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VOCAL SIGNAL DIGITAL PROCESSING. INSTRUMENT FOR ANALOG TO DIGITAL CONVERSION STUDY
Ovidiu-Andrei Schipor 1schipor@eed.usv.ro
Universitatea "Ştefan cel Mare"
Suceava Str. Universităţii nr.9720225SuceavaRomania
Felicia-Florentina Gîză
Universitatea "Ştefan cel Mare"
Suceava Str. Universităţii nr.9720225SuceavaRomania
VOCAL SIGNAL DIGITAL PROCESSING. INSTRUMENT FOR ANALOG TO DIGITAL CONVERSION STUDY
PCM methodvocal signalssignal representation
The goal of this article is to present interactive didactic software for analog to digital conversion using PCM method. After a short introduction regarding vocal signal processing we present some method for analog to digital conversion. The didactic software is an applet that can be direct accessed by any interested person.
1.Prelucrarea numerică a semnalelor
Procesarea numerică de semnal (PNS) se referă la o varietate de tehnici de îmbunătăţire a acurateţei şi siguranţei comunicaţiilor digitale. Teoria care stă la baza prelucrării numerice de semnal este suficient de complexă, însă la baza acestei tehnici de procesare se găseşte stabilirea şi standardizarea nivelelor şi a stărilor unui semnal digital. Semnalele analogice reprezintă o mărime care se modifică in mod continuu (de exemplu intensitatea sonoră a unui ton). În schimb, semnalele digitale pot prelua numai anumite valori (discrete), de exemplu valorile 0 şi 1 respectiv "sub tensiune" sau "fără tensiune". Toate circuitele de comunicaţie conţin zgomot. Acest lucru este valabil indiferent dacă semnalele sunt analogice sau digitale şi indiferent de tipul de informaţie transmis. Zgomotul este principala sursă de neplăceri pentru inginerii de comunicaţie care încearcă mereu să găsească noi metode de îmbunătăţire a raportului semnal/zgomot (S/N) în sistemele de comunicaţie. Metodele tradiţionale de optimizare a raportului semnal/zgomot includ creşterea puterii semnalului transmis şi creşterea sensibilităţii receptorului de semnal. Principalul avantaj al utilizării semnalelor digitalizate este că orice prelucrare ulterioară a acestora este în principiu lipsită de pierderi de informaţie (numerele nu sunt afectate de zgomot iar precizia calculelor poate fi matematic controlată). Un sistem de prelucrarea numerică a semnalelor îndeplineşte în esenţă un ansamblu de operaţii şi anume: -conversia semnalului analogic în semnal numeric prelucrarea semnalului numeric obţinut -conversia semnalului numeric prelucrat în semnal analogic. Dacă un semnal de intrare este analog, acesta este mai întâi convertit într-o formă digitală de un convertor analog-digital (CAD). Semnalul rezultat are două sau mai multe nivele. Ideal, aceste nivele sunt cunoscute exact, reprezentând curenţi sau tensiuni. Totuşi, deoarece semnalul de intrare conţine zgomot, nivelele nu sunt întotdeauna egale cu valorile standard. Circuitele de prelucrare a semnalului ajustează aceste nivele astfel încât să reprezinte valorile corecte. De fapt acestea elimină zgomotul.
2.Tehnici de conversie analog-numerică
Conversia analogică digitală este un proces electronic în care un semnal variabil continuu în timp şi amplitudine (analog) este transformat (cu o eroare controlată), într-un semnal digital. Intrarea unui convertor analogic digital (CAD) constă într-un nivel de tensiune care variază într-un interval infinit de valori. Exemple sunt: formele de undă sinusoidale, formele de undă care reprezintă vorbirea umană, semnalele de la o cameră video etc. Semnalele digitale sunt propagate mult mai eficient decât semnalele analogice, în mare parte datorită faptului că impulsurile digitale, care sunt foarte bine definite şi ordonate, sunt mult mai uşor de deosebit de zgomot, care este haotic. Acesta este avantajul principal al modurilor de comunicare digitală.
2.1.Modulaţia în cod a impulsurilor (PCM)
Marele progres în fabricarea componentelor electronice, în special a circuitelor înalt integrate şi cel mai înalt integrate, îl reprezintă utilizarea frecventă a tehnicii numită Pulse Code Modulation (PCM -modulaţia impulsurilor în cod) în tehnica informaţiilor şi în electronică, precum şi în electronica de divertisment, ca de exemplu la video-disc şi la discul digital (compact-disc). PCM este unul din cele mai vechi şi conceptual cele mai simple procese de conversie de la analogic la digital utilizate în semnalele vocale şi video. În cazul modulării în cod a impulsurilor (PCM), semnalele care se prelucrează (de exemplu tonuri sonore) nu se prezintă sub forma oscilaţiilor, ci sub forma numerelor binare formate din mai mulţi biţi care au fost obţinute ca rezultat al eşantionării şi cuantizării cursului oscilaţiilor. Eşantionarea are loc de cele mai multe ori cu ajutorul unui aşa-numit circuit sample-and-hold (eşantionare-şi-menţinere) care înregistrează mărimile continue de intrare (de exemplu valorile tensiunii unei oscilaţii electrice) sub formă de semnale periodice de foarte scurtă durată. Semnalul format prin eşantionare este transformat apoi într-o mărime digitală de ieşire prin intermediul unităţilor de cuantizare şi codare. Cu ajutorul modulării în cod a impulsurilor (PCM) este posibilă recunoaşterea, acoperirea sau corectarea transmiterii erorilor in cazul transmiterii semnalului. Valorile răspunzătoare de erori pot fi eliminate prin prelucrarea semnalului şi înlocuire prin media valorilor corecte învecinate.
2.2.Modulaţia numerică diferenţială
În cadrul modulaţiei numerice diferenţiale (DNUM), în locul informaţiei despre un anumit eşantion se transmite o informaţie despre diferenţa dintre acesta şi un eşantion determinat prin predicţie. Cele mai cunoscute tehnici de modulaţie numerică diferenţială sunt: -modulaţia diferenţială a impulsurilor în cod (DPCM) la care sunt generate doar diferenţele dintre amplitudini consecutive (în acest mod se diminuează rata de transfer necesară); -modulaţia delta (M) la care se generează un singur bit de diferenţă între amplitudini succesive (acest bit indică creşterea / descreşterea eşantionului curent faţă de eşantionul precedent).
2.2.Modulaţia numerică adaptativă
În cadrul modulaţiei numerice adaptive (ADPCM) se realizează corespondenţa de la eşantion la alfabet funcţie de istoria semnalului. În acest scop se constituie o stare S n a sistemului în intervalul nT, iar corespondenţa între valoarea eşantionului x(nT) şi valoarea discretă y k se va face ţinând cont şi de această stare. Acest procedeu de modulaţie este cel mai eficient.
3.Conversia analog-numerică prin metoda PCM
3.1.Eşantionarea
Fie x a (t) un semnal analogic (continuu în timp) şi {t n } n o mulţime numărabilă de valori reale distincte ordonate (t n < t m dacă n < m). Eşantionarea este transformarea semnalului x a (t) în semnalul discret x[n] definit prin relaţia:
x[n] = x a (t n ) (1) Eşantionarea uniformă este dată de relaţia:
x[n] = x a (nT) (2) unde T>0 este perioada de eşantionare, iar t n = n*T.
Figura 1. Eşantionarea (semnal sinusoidal, doua perioade, 20 eşantioane)
Prin eşantionare reţinem numai valorile continue în amplitudine şi discrete în timp. În mod ideal, eşantionarea nu are ca rezultat o pierdere de informaţie şi nici nu introduce distorsiuni în semnal dacă sunt respectate condiţiile teoremei eşantionării. Un semnal analogic poate fi refăcut din eşantioanele sale dacă a fost eşantionat la o frecvenţă de cel puţin două ori mai mare decât lărgimea de bandă a semnalului analogic (lărgimea de bandă = frecvenţa superioară -frecvenţa inferioară). De exemplu, pentru muzică, al cărui domeniu de frecvenţe se situează între 20 si 20 000 Hz (limitele analizatorului auditiv uman), sunt necesare deci cel puţin 40 000 de eşantioane pe secundă. Pentru compact-disc, semnalul se eşantionează, de exemplu, cu 44 100 de eşantioane pe secundă si astfel sunt reţinute tot atât de multe valori pe secundă. Vocea umană, poate fi redată optim prin sunete cu frecvenţe cuprinse între 100 şi 8.000 Hz.(limitele aparatului fonoarticulator). Acesta este motivul pentru care sistemele de telefonie au o gamă de frecvenţe de răspuns relativ îngustă, eliminând sunetele de înaltă frecvenţă. Drept rezultat, sunetul înregistrat de un sistem de recunoaştere a vorbirii poate fi eşantionat la o rată minimă de numai 8kHz, cu toate că 16kHz ar putea oferi rezultate mai bune, dacă sistemul dispune de suficientă putere de procesare şi de stocare de date. La ieşirea dispozitivului de eşantionare se obţine o secvenţă de impulsuri. Amplitudinea fiecărui impuls este proporţională cu amplitudinea semnalului de intrare analogic în momentul eşantionării. Din acest motiv, acest pas se numeşte modulaţie de amplitudine a impulsurilor.
3.1.Cuantizarea
Cuantizarea reprezintă transformarea semnalului continuu în amplitudine şi discret în timp într-un semnal discret în timp şi în amplitudine. Este un proces ireversibil care transformă amplitudinile notate cu x[n] sau x a [nT] în valori y k dintr-un set finit de valori. Fie D domeniul semnalului de intrare. Acesta este împărţit în L intervale:
I k = {x k < x[n] <= x k+1 }, k = 1, 2 … L (3)
Având în vedere acest lucru, se obţin nivelele de cuantizare notate cu y 1 , y 2 ...y k astfel:
x q [n] = Q(x[n]) = y[n] = y k ,
pentru x[n] I k. , iar Q(x[n]) reprezentând intervalul I k în care se găseşte x[n]. numărului de nivele de cuantizare necesită o rată de transfer mai mare. Gama dinamică (diferenţa de volum dintre cel mai slab şi cel mai puternic sunet) pentru vocea umană este mult mai mică decât pentru muzica de înaltă calitate. În cele mai multe cazuri este nevoie de doar 8 biţi pe eşantion cu toate că rezultatele sunt mult mai bune în cazul folosirii a 16 biţi pe eşantion, care este şi numărul de biţi caracteristic CD-urilor audio. Diferenţele dintre ratele de eşantionare şi numărul de biţi pot avea un impact major asupra cantităţii de date pe care calculatorul trebuie să o proceseze. O secundă de sunet digital la 8 kHz şi 8 biţi pe eşantion înseamnă doar 8.000 octeţi de date. Aceeaşi secundă de sunet digital la 16 kHz şi 16 biţi înseamnă de patru ori mai multe date: 32.000 octeţi. Standardul pentru CD-uri audio, de 44 kHz şi 16 biţi, înseamnă că o secundă de sunet necesită un spaţiu de stocare de 88.000 octeţi. Pentru rezultate superioare, cuantizarea nu ar trebui efectuată uniform. Unele semnale sunt de amplitudini joase, iar altele sunt de amplitudini ridicate. În practică, cuantizarea este neuniformă, existând mai multe nivele de cuantizare pentru amplitudinile care predomină în cadrul semnalului. Pentru o transmisie a semnalelor inteligibilă şi de o calitate acceptabilă a comunicaţiei se poate realiza reducerea vitezei de kbit/s cu algoritmi de codare şi de cuantizare vectorială. Scopul acestor algoritmi este de a transmite, memora şi sintetiza semnalul vocal de o calitate dată, utilizând mai puţini biţi. Această reducere este realizată eliminând redundanţa din semnalul vocal 4. Instrument didactic pentru studiul conversiei analog-numerice prin metoda PCM Apletul a fost realizat utilizând limbajul Java, versiunea Sun SDK 1.4. O parte din clasele utilizate au fost realizate de către autori în cadrul bursei de studiu ERASMUS-SOCRATES EUDIL-Lille Franta. Interfaţa este realizată utilizând clasele din pachetul Abstract Window Toolkit.Diferenţa dintre x[n] şi x q [n] se numeşte eroare de cuantizare sau zgomot de cuantizare:
e q = x[n] -x q [n]
(5)
Eroarea de cuantizare nu poate depăşi o jumătate din pasul de cuantizare:
2
≤ e q ≤
2
(6)
unde = pas de cuantizare.
În cazul în care eroarea de cuantizare depăşeşte limitele admise, trebuie mărit numărul de nivele de
cuantizare.
Figura 2. Cuantizarea (8 nivele de cuantizare)
3.3.Codarea
Codarea este procesul prin care fiecărei valori discrete x q [n] i se atribuie o secvenţă egală cu b biţi.
Pentru codificarea celor k nivele de cuantizare posibile sunt necesari log 2 k biţi.
Apropierea necesară faţă de procesul oscilatoriu reclamă o gradare fină a valorilor măsurătorilor
rezultate prin eşantionare, rezultând deci şiruri relativ lungi de biţi (lungimi de cuvinte). Astfel,
printr-o lungime de cuvânt de 4 biţi, pot fi redate numai 7% din procesele oscilatorii.
Figura 3. Codificarea (în cazul folosirii a 16 nivele de cuantizare)
Pentru un semnal cu frecvenţa de eşantionare de f Hz şi k nivele de cuantizare este necesară o rată
de transfer:
RT = f * log 2 k bps
(7)
De exemplu pentru transmiterea unui semnal eşantionat cu rata de eşantionare de 8 KHz şi codat pe
8 biţi (adică are 256 nivele de cuantizare) este necesară o rată de transfer de 64 Kbps. Se observă că
o creştere a
Prin conversia analog-numerică, semnalul continuu este eşantionat şi cuantizat, fiecare nivel obţinut fiind reprezentat printr-un cuvânt binar care se aplică la intrarea sistemului de prelucrare numerică. Această primă fază a prelucrării numerice este realizată cu convertoare specializate analognumerice şi este deosebit de importantă, deoarece prin aproximările pe care le efectuează, contribuie la precizia de calcul şi la raportul semnal zgomot final.
Apletul permite generarea unui semnal periodic nesinusoidal pe baza primelor 6 armonici. Ulterior, acest semnal este convertit numeric, pas cu pas, evidenţiindu-se aspectele esenţiale. Apletul se utilizează în modul următor. Apletul permite generarea unui semnal periodic nesinusoidal pe baza primelor 6 armonici. Ulterior, acest semnal este convertit numeric, pas cu pas, evidenţiindu-se aspectele esenţiale. Apletul se utilizează în modul următor:
Se introduc coeficienţii A1…A6 (sin) şi B1…B6 (cos) ai primelor 6 armonici ale semnalului analogic. Se introduc coeficienţii A1…A6 (sin) şi B1…B6 (cos) ai primelor 6 armonici ale semnalului analogic.
Se introduce frecvenţa armonicii fundamentale (F1) în format mantisă -ordin de marime. Se introduce frecvenţa armonicii fundamentale (F1) în format mantisă -ordin de marime
Se introduce numărul de perioade (Perioade) ale armonicii fundamentale ce se doresc vizualizate. Se introduce numărul de perioade (Perioade) ale armonicii fundamentale ce se doresc vizualizate.
Se introduce componenta constantă (U_ctn). Se introduce componenta constantă (U_ctn).
Se introduce numărul eşantioanelor de timp (Eşantioane). Se introduce numărul eşantioanelor de timp (Eşantioane).
Se introduce numărul de biţi utilizaţi pentru cuantizare (Biţi). Se introduce numărul de biţi utilizaţi pentru cuantizare (Biţi).
Se apasă butonul <OK> pentru a vizualiza semnalul analogic. Se apasă butonul <OK> pentru a vizualiza semnalul analogic.
Butoane de ajutor: <Autorii> şi <Ajutor>. Opt butoane permit o mai bună vizualizare a graficelor. ^ , V , > , < -Pentru Deplasarea Graficului, Se apasă butoanele <înainte> şi <înapoi> pentru vizualizarea succesivă a etapelorSe apasă butoanele <înainte> şi <înapoi> pentru vizualizarea succesivă a etapelor. Butonul <RST> permite aducerea apletului în starea iniţială. Butoane de ajutor: <Autorii> şi <Ajutor>. Opt butoane permit o mai bună vizualizare a graficelor: -^ , v , > , < -pentru deplasarea graficului;
pentru zoom pozitiv şi negativ pe axa OX (a timpului) şi OY (a valorii semnalului. -X+ , X- , Y+ , Y- , -X+ , X-, Y+ , Y-pentru zoom pozitiv şi negativ pe axa OX (a timpului) şi OY (a valorii semnalului).
Pentru o utilizare rapidă (fără introducere de date) se poate selecta un exemplu: -sinusoidă -un semnal sinusoidal pe 2 perioade; -triunghiular -un semnal triunghiular pe 2 perioade; -dreptunghiular -un semnal dreptunghiular pe 4 perioade. Pentru o utilizare rapidă (fără introducere de date) se poate selecta un exemplu: -sinusoidă -un semnal sinusoidal pe 2 perioade; -triunghiular -un semnal triunghiular pe 2 perioade; -dreptunghiular -un semnal dreptunghiular pe 4 perioade;
-o perioadă -un semnal oarecare pe o perioadă. -o perioadă -un semnal oarecare pe o perioadă.
Comunicaţii digitale avansate. Kamilo Dr, Feher, Editura Tehnică. Dr. Kamilo Feher (1993) -Comunicaţii digitale avansate, Editura Tehnică, Bucureşti, Romania
Constantin Ioan, Marghescu Ion, Transmisiuni analogice şi digitale. Bucureşti, RomaniaConstantin Ioan, Marghescu Ion (1995) -Transmisiuni analogice şi digitale, Editura Tehnică, Bucureşti, Romania
Măsurări electrice şi electronice. Cornelia Marcuta, Mihai Creţu, Editura Tehnică-Info. Cornelia Marcuta, Mihai Creţu (2002) -Măsurări electrice şi electronice, Editura Tehnică-Info, Chişinău, Republica Moldova
Error rate characteristics of oversampled analog to digital conversion. Zoran Cvetkovic, Martin Vetterli, IEEE Transactions on Information Theory. 445Zoran Cvetkovic, Martin Vetterli (2004) -Error rate characteristics of oversampled analog to digital conversion, IEEE Transactions on Information Theory vol.44, nr. 5
Current techniques of measurement, acquisition and processing test data. A L Wicks, Department of Mechanical Engineering Virginia Tech site-uri webA.L. Wicks (2003) -Current techniques of measurement, acquisition and processing test data, 2003, Department of Mechanical Engineering Virginia Tech site-uri web
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Artificial intelligence for Sustainability in Energy Industry: A Contextual Topic Modeling and Content Analys
Tahereh Saheb t.saheb@modares.ac.ir
Mohammad Dehghani mohamad.dehqani@modares.ac.ir
Management Studies Center
Industrial and Systems Engineering
Tarbiat Modares University
TehranIran
Tarbiat Modares University Tehran
Iran
Artificial intelligence for Sustainability in Energy Industry: A Contextual Topic Modeling and Content Analys
Research Assistant Professor, Science & Technology Studies Group, 1 Corresponding Author 2Artificial intelligencesustainabilityenergytopic modelingcontent analysissustainable energy
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.On May 29th 2021, we searched the following keywords inside the title, keyword, and abstract: "artificial intelligence" OR "AI" AND "sustainable" OR "sustainability" AND "energy". This search resulted in the retrieval of 981 documents. Following that, we restricted the document type to Articles and the language toEnglish. This exclusion resulted in 296 articles. Following that, we manually evaluated the titles and abstracts of the articles to identify the most pertinent ones that examined the role of artificial intelligence in ensuring the energy sector's sustainability. This screening yielded 182 publications spanning the years 2004 to 2022.Given that abstracts of research articles are the most succinct summary of key ideas [22], we included abstracts of the final publications in the study's corpus.Preprocessing and Post-Processing StagesPython 3.7.9 was utilized for pre-and post-processing, as well as for topic modeling analysis. We preprocessed our corpus using the NLTK and Scikit-learn packages, as well as Regular Expressions or RegEX. We import the word tokenize from the NLTK to begin the tokenization process. After removing punctuation, we lowercased our characters and deleted all numeric characters, punctuation, and whitespace.Additionally, we eliminated no-word repetitions and anything enclosed in parenthesis. Additionally, we eliminated the NLTK library's stopwords.We reviewed the first findings and created a manual exclusion list for more relevant topic identification during the postprocessing step. We added the core keywords (i.e. artificial intelligence, AI, energy, sustainable, sustainability) in the exclusion list to enhance the coherence of the findings. We used stemming throughout the preprocessing step; however, after observing the first results, we decided to remove the stemming to make the words displayed in the word clouds more understandable. We next used the lemmatization procedure, which we abandoned following the findings of the word clouds in order to make our topic labeling approach more comprehensible. Additionally, we estimated the TF-IDF score for each word in the corpus. We eliminated words with scores that were lower than the median of all TF-IDF values. We calculated the TF-IDF scores using the Scikit-learn package. The maximum TF-IDF score was set to 0.8 and the minimum value at 0.11. Additionally, we incorporated unigrams and bigrams.Topic ModelingWe applied the following libraries to conduct the topic modeling: Pandas to read the dataset, Gensim to perform LDA, Transformers to perform BERT, Keras to perform auto-encoding, and Seaborn and Matplotlib to visualize the results. We imported the TFID vectorizer from the Scikit-learn feature extraction and KMeans from the Scikit-learn cluster. The probabilistic topic assignment vector was constructed using LDA, while the sentence embedding vector was constructed using BERT. To begin, we used the TF-IDF,
Introduction
The rise of unsustainable practices and procedures co-occurred with the rising urbanization and civilization have driven the emergence of AI-based solutions to assist the path toward sustainability [1][2][3]. Excessive consumption and unsustainable energy sources, which have increased at an unprecedented rate due to factors such as urbanization, improper building construction, transportation, environmental changes, and population growth, have pressured the energy industry to pursue clean energy sources and smart solutions [4]. The deployment of alternative energy sources and access to sustainable energy are pillars of global economic growth [5] and fight against environmental hazards, in particular climate change [6]. Thus, the energy sector has focused its efforts not only on developing new sources of energy, but also on inventing novel technical solutions that increase the efficiency of existing mitigation measures [7]. AI-based interventions, which are available in the form of both hard and soft solutions, such as robots and algorithms and models, are one of these solutions that have come to assist humanity [8]. Artificial intelligence can provide a wide range of intelligent solutions, from predictive and prescriptive energy consumption insights to intelligent energy generation and distribution.
Parallel to the escalating discussions over sustainable energy and artificial intelligence solutions, the world is now debating the ethics of artificial intelligence and its potentially negative effects on society and the environment. Ethical AI considers not just AI's moral dimensions, but also its epistemic perspectives [9].
While prior studies have urged scholars to focus on the epistemological aspects of sustainable AI and to open the black box of algorithms to develop sustainable models and algorithms [10], other researches have concentrated on AI for social good and its favorable societal and environmental circumstances [11,12]; such as the development of sustainable AI.
In this article, we define sustainable AI as AI that is designed to achieve sustainability and is called AI for sustainability, as differed from AI that is designed to be sustainable and is called sustainability of AI [10]. In this paper, the term "sustainable AI" refers to the extent to which artificial intelligence can help society accomplish their sustainability goals [13,14]. The energy industry is one of the core industries that will benefit from sustainable AI, which will aid in the development of energy sustainability [15]. Sustainable energy strives to fulfill today's energy demand without depleting energy supplies or harming the environment. Sustainable energy systems are regarded as a requirement for achieving all the Sustainable Development Goals (SDGs) [16]. Sustainable artificial intelligence can help to expedite the development of sustainable energy [14]. To advance sustainable energy, the industry has supplied a wide variety of choices, including wind energy, fossil fuels, solar energy, and bioenergy. It's also vital to recognize how academics have dealt with the confluence of sustainability, artificial intelligence, and energy. This research is novel from various perspectives. First, this study intends to foster discussions on sustainable AI by identifying the most important research issues in the area, highlighting intellectual gaps, and proposing potential research streams. It is obvious that the energy sector and scientific research and innovation are inextricably linked. Scientific research is seen to be the cornerstone of technological advancements [17].
Identifying the intellectual frameworks of scientific research across time and the historical progression of its themes can have a huge influence on the effectiveness or failure of new technological solutions. To our knowledge, scientific research on sustainable energy is lacking a coherent understanding of how artificial intelligence has been integrated into this domain and how it should be conducted in the future. It is therefore imperative to perform a mixed-method literature review to have a deeper understanding of the deployment of AI to achieve sustainable energy in order to identify existing research gaps and potential future research streams. The second aspect of this research that distinguishes it from prior research is its novel methodology.
Extensive literature reviews are conducted by scholars using bibliometric methodologies [18][19][20] or topic modeling techniques such as Latent Dirichlet Allocation (LDA) [21,22] or qualitative content analysis [23]. As a result, we incorporated all the aforementioned review methodologies to ensure that their findings were complementary. Furthermore, because both bibliometric and LDA topic modeling are based on keyword cooccurrence analysis, we included a contextual embedding-based topic modeling analysis that incorporates use of sentences as fundamental units of analysis. This method which is the latest development in natural language processing (NLP) is offered by Google under the name of Bidirectional Encoder Representations for Transformers (BERT) [24] . BERT makes use of the Transformer library, which uses machine learning to discover contextual relationships between words in a text. Our integrated adoption of computational and advanced topic modeling tools, as well as qualitative analysis, enables us to gain highly objective, coherent, superior, and meta-analytical insight into present research on sustainable artificial intelligence in energy and to forecast its future. The final contribution of this research is that we offer a thorough list of research gaps and potential research agendas that may be used to increase the depth of research on sustainable artificial intelligence in the energy industry In sum, the theoretical contribution of this research is to extent the literatures on sustainable AI and sustainable energy by determining the key academic themes, sub-themes and cross-topic common themes addressed by scientists working on sustainable AI in energy, as well as how these subjects have evolved over time. Practically, this research attempts to enlighten policymakers, the energy sector, and engineers and developers of artificial intelligence about the productivity of science while emphasizing the challenges that require more AI-based responses. Additionally, it encourages policymakers to design artificial intelligence regulations that promote the development of sustainable AI in the energy sector while mitigating the unintended consequences of unsustainable energy sources and AI solutions.
4
The study is structured as follows: we begin with an explanation of our methodology and then go on to the findings, which include our topic modeling and content analysis of topics. We conclude the study by discussing our findings, theoretical research gaps, and potential future research directions. We also discussed the theoretical and practical contribution of the study. We conclude the paper with a conclusion.
Methodology
It is a widely held belief among researchers that each quantitative and qualitative research technique has inherent strengths and weaknesses; hence, combining both methods is advised to ensure that their results complement one another. We drew on and included four complimentary sets of research methodologies in our study. Three of these, BERT, LDA topic modeling and clustering are connected with text mining techniques. Additionally, we supplemented these quantitative findings with a qualitative topic-based content analysis. Our mixed-methods approach is new in three ways. First, we employed computational approaches such as BERT, LDA, and clustering to discover the thematic content of research on sustainable AI in energy.
Second, we conducted a comprehensive analysis of the retrieved topics using content analysis as a qualitative approach. Third, we integrated LDA and BERT topic modeling approaches in this study to achieve the highest level of topic identification accuracy. Our suggested mixed-method methodology may be used by researchers from a variety of disciplines to improve our understanding of quantitative and computational analyses through the use of topic-based content analysis.
LDA is predicated on the premise that documents are made of topics and that some words are more likely to occur in certain topics than others (Xie et al., 2020). While LDA has been regularly used by academics to identify topics, it does have some limitations due to the fact that it is a word co-occurrence analysis and so cannot incorporate the entire content of the sentence. Additionally, it does not do well on short texts [26].
Additionally, the outcomes of LDA may be challenging for humans to comprehend and consume [27]. By contrast, BERT topic modeling is focused on detecting semantic similarity and integrating topics with pretrained contextual representations [28] It substantially enhances the coherence of neural topic models by including contextual information into the topic modeling process [29]. BERT makes use of the Transformer library, which has an Autoencoder technique: an encoder that scans the text input. We combined the LDA and BERT vectors in this study to improve topic recognition and clustering. Moreover, because one of the most difficult aspects of word-sentence embedding is dealing with high dimensions, we applied the Uniform Manifold Approximation and Projection (UMAP) approach. In comparison to other approaches, UMAP is one of the most efficient implementations of manifold learning [30]. order to balance the information content of each vectors. We incorporated the Keras package to process the auto-encoder in order to learn a lower-dimensional latent space representation for the concatenated vector.
To ensure the clusters were of good quality, we calculated the Silhouette Score, which was 0.566 and near to one for LDA+BERT+ Clustering. TFIDF+clustering received a score of 0.048, while BERT+clustering received a score of 0.095 ( Figure 2). The Silhouette Score is used for cluster quality [31]. The score ranges from -1 to 1. If the score is near to one, the cluster is dense and well isolated from neighboring clusters. In comparison to other topic modeling techniques, LDA BERT Clustering is closer to 1, indicating that the clusters are of excellent quality. The final topic identification obtained by LDA+BERT+Clustering Algorithms is depicted in Figure 3. We utilized the UMAP package to do dimension reductions and set the topic count to eight. We also evaluated several topic clustering, including 10, 4, and 6. The authors determined that eight topics were better separated from one another and had a greater density within each topic; this demonstrates the excellent quality of clustering. As indicated by the percentage of documents contained inside each topic, approximately 11% of documents belong to topic 0 and approximately 16% to topic 1. Clustering resulted in a balanced distribution of documents within each topic, confirming the clustering's excellent quality.
TF-IDF Clustering BERT LDA Figure 3 The global view of the topic model on sustainable AI in energy research area. We integrated LDA, BERT and clusetering for topic modeling detection. uncovered eight different topics. These topics will be described, and then a content analysis of the papers that are associated with each one will be carried out throughout this part of the article.
Results
Descriptive Analysis
These articles were organized according to their relative likelihood of belonging to each topic. As seen in The word cloud visualization ( Figure 6.0) shows the identified topics after labeling based on the topic three keywords. The Figure 6 shows that the first three most-used terms in each subject are as follows: Topic 1(building, consumption, environment); topic 2 (design, water, decision); topic 3 (building, climate, fuel); topic 4 (decision, agriculture, improve); topic 5 (IoT, devices, consumption); topic 6 (urban, technology, industrial); topic 7 (engineering, efficiency, students); topic 8 (optimization, efficient, building).
Figure 4 The distribution of documents across topics
The evolution of topics over time
Once we scoured the corpus for hidden topics, we determined how often they appear throughout time. however, topic reached its apex in 2019 and 2020. The topic of AI for energy efficiency has shown a reasonably steady increase from 2013, with its greatest growth occurring between 2020 and 2021. In 2020, significant academic focus was given to AI-based DSSs for urban water management.
Content analysis to detect topics, sub-themes and cross-topic common themes
In this part of the paper, we conducted content analysis of detected topics for three purposes: First, to detect the general topics from articles; second, to identify the sub-themes from each topic, and third to find the cross-topic common themes.
Topic 1: Sustainable Buildings and Energy Consumption
The primary concerns of topic 1 are related to the design of automated and intelligent systems and the incorporation of cutting-edge technologies, particularly IoT and AI-based DSSs, in order to construct sustainable buildings. These buildings will be part of the sustainable cities initiative, which aims to promote sustainable energy consumption and smart grids.
One of the primary scholarly interests is the creation of sustainable buildings and smart grids for the purpose of reducing energy consumption. One way to accomplish this aim is to redefine the design and architecture of buildings, whether residential, public, commercial, industrial, or manufacturing. According to studies, the application of automation and intelligent systems in the construction of sustainable buildings will result in sustainable energy usage [32,33]. Several AI-based approaches are proposed to achieve a more sustainable building, including building management systems, knowledge-based engineering (KBE), fuzzy logic, neural [34]. From a broad standpoint, sustainable building development falls under the umbrella of sustainable smart cities and reducing building energy consumption [35]. Additionally, scholars have drawn inspiration from nature and advocated regenerative design influenced by nature for pattern detection, prediction, optimization, and planning of buildings [36]. Additionally, scholars discuss the potential of AI in reducing CO2 emissions in buildings, suggesting that AI may be used to construct smart multi-energy systems, such as those found in industrial districts, resulting in significant energy savings and CO2 emission reductions (Simeoni, Nardin and Ciotti, 2018 ). As a result, sustainable building design would be a way to combat climate change.
