In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution P...In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability;(2) known Edge Device Fault Probability Distribution (EDFPD);(3) known edge device fault number and EDFPD;(4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a Cause Event (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.展开更多
Artificial intelligence has the potential to stand as the cornerstone of human society,which could drive our civilization forward and emerge as a pivotal frontier in the ongoing technological revolution and industrial...Artificial intelligence has the potential to stand as the cornerstone of human society,which could drive our civilization forward and emerge as a pivotal frontier in the ongoing technological revolution and industrial transformation.Amidst profound shifts in the global technological landscape,smart materials,smart devices,and smart systems have become the defining pillars of our era,which will catalyze paradigm shifts in engineering science and reshape the trajectory of modern technology.展开更多
The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthca...The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.展开更多
The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-con...The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-constrained and dependentchance for the fund budget of multipurpose transit smart card systems are established with application scale and social demand as random variables, respectively aiming to maximize earnings and satisfy the service requirements the furthest; and the genetic algorithm based on stochastic simulation is adopted for model solution. The calculation results show that the fund budget differs greatly with different system objectives which can cause the systems to have distinct expansibilities, and the application scales of some applications may not satisfy user demands with limited funds. The analysis results indicate that the forecast of application scales and application future demands should be done first, and then the system objective is determined according to the system mission, which can help reduce the risks of fund budgets.展开更多
Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined pr...Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.展开更多
THE Industrial Revolution starting from about 1760 and ending at around 1840 has been viewed as the first Industrial Revolution.It features with the replacement of human and animal muscle power with steam and mechanic...THE Industrial Revolution starting from about 1760 and ending at around 1840 has been viewed as the first Industrial Revolution.It features with the replacement of human and animal muscle power with steam and mechanical power.Human income per capita had taken 800 years to double by展开更多
User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors a...User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.展开更多
The development and wider adoption of smart home technology also created an increased requirement for safe and secure smart home environments with guaranteed privacy constraints. In this paper, a short survey of priva...The development and wider adoption of smart home technology also created an increased requirement for safe and secure smart home environments with guaranteed privacy constraints. In this paper, a short survey of privacy and security in the more broad smart-world context is first presented. The main contribution is then to analyze and rank attack vectors or entry points into a smart home system and propose solutions to remedy or diminish the risk of compromised security or privacy. Further, the usability impacts resulting from the proposed solutions are evaluated. The smart home system used for the analysis in this paper is a digital- STROM installation, a home-automation solution that is quickly gaining popularity in central Europe, the findings, however, aim to be as solution independent as possible.展开更多
with the development of science and technology, smart home systems require better, faster to meet the needs of human. In order to achieve this goal, the human-machine-items all need to interact each other with underst...with the development of science and technology, smart home systems require better, faster to meet the needs of human. In order to achieve this goal, the human-machine-items all need to interact each other with understand, efficient and speedy. Cps could unify combination with the human-machine-items; realize the interaction between the physical nformation and the cyber world. However, information interaction and the control task needs to be completed in a valid time. Therefore, the transform delay control strategy becomes more and more important. This paper analysis Markov delay control strategy for smart home systems, which might help the system decrease the transmission delay.展开更多
Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular m...Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.展开更多
Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial proce...Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.展开更多
The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar green...The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar greenhouses as active energy contributors within SESs,emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling(DHC)networks.A structured classification of machine learning(ML)and deep learning(DL)techniques applied in forecasting and optimizing these processes is provided.Additionally,the evolution of DHC systems is analyzed,with a focus on fifth-generation DHC(5GDHC)networks,which facilitate bidirectional energy exchange at near-ambient temperatures.The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses.A novel framework is proposed,illustrating their role as prosumers capable of exchanging electricity,hydrogen,and thermal energy within SESs.Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency,reduce carbon emissions,and improve system resilience.Furthermore,ML-driven predictive control strategies,particularly model predictive control(MPC),are identified as essential for optimizing real-time energy flows and biomass gasification processes.This study provides a foundation for future research on the technical,economic,and environmental feasibility of integrating greenhouses into SESs.The insights presented offer a pathway toward more sustainable,AI-driven energy-sharing networks,supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.展开更多
