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Influence of Various Earth-Retaining Walls on the Dynamic Response Comparison Based on 3D Modeling

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2835-2863页

作者： Muhammad Akbar Huali Pan Jiangcheng Huang Bilal ahmed Guoqiang Ou Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu610000China Institute of International Rivers and Eco-Security Yunnan UniversityKunming650500China Department of Structural Engineering Faculty of Civil EngineeringDoctoral SchoolAkademicka 2Silesian University of TechnologyGliwice44-100Poland

The present work aims to assess earthquake-induced earth-retaining(ER)wall *** study is on the dynamics analysis of various earth-retaining wall designs in hollow precast concrete panels,reinforcement concrete facing panels,and gravity-type earth-retaining *** finite element(FE)simulations utilized a 3D plane strain condition to model full-scale ER walls and numerous nonlinear dynamics *** seismic performance of differentmodels,which includes reinforcement concrete panels and gravity-type and hollowprecast concrete ER walls,was simulated and examined using the FE *** also displays comparative studies such as stress distribution,deflection of the wall,acceleration across the wall height,lateral wall displacement,lateral wall pressure,and backfill plastic *** components of the created ER walls were found throughout this research *** is a granular reinforcement backfill,while the other is a wall-facing panel and base *** dynamic response effects of varied earth-retaining walls have also been *** was discovered that the facing panel of the model significantly impacts the earthquake-induced displacement of ER *** proposed analytical model’s validity has been evaluated and compared with the reinforcement concrete facing panels,gravity-type ER wall,scientifically available data,and American Association of State Highway and Transportation Officials(AASHTO)guidelines results based on FE *** results of the observations indicate that the hollow prefabricated concrete ER wall is the most feasible option due to its lower displacement and high-stress distribution compared to the two *** methodology and results of this study establish standards for future analogous investigations and professionals,particularly in light of the increasing computational capabilities of desktop computers.

关键词： Seismic analysis finite element modeling earth-retaining ER walls dynamic response structural resilience

Parallel Inference for Real-Time Machine Learning Applications

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Journal of Computer and Communications 2024年第1期12卷 139-146页

作者： Sultan al Bayyat Ammar alomran Mohsen alshatti ahmed almousa Rayyan almousa Yasir alguwaifli College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.

关键词： Machine Learning Models Computational Efficiency Parallel Computing Systems Random Forest Inference Hyperparameter Tuning Python Frameworks (TensorFlow PyTorch Scikit-Learn) High-Performance Computing

Sustainable pathways to ammonia: a comprehensive review of green production approaches

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CLEAN ENERGY 2024年第2期8卷 60-72页

作者： ahmed, Hafiz Sharjeel Yahya, Zaid Khan, Waqar ali Faraz, ali Univ Engn & Technol UET Lahore Pakistan NFC Inst Engn & Fertilizer Res Chem Engn Dept Faislabad Pakistan Univ Punjab Chem Engn Dept Lahore Pakistan

Ammonia production is vital in global industries but faces increasing environmental concerns, necessitating a shift towards sustainable methods. This review comprehensively explores diverse hydrogen production approaches and their potential impact on sustainable ammonia synthesis. The primary focus is investigating hydrogen production methods that enhance both efficiency and sustainability in ammonia synthesis, which is crucial for reducing its environmental footprint. Furthermore, the review highlights the pivotal role of catalysts in improving Faradaic efficiency in ammonia synthesis, emphasizing their significance in overall process efficiency. The study identifies innovative technologies that have the potential to revolutionize ammonia production while aligning with global sustainability goals. Through a thorough examination of hydrogen production methods and catalytic advances, this review provides valuable insight into greener and more efficient ammonia production practices. In conclusion, it emphasizes the importance of exploring hydrogen production methods and leveraging catalysts to improve ammonia production sustainability and efficiency. It addresses a critical research gap and paves the way for an environmentally responsible and globally aligned ammonia production industry. Hydrogen production methods that enhance both efficiency and sustainability in ammonia synthesis are reviewed, highlighting the pivotal role of catalysts in improving Faradaic efficiency in ammonia synthesis. It addresses a critical research gap and paves the way for an environmentally responsible and globally aligned ammonia production industry. Graphical Abstract

