Reliability Issues Research Articles

Growing Forward: Exploring Post-Traumatic Growth and Trait Resilience Following the COVID-19 Pandemic in England

The COVID-19 pandemic presented many potentially traumatic circumstances. Research continues to investigate pandemic-related Post-traumatic Growth (PTG). However, most studies fail to fulfil the parameters of PTG whereby a triggering event must be of seismic intensity and have ceased before PTG can manifest, producing significant validity and reliability issues. The relationships between PTG, trait resilience and fear are also under-researched, particularly in circumstances where the parameters of PTG are met. This study examined the relationship between PTG, COVID-19-related fear and trait resilience. Participants (n = 229) completed an online questionnaire incorporating the Post-Traumatic Growth Inventory and the Connor–Davidson Resilience Scale. The sample participants were moderately traumatised with moderate–low PTG (M = 50.85). Participants reported greater levels of PTG compared to participants from pre-COVID studies, notably in relation to the constructs of Relating to Other (d = 0.29), New Possibilities (d = 0.47), Personal Strength (d = 0.39), and Spiritual Change (d = 0.29). Higher levels of resilience (B = 0.48) and COVID-19-related fear (B = 0.16) were associated with greater overall PTG. Younger participants also reported greater levels of PTG (B = −0.29). The findings advance current knowledge regarding the potential relationship between fear and PTG and demonstrate that trait resilience is a promotional factor, presenting opportunity for future intervention formulation. However, reform is required within the PTG literature pool. Future research investigating PTG must reach both parameters. In circumstances where this is impossible, research concerning newfound positive cognition during adverse circumstances should be re-explored as Post-Adversarial Appreciation (PAA) to maintain validity.

Bevel Gear Suspension System

Abstract— The suspension system of an automobile plays a critical role in providing a smooth and comfortable ride while ensuring safety and stability. Traditional suspension systems commonly rely on mechanical springs for absorbing shocks and vibrations from the road. However, there is growing interest in springless suspension systems that utilize alternative technologies, such as bevel gears, for suspension and damping. This paper presents an exploration of the use of bevel gears in springless suspension systems for automobiles. The advantages and challenges of using bevel gears in place of traditional springs are discussed. Bevel gears offer a compact and lightweight solution, precise control over suspension characteristics, and potential reduction in complexity and maintenance requirements. However, challenges such as design complexities, durability and reliability issues, and increased cost need to be addressed. The principles of bevel gears and their application in suspension systems are reviewed, and the current state of research and development in this area is highlighted. The proposed approach and methodology for further investigation are discussed, emphasizing the significance of this concept in advancing the field of automotive suspension systems. Keywords---sensitive: Springless suspension, Wear, Bevel gears, Self-aligning, Comfortable ride, Damping

Kriging-Based Time-Variant Reliability Analysis Incorporating Error Distance Function and First Crossing Time Point

To address the time-variant reliability (TVR) issue during a structure’s life cycle, we propose an innovative Kriging method named EDFK-FCTP. This active-learning TVR analysis is based on an error distance function (EDF) and the first crossing time point (FCTP). We employ Latinized partially stratified sampling (LPSS) to estimate the first four-order origin moments of the FCTP surrogate model. The convergence condition for establishing the optimal surrogate model for the FCTP is determined by the geometric mean of the probability density of training samples and the maximum error of the first four-order origin moments across two adjacent iterations. Utilizing the optimal surrogate model, the probability density function (PDF) of the FCTP is solved using kernel density estimation (KDE), thereby deriving the failure probability of the structure throughout its life cycle. Example analyses demonstrate that the calculation accuracy of our proposed method fulfills engineering requirements. Notably, it presents certain advantages over alternative methods, particularly in regions of low failure probability. For complex performance functions, our method offers significant computational efficiency benefits compared to other approaches.

Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability

Effective waste management is currently one of the most influential factors in enhancing the quality of life. Increased garbage production has been identified as a significant problem for many cities worldwide and a crucial issue for countries experiencing rapid urban population growth. According to the World Bank Organization, global waste production is projected to increase from 2.01 billion tonnes in 2018 to 3.4 billion tonnes by 2050 (Kaza et al. in What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, The World Bank Group, Washington, DC, USA, 2018). In many cities, growing waste is the primary driver of environmental pollution. Nationally, governments have initiated several programs to improve cleanliness by developing systems that alert businesses when it’s time to empty the bins. Current research proposes an enhanced, accurate, real-time object detection system to address the problem of trash accumulating around containers. This system involves numerous trash cans scattered across the city, each equipped with a low-cost device that measures the amount of trash inside. When a certain threshold is reached, the device sends a message with a unique identifier, prompting the appropriate authorities to take action. The system also triggers alerts if individuals throw trash bags outside the container or if the bin overflows, sending a message with a unique identifier to the authorities. Additionally, this paper addresses the need for efficient garbage classification while reducing computing costs to improve resource utilization. Two-stage lightweight deep learning models based on YOLOv5 and YOLOv8 are adopted to significantly decrease the number of parameters and processes, thereby reducing hardware requirements. In this study, trash is first classified into primary categories, which are further subdivided. The primary categories include full trash containers, trash bags, trash outside containers, and wet trash containers. YOLOv5 is particularly effective for classifying small objects, achieving high accuracy in identifying and categorizing different types of waste products on hardware without GPU capabilities. Each main class is further subdivided using YOLOv8 to facilitate recycling. A comparative study of YOLOv8, YOLOv5, and EfficientNet models on public and newly constructed garbage datasets shows that YOLOv8 and YOLOv5 have good accuracy for most classes, with the full-trash bin class achieving the highest accuracy and the wet trash container class the lowest compared to the EfficientNet model. The results demonstrate that the system effectively addresses the reliability issues of previously proposed systems, including detecting whether a trash bin is full, identifying trash outside the bin, and ensuring proper communication with authorities for necessary actions. Further research is recommended to enhance garbage management and collection, considering target occlusion, CPU and GPU hardware optimization, and robotic integration with the proposed system.

TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework for Microservice-based Systems with Multimodal Data

Microservice-based systems often suffer from reliability issues due to their intricate interactions and expanding scale. With the rapid growth of observability techniques, various methods have been proposed to achieve failure diagnosis, including root cause localization and failure type identification, by leveraging diverse monitoring data such as logs, metrics, or traces. However, traditional failure diagnosis methods that use single-modal data can hardly cover all failure scenarios due to the restricted information. Several failure diagnosis methods have been recently proposed to integrate multimodal data based on deep learning. These methods, however, tend to combine modalities indiscriminately and treat them equally in failure diagnosis, ignoring the relationship between specific modalities and different diagnostic tasks. This oversight hinders the effective utilization of the unique advantages offered by each modality. To address the limitation, we propose TVDiag , a multimodal failure diagnosis framework for locating culprit microservice instances and identifying their failure types (e.g., Net-packets Corruption) in microservice-based systems. TVDiag employs task-oriented learning to enhance the potential advantages of each modality and establishes cross-modal associations based on contrastive learning to extract view-invariant failure information. Furthermore, we develop a graph-level data augmentation strategy that randomly inactivates the observability of some normal microservice instances to mitigate the shortage of training data. Experimental results on four datasets show that TVDiag outperforms state-of-the-art methods in multimodal failure diagnosis by at least 20.16% and 3.08% in terms of \(HR@1\) and F1-score, respectively.

CLEANSurgiTip: A Contamination Limiting, Expendable, and Novel Surgical Tool Tip for Robot-Assisted Minimally Invasive Surgery

