System Failure Research Articles

Understanding Ethiopia’s maize innovation system: Application of integrated innovation system and sustainability transition perspectives

Agricultural innovation is a top priority on the global sustainable development agenda to solve food insecurity, land degradation, climate-change issues, poverty, and unemployment. This paper aims to identify systemic problems and socio-technical transition factors that hinder the development, dissemination, and utilization of improved maize varieties in Ethiopia. Primary data were gathered through structured and semi-structured interview schedules, and then analyzed thematically. Results show that systemic failures such as the absence of key actors, institutional problems, lack of interaction between actors, and inadequate infrastructure are the major hindrances to the systems functioning. Three functions, including entrepreneurship, knowledge development, and creating legitimacy, were found to be weaker than others in the case of improved maize varieties released by Pioneer Hi-Bred Seeds Ethiopia Plc. However, these functions were stronger in the case of improved maize seed varieties released by public research centres. Knowledge dissemination and resource mobilization were the stronger functions of Pioneer Hi-Bred Seeds Ethiopia Plc., which were found to be weak in the public research centres. The result also shows that policies and governance affected the sustainable development of improved maize varieties released by Pioneer Hi-Bred Seeds Ethiopia Plc. Promoting knowledge development and resource mobilization within networks of policymakers, the public sector, and local and foreign private sectors, and a regulation on breeders’ rights to encourage the involvement of private companies in the sector are recommended to enable the innovation system to function sustainably.

System fragility analysis of highway bridge using multi-output Gaussian process regression surrogate model

This study presents a multi-output Gaussian process regression (GPR) surrogate model for seismic-fragility analysis of bridge structural systems. Multi-output GPR can model the correlations among multiple outputs, accuracy and stability are achieved with fewer training data, which reduces the computational cost of fragility analysis. Furthermore, the explainability of the constructed surrogate model is implemented by adopting an automatic relevance-determination (ARD) kernel in the GPR. The estimated hyperparameters can provide the contribution of the uncertainty of each input parameter to the outputs. The fragility analysis using the multi-output GPR surrogate model was verified by applying it to a seismic isolation highway bridge with multiple spans and a curved geometry. The effectiveness of the multi-output GPR was demonstrated by the construction of an accurate and stable surrogate model with 46 inputs and 28 outputs. The relative contributions of the uncertainties to the structural properties and input earthquake loads could also be understood. The fragility curves, at both the component and system levels, were appropriately obtained using a sufficient number of samples in a Monte Carlo calculation. Furthermore, the failure modes were evaluated, identifying which structural components contributed to the system failure. This enabled discussions on structural system failures from the viewpoint of the structural dynamic characteristics of the bridge and earthquake-load properties.

Optimizing System Efficiency and Reliability: Integrating Semi-Markov Processes and Regenerative Point Techniques for Maintenance Strategies in Plate Manufacturing

This paper offers a modeling methodology to evaluate similar systems’ performance and optimize system effectiveness with customized maintenance plans. This strategy, which combines regeneration point techniques with semi-Markov processes, is used to assess a plate manufacturing company’s system. Key insights are obtained using a thorough cost-benefit analysis that includes numerical analysis, graphical interpretations, and system efficacy indicators. The study clarifies the dynamics of the system’s reliability under different repair rates by showing an inverse relationship between Mean Time to System Failures (MTSF) and failure rate. Additionally, the analysis clarifies how profit is affected by failure rate, highlighting the necessity of efficient maintenance plans from an economic standpoint. This research highlights the growing significance of availability and dependability in technology-driven sectors and emphasizes the need for dependable systems. This paper makes a substantial contribution to the knowledge already available in the field of reliability engineering by providing a thorough framework that combines regeneration point approaches and semi-Markov processes. The metrics that are obtained offer a comprehensive understanding of the behavior of the system, which in turn helps industry practitioners make well-informed decisions.

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Reliability Analysis for Degradation-Shock Processes with State-Varying Degradation Patterns Using Approximate Bayesian Computation (ABC) for Parameter Estimation

The degradation of products is an integral part of their life-cycle, often following predictable trajectories. However, sudden, unexpected events, termed ’shocks’, can substantially alter these degradation paths. Shocks can significantly influence the pace of degradation, leading to accelerated system failure. Moreover, they may initiate changes in degradation patterns, transitioning from linear to non-linear or random trajectories. To address this challenge, we present a novel multi-state reliability model for competing failure processes that account for degradation-shock dependencies by considering the state-varying degradation pattern. The degradation process is divided into s-states, with each state treated according to its pattern based on the time-transform Wiener process. The reliability function is derived based on soft failure caused by continuous degradation involving the s-states, the sudden increase in degradation caused by random shocks, and hard failure due to some shock processes. Additionally, we performed a sensitivity analysis to determine which parameters have the most significant impact on product reliability. Due to the complexity of the likelihood function, we adopted the ABC method to estimate the model parameters. A simulation study and a practical application with micro-electro-mechanical systems (MEMS) degradation results are used to demonstrate the efficiency and effectiveness of the proposed approach.

