Reliable Service Research Articles

Inferring failure risk of on-site wastewater systems from physical and social factors

Aging infrastructure and climate change present emerging challenges for clean water supply and reliable wastewater services for communities in the United States (US). In Georgia, for example, the failure rates of on-site wastewater systems (OWTS) have increased from 10% to 35% in the last two decades as the systems age. In this work, we develop a hierarchical Bayesian model to understand the different contributions of physical and social factors driving OWTS failures using a long-term collection of 201,000 Georgia’s OWTS inspection records. The out-of-sample validation accuracy of our hierarchical Bayesian model is 70% within Georgia, outperforming other machine learning models that do not consider the multiscale nature of the problem. Overall, we find counties that experience more extreme precipitation and are situated in steeper-sloped regions are significantly associated with increased failure risks. Uncertainties, meanwhile, are largely associated with counties experiencing more precipitation and have lower median housing value.

A Game Theory Based Scheduling Approach for Charging Coordination of Multiple Electric Vehicles Aggregators in Smart Cities

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study various EVs aggregators including residential, commercial, official, university, and industry parking lots have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88% and 4.28%, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.

A Mechanoluminescence‐Based Stress Sensing Hydrogel for Intelligent Artificial Ligament

AbstractRobust and biocompatible hydrogels are recognized as promising biomimetic soft materials to improve human life quality. To ensure their stable, reliable, and safe service, the hydrogels are further required to have non‐contact and wireless stress sensing ability. Herein, a mechanoluminescence (ML) based micellar hydrogel is developed, in which the surface‐modified BaSi2O2N2: Eu2+ (M‐BSON) particles are chemically incorporated into the cross‐linked polyacrylamide/polymethyl acrylate (PAM/PMA) network structure. Because of the interactions between the M‐BSON particles and the PMA micelles, the as‐fabricated composite hydrogel exhibits enhanced mechanical properties with a mechanical strength of 2.73 MPa and a toughness of 3.40 MJ m−3, respectively. The chemical wrapping of the M‐BSON particles by the hydrophobic PMA micelles further protects the ML properties from water quenching, leading to remarkable stress‐induced luminescence under the water environment of hydrogel. Because of its desirable mechanical performance, attractive stress‐light responsiveness, and good biocompatibility, the M‐BSON incorporated hydrogel has the potential to be applied as an intelligent artificial ligament for stress self‐monitoring and failure warning. This work addresses the inhomogeneous dispersion and water quenching issues of the ML particles in hydrogel structure, which significantly promote ML applications in bionic engineering.

Minimizing downtime in E-Commerce platforms through containerization and orchestration

Minimizing downtime in e-commerce platforms is critical for maintaining financial stability and customer satisfaction. This paper explores the impact of containerization and orchestration technologies in achieving this goal. We begin by highlighting the detrimental effects of downtime and the importance of reliable e-commerce services. The role of containerization in providing consistency, isolation, and scalability is examined, along with its implementation in e-commerce platforms. We then delve into orchestration tools such as Kubernetes and Docker Swarm, discussing their benefits in automating container management and enhancing system stability. Strategies for integrating these technologies into existing infrastructures are outlined, addressing potential challenges and proposing solutions. Best practices for seamless integration, including continuous monitoring, robust security measures, and regular updates, are also presented. Finally, the paper concludes with actionable recommendations for e-commerce businesses aiming to reduce downtime and improve platform reliability through containerization and orchestration. Future directions for research and technological advancements in this field are suggested to enhance the efficacy of these solutions further.

An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

PurposeThe rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approachIn this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.FindingsSimulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.Research limitations/implicationsOne of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implicationsFrom a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.Social implicationsOur proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.Originality/valueExisting resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

Evaluating the Role of ISO Standards in Enhancing the Performance of Medical Laboratories

The role of ISO standards in enhancing the performance of medical laboratories has become increasingly significant in ensuring quality and reliability in healthcare services. This review evaluates the impact of ISO standards, particularly ISO 15189, on the operational efficiency, diagnostic accuracy, and risk management of medical laboratories. It highlights the key components of these standards, including quality management systems, personnel competence, and continuous improvement, and examines their contributions to patient safety and institutional credibility. The article also explores the challenges of implementing ISO standards, such as financial constraints and resistance to change, and proposes strategies to overcome these barriers. The findings underscore the importance of ISO standards in promoting a culture of quality and innovation within medical laboratories, ultimately improving healthcare outcomes.

Short-Term Passenger Flow Prediction in Urban Rail Transit based on Hybrid Deep Learning Models

Urban rail transit systems are essential for efficient, reliable, and environmentally friendly transportation in modern cities. Correct and effective short-term passenger flow prediction is crucial for optimizing operational efficiency, enhancing service quality, and ensuring passenger safety. Traditional prediction methods often fail to capture the complex, non-linear, and dynamic patterns in urban rail transit systems. This study uses a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to predict short-term passenger flow in urban rail transit. By integrating the strengths of CNN in capturing spatial features and LSTM in modeling temporal dependencies, the hybrid model aims to improve prediction accuracy. Using historical data from Hangzhou Metro, the study demonstrates the model's effectiveness in predicting passenger flow, which reasonably has a good fit. Additionally, models with different convolutional layers show different performances. These improved predictions offer valuable insights for transit authorities, enabling them to make more informed decisions regarding train scheduling, resource allocation, and emergency response planning. By anticipating passenger demand more accurately, authorities can optimize the deployment of trains, reduce waiting times, enhance passenger comfort, and improve overall service reliability.

A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications.

Data-driven technologies have improved the efficiency, reliability and effectiveness of healthcare services, but come with an increasing demand for data, which is challenging due to privacy-related constraints on sharing data in healthcare contexts. Synthetic data has recently gained popularity as potential solution, but in the flurry of current research it can be hard to oversee its potential. This paper proposes a novel taxonomy of synthetic data in healthcare to navigate the landscape in terms of three main varieties. Data Proportion comprises different ratios of synthetic data in a dataset and associated pros and cons. Data Modality refers to the different data formats amenable to synthesis and format-specific challenges. Data Transformation concerns improving specific aspects of a dataset like its utility or privacy with synthetic data. Our taxonomy aims to help researchers in the healthcare domain interested in synthetic data to grasp what types of datasets, data modalities, and transformations are possible with synthetic data, and where the challenges and overlaps between the varieties lie.

Perlindungan Hukum Nasabah dari Kejahatan Phising dalam Layanan Perbankan Digital di Indonesia

The digitalization of the banking sector, driven by the Fourth Industrial Revolution, has significantly impacted the ease of financial transactions through digital banking services. However, this progress also creates vulnerabilities to cybercrimes, particularly phishing, which aims to steal customers' personal data via fake websites or messages. This article examines relevant legal frameworks, including Law No. 19 of 2016 on Electronic Information and Transactions (EIT Law), the Indonesian Criminal Code (KUHP), and regulations issued by the Financial Services Authority (OJK), such as POJK No. 12/POJK.03/2018 on Digital Banking Services. Employing a normative qualitative approach, the study explores the legal protection available to customers as phishing victims and the responsibilities of banks in preventing and addressing such threats. This article recommends strengthening banks' technological security systems, enhancing customers' digital literacy, and enforcing laws more effectively to establish secure and reliable digital banking services.

Identification of theft in a power distribution network: A tree-based approach

Electricity is an essential commodity, yet it is not freely available to everyone in the country due to widespread electricity theft. This theft results in significant financial losses for Distribution Companies (DISCOMS), hindering their ability to provide reliable services to consumers. Although there are methods to curb electricity theft, DISCOMS have struggled to control it effectively. The most successful current method is the manual investigation of nodes; however, it has its drawbacks, such as a low probability of theft detection when nodes are randomly sampled and excessive time consumption when all nodes are investigated. To address these issues, a systematic approach utilizing the capture, storage, and analysis of data is essential. This paper presents a coloring-based tree approach to identify power theft without altering the existing network infrastructure. The proposed approach aims to reduce the dataset for manual inspection, thereby increasing the probability of theft detection while substantially reducing investigation time. By discouraging blind checking of nodes and providing a basis for targeted node inspections, this method benefits both DISCOMS and general consumers. In this paper, the existing methods of theft detection have been compared with the proposed approach of tree-based theft detection. The application of the proposed approach removed certain nodes from the set of nodes to be investigated for theft detection.

Vibration characteristics of rolling bearing cage under speed variation conditions

Speed variation conditions will deteriorate the stress state of the rolling bearing internal components and aggravate the vibration of the cage, thereby affecting the service life and reliability of rolling bearings. In this paper, a nonlinear dynamic analysis model of the cylindrical roller bearing is established considering the rubbing effect and hydrodynamic contact between the cage and the guiding ring, as well as the sliding between the cage and other rolling elements. The vibration characteristics of the cage is discussed under the start-stop conditions and different fluctuations of rotation rate. The results show that the vibration of the cage mainly occurs at the middle and late stages of the acceleration process, and the impact mainly occurs at the middle stage of the deceleration process under the start-stop conditions. Moreover, the fluctuation of inner ring speed will enhance the unstable motion of the cage, among which the vibration and impact of the cage are most severe under the rectangular fluctuation of speed, followed by simple harmonic and triangular fluctuations. The findings can provide a theoretical guidance for improving the performance of rolling bearings and ensuring their safe and reliable operation.

