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Computers, Materials & Continua 2020年第11期65卷 1649-1670页

作者： s.b.manoojkumaar C.Poongodi Department of Computer science and Engineering J.K.K.Munirajah College of TechnologyGobichettipalayamErode638506India Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalamErode638401India

Maximizing network lifetime is measured as the primary issue in Mobile Ad-hoc Networks(MANETs).In geographically routing based models,packet transmission seems to be more appropriate in dense *** involvement of the Heuristic model directly is not appropriate to offer an effectual solution as it becomes NP-hard issues;therefore investigators concentrate on using Meta-heuristic *** Optimization(DFO)is an effective meta-heuristic approach to resolve these problems by providing optimal ***,Meta-heuristic approaches(DFO)turn to be slower in convergence problems and need proper computational time while expanding network ***,DFO is adaptively improved as Adaptive Dragonfly Optimization(ADFO)to fit this model and re-formulated using graph-based m-connection establishment(G-𝑚𝑚CE)to overcome computational time and DFO’s convergence based problems,considerably enhancing DFO ***(G-𝑚𝑚CE),Connectivity Zone(CZ)is chosen among source to destination in which optimality should be under those connected regions and ADFO is used for effective route establishment in CZ indeed of complete networking *** measure complementary features of ADFO and(G-𝑚𝑚CE),hybridization of DFO-(G-𝑚𝑚CE)is anticipated over dense circumstances with reduced energy consumption and delay to enhance network *** simulation was performed in MATLAb environment.

关键词： Routing connectivity zone ADFO mobile ad-hoc network graph-based m-connection establishment

Intrumer:A Multi Module Distributed Explainable IDs/IPs for securing Cloud Environment

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Computers, Materials & Continua 2025年第1期82卷 579-607页

作者： Nazreen banu A s.K.b.sangeetha Department of Computer science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai600026Tamil NaduIndia

The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDs,have been developed to tackle these ***,most conventional Intrusion Detection system(IDs)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDsmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDs datasets CICIDs 2017,Honeypots,and NsL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.

关键词： Cloud computing intrusion detection system transformers and explainable artificial intelligence(XAI)

Neuroinflammation revisited through the microglial lens

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Neural Regeneration Research 2025年第7期20卷 1989-1990页

作者： Renato socodato João b.Relvas Institute of Research and Innovation in Health(i3s)and Institute for Molecular and Cell biology(IbMC) University of PortoPortoPortugal Department of biomedicine Faculty of Medicine of the University of Porto(FMUP)PortoPortugal

Neuroimmunology is emerging as a pivotal field,shedding light on the intricate dialogues between the central nervous system(CNs)and the immune *** exploration is particularly significant in understanding microglia,the CNs’s innate immune cells,beyond the conventional conflation of“neuroinflammation”and“microglial activation.”This conflation has clouded the true complexity of these processes,potentially stalling scientific progress and the development of new *** challenge the long-standing perspectives that have oversimplified these interactions,advocating for a deeper exploration of the dynamic relationship between neuroinflammation and microglial *** dissecting specific molecular pathways,we aim to illuminate their elaborate roles in neuroinflammatory responses,especially in the context of Alzheimer’s disease(AD).Here,neuroinflammation is not merely a passive observer or a direct antagonist but a complex agent in the disease’s *** article calls for a significant paradigm shift towards an integrative,multi-omics approach to *** such a comprehensive framework is crucial for advancing our understanding of neuroinflammatory conditions and paving the way for targeted therapeutic strategies for brain diseases.

关键词： inflammation simplified activation

Federated Learning’s Role in Next-Gen TV Ad Optimization

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Computers, Materials & Continua 2025年第1期82卷 675-712页

作者： Gabriela Dobrita simona-Vasilica Oprea Adela bâra Department of Economic Informatics and Cybernetics Bucharest University of Economic StudiesBucharest010374Romania

In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become *** methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data *** article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data *** combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot *** into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-sAT)and then the scheduled position of all ads will be the result of the optimization ***,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data *** FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity ***,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.

关键词： Ad scheduling prescriptive-predictive approach federated learning KNN

brain-derived neurotrophic factor signaling in the neuromuscular junction during developmental axonal competition and synapse elimination

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Neural Regeneration Research 2025年第2期20卷 394-401页

作者： Josep Tomàs Víctor Cilleros-Mañé Laia Just-borràs Marta balanyà-segura Aleksandra Polishchuk Laura Nadal Marta Tomàs Carolina silvera-simón Manel M.santafé Maria A.Lanuza Unitat d’Histologia i Neurobiologia(UHNeurob) Facultat de Medicina i Ciències de la SalutUniversitat Rovira i VirgiliReusSpain

