Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the ...Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information(i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals(i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.展开更多
This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schem...This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.展开更多
Dear Editor,I am writing in response to the article“Assessment of nurses’workplace silence behavior motives:A cross-sectional study”by Alhojairi et al.published in the September 2024 issue of the International Jour...Dear Editor,I am writing in response to the article“Assessment of nurses’workplace silence behavior motives:A cross-sectional study”by Alhojairi et al.published in the September 2024 issue of the International Journal of Nursing Sciences[1].This is a letter written by a nurse with 10 years of clinical work experience and a personal interest in team dynamics.I appreciate the authors'recommendations on mitigating workplace silence among nurses to enhance clinical work development,and I believe their proposals could be expanded further.展开更多
Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensur...Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety.This survey examines the current state of pill image recognition,focusing on advancements,methodologies,and the challenges that remain unresolved.It provides a comprehensive overview of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and aims to explore the ongoing difficulties in the field.We summarize and classify the methods used in each article,compare the strengths and weaknesses of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and review benchmark datasets for pill image recognition.Additionally,we compare the performance of proposed methods on popular benchmark datasets.This survey applies recent advancements,such as Transformer models and cutting-edge technologies like Augmented Reality(AR),to discuss potential research directions and conclude the review.By offering a holistic perspective,this paper aims to serve as a valuable resource for researchers and practitioners striving to advance the field of pill image recognition.展开更多
Kinship verification is a key biometric recognition task that determines biological relationships based on physical features.Traditional methods predominantly use facial recognition,leveraging established techniques a...Kinship verification is a key biometric recognition task that determines biological relationships based on physical features.Traditional methods predominantly use facial recognition,leveraging established techniques and extensive datasets.However,recent research has highlighted ear recognition as a promising alternative,offering advantages in robustness against variations in facial expressions,aging,and occlusions.Despite its potential,a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively.To address this challenge,we introduce the EarKinshipVN dataset,a novel and extensive collection of ear images designed specifically for kinship verification.This dataset consists of 4876 high-resolution color images from 157 multiracial families across different regions,forming 73,220 kinship pairs.EarKinshipVN,a diverse and large-scale dataset,advances kinship verification research using ear features.Furthermore,we propose the Mixer Attention Inception(MAI)model,an improved architecture that enhances feature extraction and classification accuracy.The MAI model fuses Inceptionv4 and MLP Mixer,integrating four attention mechanisms to enhance spatial and channel-wise feature representation.Experimental results demonstrate that MAI significantly outperforms traditional backbone architectures.It achieves an accuracy of 98.71%,surpassing Vision Transformer models while reducing computational complexity by up to 95%in parameter usage.These findings suggest that ear-based kinship verification,combined with an optimized deep learning model and a comprehensive dataset,holds significant promise for biometric applications.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation ...With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.展开更多
Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration...Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.展开更多
Mining activities have caused significant land degradation globally,emphasizing the need for effective restoration.Microbial inoculants offer a promising solution for sustainable remediation by enhancing soil nutrient...Mining activities have caused significant land degradation globally,emphasizing the need for effective restoration.Microbial inoculants offer a promising solution for sustainable remediation by enhancing soil nutrients,enzyme activities,and microbial communities to support plant growth.However,the mechanisms by which inoculants influence soil microbes and their relationship with plant growth require further investigation.Metagenomic sequencing was employed for this study,based on a one-year greenhouse experiment,to elucidate the effects of Bacillus thuringiensis NL-11 on the microbial functions of abandoned mine soils.Our findings revealed that the application of microbial inoculants significantly enhanced the soil total carbon(TC),total sulfur(TS),organic carbon(SOC),available phosphorus(AP),ammonium(NH4+),urease,arylsulfatase,phosphatase,β-1,4-glucosidase(BG),β-1,4-N-acetylglucosaminidase(NAG).Moreover,this led to substantial improvements in plant height,as well as aboveground and belowground biomass.Microbial inoculants impacted functional gene structures without altering diversity.The normalized abundance of genes related to the degradation of carbon and nitrogen,methane metabolism,and nitrogen fixation were observed to increase,as well as the functional genes related to phosphorus cycling.Significant correlations were found between nutrient cycling gene abundance and plant biomass.Partial Least Squares Path Model analysis showed that microbial inoculants not only directly influenced plant biomass but also indirectly affected the plant biomass through C cycle modifications.This study highlights the role of microbial inoculants in promoting plant growth and soil restoration by improving soil properties and enhancing normalized abundance of nutrient cycling gene,making them essential for the recovery of abandoned mine sites.展开更多
Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of ...Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of diesel fuel and vegetable oil is a form of biofuel.However,some properties of the mixed fuel,such as viscosity and density,are higher than those of traditional diesel fuel,affecting the injection and combustion process and reducing power and non-optimal toxic emissions,especially soot emissions.This study uses Kiva-3V software to simulate the combustion process of a diesel-vegetable oil mixture in the combustion chamber of a fishing vessel diesel engine with changes in fuel injection timing.The results show that when increasing the fuel injection timing of a diesel-vegetable oil mixture about 1–2 degrees of crankshaft rotation angle before the top dead center compared to diesel fuel injection timing,the engine power increases,and soot emissions decrease compared to no adjustment.The above simulation research results will help orient the experiments conveniently and reduce costs in the future experimental research process to quantify the fuel system adjustment of fishing vessels’diesel engines when using biofuels,including diesel-vegetable oil mixtures.Thus,the engine’s economic indicators will improve,and emissions that pollute the environment will be limited.展开更多
The effects of the Rashba spin–orbit interaction and external electric and magnetic fields on the thermodynamic properties of parabolic quantum dots are investigated.An explicit partition function is derived,and ther...The effects of the Rashba spin–orbit interaction and external electric and magnetic fields on the thermodynamic properties of parabolic quantum dots are investigated.An explicit partition function is derived,and thermodynamic quantities,including specific heat,entropy,and magnetic susceptibility,are analyzed.The behavior of Shannon entropy-related thermodynamic quantities is examined under varying magnetic fields and Hamiltonian parameters through numerical analysis.The results reveal a pronounced Schottky anomaly in the heat capacity at lower temperatures.The susceptibility exhibits a progressive enhancement and transitions to higher values with changes in the quantum dot parameters.In the presence of the Rashba spin–orbit interaction,the specific heat increases with temperature,reaches a peak,and then decreases to zero.Additionally,the susceptibility increases with theβparameter for varying Rashba spin–orbit interaction coefficients,and at a fixed temperature,it further increases with the Rashba coefficient.展开更多
Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
Nonlocal set of orthogonal product states(OPSs)can improve the confidentiality of information when it is used to design quantum cryptographic protocols.It is a difficult question how to construct a nonlocal set of OPS...Nonlocal set of orthogonal product states(OPSs)can improve the confidentiality of information when it is used to design quantum cryptographic protocols.It is a difficult question how to construct a nonlocal set of OPSs on general multipartite and high dimensional quantum systems.Different from the previous works,we first present a novel method for constructing a nonlocal product set with 3d-2 members on C^(d)■C^(d)■C^(d)quantum system for d≥3.Then,we extend this construction method to C^(d_(1))■C^(d_(2))■C^(d_(3))quantum system and■_(i=1)^(n)C^(di)quantum system respectively,where 3≤d_(1)≤d_(2)≤d_(3)≤…≤dC_(d_(i))and n≥3.The nonlocal set of OPSs constructed by our method contains fewer elements than those constructed by the existing methods,except for one special case.More importantly,the set of states constructed by our method has a completely different structure from those constructed by the existing methods since our nonlocal set does not contain a“stopper”state.Our result is helpful to further understand the different structures of nonlocal sets on multipartite systems.展开更多
In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapi...In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.展开更多
Efficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems.To alleviate the load,one innovative method is branching that adds extra layers w...Efficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems.To alleviate the load,one innovative method is branching that adds extra layers with classification exits to a pre-trained model,enabling inputs with high-confidence predictions to exit early,thus reducing inference cost.However,branching networks,not originally tailored for IoT environments,are susceptible to noisy and out-of-distribution(OOD)data,and they demand additional training for optimal performance.The authors introduce BrevisNet,a novel branching methodology designed for creating on-device branching models that are both resourceadaptive and noise-robust for IoT applications.The method leverages the refined uncertainty estimation capabilities of Dirichlet distributions for classification predictions,combined with the superior OOD detection of energy-based models.The authors propose a unique training approach and thresholding technique that enhances the precision of branch predictions,offering robustness against noise and OOD inputs.The findings demonstrate that BrevisNet surpasses existing branching techniques in training efficiency,accuracy,overall performance,and robustness.展开更多
Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus imag...Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods:The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database.The analysis included publication trends over time,citation patterns,institutional collaborations,and the emergence of keywords.Results:From 2014-2022,there was a steady increase in the number of publications,reaching a peak in 2021.India(26%),China(20.05%),and the USA(9.98%)were the major contributors to research output in this field.Among the publication venues,IEEE ACCESS stood out as the leading one,with 44 articles published.The research landscape has evolved from traditional image processing techniques to deep learning approaches.In recent years,there has been a growing emphasis on multimodal AI models.The analysis identified three distinct phases in the development of AI-based DR screening:CNN-based systems(2014-2020),Vision Transformers and innovative learning paradigms(2020-2022),and large foundation models(2022-2024).Conclusions:The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies.Future directions suggest an increased focus on the integration of telemedicine,innovative AI algorithms,and real-world implementation of these technologies in real-world settings.展开更多
Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on trai...Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.展开更多
Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most...Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.展开更多
Lake 90°E in Antarctica encompasses an area of 2000 km2,ranking it the second largest subglacial lake identified in the country by area,following Vostok Subglacial Lake.In this study,the overlying ice thickness a...Lake 90°E in Antarctica encompasses an area of 2000 km2,ranking it the second largest subglacial lake identified in the country by area,following Vostok Subglacial Lake.In this study,the overlying ice thickness and lake elevation of Lake 90°E were determined using airborne radio-echo sounding across two survey lines,conducted by the International Collaborative Exploration of the Cryosphere by Airborne Profiling in Princess Elizabeth Land(ICECAP/PEL)campaign during the 32nd Chinese National Antarctic Research Expedition(CHINARE 32,2015-2016),and the depth of lake water was inversed by coupling with synchronous airborne gravity data.The analysis revealed a 15-m elevation increase in the ice sheet surface from the southeast to the northwest,correlating with a gradient in ice thickness that progresses from thin in the southeast to thick in the northwest.The maximum water depth of Lake 90°E is estimated as 320 m along the central line,bifurcated by a topographic ridge into two zones of varying depths,with exceptionally shallow water at its periphery.Thermodynamic modeling using data from two points along the survey lines indicated that melt rates at the ice-water interface have consistently been low over the last 400,000 years,varying between 0.56-0.95 mm/yr and 2.70-3.41 mm/yr,balanced by either basal freezing to the south or downstream water loss,thereby maintaining a thermodynamically stable state.Satellite imagery and altimetry data analyses identified no significant changes in the outline or elevation of the ice surface over the past 20 years.This study presents novel insights into the physiography and thermodynamic state of Lake 90°E,establishing a foundation for future drilling initiatives.展开更多
In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green...In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green-blue infrastructure,and climate factors affecting UTR.Moving beyond traditional methods that compare urban and rural thermal differences,our research innovatively measures UTR by evaluating urban disturbances caused by extreme thermal events.To improve accuracy and reliability,we utilize an AI-powered Monte Carlo Simulation framework.Our findings emphasize the critical role of blue-green spaces in boosting UTR,whereas urban morphology often has a suppressive impact.Additionally,atmospheric humidity is identified as a critical factor affecting UTR.The study interestingly finds varied climatic responses:dense urban areas enhance resilience in arid and cold regions but reduce it in tropical and temperate zones.These findings highlight the need for a balance between sustainable urban living and infrastructure development.展开更多
基金by National Natural Science Foundation of China(Nos.61773027 and 62076014)National Key Research and Development Program Project(No.2020YFB1005903)Industrial Internet Innovation and Development Project(No.135060009002).
文摘Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information(i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals(i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.
文摘This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.
文摘Dear Editor,I am writing in response to the article“Assessment of nurses’workplace silence behavior motives:A cross-sectional study”by Alhojairi et al.published in the September 2024 issue of the International Journal of Nursing Sciences[1].This is a letter written by a nurse with 10 years of clinical work experience and a personal interest in team dynamics.I appreciate the authors'recommendations on mitigating workplace silence among nurses to enhance clinical work development,and I believe their proposals could be expanded further.