DriveMate - Vehicle Breakdown Assistance using Mobile Application

The increasing need for on-demand vehicle breakdown assistance calls for a system that connects stranded drivers with nearby mechanics through a seamless mobile interface. This paper presents a survey of existing technologies, applications, and algorithms that aid in the development of a real-time mechanic dispatch platform. The app allows for mechanic registration, user-mechanic connectivity based on GPS location, and ratings to enhance service reliability. This survey highlights methods for implementing proximity-based assistance, secure admin control for mechanic vetting, and efficient user-to-mechanic communication

Large internal stress induced nonlinear current-voltage behavior in nanodiamond strengthened ZnO ceramics

The modulation of the electrostatic potential barrier at grain boundaries determines the performance of many ceramic-based electronics such as varistors. However, conventional protocols relying on complex doping and annealing processes inevitably increase the inhomogeneity of microstructure, which may jeopardize the performance stability and mechanical reliability in service. Instead of doping, herein we demonstrate an effective strategy to modulate the potential barrier in ZnO-based low-voltage varistors by exploiting internal stress-induced piezoelectric polarization. The local residual stress as large as ~1 GPa can be created in the ZnO matrix by incorporating ultra-stiff nanodiamond particles using a cold sintering process. As a result, the composite with only 2 wt% of nanodiamond exhibits a prominent nonlinear current-voltage response at a low switch voltage of 15.7 V/mm, which is ascribed to the depressed barrier height induced by the distinct effects of positively and negatively charged polarization on grain boundaries. More strikingly, the large internal stress can significantly enhance the strength of the composite by more than 230% compared with the monolith, owing to the highly strengthened grain boundaries and crack-tip bridging from prestressed nanodiamonds. These findings add internal stress as a new dimension to design mechanically robust ceramic electronics with high performance.

UNC Charlotte Autonomous Shuttle Pilot Study: An Assessment of Operational Performance, Reliability, and Challenges

This paper presents the findings from an autonomous shuttle pilot program conducted at the University of North Carolina at Charlotte between June and December 2023 as part of the North Carolina Department of Transportation’s Connected Autonomous Shuttle Supporting Innovation (CASSI) initiative. The shuttle completed 825 trips, transporting 565 passengers along a 2.2-mile mixed-traffic campus route. The study evaluates the shuttle’s operational performance, reliability, and challenges using data from onboard sensors, system logs, and operator reports. Key analyses are divided into four areas: service reliability, which assesses autonomy disengagements caused by signal loss, technical issues, and environmental factors; service robustness, focusing on the shuttle’s ability to maintain operations under adverse conditions; performance metrics, including average speed, autonomy percentage, and battery usage; and service usage, which examines the number of trips and passengers to gauge efficiency. Signal loss and battery-related issues were the primary causes of service interruptions, while environmental factors like weather and vegetation also affected shuttle performance. Recommendations include enhancing vehicle-to-infrastructure communication and optimizing battery management.

Administrative and legal framework for the introduction of the electronic notarial system in Ukraine

The article is dedicated to the study of the administrative and legal foundations for the implementation of the electronic notarial system in Ukraine. The author examines the process of transitioning from the traditional paper-based notarial system to a digital one, highlighting the benefits of increased efficiency, document security, and accessibility. The study focuses on the phased approach adopted by the government to implement the e-notary system, outlining key stages such as the creation of electronic registries, the development of a digital archive for notarial acts, and the introduction of a system for online notarial services. These developments represent a significant modernization of Ukraine’s notarial services. The author emphasizes the need for substantial legal reforms to support the full-scale digitalization of notarial services. It is highlighted that the Ukrainian Law on Notariate currently lacks explicit provisions for the use of digital technologies, which necessitates amendments to accommodate electronic notarial acts, remote notarization, and the integration of digital signatures. Additionally, the article discusses the challenge of aligning the e-notary system with European standards, especially regarding the cross-border recognition of electronic documents. The alignment of Ukrainian notarial laws with international frameworks is crucial for ensuring the system’s compatibility with global legal standards. The article identifies several key advantages of the e-notary system, including the automation of processes, which significantly reduces the time and administrative burden involved in notarial acts. Enhanced security measures, such as digital signatures and QR codes, provide protection against forgery and unauthorized alterations of notarial documents. Furthermore, the integration of the e-notary system with state databases facilitates efficient information exchange, thereby improving the accuracy and reliability of notarial services. These factors contribute to a more transparent and effective notarial system that can better meet the needs of modern society. Of particular note is the author’s focus on the importance of cybersecurity and data protection in ensuring the long-term success of the e-notary system. The article underscores the need for robust security measures to protect sensitive legal information from cyber threats, which are increasingly becoming a concern in the digital age. The author concludes by outlining prospects for further research, particularly in the areas of legal reform, public trust in digital notarial services, and the technological infrastructure needed to ensure the resilience and security of the electronic notarial system.

