Alexa,let's train now!——A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging

Background:There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging.Consequently,the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions.In this context,digital and home-based physical training interventions might be a promising alternative to center-based intervention programs.Thus,this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance.Methods:In this pre-registered systematic review(PROSPERO;ID:CRD42022320031),5 electronic databases(PubMed,Web of Science,Psyclnfo,SPORTDiscus,and Cochrane Library)were searched by 2 independent researchers(FH and PT)to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults.The systematic literature search yielded 8258 records(extra17 records from other sources),of which 27 controlled trials were considered relevant.Two reviewers(FH and PT)independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale).Results:Of the 27 reviewed studies,15 reported positive effects on cognitive and motor-cognitive outcomes(i.e.,performance improvements in measures of executive functions,working memory,and choice stepping reaction test),and a considerable heterogeneity concerning study-related,population-related,and intervention-related characteristics was noticed.A more detailed analysis suggests that,in particular,interventions using online classes and technology-based exercise devices(i.e.,step-based exergames)can improve cognitive performance in healthy older adults.Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence(≤85%)and monitoring of the level of regular physical activity in the control group.Conclusion:The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall,though there is limited evidence that specific types of digital and home-based physical training interventions(e.g.,online classes and step-based exergames)can be an effective strategy for improving cognitive performance in older adults.However,due to the limited number of available studies,future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry

Biomimetic materials that use natural wisdom to solve practical problems are developing rapidly.The trend for systematic biomimicry is towards in-situ characterization of naturalcreatures with high spatial resolutions.Furthermore,rapid reconstruction of digital twin models with the same complex features as the prototype is indispensable.However,it faces bottlenecks and limits in fast characterization and fabrication,precise parameter optimization,geometricdeviations control,and quality prediction.To solve these challenges,here,we demonstrate astate-of-the-art method taking advantage of micro-computed tomography and three-dimensional printing for the fast characterization of the pitcher plant Nepenthes x ventrata and fabrication of its biomimetic model to obtain a superior drainage controller with multiscale structures withprecise surface morphology optimization and geometric deviation control.Thefilm-rupture-based drainage dynamic and mechanisms are characterized by x-ray and high-speed videography,which determines the crucial structures for unique directionaldrainage.Then the optimized artificial pitchers are further developed into sustained drainage devices with novel applications,such as detection,reaction,and smoke control.

Digital Twin Technology of Human-Machine Integration in Cross-Belt Sorting System

The Chinese express delivery industry processes nearly 110 billion items in 2022,averaging an annual growth rate of 200%.Among the various types of sorting systems used for handling express items,cross-belt sorting systems stand out as the most crucial.However,despite their high degree of automation,the workload for operators has intensified owing to the surging volume of express items.In the era of Industry 5.0,it is imperative to adopt new technologies that not only enhance worker welfare but also improve the efficiency of cross-belt systems.Striking a balance between efficiency in handling express items and operator well-being is challenging.Digital twin technology offers a promising solution in this respect.A realization method of a human-machine integrated digital twin is proposed in this study,enabling the interaction of biological human bodies,virtual human bodies,virtual equipment,and logistics equipment in a closed loop,thus setting an operating framework.Key technologies in the proposed framework include a collection of heterogeneous data from multiple sources,construction of the relationship between operator fatigue and operation efficiency based on physiological measurements,virtual model construction,and an online optimization module based on real-time simulation.The feasibility of the proposed method was verified in an express distribution center.

Evaluation Test Research of DR Inspection System for Circumferential Welds in Long Distance Pipeline

