Mount Kandil is situated in the eastern sector of the EAHP(Eastern Anatolian High Plateau),to the south of the Lesser Caucasus.The mountain lies at the westernmost end of the Aras Mountains,which extends approximately...Mount Kandil is situated in the eastern sector of the EAHP(Eastern Anatolian High Plateau),to the south of the Lesser Caucasus.The mountain lies at the westernmost end of the Aras Mountains,which extends approximately 80 km along a NW-SE axis.With a summit reaching~3214 m(a.s.l.),Mount Kandil is a stratovolcano that,like many other peaks within the EAHP and the Lesser Caucasus,experienced significant environmental changes during Late Pleistocene.Among these,glacial processes stand out as the most profound,having distinctly shaped the mountains geomorphic landscape.This study presents,for the first time,a comprehensive analysis of the glacial morphology of Mount Kandil based on multiple datasets.Field-based morphological observations indicate that an area of approximately 32.62 km^(2)has been sculpted by glacial activity.Within six glaciated regions on Mount Kandil,25 cirques and 6 glacial valleys have been identified.In addition,moraines in various locations exhibit characteristic morphologies.Furthermore,valley glaciers are inferred to have descended to altitudes as low as~2000 m.The paleoequilibrium line(p ELA)was estimated to use AABR method within GIS,yielding a mean pELA of~2730 m.Ice thickness modelling indicates that the thickness of glaciers in the Kandil Mountain valleys reaches up to~350 m.Due to its orographic extension,Mount Kandil is exposed to humid northwest winds and receives substantial frontal precipitation(~686 mm annually).The compiled geomorphic,cartographic and morphometric parameters suggest that the glaciation dynamics of Mount Kandil—situated between the Southeastern Taurus and the Lesser Caucasus—closely resemble those observed in the Lesser Caucasus.This indicates that glaciation was primarily governed by northern atmospheric systems with additional influences from southerly or westerly winds.The integrated data also underscores the role of multiple atmospheric systems in controlling the glaciation regime around the Lesser Caucasus.Additionally,findings on regional pELA question the common belief that pELA increases eastward in EAHP.展开更多
Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constrain...Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constraints,including relocated aftershocks,geological observations,and geophysical information,to adaptively model fault geometry and slip in diverse scenarios such as multi-segment and multi-event ruptures.The framework combines adaptive fault construction with a scenario-driven Bayesian joint inversion approach.Fault geometries are first built from prior constraints,such as surface ruptures and aftershocks,and then refined through probabilistic inference when such data are inadequate.To enhance computational efficiency,we introduce a Sequential Monte Carlo Fukuda-Johnson(SMC-FJ)strategy.This separates nonlinear parameters-including geometry,data weights,and smoothing factors-from linear slip parameters,which are conditionally solved via constrained least squares.Geometry updates follow a hierarchical adjustment scheme based on point,line,and structural units,enabling flexibility across rupture complexities.Synthetic tests and four case studies,including the 2022 Menyuan,2023 Türkiye,2022 Luding,and 2019 Ridgecrest earthquakes,demonstrate robustness under various constraints.For the Menyuan earthquake,full Bayesian inversion confirms that the fault geometry constrained by relocated aftershocks is highly accurate and needs only minor adjustment to match the observed surface deformation.The results further show that gradual changes in fault strike and dip modulated rupture arrest and postseismic stress accumulation,highlighting the critical role of inherited geometric structure in controlling rupture termination and delayed seismic activation.展开更多
In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a ...In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial fo...Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics.While traditional experimental approaches are time-consuming and expensive,computational methods have emerged as viable alternatives,including similarity-based and machine learning(ML)-/deep learning(DL)-based methods.However,existing methods often struggle with robustness and generalizability.To address these challenges,we propose HyPepTox-Fuse,a novel framework that fuses protein language model(PLM)-based embeddings with conventional descriptors.HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture.A robust feature ranking and selection pipeline further refines conventional descriptors,thus enhancing prediction performance.Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations,offering a scalable and reliable tool for peptide toxicity prediction.Moreover,we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse,highlighting its effectiveness in enhancing model performance.Furthermore,the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/.The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets.展开更多
Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experienci...Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer cons...The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer considerable potential for automating this task,their practical application in scientific domains is nonetheless constrained by high application programming interface(API)costs and computational resources required for fine-tuning.To address these limitations,a cognition-enhanced instruction framework(CEIF)is proposed,wherein a high-performance teacher model(such as DeepSeek-R1)provides dynamic feedback,prompt refinement,and training data optimization to guide the learning process of low-parameter models.Experimental results demonstrate that the low-parameter models(6B-9B)optimized via the CEIF achieve approximately 85%accuracy in battery literature extraction tasks,approaching the performance of GPT-4 while requiring only a single NVIDIA RTX 3090 GPU.Furthermore,the emergence of an"Aha moment"characterized by rapid performance improvement during specialized learning is observed,offering novel theoretical insights for the design and optimization of domainspecific models.展开更多
Covalent organic frameworks(COFs)are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures.Their crystallization is achieved by balancing thermal reversibility with t...Covalent organic frameworks(COFs)are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures.Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks.Ionic covalent organic frameworks(ICOFs)are a subclass that incorporates ions in positive,negative,or zwitterionic forms into the frameworks.In particular,spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs.Unlike electroneutral COFs,ICOFs can be tailored by adjusting the types and arrangements of ions,influencing their formation mechanisms and physical properties.This study focuses on analyzing the graph-based structural characteristics of ICOFs with spiroborate linkages.We compute graph based entropy using hybrid topological descriptors that capture both local and global structural patterns.Furthermore,statistical regression models are developed to predict graph energies of larger-dimensional ICOF structures based on these descriptors.To ensure the robustness and accuracy of our results,we validated our findings using a pseudocode algorithm specifically designed for computing degree-based topological indices.This computational validation confirms the consistency of the derived descriptors and supports their applicability in quantitative structure-property relationship(QSPR)modeling.Overall,this approach provides valuable insights for future applications in material design and property prediction within the framework of ICOFs.展开更多
Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This re...Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This review systematically examines the global landscape of traditional medicine-modern medicine integration by analyzing policy developments,regulatory frameworks,and clinical implementation models across various regions,including Asia,Africa,Europe,and the USA.The scope of the review encompasses five key domains:(1)global policy initiatives,(2)regulatory and institutional frameworks,(3)clinical integration models,(4)impacts and outcomes of integrative practices,and(5)challenges and barriers to implementation.Based on peer-reviewed literature and official health policy documents published between 2000 and 2025,the present review investigates how countries have operationalized clinical integration models combining traditional and complementary medicine.Although interest in traditional and complementary medicine has grown worldwide,persistent challenges,such as limited scientific validation,lack of standardization,and professional resistance,continue to hinder progress.This review concludes that successful and sustainable integration requires evidence-based clinical approaches,inclusive regulatory reforms,and coordinated policy strategies.Countries such as China,India,and Brazil have made significant advances,offering valuable models for future implementation worldwide.展开更多
We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We...We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.展开更多
Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the...Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.展开更多
