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A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and Security Center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

Comparative Evaluation and Performance Analysis of Heat Exchangers Featured with Peened Surface

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Journal of Thermal Science 2025年第1期34卷 50-61页

作者： DINESHBABU Chinnadurai SHIVA SANKARAN Nallasamy VENKATESH RAJA Krishnan VENKATESH Rathinavelu department of Mechanical Engineering Kongunadu College of Engineering and Technology department of Mechanical Engineering Selvam College of Technology department of Mechanical Engineering Sona College of Technology department of Mechanical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences (SIMATS)Saveetha University

Heat exchangers have the potential for various engineering applications,and their performance is enriched by surface *** proposed system enriches the heat exchanger's performance by adapting a peened stainless steel（SS） surface blasted with copper,grinding,sand,and *** influences peened SS surface modified with a copper blast,grinding,sandblast,and iron blast on the surface morphology of shell and tube heat exchanger surface（STHE） is analyzed via tungsten heated cathode electron gun featured scanning electron microscope and observed coarse grain *** SS surface featured STHE performance is experimentally evaluated by different flow rates（30,60,90,and 120 L/min） underwater fluid medium functioned by the temperature ranges of 25-75℃ and its results are compared with computational fluid dynamic（CFD）/heat transfer research（HTRI） analyzed *** 60 L/min flow rate was spotted as an optimum value for both shell and tube side *** STHE is operated with a 60 L/min flow rate under the different peened surfaces and evaluated its Stanton number,Nusselt number,logarithmic mean temperature difference（LMTD）,overall heat transfer coefficient,number of transfer units（NTU）,effectiveness,and exergy *** iron blasted SS peened surface was observed to have better SHTE performance like Stanton number（0.0012）,Nusselt number（2180）,reduced LMTD of 31℃,improved overall heat transfer co-efficient of 2400 W/（m2·K）,better NTU of 0.5532,good effectiveness value of 0.451 62,and hiked exergy efficiency of 10% respectively.

关键词： penned surface performance analysis shell and tube heat exchanger stainless steel

Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

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Computers, Materials & Continua 2025年第1期82卷 259-278页

作者： Hoon Ko Marek R.Ogiela Division of Computer Science and Engineering Sunmoon UniversityAsan31460Republic of Korea AGH University of Krakow Cryptography and Cognitive Informatics LaboratoryKrakow30-059Poland

This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain *** combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical *** participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security *** multi faceted exploration led to an indepth evaluation of the model’s performance and ***,the correlation between accuracy,detection rate,and error rate was *** analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance *** study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical *** blockchain to generative AI-created medical content addresses key personal information protection *** utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical *** approach not only enhances security but also enables transparent and tamperproof ***,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient *** conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient *** proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content *** research and advancements

关键词： Digitalmedical content medical diagnostic visualization security analysis generativeAI blockchain vulnerability pattern recognition

Few-shot learning for screening 2D Ga2CoS4-x supported single-atom catalysts for hydrogen production

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Journal of Energy Chemistry 2025年第1期100卷 665-673页

作者： Nabil Khossossi Poulumi Dey department of Mechanical Engineering (ME) Delft University of Technology

Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction（HER）,which faces challenges of slow kinetics and high *** electrocatalysts,particularly single-atom catalysts （SACs） on two-dimensional （2D） materials,are *** study presents a few-shot machine learning （ML） assisted high-throughput screening of 2D septuple-atomic-layer Ga2CoS4-xsupported SACs to predict HER catalytic ***,density functional theory （DFT）calculations showed that 2D Ga2CoS4is inactive for ***,defective Ga2CoS4-x（x=0–0.25）monolayers exhibit excellent HER activity due to surface sulfur vacancies （SVs）,with predicted overpotentials （0–60 mV） comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER *** demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D *** DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was *** intrinsic descriptor linking SAC atomic properties to HER overpotential was *** study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.

关键词： Hydrogen production Electrocatalyst 2D material Density functional theory Machine learning Surface sulfur vacancy

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34卷 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.

关键词： Al2O3 concentration CI engine Dairy Scum Methyl Ester Biodiesel

Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Biosensor Complex mixture Dopamine Machine learning MoS2 Multiwalled carbon nanotubes

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

A Decentralized and TCAM-Aware Failure Recovery Model in Software Defined Data Center Networks

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Computers, Materials & Continua 2025年第1期82卷 1087-1107页

作者： Suheib Alhiyari Siti Hafizah AB Hamid Nur Nasuha Daud department of Computer System and technology University of MalayaKuala Lumpur50603Malaysia

Link failure is a critical issue in large networks and must be effectively *** software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive *** schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow *** SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes *** particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade *** paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource *** DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory *** employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and *** employs flow entry aggregation techniques to reduce switch memory *** of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC *** reduces the switches’Ternary Content-Addressable Memory(TCAM)***,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network *** performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN *** different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed vario

关键词： Software defined networking failure detection failure recovery restoration protection TCAM size

Preparation and Performance of Ternary Early Strength Agent and Quercetin Composite Cement Sealing Material

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Journal of Wuhan University of technology(Materials Science) 2025年第1期40卷 130-140页

作者： LIU Jian CHEN Meiting JI Xiaoli XU Chao WANG Chunmei department of Safety Science and Engineering Anhui University of Science and TechnologyHuainan 232001China Key Laboratory of Mine Safety and High Efficient Mining Jointly Built by Province and Education Ministry Anhui University of Science and TechnologyHuainan 232001China department of Chemical Engineering and Explosives Anhui University of Science and TechnologyHuainan 232001China

A ternary early-strengthening agent consisting of calcium formate+triethanolamine+lithium sulfate was compounded with quercetin to shorten the setting time of cementitious materials while ensuring their early *** optimum ratio of the three early-strengthening agents was determined as 0.5%calcium formate+0.04%triethanolamine+0.4%lithium sulfate by response surface *** effects of the ternary early-strengthening agent composed of calcium formate+triethanolamine(TEA)+lithium sulfate on cementitious pore sealing materials under the synergistic effect of quercetin were studied by means of the performance tests of compressive strength,fluidity,and setting time,and the microstructural characterizations of X-ray powder diffractometer(XRD),thermogravimetry(TG-DSC)and scanning electron microscopy(SEM).The study shows that the synergistic effect of ternary early-strengthening agent and quercetin forms a multi-performance composite admixture for cementitious *** best performance was obtained with the compounding scheme of 0.5%calcium formate+0.04%triethanolamine+0.4%lithium sulfate ternary early-strengthening agent and 0.05%*** compressive strength of 1,3,7,and 28 d are 94.8%,39.8%,42%,and 28%higher than those of the blank group,*** initial time and final setting time are 41 and 57 minutes,*** to the microscopic analysis,the network and fibrous C-S-H gels generated by ternary early-strengthening agents are attached to the surface promoted by quercetin,which forms skeleton support while thickening and solidifying the cement slurry,which enhances the early compressive strength of the cement-based materials.

关键词： sealing material ternary early-strengthening agent quercetin synergetic effect response surface methodology

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