Automated Virtual Machine Migration with Load Shifting Technique Avoiding Latency in Cloud Environment

Efficient management of Virtual Machines (VMs) in cloud computing is one key aspect to achieve the best utilization of resources and performance. The study is about improving Automated Virtual Machine Migration with a Load Shifting Technique in order to reduce latency through the cloud domain. This methodology dynamically distribute the load across multiple VMs by migrating their workloads to other physical hosts determined according certain resource utilization metrics available in real-time. The dynamic and unpredictable nature of cloud workloads requires a solution that is more responsive in the way it manages VMs. Live migration and pre-copy migration offer suboptimal performance in terms of time to migrate, downtime due to the prolonged copying period as heaps swell/collapse during live migrations. Utilizing sophisticated algorithms and real-time monitoring to avoid these pitfalls, our methodology allows us for proper load balancing with minimal latency. The primary elements of the proposed framework are a detailed monitoring module, an intelligent load shifting algorithm, high-performance migration method and feedback loop for continuous optimization. The monitoring module is responsible for collecting on-the-fly information about CPU, memory, network traffic and disk I/O such that can be exploited by the load shifting algorithm. The algorithm processes this data to decide which VMs should be migrated with the purpose of ensuring load balancing and decreasing latency achieved using these migrations. In order to test the proposed approach, comprehensive simulations and comparatives analyses utilizing other models were performed. This results in better latency reduction, increased resource utilization and improved overall system performance. In fact, the proposed method can handle not only current challenges in cloud computing such as load balancing and latency issued but also a scalable and effective approach for general cloud infrastructure usage. Finally, the Automated Virtual Machine Migration with Load Shifting Technique proves to be a reliable and successful solution for assisting in managing dynamic cloud workloads. Through real-time monitoring, advanced algorithms and continuous optimization this method has now been able to improve cloud services performance and reliability thereby allowing it to better serve service level agreements as well meet up with user satisfaction. We hope to adapt the algorithm and generalize it for even more advanced predictive models in future iterations, as well as evaluation of practical implementation on cloud-based deployment environments.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Optimizing traffic management for public services during high-demand periods using cloud load balancers

In times of high demand, effective traffic management is essential for public service platforms to maintain reliable, continuous access for users. This paper explores the role of cloud load balancers as a solution for optimizing traffic distribution in public services during peak periods. By dynamically distributing network traffic across multiple servers, cloud load balancers enhance system scalability, flexibility, and response time, which are critical for minimizing service disruptions. The paper further discusses best practices for configuring load balancers, including geographic balancing and redundancy, and highlights the importance of integrating dynamic scaling and monitoring tools to adapt to sudden traffic surges. Additionally, the potential of artificial intelligence (AI) and predictive analytics in advancing cloud load balancing is considered, emphasizing their value for predictive scaling and automated traffic management. Key challenges, including data privacy and dependency on third-party infrastructure, are also addressed, with recommendations for enhancing load balancer deployment in public service applications. This paper concludes with strategic recommendations for public service providers to leverage cloud technology effectively, ensuring improved reliability, security, and cost-efficiency in service delivery. Keywords: Cloud Load Balancers, Traffic Management, Public Service Platforms, Dynamic Scaling, Predictive Analytics, Service Reliability.

Robust dynamic real-time control strategies for high-frequency bus service: a multi-agent reinforcement learning framework

This study addresses the multifaceted challenge of ensuring the regularity of bus services, minimizing bus bunching, and facilitating synchronized bus connections across routes. An enhanced multi-agent reinforcement learning algorithm, namely the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, is proposed to implement real-time control strategies for addressing these issues simultaneously. The merit of the modified MADDPG algorithm lies in its ability to continuously learn while adeptly navigating the non-stationary operating nature of bus system networks. A case study of a bus corridor is used to train and test the algorithm. Four robust scenarios, each presenting varying degrees of travel time and dwell time variations, are designed to assess the algorithm’s robustness. Results indicate that the MADDPG algorithm can significantly increase the likelihood of synchronized bus transfers across multiple routes by two or three times while maintaining the service reliability on each route. Moreover, the flexibility of the MADDPG algorithm in training bus policies allows it to effectively adapt to up to 90% variations in bus travel times and demand changes, even amid disruptive events in real-world scenarios.