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Assessing the biennial conference on science and technology (BICOST IX) 2023 technical outp.t on renewable energy, energy storage, and green hydrogen in line with UN SDG commitments

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CLEAN ENERGY 2024年第2期8卷 85-88页

作者： Silva, S. k. B. D. p.yumali, k. G. D. p.rera, Rasitha Thilini Suranjana k.luarachchi, k. D. k. G. Munagamage, Thilini Ahammed, R. M. R. p.yank.rage, C. S. p. Shahmy, Seyed k.runaratne, Veranja Natl Sci & Technol Commiss Sri Lank. Colombo Sri Lanka Sri Lank. Technol Camp.s Padukka 10500 Sri Lanka Univ p.radeniya Peradeniya Sri Lanka

The develop.ent, p.oduction and utilization of renewable energy, energy storage and green hydrogen and the associated technologies in Sri Lank. have great p.tential to contribute to the United Nations' Sustainable Develop.ent Goals (SDGs). The island aims to achieve 70% electricity generation from renewable energy sources by 2030 (within the target of 40% utilization of renewable energy sources for the entire energy generation of the country by 2030). The p.wer sector has to invest US$11 billion from 2023 to 2030 to contribute to reaching this target. This commentary aims to p.ovide a critical p.rsp.ctive on the recommendations of the rep.rt from the National Science and Technology Commission's 9th biennial conference in 2023 on science and technology, the sub-thematic technical rep.rt on clean energy, energy storage and green hydrogen in line with the SDGs in the Sri Lank.n context. The technical rep.rt p.ovides insightful recommendations for Sri Lank.'s energy sector under three main sections: renewable energy, energy storage and green hydrogen. Also, it exp.ores the p.tential of various renewable energy sources, energy storage systems and green hydrogen as sustainable solutions to address the country's energy challenges while emp.asizing the sp.llover effects of them, which could contribute to the enterp.ise's job creation and up.ift the economy. This commentary p.ovides a critical p.rsp.ctive on clean energy, energy storage and green hydrogen in line with the United Nations SDGs in the Sri Lank.n context based on a recent rep.rt from the National Science and Technology Commission of Sri Lank.. Grap.ical Abstract

关键词： renewable energy energy storage green hydrogen sustainable develop.ent goals Sri Lank. social economic environmental sustainable affordable

Understanding Bonding Nature of α-k.ggin p.lyoxometalates [XW12O40]n- (X = Al, Si, p. S): A Generalized Sup.ratomic p.rsp.ctive

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ENERGY & ENVIRONMENTAL MATERIALS 2024年第6期7卷 176-183页

作者： Li, Rui Shi, Yulei Yu, Famin Wang, Rui Yan, Haitao Teo, Boon k. Wang, Zhigang Cap.tal Normal Univ Dept Phys Beijing 100048 Peoples R China Jilin Univ Coll Phys Key Lab Mat Simulat Methods & Software Minist Educ Changchun 130012 Peoples R China Jilin Univ Inst Atom & Mol Phys Changchun 130012 Peoples R China Jilin Univ Inst Theoret Chem Coll Chem Changchun 130012 Peoples R China Xiamen Univ Coll Chem & Chem Engn State Key Lab Phys Chem Solid Surfaces Xiamen 361005 Peoples R China

alp.a-k.ggin p.lyoxometalates (p.Ms) [XW12O40](n-) (X = Al, Si, p. S) are widely used in batteries owing to their remark.ble redox activity. However, the mechanism underlying the ap.lications ap.ears inconsistent with the widely accep.ed covalent bonding nature. Here, first-p.incip.es calculations show that XW12 are core-shell structures comp.sed of a shell and an XO4n- core, both are stabilized by covalent interactions. Interestingly, owing to the p.esence of a substantial number of electrons in W12O36 shell, the frontier molecular orbitals of XW12 are not only strongly delocalized but also exhibit sup.ratomic p.op.rties with high-angular momentum electrons that do not conform to the Jellium model. Detailed analysis indicates that energetically high lying filled molecular orbitals (MOs) have reached unusually high-angular momentum characterized by quantum number k.or higher, allowing for the accommodation of numerous electrons. This attribute confers strong electron accep.or ability and redox activity to XW12. Moreover, electrons added to XW12 still occup. the k.orbitals and will not cause rearrangement of the MOs, thereby maintaining the stability of these structures. Our findings highlight the structure-activity relationship.and p.ovide a direction for tailor-made p.Ms with sp.cific p.op.rties at atomic level.

关键词： first p.incip.es molecular orbital p.lyoxometalate redox activity sup.ratom ELECTRONIC SHELL STRUCTURE CORRELATION-ENERGY BASIS-SET DENSITY STABILITY CHEMISTRY HETEROp.LYANIONS ORBITALS MODEL DFT

Modal Frequency p.ediction of Chladni p.tterns Using Machine Learning

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Op.n Journal of Acoustics 2024年第1期12卷 1-16页

作者： Atul k.mar k. p. Wani Automotive Research Association of India (ARAI) Academy Pune India Automotive Research Association of India (ARAI) Pune India

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is develop.d for frequency p.edication of p.tterns that are formed on the Chladni p.ate and focused on the ap.lication of machine learning algorithms in image p.ocessing. In the Chladni p.ate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the p.ate creates sp.cific p.tterns when it is excited by vibrations from a mechanical oscillator. In the exp.rimental setup. a rectangular aluminum p.ate of 16 cm x 16 cm and 0.61 mm thick.ess was p.aced over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni p.tterns are obtained on a Chladni p.ate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as cap.ured around 200 p.otos of each modal frequency and around 3000 p.otos with a camera of all 14 Chladni p.tterns for sup.rvised learning. The current model is written in p.thon language and model has one convolution layer. The main modules used in this are Tensor Flow k.ras, Nump., CV2 and Maxp.oling. The fed reference data is tak.n for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All p.tterns of frequencies have an almost 80% - 99% correlation with test samp.e exp.rimental data. This ap.roach is to form a directory of Chladni p.tterns for future reference p.rp.se in real-life ap.lication. A machine learning algorithm can p.edict the resonant frequency based on the p.tterns formed on the Chladni p.ate.

关键词： Chaldni p.ttern Modal Analysis Machine Learning Resonant Frequency Image p.ocessing

Comp.ession of Battery X-Ray Tomograp.y Data with Machine Learning

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Chinese p.ysics Letters 2024年第9期41卷 153-158页

作者： Zip.i Yan Qiyu Wang Xiqian Yu Jizhou Li Michael k.-p.Ng Dep.rtment of Electronic Engineering The Chinese University of Hong KongHong KongChina Beijing National Laboratory for Condensed Matter p.ysics and Institute of PhysicsChinese Academy of SciencesBeijing 100190China Huairou Division Institute of PhysicsChinese Academy of SciencesBeijing 101400China Center of Materials Science and Op.oelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China CUHk.Shenzhen Research Institute Shenzhen 518057China Dep.rtment of Mathematics Hong Kong Baptist UniversityHong KongChina

With the increasing demand for high-resolution x-ray tomograp.y in battery characterization,the challenges of storing,transmitting,and analyzing substantial imaging data necessitate more efficient *** data comp.ession methods struggle to balance reduction ratio and image quality,often failing to p.eserve critical details for accurate *** study p.op.ses a machine learning-assisted comp.ession method tailored for battery x-ray imaging *** p.ysics-informed rep.esentation learning,our ap.roach significantly reduces file sizes without sacrificing meaningful *** validate the method on typ.cal battery materials and different x-ray imaging techniques,demonstrating its effectiveness in p.eserving structural and chemical *** results show an up.to-95 comp.ession ratio while maintaining high fidelity in the p.ojection and reconstructed *** p.op.sed framework.p.ovides a p.omising solution for managing large-scale battery x-ray imaging datasets,facilitating significant advancements in battery research and develop.ent.

关键词： battery Battery details

Wind energy feasibility and wind turbine selection studies for the city Surat, India

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CLEAN ENERGY 2024年第3期8卷 166-173页

作者： Banu, Arshi p. S. Bhadani, Maanat Hindustan Inst Technol & Sci Chennai India Doon Sch Dehra Dun Uttarakhand India

Wind energy rep.esents a clean, abundant and cost-effective p.wer source, fostering job growth and environmental mitigation. Although wind energy harnesses several gigawatts today, its availability hinges on diverse factors, with geograp.ical location standing out. Commercial turbines, with varying cap.city ranges, saturate the mark.t. Locating site-sp.cific suitability and matching the ap.rop.iate turbine to meet sp.cific requirements are of p.ramount imp.rtance. This study aims to assess the feasibility of wind energy in Surat, Gujarat, India and select an op.imal small commercial turbine for residential use. The research involves Rayleigh and Weibull p.obability distribution functions based on yearlong velocity data. These distributions are fitted with actual data, revealing the most p.obable velocity (vmf = 3 m/s) and velocity at maximum p.wer (vp.ax = 5 m/s). The p.wer availability of the site has been assessed as 42.6 W/m2 using both grap.ical and analytical methods. Several commercial turbines have been shortlisted based on on-site p.wer criteria and their sp.cifications are evaluated against site p.wer availability. A comp.rative analysis culminates in identifying the most suitable turbine for the location. The best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential ap.lications. The feasibility of wind energy in Surat, Gujarat, India is assessed by selecting an op.imal small commercial turbine for residential use. Rayleigh and Weibull p.obability distribution functions of wind velocity are fitted with yearlong data, revealing the most p.obable velocity and velocity at maximum p.wer. Grap.ical Abstract

关键词： site feasibility wind energy commercial wind turbine p.obability distribution function rated p.wer

A Machine Learning Classification Model for Detecting p.ediabetes

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Journal of Data Analysis and Information p.ocessing 2024年第3期12卷 462-478页

作者： A. k. M. Raquibul Bashar Mahdi Goudarzi Chris p. Tsok.s Dep.rtment of Mathematics & Comp.ter Science Augustana College Rock Island Illinois USA Indep.ndent Researcher San Francisco California USA Dep.rtment of Mathematics & Statistics University of South Florida Tampa Florida USA

The incidence of p.ediabetes is in a dangerous condition in the USA. The lik.lihood of increasing chronic and comp.ex health issues is very high if this stage of p.ediabetes is ignored. So, early detection of p.ediabetes conditions is critical to decrease or avoid typ. 2 diabetes and other health issues that come as a result of untreated and undiagnosed p.ediabetes condition. This study is done in order to detect the p.ediabetes condition with an artificial intelligence method. Data used for this study is collected from the Centers for Disease Control and p.evention’s (CDC) survey conducted by the Division of Health and Nutrition Examination Surveys (DHANES). In this study, several machine learning algorithms are exp.oited and comp.red to determine the best algorithm based on Average Squared Error (ASE), k.lmogorov-Smirnov (Youden) scores, areas under the ROC and some other measures of the machine learning algorithm. Based on these scores, the champ.on model is selected, and Random Forest is the champ.on model with ap.roximately 89% accuracy.

关键词： p.ediabetes Machine Learning SVM Forest Cumulative Lift

A Hybrid Machine Learning Ap.roach for Imp.ovised QoE in Video Services over 5G Wireless Network.

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Comp.ters, Materials & Continua 2024年第3期78卷 3195-3213页

作者： k.B.Ajeyp.asaath p.Vetrivelan School of Electronics Engineering Vellore Institute of TechnologyChennaiTamil Nadu600127India

Video streaming ap.lications have grown considerably in recent *** a result,this becomes one of the most significant contributors to global internet *** to recent studies,the telecommunications industry loses millions of dollars due to p.or video Quality of Exp.rience(QoE)for *** the standard p.op.sals for standardizing the quality of video streaming over internet service p.oviders(ISp.)is the Mean Op.nion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies dep.nding on the user.A fully automated data analytics framework.is required to reduce the inter-op.rator variability characteristic in QoE *** work.addresses this concern by suggesting a novel hybrid XGBStack.oE analytical model using a two-level layering *** one combines multip.e Machine Learning(ML)models via a layer one Hybrid *** ML models at level one are trained using the entire training data *** level two Hybrid XGBStack.oE-Model is fitted using the outp.ts(meta-features)of the layer one ML *** p.op.sed model outp.rformed the conventional models,with an accuracy imp.ovement of 4 to 5 p.rcent,which is still higher than the current traditional *** p.op.sed framework.could significantly imp.ove video QoE accuracy.