Digital radiographic(DR)testing equipment has been widely promoted and applied in the inspection of circumferential welds in oil and gas pipelines.In order to establish a comprehensive quality control system for digital radiographic testing and fully evaluate the integrated system inspection ability of equipment,personnel,and processes,a scientific and standardized evaluation method to the system is very necessary.Here investigates the precedents of relevant non-destructive testing evaluation methods at home and abroad,considers the testing characteristics of DR equipment,develops a complete set of DR testing system evaluation procedures.It deeply studies the adaptability methods of program processes from defect production to slicing processing and data statistical calculation for digital radiographic testing evaluation.To check the repeatability and reliability of the detectable system,five process welds with 200 real metallographic defects were fabricated in the laboratory.From the detected results,the DR system has good repeatability in image quality,and the detectable defect size reaches 0.85 mm under achieving 90%detection probability at a confidence level of 95%,the error of detected defect length is±2 mm,and the error of detected defect localization is±5 mm.The qualitative and quantitative detection of defects are accurate and reliable.The test further confirmed the reliable detection ability of the DR detection system,and fully validated the scientific and practical evaluation method designed.The research on the evaluation test method can serve as an important link in the quality control system for the on-site application of digital ray equipment in long-distance pipelines.The designed program,test,and evaluation content can serve as an important basis for the formulation of relevant specifications or standards.

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software devel-opment.However,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity measures.This paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of SPL.In the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and business.Specifically,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient ecosystems.Our experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in SPLs.As digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Enabling Technologies for Autonomous Robotic Systems in Manufacturing

Research of autonomous manufacturing systems is motivated both by the new technical possibilities of cyber-physical systems and by the practical needs of the industry.Autonomous operation in semi-structured industrial environments can now be supported by advanced sensor technologies,digital twins,artificial intelligence and novel communication techniques.These enable real-time monitoring of production processes,situation recognition and prediction,automated and adaptive(re)planning,teamwork and performance improvement by learning.This paper summarizes the main requirements towards autonomous industrial robotics and suggests a generic workflow for realizing such systems.Application case studies will be presented from recent practice at HUN-REN SZTAKI in a broad range of domains such as assembly,welding,grinding,picking and placing,and machining.The various solutions have in common that they use a generic digital twin concept as their core.After making general recommendations for realizing autonomous robotic solutions in the industry,open issues for future research will be discussed.

Control Strategies for Digital Twin Systems

With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Building a Smart Management System for the Field Training Course at the Faculty of Specific Education-Mansoura University

Field training is the backbone of the teacher-preparation process.Its importance stems from the goals that colleges of education aim to achieve,which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training.Currently,trainee students attendance in field training is recordedmanually through signatures on attendance sheets.However,thismethod is prone to impersonation,time wastage,and misplacement.Additionally,traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes.Field training also lacks modern technology that the supervisor can use in case of his absence from school to monitor the trainee students’implementation of the required activities and tasks.These shortcomings do not meet the needs of the digital era that universities are currently experiencing.As a result,this paper presents a smart management system for field training based on Internet of Things(IoT)and mobile technology.It includes three subsystems:attendance,monitoring,and evaluation.The attendance subsystem uses an R307 fingerprint sensor to record trainee students’attendance.The Arduino Nano microcontroller transmits attendance data to the proposed Android application via an ESP-12F Wi-Fi module,which then forwards it to the Firebase database for storage.The monitoring subsystem utilizes Global Positioning System(GPS)technology to continually track trainee students’locations,ensuring they remain at the school during training.It also enables remote communication between trainee students and supervisors via audio,video,or text by integrating video call and chat technologies.The evaluation subsystem is based on three items:an online exam,attendance,and implementation of required activities and tasks.Experimental results have demonstrated the accuracy and efficiency of the proposed management system in recording attendance,as well as in monitoring and evaluating trainee students during field traiing.

Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation

By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.

Experimental Study on Full-Surface Buckling of Variable Curvature Cylindrical Shell Using Multi-camera 3D-DIC System

To achieve full-surface strain measurement of variable curvature objects,a 360°3D digital image correlation(DIC)system is proposed.The measurement system consists of four double-camera systems,which capture the object’s entire surface from multiple angles,enabling comprehensive full-surface measurement.To increase the stitching quality,a hierarchical coordinate matching method is proposed.Initially,a 3D rigid body calibration auxiliary block is employed to track motion trajectory,which enables preliminary matching of four 3D-DIC sub-systems.Subsequently,secondary precise matching is performed based on feature points on the test specimen’s surface.Through the hierarchical coordinate matching method,the local 3D coordinate systems of each double-camera system are unified into a global coordinate system,achieving 3D surface reconstruction of the variable curvature cylindrical shell,and error analysis is conducted on the results.Furthermore,axial compression buckling experiment is conducted to measure the displacement and strain fields on the cylindrical shell’s surface.The experimental results are compared with the finite element analysis,validating the accuracy and effectiveness of the proposed multi-camera 3D-DIC measuring system.