Advertising avoidance is resistance to advertising intrusion.This issue has been the subject of much academic research in recent years.To guide scholars to better carry out relevant research and promote enterprises to...Advertising avoidance is resistance to advertising intrusion.This issue has been the subject of much academic research in recent years.To guide scholars to better carry out relevant research and promote enterprises to better implement advertising activities,this study intends to summarize the relevant research on advertising avoidance in recent years.The specific method is to use the core literature meta-analysis method to identify,filter,and screen relevant literature published in core journals from 1997 to 2020 with the keywords advertising avoidance and advertising resistance.We review the collected articles from the following perspectives:the definition and classification,external stimulating factors,internal perception factors,and moderating factors of advertising avoidance.On this basis,the SOMR model of advertising avoidance is constructed according to the SOR model.Finally,some prospects for future related research are presented.展开更多
Blockchain technology has a unique ability to automate accounting processes that are a part of regulatory requirements in all commercial enterprises.Moreover,it can hold verified accounting records and eliminate the n...Blockchain technology has a unique ability to automate accounting processes that are a part of regulatory requirements in all commercial enterprises.Moreover,it can hold verified accounting records and eliminate the need for a trusted third party.Despite blockchain’s potential to transform the nature of traditional accountancy procedures,adoption by the accounting industry is somewhat limited.Knowledge in this domain is lacking,and research on the antecedents influencing the adoption of blockchainbased accounting systems is scarce.This study is rooted in the technology-organization-environment(TOE)framework,presenting a trust-centric adoption model based on diligent analysis of blockchain and technology adoption literature.The model proposes that TOE factors mediate trust’s role in adopting blockchain for accounting applications.The model was validated based on qualitative semi-structured interviews of twelve industry leaders and was comprehensively tested using a quantitative surveybased methodology with accounting professionals knowledgeable about blockchain technology.The data collected was analyzed using PLS-SEM.The results demonstrate the role of trust and the mediating effect of the theorized TOE variables on adopting blockchain-based accounting solutions.The results’implications for research and practice are discussed.展开更多
In this paper,based on the structure-behavior coupling paradigm,we propose the concept of deviation of central town to describe the geography-market distance between farmers and the central regional town.Using the sur...In this paper,based on the structure-behavior coupling paradigm,we propose the concept of deviation of central town to describe the geography-market distance between farmers and the central regional town.Using the survey data from farmers in a poverty-stricken village in Western China,the impact of deviation of central town on farmers'livelihood strategies is analyzed.The results indicate that farmers exhibit spatial heterogeneity in their livelihood strategies.Those with low deviation show a strong inclination towards working in urban areas,while those with high deviation tend to integrate into rural industries.The deviation of central town influences farmers'livelihood strategies through the information effect,which is also affected by the level of rural infrastructure and public services,labor force structure and assistance policies.The obtained results are expected to provide guidance for promoting the integration of farmers into the urban-rural economic cycle based on sustainable livelihoods and connecting poverty alleviation with rural revitalization.展开更多
Photo-responsive metal-organic frameworks(MOFs)have evoked considerable attention due to their potential application in inkless printing paper.However,the poor cycling performance and low printing resolution greatly i...Photo-responsive metal-organic frameworks(MOFs)have evoked considerable attention due to their potential application in inkless printing paper.However,the poor cycling performance and low printing resolution greatly inhibit their practical application.Herein,a novel MOF based on naphthalenediimide derivate moiety,[La(H_(2)O)(BINDI)_(0.5)(DMF)_(3)][NO_(3)](1,H_(4)BINDI=N,N'-bis(5-isophthalic acid)naphthalenediimide),was successfully synthesized for inkless erasable printing.This material exhibits reversible photochromic behavior and good stability.The inkless printing paper coated with 1 delivers high resolution reaching up to 0.2 mm,comparable to commercial printers.Furthermore,the stable framework and suitable reversibility enable excellent cycling performance with 197 cycles,surpassing almost all reported MOFs.This work sheds light on new opportunities in designing outstanding photochromic MOFs for ink-free printing.展开更多
Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper review...Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper reviews critical parameters influencing mechanical properties in 35 RAC studies,compiles four datasets encompassing these parameters,and compiles the performance and key findings of 77 published data-driven models.Baseline capability tests are conducted on the nine most used models.The paper also outlines advanced methodological frameworks for future RAC research,examining the principles and challenges of physics-informed neural networks(PINNs)and generative adversarial networks(GANs),and employs SHAP and PDP tools to interpret model behaviour and enhance transparency.Findings indicate a clear trend toward integrated systems,hybrid models,and advanced optimization strategies,with integrated tree-based models showing superior performance across various prediction tasks.Based on this comprehensive review,we offer a recommendation for future research on how AI can be effectively oriented in RAC studies to support practical deployment and build confidence in data-driven approaches.展开更多
The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across divers...The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across diverse geological settings.Large-scale models(LSMs),with vast parameter spaces and extensive training datasets,excel in solving complex visual problems.This study explores the potential of using one such LSM,Segment anything model(SAM),to identify facet-type discontinuities across several outcrops via interactive prompting.The findings demonstrate that SAM effectively segments two-dimensional(2D)discontinuities,with its generalization capability validated on a dataset of 2426 identified discontinuities across 170 outcrops.The model achieves 0.78 mean IoU and 0.86 average precision using 11-point prompts.To extend to three dimensions(3D),a framework integrating SAM with Structure-from-Motion(SfM)was proposed.By utilizing the inherent but often overlooked relationship between image pixels and point clouds in SfM,the identification process was simplified and generalized across photogrammetric devices.Benchmark studies showed that the framework achieved 0.91 average precision,identifying 87 discontinuities in Dataset-3D.The results confirm its high precision and efficiency,making it a valuable tool for data annotation.The proposed method offers a practical solution for geological investigations.展开更多
Background:Refined models of kidney disease are critical to better understand disease processes and study novel treatments while minimizing discomfort in research animals.The objective of this study was to report a te...Background:Refined models of kidney disease are critical to better understand disease processes and study novel treatments while minimizing discomfort in research animals.The objective of this study was to report a technique for minimally invasive partial kidney embolism in cats and describe outcomes following transcatheter administration of embolic microspheres with subsequent contralateral nephrectomy.Methods:Eleven,apparently healthy,male,purpose-bred cats underwent unilateral kidney embolism with 0.25 or 0.5 mL of embolic microparticle(40-120μm)suspension(0.2 mL microspheres/mL)delivered into the right renal artery under fluoroscopic guidance,followed 5 months later by contralateral nephrectomy.One month after nephrectomy,blood and urinary markers of kidney function were evaluated,and embolized kidneys were harvested for histopathology evaluation.Results:Renal artery embolization was possible in all cats.Two cats did not complete the study,one after experiencing congestive heart failure(n=1)and the other following evidence of complete kidney embolism precluding nephrectomy(n=1)postembolization.At study end,compared to baseline,cats had significant increases in median(range)serum creatinine(159.1μmol/L[141.4-530.4]versus 128.2μmol/L[92.8-150.3];p=0.0004),urea nitrogen(15.71 mmol/L[9.29-47.85]versus 7.50 mmol/L[6.07-8.57];p<0.0001),and symmetric dimethylarginine(0.74μmol/L[0.59-3.12]versus 0.67μmol/L[0.54-0.72];p=0.0288)concentrations.No differences in markers of kidney function were documented between dose groups.Conclusions:M inimally invasive kidney embolism is a promising technique for modeling kidney disease in cats.Understanding optimal dose,timing of nephrectomy,and longer-term consequences requires additional work.展开更多
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu...Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.展开更多
基金supported by Van Yüzüncü Yıl University,Scientific Research Projects Coordination Unit(Project No:SDK-2025-11935)Van Yüzüncü Yıl University,Scientific Research Projects Coordination Unit for supporting the study。