文摘Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety.This survey examines the current state of pill image recognition,focusing on advancements,methodologies,and the challenges that remain unresolved.It provides a comprehensive overview of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and aims to explore the ongoing difficulties in the field.We summarize and classify the methods used in each article,compare the strengths and weaknesses of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and review benchmark datasets for pill image recognition.Additionally,we compare the performance of proposed methods on popular benchmark datasets.This survey applies recent advancements,such as Transformer models and cutting-edge technologies like Augmented Reality(AR),to discuss potential research directions and conclude the review.By offering a holistic perspective,this paper aims to serve as a valuable resource for researchers and practitioners striving to advance the field of pill image recognition.
文摘Kinship verification is a key biometric recognition task that determines biological relationships based on physical features.Traditional methods predominantly use facial recognition,leveraging established techniques and extensive datasets.However,recent research has highlighted ear recognition as a promising alternative,offering advantages in robustness against variations in facial expressions,aging,and occlusions.Despite its potential,a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively.To address this challenge,we introduce the EarKinshipVN dataset,a novel and extensive collection of ear images designed specifically for kinship verification.This dataset consists of 4876 high-resolution color images from 157 multiracial families across different regions,forming 73,220 kinship pairs.EarKinshipVN,a diverse and large-scale dataset,advances kinship verification research using ear features.Furthermore,we propose the Mixer Attention Inception(MAI)model,an improved architecture that enhances feature extraction and classification accuracy.The MAI model fuses Inceptionv4 and MLP Mixer,integrating four attention mechanisms to enhance spatial and channel-wise feature representation.Experimental results demonstrate that MAI significantly outperforms traditional backbone architectures.It achieves an accuracy of 98.71%,surpassing Vision Transformer models while reducing computational complexity by up to 95%in parameter usage.These findings suggest that ear-based kinship verification,combined with an optimized deep learning model and a comprehensive dataset,holds significant promise for biometric applications.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
基金supported by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia,and these results are parts of Grant No.451-03-66/2024-03/200132 with the University of Kragujevac-Faculty of Technical Sciences Cacak.
文摘With the increasing use of web applications,challenges in the field of cybersecurity are becoming more complex.This paper explores the application of fine-tuned large language models(LLMs)for the automatic generation of synthetic attacks,including XSS(Cross-Site Scripting),SQL Injections,and Command Injections.A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence.The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks.This approach not only improves the model’s precision and dependability but also serves as a practical resource for cybersecurity professionals to enhance the security of web applications.The methodology and structured implementation underscore the importance and potential of advanced language models in cybersecurity,illustrating their effectiveness in generating high-quality synthetic data for penetration testing purposes.The research results demonstrate that this approach enables the identification of vulnerabilities that traditional methods may not uncover,providing deeper insights into potential threats and enhancing overall security measures.The performance evaluation of the model indicated satisfactory results,while further hyperparameter optimization could improve accuracy and generalization capabilities.This research represents a significant step forward in improving web application security and opens new opportunities for the use of LLMs in security testing,thereby contributing to the development of more effective cybersecurity strategies.
基金supported by the Instrument Developing Project of Magnetic Resonance Union of Chinese Academy of Sciences,Grant No.2022GZL002.
文摘Gradient coil is an essential component of a magnetic resonance imaging(MRI)scanner.To achieve high spatial resolution and imaging speed,a high-efficiency gradient coil with high slew rate is required.In consideration of the safety and comfort of the patient,the mechanical stability,acoustic noise and peripheral nerve stimulation(PNS)are also need to be concerned for practical use.In our previous work,a high-efficiency whole-body gradient coil set with a hybrid cylindrical-planar structure has been presented,which offers significantly improved coil performances.In this work,we propose to design this transverse gradient coil system with transformed magnetic gradient fields.By shifting up the zero point of gradient fields,the designed new Y-gradient coil could provide enhanced electromagnetic performances.With more uniform coil winding arrangement,the net torque of the new coil is significantly reduced and the generated sound pressure level(SPL)is lower at most tested frequency bands.On the other hand,the new transverse gradient coil designed with rotated magnetic gradient fields produces considerably reduced electric field in the human body,which is important for the use of rapid MR sequences.It's demonstrated that a safer and patient-friendly design could be obtained by using transformed magnetic gradient fields,which is critical for practical use.
基金supported by the Jiangsu Science and Technology Plan Project(No.BE2022420)the Innovation and Promotion of Forestry Science and Technology Program of Jiangsu Province(No.LYKJ[2021]30)+2 种基金the Scientific Research Project of Baishanzu National Park(No.2021ZDLY01)the Ningxia key research and development plan(No.2021BEG02010)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Mining activities have caused significant land degradation globally,emphasizing the need for effective restoration.Microbial inoculants offer a promising solution for sustainable remediation by enhancing soil nutrients,enzyme activities,and microbial communities to support plant growth.However,the mechanisms by which inoculants influence soil microbes and their relationship with plant growth require further investigation.Metagenomic sequencing was employed for this study,based on a one-year greenhouse experiment,to elucidate the effects of Bacillus thuringiensis NL-11 on the microbial functions of abandoned mine soils.Our findings revealed that the application of microbial inoculants significantly enhanced the soil total carbon(TC),total sulfur(TS),organic carbon(SOC),available phosphorus(AP),ammonium(NH4+),urease,arylsulfatase,phosphatase,β-1,4-glucosidase(BG),β-1,4-N-acetylglucosaminidase(NAG).Moreover,this led to substantial improvements in plant height,as well as aboveground and belowground biomass.Microbial inoculants impacted functional gene structures without altering diversity.The normalized abundance of genes related to the degradation of carbon and nitrogen,methane metabolism,and nitrogen fixation were observed to increase,as well as the functional genes related to phosphorus cycling.Significant correlations were found between nutrient cycling gene abundance and plant biomass.Partial Least Squares Path Model analysis showed that microbial inoculants not only directly influenced plant biomass but also indirectly affected the plant biomass through C cycle modifications.This study highlights the role of microbial inoculants in promoting plant growth and soil restoration by improving soil properties and enhancing normalized abundance of nutrient cycling gene,making them essential for the recovery of abandoned mine sites.
文摘Limiting environmental pollution from exhaust emissions from internal combustion engines includes many measures,including encouraging biofuel use because biofuel is environmentally friendly and renewable.A mixture of diesel fuel and vegetable oil is a form of biofuel.However,some properties of the mixed fuel,such as viscosity and density,are higher than those of traditional diesel fuel,affecting the injection and combustion process and reducing power and non-optimal toxic emissions,especially soot emissions.This study uses Kiva-3V software to simulate the combustion process of a diesel-vegetable oil mixture in the combustion chamber of a fishing vessel diesel engine with changes in fuel injection timing.The results show that when increasing the fuel injection timing of a diesel-vegetable oil mixture about 1–2 degrees of crankshaft rotation angle before the top dead center compared to diesel fuel injection timing,the engine power increases,and soot emissions decrease compared to no adjustment.The above simulation research results will help orient the experiments conveniently and reduce costs in the future experimental research process to quantify the fuel system adjustment of fishing vessels’diesel engines when using biofuels,including diesel-vegetable oil mixtures.Thus,the engine’s economic indicators will improve,and emissions that pollute the environment will be limited.
文摘The effects of the Rashba spin–orbit interaction and external electric and magnetic fields on the thermodynamic properties of parabolic quantum dots are investigated.An explicit partition function is derived,and thermodynamic quantities,including specific heat,entropy,and magnetic susceptibility,are analyzed.The behavior of Shannon entropy-related thermodynamic quantities is examined under varying magnetic fields and Hamiltonian parameters through numerical analysis.The results reveal a pronounced Schottky anomaly in the heat capacity at lower temperatures.The susceptibility exhibits a progressive enhancement and transitions to higher values with changes in the quantum dot parameters.In the presence of the Rashba spin–orbit interaction,the specific heat increases with temperature,reaches a peak,and then decreases to zero.Additionally,the susceptibility increases with theβparameter for varying Rashba spin–orbit interaction coefficients,and at a fixed temperature,it further increases with the Rashba coefficient.
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
基金supported by the National Natural Science Foundation of China(Grant No.62171264)the Natural Science Foundation of Shandong Province of China(Grant No.ZR2023MF080)the Natural Science Foundation of Beijing(Grant No.4252014).
文摘Nonlocal set of orthogonal product states(OPSs)can improve the confidentiality of information when it is used to design quantum cryptographic protocols.It is a difficult question how to construct a nonlocal set of OPSs on general multipartite and high dimensional quantum systems.Different from the previous works,we first present a novel method for constructing a nonlocal product set with 3d-2 members on C^(d)■C^(d)■C^(d)quantum system for d≥3.Then,we extend this construction method to C^(d_(1))■C^(d_(2))■C^(d_(3))quantum system and■_(i=1)^(n)C^(di)quantum system respectively,where 3≤d_(1)≤d_(2)≤d_(3)≤…≤dC_(d_(i))and n≥3.The nonlocal set of OPSs constructed by our method contains fewer elements than those constructed by the existing methods,except for one special case.More importantly,the set of states constructed by our method has a completely different structure from those constructed by the existing methods since our nonlocal set does not contain a“stopper”state.Our result is helpful to further understand the different structures of nonlocal sets on multipartite systems.
基金supported by the Basic Scientific Research Business Fund Project of Higher Education Institutions in Heilongjiang Province(145409601)the First Batch of Experimental Teaching and Teaching Laboratory Construction Research Projects in Heilongjiang Province(SJGZ20240038).
文摘In the wake of major natural disasters or human-made disasters,the communication infrastruc-ture within disaster-stricken areas is frequently dam-aged.Unmanned aerial vehicles(UAVs),thanks to their merits such as rapid deployment and high mobil-ity,are commonly regarded as an ideal option for con-structing temporary communication networks.Con-sidering the limited computing capability and battery power of UAVs,this paper proposes a two-layer UAV cooperative computing offloading strategy for emer-gency disaster relief scenarios.The multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm integrated with prioritized experience replay(PER)is utilized to jointly optimize the scheduling strategies of UAVs,task offloading ratios,and their mobility,aiming to diminish the energy consumption and delay of the system to the minimum.In order to address the aforementioned non-convex optimiza-tion issue,a Markov decision process(MDP)has been established.The results of simulation experiments demonstrate that,compared with the other four base-line algorithms,the algorithm introduced in this paper exhibits better convergence performance,verifying its feasibility and efficacy.
基金Australian Research Council,Grant/Award Numbers:DE200101465,DP240101108。
文摘Efficiently executing inference tasks of deep neural networks on devices with limited resources poses a significant load in IoT systems.To alleviate the load,one innovative method is branching that adds extra layers with classification exits to a pre-trained model,enabling inputs with high-confidence predictions to exit early,thus reducing inference cost.However,branching networks,not originally tailored for IoT environments,are susceptible to noisy and out-of-distribution(OOD)data,and they demand additional training for optimal performance.The authors introduce BrevisNet,a novel branching methodology designed for creating on-device branching models that are both resourceadaptive and noise-robust for IoT applications.The method leverages the refined uncertainty estimation capabilities of Dirichlet distributions for classification predictions,combined with the superior OOD detection of energy-based models.The authors propose a unique training approach and thresholding technique that enhances the precision of branch predictions,offering robustness against noise and OOD inputs.The findings demonstrate that BrevisNet surpasses existing branching techniques in training efficiency,accuracy,overall performance,and robustness.
基金supported by the National Natural Science Foundation of China(62402009)the Science and Technology Development Fund of Macao(0013-2024-ITP1).
文摘Objective:Diabetic retinopathy(DR)screening using artificial intelligence(AI)has evolved significantly over the past decade.This study aimed to analyze research trends,developments,and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods:The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database.The analysis included publication trends over time,citation patterns,institutional collaborations,and the emergence of keywords.Results:From 2014-2022,there was a steady increase in the number of publications,reaching a peak in 2021.India(26%),China(20.05%),and the USA(9.98%)were the major contributors to research output in this field.Among the publication venues,IEEE ACCESS stood out as the leading one,with 44 articles published.The research landscape has evolved from traditional image processing techniques to deep learning approaches.In recent years,there has been a growing emphasis on multimodal AI models.The analysis identified three distinct phases in the development of AI-based DR screening:CNN-based systems(2014-2020),Vision Transformers and innovative learning paradigms(2020-2022),and large foundation models(2022-2024).Conclusions:The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies.Future directions suggest an increased focus on the integration of telemedicine,innovative AI algorithms,and real-world implementation of these technologies in real-world settings.
文摘Spiking neural networks(SNN)represent a paradigm shift toward discrete,event-driven neural computation that mirrors biological brain mechanisms.This survey systematically examines current SNN research,focusing on training methodologies,hardware implementations,and practical applications.We analyze four major training paradigms:ANN-to-SNN conversion,direct gradient-based training,spike-timing-dependent plasticity(STDP),and hybrid approaches.Our review encompasses major specialized hardware platforms:Intel Loihi,IBM TrueNorth,SpiNNaker,and BrainScaleS,analyzing their capabilities and constraints.We survey applications spanning computer vision,robotics,edge computing,and brain-computer interfaces,identifying where SNN provide compelling advantages.Our comparative analysis reveals SNN offer significant energy efficiency improvements(1000-10000×reduction)and natural temporal processing,while facing challenges in scalability and training complexity.We identify critical research directions including improved gradient estimation,standardized benchmarking protocols,and hardware-software co-design approaches.This survey provides researchers and practitioners with a comprehensive understanding of current SNN capabilities,limitations,and future prospects.
文摘Electric Vehicle Charging Systems(EVCS)are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet ofThings(IoT)environments,raising significant security challenges.Most existing research primarily emphasizes network-level anomaly detection,leaving critical vulnerabilities at the host level underexplored.This study introduces a novel forensic analysis framework leveraging host-level data,including system logs,kernel events,and Hardware Performance Counters(HPC),to detect and analyze sophisticated cyberattacks such as cryptojacking,Denial-of-Service(DoS),and reconnaissance activities targeting EVCS.Using comprehensive forensic analysis and machine learning models,the proposed framework significantly outperforms existing methods,achieving an accuracy of 98.81%.The findings offer insights into distinct behavioral signatures associated with specific cyber threats,enabling improved cybersecurity strategies and actionable recommendations for robust EVCS infrastructure protection.
基金the National Natural Science Foundation of China under Grants 42376253,42201489,and 42474056Shanghai Science and Technology Development Funds under Grant 21ZR1469700.
文摘Lake 90°E in Antarctica encompasses an area of 2000 km2,ranking it the second largest subglacial lake identified in the country by area,following Vostok Subglacial Lake.In this study,the overlying ice thickness and lake elevation of Lake 90°E were determined using airborne radio-echo sounding across two survey lines,conducted by the International Collaborative Exploration of the Cryosphere by Airborne Profiling in Princess Elizabeth Land(ICECAP/PEL)campaign during the 32nd Chinese National Antarctic Research Expedition(CHINARE 32,2015-2016),and the depth of lake water was inversed by coupling with synchronous airborne gravity data.The analysis revealed a 15-m elevation increase in the ice sheet surface from the southeast to the northwest,correlating with a gradient in ice thickness that progresses from thin in the southeast to thick in the northwest.The maximum water depth of Lake 90°E is estimated as 320 m along the central line,bifurcated by a topographic ridge into two zones of varying depths,with exceptionally shallow water at its periphery.Thermodynamic modeling using data from two points along the survey lines indicated that melt rates at the ice-water interface have consistently been low over the last 400,000 years,varying between 0.56-0.95 mm/yr and 2.70-3.41 mm/yr,balanced by either basal freezing to the south or downstream water loss,thereby maintaining a thermodynamically stable state.Satellite imagery and altimetry data analyses identified no significant changes in the outline or elevation of the ice surface over the past 20 years.This study presents novel insights into the physiography and thermodynamic state of Lake 90°E,establishing a foundation for future drilling initiatives.
基金financed by‘Data Analysis of Thermal Environment and Low-Carbon Intelligent Optimization Design of Urban Ecological Layout’s Impact’under National Natural Science Foundation of China(Grant No.524B200113)‘Basic Theory of Sustainable Urban Planning,Construction,and Governance’under the 14th Five-Year Plan of the State Key Research and Development Program of the People’s Republic of China(Grant No.2022YFC3800205)+1 种基金‘Key Technologies for Regional Carbon Neutral Mega-City Planning and Design’under Shanghai Science and Technology Support Program for Carbon(Grant No.22DZ1207800)Shanghai Intelligent Science and Technology IV Summit Discipline‘Cross-Innovation Science and Education Integration Fund’.
文摘In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green-blue infrastructure,and climate factors affecting UTR.Moving beyond traditional methods that compare urban and rural thermal differences,our research innovatively measures UTR by evaluating urban disturbances caused by extreme thermal events.To improve accuracy and reliability,we utilize an AI-powered Monte Carlo Simulation framework.Our findings emphasize the critical role of blue-green spaces in boosting UTR,whereas urban morphology often has a suppressive impact.Additionally,atmospheric humidity is identified as a critical factor affecting UTR.The study interestingly finds varied climatic responses:dense urban areas enhance resilience in arid and cold regions but reduce it in tropical and temperate zones.These findings highlight the need for a balance between sustainable urban living and infrastructure development.
