Home Devices Research Articles

Subway Train Bearing Fault Detection Under Variable Speed Based on Speed-Guided ACMD and Order Tracking

Subway Train Bearing Fault Detection Under Variable Speed Based on Speed-Guided ACMD and Order Tracking

Plasmon-Driven Gold Nanopillar Multiarrayed Gene Amplification Methodology for the High-Throughput Discrimination of Pathogens.

Molecular diagnosis limitations, including complex treatment processes, low cost-effectiveness, and operator-dependent low reproducibility, interrupt the timely prevention of disease spread and the development of medical devices for home and outdoor uses. A newly fabricated gold nanopillar array-based film is presented for superior photothermal energy conversion. Magnifying the metal film surface-to-volume ratio increases the photothermal energy conversion efficiency, resulting in a swift reduction in the gene amplification reaction time. Plasmonic energy-based ultrafast gene amplification and facile confirmation methodology offer a rapid disease discrimination platform for high-throughput multiplexed diagnosis. The superior performance of the gold nanopillar arrayed film is demonstrated by measuring the amount of pathogen (Vibrio cholerae) with a sensitivity of 101 cfu mL-1 in 5.5min. The newly engineered gold nanopillar arrayed film can be utilized to diagnose universal pathogens to achieve an increasingly successful complete cure.

Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise

Multi-object tracking (MOT) aims to detect objects in video sequences and associate them across frames. Currently, the mainstream research direction regarding MOT is the tracking-by-detection (TBD) framework. Tracking results are highly sensitive to detection outputs, and challenges from object occlusion and complex motion present significant obstacles in the field of MOT. To reduce dependence on detection outputs, we propose a method that integrates predictive information to improve Non-Maximum Suppression (NMS). By applying secondary modulation to the suppression scores and dynamically adjusting the suppression threshold using tracking information, our method better retains candidate boxes for occluded objects. Furthermore, to track occluding and overlapping objects more effectively, we introduce an adaptive measurement noise method that adjusts the measurement noise to mitigate the impact of object occlusion or overlap on tracking accuracy. Additionally, we enhance the affinity matrix in the association algorithm by incorporating height information, thereby improving the stability of complex moving objects. Our method outperforms the baseline model ByteTrack on the DanceTrack dataset, increasing Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and the ID F1 Score (IDF1) by 10.2%, 3.0%, and 4.8%, respectively.

A New Integrated Criteria Weighting Approach for Determining Production Priority in Demand Management

In the demand management process; it is tried to balance the customers' needs with the company's supply possibilities. Effective demand management starts with effective stock control and tracking of customer orders and ends with customer satisfaction. Correctly determining production priorities is one of the ways to increase customer satisfaction. In this context, a new approach has been developed to determine the production priority needed in the demand management process. A new integrated approach based on Multi-Criteria Decision Making (MCDM) is proposed to determine production priorities. In the first stage of this three-stage approach, customer alternatives are determined. In the second stage, production priority criteria are determined depending on customer characteristics. In the third stage; with LBWA (Level Based Weight Assessment) and CRITIC (The Criteria Importance Through Intercriteria Correlation) methods, objective and subjective criterion weights are calculated by considering expert opinions and real data. Then, integrated criterion weights are determined with the SOWIA (Subjective and Objective Weight Integrated Approach) method using objective and subjective criterion weights. In the literature; few studies determine production priority using the MCDM method. Additionally, no study has been found in the literature in which quantitative and qualitative methods are used integrated in the ceramic industry. Therefore, it is thought that this study will contribute to the literature.