Abstract Over the last two decades, Robot-Assisted Minimally Invasive Surgery (RAMIS) has become more accessible and has contributed to improved patient outcomes. However, its adaptation in resource-constrained environments is limited due to the cost of the consumables, such as surgical instruments and access to sterilization facilities. These surgical instruments often have a tooltip with intricate geometry, making it difficult to sterilize and reuse for multiple surgeries. This research introduces a novel MIS instrument, the CLEANSurgiTip, which features a disposable grasper designed to address biofouling while reducing overall costs through minimized waste. Unlike traditional tendon-driven systems that suffer reliability issues and require frequent replacements, the CLEANSurgiTip utilizes rigid shafts to transmit motion to a distal 2+1 degrees of freedom grasper (pitch, yaw, grasp). These rigid shafts ensure a consistent cross-section, simplifying the sealing process against bodily fluids. The transmission mechanism is simplified using an elbow mechanism and flexible shafts in place of conventional gears for orthogonal motion transmission. The flexible shaft aids in decoupling the pitch and the yaw motion. The tooltip design promotes a simplified interface between the disposable grasper and the reusable shaft, facilitating easy detachment. The paper also presents a novel reusable tool shaft to drive the proposed tooltip.

Analysis of the influence of defect evolution on the performance and damage of 4H-SiC photoconductive semiconductor switches

This study investigated the failure mechanism of a series-connected structured photoconductive semiconductor switches (PCSSs) made of vanadium-doped 4H-SiC. For a long time, SiC PCSSs have face reliability issues in high-voltage and high-repetition-frequency environments. Degradation and breakdown mechanisms were analyzed using a combination of scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and current deep-level transient spectroscopy (I-DLTS). The vertical 4H-SiC PCSS in the series-connected structure failed after 9686 triggering pulses with a 532 nm Q-switched Nd: YAG laser with a DC voltage bias of 35 kV. The SEM and EDS showed a through-ablation channel through the substrate and connecting the electrodes, and a large amount of carbon accumulation was found inside the through-ablation channel. The I-DLTS test found that deep-level defects changed, in particular, defects associated with carbon vacancies Z1/2 and a newly formed defect DT2 near 410 K, indicating that high-speed electron collisions and defect accumulation can lead to lattice reorganization. This work highlights the effect of defects on device performance and the role of defect dynamics in impact ionization detrapping in device failure. It provides insights into improving PCSS performance in high-voltage applications. The link between defect behavior and device lifetime is also further investigated.

Enablers and barriers to shifting to sustainable mobility. Takeaways from a sustainability campaign in Flanders, Belgium

Despite Flanders’ high cycling rates and good public transportation coverage, car ownership has been rising steadily, conflicting with sustainability goals. Since cars are often the default transport mode, changing behaviour can be challenging, so understanding barriers and enablers to transition at the individual level is crucial. We use focus groups to identify obstacles to sustainable mobility experienced by participants in the “30 Days with Less Cars” campaign held in June 2022, which encouraged sustainable travel and a less car-dependent lifestyle. Our findings constitute insights into barriers and enablers for adopting more sustainable mobility behaviour among participants mindful of sustainable mobility. Key barriers included the lack of alternative transportation options, especially in non-urban areas and outside business hours, as well as public transport reliability issues and the comfort of private cars. Difficulties associated with car ownership (such as cost) and the availability of alternatives are significant enablers for sustainable mobility.

Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO2 concentrations below 1000 ppm, and up to 12.5% if CO2 concentrations are slightly above 1000 ppm for 1.5% of the time.

3D kinetic Monte Carlo-based investigation of the influence of dopants on the reliability of VCM ReRAM

Variability and retention are critical reliability challenges for redox-based resistive switching random access memory (ReRAM) devices operating via the valence change mechanism (VCM). Variability arises from stochastic oxygen vacancy dynamics at short timescales, while retention failure manifests itself by the long-term instability of the programmed states. Both challenges become more severe in large-scale memory arrays, requiring a better understanding of the mechanisms and effective optimization strategies. In this work, we employ an advanced 3D Kinetic Monte Carlo simulation model to systematically investigate the influence of dopants on these reliability issues. For variability, we demonstrate that dopants significantly enhance the signal-to-noise ratio by stabilizing oxygen vacancy dynamics through a trapping effect, which reduces the frequency of current fluctuations. For retention, we show that dopants improve the stability of current distributions over time, where they effectively suppress distribution broadening and slow mean current degradation. Our simulation results provide valuable insights into optimizing the reliability of VCM ReRAM via dopants for practical applications. While these findings align with existing experimental data, the scope of experimental studies on doped VCM ReRAM devices remains limited, particularly concerning large-scale statistics and short-term variability. Therefore, our simulations not only corroborate previous experimental observations but also highlight the need for further experimental investigations to enhance the performance of VCM ReRAM devices for next-generation memory applications.