文摘Mount Kandil is situated in the eastern sector of the EAHP(Eastern Anatolian High Plateau),to the south of the Lesser Caucasus.The mountain lies at the westernmost end of the Aras Mountains,which extends approximately 80 km along a NW-SE axis.With a summit reaching~3214 m(a.s.l.),Mount Kandil is a stratovolcano that,like many other peaks within the EAHP and the Lesser Caucasus,experienced significant environmental changes during Late Pleistocene.Among these,glacial processes stand out as the most profound,having distinctly shaped the mountains geomorphic landscape.This study presents,for the first time,a comprehensive analysis of the glacial morphology of Mount Kandil based on multiple datasets.Field-based morphological observations indicate that an area of approximately 32.62 km^(2)has been sculpted by glacial activity.Within six glaciated regions on Mount Kandil,25 cirques and 6 glacial valleys have been identified.In addition,moraines in various locations exhibit characteristic morphologies.Furthermore,valley glaciers are inferred to have descended to altitudes as low as~2000 m.The paleoequilibrium line(p ELA)was estimated to use AABR method within GIS,yielding a mean pELA of~2730 m.Ice thickness modelling indicates that the thickness of glaciers in the Kandil Mountain valleys reaches up to~350 m.Due to its orographic extension,Mount Kandil is exposed to humid northwest winds and receives substantial frontal precipitation(~686 mm annually).The compiled geomorphic,cartographic and morphometric parameters suggest that the glaciation dynamics of Mount Kandil—situated between the Southeastern Taurus and the Lesser Caucasus—closely resemble those observed in the Lesser Caucasus.This indicates that glaciation was primarily governed by northern atmospheric systems with additional influences from southerly or westerly winds.The integrated data also underscores the role of multiple atmospheric systems in controlling the glaciation regime around the Lesser Caucasus.Additionally,findings on regional pELA question the common belief that pELA increases eastward in EAHP.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130101,42474002,42374003&42564002)the Jiangxi Provincial Natural Science Foundation(Grant No.20252BAC240262).
文摘Accurate fault modeling is essential for understanding earthquake rupture processes and improving seismic hazard assessment.We present a unified framework that integrates geodetic data with multidisciplinary constraints,including relocated aftershocks,geological observations,and geophysical information,to adaptively model fault geometry and slip in diverse scenarios such as multi-segment and multi-event ruptures.The framework combines adaptive fault construction with a scenario-driven Bayesian joint inversion approach.Fault geometries are first built from prior constraints,such as surface ruptures and aftershocks,and then refined through probabilistic inference when such data are inadequate.To enhance computational efficiency,we introduce a Sequential Monte Carlo Fukuda-Johnson(SMC-FJ)strategy.This separates nonlinear parameters-including geometry,data weights,and smoothing factors-from linear slip parameters,which are conditionally solved via constrained least squares.Geometry updates follow a hierarchical adjustment scheme based on point,line,and structural units,enabling flexibility across rupture complexities.Synthetic tests and four case studies,including the 2022 Menyuan,2023 Türkiye,2022 Luding,and 2019 Ridgecrest earthquakes,demonstrate robustness under various constraints.For the Menyuan earthquake,full Bayesian inversion confirms that the fault geometry constrained by relocated aftershocks is highly accurate and needs only minor adjustment to match the observed surface deformation.The results further show that gradual changes in fault strike and dip modulated rupture arrest and postseismic stress accumulation,highlighting the critical role of inherited geometric structure in controlling rupture termination and delayed seismic activation.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13。
文摘In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT,Republic of Korea(Grant No.:RS-2024-00344752)supported by the Department of Integrative Biotechnology,Sungkyunkwan University(SKKU)and the BK21 FOUR Project,Republic of Korea.
文摘Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics.While traditional experimental approaches are time-consuming and expensive,computational methods have emerged as viable alternatives,including similarity-based and machine learning(ML)-/deep learning(DL)-based methods.However,existing methods often struggle with robustness and generalizability.To address these challenges,we propose HyPepTox-Fuse,a novel framework that fuses protein language model(PLM)-based embeddings with conventional descriptors.HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture.A robust feature ranking and selection pipeline further refines conventional descriptors,thus enhancing prediction performance.Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations,offering a scalable and reliable tool for peptide toxicity prediction.Moreover,we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse,highlighting its effectiveness in enhancing model performance.Furthermore,the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/.The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets.
基金supported by the Brain&Behavior Research Foundation(30233).
文摘Depressive disorder is a chronic,recurring,and potentially life-endangering neuropsychiatric disease.According to a report by the World Health Organization,the global population suffering from depression is experiencing a significant annual increase.Despite its prevalence and considerable impact on people,little is known about its pathogenesis.One major reason is the scarcity of reliable animal models due to the absence of consensus on the pathology and etiology of depression.Furthermore,the neural circuit mechanism of depression induced by various factors is particularly complex.Considering the variability in depressive behavior patterns and neurobiological mechanisms among different animal models of depression,a comparison between the neural circuits of depression induced by various factors is essential for its treatment.In this review,we mainly summarize the most widely used behavioral animal models and neural circuits under different triggers of depression,aiming to provide a theoretical basis for depression prevention.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金supported by the National Natural Science Foundation of China(NSFC)under grant numbers of 52277222,52406256,and 52177217the Shanghai Science and Technology Development Fund under grant number 22ZR14445000the Artificial Intelligence for Research Paradigm Reform Enabling Discipline Leapfrog Program Project Funding Grant。
文摘The explosive growth of lithium-ion battery literature has led to severe knowledge overload,challenging researchers'ability to efficiently extract structured information.While large language models(LLMs)offer considerable potential for automating this task,their practical application in scientific domains is nonetheless constrained by high application programming interface(API)costs and computational resources required for fine-tuning.To address these limitations,a cognition-enhanced instruction framework(CEIF)is proposed,wherein a high-performance teacher model(such as DeepSeek-R1)provides dynamic feedback,prompt refinement,and training data optimization to guide the learning process of low-parameter models.Experimental results demonstrate that the low-parameter models(6B-9B)optimized via the CEIF achieve approximately 85%accuracy in battery literature extraction tasks,approaching the performance of GPT-4 while requiring only a single NVIDIA RTX 3090 GPU.Furthermore,the emergence of an"Aha moment"characterized by rapid performance improvement during specialized learning is observed,offering novel theoretical insights for the design and optimization of domainspecific models.
文摘Covalent organic frameworks(COFs)are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures.Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks.Ionic covalent organic frameworks(ICOFs)are a subclass that incorporates ions in positive,negative,or zwitterionic forms into the frameworks.In particular,spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs.Unlike electroneutral COFs,ICOFs can be tailored by adjusting the types and arrangements of ions,influencing their formation mechanisms and physical properties.This study focuses on analyzing the graph-based structural characteristics of ICOFs with spiroborate linkages.We compute graph based entropy using hybrid topological descriptors that capture both local and global structural patterns.Furthermore,statistical regression models are developed to predict graph energies of larger-dimensional ICOF structures based on these descriptors.To ensure the robustness and accuracy of our results,we validated our findings using a pseudocode algorithm specifically designed for computing degree-based topological indices.This computational validation confirms the consistency of the derived descriptors and supports their applicability in quantitative structure-property relationship(QSPR)modeling.Overall,this approach provides valuable insights for future applications in material design and property prediction within the framework of ICOFs.
文摘Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This review systematically examines the global landscape of traditional medicine-modern medicine integration by analyzing policy developments,regulatory frameworks,and clinical implementation models across various regions,including Asia,Africa,Europe,and the USA.The scope of the review encompasses five key domains:(1)global policy initiatives,(2)regulatory and institutional frameworks,(3)clinical integration models,(4)impacts and outcomes of integrative practices,and(5)challenges and barriers to implementation.Based on peer-reviewed literature and official health policy documents published between 2000 and 2025,the present review investigates how countries have operationalized clinical integration models combining traditional and complementary medicine.Although interest in traditional and complementary medicine has grown worldwide,persistent challenges,such as limited scientific validation,lack of standardization,and professional resistance,continue to hinder progress.This review concludes that successful and sustainable integration requires evidence-based clinical approaches,inclusive regulatory reforms,and coordinated policy strategies.Countries such as China,India,and Brazil have made significant advances,offering valuable models for future implementation worldwide.
基金funded by the Chongqing Water Resources Bureau,China(Project No.CQS24C00836).
文摘We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.
基金the financial support from the Fujian Science Foundation for Outstanding Youth(2023J06039)the National Natural Science Foundation of China(Grant No.41977259,U2005205,41972268)the Independent Research Project of Technology Innovation Center for Monitoring and Restoration Engineering of Ecological Fragile Zone in Southeast China(KY-090000-04-2022-019)。
文摘Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.
文摘Advertising avoidance is resistance to advertising intrusion.This issue has been the subject of much academic research in recent years.To guide scholars to better carry out relevant research and promote enterprises to better implement advertising activities,this study intends to summarize the relevant research on advertising avoidance in recent years.The specific method is to use the core literature meta-analysis method to identify,filter,and screen relevant literature published in core journals from 1997 to 2020 with the keywords advertising avoidance and advertising resistance.We review the collected articles from the following perspectives:the definition and classification,external stimulating factors,internal perception factors,and moderating factors of advertising avoidance.On this basis,the SOMR model of advertising avoidance is constructed according to the SOR model.Finally,some prospects for future related research are presented.
文摘Blockchain technology has a unique ability to automate accounting processes that are a part of regulatory requirements in all commercial enterprises.Moreover,it can hold verified accounting records and eliminate the need for a trusted third party.Despite blockchain’s potential to transform the nature of traditional accountancy procedures,adoption by the accounting industry is somewhat limited.Knowledge in this domain is lacking,and research on the antecedents influencing the adoption of blockchainbased accounting systems is scarce.This study is rooted in the technology-organization-environment(TOE)framework,presenting a trust-centric adoption model based on diligent analysis of blockchain and technology adoption literature.The model proposes that TOE factors mediate trust’s role in adopting blockchain for accounting applications.The model was validated based on qualitative semi-structured interviews of twelve industry leaders and was comprehensively tested using a quantitative surveybased methodology with accounting professionals knowledgeable about blockchain technology.The data collected was analyzed using PLS-SEM.The results demonstrate the role of trust and the mediating effect of the theorized TOE variables on adopting blockchain-based accounting solutions.The results’implications for research and practice are discussed.
文摘In this paper,based on the structure-behavior coupling paradigm,we propose the concept of deviation of central town to describe the geography-market distance between farmers and the central regional town.Using the survey data from farmers in a poverty-stricken village in Western China,the impact of deviation of central town on farmers'livelihood strategies is analyzed.The results indicate that farmers exhibit spatial heterogeneity in their livelihood strategies.Those with low deviation show a strong inclination towards working in urban areas,while those with high deviation tend to integrate into rural industries.The deviation of central town influences farmers'livelihood strategies through the information effect,which is also affected by the level of rural infrastructure and public services,labor force structure and assistance policies.The obtained results are expected to provide guidance for promoting the integration of farmers into the urban-rural economic cycle based on sustainable livelihoods and connecting poverty alleviation with rural revitalization.
基金supported by the National Natural Science Foundation of China(Nos.21905142 and 22035003)the Programme of Introducing Talents of Discipline to Universities(No.B18030)。
文摘Photo-responsive metal-organic frameworks(MOFs)have evoked considerable attention due to their potential application in inkless printing paper.However,the poor cycling performance and low printing resolution greatly inhibit their practical application.Herein,a novel MOF based on naphthalenediimide derivate moiety,[La(H_(2)O)(BINDI)_(0.5)(DMF)_(3)][NO_(3)](1,H_(4)BINDI=N,N'-bis(5-isophthalic acid)naphthalenediimide),was successfully synthesized for inkless erasable printing.This material exhibits reversible photochromic behavior and good stability.The inkless printing paper coated with 1 delivers high resolution reaching up to 0.2 mm,comparable to commercial printers.Furthermore,the stable framework and suitable reversibility enable excellent cycling performance with 197 cycles,surpassing almost all reported MOFs.This work sheds light on new opportunities in designing outstanding photochromic MOFs for ink-free printing.
文摘Data-driven research on recycled aggregate concrete(RAC)has long faced the challenge of lacking a unified testing standard dataset,hindering accurate model evaluation and trust in predictive outcomes.This paper reviews critical parameters influencing mechanical properties in 35 RAC studies,compiles four datasets encompassing these parameters,and compiles the performance and key findings of 77 published data-driven models.Baseline capability tests are conducted on the nine most used models.The paper also outlines advanced methodological frameworks for future RAC research,examining the principles and challenges of physics-informed neural networks(PINNs)and generative adversarial networks(GANs),and employs SHAP and PDP tools to interpret model behaviour and enhance transparency.Findings indicate a clear trend toward integrated systems,hybrid models,and advanced optimization strategies,with integrated tree-based models showing superior performance across various prediction tasks.Based on this comprehensive review,we offer a recommendation for future research on how AI can be effectively oriented in RAC studies to support practical deployment and build confidence in data-driven approaches.
基金support in dataset preparation.This study was funded by National Natural Science Foundation of China(Nos.42422704 and 52379109)Opening the fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Chengdu University of Technology)(No.SKLGP2024K028)Science and Technology Research and Design Projects of China State Construction Engineering Corporation Ltd.(No.CSCEC-2024-Q-68).
文摘The identification of rock mass discontinuities is critical for rock mass characterization.While high-resolution digital outcrop models(DOMs)are widely used,current digital methods struggle to generalize across diverse geological settings.Large-scale models(LSMs),with vast parameter spaces and extensive training datasets,excel in solving complex visual problems.This study explores the potential of using one such LSM,Segment anything model(SAM),to identify facet-type discontinuities across several outcrops via interactive prompting.The findings demonstrate that SAM effectively segments two-dimensional(2D)discontinuities,with its generalization capability validated on a dataset of 2426 identified discontinuities across 170 outcrops.The model achieves 0.78 mean IoU and 0.86 average precision using 11-point prompts.To extend to three dimensions(3D),a framework integrating SAM with Structure-from-Motion(SfM)was proposed.By utilizing the inherent but often overlooked relationship between image pixels and point clouds in SfM,the identification process was simplified and generalized across photogrammetric devices.Benchmark studies showed that the framework achieved 0.91 average precision,identifying 87 discontinuities in Dataset-3D.The results confirm its high precision and efficiency,making it a valuable tool for data annotation.The proposed method offers a practical solution for geological investigations.
文摘Background:Refined models of kidney disease are critical to better understand disease processes and study novel treatments while minimizing discomfort in research animals.The objective of this study was to report a technique for minimally invasive partial kidney embolism in cats and describe outcomes following transcatheter administration of embolic microspheres with subsequent contralateral nephrectomy.Methods:Eleven,apparently healthy,male,purpose-bred cats underwent unilateral kidney embolism with 0.25 or 0.5 mL of embolic microparticle(40-120μm)suspension(0.2 mL microspheres/mL)delivered into the right renal artery under fluoroscopic guidance,followed 5 months later by contralateral nephrectomy.One month after nephrectomy,blood and urinary markers of kidney function were evaluated,and embolized kidneys were harvested for histopathology evaluation.Results:Renal artery embolization was possible in all cats.Two cats did not complete the study,one after experiencing congestive heart failure(n=1)and the other following evidence of complete kidney embolism precluding nephrectomy(n=1)postembolization.At study end,compared to baseline,cats had significant increases in median(range)serum creatinine(159.1μmol/L[141.4-530.4]versus 128.2μmol/L[92.8-150.3];p=0.0004),urea nitrogen(15.71 mmol/L[9.29-47.85]versus 7.50 mmol/L[6.07-8.57];p<0.0001),and symmetric dimethylarginine(0.74μmol/L[0.59-3.12]versus 0.67μmol/L[0.54-0.72];p=0.0288)concentrations.No differences in markers of kidney function were documented between dose groups.Conclusions:M inimally invasive kidney embolism is a promising technique for modeling kidney disease in cats.Understanding optimal dose,timing of nephrectomy,and longer-term consequences requires additional work.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-2-02038).
文摘Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.