Several additional studies integrate AI solutions with other cutting-edge technologies, most notably the Internet of Things and big data, to improve not only the design and optimization of sustainable buildings, but also the efficiency of their power usage (Chui, Lytras and Visvizi, 2018). For instance, one project focused on the application of IoT in public buildings in order to discover and anticipate energy usage trends [39]. A preceding study, for illustration, outlines the obstacles involved in understanding the semantics of IoT devices using machine learning models. Image Encoded Time Series has been identified as an alternate method to other statistical feature-based inference [35]. Sustainability analysts from [40] and [41] studies have also advocated for continual monitoring of sustainability metrics by integrating AI with DSSs or ambient intelligence.
Both residential buildings and plants and commercial buildings and offices have the same issue in regard to energy usage. Previous studies incorporated multi-objective and multi-attribute decision making modeling as well as impact evaluation of the emission outputs to help designers and manufacturers to make environmentally sustainable decisions about the designs and production of facilities [42]. Researchers also believe that in order to provide bulk energy consumption forecast, control, and management, simulation techniques could be utilized [15], for instance in public buildings, offices and factories. Due to new modes of consumption and distributed intelligence, the electrical power grids have been also influenced, and as a result, smart energy grids have been generated to achieve sustainability [43].
Topic 2: AI-based DSSs for Sustainable Urban Water Management
The second topic is sustainable water management, which includes utilizing AI to create DSSs for consumption and water usage. Forecasting, real-time monitoring, and customized and adjustable pricing and tariffs are the primary strategies. AI is used with other sophisticated technologies to assist in the development of a smart city.
The previous studies have postulated several approaches, such as optimization and AI-based decision support systems, for water infrastructure management [44], better delivery of public services of smart cities such as water treatment and supply [45], AI-based water pricing and tariff options [46] and sustainable water consumption [47]. For this goal, AI is integrated with recent technological advances in urban life. This includes using open source data, employing deep learning algorithms, and developing smart street lighting systems. Such decisions about social impacts of smartphone applications or smart travel behavior are also examined [48].
AI techniques are utilized to anticipate water resource management [49], such as water quality by adopting algorithms such as neuro-fuzzy inference system [50]. Real-time optimization of water resources and cloud technologies are integrated with visual recognition techniques and created to improve efficiency with irrigation systems [51]. A study conducted on ecological water governance implementation using AI found that including algorithms into the system yields higher-quality information and better prediction models for accurate evaluation of water quality [52]. AI may be used for tracking water use and demand as well as forecasting water quality, but it can also be used for estimating water infrastructure maintenance, monitoring dam conditions, water-related diseases and disasters [53] and water reuse [54].
By critiquing conventional decision support systems, research offer alternatives based on artificial intelligence, such as a systematic decision process [55], sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems to develop a sustainable desalination plant [56] or an comprehensive and flexible decision-making process fueled by social learning and engagement aimed at ensuring the urban water system's environmental and energy sustainability [57]. One research offers a unique DSS for analyzing the energy effect of each of the urban water cycle's macro-sectors, including assessing the system's energy balance and proposing potential energy-efficient solutions ( Puleo et al., 2016).
Topic 3: Climate Artificial Intelligence (Climate Informatics)
Climate informatics, specially climate artificial intelligence as a new field of study is concerned with issues such as AI-based DSSs to reduce greenhouse gas emissions, optimizing grid assets, enhancing climate resiliency and reliability, increasing energy efficiency, forecasting energy consumption and modeling earth systems. Moreover, within this topic, scholars have addressed the issue of explainable and trustworthy AL models due to the controversial nature of climate change.
Climate change has compelled societies to seek alternate energy sources and fuels [59]. Climate informatics [60], such as several AI-based solutions, including novel algorithms and DSSs, have been hugely beneficial in lowering greenhouse gas emissions in the energy sector. By improving grid assets, and strengthening climate adaptability these innovations have greatly contributed to this ultimate goal [15]. Reliable and explainable artificial intelligence models, as advocated in prior studies, might help stakeholders and decision-makers achieve climate-resilient and sustainable development goals [61]. By integrating advanced machine learing techniques, AI can propose fresh insights in complex climate simulations in the field of climate modeling [62].
Energy consumption patterns might undergo considerable changes due to climatic change, which means AI forecasts can aid in estimating future energy use for various climate scenarios [63]. It's not only businesses and other organizations that are using AI algorithms these days-AI algorithms are also being utilized to foster sustainable urban growth and mitigate climate change by examining how future urban expansion will affect material and energy flows [64]. Fossil fuel, used as the primary energy source, is the primary contributor to human greenhouse gases that influence the climate. AI is extensively utilized for decreasing carbon footprints and for avoiding fossil fuel combustion [65] as prior studies show that AI can act as an automated carbon tracker [66]. Artificial intelligence-powered technologies may help investors in analyzing a company's climate effect while making investment choices [67]. By drawing attention to climate change through visualization techniques, they help to educate the public on the effects of climate change [68] Ultimately, AI algorithms may provide great resources for climate change conflicts, including in the field of modeling earth systems [69], teleconnections [70], weather forecasting ( McGovern and Elmore, 2017), future climate scenarios [72], climate impacts [73] and climate extremes [74].
Topic 4: Agriculture 4.0 and Sustainable Sources of Energy
The fourth area that academics in the field of sustainable AI for energy extensively address is the development of smart agriculture and sustainable energy sources. The primary issue in this subject is how to combine advanced technologies like IoT, drones, and renewable energy with AI in order to create automated and real-time systems.
According to some researchers, the agriculture industry is suffering from an insufficient application of responsible innovation [75]. As a result, the researchers are calling for a system referred to as Responsible Agriculture 4.0, which incorporates drones, IoT, robotics, vertical farms, AI, and solar and wind power linked to microgrids [76][77][78]. When it comes to the productivity of agriculture, factors such as the cost of energy for cultivation are equally significant [79]. Based on the premise that most agricultural machinery operates on fossil fuels, it may potentially contribute to climate change. Thus, new energy solutions, and AI-based approaches are provided. One way in which bioproduction and renewable energy may positively influence sustainable agriculture and farming is via the development of bioproduction and renewable energy [80].
Proposing new AI methods to forecast agricultural energy use has also been researched [79]. biomass may also be used to provide sustainable energy in agriculture, and care should be taken to avoid any injuries [81].
Real-time alerting systems, AI-based DSSs, real-time DSS forecasting models, and alternative energy sources such as solar and wind play a vital role in sustainable agriculture [82]. Maximizing agricultural production and economic stabilization while minimizing the use of natural resources and their harmful environmental consequences may be accomplished using renewable energy and AI [82]. Artificial intelligence enables academics to provide accurate forecasts of agricultural energy use [83]. Especially, a drastic shift toward sustainability in agricultural practices has occurred because of its confluence with other cutting-edge 14 technology, including sensors, DSSs, greenhouse monitoring, intelligent farm equipment, and drone-based crop imaging. [84].
15 AI is used in tandem with a number of cutting-edge technologies for sustainable energy development, such as improved energy conservation [85] and building intelligent energy management [86] such as building management systems [35]. Internet of Things (IoT) is one of the most promising and pervasive technologies [85]; whose integration with AI has generated a revolution in the energy sector. There are many functions in creating sustainable energy in the IoT-enabled smart city dubbed City 4.0 [87] such as simulation and optimization of power plant energy sustainability [86]. City systems such as water and electricity, as well as other infrastructures, such as data analytics, will be driven by sensor and data collection in the smart city [87].
A significant use of IoT is in the design of intelligent buildings, which with AI included may support a goal of energy or water conservation [39,88], for instance, by educating the citizens on how to use energy more effectively and giving them warnings if they are using excessive amounts of energy. [89]. IoT is integral to modern grid development as well. In particular, it seeks to transform the traditional, fossil-fuel-based power grids with distributed energy resources and integrate it with cutting-edge technology such as artificial intelligence for improved grid management [90]. In the same manner, Blockchain has also been considered to be a viable alternative for smart cities. Fusing blockchain with AI may be leveraged for smart services, including energy load forecasting, categorizing customers, and evaluating energy load [91]. Smart connected devices such as IoT devices have successfully employed blockchain in time to retain these devices safe and secure in a blockchain network [92].
The effect of IoT and AI on agriculture and food sectors is also substantial [93,94]. Manufacturing facilities such as food factories and plants may be transformed more intelligent and more environmentally friendly via the use of IoT and AI, which merge with nonthermal and advanced thermal technologies [94]. Sustainable and green IoT are other topics covered in this subject. The two main objectives of the literature on green IoT are to increase the recyclability and usefulness of IoT devices, as well as to minimize the carbon footprints of such devices. The second objective is to incorporate more effective life cycle assessment (LCA) methods integrating artificial intelligence (AI) in order to cut costs and time [95]. Another of the many topics that apply to IoT is with developing smart campuses, which are carbon neutral, energy efficient, use less water, and are laced with various high-quality green energy tools [96] and smart teaching and learning platforms [97].
Researchers have identified the positive traits of IoT devices, but they've also forewarned about the possible risks of the devices and proposed various techniques for detecting weaknesses [93] or challenges regarding the heterogeneity of smart devices and their associated meta-data [35].
Topic 6: AI-based Evaluation of Renewable Energy Technologies
Scholarly interest has been generated by the discussion of leveraging AI for DSSs to enhance the efficiency of conventional system evaluations for renewable energy technologies. To a great extent, a sustainable future will depend on maximizing the use of energy sources that cannot be depleted [98]. Artificial intelligence is important for the survival of the future by leveraging a wide range of renewable energy technologies such as biomass energy, wind energy, solar energy, geothermal energy, hydro energy, marine energy, bioenergy, hydrogen energy, and hybrid energy [99]. AI is used to evaluate renewable energy solutions based on their cost of energy production, carbon footprint, affordability of renewable resources, and energy conversion efficiency [100]. Artificial intelligence will ensure the most effective use of these resources while also pushing for improved management and distribution systems [14]. Distributed energy management, generating, forecasting, grid health monitoring, and fault detection are also made more efficient by using automated AI systems [101]. AI can help disperse the supply and demand of energy in real-time and improve energy consumption and storage allocation (Sun, Dong and Liang, 2016).
To mitigate against the barrier of utilizing renewable energy technology, the following measures are taken:
Renewable energy sustainability is evaluated [103]; in addition, the turbulent and sporadic character of renewable energy data is addressed [104]. One research group claims that standard techniques such as LCA and EIA (Environmental Impact Assessment) may be improved by developing more advanced digital intelligent decision-making systems, or DSSs. It is feasible that improved assessments of renewable energy sources may be achieved via intelligent and automated technologies [105]. With the smart mechanisms in place, long-term detrimental consequences can be calculated, as well as visible and invisible factors [106].
Artificial intelligence (AI) increases the adaptability of power systems, providing DSSs for energy storage applications [107]. For instance, to ensure more use of battery-electric buses, and minimize the effect on the power grids, the researchers developed an AI-powered DSS [108]. Another research leveraged AI to create a DSS for forecasting future energy consumption patterns, and to provide a solution for utilizing renewable energy alternatives [109].
