Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic bonds.This tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membran...Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic bonds.This tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membranes can provide numerous advantages by removing thick,inflexible,and costly substrates.Herein,cavities with various sizes were introduced by overgrowing target layers,such as undoped GaN and green LEDs,on nanoporous templates prepared by electrochemical etching of n-type GaN.The large primary interfacial toughness was effectively reduced according to the design of the cavity density,and the overgrown target layers were then conveniently exfoliated by engineering tensile-stressed Ni layers.The resulting III-nitride membranes maintained high crystal quality even after exfoliation due to the use of GaN-based nanoporous templates with the same lattice constant.The microcavity-assisted crack propagation process developed for the current III-nitride membranes forms a universal process for developing various kinds of large-scale and high-quality semiconductor membranes.展开更多
1.Background The use of engineering tools,design,research,and thinking to create environments and capabilities whereby individuals who are currently under-employed or unemployed due to a physical disability(e.g.,amput...1.Background The use of engineering tools,design,research,and thinking to create environments and capabilities whereby individuals who are currently under-employed or unemployed due to a physical disability(e.g.,amputation or spinal cord injury)or neurological difference(e.g.,autism)are enabled to become fully productive and employed members of society has been the implicit goal of decades of research at Vanderbilt University and elsewhere.At Vanderbilt University,progress in these areas has been greatly facilitated by the proximity of the School of Engineering to the world-class Vanderbilt University Medical Center and the resulting close collaboration between engineering and medical researchers.展开更多
The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy woul...The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy would be waste in this method. While in co-generation power plant, this number could improve to more than 50%. Therefore, to achieve higher efficiency in utilizing biomass energy, co-generation power plants is proposed by using biogas as fuel instead of natural gas. Proposed system would be supplied thermal and electrical energy for non-urban areas of Iran. In this regard, process of fermentation and gas production from biomass in a vertical digester is studied and simulated using analytic methods. Various factors affecting the fermentation, such as temperature, humidity, PH and optimal conditions for the extraction of gas from waste agriculture and animal are also determined. Comparing between the pollution emission from fossil fuel power plants and power plants fed by biomass shows about 88% reduction in greenhouse emission which significant number.展开更多
This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃...This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.展开更多
Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral r...Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.展开更多
2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultravio...2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultraviolet(EUV)lithography for deep-nanoscale silicon(Si)electronics.Numerous semiconductor companies have recognized NIL's manufacturing quality and are actively being evaluated for the production of the most advanced semiconductor devices.Nanoimprinting's potential extends beyond silicon chip fabrication and wafer-scale applica-tions.With its high throughput and 3D patterning capabilities,NIL is becoming a key technology for fabricating emerging devices,such as flat optics and augmented reality glasses.This review summarizes the key developments and applications of nanoimprint lithography,with a particular focus on the latest industry advancements in nano-Si device manufacturing and nanophotonics applications.展开更多
Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an...Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.展开更多
Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors....Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors.Traditional methods,such as static and dynamic analysis,suffer from polymorphism,code obfuscation,and high resource demands.This paper introduces a multi-stage approach to enhance behavioral analysis for Android ransomware detection,focusing on a reduced set of distinguishing features.The approach includes ransomware app collection,behavioral profile generation,dataset creation,feature identification,reduction,and classification.Experiments were conducted on∼3300 Android-based ransomware samples,despite the challenges posed by their evolving nature and complexity.The feature reduction strategy successfully reduced features by 80%,with only a marginal loss of detection accuracy(0.59%).Different machine learning algorithms are employed for classification and achieve 96.71%detection accuracy.Additionally,10-fold cross-validation demonstrated robustness,yielding an AUC-ROC of 99.3%.Importantly,latency and memory evaluations revealed that models using the reduced feature set achieved up to a 99%reduction in inference time and significant memory savings across classifiers.The proposed approach outperforms existing techniques by achieving high detection accuracy with a minimal feature set,also suitable for deployment in resource-constrained environments.Future work may extend datasets and include iOS-based ransomware applications.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance b...Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.展开更多