Acute Respiratory Failure in Children: A Clinical Update on Diagnosis.

Acute respiratory failure (ARF) is a sudden failure of the respiratory system to ensure adequate gas exchanges. Numerous clinical conditions may cause ARF, including pneumonia, obstructive lung diseases (e.g., asthma), restrictive diseases such as neuromuscular diseases (e.g., spinal muscular atrophy and muscular dystrophy), and albeit rarely, interstitial lung diseases. Children, especially infants, may be more vulnerable to ARF than adults due to anatomical and physiological features of the respiratory system. Assessing respiratory impairment in the pediatric population is particularly challenging as children frequently present difficulties in reporting symptoms and due to compliance and cooperation in diagnostic tests. The evaluation of clinical and anamnestic aspects represents the cornerstone of ARF diagnosis: first level exams (e.g., arterial blood gas analysis) confirm and evaluate the severity of the ARF and second level exams help to uncover the underlying cause. Prompt management is critical, with supplemental oxygen, mechanical ventilation, and the treatment of the underlying problem. The aim of this review is to provide a comprehensive summary of the current state of the art in diagnosing pediatric ARF, with a focus on pathophysiology, novel imaging applications, and new perspectives, such as biomarkers and artificial intelligence.

Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.

The blind HR: Why do people leave managers not companies? A case of inconsistency in performance appraisal and performance outcome

Learning outcomes The learning outcomes are as follows: to benchmark and compare the theoretical models of the performance management and appraisal processes. (Questions 1 and 2) Remembering-in Bloom’s Taxonomy; to understand the importance of practicing fair performance appraisal process. (Question 4) Understanding-in Bloom’s Taxonomy; to analyze the implementation and effectiveness of 180-degree performance appraisal method and rating system prevalent in the IT Sector. (Question 1) Applying and Analyzing-in Bloom’s Taxonomy; to assess the impact of perceptual biases on human behavior and performance (Questions 2 and 3) Evaluating-in Bloom’s Taxonomy. Case overview/synopsis The case study entitled “Is HR Blind? Why do People Leave Managers Not Companies? A Case of Unfair Performance Appraisal and Biases” is a classic example of a flawed and biased performance appraisal process and perceptual biasness, which resulted in the loss of a valuable and talented resource in a leading Indian IT MNC. The present case had been based upon the real-life experience of an employee (i.e. Rahul Verma), who worked with the company from year 2010 to 2021. It was among the top ten IT MNCs employing about 0.1 million people. The objective of the case was to highlight real time issues existing with HR practices, mainly in IT sector organizations. For example, in the present case, do the HR seek proper justification from the manager before taking a harsh decision like forcibly asking an employee to sign a termination contract without looking at the contributions of his qualitative performance or even performance rating (refer to the transcript) for that matter? Was the job of the HR to only ensure how to fit in employees in the faulty bell curve system? Whether the performance appraisal system being followed at the company is adequately capable of identifying and recognizing the talent. Do the different functions really work cohesively and organically toward achieving the intended goals and objectives of the organization? Was this a failure of the manager in recognizing talent or something went wrong at the employee’s part? Was this a failure of the entire HR system or performance management process at the organization that was unable to filter out the capable and skilled resources out of the crowd? Was this a problem of organizational culture that put on stake its most critical resource – the human capital – by allowing the appraisers to evaluate them just because of the hierarchical structure, and not because they are not being competent enough to perform this most critical job objectively? Who ensures the appraiser is free from any kind of prejudice or bias and is capable of fairly assessing the talent resource? So, the present case was a deliberate attempt to throw out these burning questions to the practitioners and students to ponder upon. Does HR really follow the blind process merely acting on the feedback received from the different units of the organization? With the help of strong theoretical foundation and practical applications, the following objectives and questions have been framed to deliberate and propose the workable solutions for the benefits of the relevant stakeholders. Complexity academic level HR practitioners, HR managers, supervisors, senior management and HR students, IT heads, project managers. Supplementary material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management.

Challenges Afflicting Prisons – A First-hand Observation

Prisons encounter numerous challenges stemming from systemic, social, and administrative shortcomings. Overcrowding stands out as a significant problem, with facilities operating well beyond their intended capacities, leading to strained resources, increased tensions, and substandard living conditions. The high volume of pretrial detainees exacerbates overcrowding, prolongs the wait for justice, and creates excessive difficulties for those who are imprisoned. Deficiencies in infrastructure, a lack of initiatives to build new prisons or expand the capacity of current facilities, insufficient medical care - particularly in mental health services - and shortages of staff all jeopardize the well-being and safety of inmates. Additionally, gender-specific issues, such as insufficient facilities for women and the absence of open prisons, underscore the system's failure to support marginalized groups. Furthermore, the scarcity of rehabilitation programs hinders the reintegration of prisoners into society. Malpractices, gang activity, and substance abuse in prisons worsen these conditions, leading to violence and fatalities that hinder reform initiatives. Addressing these interconnected challenges requires extensive reforms that involve robust administrative commitment, investments in infrastructure and workforce, improvements to the legal framework, and the creation of rehabilitation programs aimed at cultivating a more humane and efficient prison system.

The effects of load demand uncertainty on cascading overload failures in power systems

The effects of load demand uncertainty on cascading overload failures in power systems

A proposed Critical Theory of Music Vilification to explain “problem music”

Links between certain music genres and detrimental behaviors and impacts have often resulted in labeling specific music genres as problematic. The resultant concept of so-called problem music (SCPM) typically focuses on hip hop and rock as well as their related genres. However, researchers have repeatedly found a lack of causal empirical evidence of SCPM and noted that the essential aspect is not the music genre, but rather how the music functions for the individual. This paper proposes the Critical Theory of Music Vilification (CTMV) to explain the vilification of SCPM. First, individuals exist within greater societal systems that oppress and marginalize people. Drawing from theories of social and musical identities, people may embrace SCPM to critique oppressive systems and develop social cohesion through in-group identities. Based on theories of stereotypes in music, societal systems often further ostracize people based on their music preferences and musical identities and people may experience harm. The experienced vilification and harm can reinforce people’s music preferences and corresponding musical identities. Societal systems then may vilify the music that was meant to critique their own systemic and policy failures, resulting in a cycle of music vilification. By including systems of oppression, classism and stereotypes in music, and music preferences and musical identities that critique systems and provide social cohesion, the CTMV provides context to SCPM as a socially constructed idea lacking empirical evidence. Implications, limitations, and suggestions for future research are provided.

Create and strengthening innovation ecosystem: roles of policy-affiliated transition intermediaries in latecomers

Purpose This study aims to explore the functions of policy-affiliated intermediaries and their roles in driving innovation in latecomers. Design/methodology/approach Latecomers have recently recognized the significance of an innovation ecosystem in transitioning to an innovation-driven economy. However, these countries encounter various market and system failures, making policy-affiliated transition intermediaries crucial in orchestrating innovation ecosystems. To achieve this, this study used a case study of a policy-affiliated intermediary and conducted interviews with 19 managers. The results from the content analysis revealed that innovation intermediaries in latecomers perform more comprehensive duties than those mentioned in the literature in the transition to innovation-based development. Findings Results show that the most important of policy-affiliated innovation intermediaries’ roles is orientation to the government and private sector market, attracting the participation of the private sector through the implementation of tax credits and the development of the corporate innovation ecosystem, developing science and technology parks, developing laboratory research infrastructure, supporting prominent and capable professors to create innovation networks, developing innovation zones around research institutions and leading universities and developing international scientific cooperation. These are new and important functions that innovation intermediaries should play in the transition process. Originality/value The research found that the innovation intermediary acts as a change agent in the mental model of other executive and decision-making bodies. Also result shows that this intermediaries can play more roles than what is mentioned in the literature for the development of innovation.

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model.

Bond-associated non-ordinary state-based peridynamics for simulating damage evolution of thermal barrier coatings in aero-engine turbine blades

The failure mode of thermal barrier coatings (TBC) systems in aeroengine turbine blades is very complex because of the harsh service conditions. A peridynamic (PD) model is established to simulate the damage evolution of TBC with uniform thermally grown oxide (TGO) growth under cycle load. The peridynamic differential operator is introduced to solve the zero-energy mode, and thermo-elastic deformation is considered. Moreover, the influence of high-temperature holding time, initial oxide layer thickness, and interface morphology on the evolution of the stress distribution and interface damage is discussed. The newly proposed PD model can effectively capture the interface cracking of TBC systems and it is conducive to the study of the failure of TBC systems.

Significant improving magnetic properties and thermal conductivity of FeBSiPCNbCu nanocrystalline powder cores by designing novel ZnO insulating layer

As the application scenarios of soft magnetic composites (SMCs) tend to be high frequency, reducing their high-frequency core loss (Pcv) and improve the heat-sinking capability to avoiding thermal failure of electronic system are still greatly challenging. In present work, we successfully designed and prepared novel ZnO insulating layers on the surface of FeBSiPCNbCu nanocrystalline powders and discussed their growth processes. In addition, we investigated the effects of the ZnO insulating layers on the magnetic properties and heat-sinking capability of the FeBSiPCNbCu nanocrystalline magnetic powder cores (NMPCs) and explored the related mechanisms. The experimental results show that the hydrolysis of 1.75 wt% zinc acetate dihydrate under the coupling of 1 wt% polyvinylpyrrolidone forms uniform and thin ZnO insulating layers on the powders surface. The FeBSiPCNbCu@ZnO NMPCs annealed at 510 °C for 60 min exhibit excellent magnetic properties and superior heat-sinking capability with a low core loss of 698.4 mW/cm3 (20 mT, 2 MHz), high DC bias permeability of 68.3% (100 Oe) and heat conductivity coefficient of 2.22 W/(m•K) as compared with those of 859.8 mW/cm3, 57.1% and 1.86 W/(m•K), respectively, for the NMPCs without ZnO insulating layer.

Surface engineering of silica to control creep failure resistance of natural rubber-based composites for potential application in the suspension of rolling stock

Immobilization of polymer chains by silica on one side could increase the viscoelastic energy loss and on the other could postpone chain relaxations and temporarily conceal creep deformation. It is not yet clear how the formation of an interphase layer contributes to the change of creep resistance. Bifunctional silane, “Bis(triethoxysilylpropyl) disulfide” is employed to induce various degrees of covalent bonds at the rubber-silica interface, and also brings multiple degrees of immobilization. Interestingly, it was found that by controlling interfacial phenomena, the creep rate can be altered in a wide range of 4.35–1.65 (%/decade). It was mechanistically explained how the maximum creep resistance is observed for the sample having a medium level of surface treatment. The formation of a filler network indicated an essential role in increasing the high-temperature creep failure of systems having no favorable polymer-filler interaction.

Evaluation of the unavailability of the primary circuit of Triga SSR reactor, importance factors and risk criteria for its components

Abstract Probabilistic Safety Assessment is a tool for improving a nuclear installation and its safety in all its function phases. PSA provides a methodical way of identifying sequences resulting from a wide range of initiating events concerning an accident, and includes systematic and realistic determination of accident frequencies and their consequences. This paper analyzes the unavailability of the TRIGA SSR reactor primary circuit, obtaining the system unavailability value, identifying main contributors to system failure, and calculating risk importance factors. Two cases were considered: one without considering Common Cause Failure (CCF) in the developed fault tree, and the second considering CCF on pumps and heat exchangers lines. The aim was to assess their influence on the primary system’s unavailability. Two importance factors, Fussell–Vesely and Risk Achievement Worth, were employed to identify components with a strong impact on the risk (probability of system failure). These factors aid in enhancing maintenance activities and testing. The analysis utilized the EDFT code, part of the PSAMAN code package developed in SCN-Pitesti.

Alternative Pharmaceutical Innovation Models in Competitive Markets: A Collaborative Approach to Develop a Novel Drug for Hepatitis C.

Alternative innovation models have emerged to address failures of the traditional pharmaceutical system, particularly for diseases where market incentives do not attract sufficient research and development efforts. However, the feasibility of such models for diseases with significant markets is not well-established. This article analyses the development of a novel drug (ravidasvir) for the treatment of hepatitis C, a highly profitable market. Data from qualitative research methods, including literature reviews and semi-structured interviews, was analyzed using a novel conceptual framework focusing on actors, resources, organizational practices, and outcomes. Dissimilar to other projects, ravidasvir did not involve any major pharmaceutical companies. Rather, it leveraged the capacities of actors less traditionally involved in the development of novel medicines by constructing a collaborative network of private and public partners from low- and middle-income countries with a shared goal. The collaboration was successful in developing a highly effective, easy-to-use, and affordable medicine and contributed significantly to capacity-strengthening. However, the case also highlighted that strategic behavior by competing for-profit firms could pose significant challenges and that changing external conditions reduced the potential public health impact of the drug. Lessons from ravidasvir can inform future efforts to develop alternative innovation models for therapeutic areas with significant commercial interest.

Limiting Behavior of Mixed Coherent Systems With Lévy‐Frailty Marshall–Olkin Failure Times

ABSTRACTIn this article, we show a limit result for the reliability function of a system—that is, the probability that the whole system is still operational after a certain given time—when the number of components of the system grows to infinity. More specifically, we consider a sequence of mixed coherent systems whose components are homogeneous and non‐repairable, with failure‐times governed by a Lévy‐Frailty Marshall–Olkin (LFMO) distribution—a distribution that allows simultaneous component failures. We show that under integrability conditions the reliability function converges to the probability of a first‐passage time of a Lévy subordinator process. To the best of our knowledge, this is the first result to tackle the asymptotic behavior of the reliability function as the number of components of the system grows. To illustrate our approach, we give an example of a parametric family of reliability functions where the system failure time converges in distribution to an exponential random variable, and give computational experiments testing convergence.