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Geochemistry and depositional environment of fuchsite quartzites from sargur Group,western Dharwar Craton,India

Acta Geochimica 2024年第3期43卷 555-570页

作者： C.s.sindhuja G.Harshitha C.Manikyamba K.s.V.subramanyam CsIR(Council of scientific and Industrial Research)-National Geophysical Research Institute(NGRI) Uppal RoadHyderabad 500007India

Meso-Neoarchean fuchsite quartzites are present in different stratigraphic positions of Dharwar Craton including the oldest(~3.3 Ga)sargur Group of western Dharwar *** present study deals with the petro-graphic and geochemical characteristics of the fuchsite quartzites from the Ghattihosahalli belt to evaluate their genesis,depositional setting and the enigma involved in the ancient sedimentation *** major mineral assemblages include quartz,fuchsite,and feldspars along with accessory kyanite and *** geochemical com-positions are characterized by high siO_(2),Al_(2)O_(3),low MgO,CaO,strongly enriched Cr(1326–6899 ppm),Ba(1165–3653 ppm),sr(46–210 ppm),V(107–868 ppm)and Zn(11–158 ppm)contents compared to the upper continental crust(UCC).The UCC normalized rare earth element(REE)patterns are characterized by depleted light REE[(La/sm)UCC=0.33–0.95]compared to heavy REE[(Gd/Yb)_(UCC)=0.42–1.65]with conspicuous positive Eu-anomalies(Eu/Eu^(*)=1.35–18.27)characteristic of hydrothermal solutions evidenced through the interlayered *** overall major and trace element systematics reflect a combined mafic-felsic provenance and suggest their deposition at a passive continental margin *** comprehensivefield,petrographic,and geo-chemical studies indicate that these quartzites are infiltrated by Cr-richfluids released during high-grade metamorphism of associated ultramafic *** sargur and the subse-quent Dharwar orogeny amalgamated diverse lithounits from different tectonic settings,possibly leading to the release of Cr-richfluids and the formation of fuchsite quartzite during or after the *** sug-gest a pre-existing stable crust prior to the sargur Group and the link between orogenic events and various mineral deposits in the Dharwar Craton.

关键词： Dharwar Craton Ghattihosahalli Fuchsite quartzite Provenance Depositional setting

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Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT scan: From Concepts toward Clinical Explainability

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Computers, Materials & Continua 2024年第1期78卷 1171-1187页

作者： Mohamed Abdel-Basset Hossam Hawash Mohamed Abouhawwash s.s.Askar Alshaimaa A.Tantawy Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT *** study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 *** paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT ***,an efficient U-shaped transformer network is integrated for lung image ***,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input ***,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer *** evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical *** Conformer Network achieves 97.40% of detection accuracy under cross-validation *** model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our *** findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

关键词： Deep learning COVID-19 multi-modal medical image fusion diagnostic image fusion

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Role of climatic variability in shaping intraspecific variation of thermal tolerance in Mediterranean water beetles

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Insect science 2024年第1期31卷 285-298页

作者： susana Pallarés David Garoffolo Belén Rodríguez David sánchez-Fernández Department of Zoology University of SevilleSevilleSpain Faculty of Biology Department of Ecology and HydrologyUniversity of MurciaCampus EspinardoMurciaSpain

The climatic variability hypothesis(CVH)predicts that organisms in more thermally variable environments have wider thermal breadths and higher thermal plasticity than those from more stable ***,due to evolutionary trade-offs,taxa with greater absolute thermal limits may have little plasticity of such limits(trade-off hypothesis).The CVH assumes that climatic variability is the ultimate driver of thermal tolerance variation across latitudinal and altitudinal gradients,but average temperature also varies along such *** explored intraspecific variation of thermal tolerance in three typical Mediterranean saline water beetles(families Hydrophilidae and Dytiscidae).For each species,we compared two populations where the species coexist,with similar annual mean temperature but contrasting thermal variability(continental *** population).We estimated thermal limits of adults from each population,previously acclimated at 17,20,or 25℃.We found species-specific patterns but overall,our results agree with the CVH regarding thermal ranges,which were wider in the continental(more variable)*** the two hydrophilid species,this came at the cost of losing plasticity of the upper thermal limit in this population,supporting the trade-off hypothesis,but not in the dytiscid *** results support the role of local adaptation to thermal variability and trade-offs between basal tolerance and physiological plasticity in shaping thermal tolerance in aquatic ectotherms,but also suggest that intraspecific variation of thermal tolerance does not fit a general pattern among aquatic *** such intraspecific variation could lead to inaccurate predictions of the vulnerability of aquatic insects to global warming.

关键词： acclimation capacity aquatic insects climate change heat coma temperature supercooling point thermal plasticity

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Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

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Transactions of Tianjin University 2024年第2期30卷 152-166页

作者： Mohd H.s.Alrashdan Department of Electrical Engineering Al-Hussein Bin Talal UniversityMaan 71110Jordan

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17

关键词： Lithium-ion batteries Average electric power during charging Taguchi method COMsOL Multiphysics software C rate L27 orthogonal array

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TOEPLITZ DETERMINANTs IN ONE AND HIGHER DIMENsIONs

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Acta Mathematica scientia 2024年第5期44卷 1931-1944页

作者： surya GIRI s.sivaprasad KUMAR Department of Applied Mathematics Delhi Technological UniversityNew Delhi110042India

In this study,we derive the sharp bounds of certain Toeplitz determinants whose entries are the coefficients of holomorphic functions belonging to a class defined on the unit disk ***,these results are extended to a class of holomorphic functions on the unit ball in a complex Banach space and on the unit polydisc inℂ*** obtained results provide the bounds of Toeplitz determinants in higher dimensions for various subclasses of normalized univalent functions.

