The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semicon...We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.展开更多
Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variatio...Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless...Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless,unobtrusive BP monitoring systems,offering an alternative to traditional sphygmomanometers.However,extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources,power efficiency,and deployment scalability.This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring,emphasizing its potential for personalized,scalable,and accessible healthcare.We systematically analyze the end-to-end system architecture,from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments.We further discuss clinical validation metrics and implementation barriers and prospective strategies.To bridge lab-to-field translation,we propose an innovative"sensor-model-deployment-assessment"co-design framework.This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.展开更多
This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene...This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.展开更多
DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in...DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we emp...As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we employed high-quality crystalline TiN/Al_(2)O_(3)/BaTiO_(3)/Pt RRAM with an optimized thin Al_(2)O_(3) interlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the interlayer affects the memory window size. After insertion of the Al_(2)O_(3) interlayer, the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in performance. Moreover, the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magnitude, respectively, thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude, respectively. The device also exhibits multilevel resistive switching behavior when varying the applied voltage. Finally, we also developed a 6 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal variation. Hence, our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.展开更多
Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2....Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.展开更多
The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation...The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.展开更多
Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanism...Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.展开更多
The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from ...The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.展开更多
This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational ...This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.展开更多
This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We syste...This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.展开更多
Differential inequalities generated in an extended Lyapunov framework are employed in the stability and instability analyses of a class of switched continuous-time second-and higher order linear systems with an arbitr...Differential inequalities generated in an extended Lyapunov framework are employed in the stability and instability analyses of a class of switched continuous-time second-and higher order linear systems with an arbitrary number of switching matrices.The exponential stability and instability(ESI)conditions so obtained involve the supremum and infimum of ratios of certain quadratic forms of the matrices,leading to global time-averages of their activity intervals.Further,motivated by linear switching system examples of(i)instability with stable matrices and(ii)stability with unstable matrices(found in the literature primarily for second-order systems),the proposed framework is generalized to establish ESI conditions that include both the activity intervals of the matrices and their switching rates,the latter being governed by a certain logarithmic measure of the normalized magnitudes of discontinuities caused by switching.In effect,(the new,globally averaged)dwell-time is flexibly traded,apparently for the first time,but under specific conditions(related,in part,to the eigenvalues of the matrices),for switching discontinuity-based conditions.Two further novel aspects of the proposed approach are:(i)For second-order matrices,switching lines in phase space can be chosen for periodic switching to stabilize or destabilize the system,and even generate oscillations,depending on the eigenvalues of the system matrices.But for third-(and higher)order matrices,such an analytically tractable(and controlled)periodical switching entails solution of an explicit non-convex multi-parameter optimization problem for which a stochastic optimization algorithm from the literature can be invoked.(ii)Lower and upper bounds on the solutions of the system equations can be quantified to reflect the stability/instability/oscillatory property of the system.Illustrative examples,which demonstrate the novelty of the derived stability and instability conditions,are presented in part 2 which is advisedly to be read along with this part 1 for a coherent merging of theory with practice.展开更多
In this second part of the paper,bearing the same title as above,but with the last hyphenated phrase replaced by part 1(Theory),the exponential stability and instability(ESI)Theorems 1–4 of part 1 are illustrated by ...In this second part of the paper,bearing the same title as above,but with the last hyphenated phrase replaced by part 1(Theory),the exponential stability and instability(ESI)Theorems 1–4 of part 1 are illustrated by applying them to second-andby,say,third-order linear switched systems with different eigenvalue structures to demonstrate the versatility,novelty and superiority(over many of the results found in the literature,especially for second-order switched lined systems)of the new theoretical results.The computational procedure that is employed with reference to the third-order systems is generic,in the sense that it is applicable to higher(i.e.,greater than third-)order linear switched systems.A pseudo-code for a computer implementation of the stability/instability conditions is also presented.With the principal aim of facilitating an independent reading of this part 2 of the paper,some crucial mathematical notations,definitions and results of part 1 have been repeated,thereby making the contents as self-contained as possible.展开更多