GENERATING VIDEO VISITS FOR RURAL OLDER VETERANS IN TELE-GERIATRIC MENTAL HEALTH SERVICES

Abstract Telehealth services increase access to specialty mental health services for rural older adults. Within the Veterans Health Administration (VHA), older Veterans are more likely to receive audio-only mental telehealth services compared with younger Veterans. Using a mixed methods approach, we examined the use of video telehealth services by geriatric mental health (GMH) providers in four regional VHA telehealth hubs. Between October 2022 and September 2023, 351 rural Veterans with a mean age of 75.1 years (SD = 8.8) received services from one or more GMH provider. Patients were mostly white (88.6%), males (95.4%) with high care needs (CAN = 81.6 out of 100, indicating highest need). Of the 1,440 encounters delivered, 49% were video to home, 27% were video to clinic, 19% were telephone, and 5% were e-consultation services. Through interviews with GMH providers, we identified technology-related barriers and solutions used to help rural Veterans be seen via video. Findings suggested that barriers to video services included lack of home internet and/or devices, poor connection, and audio or video issues. Solutions included using VHA resources to obtain iPads for patients who lacked connectivity/devices and prioritizing getting patients into local clinic when needed for video visits. Providers described utilizing the telehealth clinical technician to help overcome patient barriers and discomfort with digital technology through teaching patients and caregivers to use home telehealth software. With troubleshooting, use of resources and training, high quality video telehealth services can be delivered successfully to older rural Veterans and increase access to GMH care.

CPUs and GPUs in Smart Home Devices: Performance, Applications, and Future Directions

This paper explores the performance and applications of central processing units (CPUs) and graphics processing units (GPUs) in smart home devices. It delves into the background and trends of smart home evolution, highlighting the applied technologies and the roles of CPU and GPU. The literature review presents advancements in CPU and GPU technologies for smart home devices, including their developments in home data processing, image recognition, and device interconnection. The methodology section discusses the architecture design of CPU and GPU in smart home devices, emphasizing multi-core design, parallel processing, and cache optimization. The applications of CPUs and GPUs are examined in smart home appliances and home entertainment systems, including their roles in device control, energy efficiency management, intelligent operation interfaces, 4K video processing, game graphics rendering, and intelligent recommendation systems. The experiment evaluates the performance of CPUs and GPUs in smart home devices through a simulated home environment and specific performance indicators. Finally, the conclusion emphasizes the importance of high-performance computing chips in smart home devices. It suggests future research directions, including optimizing performance, integrating AI technology, and developing new computing chips.

Nasal High-Flow Therapy Versus Low-Flow Oxygen Therapy Post-Discharge in Infants With Bronchopulmonary Dysplasia: A Retrospective Observational Study.

Low-flow oxygen therapy (LFOT) has been the only option for infants with bronchopulmonary dysplasia (BPD) requiring supplemental oxygen after discharge for years. Nasal high-flow therapy (NHFT) has more recently become available as a home device. We compared the impact of NHFT and LFOT on respiratory morbidity and lung function over the first 4 years in infants with BPD requiring respiratory support post-discharge. We retrospectively analyzed data on respiratory morbidity and lung function assessed by tidal breathing flow-volume loop (TBFVL) analysis available from follow-up visits of infants with BPD discharged home with LFOT or NHFT up to 4 years. We compared long-term respiratory outcomes by applying a mixed model, adjusting for mechanical ventilation, overall respiratory support duration, pulmonary hypertension, oxygen therapy, caffeine therapy, and smoking exposure. We included 26 infants discharged with LFOT and 47 with NHFT. The two groups were homogeneous for baseline demographics and BPD severity. Infants in the NHFT group were weaned from respiratory support earlier compared to the LFOT group (median [IQR] 8.0 [4.0; 12.0] vs 14.5 [6.0; 21.5] months, p 0.046). After applying a mixed model, the NHFT group showed a lower incidence of upper (p = 0.025) and lower respiratory tract infections (p = 0.003), wheezing (p: 0.001), and need for bronchodilator (p = 0.028) and systemic steroids (p < 0.001) during the initial 4 years. Lung function testing did not highlight between-group differences. NHFT as an alternative to LFOT may positively impact long-term clinical outcomes of infants with BPD requiring noninvasive respiratory support post-discharge.

