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Geoscience Frontiers 2024年第4期15卷 411-426页

作者： Aifeng Liu Muhammad Imran Abdelmohsen A.Nassani Rima H.Binsaeed khalid Zaman International Business School Shaanxi Normal UniversityXi’anChina Department of Economics The University of HaripurHaripur Khyber Pakhtunkhwa 22620Pakistan Department of Management College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh 11587Saudi Arabia

Standards for Low-Carbon Energy Portfolios(LC-EPS)mandate that a certain percentage of a region's electricity generation originate from zero-or low-emissions *** 1995Q1 through 2020Q4,the study used the ARDL-Bounds testing technique to estimate coefficient parameters,Granger causality to draw causal inferences,and variance decomposition analysis to anticipate the factors that will have the greatest impact on carbon emissions in the US *** power significantly impacts carbon emissions,as seen by an inverted U-shaped environmental Kuznets curve,whereas long-term impact of innovation leads to lower *** the other hand,exports of sophisticated technology reduce carbon *** growth has a discernible effect on carbon emissions,nuclear power,innovation,and environmentally friendly ***-tech exports will likely impact carbon emissions most,followed by a demand for nuclear power,innovation,economic expansion,and sustainable finance for the next ten *** results give policymakers helpful insight into how the US economy may reduce carbon emissions and fight climate change via renewable energy and green finance.

关键词： Nuclear energy demand High technology exports Innovation Economic growth Green finance USA

Enhancing the Decomposition of Paper Cups Using Galleria Mellonella and Eisenia Fetida

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Journal of Renewable Materials 2024年第2期12卷 349-367页

作者： Shadi Moqbel Habib al-Ghoul Abd al-Majeed al-Ghzawi Rami Mukbel Civil Engineering Department The University of JordanAmman11942Jordan Department of Chemistry Technical University of MunichMunichGermany Plant Production Jordan University of Science and TechnologyIrbid22110Jordan Basic Medical Veterinary Sciences Jordan University of Science and TechnologyIrbid22110Jordan

The composition of paper cups creates a challenge for the recycling industry,as the paperboard–plastic film composite is hard to ***,paper cups are sent to landfills or waste *** study explores the combined use of red worms(Eisenia fetida)and Greater wax moth(Galleria mellonella)in the biodegradation of paper *** study investigates the conditions and combinations that promote using Eisenia fetida and Galleria mellonella for degrading paper *** study considered the influence of environmental temperature,the presence of food waste,varying the number of Eisenia fetida worms,and the presence of a Galleria mellonella growth-slowing agent on the degradation *** achieve the study objectives,the study followed a quantitative *** study monitored the degradation of paper cup cuts that were placed in jars containing different combinations of Eisenia fetida worms,Galleria mellonella larvae,food waste,bedding material,and Galleria mellonella growth-slowing *** study found that the best operating temperature is *** study found that using food waste improves the performance of Eisenia fetida worms and Galleria mellonella larvae *** study found that adding a Galleria mellonella growth-slowing agent slightly enhances the degradation of the paper ***,a numerical model was obtained to simulate the paper cup degradation efficiency.

关键词： Paper cups resource recovery recycling circular economy Galleria mellonella Eisenia fetida

Analysis of particle dispersion and cavity formation during bulk particle water entry using fully coupled CFD-DEM-VOF approach

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Particuology 2024年第7期90卷 558-580页

作者： Utkan Çalışkan Sanja Mišković Norman B.Keevil Institute of Mining Engineering University of British Columbia2329 West MallVancouverBC V6T 1Z4Canada

This paper presents the development and validation of a fully coupled computational fluid dynamics—discrete element method—volume of fluid(CFD-DEM-VOF)model to simulate the complex behavior of particle-laden flows with free *** coupling between the fluid and particle phases is established through the implemented continuity,momentum,and alpha transport *** coupled particle forces such as drag,pressure gradient,dense virtual mass,viscous,and interface forces are also integrated,with drag and dense virtual mass forces being dependent on local *** integrated conservative alpha transport equation ensures phase volume conservation during interactions between particles and ***,we have implemented a trilinear interpolation method designed to operate on unstructured hexahedral *** method has been tested for its ability to properly resolve the coupling effects in the numerical simulations,particularly in cases with a relatively low cell-size *** model is validated through three distinct test cases:single particle water entry,dam break with particles,and water entry of a group of particles *** experimental setup is built to study the dynamics of the water entry of a group of particles,where three key flow features are analyzed:the evolution of average particle velocity,cavity shape,and particle dispersion cloud profiles in *** tests involve four different scenarios,including two different water levels(16.1 and 20.1 cm)and two different particle densities(2650 and 4000 kg/m3).High-speed videometry and particle tracking velocimetry(using ImageJ/TrackMate)methods are employed for experimental data *** is demonstrated that numerical results are in excellent agreement with theoretical predictions and experimental *** study highlights the significance of vortices in cavity shaping and particle *** validated CFD-DEM-VOF model constitutes a robust tool for simulating particle-laden flows,cont

关键词： CFD-DEM-VOF Trilinear interpolation Particle-laden flow Free-surface Particle water entry Volume conservation

Embryonic thermal manipulation:a potential strategy to mitigate heat stress in broiler chickens for sustainable poultry production

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Journal of Animal Science and Biotechnology 2024年第4期15卷 1347-1375页

作者： Sadid al Amaz Birendra Mishra Department of Human Nutrition Food and Animal SciencesCollege of Tropical Agriculture and Human ResourcesUniversity of Hawaii at ManoaAgSci 2161955 East‑West RdHonoluluHI 96822USA

Due to high environmental temperatures and climate change, heat stress is a severe concern for poultry health and production, increasing the propensity for food insecurity. With climate change causing higher temperatures and erratic weather patterns in recent years, poultry are increasingly vulnerable to this environmental stressor. To mitigate heat stress, nutritional, genetic, and managerial strategies have been implemented with some success. However, these strategies did not adequately and sustainably reduce the heat stress. Therefore, it is crucial to take proactive measures to mitigate the effects of heat stress on poultry, ensuring optimal production and promoting poultry well-being. Embryonic thermal manipulation(TM) involves manipulating the embryonic environment's temperature to enhance broilers' thermotolerance and growth performance. One of the most significant benefits of this approach is its cost-effectiveness and saving time associated with traditional management practices. Given its numerous advantages, embryonic TM is a promising strategy for enhancing broiler production and profitability in the poultry industry. TM increases the standard incubation temperature in the mid or late embryonic stage to induce epigenetic thermal adaption and embryonic metabolism. Therefore, this review aims to summarize the available literature and scientific evidence of the beneficial effect of pre-hatch thermal manipulation on broiler health and performance.

关键词： Broiler Embryo Epigenetics Global warming Thermoregulation

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad khalid Imam Rahmani Baijnath Kaushik Rania almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

Deep eutectic solvents:Green multi-task agents for sustainable super green hydrogen technologies

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Journal of Energy Chemistry 2024年第5期92卷 357-382页

作者： Raiyan al-Farsi Maan Hayyan Renewable Energy Engineering Program Faculty of Engineering and TechnologyMuscat UniversityMuscat 130Oman Chemical Engineering Program Faculty of Engineering and TechnologyMuscat UniversityMuscat 130Oman

While reliance on renewable energy resources has become a reality, there is still a need to deploy greener and more sustainable methods in order to achieve sustainable development goals. Indeed, green hydrogen is currently believed to be a reliable solution for global warming and the pollution challenges arising from fossil fuels, making it the resilient fuel of the future. However, the sustainability of green hydrogen technologies is yet to be achieved. In this context, generation of green hydrogen with the aid of deep eutectic solvents(DESs) as green mixtures has been demonstrated as a promising research area. This systematic review article covers green hydrogen generation through water splitting and biomass fermentation when DESs are utilized within the generation process. It also discusses the incorporation of DESs in fuel cell technologies. DESs can play a variety of roles such as solvent, electrolyte, or precursor;colloidal suspension and reaction medium;galvanic replacement, shape-controlling, decoration, or extractive agent;finally oxidant. These roles are relevant to several methods of green hydrogen generation, including electrocatalysis, photocatalysis, and fermentation. As such, it is of utmost importance to screen potential DES formulations and determine how they can function in and contribute throughout the green hydrogen mobility stages. The realization of super green hydrogen generation stands out as a pivotal milestone in our journey towards achieving a more sustainable form of development;DESs have great potential in making this milestone achievable. Overall, incorporating DESs in hydrogen generation constitutes a promising research area and offers potential scalability for green hydrogen production, storage,transport, and utilization.

关键词： Watersplitting Biohydrogen Super green hydrogen Electrocatalysis Photocatalysis Fuel cell Power-to-X

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb khalid Javeed Musaed alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning

Comparative analysis of CO_(2)emissions and economic performance in the United States and China:Navigating sustainable development in the climate change era

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Geoscience Frontiers 2024年第5期15卷 239-255页

作者： khalid Mehmood Syed Tauseef Hassan Xuchun Qiu Shahid ali Institute of Environmental Health and Ecological Security School of the Environment and Safety EngineeringJiangsu UniversityZhenjiangJiangsu 212013China Department of Trade and Finance Faculty of Economics and ManagementCzech University of Life Sciences PragueKamycka 129PrahaZIP 16500Czechia School of Economics and Management Anhui Polytechnic UniversityWuhu 241000China Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment Suzhou University of Science and TechnologySuzhou 215009China

Economic growth has brought a global climate change into the spotlight,and CO_(2)emissions demonstrate significant challenges in reducing environmental shifts ***,the United States and China contribute the largest amount of CO_(2)*** purpose of this study is to examine the relationship between different types of CO_(2)emissions and economic growth by using a modeling *** analyze total CO_(2)emissions,coal CO_(2)emissions,oil CO_(2)emissions,the global share of coal CO_(2)emissions,the global share of oil CO_(2)emissions,and economic *** study provides unique insights into how to simultaneously reduce CO_(2)emissions and sustain economic growth.A bootstrap autoregressive distributed lag(BARDL)simulation method is utilized to examine the long-and short-run effects of predictors on CO_(2)*** CO_(2)emissions are found to have a significant positive effect on economic growth in the short run but a negative impact on economic growth over the long run in the United *** United States needs to implement stronger measures to balance coal CO_(2)emissions with economic growth for sustainable *** contrast,oil CO_(2)emissions have positive effect for China in both the long run and short ***,China can continue to reduce CO_(2)emissions from oil while maintaining positive economic *** China’s policies promoting cleaner energy alternatives can be adapted and implemented to maintain a balance between economic growth and carbon *** study has valuable insights for policymakers seeking to balance economic growth with carbon reduction *** emphasizes the need to better understand the complex relationship between CO_(2)emissions and economic growth.

关键词： CO_(2)emission Sustainable development Climate change United States China BARDL

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood Syed Muhammad Usman Musaed alhussein Khursheed Aurangzeb Shehzad khalid Muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 633-659页

作者： Nadeem Ahmed khalid Mohammadani ali Kashif Bashir Marwan Omar Angel Jones Fayaz Hassan School of Electronic Science Beijing University of Post and TelecommunicationBeijing100086China Department of Computer Science Huanggang Normal UniversityHuanggangChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabad502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Information Technology and Management Illinois Institute of TechnologyChicagoUSA College of Computing Capital Technology UniversityLaurelUSA

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes *** the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against *** additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network *** testing of our solution has revealed its remarkable ability to enhance communication in ***,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and *** results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

关键词： Black hole attack IoV vehicular ad hoc network AODV routing protocol