Titanium dioxide(TiO_(2))is an extremely promising anode material for lithium-ion batteries due to its low cost,minimal volume change,and extended cycle life.However,its electrochemical performance is severely hindere...Titanium dioxide(TiO_(2))is an extremely promising anode material for lithium-ion batteries due to its low cost,minimal volume change,and extended cycle life.However,its electrochemical performance is severely hindered by inherent issues such as poor ionic and electronic conductivity.Here,we design a dual-phase conductor Co@TiO_(2),which contributes a synergistic storage mode consisting of a Li-accepting and an electron-accepting phase.In situ magnetic characterization and experimental results reveal the space charge storage mechanism in addition to traditional insertion mechanisms.Based on these mechanisms,the specific capacity and rate performance of the Co@TiO_(2)electrode have been greatly enhanced.Under a current density of 200 mA·g^(-1),the specific capacity of Co@TiO_(2)reaches 397.2 mAh·g^(-1).Upon increasing the current density to 10 A·g^(-1),the electrode still maintains a capacity of 83.1 mAh·g^(-1)after 900 cycles.This result offers a fresh perspective on the structural design of new anode materials to achieve high energy density.展开更多
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
基金the School of Engineering and Built Environment at Anglia Ruskin University,UK,for the supportthe support of IRC-CSS and the Electrical Engineering Department,KFUPM,Saudi Arabia。
文摘We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.
基金support from the Roy A.Wilkens Professorship Endowment。
文摘Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
基金the Chinese University of Hong Kong for providing research resources and institutional support
文摘Accurate blood pressure(BP)monitoring is essential for preventing and managing cardiovascular disease.Advancements in materials science,medicine,flexible electronic,and artificial intelligence(AI)have enabled cuffless,unobtrusive BP monitoring systems,offering an alternative to traditional sphygmomanometers.However,extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources,power efficiency,and deployment scalability.This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring,emphasizing its potential for personalized,scalable,and accessible healthcare.We systematically analyze the end-to-end system architecture,from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments.We further discuss clinical validation metrics and implementation barriers and prospective strategies.To bridge lab-to-field translation,we propose an innovative"sensor-model-deployment-assessment"co-design framework.This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.
基金the Engineering and Physical Sciences Research Council(EPSRC)for funding the researchUK India Education Research Initiative(UKIERI)for funding support.
文摘This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.
基金the National Natural Science Foundation of China(62271485,61903363,U1811463,62103411,62203250)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1)。
文摘DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by the National Research Foundation of Korea funded by the Korean Government(grant No.RS-2023-00208801).
文摘As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we employed high-quality crystalline TiN/Al_(2)O_(3)/BaTiO_(3)/Pt RRAM with an optimized thin Al_(2)O_(3) interlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the interlayer affects the memory window size. After insertion of the Al_(2)O_(3) interlayer, the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in performance. Moreover, the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magnitude, respectively, thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude, respectively. The device also exhibits multilevel resistive switching behavior when varying the applied voltage. Finally, we also developed a 6 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal variation. Hence, our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.
文摘Air pollution,specifically fine particulate matter(PM2.5),represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems.Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks;however,the inherent nonlinearity and dynamic variability of air quality data present significant challenges.This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and the hybrid CNN-LSTM as well as statistical models,AutoRegressive Integrated Moving Average(ARIMA)and Maximum Likelihood Estimation(MLE)for hourly PM2.5 forecasting.Model performance is quantified using Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R^(2))metrics.The comparative analysis identifies optimal predictive approaches for air quality modeling,emphasizing computational efficiency and accuracy.Additionally,CNN classification performance is evaluated using a confusion matrix,accuracy,precision,and F1-score.The results demonstrate that the Hybrid CNN-LSTM model outperforms standalone models,exhibiting lower error rates and higher R^(2) values,thereby highlighting the efficacy of deep learning-based hybrid architectures in achieving robust and precise PM2.5 forecasting.This study underscores the potential of advanced computational techniques in enhancing air quality prediction systems for environmental and public health applications.
基金supported by the National Natural Science Foundation of China(Grant No.62202210).