During the development of the nervous system,there is an overproduction of neurons and *** competition between neighboring nerve endings and synapses performing different activity levels leads to their elimination or *** have extensively studied the involvement of the brain-derived neurotrophic factor-Tropomyosin-related kinase b receptor neurotrophic retrograde pathway,at the neuromuscular junction,in the axonal development and synapse elimination process versus the synapse *** purpose of this review is to describe the neurotrophic influence on developmental synapse elimination,in relation to other molecular pathways that we and others have found to regulate this *** particular,we summarize our published results based on transmitter release analysis and axonal counts to show the different involvement of the presynaptic acetylcholine muscarinic autoreceptors,coupled to downstream serine-threonine protein kinases A and C(PKA and PKC)and voltage-gated calcium channels,at different nerve endings in developmental *** dynamic changes that occur simultaneously in several nerve terminals and synapses converge across a postsynaptic site,influence each other,and require careful studies to individualize the mechanisms of specific *** describe an activity-dependent balance(related to the extent of transmitter release)between the presynaptic muscarinic subtypes and the neurotrophin-mediated Trkb/p75NTR pathways that can influence the timing and fate of the competitive interactions between the different axon *** downstream displacement of the PKA/PKC activity ratio to lower values,both in competing nerve terminals and at postsynaptic sites,plays a relevant role in controlling the elimination of supernumerary ***,calcium entry through L-and P/Q-subtypes of voltage-gated calcium channels(both channels are present,together with the N-type channel in developing nerve terminals)contributes to red

关键词： acetylcholine release adenosine receptors axonal competition brain-derived neurotrophic factor calcium channels motor end-plate muscarinic acetylcholine receptors postnatal synapse elimination serine kinases tropomyosin-related kinase receptorb

Combinatorial therapies for spinal cord injury repair

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Neural Regeneration Research 2025年第5期20卷 1293-1308页

作者： Carla s.sousa Andreia Monteiro António J.salgado Nuno A.silva Life and Health sciences Research Institute(ICVs) School of MedicineUniversity of MinhoCampus de Gualtar-gualtarBragaPortugal ICVs/3b’s Associate Lab PT Government Associated LabCampus de Gualtar-gualtarBragaPortugal

spinal cord injuries have profound detrimental effects on individuals, regardless of whether they are caused by trauma or non-traumatic events. The compromised regeneration of the spinal cord is primarily attributed to damaged neurons, inhibitory molecules, dysfunctional immune response, and glial scarring. Unfortunately, currently, there are no effective treatments available that can fully repair the spinal cord and improve functional outcomes. Nevertheless, numerous pre-clinical approaches have been studied for spinal cord injury recovery, including using biomaterials, cells, drugs, or technological-based strategies. Combinatorial treatments, which target various aspects of spinal cord injury pathophysiology, have been extensively tested in the last decade. These approaches aim to synergistically enhance repair processes by addressing various obstacles faced during spinal cord regeneration. Thus, this review intends to provide scientists and clinicians with an overview of pre-clinical combinatorial approaches that have been developed toward the solution of spinal cord regeneration as well as update the current knowledge about spinal cord injury pathophysiology with an emphasis on the current clinical management.

关键词： electric stimulation neural tissue regeneration neuroprotection polytherapy spinal cord injury

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

A Comprehensive survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in Engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain saxena Amit Pundir sanjeev singh Akshat Gaurav savi bansal Razaz Waheeb Attar Mosiur Rahman brij b.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer science and Information Engineering Asia UniversityTaichung413Taiwan symbiosis Centre for Information Technology(sCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

Nonsmooth semi-Infinite Minmax Programming Involving Generalized(Φ, ρ)-Invexity

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Journal of systems science & Complexity 2015年第4期28卷 857-875页

作者： UPADHYAY b b MIsHRA s K Department of basic sciences & Humanities National Institute of Technology

This paper introduces some new generalizations of the concept of （~, p）-invexity for non- differentiable locally Lipschitz functions using the tools of Clarke subdifferential. These functions are used to derive the necessary and sufficient optimality conditions for a class of nonsmooth semi-infinite minmax programming problems, where set of restrictions are indexed in a compact set. Utilizing the sufficient optimality conditions, the authors formulate three types of dual models and establish weak and strong duality results. The results of the paper extend and unify naturally some earlier results from the literature.

关键词： Duality, generalized invexity locally Lipschitz functions, minmax programming, semi-infinite programming.

Machine learning empowers efficient design of ternary organic solar cells with PM6 donor

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Journal of Energy Chemistry 2025年第1期100卷 337-347页

作者： Kiran A.Nirmal Tukaram D.Dongale santosh s.sutar Atul C.Khot Tae Geun Kim school of Electrical Engineering Korea University Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and Biotechnology Shivaji University Yashwantrao Chavan school of Rural Development Shivaji University

Organic solar cells(OsCs) hold great potential as a photovoltaic technology for practical ***, the traditional experimental trial-and-error method for designing and engineering OsCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OsCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OsCs(TOsCs) based on a PM6 ***, we collected the device parameters of PM6-based TOsCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOsCs. Finally, we validated the ML outcomes by fabricating PM6-based TOsCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关键词： Random forests