Topic 7: Smart Campus & Engineering Education
It is possible to break down the discussions inside this topic into two distinct types: those about engineering education and those which deal with using AI and IoT to construct intelligent campuses to help maintain sustainability objectives. The two themes represent two elements of education: one dealing with the learning contents, and the other with behavioral outcomes of developing smart campuses.To build a model of smart campuses, we should focus on incorporating IoT into the infrastructure, with subsequent implementations of smart apps and services, with smart educational tools and pedagogies and smart analysis as well [97]. A smart campus is in charge of energy consumption scheduling, while its telecommunications infrastructure serves as the place where data transfers are conducted [110]. Integrating cutting-edge technology, a smart campus captures real-time data on energy usage, renewable energy power generation , air quality, and more [111].
Another point of view is that higher education should equip itself with relevant skills and competences to help in realizing long-term sustainable objectives [112]. The energy sustainability in this respect may be addressed via engineering education and engineering assistance for high-level strategic decision-making [113].
This objective can be achieved by using innovative instructional programs, alongside cutting-edge technology such as artificial intelligence and the Internet of Things. A living lab campus equipped with technology, as well as a deep well of talent and competency, may serve as a digital platform for education and sustainable growth [114]. For illustration, to support ongoing research, teaching, and learning on sustainable development, the University of British Columbia (UBC) implemented the Campus as a Living Laboratory project, which included AI and IoT and other cutting-edge technologies [115]. Furthermore, there have been several research done to help AI seamlessly integrate with current educational institutions in order to aid in sustainable development learning [116].
Topic 8: AI for Energy Optimization
Conventional optimization methods may be a roadblock for making progress toward sustainability, and AIbased solutions can help eliminate such roadblocks. Whilst renewable energy sources, like solar and wind, have many merits, there are some downsides to consider. They are usually not always available and often rely on the climate, which renders employing them complicated [117]. A proper optimization of energy may be utilized to minimize greenhouse gas emissions and cut energy usage. Efforts to reduce costs and side effects of energy consumption are facilitated using optimization models [118]. Computational and intelligent resources have enabled academics to progress with optimization problems by employing advanced AI methods. Manufacturers have developed numerous energy-efficient appliances for this reason. Even if the deployment of digital technologies in buildings will likely lead to improved energy efficiency, that is not the sole solution. Studies recommend implementing energy-saving measures that don't just target environmental variables, but also include building inhabitants' comfort and preferences, which is achievable via the integration of AI-augmented algorithms [119]. For illustration, AI algorithms that not only monitor current actions but also give real-time alerts and warnings to users and providers allow optimization to be significantly accelerated. Some approaches, such as algorithms that use energy consumption data to lower energy costs in buildings that use advanced AI, are only one example of how AI and advanced technology may be used to benefit society [120].
Weather has a direct effect on energy consumption, which is indisputable. To ensure the winter heating demand of non-residential buildings was calculated correctly, researchers used an optimized artificial neural network method to determine and forecast this need [121]. By utilizing AI along with the use of smart metering and non-intrusive load monitoring, one may improve energy efficiency by evaluating the electricity use of appliances [38]. Using a new approach, researchers found that the GP model was capable of making accurate predictions and a multi-objective genetic algorithm, NSGA-II, was also capable of optimizing sustainable building design [32]. The use of a fuzzy-enhanced energy system model to represent a route to a sustainable energy system has also been presented in another research [122]. The views of other researchers in the field include techniques based on artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybrids, all of which rely on biological inspiration. These findings imply that sustainable energy development is computationally challenging conventional optimization, demanding advanced techniques [123].
Discussion, Theoretical Gaps, and Future Strands of Research
To For topic 1, the key problems are the importance of sustainable buildings for smart city development and smart grid services. The issue of AI and its application in decision-making, pricing, forecasting, and sustainable consumption are all addressed in this topic. To reach sustainability, various cutting-edge technologies are tied to AI. One problem which may be especially neglected is the use of AI technology to make buildings eco-friendlier and enhance their inhabitants' feeling of accountability toward sustainability.
One approach might be to design real-time warning systems to ensure people are prohibited from excessive energy use, while also ensuring that they benefit from the AI-based solutions. Convergence research may also explore how green architecture is uniquely enabled to deal with complex issues, including environmental efficiency, such as using eco-lighting, natural ventilation, shading, green roofs, and artificial intelligence. Most of prior research focuses on eco-design and overlooks other factors of green architecture. However, there is a dearth of distributed energy resource optimization models, particularly due to the emergence of blockchain.
Figure 6 Identified cross-topic common themes
As shown in Figure 8, we discovered six core problems that were prevalent throughout the majority of the topics. For example, tariff and price models based on artificial intelligence are prevalent in topics 1 and 2;
while economic issues in general are a concern in topics 4, 6, and 8. The dilemma of sustainable consumption is prevalent in all of these topics, demonstrating the critical role of AI in attaining sustainable energy use.
Forecasting is inextricably connected to sustainable consumption, since more than half of the topics cover both; demonstrating the progress of AI forecasting algorithms for sustainable consumption. Forecasting, on the other hand, is not restricted to anticipating consumption patterns.
The topic's second significant recurring theme is the development of AI-based DSSs. The majority of research have contested traditional DSSs and devised decision-making systems based on artificial intelligence.
Sustainable building, urban water management, climate change, and renewable energy evaluation have all been substantially influenced by AI-based DSSs. Automated and real-time systems enabled by artificial intelligence are also discussed in relation to buildings, agriculture, the Internet of Things, and renewable energy technologies. Scholars have combined various digital technologies to promote sustainability in the energy sector via the management of buildings, water, agriculture, IoT, and smart campuses.
Theoretical and Practical Contribution
Theoretical Contribution
Our results supplement existing work on sustainable AI and sustainable energy by delivering the following results. Results from this study provide and highlight a thematic map of the sustainable AI research topics existing in several fields, such as energy, ethics, and management. We developed a novel mixed-method approach, the contextual topic modeling and content analysis, to visualize the latent knowledge structures pertaining to AI and sustainability and energy. This yielded in a conceptual framework representing the main topics, subtopics and common terms in each topic pertaining to sustainable AI in energy. Using LDA and BERT, eight themes related to AI in the sustainability and energy sectors were discovered. We provided the most likely terms for each topic, as well as the distribution of articles and topics throughout time. Finally, by using a thematic analysis method, we identified and qualitatively analyzed the hidden themes.
Second, we examined and analyzed hidden sub-themes within each topic, as well as common themes between topics, using a content analysis method. Figure 8 illustrates the sub-domain themes within each topic, whereas Figure 9 depicts the common cross-topic themes. Our content analysis of each topic reveals six recurring themes: sustainable consumption, AI-based DSSs, forecasting models, economic and pricing problems, automated and real-time systems, and convergence with digital technology. To further our knowledge, we highlighted how these themes intersect across topics in order to articulate the commonalities across topics. These six separate but related topics demonstrate that sustainable AI solutions can be observed at a range of behavioral, decision-making, economic, operational, and technical dimensions. At the behavioral level, shifts in consumption patterns are illustrated; at the decision-making level, decision automation is outlined; at the economic level, personalized tariffing is demonstrated; at the operational level, automation and real-time operations are addressed; and at the technological level, convergence with other technologies is studied.
Practical Implications
This research provides energy engineers, social scientists, scientists, and policymakers with a variety of insights. Engineers may develop sustainable energy products and services. Energy scientists can also integrate sustainability considerations into their research and development of new energy sources such as renewable energy. In their discussions on AI and energy, social scientists may also emphasize ethical problems, including sustainability. Additionally, policymakers may create and construct new laws and policy initiatives aimed at mitigating the harmful effects of unsustainable energy on society and the environment.
Conclusion
To discover heavily discussed scholarly topics, our study utilized a new topic modeling technique. While this illustration depicts the trajectory of previous efforts, it also prompted us to propose a number of possible future research strands targeted at increasing energy sector sustainability via the application of artificial intelligence technology. The aim of this study is to further the conversation on sustainable AI and energy, as well as their intersection, in order to get a deeper understanding of how AI may be incorporated to achieve sustainability in the energy sector.
Figure 1 Figure 2
12The concatenating and encoding LDA and BERT vectors to extract contextual topics The separate and independent results of topic modeling of research on sustainable AI in energy by using TF-IDF, BERT and LDA algorithms
Figure 3 .
30 shows a representation of the topic model on sustainable AI in energy research field with respect to the overall global view. This visualization represents the topic modeling results, where topics are illustrated as clusters on a two-dimensional plane. Also shown in Figure 4 is the word cloud visualization of the topics with the most frequently used terms in each topic. Topics 1, 2, and 3 represent the greatest research interest in the model based on 8 topics and including 21.67%, 17.22%, and 15.0% of the corpus. Our research
Figure 4 .
40, the three most-covered topics by academia are topic 1: Sustainable buildings (22.5%), Topic 2: AIbased DSSs for urban water management (16.5%) and Topic 3: Climate Artificial Intelligence (14.8%). About 54% of the articles in the corpus are concerned with these three themes.
Figure 5
5depicts the ratios of all the eight topics (beginning in 2004 and extending into 2021). Since 2018 forward, topics have garnered a substantial amount of academic interest. Specifically, the first topic, which is about the design of sustainable buildings and minimizing energy usage via the application of artificial intelligence. This subject gained considerable attention between 2012 and 2014, but then slipped off the spotlight between 2015 and 2018. The discussions about AI-based evaluation of renewable energy solutions peaked around 2008 but then became less prominent until 2019. Climate artificial intelligence experienced two distinct phases, with the second one peaking in 2015 and 2016 and the first between 2009 and 2012;
Figure 5
5The evolution of topics over time
Figure 6
6Topics detected by the combination of LDA+BERT+Clustering algorithms on sustainable AI in energy sector
identify the relevant research topics in the literature on artificial intelligence for sustainability in the energy industry, we performed a contextual topic modeling combined with qualitative cluster analysis. We went beyond previous approaches in developing this novel analysis by combining three algorithms of topic modeling (LDA, BERT, and clustering) with content analysis. In this research, eight academic topics were discovered including sustainable buildings and energy consumption, AI based DSSs for sustainable urban water management, climate artificial intelligence, agriculture 4.0 and sustainable sources of energy, convergence of IoT and AI for sustainable smart cities, AI-based evaluation of renewable energy technologies, smart campus and engineering education and AI for energy optimization. Concerns and problems addressed in each topic are summarized in Figure 7. The Figure illustrates that each topic addresses a number of specific issues, which some of them overlap.
Figure 7
7Possible future streams of research pertaining to each topic
networks, genetic algorithms, and Monte-Carlo simulationTopic 1: Sustainable Buildings and
Energy Consumption
Topic 2: AI-based DSSs for Sustainable
Urban Water Management
Topic 3: Climate Artificial Intelligence
Topic 4: Agriculture 4.0 and
Sustainable Sources of Energy
Topic 5: Convergence of IoT & AI for
Sustainable Smart Cities
Topic 6: AI-based Evaluation of
Renewable Energy Technologies
topic 7: Smart Campus & Engineering
Education
Topic 8: AI for Energy Optimization
Convergence of IoT & AI for Sustainable Smart CitiesA significant step in the implementation of sustainable energy solutions is to implement smart cities and services using internet of things technology. This topic exhibits how AI and IoT operate together to drive environmental progress. Much of this topic focuses on measure such as smart buildings, smart grid systems, green IoT, and smart campuses.Topic 1: Sustainable
Buildings and Energy
Consumption
Topic 2: AI-based DSSs for
Sustainable Urban Water
Management
Topic 3: Climate Artificial
Intelligence (Climate
Informatics)
Topic 4: Agriculture 4.0 and
Sustainable Sources of
Energy
Topic 5: Convergence of IoT
& AI for Sustainable Smart
Cities
Topic 6: AI-based Evaluation
of Renewable Energy
Technologies
Topic 7: Engineering
Education & Smart Campus
Topic 8: AI for Energy
Optimization
16
Topic 5:
future study area is the confluence of smart grids, renewable energy, and 5G technology, since these technologies have the potential to generate enormous volumes of big data. Furthermore, the use of AI in transportation seems worthy of analysis, for example, with regard to traffic predictions, public transit planning, and so on.The agricultural 4.0 and sustainable energy sources are examined in Topic 4. Many problems relevant to the subject of "prosperity, sustainable consumption, forecasting, and convergence with other automated and realtime technologies" are covered in this topic. There is only a limited body of studies dedicated to precision farming and digital mapping, but both developments promise to lead to better knowledge of the environment and to improved energy management. Precision farming by assessing soil nutrients, detecting humidity in the air, and monitoring crops allows farmers to leverage digital maps for better energy management and fight against climate change. Other related areas of study include developing automated working environments. It is worthwhile to investigate the effect that artificial intelligence and other green technologies will have onthe working conditions of farmers and farm operators, since AI may help with deeper speculations of working conditions in farms.In Topic 5, convergent IoT and AI technologies for smart city development were addressed. The primary goal of this topic was to discuss issues around sustainable consumption, LCA analysis, and the development of intelligent energy grids. Pervasive Wi-Fi connection, due to its ability to save energy, is critical in this subject. Additionally, a significant problem is open data sharing in energy management. AI-based assessment of renewable energy technologies, such as DSSs, financial problems, sustainable consumption, and automated and real-time systems are all issues in this topic that focus on renewable energy. One potential study path in this topic involves the challenges that AI algorithms and models face when attempting to evaluate renewable energy solutions. Other sophisticated AI systems, such as deep learning, make use of supervised learning using human-annotated data, and thus they are limited when it comes to complicated situations.The subject of smart campus and engineering education is examined in the seventh topic. Labs that facilitate continuous innovation are discussed in this article, as well as the idea of sustainable consumption, AI skills, and convergence with other technologies. There is an imperative requirement for further research to clarify how AI might be leveraged for practical learning and training for a range of stakeholders across businesses, farmers, residents, and employees in relation to energy management. AI is discussed in relation to energy optimization in Topic 8 of the study. This subject covers many elements of sustainable optimization, including forecasting, consumption, affordable pricing, and societal and financial impacts.Topic 2 addresses sustainable urban water management via the use of AI-based DSSs. Conventional DSSs
were under criticism from academics who suggested alternatives, and innovative approaches to DSSs were
revealed, particularly with regard to water utilities in a smart city. The second discussion point, focused on
sustainable consumption and real-time and predictive modeling, is also addressed in topic 2. Mitigating
urban problems, notably air pollution, waste management, and wastewater management, are applicable here
to exemplify how smart energy management leveraging AI improves environmental sustainability. Topic 3
deals with the connection between climate change and artificial intelligence, and the emergence of the climate
informatics field. This topic highlights the role of trustworthy of explainable AI algorithms, an issue which is
marginalized in other topics. As a result, a future potential study direction may be the development of ethical
artificial intelligence in other topics to help with the sustainable management of energy. One prospective
Figure 5 Sub-themes extracted from each topic
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EFFET DU CHLORURE DE SODIUM (NaCl) SUR LA CROISSANCE DE SIX ESPECES d'Acacia EFFECT OF SODIUM CHLORIDE (NaCl) ON THE GROWTH OF SIX Corresponding and Author
|Received | 01 March 2017| |Accepted | 29 March 2017| |Published 10 April 2017 |
|khalil Chérifi
Laboratory of Biotechnology and Valorization of Natural Resources | Faculty of sciences | Ibn Zohr University |
P.O. Box 8106 | 8000Agadir| Morocco |
|abdelmjid Anagri
Laboratory of Biotechnology and Valorization of Natural Resources | Faculty of sciences | Ibn Zohr University |
P.O. Box 8106 | 8000Agadir| Morocco |
| El
Houssine Boufous
American Journal of Innovative Research and Applied Sciences. ISSN
Department of Biochemistry and Microbiology | Laval University | Quebec City (Quebec)
2429-5396 I www.american-jirasCanada |
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| Abelhamid
El Mousadik
Laboratory of Biotechnology and Valorization of Natural Resources | Faculty of sciences | Ibn Zohr University |
P.O. Box 8106 | 8000Agadir| Morocco |
Acacia Species
American Journal of Innovative Research and Applied Sciences. ISSN
2429-5396I www.american-jiras
EFFET DU CHLORURE DE SODIUM (NaCl) SUR LA CROISSANCE DE SIX ESPECES d'Acacia EFFECT OF SODIUM CHLORIDE (NaCl) ON THE GROWTH OF SIX Corresponding and Author
|Received | 01 March 2017| |Accepted | 29 March 2017| |Published 10 April 2017 |105 ORIGINAL ARTICLE See: http://creativecommons.org/licenses/by-nc/4.0/Mots-clés: Tolérance à la salinitéVariabilitéAcaciaAmélioration des plantesRéhabilitation Keywords: Salt toleranceVariabilityAcaciaPlant breedingRehabilitation
RESUMEIntroduction : Au cours de ces dernières décennies on assiste à une diminution progressive des superficies cultivables dans les régions arides et semi-arides à cause de l'accumulation des sels liée à la rareté des précipitations, au mauvais drainage, à la sècheresse prolongées et à l'absorption de l'eau par les plantes. Contexte : Devant l'ampleur de ce problème, il s'avère donc nécessaire de proposer des programmes d'évaluation et de conservation des espèces menacées d'extinction. Le repérage d'espèces plus adaptées et la sélection des variétés tolérantes à la salinité resteraient la voie économique la plus efficace pour l'exploitation des terrains affectés. Objectifs : L'objectif de cette étude est de déterminer la capacité de tolérance à la salinité au cours du développement végétatif chez six espèces appartenant au genre Acacia. Ceci dans le but d'élaborer une classification des seuils de tolérance au stress salin, critère important dans le choix des espèces à retenir dans un programme de mise en valeur des zones affectées par la salinité. Méthodes : L'effet du stress salin a été abordé sur un certain nombre de caractères agro-morphologiques en conditions contrôlées. Les concentrations de NaCl appliquées, en plus du témoin, sont : 100 mM, 200 mM, 300 mM et 400 mM. Résultats : Les résultats ont montré une variabilité non négligeable dans le comportement des plantes des différentes espèces en fonction du stress salin. Chez les six espèces d'Acacia, le sel a exercé un effet dépressif sur tous les paramètres de croissance étudiés. Toutefois, le taux de réduction diffère selon l'intensité de stress salin et le degré de sensibilité ou de tolérance de l'espèce. La croissance en hauteur, le nombre de feuilles et la biomasse sèche totale sont vraisemblablement les paramètres les plus affectés. Cependant, il est important de signaler que toutes les espèces d'Acacia considérées dans ce travail ont survécu, même à 400 mM de NaCl, et ont présenté différents degrés de tolérance à la salinité. Dans cette situation, les espèces A. horrida et A. raddiana s'avèrent globalement les plus performantes au stade végétatif. Conclusions : La variabilité génétique, dévoilée par ces espèces dans les différentes conditions de stress salin, permettrait un choix d'écotypes pouvant entrer dans des schémas de sélection et d'amélioration variétale pour la réhabilitation des parcours dégradés surtout en zones affectées par la salinité.ABSTRACTBackground: Salinity is one of the major abiotic stresses affecting plant production in arid and semi-arid regions. It causes reduction of cultivable area and combined with other factors, presents a serious threat to food stability in these areas. Context: In front of this problem, the selection of salt tolerant species and varieties remains the best economic approach for exploitation and rehabilitation of salt-affected regions. Objective: The purpose of this study was to assess and compare the seed germination response of six Acacia species under different NaCl concentrations in order to explore opportunities for selection and breeding salt tolerant genotypes. Methods: The salinity effect was examined by measuring some agro-morphological parameters in controlled growth environment using five treatment levels: 0, 100, 200, 300 and 400 mM of NaCl. Results: The analyzed data revealed significant variability in salt response within and between species. All growth parameters were progressively reduced by increased NaCl concentrations. Growth in height, leaf number and total plant dry weight were considered as the most sensitive parameters. However, the growth reduction varied among species in accordance with their tolerance level. It is important to note that all species survived at the highest salinity (400 mM). Whereas A. horrida and A. raddiana were proved to be often the best tolerant, they recorded the lowest reduction percentage at this stage. Conclusion: The genetic variability found in the studied species at seedling stage may be used to select genotypes particularly suitable for rehabilitation and exploitation of lands affected by salinity.
INTRODUCTION
La salinisation est un processus important de dégradation des sols. Elle constitue un facteur limitant à la croissance et au développement des plantes. Les conséquences de ce phénomène qui ne cesse de prendre de l'ampleur, se manifestent par la toxicité directe due à l'accumulation excessive des ions (Na + et Cl -) dans les tissus des organes, et à un déséquilibre nutritionnel imputable essentiellement à des compétitions entre les éléments minéraux, tel que le sodium avec le potassium et le calcium, le chlorure avec le nitrate, le phosphate et le sulfate [1,2]. En conséquence, les glycophytes les plus tolérantes seront celles qui, tout en utilisant le Na + comme osmoticum, conserveront une forte sélectivité vis à vis du K + [3]. La stratégie utilisée par les végétaux pour éviter les problèmes d'excès d'ions tout en réalisant leur équilibre osmotique est la compartimentation cellulaire, qui se traduit par une accumulation préférentielle du Na + dans la vacuole [4]. Cependant, chez les glycophytes tolérantes, on discerne également une compartimentation à l'échelle de la plante, surtout dans les organes jeunes où la teneur en Na+ reste faible [5,6].
La variabilité pour la tolérance à la salinité a été étudiée chez beaucoup d'espèces [5,[7][8][9][10][11]. Pour des raisons pratiques, de nombreuses explorations de cette variabilité ont été abordées au stade végétatif sur des plantes très jeunes. Cette approche est justifiée par le fait que la réponse des plantules est parfois fortement prédictive de celle des plantes adultes [12][13][14]. Dans certains cas, l'écart entre la tolérance au stade plantule et celle au stade adulte peut justifier les différences entre les mécanismes impliqués d'un stade de développement à un autre [15][16][17][18][19]. Plusieurs recherches ont montré que la croissance en hauteur [20,21], la production de biomasse des tiges et des racines [22,23] est négativement affectée par l'augmentation de la salinité.
Face à la salinisation des sols qui constitue l'un des facteurs abiotiques majeurs réduisant le rendement agricole, l'introduction d'espèces végétales tolérantes à la salinité est une stratégie alternative recommandée pour valoriser les sols touchés par ce phénomène. Cette approche, permettraient d'améliorer le couvert végétal et résoudre les problèmes de régénération de certaines espèces forestières en zones arides et semi-arides, particulièrement celles appartenant au genre Acacia qui représentent certainement une richesse écologique menacée en Afrique du Nord [24]. Acacia, appartenant à la famille des Fabacées, est identifié comme étant un genre cosmopolite, varié et riche. Ces espèces à usage multiple peuvent coloniser des sols pauvres grâce à leur capacité de fixer l'azote atmosphérique par leur association symbiotique avec le Rhizobium des nodosités racinaires. Au Maroc les écosystèmes à base d'Acacia représentent un enjeu stratégique pour les régions semi-arides, arides et sahariennes du pays aussi bien sur le plan écologique que socio-économique. Ces écosystèmes rares et originaux, peuvent constituer une protection naturel contre la désertification.et fournir un intérêt multifonctionnel et multi-usager, tels que leurs utilisations dans la production de bois d'énergie et de service, de gomme arabique, de substances pharmaceutiques, de fourrage, de produits mellifères, ainsi que leur utilisation en reboisement et en foresterie urbaine [25]. La maîtrise des exigences de croissance des plantules est une étape importante dans le succès des opérations de reboisement de ces espèces. Ce stade, très important pour le développement des plantes, est surtout contrôlé par des facteurs génétiques et environnementales, en particulier la salinité [26]. Malheureusement, au Maroc, peu de travaux de recherche sur le degré de tolérance à la salinité chez les Acacia ont été effectués. Notre présente étude s'inscrit dans le cadre d'une évaluation de la variabilité des réponses au stade plantule de six espèces d'Acacia soumises à des doses croissantes de NaCl. L'effet du stress salin a été abordé sur un certain nombre de caractères agro-morphologiques en conditions contrôlées. Ceci dans le but d'identifier le matériel végétal le plus performant pour des programmes de restauration et de valorisation de la productivité végétale, particulièrement pour la réhabilitation des parcours dégradés, c'est le cas d'Acacia gummifera et Acacia raddiana, ainsi que pour des projets de reboisements en milieux affectés par la salinité, dans le cas des espèces exotiques, comprenant Acacia eburnea, Acacia cyanophylla, Acacia cyclops et Acacia horrida.
MATERIELS ET METHODES
Matériel végétal
L'analyse de la diversité de la tolérance à la salinité a porté sur deux espèces autochtones, représentées par Acacia gummifera et Acacia raddiana et quatre espèces exotiques, représentées par Acacia eburnea, Acacia cyanophylla, Acacia cyclops et Acacia horrida. La majeure partie des graines des différentes espèces testées ont été collectées sous forme de gousses dans différentes régions du sud-ouest marocain (Tableau 1). Elles nous ont été aimablement fournies par la station régionale des semences forestières de Marrakech et ont été conservées au froid (4°C) jusqu'à l'analyse.
Caractères mesurés
Après huit semaines de culture, quatre paramètres ont été mesurés dans différentes conditions de stress salin (Tableau 2). Les caractères retenus se rapportent au développement végétatif des plantes ainsi qu'à l'estimation de la valeur fourragère : Nombre totale de feuilles (Nbr.Fll). Ces mêmes critères ont été aussi utilisés par d'autres auteurs dans l'estimation de la croissance de la biomasse chez certaines espèces d'Acacia cultivées sous stress salin [29,30].
Tableau 2:
Le tableau montre les paramètres mesurés dans l'évaluation de l'effet de la salinité au stade végétatif.
Code des caractères Signification
Analyses statistiques
Les six espèces ont été traitées selon un dispositif complètement randomisé, à raison de 10 plantes par population et par traitement. Les données relatives aux pourcentages de réduction des différents paramètres de croissance ont été transformées en arcsin racine carrée avant d'être soumises à l'analyse de variance à deux critères de classification (espèce et [NaCl]). La comparaison des moyennes entre les différentes espèces, pour chaque traitement, a été réalisée par le test de Newman et Keuls. Pour chaque concentration, les espèces dont les moyennes ne sont pas significativement différentes ont été regroupées dans une même ellipse sur les graphiques. Les traitements des données ont été réalisés par le logiciel Statistica (Version 6) [31].
RESULTATS
Le tableau 3 résume l'analyse de variance de l'effet espèce et de l'effet NaCl ainsi que leur interaction. Pour l'ensemble des caractères, le teste ANOVA montre un effet très hautement significatif entres les espèces et entre les différents traitements de sel. Ces différences sont plus marquées dans le cas des caractères se rapportant à la croissance en longueur des plantules. De la même manière, l'interaction (NaCl * Espèce) révèle aussi un effet très hautement significatif pour les caractères longueur de la tige et le nombre de feuilles et un effet significatif si on considère le poids total de la matière sèche tandis que pour le diamètre du collet l'interaction n'est pas significative. En conséquence, pour ce dernier critère, la salinité agit sur les différentes espèces de la même manière, quel que soit la concentration. Pour le reste des paramètres étudiés, l'effet de NaCl diffère d'une espèce à l'autre.
Tableau 3: Le tableau montre l'analyse de la variance à deux critères de classification abordée sur les différents caractères végétatifs mesurés chez les espèces d'Acacia étudiées.
Caractères
Diamètre du collet (D.Coll)
L'augmentation de la concentration en sel dans la solution d'eau d'irrigation, diminue de façon significative le taux de croissance relative du diamètre du collet chez toutes les espèces considérées (figure 4). Pour ce critère on note une réduction plus faible par rapport aux autres caractères étudiés. À des concentrations allant de 200 à 400 mM, la réduction du diamètre du collet chez A. gummifera est significativement plus importante que celles des autres espèces. En effet, à 400 mM on note une régression qui peut atteindre 52% chez cette espèce comparativement à A. horrida chez laquelle la régression n'a été que de 14%. Les espèces A. horrida, A. raddiana et A. cyanophylla forment un groupe homogène et réagissent de la même manière au stress salin avec les réductions les plus faibles.
Nombre de feuilles (Nbr.Fll)
Pour ce caractère, on note une classification moins nette des différentes espèces surtout au niveau des concentrations modérées en sel (figure 5). L'espèce A. cyanophylla semble encore une fois la plus affectée par le sel et montre une réduction de sa production foliaire atteignant les 86% à la concentration 400 mM de NaCl. En générale cette espèce exhibe la plus grande sensibilité à la salinité à ce stade de développement. D'un autre côté, A. raddiana a présenté une faible réduction du nombre de feuilles par plante à la concentration 300 mM de NaCl. On note, dans ce cas, une réduction de 33% chez l'espèce la plus tolérante contre 63% chez A. cyanophylla pour atteindre à la concentration de 400 mM une réduction de 48% chez A. raddiana et 86% chez l'espèce la plus sensible.
Biomasse totale (PS)
L'augmentation de la concentration de NaCl a un effet significatif sur la biomasse sèche de toutes les parties de la plante (feuilles, tiges et racines) des espèces testées (figure 6). La plus grande variabilité a été observée pour les deux concentrations 300 et 400 mM de NaCl. Comme pour les caractères nombre de feuilles et longueur de la tige, A. cyanophylla se distingue, encore une fois, des autres espèces en affichant pour ce critère, toutes concentrations confondues, les réductions les plus élevées. Par ailleurs, A. horrida et A. raddiana semblent être, en générale, moins affectées pour ce caractère, elles ont montrée de ce fait les plus faibles régressions de la matière sèche totale élaborée, variant en moyenne entre 13 et 34% contre 40 et 77% pour A cyanophylla, la plus sensible. Les autres espèces occupent une situation intermédiaire et montrent une grande variabilité au niveau des doses élevées en NaCl (400 mM) par rapport aux concentrations faibles.
DISCUSSIONS
Les résultats présentés dans cette partie, montrent que la salinité réduit en générale la croissance des plantules chez l'ensemble des espèces étudiées. Néanmoins, une grande variabilité entre les espèces a été révélée à ce stade. Les interactions très hautement significatives entre les deux effets (espèce*salinité), observées dans notre cas, montre la possibilité d'une sélection essentiellement sur la base des caractères : Longueur de la tige, nombre de feuille et la biomasse totale. Toutefois, on a constaté qu'une espèce performante pour un caractère donné n'est pas forcément la meilleure pour un autre critère. C'est le cas d'A. cyanophylla dont le nombre de feuilles, la biomasse totale et la longueur de la tige paraissaient relativement affectée par le sel, mais dans le cas du caractère relatif au diamètre du collet elle était classée parmi les espèces les plus tolérantes.
Nos résultats sont en concordance avec les travaux de Nguyen et al. [32] dans lesquels ils ont révélés que les deux espèces Acacia auriculiformis et Acacia mangium ont réagi également par une réduction de la croissance de la partie aérienne en réponse au stress salin. Cet effet est fréquent chez les glycophytes [33], où la diminution de la croissance de l'appareil végétatif observée peut être expliquée par une augmentation de la pression osmotique provoquée par NaCl, ce qui bloque l'absorption de l'eau par les racines. Les plantes s'adaptent ainsi au stress salin par la réduction de leur croissance afin d'éviter les dommages causés par le sel [34,35]. Les effets de la salinité sur la croissance des plantules cultivés en conditions semi contrôlées, dépendent de plusieurs facteurs. Ils varient selon la teneur de NaCl appliquée, l'espèce, la provenance, le stade végétatif et la partie de la plante [2]. Les effets de la salinité se manifestent principalement par un ralentissement de la croissance de l'appareil végétatif. D'autre part, il est important de signaler que toutes les espèces d'Acacia considérées dans ce travail ont survécu, même à 400 mM de NaCl, alors que selon Ghulam et al. [36], ce niveau de salinité a été nuisible pour A. nilotica tandis que A. ampliceps tolère cette concentration. La comparaison de la tolérance à la salinité chez cinq espèces d'Acacia : A. ampliceps, A. salicina, A. ligulata, A. holosericea et A. mangium a révélé que A. ampliceps était la plus tolérante et a survécu même à 428 mM en NaCl, concentration à laquelle toutes les autres espèces ont été sévèrement affectées [37].
Pour le paramètre matière sèche, les réductions les plus faibles sont enregistrées chez A. horrida et A. raddiana. Le pourcentage de réduction de la matière sèche, généralement considéré comme indice de sensibilité des plantes vis-à-vis du stress salin, montre que la concentration 400 mM NaCl est insuffisante pour engendrer une réduction relative de 50 % par rapport aux témoins, seuil très utilisé pour le classement de la tolérance des plantes [21]. En outre, même à une concentration plus faible (300 mM) les autres espèces manifestent des réductions plus marquées.
Les feuilles sont les parties les plus sensibles de la plante au stress salin. Cependant, chez l'ensemble des espèces on assiste à une réduction significative du nombre de feuilles par rapport au témoin surtout à partir de 300 mM de NaCl. Des résultats similaires ont été rapportés sur d'autres espèces par [38][39][40][41]. D'autre part, au cours de l'expérimentation on a constaté que la croissance foliaire est également très affectée par l'augmentation du stress salin quelle que soit l'espèce. L'expansion des feuilles est considérablement inhibée par le sel, les nouvelles feuilles se développent lentement et le vieillissement des feuilles âgées s'accélère. D'ailleurs, quand la surface foliaire est réduite par la salinité, la production de carbohydrates devient insuffisante pour supporter la croissance et le rendement [42].
La réduction de la croissance, dans les conditions d'un stress salin est attribuée à plusieurs facteurs, parmi lesquelles l'accumulation des ions, aussi bien en Na + qu'en Clà des teneurs élevées dans les tissus foliaires qui est la cause principale des contraintes ioniques au niveau des tissus de la plante [43]. Selon ces auteurs, le stress salin cause un déséquilibre nutritionnel qui en résulte l'inhibition de l'absorption des éléments nutritifs essentiels comme le Ca 2+ , K + , Mg 2+ , NO3par les phénomènes de compétition minérale de fixation apoplasmique [44]. En outre, il est établi qu'un supplément de Ca 2+ dans le milieu de culture améliore les conditions de croissance sous stress salin [45]. Le dérèglement de l'absorption du calcium inhibe également l'établissement de la nodulation et la fixation d'azote chez les légumineuses [46]. Il parait que cet ion est impliqué dans le processus de la reconnaissance Rhizobium-poil absorbant [47].
Par ailleurs, la diminution de la croissance des parties aérienne peut aussi être expliquer par des perturbations des taux des régulateurs de croissance dans les tissus, particulièrement l'acide abscissique et les cytokinines induites par le sel [48], mais aussi à une diminution de la capacité photosynthétique provoqué par la diminution de la conductance stomatique de CO 2 sous la contrainte saline. Dans tous les cas, cette réduction de la croissance des différentes parties aériennes est considérée comme une stratégie adaptative nécessaire à la survie des plantes exposées à la salinité [49]. Ceci permet à la plante d'emmagasiner de l'énergie nécessaire pour faire face au stress afin de réduire les dommages irréversibles occasionnés, quand le seuil de la concentration létale est atteint [39].
De plus, il a été constaté que la tolérance au stade germination, dans les conditions de nos expériences, ne reflète pas dans tous les cas celle au stade végétatif. En effet, l'espèce A. horrida, classée parmi les plus sensibles au sel durant la germination, manifeste une tolérance importante vis-à-vis de NaCl au stade plantule. Par contre l'espèce A. raddiana, reconnue par sa tolérance à la salinité, très marquée, au stade germination, conserve globalement sa performance aux stades avancés. Cependant, la germination sous contrainte saline n'est pas suffisante pour identifier des espèces tolérantes au sel [26,50]. Dans ce contexte, de nombreux auteurs ont montré que la réponse à la salinité variait selon le stade de développement de la plante [51][52][53]. Toutefois, la germination et les premiers stades de la croissance seraient les phases les plus sensibles [54].
CONCLUSION
Le stress salin exerce chez les Six espèces d'Acacia un effet dépressif sur tous les paramètres de croissance étudiés. Toutefois, le taux de réduction diffère selon l'intensité de stress salin et le degré de sensibilité ou de tolérance de l'espèce. La croissance en hauteur, le nombre de feuilles et la biomasse sèche totale sont vraisemblablement les plus affectés. Cependant, toutes les espèces d'Acacia considérées dans ce travail ont survécu, même à 400 mM de NaCl et présentent différents degrés de tolérance à la salinité. Les espèces A. horrida et A. raddiana s'avèrent globalement les plus performantes. Cette variabilité génétique dévoilée par ces espèces dans les différentes conditions de stress salin, surtout sous des seuils élevés en NaCl allant jusqu'à 400 mM, constitue un atout intéressant qui peut être utilisé aussi bien dans le choix des espèces à retenir pour améliorer la tolérance à la salinité que dans les programmes de valorisation et de réhabilitation des sols salés.
Par ailleurs, ces recherches doivent être poursuivies par des expériences à des stades végétatifs plus avancés afin de confirmer les résultats constatés aux stades germination et juvénile. Toutefois, le dosage et l'identification des osmorégulateurs tels que la proline, pourrait mieux éclaircir les mécanismes d'ajustement osmotique nécessaires à ces plantes pour s'adapter au stress salin. Cela pourrait expliquer, par la même, leur tendance à l'halophilie, observée au cours des deux stades étudiés.
Reconnaissance : Nous exprimons toute notre reconnaissance et nos remerciements aux responsables de la Station Régionale des Semences Forestières de Marrakech (Maroc) pour leurs informations et orientations au cours des prospections pour la récolte des gousses.
Diamètre au collet (D.Coll) : Le diamètre au collet en (mm), mesuré à l'aide d'un pied à coulisse (figure 1). Longueur finale de la tige (Lg.Tige) : Mesurée en utilisant une règle graduée du collet à l'insertion du méristème apicale (figure 2). Poids de la matière sèche (PS) : Ce paramètre est nettement plus fiable et plus simple. La biomasse totale (en mg de matière sèche) est séchée dans l'étuve à 87°C puis pesée 48 heures plus tard.
Figure 1 :
1Mesure du diamètre au collet à l'aide d'un pied à coulisse.
Figure 2 :
2Mesure de la longueur de la tige à l'aide d'une règle graduée.
3. 1 .
1Etude des caractères pris séparément pour l'ensemble des espèces étudiées 3.1.1. Longueur de la tige (Lg.Tige) L'analyse de variance pour la longueur de la tige révèle un effet très hautement significatif des deux facteurs, salinité et espèce ainsi que leur interaction. Le classement des différentes espèces par le test de Newman et Kheuls selon leur tolérance à la salinité, montre une régression de la croissance en hauteur de la tige des plantules chez les différentes espèces étudiées (figure 3). Cependant, les réductions les plus importantes ont été notées pour A. cyanophylla, surtout dans le cas des fortes concentrations. Elle enregistre jusqu'à 92% pour une concentration de 400 mM. Par ailleurs, A. horrida semble la moins perturbée par le sel, du moins pour ce caractères, et affiche les valeurs les plus faibles. Les pourcentages de réduction varient dans ce cas entre 10 % et 47 %, respectivement pour les concentrations 100 et 400 mM. Le reste des espèces réagissent avec modération et occupent une situation relativement intermédiaire entre ces deux dernières espèces.
Figure 3 :
3Représentation des moyennes calculées pour chaque espèce étudiées par traitement de NaCl pour la longueur de la tige. (Les moyennes des espèces groupées dans la même ellipse ne sont pas significativement différentes selon le test de Newman et Keuls à 5%).
Figure 4 :
4Représentation des moyennes calculées pour chaque espèce étudiées par traitement de NaCl pour le diamètre du collet. (Les moyennes des espèces groupées dans la même ellipse ne sont pas significativement différentes selon le test de Newman et Keuls à 5%).
Figure 5 :
5Représentation des moyennes calculées pour l'ensemble des espèces étudiées par traitement en NaCl pour le nombre de feuilles. (Les moyennes des espèces groupées dans la même ellipse ne sont pas significativement différentes selon le test de Newman et Keuls à 5%).
Figure 6 :
6Représentation des moyennes calculées pour l'ensemble des espèces étudiées par traitement en NaCl pour la biomasse totale (PS). (Les moyennes des espèces groupées dans la même ellipse ne sont pas significativement différentes selon le test de Newman et Keuls à 5%).
Tableau 1 :
1Le tableau montre la localisation géographique des différents échantillons d'espèces étudiées.Espèces
Provenance
Région de provenance
A. gummifera
Reserve de faune de Rmila (Marrakech)
Haut Atlas occidental
A. raddiana
Reserve de faune de Rmila (Marrakech)
Haut Atlas occidental
A. cyclops
Région d'Essaouira
Souss Nord
A. cyanophylla
Canal de Rocade (Marrakech)
Haut Atlas occidental
A. horrida
Commune rurale de Saada (Marrakech)
Haut Atlas occidental
A. eburnea
Commune rurale de Saada (Marrakech)
Haut Atlas occidental
Lg.Tige D.Coll Nbr.Fll PS Longueur de la tige Diamètre au collet Nombre totale de feuilles Biomasse totale (en mg de matière sèche)
CM : Carré moyen ; NS : test non significatif ; * : test significatif ; *** : test très hautement significatifCM Espèce
CM Sel
CM Interaction
F Espèce
F Sel
F Interaction
Lg.Tige
11439,2
20596,0
555,2
121,697***
219,111***
5,907***
D.Coll
6051,7
2732,7
114,2
19,932***
9,000***
0,376 NS
Nbr.Fll
3425,2
11918,6
317,0
32,291***
112,363***
2,988***
PS
6379,6
10101,6
279,3
42,478***
67,261***
1,860*
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Improving drought tolerance of germinating seeds by exogenous application of gibberellic acid (GA3) in rapeseed (Brassica napus L. Z Li, G Lu, X Zhang, C Zou, Y Cheng, P Zheng, Seed Science and Technology. 38Li, Z., Lu, G., Zhang, X., Zou, C., Cheng, Y., and Zheng, P., Improving drought tolerance of germinating seeds by exogenous application of gibberellic acid (GA3) in rapeseed (Brassica napus L.). Seed Science and Technology, 2010. 38(2): p. 432-440. Available on: http://www.ingentaconnect.com/content/ista/sst/2010/00000038/00000002/art00016
Effet du Chlorure de sodium (NaCl) sur la croissance de six espèces d'Acacia. Citer, K Chérifi, A Anagri, E Boufous, A El Mousadik, American Journal of Innovative Research and Applied Sciences. 44Citer cet article: Chérifi K., Anagri, A., Boufous E. and El Mousadik A. Effet du Chlorure de sodium (NaCl) sur la croissance de six espèces d'Acacia. American Journal of Innovative Research and Applied Sciences. 2017; 4(4): 105-113.
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"\nScenario-based Optimization Models for Power Grid Resilience to Extreme Flooding Events\n\n\nAshu(...TRUNCATED)
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"\nMémoire de Mastère\n2016-2017 5 Oct 2018\n\nMémoire de Mastère\n2016-2017 5 Oct 2018Soutenu l(...TRUNCATED)
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arxiv
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"\nCosmologia e Representação 1\n\n\nMarcelo Byrro Ribeiro \nInstituto de Física Universidade Fed(...TRUNCATED)
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arxiv
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"\nNovel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data (...TRUNCATED)
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"\nEffect of Rain Scavenging on Altitudinal Distribution of Soluble Gaseous Pollutants in the Atmosp(...TRUNCATED)
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"\nL'usage de la combinatoire chez Girard Desargues : le cas du théorème de Ménélaüs\n20 janvie(...TRUNCATED)
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arxiv
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"\nEffective radiative forcing in the aerosol-climate model CAM5.3- MARC-ARG\n\n\nBenjamin S Grandey(...TRUNCATED)
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arxiv
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