关键词： ammonia production green alternatives sustainability catalyst ELECTROCATalYTIC N-2 REDUCTION ELECTROCHEMICal SYNTHESIS NITROGEN ELECTROSYNTHESIS CATalYSTS CARRIER WATER

A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： ahmed Barnawi Krishan Kumar Neeraj Kumar Bander alzahrani Amal almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

From seawater to hydrogen via direct photocatalytic vapor splitting: A review on device design and system integration

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Frontiers in Energy 2024年第3期18卷 291-307页

作者： Hongxia LI Khaja WAHAB ahmed Mohamed A.ABDELSalAM Michael FOWLER Xiao-Yu WU Technology Innovation Institute Masdar CityAbu Dhabi 9639United Arab Emirates Department of Chemical Engineering University of WaterlooWaterlooON N2L 3G1Canada Department of Mechanical and Nuclear Engineering Khalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L 3G1Canada

Solar-driven hydrogen production from seawater attracts great interest for its emerging role in decarbonizing global energy consumption. Given the complexity of natural seawater content, photocatalytic vapor splitting offers a low-cost and safe solution, but with a very low solar-to-hydrogen conversion efficiency. With a focus on cutting-edge photothermal–photocatalytic device design and system integration, the recent research advances on vapor splitting from seawater, as well as industrial implementations in the past decades were reviewed. In addition, the design strategies of the key processes were reviewed, including vapor temperature and pressure control during solar thermal vapor generation from seawater, capillary-fed vaporization with salt repellent, and direct photocatalytic vapor splitting for hydrogen production. Moreover, the existing laboratory-scale and industrial-scale systems, and the integration principles and remaining challenges in the future seawater-to-hydrogen technology were discussed.

关键词： seawater hydrogen photocatalytic vapor splitting solar-driven

Fentanyl and xylazine crisis:Crafting coherent strategies for opioid overdose prevention

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World Journal of Psychiatry 2024年第6期14卷 760-766页

作者： Lakshit Jain Jasleen Kaur Shahana Ayub Danya Ansari Rizwan ahmed Abdul Qadir Dada Saeed ahmed Department of Psychiatry University of ConnecticutFarmingtonCT 06032United States Addiction Services Division Connecticut Valley HospitalMiddletownCT 06457United States Department of Psychiatry Institute of LivingHartfordCT 06102United States Department of Medicine Islamabad Medical and Dental CollegeIslamabad 44000Pakistan Department of Medicine Liaquat College of Medicine and DentistryKarachi 75290Pakistan Department of Medicine Trinity School of MedicineRoswellGA 30075United States Addiction Services and Dual Diagnosis Unit Saint Francis Hospital and Medical CenterHartfordCT 06105United States

The United States is in the throes of a severe opioid overdose epidemic,primarily fueled by the pervasive use of fentanyl and the emerging threat of xylazine,a veterinary sedative often mixed with *** high potency and long duration of fentanyl is compounded by the added risks from xylazine,heightening the lethal danger faced by opioid *** such as enhanced surveillance,public awareness campaigns,and the distribution of fentanylxylazine test kits,and naloxone have been undertaken to mitigate this ***-related overdose deaths persist despite these efforts,partly due to inconsistent policies across states and resistance towards adopting harm reduction strategies.A multifaceted approach is imperative in effectively combating the opioid overdose *** approach should include expansion of treatment access,broadening the availability of medications for opioid use disorder,implementation of harm reduction strategies,and enaction of legislative reforms and diminishing stigma associated with opioid use disorder.

关键词： Fentanyl Xylazine Opioid overdose Epidemic Opioid use disorder Buprenorphine Medications for opioid use disorder

Standardization of meibomian gland dysfunction in an Egyptian population sample using a non-contact meibography technique

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International Journal of Ophthalmology(English edition) 2024年第1期17卷 61-65页

作者： ahmed Mohamed Karara Zeinab El-Sanabary Mostafa ali El-Helw Tamer ahmed Macky Mohamad Amr Salah Eddin Abdelhakim Department of Ophthalmology Kasr Al-Ainy HospitalCairo UniversityCairo 11431Egypt

AIM:To develop normative data for meibomian gland dysfunction(MGD)parameters,using non-contact meibography technique of Sirius Costruzione Strumenti Oftalmici(CSO)machine,in an Egyptian population ***:Observational,cross-sectional,analytic study,in which 104 Egyptian volunteers were *** upper lids were examined,using“Sirius CSO”*** eyelid was given a degree of meibomian gland loss(MGL),which was calculated by the software of the ***:Mean percentage MGL in right upper lid was of 30.9%±12.6%,and that of left upper lid was 32.6%±11.8%.Thirty-four volunteers(32.7%)had first-degree MGL in their right upper lid,and 67.3%had second-degree *** volunteer(1%)had zero-degree MGL in left upper lid,28(26.9%)had first-degree loss,and 75(72.1%)had second-degree *** of MGL in right upper eyelid was not related to age,but degree of MGL in left upper eyelid increased with *** was statistically significant difference between both genders for degree of MGL in right eye(P=0.036)and in left eye(P=0.027).CONCLUSION:Noncontact meibography is a useful non-invasive tool for diagnosing *** is diagnosed in 100%of apparently normal individuals;26.9%-32.7%of which have first-degree MGL,and 67.3%-72.1%have second-degree MGL.

关键词： Egyptian population meibomian gland dysfunction non-contact meibography standardization upper lid

Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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Computers, Materials & Continua 2024年第9期80卷 3967-3990页

作者： DeemaMohammed alsekait ahmed Younes Shdefat AymanNabil Asif Nawaz Muhammad Rizwan Rashid Rana Zohair ahmed Hanaa Fathi Diaa Salama Abd Elminaam Department of Computer Science and Information Technology Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia College of Engineering and Technology American University of the Middle EastEgaila15453Kuwait Faculty of Computers Science Misr International UniversityCairo11800Egypt University Institute of Information Technology—PMAS Arid Agriculture University Rawalpjndi46000Pakistan Department of Computer Science International Islamic UniversityIslamabad04436Pakistan Jadara Research Center Jadara UniversityIrbid21110Jordan Information System Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13511Egypt MEU Research Unit Middle East UniversityAmman11831Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic *** diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality ***,these methods often require significant time and expertise,making them less accessible in resource-limited *** technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare *** study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant *** is performed using Optimized *** approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health *** proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality *** findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

关键词： Heart diseases magnetic resonance imaging electrocardiogram deep learning classification

Nomograms and prognosis for superficial esophageal squamous cell carcinoma

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World Journal of Gastroenterology 2024年第10期30卷 1291-1294页

作者： Hong Tao Lin ahmed Abdelbaki Somashekar G Krishna Department of Internal Medicine Division of GastroenterologyHepatology and NutritionThe Ohio State University Wexner Medical CenterColumbusOH 43210United States

In recent years,endoscopic resection,particularly endoscopic submucosal dis-section,has become increasingly popular in treating non-metastatic superficial esophageal squamous cell carcinoma(ESCC).In this evolving paradigm,it is crucial to identify factors that predict higher rates of lymphatic invasion and poorer *** tumor size,deeper invasion,poorer differentiation,more infiltrative growth patterns(INF-c),higher-grade tumor budding,positive lymphovascular invasion,and certain biomarkers have been associated with lymph node metastasis and increased morbidity through retrospective reviews,leading to the construction of comprehensive nomograms for outcome *** validated by future prospective studies,these nomograms would prove highly applicable in guiding the selection of treatment for superficial ESCC.

关键词： Esophageal cancer Esophageal squamous cell carcinoma Esophageal resection Endoscopic mucosal resection Endoscopic submucosal dissection Lymph node metastasis