BACKGROUND Cirrhotic patients face heightened energy demands,leading to rapid glycogen depletion,protein degradation,oxidative stress,and inflammation,which drive disease progression and complications.These disruption...BACKGROUND Cirrhotic patients face heightened energy demands,leading to rapid glycogen depletion,protein degradation,oxidative stress,and inflammation,which drive disease progression and complications.These disruptions cause cellular damage and parenchymal changes,resulting in vascular alterations,portal hypertension,and liver dysfunction,significantly affecting patient prognosis.AIM To analyze the association between Child–Turcotte–Pugh(CTP)scores and di-fferent nutritional indicators with survival in a 15-year follow-up cohort.METHODS This was a retrospective cohort study with 129 cirrhotic patients of both sexes aged>18 years.Diagnosis of cirrhosis was made by liver biopsy.The first year of data collection was 2007,and data regarding outcomes were collected in 2023.Data were gathered from medical records,and grouped by different methods,including CTP,handgrip strength,and triceps skinfold cutoffs.The prognostic values for mortality were assessed using Kaplan–Meier curves and multivariate binary logistic regression models.RESULTS The coefficient for CTP was the only statistically significant variable(Wald=5.193,P=0.023).This suggests that with a negative change in CTP classification score,the odds of survival decrease 52.6%.The other evaluated variables did not significantly predict survival outcomes in the model.Kaplan–Meier survival curves also indicated that CTP classification was the only significant predictor.CONCLUSION Although different classifications showed specific differences in stratification,only CTP showed significant predictive potential.CTP score remains a simple and effective predictive tool for cirrhotic patients even after longer follow-up.展开更多
In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reve...In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehens...Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after experimentation.Simultaneously,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is introduced.It quantifies CO_(2)needed for mineralization of soluble minerals.However,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage capacities.Notably,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller pores.As pressure levels rise,the magnitude of pore alterations progressively increases.At a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small pores.The observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.展开更多
Cultural ecosystem services(CES),which encompass recreational and aesthetic values,contribute to human wellbeing and yet are often underrepresented in forest management planning due to challenges in quantifying these ...Cultural ecosystem services(CES),which encompass recreational and aesthetic values,contribute to human wellbeing and yet are often underrepresented in forest management planning due to challenges in quantifying these services.This study introduces the Recreational and Aesthetic Values of Forested Landscapes(RAFL)index,a novel framework combining six measurable recreational and aesthetic components:Stewardship,Naturalness,Complexity,Visual Scale,Historicity,and Ephemera.The RAFL index was integrated into a Linear Programming(LP)Resource Capability Model(RCM)to assess trade-offs between CES and other ecosystem services,including timber production,wildfire resistance,and biodiversity.The approach was applied in a case study in Northern Portugal,comparing two forest management scenarios:Business as Usual(BAU),dominated by eucalyptus plantations,and an Alternative Scenario(ALT),focused on the conversion to native species:cork oak,chestnut,and pedunculate oak.Results revealed that the ALT scenario consistently achieved higher RAFL values,reflecting its potential to enhance CES,while also supporting higher biodiversity and wildfire resilience compared to the BAU scenario.Results highlighted further that management may maintain steady timber production and wildfire regulatory services while addressing concerns with CES.This study provides a replicable methodology for quantifying CES and integrating them into forest management frameworks,offering actionable insights for decision-makers.The findings highlight the effectiveness of the approach in designing landscape mosaics that provide CES while addressing the need to supply provisioning and regulatory ecosystem services.展开更多
With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing archit...With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.展开更多
Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim...Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.展开更多
While Metaheuristic optimization techniques are known to work well for clustering and large-scale numerical optimization,algorithms in this category suffer from issues like reinforcement stagnation and poor late-stage...While Metaheuristic optimization techniques are known to work well for clustering and large-scale numerical optimization,algorithms in this category suffer from issues like reinforcement stagnation and poor late-stage refinement.In this paper,we propose the Improved Geyser-Inspired Optimization Algorithm(IGIOA),an enhancement of the Geyser-Inspired Optimization Algorithm(GIOA),which integrates two primary components:the Adaptive Turbulence Operator(ATO)and the Dynamic Pressure Equilibrium Operator(DPEO).ATO allows IGIOA to periodically disrupt stagnation and explore different regions by using turbulence,while DPEO ensures refinement in later iterations by adaptively modulating convergence pressure.We implemented IGIOA on 23 benchmark functions with both unimodal and multimodal contours,in addition to eight problems pertaining to cluster analysis at the UCI.IGIOA,out of all the tested methods,was able to converge most accurately while also achieving a stable convergence rate.The mitigation of premature convergence and low-level exploitation was made possible by the turbulence and pressure-based refinements.The findings from the tests confirm that the adaptation of baseline strategies by IGIOA helps deal with complex data distributions more effectively.However,additional hyperparameters which add complexity are introduced,along with increased computational cost.These include automatic tuning of parameters,ensemble or parallel variations,and hybridization with dedicated local search strategies to extend the reach of IGIOA for general optimization while also specializing it for clustering focused tasks and applications.展开更多
Personalized health services are of paramount importance for the treatment and prevention of cardiorespiratory diseases,such as hypertension.The assessment of cardiorespiratory function and biometric identification(ID...Personalized health services are of paramount importance for the treatment and prevention of cardiorespiratory diseases,such as hypertension.The assessment of cardiorespiratory function and biometric identification(ID)is crucial for the effectiveness of such personalized health services.To effectively and accurately monitor pulse wave signals,thus achieving the assessment of cardiorespiratory function,a wearable photonic smart wristband based on an all-polymer sensing unit(All-PSU)is proposed.The smart wristband enables the assessment of cardiorespiratory function by continuously monitoring respiratory rate(RR),heart rate(HR),and blood pressure(BP).Furthermore,it can be utilized for biometric ID purposes.Through the analysis of pulse wave signals using power spectral density(PSD),accurate monitoring of RR and HR is achieved.Additionally,utilizing peak detection algorithms for feature extraction from pulse signals and subsequently employing a variety of machine learning methods,accurate BP monitoring and biometric ID have been realized.For biometric ID,the accuracy rate is 98.55%.Aiming to monitor RR,HR,BP,and ID,our solution demonstrates advantages in integration,functionality,and monitoring precision.These enhancements may contribute to the development of personalized health services aimed at the treatment and prevention of cardiorespiratory diseases.展开更多
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
基金The work was supported by King Abdullah University of Science and Technology(KAUST)baseline funding BAS/1/1614-01-01 and King Abdulaziz City for Science and Technology(Grant No.KACST TIC R2-FP-008)This work was also supported by Korea Photonics Technology Institute(Project No.193300029).
文摘Epitaxially grown III-nitride alloys are tightly bonded materials with mixed covalent-ionic bonds.This tight bonding presents tremendous challenges in developing III-nitride membranes,even though semiconductor membranes can provide numerous advantages by removing thick,inflexible,and costly substrates.Herein,cavities with various sizes were introduced by overgrowing target layers,such as undoped GaN and green LEDs,on nanoporous templates prepared by electrochemical etching of n-type GaN.The large primary interfacial toughness was effectively reduced according to the design of the cavity density,and the overgrown target layers were then conveniently exfoliated by engineering tensile-stressed Ni layers.The resulting III-nitride membranes maintained high crystal quality even after exfoliation due to the use of GaN-based nanoporous templates with the same lattice constant.The microcavity-assisted crack propagation process developed for the current III-nitride membranes forms a universal process for developing various kinds of large-scale and high-quality semiconductor membranes.
基金support has been provided by US National Science Foundation(OIA-1936970)a Howard Hughes Medical Institute professorship award.
文摘1.Background The use of engineering tools,design,research,and thinking to create environments and capabilities whereby individuals who are currently under-employed or unemployed due to a physical disability(e.g.,amputation or spinal cord injury)or neurological difference(e.g.,autism)are enabled to become fully productive and employed members of society has been the implicit goal of decades of research at Vanderbilt University and elsewhere.At Vanderbilt University,progress in these areas has been greatly facilitated by the proximity of the School of Engineering to the world-class Vanderbilt University Medical Center and the resulting close collaboration between engineering and medical researchers.
文摘The main objective of this study is estimating environmental pollution of hybrid biomass and co-generation power plants. Efficiency of direct tapping of biomass is about 15%-20%. Consequently, about 80% of energy would be waste in this method. While in co-generation power plant, this number could improve to more than 50%. Therefore, to achieve higher efficiency in utilizing biomass energy, co-generation power plants is proposed by using biogas as fuel instead of natural gas. Proposed system would be supplied thermal and electrical energy for non-urban areas of Iran. In this regard, process of fermentation and gas production from biomass in a vertical digester is studied and simulated using analytic methods. Various factors affecting the fermentation, such as temperature, humidity, PH and optimal conditions for the extraction of gas from waste agriculture and animal are also determined. Comparing between the pollution emission from fossil fuel power plants and power plants fed by biomass shows about 88% reduction in greenhouse emission which significant number.
文摘This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.
基金supported by the German Research Foundation(DFG)(Grant Nos.LE 992/14-3 and LE 992/15-3).
文摘Microsphere and microcylinder-assisted microscopy(MAM)has grown steadily over the last decade and is still an intensively studied optical far-field imaging technique that promises to overcome the fundamental lateral resolution limit of microscopy.However,the physical effects leading to resolution enhancement are still frequently debated.In addition,various configurations of MAM operating in transmission mode as well as reflection mode are examined,and the results are sometimes generalized.We present a rigorous simulation model of MAM and introduce a way to quantify the resolution enhancement.The lateral resolution is compared for microscope arrangements in reflection and transmission modes.Furthermore,we discuss different physical effects with respect to their contribution to resolution enhancement.The results indicate that the effects impacting the resolution in MAM strongly depend on the arrangement of the microscope and the measurement object.As a highlight,we outline that evanescent waves in combination with whispering gallery modes also improve the imaging capabilities,enabling super-resolution under certain circumstances.This result is contrary to the conclusions drawn from previous studies,where phase objects have been analyzed,and thus further emphasizes the complexity of the physical mechanisms underlying MAM.
基金the National Science Foundation for the partial support(NSF-2213684),and LJG acknowledges an Emmett Leith Collegiate Professorship for this writing.
文摘2025 marks the 30th anniversary of nanoimprint lithography(NIL).Since its inception in 1995,and through global efforts over the past three decades,nanoimprint has emerged as the primary alternative to extreme ultraviolet(EUV)lithography for deep-nanoscale silicon(Si)electronics.Numerous semiconductor companies have recognized NIL's manufacturing quality and are actively being evaluated for the production of the most advanced semiconductor devices.Nanoimprinting's potential extends beyond silicon chip fabrication and wafer-scale applica-tions.With its high throughput and 3D patterning capabilities,NIL is becoming a key technology for fabricating emerging devices,such as flat optics and augmented reality glasses.This review summarizes the key developments and applications of nanoimprint lithography,with a particular focus on the latest industry advancements in nano-Si device manufacturing and nanophotonics applications.
基金funded by the National University of Sciences and Technology(NUST)supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1IIA3049788).
文摘Ransomware,particularly crypto-ransomware,remains a significant cybersecurity challenge,encrypting victim data and demanding a ransom,often leaving the data irretrievable even if payment is made.This study proposes an early detection approach to mitigate such threats by identifying ransomware activity before the encryption process begins.The approach employs a two-tiered approach:a signature-based method using hashing techniques to match known threats and a dynamic behavior-based analysis leveraging Cuckoo Sandbox and machine learning algorithms.A critical feature is the integration of the most effective Application Programming Interface call monitoring,which analyzes system-level interactions such as file encryption,key generation,and registry modifications.This enables the detection of both known and zero-day ransomware variants,overcoming limitations of traditional methods.The proposed technique was evaluated using classifiers such as Random Forest,Support Vector Machine,and K-Nearest Neighbors,achieving a detection accuracy of 98%based on 26 key ransomware attributes with an 80:20 training-to-testing ratio and 10-fold cross-validation.By combining minimal feature sets with robust behavioral analysis,the proposed method outperforms existing solutions and addresses current challenges in ransomware detection,thereby enhancing cybersecurity resilience.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A3049788).
文摘Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors.Traditional methods,such as static and dynamic analysis,suffer from polymorphism,code obfuscation,and high resource demands.This paper introduces a multi-stage approach to enhance behavioral analysis for Android ransomware detection,focusing on a reduced set of distinguishing features.The approach includes ransomware app collection,behavioral profile generation,dataset creation,feature identification,reduction,and classification.Experiments were conducted on∼3300 Android-based ransomware samples,despite the challenges posed by their evolving nature and complexity.The feature reduction strategy successfully reduced features by 80%,with only a marginal loss of detection accuracy(0.59%).Different machine learning algorithms are employed for classification and achieve 96.71%detection accuracy.Additionally,10-fold cross-validation demonstrated robustness,yielding an AUC-ROC of 99.3%.Importantly,latency and memory evaluations revealed that models using the reduced feature set achieved up to a 99%reduction in inference time and significant memory savings across classifiers.The proposed approach outperforms existing techniques by achieving high detection accuracy with a minimal feature set,also suitable for deployment in resource-constrained environments.Future work may extend datasets and include iOS-based ransomware applications.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金Supported by the Bavarian Academic Forum(BayWISS),as a part of the joint academic partnership digitalization program.
文摘Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and entertainment.However,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains challenging.Methods This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and VR.Therefore,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image graph.Using this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of views.The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image graph.The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time.In addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light intensities.Results Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,respectively.While achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering approaches.Conclusions Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR environments.Nevertheless,the handling of morphing artifacts in the parallax image regions remains a topic for future research.
文摘BACKGROUND Cirrhotic patients face heightened energy demands,leading to rapid glycogen depletion,protein degradation,oxidative stress,and inflammation,which drive disease progression and complications.These disruptions cause cellular damage and parenchymal changes,resulting in vascular alterations,portal hypertension,and liver dysfunction,significantly affecting patient prognosis.AIM To analyze the association between Child–Turcotte–Pugh(CTP)scores and di-fferent nutritional indicators with survival in a 15-year follow-up cohort.METHODS This was a retrospective cohort study with 129 cirrhotic patients of both sexes aged>18 years.Diagnosis of cirrhosis was made by liver biopsy.The first year of data collection was 2007,and data regarding outcomes were collected in 2023.Data were gathered from medical records,and grouped by different methods,including CTP,handgrip strength,and triceps skinfold cutoffs.The prognostic values for mortality were assessed using Kaplan–Meier curves and multivariate binary logistic regression models.RESULTS The coefficient for CTP was the only statistically significant variable(Wald=5.193,P=0.023).This suggests that with a negative change in CTP classification score,the odds of survival decrease 52.6%.The other evaluated variables did not significantly predict survival outcomes in the model.Kaplan–Meier survival curves also indicated that CTP classification was the only significant predictor.CONCLUSION Although different classifications showed specific differences in stratification,only CTP showed significant predictive potential.CTP score remains a simple and effective predictive tool for cirrhotic patients even after longer follow-up.
文摘In recent years,there has been a concerted effort to improve anomaly detection tech-niques,particularly in the context of high-dimensional,distributed clinical data.Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy,personalising treatment plans,and optimising resource allocation to enhance clinical outcomes.Nonetheless,this domain faces unique challenges,such as irregular data collection,inconsistent data quality,and patient-specific structural variations.This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges.The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data,facilitating efficient anomaly identification.Subsequently,a stochastic method based on the Interquartile Range filters unreliable data points,ensuring that medical tools and professionals receive only the most pertinent and accurate information.The primary objective of this study is to equip healthcare pro-fessionals and researchers with a robust tool for managing extensive,high-dimensional clinical datasets,enabling effective isolation and removal of aberrant data points.Furthermore,a sophisticated regression model has been developed using Automated Machine Learning(AutoML)to assess the impact of the ensemble abnormal pattern detection approach.Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML.Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhance-ment in AutoML performance,with an average improvement of 0.041 in the R2 score,surpassing the effectiveness of traditional regression models.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
基金support of the National Natural Science Foundation of China(Grant Nos.52174030,52474042 and 52374041)the Postgraduate Innovation Fund Project of Xi'an Shiyou University(No.YCX2411001)the Natural Science Basic Research Program of Shaanxi(Program Nos.2024JCYBMS-256 and 2022JQ-528)。
文摘Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration stability.To investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after experimentation.Simultaneously,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is introduced.It quantifies CO_(2)needed for mineralization of soluble minerals.However,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage capacities.Notably,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller pores.As pressure levels rise,the magnitude of pore alterations progressively increases.At a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small pores.The observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.
基金supported by the Forest Research Centre,a research unit funded by Fundacao para a Ciencia e a Tecnologia I.P.(FCT),Portugal(UIDB/00239/2020)the Associated Laboratory TERRA(LA/P/0092/2020)+4 种基金Additional funding was provided through the Ph.D.grant awarded to Dagm Abate(UI/BD/151525/2021)by two key projects:H2020-MSCA-RISE-2020/101007950,titled“DecisionES-Decision Support for the Supply of Ecosystem Services under Global Change,”funded by the Marie Curie International Staff Exchange Scheme,H2020-LCGD-2020-3/101037419,titled“FIRE-RES-Innovative technologies and socio-ecological economic solutions for fireresilient territories in Europe,”funded by the EU Horizon 2020—Research and Innovation Framework Programmesupported by a project MODFIRE—a multiple criteria approach to integrate wildfire behavior in forest management planning with reference PCIF/MOS/0217/2017a contract from Dr.Susete Marques in the scope of Norma Transitoria—DL57/2016/CP1382/CT15a grant from Fundacao para a Ciencia e a Tecnologia(FCT),Portugal to Dr.Guerra-Hernandez(CEECIND/02576/2022).
文摘Cultural ecosystem services(CES),which encompass recreational and aesthetic values,contribute to human wellbeing and yet are often underrepresented in forest management planning due to challenges in quantifying these services.This study introduces the Recreational and Aesthetic Values of Forested Landscapes(RAFL)index,a novel framework combining six measurable recreational and aesthetic components:Stewardship,Naturalness,Complexity,Visual Scale,Historicity,and Ephemera.The RAFL index was integrated into a Linear Programming(LP)Resource Capability Model(RCM)to assess trade-offs between CES and other ecosystem services,including timber production,wildfire resistance,and biodiversity.The approach was applied in a case study in Northern Portugal,comparing two forest management scenarios:Business as Usual(BAU),dominated by eucalyptus plantations,and an Alternative Scenario(ALT),focused on the conversion to native species:cork oak,chestnut,and pedunculate oak.Results revealed that the ALT scenario consistently achieved higher RAFL values,reflecting its potential to enhance CES,while also supporting higher biodiversity and wildfire resilience compared to the BAU scenario.Results highlighted further that management may maintain steady timber production and wildfire regulatory services while addressing concerns with CES.This study provides a replicable methodology for quantifying CES and integrating them into forest management frameworks,offering actionable insights for decision-makers.The findings highlight the effectiveness of the approach in designing landscape mosaics that provide CES while addressing the need to supply provisioning and regulatory ecosystem services.
基金supported by the National Natural Science Foundation of China(Nos.U20A20209 and 62304228)the China National Postdoctoral Program for Innovative Talents(No.BX2021326)+3 种基金the China Postdoctoral Science Foundation(No.2021M703310)the Zhejiang Provincial Natural Science Foundation of China(No.LQ22F040003)the Ningbo Natural Science Foundation of China(No.2023J356)the State Key Laboratory for Environment-Friendly Energy Materials(No.20kfhg09).
文摘With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.
文摘Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.
基金King Saud University for funding this work through Researchers Supporting Project number(RSPD2024R697),King Saud University,Riyadh,Saudi Arabiafinancial support European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_/0000048 via the Operational Programme Just Transition.
文摘While Metaheuristic optimization techniques are known to work well for clustering and large-scale numerical optimization,algorithms in this category suffer from issues like reinforcement stagnation and poor late-stage refinement.In this paper,we propose the Improved Geyser-Inspired Optimization Algorithm(IGIOA),an enhancement of the Geyser-Inspired Optimization Algorithm(GIOA),which integrates two primary components:the Adaptive Turbulence Operator(ATO)and the Dynamic Pressure Equilibrium Operator(DPEO).ATO allows IGIOA to periodically disrupt stagnation and explore different regions by using turbulence,while DPEO ensures refinement in later iterations by adaptively modulating convergence pressure.We implemented IGIOA on 23 benchmark functions with both unimodal and multimodal contours,in addition to eight problems pertaining to cluster analysis at the UCI.IGIOA,out of all the tested methods,was able to converge most accurately while also achieving a stable convergence rate.The mitigation of premature convergence and low-level exploitation was made possible by the turbulence and pressure-based refinements.The findings from the tests confirm that the adaptation of baseline strategies by IGIOA helps deal with complex data distributions more effectively.However,additional hyperparameters which add complexity are introduced,along with increased computational cost.These include automatic tuning of parameters,ensemble or parallel variations,and hybridization with dedicated local search strategies to extend the reach of IGIOA for general optimization while also specializing it for clustering focused tasks and applications.
基金funded by the National Key R&D Program of China(2022YFE0140400)the National Natural Science Foundation of China(62405027, 62111530238, 62003046)+3 种基金Supporting project of major scientific research projects of Beijing Normal University at Zhuhai (ZHPT2023007)supported by the Tang Scholar of Beijing Normal Universityco-funded by the financial support of the European Union under the REFRESH-Research Excellence For REgion Sustainability and High-tech Industries project number CZ.10.03.01/00/22003/0000048 via the Operational Programme Just Transitionthe scope of the projects CICECO-Aveiro Institute of Materials, UIDB/50011/2020 (DOI 10.54499/UIDB/50011/2020), UIDP/50011/2020 (DOI 10.54499/UIDP/50011/2020) & LA/P/0006/2020 (DOI 10.54499/LA/P/0006/2020) financed by national funds through the FCT/MCTES (PIDDAC)
文摘Personalized health services are of paramount importance for the treatment and prevention of cardiorespiratory diseases,such as hypertension.The assessment of cardiorespiratory function and biometric identification(ID)is crucial for the effectiveness of such personalized health services.To effectively and accurately monitor pulse wave signals,thus achieving the assessment of cardiorespiratory function,a wearable photonic smart wristband based on an all-polymer sensing unit(All-PSU)is proposed.The smart wristband enables the assessment of cardiorespiratory function by continuously monitoring respiratory rate(RR),heart rate(HR),and blood pressure(BP).Furthermore,it can be utilized for biometric ID purposes.Through the analysis of pulse wave signals using power spectral density(PSD),accurate monitoring of RR and HR is achieved.Additionally,utilizing peak detection algorithms for feature extraction from pulse signals and subsequently employing a variety of machine learning methods,accurate BP monitoring and biometric ID have been realized.For biometric ID,the accuracy rate is 98.55%.Aiming to monitor RR,HR,BP,and ID,our solution demonstrates advantages in integration,functionality,and monitoring precision.These enhancements may contribute to the development of personalized health services aimed at the treatment and prevention of cardiorespiratory diseases.
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