Resolving performance reliability issues in BiFeO3 domain wall memories

Resolving performance reliability issues in BiFeO3 domain wall memories

A Critical Appraisal of Alternative Adsorption Refrigeration System Design Configurations: Enhancing Current Technologies with Innovative Approaches

Adsorption cooling technology (ACT) offers the potential for energy savings and supports environmental sustainability, aligning with current efforts to mitigate the impacts of global warming. However, the current technological advancements to improve its performance and maintain its presence in the market, appear insufficient to compete with the traditional vapour compression cooling systems due to poor energy performance, low COP, high initial cost, low specific energy density, intermittency issue, and reliability issues. The existing ACT operation relies on the use of static adsorbent beds which suffer from poor heat transfer process. Therefore, many research studies focus on addressing design challenges including increasing specific adsorption cooling capacity by using engineered porous materials in combination with environmentally friendly refrigerants. These new advancements can lead to significant improvement in the adoption of adsorption cooling technology beyond the current niche market. The focus of this paper is to appraise critically the various possible innovative mechanical arrangements of the adsorption solid desiccant components for application in heating, cooling and thermal energy storage. The work highlights the design innovation of the solid desiccant bed, particularly in the use of fluidised and moving adsorbent material between the heat source and heat sink. Therefore, the concept of the configurations in this appraisal with their pertinent potentials is intended to overcome the aforementioned identified bottlenecks often encountered by ACT. This appraisal is aimed to provide the required impetus for fast tracking the further development and wider application of adsorption cooling technology.

The role of Artificial Intelligence (AI) in tests and assessment from the perspective of teachers in Jeddah education

With AI's growing integration into various sectors, its potential to transform educational assessment practices is increasingly apparent. This research aims to examine how school teachers perceive the use of AI in creating, administering, and evaluating assessments, as well as its potential benefits and challenges. A quantitative-methods approach was employed, with 219 participants being surveyed. The results indicate that the perspectives of school teachers in Jeddah reveal a nuanced view of AI in educational assessments. The study also identifies several significant challenges that hinder the integration of AI in assessment in Jeddah’s educational sector. These challenges include a lack of training, financial and resource constraints, ethical concerns, resistance to change, and technical reliability issues. No significant differences were found in the perceptions of AI’s importance in assessment according to gender, school level, or experience. However, public-school teachers demonstrated significantly higher mean scores compared to private-school teachers, indicating a stronger belief in AI’s potential to enhance assessment practices.

Pengembangan Sistem Informasi Koperasi Sekolah

Besides providing education, schools also play a crucial role in addressing students' learning needs through school cooperatives. These cooperatives sell various products: stationery, uniforms, and other learning materials. Observations took place at a school in Medan with approximately 4,300 students. The school cooperative administrators face several challenges that necessitate digitization. These challenges include delays in service during peak times, reliance on Microsoft Excel sheets for data processing — rather complex and prone to reliability issues — and the burdensome sale of printed books before the new academic year, which strains the cooperative administrators and other school departments. In response to these issues, a desktop-based application and a website were developed using the Rapid Application Development (RAD) methodology. The desktop application, constructed using the C# programming language within the Windows Forms .NET Framework, is designed to streamline the cooperative's daily operational activities. The website, developed with Next.js and Node.js, incorporates web services and real-time virtual bank account payment features. These digital solutions enable cooperative administrators to manage products, purchases, sales, report generation, and online transactions more effectively and efficiently, thus resolving the identified challenges.

MoS2 Transistors with 4 nm hBN Gate Dielectric and 0.46 V Threshold Voltage.