关键词： holomorphic mappings starlike mappings Toeplitz determinants coefficient inequalities

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MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature selection,and Explainable AI

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Computers, Materials & Continua 2024年第5期79卷 2229-2251页

作者： Moshe Dayan sirapangi s.Gopikrishnan school of Computer science and Engineering VIT-AP UniversityAmaravathiAndhra Pradesh522241India

Medical Internet of Things(IoT)devices are becoming more and more common in *** has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized *** methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT *** is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease *** using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be *** Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing *** metrics show the model’s superiority in real-time health *** proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay *** prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable *** research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

关键词： Predictive health modeling Medical Internet of Things explainable artificial intelligence personalized feature selection preemptive analysis

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Wind energy feasibility and wind turbine selection studies for the city surat,India

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Clean Energy 2024年第3期8卷 166-173页

作者： Arshi Banu P s Maanat Bhadani Hindustan Institute of Technology and science ChennaiIndia The Doon school UttarakhandDehradunIndia

Wind energy represents a clean,abundant and cost-effective power source,fostering job growth and environmental *** wind energy harnesses several gigawatts today,its availability hinges on diverse factors,with geographical location standing *** turbines,with varying capacity ranges,saturate the *** site-specific suitability and matching the appropriate turbine to meet specific requirements are of paramount *** study aims to assess the feasibility of wind energy in surat,Gujarat,India and select an optimal small commercial turbine for residential *** research involves Rayleigh and Weibull probability distribution functions based on yearlong velocity *** distributions are fitted with actual data,revealing the most probable velocity(v_(mf)=3 m/s)and velocity at maximum power(v_(pmax)=5 m/s).The power availability of the site has been assessed as 42.6 W/m^(2)using both graphical and analytical *** commercial turbines have been shortlisted based on on-site power criteria and their specifications are evaluated against site power availability.A comparative analysis culminates in identifying the most suitable turbine for the *** best suitable turbine for the site with an annual energy yield of 8 MW has been suggested amongst selected turbines for small-scale residential applications.

关键词： site feasibility wind energy commercial wind turbine probability distribution function rated power

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Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETs)

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Computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya s.Ansari Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial *** study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic *** are wireless networks that are based on mobile devices and may *** distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion *** study suggests a unique Chaotic spatial Fuzzy Polynomial Neural Network(CsFPNN)technique to assess real-time data acquired from various sources within *** framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real ***-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce *** framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban *** simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and *** results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CsFPNN)

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Towards sustainable solar cells:unveiling the latest developments in bio-nano materials for enhanced DssC efficiency

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Clean Energy 2024年第3期8卷 238-257页

作者： sethu Narayanan Tamilselvan s.shanmugan Research Centre for solar Energy Department of Engineering PhysicsCollege of EngineeringKoneru Lakshmaiah Education FoundationGreen FieldsGuntur DistrictVaddeswaramAndhra Pradesh 522502India

Fossil fuels-instrumental in powering the nineteenth-century Industrial Revolution-are now under heightened scrutiny due to environmental concerns and carbon ***,there is a global impetus towards achieving carbon-neutral energy production,with solar energy emerging as a cornerstone in this *** the past three decades,dye-sensitized solar cells(DssCs)have gained increasing prominence due to their unique *** review elucidates several crucial elements essential to DssC construction,notably bio-nano materials,prized for their structural flexibility,cost-effectiveness and high molar absorption *** delves into the intricate relationship between molecular structure and DssC performance,showcasing significant advancements with light-to-power conversion efficiencies reaching 11%.A comprehensive analysis of the photovoltaic performance of DssCs utilizing bio-nano-based dyes offers invaluable insights into the state of this rapidly evolving *** components under scrutiny include transparent conductive oxide(TCO)layers,physical techniques for metal oxide deposition onto TCO thin films,diverse dye variants and foundational knowledge of components ripe for growth and ***,the review outlines the latest breakthroughs in the field,providing glimpses into the current status of prototype DssCs.

关键词： DssC performance TCO thin films environmental solution

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An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in streaming Data

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China Communications 2024年第5期21卷 249-260页

作者： R.Rajakumar s.sathiya Devi srinivasa Ramanujan Centre SASTRA Deemed UniversityKumbakonamTamil Nadu 612001India University College of Engineering BIT CampusAnna UniversityTiruchirappalli 620024India

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,*** recent developments of deep learning(DL)models find helpful in the detection and classification of *** article designs an oversampling with an optimal deep learning-based streaming data classification(Os-ODLsDC)*** aim of the OsODLsDC model is to recognize and classify the presence of anomalies in the streaming *** proposed Os-ODLsDC model initially undergoes preprocessing *** streaming data is unbalanced,support vector machine(sVM)-synthetic Minority Over-sampling Technique(sVM-sMOTE)is applied for oversampling ***,the Os-ODLsDC model employs bidirectional long short-term memory(Bi LsTM)for AD and ***,the root means square propagation(RMsProp)optimizer is applied for optimal hyperparameter tuning of the Bi LsTM *** ensuring the promising performance of the Os-ODLsDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDs 2018,KDD-Cup 1999,and NsL-KDD datasets.

关键词： anomaly detection deep learning hyperparameter optimization oversampling sMOTE streaming data

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