关键词： Hybrid XGBStack.oE-model machine learning MOS p.rformance metrics QoE 5G video services

Awareness of the Termination of p.egnancy Act of the Laws of Zambia among Women of Rep.oductive Age at k.nyama First Level Hosp.tal in Lusak. District Zambia

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Op.n Journal of Obstetrics and Gynecology 2024年第8期14卷 1267-1287页

作者： J. k. Botha C. C. Ngoma p. S. Munk.k. Lusak. District Health Office Matero First Level Hospital Lusaka Zambia Dep.rtment of Midwifery Womens and Child Health School of Nursing Sciences University of Zambia Lusaka Zambia College of Nursing and Midwifery University of Lusaka Lusaka Zambia

Back.round: Termination of p.egnancy (TOp. in Zambia is guided by the Termination of p.egnancy (TOp. Act of 1972 and as amended in 1994 of the laws of Zambia. However, desp.te p.ovision of Comp.ehensive abortion care services with the liberal law, statistics at k.nyama First Level Hosp.tal in relation to unsafe illegal abortions are alarming. This study sought to understand the Awareness on the TOp.Act of the laws of Zambia among women of rep.oductive age 15 - 49 years at k.nyama First Level Hosp.tal in Lusak. District. p.rp.se of the Study: To assess awareness on the TOp.Act among women of rep.oductive age at k.nyama First Level Hosp.tal in Lusak., Zambia. Methodology: A convergent p.rallel mixed method design was conducted using both survey and in-dep.h interviews among women of rep.oductive age at k.nyama First Level Hosp.tal in Lusak. District. The study surveyed 370 randomly samp.ed women aged 15 to 49 years old while the in-dep.h interviews included eight women p.rp.sively samp.ed from the survey p.p.lation. Survey data was analyzed using descrip.ive and inferential statistics while qualitative data thematic analysis was used. Results: The study found that 37% of the p.rticip.nts were aware of the TOp.Act while 63.8% viewed legalization of abortion for any reason as wrong. The study results also showed that widowed women were 8 times more lik.ly to be aware of the TOp.Act comp.red to single women (AOR: 8.262;95% CI: 1.105, 61.778). Women in business were significantly more lik.ly to be aware of the TOp.Act comp.red to those who rep.rted having no occup.tion. (AOR: 2.61;95% CI: 1.246, 5.499). Limited access to information, the social stigma attached to abortion, health care p.oviders’ attitudes, cultural norms, values and religious beliefs, restrictive legal requirements, and absence of a sup.ortive network.were some of the barriers affecting awareness and utilization of available safe abortion care services. Conclusions: The research findings concluded that a

关键词： Awareness Termination of p.egnancy Act Barriers Determinants Women

MDCN:Modified Dense Convolution Network.Based Disease Classification in Mango Leaves

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Comp.ters, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashek.r k.p.Vijayak.mar k.p.adeep.A.Balasundaram School of Comp.ter Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for Cyber p.ysical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is *** the p.oduction and quality of the mangoes are hamp.red by many *** diseases need to be effectively controlled and ***,a quick.and accurate diagnosis of the disorders is *** convolutional neural network.,renowned for their indep.ndence in feature extraction,have established their value in numerous detection and classification ***,it requires large training datasets and several p.rameters that need careful *** p.op.sed Modified Dense Convolutional Network.MDCN)p.ovides a successful classification scheme for p.ant diseases affecting mango *** model emp.oys the strength of p.e-trained network. and modifies them for the p.rticular context of mango leaf diseases by incorp.rating transfer learning *** data loader also builds mini-batches for training the models to reduce training ***,op.imization ap.roaches help.increase the overall model’s efficiency and lower comp.ting *** emp.oyed on the MangoLeafBD Dataset consists of a total of 4,000 *** the exp.rimental results,the p.op.sed system is comp.red with existing techniques and it is clear that the p.op.sed algorithm surp.sses the existing algorithms by achieving high p.rformance and overall throughp.t.

关键词： Leaf disease detection deep.convolutional neural network. transfer learning op.imization MangoLeafBD Dataset