The use of two-dimensional (2D) semiconducting materials (MoS2, WS2) as a channel in field-effect transistors may help extend Moore's law and produce devices beyond the complementary metal-oxide-semiconductor (CMOS) technology. Traditional dielectrics used in microelectronics (SiO2, HfO2, Al2O3) form a defective interface with the 2D semiconductor─because the latter does not have dangling bonds─leading to multiple device reliability issues and premature failure. Using 2D hexagonal boron nitride (hBN) as the gate dielectric sounds like a potential solution because it can form a clean van der Waals interface with the 2D semiconductor. However, its relative permittivity is only 3.6, which has raised concerns: it is believed that a MoS2 transistor with hBN gate dielectric cannot be switched ON without the apparition of gate leakage current. Here, we show that transistors with a Pt/4 nm-hBN/MoS2 vertical structure can exhibit ON/OFF current ratios above 105, low threshold voltage of 0.46 V, and low gate leakage current density (JG) of 10-4 A/cm2. Moreover, our Pt/hBN/MoS2 transistors show acceptable performance even after 1000 switching cycles: ON/OFF current ratio is above 104 and JG below 10-4 A/cm2. Our study indicates that hBN may be a suitable gate dielectric for some types of nanosized MoS2 transistors.

Opportunities and Challenges of Online Exit Exams in Higher Education: A Systematic Review

This systematic review investigates the opportunities and challenges of online exit exams in higher education, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework and PICO (Population, Intervention, Comparison, Outcome) model to ensure methodological rigor. The review synthesizes findings from 34 studies spanning 1994 to 2024, covering diverse geographic regions including Europe, North America, Africa, Asia, and Australia. The studies utilized a range of methodologies, including descriptive case studies, literature reviews, empirical research, policy analyses, and quantitative investigations, providing a comprehensive understanding of the impact of online exit exams on educational quality, teaching practices, and student outcomes. Key opportunities identified include enhanced accessibility for remote learners, cost-efficiency through reduced logistical demands, data-driven improvements via analytics, flexibility for diverse student needs, and standardized testing frameworks. However, significant challenges persist, such as the digital divide, concerns about academic integrity, technical reliability issues, limitations in assessing practical skills, and psychological stress among students. A comparative analysis reveals complex interrelationships between these themes, highlighting tensions between accessibility and equity, cost-efficiency and security, and standardization and cultural relevance. The review underscores the need for robust technical infrastructure, faculty training, and adaptive strategies to balance rigorous assessment standards with equitable practices. Future research should prioritize globally representative studies addressing gaps such as publication bias and exclusion of non-English literature, while exploring intersections of technological readiness, institutional culture, and socio-economic factors.

Classification of Grades of Subchondral Sclerosis from Knee Radiographic Images Using Artificial Intelligence.

Osteoarthritis (OA) is the most common joint disease, affecting over 300 million people worldwide. Subchondral sclerosis is a key indicator of OA. Currently, the diagnosis of subchondral sclerosis is primarily based on radiographic images; however, reliability issues exist owing to subjective evaluations and inter-observer variability. This study proposes a novel diagnostic method that utilizes artificial intelligence (AI) to automatically classify the severity of subchondral sclerosis. A total of 4019 radiographic images of the knee were used to train the 3-Layer CNN, DenseNet121, MobileNetV2, and EfficientNetB0 models. The best-performing model was determined based on sensitivity, specificity, accuracy, and area under the curve (AUC). The proposed model exhibited outstanding performance, achieving 84.27 ± 1.03% sensitivity, 92.46 ± 0.49% specificity, 84.70 ± 0.98% accuracy, and 95.17 ± 0.41% AUC. The analysis of variance confirmed significant performance differences across models, age groups, and sexes (p < 0.05). These findings demonstrate the utility of AI in diagnosing and treating knee subchondral sclerosis and suggest that this approach could provide a new diagnostic method in clinical medicine. By precisely classifying the grades of subchondral sclerosis, this method contributes to improved overall diagnostic accuracy and offers valuable insights for clinical decision-making.

A novel directional pilot protection method for EHV transmission lines based on S-transform and SVM

To address the issues of insufficient reliability and low sensitivity of traveling-wave (TW)-based protection caused by fault condition attributes, i.e., fault resistance and fault angle, this paper proposes a novel directional pilot protection method. By studying the propagation of fault TW on transmission lines, the relationship between the energy of the transient voltage components and the fault direction is established. The S-transform is employed to extract the time–frequency information of the fault transient voltage components, the direction identification criterion based on the energy difference between the reverse and forward transient voltage components is constructed. Support Vector Machine (SVM) is used to incorporate the fault angle and fault resistance into the fault characteristics. When the trained-SVM is employed for fault identification, it automatically applies different protection action values based on various fault condition attributes. The method is validated through extensive EMTP-RV fault simulations. The results indicate that the proposed method achieves 100% accuracy in fault identification and effectively eliminates the influence of fault condition attributes.Article HighlightsA novel solution is proposed for the problem that traveling wave protection is difficult to apply under weak fault conditions. The difference between forward and reverse traveling wave energy during faults is investigated. The S-transform can efficiently extract traveling wave transient information.

Time-variant system reliability analysis via stochastic process discretization and most probable point trajectory approximation

Time-variant reliability analysis for single failure mode has advanced considerably and now exhibits great potential. However, in practical engineering applications, structures and systems generally invoke multiple failure modes. This poses a lasting challenge for current reliability analysis and simultaneously entails very high, if not intractable, computational cost. Therefore, developing an accurate time-variant system reliability model that accounts for multiple failure modes and its commensurate solution strategy is imperative. To this objective, this study develops an innovative method to assess time-variant system reliability by utilizing the Kriging model. First, multiple failure modes are decomposed into separate time-variant reliability assessments, each addressing a single failure mode. Inspired by the discretization of stochastic processes, each time-variant function in the structural system is assumed to be a set of time-invariant reliability problems. Subsequently, for each time-invariant reliability issue, the most probable point (MPP) is determined and the performance function is linearized and expanded accordingly. On this basis, the Kriging model of the MPP at discrete time is constructed. To enhance the model’s precision, an active learning technique is employed for ongoing model updates. Finally, the reliability index and failure probability are determined by solving time-invariant system reliability models. The proposed method’s applicability and effectiveness are demonstrated through exemplifications of variable systems.

A Study on Challenges faced by Bangalore Residents in Accessing and Utilizing House Cleaning Services

With increasing urbanization and busy lifestyles, the demand for professional house cleaning services in Bangalore has grown significantly. However, many residents face challenges in accessing and utilizing these services, impacting their overall satisfaction and willingness to rely on them regularly. This study aims to identify the key difficulties faced by Bangalore residents in availing house cleaning services, including issues related to affordability, service availability, quality concerns, and accessibility. It also examines consumer preferences, adoption patterns, and the role of digital platforms in service procurement. To achieve these objectives, primary data was collected through a structured survey targeting Bangalore residents. The findings indicate that while digital platforms have made booking services more convenient, many consumers still struggle with high costs, inconsistent service quality, and scheduling difficulties. Trust and reliability issues also emerge as major concerns, with customers expressing hesitation in hiring unknown service providers. Additionally, limited awareness regarding service options and lack of customer-centric policies further contribute to the challenges faced by users. Despite these obstacles, the study finds that a significant portion of respondents are willing to recommend cleaning services, provided key concerns such as pricing and service quality are addressed. This study underscores the need for improving the affordability and reliability of house cleaning services while enhancing consumer awareness and trust. Service providers must focus on standardizing quality, ensuring transparent pricing, and leveraging technology to streamline operations. Additionally, regulatory bodies and policymakers can play a role in setting industry standards to ensure fair practices. Addressing these challenges can contribute to a more efficient and consumer-friendly house cleaning service industry in Bangalore, ultimately improving urban living standards. The insights from this research can help service providers, businesses, and policymakers in shaping strategies to enhance service delivery and consumer satisfaction. Keywords: House Cleaning Services, Consumer Challenges, Service Accessibility, Affordability and Quality, Digital Service Platforms, Urban Household Hygiene