文摘The application of generative artificial intelligence(AI)is bringing about notable changes in anime creation.This paper surveys recent advancements and applications of diffusion and language models in anime generation,focusing on their demonstrated potential to enhance production efficiency through automation and personalization.Despite these benefits,it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models.We conduct an in-depth survey of cutting-edge generative AI technologies,encompassing models such as Stable Diffusion and GPT,and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics.Review of the surveyed literature indicates the achievement of considerable maturity in the capacity of AI models to synthesize high-quality,aesthetically compelling anime visual images from textual prompts,alongside discernible progress in the generation of coherent narratives.However,achieving perfect long-form consistency,mitigating artifacts like flickering in video sequences,and enabling fine-grained artistic control remain critical ongoing challenges.Building upon these advancements,research efforts have increasingly pivoted towards the synthesis of higher-dimensional content,such as video and three-dimensional assets,with recent studies demonstrating significant progress in this burgeoning field.Nevertheless,formidable challenges endure amidst these advancements.Foremost among these are the substantial computational exigencies requisite for training and deploying these sophisticated models,particularly pronounced in the realm of high-dimensional generation such as video synthesis.Additional persistent hurdles include maintaining spatial-temporal consistency across complex scenes and mitigating ethical considerations surrounding bias and the preservation of human creative autonomy.This research underscores the transformative potential and inherent complexities of AI-driven synergy within the creative industries.We posit that future research should be dedicated to the synergistic fusion of diffusion and autoregressive models,the integration of multimodal inputs,and the balanced consideration of ethical implications,particularly regarding bias and the preservation of human creative autonomy,thereby establishing a robust foundation for the advancement of anime creation and the broader landscape of AI-driven content generation.
基金funded by Ajman University,AU-Funded Research Grant 2023-IRG-ENIT-22.
文摘Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.
文摘The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.
文摘This review paper examines the various types of electrical generators used to convert wave energy into electrical energy.The focus is on both linear and rotary generators,including their design principles,operational efficiencies,and technological advancements.Linear generators,such as Induction,permanent magnet synchronous,and switched reluctance types,are highlighted for their direct conversion capability,eliminating the need for mechanical gearboxes.Rotary Induction generators,permanent magnet synchronous generators,and doubly-fed Induction generators are evaluated for their established engineering principles and integration with existing grid infrastructure.The paper discusses the historical development,environmental benefits,and ongoing advancements in wave energy technologies,emphasizing the increasing feasibility and scalability of wave energy as a renewable source.Through a comprehensive analysis,this review provides insights into the current state and future prospects of electrical generators in wave energy conversion,underscoring their potential to significantly reduce reliance on fossil fuels and mitigate environmental impacts.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)funded this review study.
文摘This reviewpresents a comprehensive technical analysis of deep learning(DL)methodologies in biomedical signal processing,focusing on architectural innovations,experimental validation,and evaluation frameworks.We systematically evaluate key deep learning architectures including convolutional neural networks(CNNs),recurrent neural networks(RNNs),transformer-based models,and hybrid systems across critical tasks such as arrhythmia classification,seizure detection,and anomaly segmentation.The study dissects preprocessing techniques(e.g.,wavelet denoising,spectral normalization)and feature extraction strategies(time-frequency analysis,attention mechanisms),demonstrating their impact on model accuracy,noise robustness,and computational efficiency.Experimental results underscore the superiority of deep learning over traditional methods,particularly in automated feature extraction,real-time processing,cross-modal generalization,and achieving up to a 15%increase in classification accuracy and enhanced noise resilience across electrocardiogram(ECG),electroencephalogram(EEG),and electromyogram(EMG)signals.Performance is rigorously benchmarked using precision,recall,F1-scores,area under the receiver operating characteristic curve(AUC-ROC),and computational complexitymetrics,providing a unified framework for comparing model efficacy.Thesurvey addresses persistent challenges:synthetic data generationmitigates limited training samples,interpretability tools(e.g.,Gradient-weighted Class Activation Mapping(Grad-CAM),Shapley values)resolve model opacity,and federated learning ensures privacy-compliant deployments.Distinguished from prior reviews,this work offers a structured taxonomy of deep learning architectures,integrates emerging paradigms like transformers and domain-specific attention mechanisms,and evaluates preprocessing pipelines for spectral-temporal trade-offs.It advances the field by bridging technical advancements with clinical needs,such as scalability in real-world settings(e.g.,wearable devices)and regulatory alignment with theHealth Insurance Portability and Accountability Act(HIPAA)and General Data Protection Regulation(GDPR).By synthesizing technical rigor,ethical considerations,and actionable guidelines for model selection,this survey establishes a holistic reference for developing robust,interpretable biomedical artificial intelligence(AI)systems,accelerating their translation into personalized and equitable healthcare solutions.
文摘Differential inequalities generated in an extended Lyapunov framework are employed in the stability and instability analyses of a class of switched continuous-time second-and higher order linear systems with an arbitrary number of switching matrices.The exponential stability and instability(ESI)conditions so obtained involve the supremum and infimum of ratios of certain quadratic forms of the matrices,leading to global time-averages of their activity intervals.Further,motivated by linear switching system examples of(i)instability with stable matrices and(ii)stability with unstable matrices(found in the literature primarily for second-order systems),the proposed framework is generalized to establish ESI conditions that include both the activity intervals of the matrices and their switching rates,the latter being governed by a certain logarithmic measure of the normalized magnitudes of discontinuities caused by switching.In effect,(the new,globally averaged)dwell-time is flexibly traded,apparently for the first time,but under specific conditions(related,in part,to the eigenvalues of the matrices),for switching discontinuity-based conditions.Two further novel aspects of the proposed approach are:(i)For second-order matrices,switching lines in phase space can be chosen for periodic switching to stabilize or destabilize the system,and even generate oscillations,depending on the eigenvalues of the system matrices.But for third-(and higher)order matrices,such an analytically tractable(and controlled)periodical switching entails solution of an explicit non-convex multi-parameter optimization problem for which a stochastic optimization algorithm from the literature can be invoked.(ii)Lower and upper bounds on the solutions of the system equations can be quantified to reflect the stability/instability/oscillatory property of the system.Illustrative examples,which demonstrate the novelty of the derived stability and instability conditions,are presented in part 2 which is advisedly to be read along with this part 1 for a coherent merging of theory with practice.
文摘In this second part of the paper,bearing the same title as above,but with the last hyphenated phrase replaced by part 1(Theory),the exponential stability and instability(ESI)Theorems 1–4 of part 1 are illustrated by applying them to second-andby,say,third-order linear switched systems with different eigenvalue structures to demonstrate the versatility,novelty and superiority(over many of the results found in the literature,especially for second-order switched lined systems)of the new theoretical results.The computational procedure that is employed with reference to the third-order systems is generic,in the sense that it is applicable to higher(i.e.,greater than third-)order linear switched systems.A pseudo-code for a computer implementation of the stability/instability conditions is also presented.With the principal aim of facilitating an independent reading of this part 2 of the paper,some crucial mathematical notations,definitions and results of part 1 have been repeated,thereby making the contents as self-contained as possible.
基金financially supported by the National Natural Science Foundation of China(Nos.92372127 and 22179066)Natural Science Foundation of Shandong,China(Nos.ZR2023JQ017,202210060028,and ZR2021QE061)
文摘Titanium dioxide(TiO_(2))is an extremely promising anode material for lithium-ion batteries due to its low cost,minimal volume change,and extended cycle life.However,its electrochemical performance is severely hindered by inherent issues such as poor ionic and electronic conductivity.Here,we design a dual-phase conductor Co@TiO_(2),which contributes a synergistic storage mode consisting of a Li-accepting and an electron-accepting phase.In situ magnetic characterization and experimental results reveal the space charge storage mechanism in addition to traditional insertion mechanisms.Based on these mechanisms,the specific capacity and rate performance of the Co@TiO_(2)electrode have been greatly enhanced.Under a current density of 200 mA·g^(-1),the specific capacity of Co@TiO_(2)reaches 397.2 mAh·g^(-1).Upon increasing the current density to 10 A·g^(-1),the electrode still maintains a capacity of 83.1 mAh·g^(-1)after 900 cycles.This result offers a fresh perspective on the structural design of new anode materials to achieve high energy density.