In order to deliver and/or release anti-cancer therapeutics at the tumor sites, novel environment-responsive drug delivery systems are designed to specifically respond to tumor microenvironment （such as low pH and hy...In order to deliver and/or release anti-cancer therapeutics at the tumor sites, novel environment-responsive drug delivery systems are designed to specifically respond to tumor microenvironment （such as low pH and hypoxia）. Due to their extraordinary advantages, these environment-responsive drug delivery systems can improve antitumor efficacy, and most importantly, they can decrease toxicity associated with the anti-cancer therapeutics. This review highlights different mechanisms of environmentresponsive drug delivery systems and their applications for targeted cancer therapy.展开更多
Sorafenib,a molecular targeted multi-kinase inhibitor,has received considerable interests in recent years due to its significant profiles of efficacy in cancer therapy.However,poor pharmacokinetic properties such as l...Sorafenib,a molecular targeted multi-kinase inhibitor,has received considerable interests in recent years due to its significant profiles of efficacy in cancer therapy.However,poor pharmacokinetic properties such as limited water solubility,rapid elimination and metabolism lead to low bioavailability,restricting its further clinical application.Over the past decade,with substantial progress achieved in the development of nanotechnology,various types of smart sorafenib nanoformulations have been developed to improve the targetability as well as the bioavailability of sorafenib.In this review,we summarize various aspects from the preparation and characterization to the evaluation of antitumor efficacy of numerous stimuli-responsive sorafenib nanodelivery systems,particularly with emphasis on their mechanism of drug release and tumor microenvironment response.In addition,this review makes great effort to summarize the nanosystem-based combination therapy of sorafenib with other antitumor agents,which can provide detailed information for further synergistic cancer therapy.In the final section of this review,we also provide a detailed discussion of future challenges and prospects of designing and developing ideal sorafenib nanoformulations for clinical cancer therapy.展开更多
With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and ...With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.展开更多
In today's fast-evolving technological landscape,the growing deployment of smart systems demands more efficient and adaptive information acquisition capabilities.Traditional contact-based sensors[1,2]are proving i...In today's fast-evolving technological landscape,the growing deployment of smart systems demands more efficient and adaptive information acquisition capabilities.Traditional contact-based sensors[1,2]are proving increasingly inadequate,facing limitations such mechanical abrasion,reduced stability,and constrained adaptability to dynamic environments,particularly evident in applications like smart homes and medical monitoring.The advent of non-contact sensing presents a transformative solution,offering safer,more durable,and versatile alternatives across diverse sectors.Leading this transition,tele-perception emerges as a cutting-edge paradigm,extending beyond conventional non-contact methods through the use of advanced electret materials that enable stable,long-range perception.Tele-perception technologies facilitate stimulus detection without physical contact,establishing a foundational component of adaptive embodied artificial intelligence systems across diverse fields including robotics,autonomous driving,and human-machine interaction(HMI).展开更多
The escalating demands for smart biomedical systems ignite a significantly growing influence of three-dimensional(3D)printing technology.Recognized as a revolutionary and potent fabrication tool,3D printing possesses ...The escalating demands for smart biomedical systems ignite a significantly growing influence of three-dimensional(3D)printing technology.Recognized as a revolutionary and potent fabrication tool,3D printing possesses unparalleled capabilities for generating customized functional devices boast-ing intricate and meticulously controlled architectures while enabling the integration of multiple functional materials.These distinctive advantages arouse a growing inclination toward customization and miniaturization,thereby facilitating the development of cutting-edge biomedical systems.In this comprehensive review,the prevalent 3D printing technologies employed in biomedical applications are presented.Moreover,focused attention is paid to the latest advancements in harnessing 3D printing to fabricate smart biomedical systems,with specific emphasis on exemplary ongoing research encompassing biomedical examination systems,biomedical treatment sys-tems,as well as veterinary medicine.In addition to illuminating the promising potential inherent in 3D printing for this rapidly evolving field,the prevailing challenges impeding its further progression are also discussed.By shedding light on recent achievements and persisting obstacles,this review aims to inspire future breakthroughs in the realm of smart biomedical systems.展开更多
As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study ai...As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.展开更多
The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuit...The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuition,historical knowledge,and manual research.However,with the advancement of AI-driven Smart Negotiation Assistants,procurement teams can leverage real-time market intelligence,price benchmarks,and predictive analytics to autonomously negotiate contracts.This paper introduces an AI-powered Pro curement Chatbot,capable of conducting supplier negotiations with minimal human intervention.The system utilizes machine learning(ML),natural lan guage processing(NLP),and historical transaction data to negotiate terms,secure cost savings,and ensure compliance with procurement policies.Realworld case studies,including automated software licensing negotiations and dynamic supplier pricing adjustments,demonstrate how AI-driven negotiations can save millions in procurement costs,reduce cycle times by up to 40%,and mitigate supplier risks[1].The paper also explores technical architecture,algorithmic models,and deployment strategies for integrating AI negotiation assistants into enterprise procurement workflows.Furthermore,it highlights regulatory and ethical considerations in AI-driven procurement,emphasizing transparency and fairness.By leveraging AI-driven negotiation chatbots,businesses can achieve autonomous,efficient,and data-driven procurement processes,ensuring better supplier relationships and long-term cost savings.展开更多
Through the phenomenological approach,the nonlinear constitutive equations coupling the electro/magnetic therrnoelastic media are derived.Several nonlinear variational principles for piezothermoelastic continua are pr...Through the phenomenological approach,the nonlinear constitutive equations coupling the electro/magnetic therrnoelastic media are derived.Several nonlinear variational principles for piezothermoelastic continua are presented and employed to formulate the incremental variational princi- ples which are of important significance in practical applications such as the nonlinear finite element analysis,the buckling,postbuckling and dynamic stability analyses of piezoelectric smart structures.展开更多
基金supported by the National Natural Science Foundation of China(No.52004120).
文摘In a smart system, the faults of edge devices directly impact the system’s overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability;(2) known Edge Device Fault Probability Distribution (EDFPD);(3) known edge device fault number and EDFPD;(4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a Cause Event (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.
文摘Artificial intelligence has the potential to stand as the cornerstone of human society,which could drive our civilization forward and emerge as a pivotal frontier in the ongoing technological revolution and industrial transformation.Amidst profound shifts in the global technological landscape,smart materials,smart devices,and smart systems have become the defining pillars of our era,which will catalyze paradigm shifts in engineering science and reshape the trajectory of modern technology.
基金funded by King Saud University through Researchers Supporting Program Number (RSP2024R499).
文摘The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.
基金The Key Technology R& D Program of Jiangsu Scienceand Technology Department(No.BE2006010)the Key Technology R& DProgram of Nanjing Science and Technology Bureau(No.200601001)Sci-ence and Technology Research Projects of Nanjing Metro Headquarters(No.8550143007).
文摘The fund budget of multipurpose transit smart card systems is studied by stochastic programming to assign limited funds to different applications reasonably. Under the constraints of a gross fund, models of chance-constrained and dependentchance for the fund budget of multipurpose transit smart card systems are established with application scale and social demand as random variables, respectively aiming to maximize earnings and satisfy the service requirements the furthest; and the genetic algorithm based on stochastic simulation is adopted for model solution. The calculation results show that the fund budget differs greatly with different system objectives which can cause the systems to have distinct expansibilities, and the application scales of some applications may not satisfy user demands with limited funds. The analysis results indicate that the forecast of application scales and application future demands should be done first, and then the system objective is determined according to the system mission, which can help reduce the risks of fund budgets.
文摘Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.
文摘THE Industrial Revolution starting from about 1760 and ending at around 1840 has been viewed as the first Industrial Revolution.It features with the replacement of human and animal muscle power with steam and mechanical power.Human income per capita had taken 800 years to double by
基金supported by the National Natural Science Foundation of China(62071069)。
文摘User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.
文摘The development and wider adoption of smart home technology also created an increased requirement for safe and secure smart home environments with guaranteed privacy constraints. In this paper, a short survey of privacy and security in the more broad smart-world context is first presented. The main contribution is then to analyze and rank attack vectors or entry points into a smart home system and propose solutions to remedy or diminish the risk of compromised security or privacy. Further, the usability impacts resulting from the proposed solutions are evaluated. The smart home system used for the analysis in this paper is a digital- STROM installation, a home-automation solution that is quickly gaining popularity in central Europe, the findings, however, aim to be as solution independent as possible.
文摘with the development of science and technology, smart home systems require better, faster to meet the needs of human. In order to achieve this goal, the human-machine-items all need to interact each other with understand, efficient and speedy. Cps could unify combination with the human-machine-items; realize the interaction between the physical nformation and the cyber world. However, information interaction and the control task needs to be completed in a valid time. Therefore, the transform delay control strategy becomes more and more important. This paper analysis Markov delay control strategy for smart home systems, which might help the system decrease the transmission delay.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)Support Program(IITP-2023-2018-0-01799)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and also the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1F1A1063134).
文摘Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.
文摘Membrane fouling is a persistent challenge in membrane-based technologies,significantly impacting efficiency,operational costs,and system lifespan in applications like water treatment,desalination,and industrial processing.Foul-ing,caused by the accumulation of particulates,organic compounds,and microorganisms,leads to reduced permeability,increased energy demands,and frequent maintenance.Traditional fouling control approaches,relying on empirical models and reactive strategies,often fail to address these issues efficiently.In this context,artificial intelligence(AI)and machine learning(ML)have emerged as innovative tools offering predictive and proactive solutions for fouling man-agement.By utilizing historical and real-time data,AI/ML techniques such as artificial neural networks,support vector machines,and ensemble models enable accurate prediction of fouling onset,identification of fouling mechanisms,and optimization of control measures.This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation,discussing advanced algorithms,the role of sensor-based monitoring,and the importance of robust datasets in enhancing predictive accuracy.Case studies highlighting successful AI/ML applications across various membrane processes are presented,demonstrating their transformative potential in improving system performance.Emerging trends,such as hybrid modeling and IoT-enabled smart systems,are explored,alongside a criti-cal analysis of research gaps and opportunities.This review emphasizes AI/ML as a cornerstone for sustainable,cost-effective membrane operations.
文摘The integration of solar greenhouses into smart energy systems(SESs)remains largely unexplored,despite their potential to enhance energy sharing and hydrogen production.This review investigates the role of solar greenhouses as active energy contributors within SESs,emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling(DHC)networks.A structured classification of machine learning(ML)and deep learning(DL)techniques applied in forecasting and optimizing these processes is provided.Additionally,the evolution of DHC systems is analyzed,with a focus on fifth-generation DHC(5GDHC)networks,which facilitate bidirectional energy exchange at near-ambient temperatures.The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses.A novel framework is proposed,illustrating their role as prosumers capable of exchanging electricity,hydrogen,and thermal energy within SESs.Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency,reduce carbon emissions,and improve system resilience.Furthermore,ML-driven predictive control strategies,particularly model predictive control(MPC),are identified as essential for optimizing real-time energy flows and biomass gasification processes.This study provides a foundation for future research on the technical,economic,and environmental feasibility of integrating greenhouses into SESs.The insights presented offer a pathway toward more sustainable,AI-driven energy-sharing networks,supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.
基金National Basic Research Program of China(Grant No.973 Program,2013CB932500)National Natural Science Foundation of China(Grant No.81273458)Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110071130011)
文摘In order to deliver and/or release anti-cancer therapeutics at the tumor sites, novel environment-responsive drug delivery systems are designed to specifically respond to tumor microenvironment （such as low pH and hypoxia）. Due to their extraordinary advantages, these environment-responsive drug delivery systems can improve antitumor efficacy, and most importantly, they can decrease toxicity associated with the anti-cancer therapeutics. This review highlights different mechanisms of environmentresponsive drug delivery systems and their applications for targeted cancer therapy.
基金This project was supported by the National Natural Science Foundation of China(81972832)Project supported by College Students’innovation and entrepreneurship training program of Fujian Province(S202010386051).
文摘Sorafenib,a molecular targeted multi-kinase inhibitor,has received considerable interests in recent years due to its significant profiles of efficacy in cancer therapy.However,poor pharmacokinetic properties such as limited water solubility,rapid elimination and metabolism lead to low bioavailability,restricting its further clinical application.Over the past decade,with substantial progress achieved in the development of nanotechnology,various types of smart sorafenib nanoformulations have been developed to improve the targetability as well as the bioavailability of sorafenib.In this review,we summarize various aspects from the preparation and characterization to the evaluation of antitumor efficacy of numerous stimuli-responsive sorafenib nanodelivery systems,particularly with emphasis on their mechanism of drug release and tumor microenvironment response.In addition,this review makes great effort to summarize the nanosystem-based combination therapy of sorafenib with other antitumor agents,which can provide detailed information for further synergistic cancer therapy.In the final section of this review,we also provide a detailed discussion of future challenges and prospects of designing and developing ideal sorafenib nanoformulations for clinical cancer therapy.
文摘With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.
基金supported by the Beijing Natural Science Foundation (IS23040)the National Natural Science Foundation of China (22479016)。
文摘In today's fast-evolving technological landscape,the growing deployment of smart systems demands more efficient and adaptive information acquisition capabilities.Traditional contact-based sensors[1,2]are proving increasingly inadequate,facing limitations such mechanical abrasion,reduced stability,and constrained adaptability to dynamic environments,particularly evident in applications like smart homes and medical monitoring.The advent of non-contact sensing presents a transformative solution,offering safer,more durable,and versatile alternatives across diverse sectors.Leading this transition,tele-perception emerges as a cutting-edge paradigm,extending beyond conventional non-contact methods through the use of advanced electret materials that enable stable,long-range perception.Tele-perception technologies facilitate stimulus detection without physical contact,establishing a foundational component of adaptive embodied artificial intelligence systems across diverse fields including robotics,autonomous driving,and human-machine interaction(HMI).
基金National Natural Science Foundation of China,Grant/Award Numbers:22138013,22208376Ministry of Education Industry-University Cooperative Education Project,Grant/Award Number:202102152006startup support grant from China University of Petroleum(East China),Grant/Award Number:22CX06025A。
文摘The escalating demands for smart biomedical systems ignite a significantly growing influence of three-dimensional(3D)printing technology.Recognized as a revolutionary and potent fabrication tool,3D printing possesses unparalleled capabilities for generating customized functional devices boast-ing intricate and meticulously controlled architectures while enabling the integration of multiple functional materials.These distinctive advantages arouse a growing inclination toward customization and miniaturization,thereby facilitating the development of cutting-edge biomedical systems.In this comprehensive review,the prevalent 3D printing technologies employed in biomedical applications are presented.Moreover,focused attention is paid to the latest advancements in harnessing 3D printing to fabricate smart biomedical systems,with specific emphasis on exemplary ongoing research encompassing biomedical examination systems,biomedical treatment sys-tems,as well as veterinary medicine.In addition to illuminating the promising potential inherent in 3D printing for this rapidly evolving field,the prevailing challenges impeding its further progression are also discussed.By shedding light on recent achievements and persisting obstacles,this review aims to inspire future breakthroughs in the realm of smart biomedical systems.
文摘As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.
文摘The rise of artificial intelligence(AI)in procurement has transformed how organizations engage with suppliers,optimize spending,and drive contract negotiations.Traditional procurement negotiations rely on human intuition,historical knowledge,and manual research.However,with the advancement of AI-driven Smart Negotiation Assistants,procurement teams can leverage real-time market intelligence,price benchmarks,and predictive analytics to autonomously negotiate contracts.This paper introduces an AI-powered Pro curement Chatbot,capable of conducting supplier negotiations with minimal human intervention.The system utilizes machine learning(ML),natural lan guage processing(NLP),and historical transaction data to negotiate terms,secure cost savings,and ensure compliance with procurement policies.Realworld case studies,including automated software licensing negotiations and dynamic supplier pricing adjustments,demonstrate how AI-driven negotiations can save millions in procurement costs,reduce cycle times by up to 40%,and mitigate supplier risks[1].The paper also explores technical architecture,algorithmic models,and deployment strategies for integrating AI negotiation assistants into enterprise procurement workflows.Furthermore,it highlights regulatory and ethical considerations in AI-driven procurement,emphasizing transparency and fairness.By leveraging AI-driven negotiation chatbots,businesses can achieve autonomous,efficient,and data-driven procurement processes,ensuring better supplier relationships and long-term cost savings.
基金The work is supported by the National Natural Science Foundationthe Doctoral Education Foundationthe Aerospace Foundation
文摘Through the phenomenological approach,the nonlinear constitutive equations coupling the electro/magnetic therrnoelastic media are derived.Several nonlinear variational principles for piezothermoelastic continua are presented and employed to formulate the incremental variational princi- ples which are of important significance in practical applications such as the nonlinear finite element analysis,the buckling,postbuckling and dynamic stability analyses of piezoelectric smart structures.