Enhancing transjugular intrahepatic portosystemic shunt procedure efficiency with digital subtraction angiography image overlay technology in esophagogastric variceal bleeding

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a pivotal intervention for managing esophagogastric variceal bleeding in patients with chronic hepatic schistosomiasis.AIM To evaluate the efficacy of digital subtraction angiography image overlay tech-nology(DIT)in guiding the TIPS procedure.METHODS We conducted a retrospective analysis of patients who underwent TIPS at our hospital,comparing outcomes between an ultrasound-guided group and a DIT-guided group.Our analysis focused on the duration of the portosystemic shunt puncture,the number of punctures needed,the total surgical time,and various clinical indicators related to the surgery.RESULTS The study included 52 patients with esophagogastric varices due to chronic hepatic schistosomiasis.Results demonstrated that the DIT-guided group expe-rienced significantly shorter puncture times(P<0.001)and surgical durations(P=0.022)compared to the ultrasound-guided group.Additionally,postoperative assessments showed significant reductions in aspartate aminotransferase,B-type natriuretic peptide,and portal vein pressure in both groups.Notably,the DIT-guided group also showed significant reductions in total bilirubin(P=0.001)and alanine aminotransferase(P=0.023).CONCLUSION The use of DIT for guiding TIPS procedures highlights its potential to enhance procedural efficiency and reduce surgical times in the treatment of esophagogastric variceal bleeding in patients with chronic hepatic schistoso-miasis.

Digital Human Rights from the Perspective of System Theory——Concept Definition, Social Function, and Constitutional Basis

The three core issues in the“digital human rights”debate are whether“digital human rights”are possible,necessary,and feasible.Both sides of the debate focus on discovering the value of“digital human rights”to individuals from a semantic level,but ignore the significance of“digital human rights”to the whole society and its subsystems at the level of social structure.By introducing Niklas Luhmann's System Theory,this observation blind spot can be eliminated.Fundamental rights are devoted to directly shaping not a physiological-psychological“individual”as a social environment but a social“person”that can be included by social systems.It is clear that digital human rights are the right to participate in digital communication of a“human”as a“person”,so they are possible in terms of conceptual definition.Digital human rights can help“people”lower the threshold for participation in digital communication,limit the excessive expansion of social systems,and promote the free and complete expression of body and mind,so they are necessary for social functions.There are limitations in the existing two ideas of“incorporating digital human rights into the constitution”.Based on the new construction idea of System Theory of Law,digital human rights as the right to participate in digital communication can be typified into digital communication in social sub-fields such as politics,economy,science,and art.The right to participate constructs a complete digital human rights system,making it feasible on the basis of the constitution.

Enhancing User Security on Instagram: A Multifaceted AI System for Filtering Abusive Comments

Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.

The Dynamic System Theory of Legal Protection for Personal Information Rights Infringement

The advent of the big data era has presented unprecedented challenges to remedies for personal information infringement in areas such as damage assessment,proof of causation,determination of illegality,fault assessment,and liability.Traditional tort law is unable to provide a robust response for these challenges,which severely hinders human rights protection in the digital society.The dynamic system theory represents a third path between fixed constitutive elements and general clauses.It both overcomes the rigidity of the“allor-nothing”legal effect evaluation mechanism of the“element-effect”model and avoids the uncertainty of the general clause model.It can effectively enhance the flexibility of the legal system in responding to social changes.In light of this,it is necessary to construct a dynamic foundational evaluation framework for personal information infringement under the guidance of the dynamic system theory.By relying on the dynamic interplay effect of various foundational evaluation elements,this framework can achieve a flexible evaluation of the constitutive elements of liability and the legal effects of liability for personal information infringement.Through this approach,the crisis of personal information infringement in the era of big data can be mitigated,and the realization of personal information rights as digital human rights can be promoted.