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Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 1-48页

作者： Hariharasakthisudhan Ponnarengan Sivakumar Rajendran Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj department of Mechanical engineering Dr.Mahalingam College of Engineering and TechnologyPollachiCoimbatoreTamil Nadu642003India department of Mechanical engineering JKKN College of Engineering and TechnologyKumarapalayamNamakkalTamil Nadu637209India department of Mechanical engineering Gharda Institute of TechnologyLavelMaharashtra415708India department of Electrical and Electronics engineering Sri Eshwar College of EngineeringCoimbatoreTamilnadu641202India department of Mechanical engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical *** review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health *** findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and *** synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment ***,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease *** review emphasizes the need for interdisciplinary collaboration to further advance this *** research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and *** fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

关键词： Computational models biomedical engineering bioinformatics machine learning wearable technology

Efficient photocatalytic reduction of CO2 to formate by a molecular noble metal-free system

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Science China Chemistry 2025年第1期 152-156页

作者： Lihua Zhang Lingjing Chen Huatian Shi Yue Wei Gui Chen Tai-Chu Lau School of Environment and Civil engineering Research Center for Eco-environmental Engineering Dongguan University of Technology department of Chemistry and Institute of Molecular Functional Materials City University of Hong Kong

Reducing CO2to carbon-neutral solar fuels can not only alleviate the energy demand but also mitigate the greenhouse *** present,developing an efficient non-noble metal photo/electrocatalytic system for converting CO2to a specific product beyond CO is still a big ***,we report a new cobalt complex bearing 2,2′-bi-1,10-phenanthroline ligand that can photocatalytically reduce CO2to formate （HCOO-）.Turnover number （TON） for HCOO-up to 677 with 99%selectivity can be achieved using organic dyes purpurin as photosensitizer and 1,3-dimethyl-2-phenyl-2,3-dihydro-1H-benzo[d]imidazole （BIH） as sacrificial reductant in a CO2-saturated NMP/TEA （v/v,3:1） solution under visible light *** potential electrolysis at-1.41 V （***/Ag Cl） in the presence of 12 m M Et3NHBF4also produced HCOO-with Faradaic efficiency of 39%.Spectroscopic and electrochemical studies suggest that a[CoI-H]species is the key intermediate for CO2reduction.

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of Computer Science and engineering College of Engineering GuindyAnna UniversityChennai600025India department of Information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

Co/Co_(7)Fe_(3)heterostructures with controllable alloying degree on carbon spheres as bifunctional electrocatalyst forrechargeable zinc-air batteries

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 476-487页

作者： Junkang Chen Yongyue Zhuang Yanxin Qiao Yu Zhang Aihua Yuan Hu Zhou School of Materials Science and engineering Jiangsu University of Science and TechnologyZhenjiang 212003China School of environmental and Chemical engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

Exploring efficient and nonprecious metal electrocatalysts of oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)is crucial for developing rechargeable zinc-air batteries(ZABs).Herein,an alloying-degree control strategy was employed to fabricate nitrogen-doped carbon sphere(NCS)decorated with dual-phase Co/Co_(7)Fe_(3)heterojunctions(CoFe@NCS).The phase composition of materials has been adjusted by controlling the alloying *** optimal CoFe_(0.08)@NCS electrocatalyst displays a half-wave potential of 0.80 V for ORR and an overpotential of 283 mV at 10 mA·cm^(-2)for OER in an alkaline *** intriguing bifunctional electrocatalytic activity and durability is attributed to the hierarchically porous structure and interfacial electron coupling of highly-active Co_(7)Fe_(3)alloy and metallic Co *** the CoFe_(0.08)@NCS material is used as air-cathode catalyst of rechargeable liquid-state zinc-air battery(ZAB),the device shows a high peak power-density(157 mW·cm^(-2))and maintains a stable voltage gap over 150 h,outperforming those of the benchmark(Pt/C+RuO_(2))-based *** particular,the as-fabricated solid-state flexible ZAB delivers a reliable compatibility under different bending *** work provides a promising strategy to develop metal/alloy-based electrocatalysts for the application in renewable energy conversion technologies.

关键词： bifunctional electrocatalysts oxygen reduction reaction oxygen evolution reaction zinc-air battery metal/alloy carbon sphere

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An SPH Framework for Earthquake-Induced Liquefaction Hazard Assessment of Geotechnical Structures

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Computer Modeling in engineering & Sciences 2025年第1期142卷 251-277页

作者： Sourabh Mhaski G.V.Ramana department of Civil engineering Indian Institute of Technology DelhiNew Delhi110016India

Earthquake-induced soil liquefaction poses significant risks to the stability of geotechnical structures *** understanding of the liquefaction triggering,and the post-failure large deformation behaviour is essential for designing resilient *** present study develops a Smoothed Particle Hydrodynamics(SPH)framework for earthquake-induced liquefaction hazard assessment of geotechnical *** coupled flowdeformation behaviour of soils subjected to cyclic loading is described using the PM4Sand model implemented in a three-phase,single-layer SPH framework.A staggered discretisation scheme based on the stress particle SPH approach is adopted to minimise numerical inaccuracies caused by zero-energy modes and tensile ***,non-reflecting boundary conditions for seismic analysis of semi-infinite soil domains using the SPH method are *** numerical framework is employed for the analysis of cyclic direct simple shear test,seismic analysis of a level ground site,and liquefaction-induced failure of the Lower San Fernando *** agreement for liquefaction triggering and post-failure behaviour demonstrates that the SPH framework can be utilised to assess the effect of seismic loading on field-scale geotechnical *** present study also serves as the basis for future advancements of the SPH method for applications related to earthquake geotechnical engineering.

关键词： Earthquake seismic liquefaction stress particle PM4Sand smoothed particle hydrodynamics(SPH)

Magnetron sputtered nickel oxide with suppressed interfacial defect states for efficient inverted perovskite solar cells

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Journal of Energy Chemistry 2025年第1期100卷 348-355页

作者： Guoqiang Ma Qin Tan Zhaoning Li Jingwei Xiu Jiafeng Wang Tianle Cheng Dong He Qiang Sun Xuhang Ma Francesco Lamberti Zhubing He department of Materials Science and engineering Institute of Innovative Materials (I2M) Shenzhen Key Laboratory of Full Spectral Solar Electricity Generation (FSSEG)Southern University of Science and Technology (SUSTech) Core Research Facilities Southern University of Science and Technology department of Chemical Sciences Department of Industrial Engineering University of Padova

Widely used spin-coated nickle oxide （NiOx） based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impair device performance in a long-term view,even with surface molecule *** this study,we developed high-quality magnetron-sputtered NiOxthin films through detailed process optimization,and compared systematically sputtered and spin-coated NiOxthin film surfaces from materials to *** sputtered NiOxfilms exhibit improved crystallinity,smoother surfaces,and significantly reduced Ni3+or Ni vacancies compared to their spin-coated ***,the interface between the perovskite and sputtered NiOxfilm shows a substantially reduced density of defect *** solar cells （PSCs） fabricated with our optimally sputtered NiOxfilms achieved a high power conversion efficiency （PCE） of up to 19.93%and demonstrated enhanced stability,maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun ***,with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10（9H）-yl)butyl)p hosphonic acid （DMAcPA）,the device PCE was further promoted to 23.07%,which is the highest value reported for sputtered NiOxbased PSCs so far.

关键词： Nickle oxide Magnetron sputter Defect Stability Perovskite solar cell

Nano-Enhanced Dairy Scum Biodiesel Blends:Unleashing the Power of Aluminium Oxide for Sustainable and Efficient Engine Performance

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Journal of Thermal Science 2025年第1期 283-302页

作者： Veeranna MODI Prasad B.RAMPURE Sunil Kumar SHETTY Madeva NAGARAL Channa Keshava NAIK N Johar MGM Muhammad Irsyad ABDULLAH Abdullah I.AL-MANSOUR Shamshad ALAM KLE College of engineering and technology ChikodiVisvesvaraya Technological University Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT)Department of Mechanical Engineering Aircraft Research and Design Centre HAL department of Mechanical engineering BGS College of Engineering and TechnologyBengaluruAffiliated to Visvesvaraya Technological University Management and Science University department of Civil engineering College of EngineeringKing Saud University

This study investigates the effect of aluminium oxide nanoparticlesaddition to biodiesel blends made from dairy milk scum on performance and combustion characteristics of the diesel *** dispersion of Al2O3nanoparticles in B20 blends at different concentrations was done with the help of *** number of blends were prepared for the *** Machine learning algorithms(Random Forest（RF）and CatBoost) was used for the *** results show that in comparison to biodiesel blends without nanoparticles,the kinematic viscosity and density is higher for fuel blends with *** these Fuel blends have higher calorific *** blends exhibited reduced Brake Specific Fuel Consumption（BSFC） of2.85% than the blends without nano particles(Dairy Scum Methyl Ester Biodiesel 20%+Neat Diesel 80%（in volume）,DSMEB20),57.14% less CO,40.8% less hydrocarbon,and increased NOxemissions compared to conventional diesel,contributing to the development of environmentally friendly and renewable biofuel blends with *** B20NP30 is the optimal blend for performance and emission *** study concludes with findings on enhanced Brake Thermal Efficiency（BTE） of 26.29% in 3×10-5（in volume） Al2O3nanoparticle-blended DSME B20 and other DSME B20 fuel blends,emphasizing the importance of optimal nanoparticle *** correlation matrix shows how engine load,efficiency measures（BTE,BSFC）,and emissions（CO,CO2,NOx,Smoke） are connected in complex *** results help us understand the complicated dynamics of engine performance and emission characteristics ***'s diagram for BTE and BSFC shows that CatBoost-based BTE models perform superior to RF-based models during the training as well as testing *** results were obtained for CO and CO2emission results.

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Maximizing biomass utilization: An integrated strategy for coproducing multiple chemicals

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Journal of Energy Chemistry 2025年第1期100卷 180-191页

作者： Byeongchan Ahn Sua Jo Jonggeol Na J.Jay Liu Young-Ju Kim Wangyun Won department of Chemical and Biological engineering Korea University department of Chemical engineering (Integrated process) Kyung Hee University department of Chemical engineering and Materials Science Ewha Womans University department of Chemical engineering Pukyong National University Resources engineering Plant Research department Korea Institute of Geoscience and Mineral Resources

Lignocellulosic biomass is one of the viable solutions to alleviate the global warming. However, the limited utilization of biomass majorly focused on cellulose and hemicellulose restricts the economic and environmental feasibilities. To cope with this issue, we proposed an integrated process of co-producing 1,6-hexanediol（1,6-HDO） with tetrahydrofuran and adipic acid from biomass, referred to as Strategy A. To compare the impacts of lignin upgrading and feedstock, Strategy B, which co-produces tetrahydrofuran alone, and Strategy C, which is the traditional route to produce 1,6-HDO from fossil fuels, were used. Heat networks are also designed to reduce operating costs and indirect carbon emissions due to energy consumption, saving 87% and 83% of the heat and cooling requirements, respectively, in Strategy A. The market competitiveness of Strategy A was evaluated by determining the minimum selling price through techno-economic analysis, and sustainability was thoroughly investigated by quantifying the environmental impacts through both midpoint and endpoint life-cycle assessments（LCAs）.Strategy A was found to be the most favorable both economically（USRDSCHARDOLLAR3,402/ton） and environmentally（-26.9 kg CO2eq.）. This indicates that lignin valorization is not only economically but also environmentally preferred. Finally, changes in economic and environmental feasibilities depending on economic, process, and environmental parameters were investigated using sensitivity and uncertainty analyses. The results of these analyses provide valuable insight into bio-based chemical production.

关键词： Biorefinery Carbon mitigation Climate change Global warming Lignin utilization Bioeconomy

Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University department of Electrical engineering and Automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity