Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

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.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

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%.

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems:A Survey

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.

Fund budget model for multipurpose transit smart card systems

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.

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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.

IoT-based Smart and Complex Systems：A Guest Editorial Report

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

A Novel User Behavior Prediction Model Based on Automatic Annotated Behavior Recognition in Smart Home Systems

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.

A Solution-Based Analysis of Attack Vectors on Smart Home Systems

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.

Analysis Markov Delay Control Strategy for Smart Home Systems

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.

Temperature-Based Fan Speed Optimization Strategy

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.

Recent advances of sorafenib nanoformulations for cancer therapy:Smart nanosystem and combination therapy

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.

A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

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.

Analyzing the Impact of Blockchain Models for Securing Intelligent Logistics through Unified Computational Techniques

Blockchain technology has revolutionized conventional trade.The success of blockchain can be attributed to its distributed ledger characteristic,which secures every record inside the ledger using cryptography rules,making it more reliable,secure,and tamper-proof.This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context.Furthermore,it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure.To realize the full potential of the accurate and efficacious use of blockchain in the transportation sector,it is essential to understand the most effective mechanisms of this technology and identify the most useful one.As a result,the present work offers a priority-based methodology that would be a useful reference for security experts in managing blockchain technology and its models.The study uses the hesitant fuzzy analytical hierarchy process for prioritizing the different blockchain models.Based on the findings of actual performance,alternative solution A1 which is Private Blockchain model has an extremely high level of security satisfaction.The accuracy of the results has been tested using the hesitant fuzzy technique for order of preference by similarity to the ideal solution procedure.The study also uses guidelines from security researchers working in this domain.

VARIATIONAL PRINCIPLFS OF NONLINEAR PIEZOTHERMOELASTIC MEDIA

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.

IEC 61850 as Backbone for Smart PAC Systems

This paper discusses a working definition of smartness in protection,automation and control systems(PACS)in substations.A summary is given about the standard IEC 61850 features that support smartness,i.e.,the data model,the multiple communication services,and the system configuration description language(SCL).With help of known examples of application functions for control,supervision,and protection,the building of smart systems out of the modules of IEC 61850 is demonstrated.From the possible architectures,the standardized redundancy is explained.The process bus is exploited in detail since it is seen by users as the core of a smart substation.In this context,attention is given also to the time synchronization in theµs range over the communication network,a rather new part of the standard.In the last part,all features are summarized and it is concluded that IEC 61850 may really be named the backbone for smart PAC systems.

Smart manufacturing systems for Industry 4.0： Conceptual framework, scenarios, and future perspectives

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems （CPS）, which mark the advent of the fourth stage of industrial production （i.e., Industry 4.0）. The widespread application of CPS in manufacturing environments renders manufacturing sys- tems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

Environment-responsive drug delivery systems for targeted cancer therapy

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.

Robust image processing algorithm for computational resource limited smart apple sunburn sensing system

Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses.Typically,when the fruit surface temperature(FST)rises above critical limits for a prolonged duration,the fruit may suffer several physiological disorders including sunburn.To manage apple sunburn,monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring.Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources.Therefore,key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer.Algorithm logic flow includes color space transformation,k-means++classification and morphological operators prior to fruit segmentation and FST estimation.The developed algorithm demonstrated the segmentation accuracy of 57.78%(missing error=12.09%and segmentation error=0.13%).This aided successful apple FST estimation that was 10–18C warmer than ambient air temperature.Moreover,algorithm reduced the imagery data processing time cost of the smart sensing systemfrom 87 s to 44 s using image compression approach.

Temperature and Tumor Microenvironment Dual Responsive Mesoporous Magnetic Nanospheres for Magnetothermal Effect-Induced Cancer Theranostics

The development of smart drug delivery systems(SDDSs)based on engineered nanomaterials is important for clinical applications.Nevertheless,controllable administration of chemotherapeutic drugs for deep tumors and the avoidance of side effects caused by off-targeting during delivery remain a great challenge.Herein,a stimulus-responsive system of mesoporous nanospheres(composed of Cu@Fe_(2)C@mSiO_(2))with good magnetothermal effect is introduced into the tumor microenvironment.This system plays an important role in image-guided controllable targeted drug delivery that is independent of tumor depth.Aggregation-induced emission luminogen-based fluorescence imaging and magnetic resonance imaging were utilized since these techniques visualize the delivery process in real time.In addition,the degraded nanocarriers showed high catalytic activity for Fenton and Fenton-like reactions,upregulating the level of hydroxyl radicals(•OH)in cancer cells to realize chemodynamic therapy.The induced•OH led to the overexpression of pho-STAT3,activating the STAT3 signaling pathway,eventually inducing cancer cell apoptosis.Through metabolic monitoring,this SDDS is removed from the body after its degradation in vivo.The synergistically enhanced therapeutic effect was obtained in the chemo-chemodynamic therapy of 4T1 tumor-bearing mice,offering a platform for efficient cancer therapy with a personalized theranostic strategy.

Towards IoT-based sustainable digital communities

Seeking continuous development,a modern community must also be able to adapt to future possible challenges using constrained or limited resources.As a revolutionary communication paradigm,the Internet of Things(IoT)empowers the cutting-edge and emerging applications which enable manifold new intelligent services towards a smart community.The sophisticated ecosystem of a digital community is made feasible by the IoT infrastructure,which also provides community control with access to a wealth of actual data.In addition,IoT platforms empower the ubiquitous computing ability,providing more potentials to the actuators in perception layer in the IoT architecture.With more and more population in the urban areas,sustainability issues have become a key factor to consider in the development of a digital community.We give a modern survey in this study on the most recent developments in IoT for sustainable digital communities.After carefully examining the most recent literature,we specifically highlight the various smart digital community application scenarios,such as smart buildings,energy management,green transportation,trash management,etc.We also look into a number of major issues facing the use of IoT technology in digital communities.Furthermore,we discuss potential future applications and future research areas for IoT,the critical component of sustainable digital communities.