Academician of the All-Ukrainian Academy of Sciences Petro Mykhaylovych Suprunenko: Life and activity

The article updates the most significant information about the outstanding scientist-mechanic and railwayman of the first third of the 20th century Academician of the All-Ukrainian Academy of Sciences Petro Mykhaylovych Suprunenko (1893–1938). It is proven that his activities in the field of railway development are a significant contribution to the development of world science and technology. The main scientific works of the scientist and engineer are devoted to important problems of transport mechanics. He paid special attention to locomotive and wagon construction (he was a wagon designer). Wagons were built on the basis of a large amount of experimental material, which at one time received significant public resonance and scientific and technical recognition. It is emphasized that P. M. Suprunenko was one of the first railway engineers who proposed a scientific analysis of the interaction of the track with the rolling stock of railways. The article notes that today P. M. Suprunenko is mentioned very little, only fragments of his biography are described, only some of his scientific works are characterized. In the Institute of Transport Mechanics of the All-Ukrainian Academy of Sciences headed by him, the following research was successfully developed under his leadership: methodological problems of transport mechanics were highlighted in order to comprehensively solve the tasks of modernization of the railway industry; research of non-stationary dynamic processes of interaction of rolling stock (train) with the track system (especially the study of the resonance phenomenon); research of the theory of calculations of locomotive traction on railway tracks in order to optimize elements of transport mechanics and increase their efficiency, etc. The article describes in detail each of these areas of research by P. M. Suprunenko. In general, his research focused on problems of railway transport. In the field of train traction theory, P. M. Suprunenko created a number of new methods for graphical integration of differential equations of train motion, paid significant attention to improving existing ones and creating new devices designed to measure the characteristics of various processes occurring in rolling stock and tracks. The scientist's achievements in the initial period of railway transport construction in Ukraine, especially on the South-Western Railways, are confirmed. The article shows that the role of P. M. Suprunenko was in his scientific work as an "idea generator" and a leading theorist in combination with engineering activities.

AI-DRIVEN BRIDGING LOCAL SELLERS AND GLOBAL BUYERS

This project aims to create an accessible digital platform tailored for small-scale businesses, enabling them to engage in cross-border e-commerce and global trade with minimal complexity. By leveraging the MERN stack (MongoDB, Express, React, Node.js), the platform provides a responsive and scalable solution that connects local producers with a global customer base. Small-scale industries often face barriers in reaching international markets, from supply chain complexities to payment processing challenges.This platform addresses these obstacles by simplifying logistics, incorporating multi-currency support, and securing transactions through integrated payment gateways.The core features, such as real-time order tracking and streamlined supply chain management, empower small businesses to operate with the same efficiency as larger enterprises. Buyers can browse products from local producers, pay in their currency, and track shipments globally, making cross- border shopping more accessible. This project bridges the gap between local and international markets, fostering growth for small-scale industries and expanding their reach to a worldwide audience. By reducing the traditional costs and complications associated with global trade, this platform offers an efficient pathway for small businesses to scale their operations beyond local borders. Keywords: AI-Driven Solutions, Cross-Border E- Commerce, Small-Scale Industries, Global Buyers and Local Seller, MERN Stack, Inventory Management, Real-Time Order Tracking, Secure Payment Gateways, Logistics Simplification, Customer Engagement, Customer Trust and Satisfaction.

(Re)Defining Smart Home Through an HCI Perspective: A Systematic Review of over Two Decades of Smart Home Conceptualization and Research

Smart homes have increasingly become subjects of frequent discussion in consumer and academic communities. Despite the decades-long advancement of smart homes, there is not a clear and consistent definition for a smart home that encompasses the diversity of research and development taking place in the field. The present work aims to provide a systematic evidence-based synthesis of ostensive and formal definitions over the past two decades. In addition, we investigate how these definitions have changed over time and whether these definitions differ based on academic discipline. There were 355 definitions analyzed across 333 articles. Most of these definitions were original and published by engineering, computer science, and/or mathematics researchers. The greatest changes in definitions over time are related to the types of popular smart home technologies present during the decade. Specifically, IoT home devices and sensors showed a considerable increase in mentions beginning in 2010. We conclude the publication by proposing a new roadmap for conceptualizing and defining smart homes.

Charge lifetime improvement of the Continuous Electron Beam Accelerator Facility photogun with a biased anode

GaAs photocathodes in dc high-voltage photoguns are highly susceptible to ion back-bombardment, which reduces the photocathode quantum efficiency and limits the useful operating lifetime for producing polarized electron beams. This paper demonstrates that applying a small positive bias to the photogun anode can significantly suppress ion back-bombardment and increase charge lifetime. This technique was studied extensively using the Continuous Electron Beam Accelerator Facility photogun, where highly polarized electron beams created using a strained-superlattice GaAs/GaAsP photocathode were used and charge lifetimes improved by almost a factor of 2. A new simulation code was developed to model ion production and tracking in order to better understand and explain the factors that led to the performance improvement. Results of the experiments and simulations are discussed in detail. Published by the American Physical Society 2024

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

A Granular-Computing-Based Data-Sharing Decision-Making Method for Enabling Blockchain-Based Order Tracking in Social Manufacturing

A Granular-Computing-Based Data-Sharing Decision-Making Method for Enabling Blockchain-Based Order Tracking in Social Manufacturing

Understanding Wireless Debugging with Sniffers in Smart Home Environments

Wireless communication is becoming increasingly prevalent in smart home environments; the need for effective debugging methodologies has become paramount for system reliability and performance optimization. This comprehensive article analysis explores the implementation of wireless sniffers across multiple protocols, including Wi-Fi, Bluetooth, and Zigbee, with particular emphasis on their application in contemporary IoT devices. Through a detailed examination of chipset capabilities from major manufacturers, including Qualcomm Atheros, Texas Instruments, and Nordic Semiconductor, this article presents a structured approach to wireless debugging that encompasses packet capture mechanisms, signal analysis, and protocol verification. The investigation demonstrates how modern sniffer technologies can effectively diagnose common issues such as packet loss, signal interference, and protocol misconfigurations in smart home devices, including voice assistants, security systems, and environmental controls. By synthesizing practical implementation strategies with technical analysis, this article provides developers and system architects with actionable methodologies for improving wireless system reliability while highlighting the critical role of protocol analysis in ensuring robust IoT deployments.

Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM

In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model’s real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments.

An iterative approach for estimating domain-specific cognitive abilities from large scale online cognitive data.

Online cognitive tasks are gaining traction as scalable and cost-effective alternatives to traditional supervised assessments. However, variability in peoples' home devices, visual and motor abilities, and speed-accuracy biases confound the specificity with which online tasks can measure cognitive abilities. To address these limitations, we developed IDoCT (Iterative Decomposition of Cognitive Tasks), a method for estimating domain-specific cognitive abilities and trial-difficulty scales from task performance timecourses in a data-driven manner while accounting for device and visuomotor latencies, unspecific cognitive processes and speed-accuracy trade-offs. IDoCT can operate with any computerised task where cognitive difficulty varies across trials. Using data from 388,757 adults, we show that IDoCT successfully dissociates cognitive abilities from these confounding factors. The resultant cognitive scores exhibit stronger dissociation of psychometric factors, improved cross-participants distributions, and meaningful demographic's associations. We propose that IDoCT can enhance the precision of online cognitive assessments, especially in large scale clinical and research applications.

Smart Security : A Comprehensive Exploration of AI and IoT in Cybersecurity

This paper investigates the transformative roles of Artificial Intelligence (AI) and the Internet of Things (IoT) in cybersecurity, especially as the complexity and connectivity of digital networks intensify. The rapid adoption of IoT devices in industries and homes alike has significantly broadened the cyber threat landscape, introducing countless points of vulnerability. Cybercriminals now target the vast and often unsecured data generated by IoT devices, exploiting weak access controls, outdated software, and inadequate encryption. In response, AI has become a critical tool in modern cybersecurity strategies, offering enhanced methods for threat detection, predictive analytics, and automated responses. AI-driven systems can proactively analyze patterns in network activity, detect anomalies, and anticipate future threats, all in real-time, providing a strong defence against increasingly sophisticated attacks. Additionally, AI facilitates automation in incident response, enabling immediate isolation or neutralization of threats before significant damage occurs. However, integrating IoT and AI in cybersecurity introduces new challenges, particularly concerning device interoperability, resource limitations in IoT devices, and privacy concerns due to the extensive data generated and processed. This paper explores these technologies in depth, examining their current applications, limitations, and the potential they hold for building smarter, more resilient cybersecurity frameworks in the future.

Predictors and Prevalence of Severe Obstructive Sleep Apnea: A Cross-Sectional Study in Erbil, Kurdistan Region, Iraq.

Background Obstructive sleep apnea (OSA) is a common sleep disorder that's characterized by episodes of a complete or partial collapse of the upper airway with an associated decrease in oxygen saturation or arousal from sleep. According to theAmerican Academy of Sleep Medicine (AASM), OSA is categorized based on polysomnography findings into mild, moderate, and severe. Objectives This study aims at determining the prevalence of the severities of OSA in Erbil, Kurdistan Region of Iraq, as well as discovering the predictors for severe OSA. Methods This was a cross-sectional study that was carried out from December 2021 to July 2023 on patients displaying OSA symptoms in a sleep study section of a private clinic in Erbil, Kurdistan Region of Iraq. A detailed questionnaire was designed to collect the data, and IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, New York, United States) was used to analyze it. The polysomnography device used for the diagnosis of OSA was a Philips Respironics Alice NightOne home device, and Philips Respironics Sleepware G3 (Koninklijke Philips N.V., Amsterdam, Netherlands) was used to analyze the sleep data. Results A sample size of 328 OSA cases was analyzed. The results revealed a prevalence of 47% (155) for severe OSA. Apnea-hypopnea index (AHI) was negatively correlated with lowest and average oxygen saturation, while it was positively correlated with time spent with oxygen saturation under 89%, body mass index (BMI), and weight of the participants. Furthermore, stepwise multiple regression tests revealed BMI, age, gender, and heart failure as independent predictors for AHI. Conclusion This study highlights the links between OSA and various chronic health conditions. Furthermore, it underscores the importance of factors like age, obesity, and gender in influencing OSA severity. The identification of predictors for OSA severity can assist in risk assessment and personalized interventions.

Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning

This paper introduces JEMADAR-AI, an approach to energy management within smart grids, leveraging an edge-cloud continuum architecture coupled with Deep Q-Learning to optimize the operation of smart home devices. The main hypothesis of this work is that combining advanced machine learning models with edge-cloud computing can significantly improve energy efficiency and cost savings in smart grids. The proposed system utilizes SARIMA for seasonal trends and LSTM for long-term dependency models to forecast energy consumption and production, enabling proactive decision-making to balance supply and demand in real-time. JEMADAR-AI employs a deep reinforcement learning algorithm (Deep Q-Learning) to optimize appliance operations, dynamically adjusting energy usage based on predicted demand and supply fluctuations. This ensures that household energy consumption aligns with production capabilities, particularly during periods of renewable energy generation. The architecture combines the high processing power of cloud computing for long-term forecasting with the low-latency responsiveness of edge computing for real-time appliance control. This Edge-Cloud Continuum approach provides an efficient solution for managing energy in distributed smart grids. The experimental results, obtained using Gridlab-D and Omnet++ simulations, demonstrate that JEMADAR-AI improves decision-making speed by 32.25% and reduces household energy bills by 22.11% compared to traditional cloud-based systems.

Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack.

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method's efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds.