Graceful Degradation Research Articles

Mastering Real-Time Data Processing Applications : Optimization Strategies for Peak Performance

This comprehensive article explores strategies for optimizing real-time data processing applications in the era of big data and distributed systems. It examines the growing demand for real-time analytics across industries and organizations' challenges in maintaining low latency and scalability. The article discusses key optimization techniques, including leveraging the Akka framework's actor model and supervision strategies, implementing efficient data ingestion through stream processing and partitioning, utilizing caching strategies, employing robust monitoring and auto-scaling mechanisms, ensuring fault tolerance through checkpointing and graceful degradation, and adopting asynchronous communication patterns. Throughout the article, real-world case studies and performance metrics demonstrate the significant improvements these strategies can bring to system throughput, latency, and resilience in various sectors such as finance, e-commerce, telecommunications, and manufacturing.

Rx for Legal Troubles: CEO Medical Degree and Corporate Litigation

ABSTRACTResearch Question/IssueThis study examines the relationship between CEOs' medical degrees (MDs) and corporate litigations targeting US pharmaceutical firms.Research Findings/InsightsCEOs with a medical background are significantly more susceptible to securities class actions, while facing fewer patent lawsuits. The results indicate that the lack of management skills among MD CEOs comes at the cost of firm value. After lawsuit announcements, companies led by MD CEOs experience a significantly negative effect on their stock prices. Markets show no tolerance for failures by MD CEOs, as evidenced by the impact of clinical trial disappointments.Theoretical/Academic ImplicationsThe findings contribute to our understanding of the impact of CEOs' medical backgrounds on corporate litigation outcomes. The results suggest that when examining how a CEO's qualifications affect legal issues and the value of a company, we should consider the unique context of having an MD.Practitioner/Policy ImplicationsPractically, the study implies that firms should carefully consider the balance of managerial and medical expertise in CEO appointments, recognizing the potential legal and financial consequences. Policymakers might see it as beneficial to encourage a mix of different skills among top executives. This could make pharmaceutical companies more resilient in the face of legal issues, which, in turn, helps maintain confidence among investors.

PDFO: a cross-platform package for Powell’s derivative-free optimization solvers

AbstractThe late Professor M. J. D. Powell devised five trust-region methods for derivative-free optimization, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. He carefully implemented them into publicly available solvers, renowned for their robustness and efficiency. However, the solvers were implemented in Fortran 77 and hence may not be easily accessible to some users. We introduce the PDFO package, which provides user-friendly Python and MATLAB interfaces to Powell’s code. With PDFO, users of such languages can call Powell’s Fortran solvers easily without dealing with the Fortran code. Moreover, PDFO includes bug fixes and improvements, which are particularly important for handling problems that suffer from ill-conditioning or failures of function evaluations. In addition to the PDFO package, we provide an overview of Powell’s methods, sketching them from a uniform perspective, summarizing their main features, and highlighting the similarities and interconnections among them. We also present experiments on PDFO to demonstrate its stability under noise, tolerance of failures in function evaluations, and potential to solve certain hyperparameter optimization problems.

Component-based modeling of cascading failure propagation in directed dual-weight software networks

Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.

Model for Anticipating Frank Failures in Computing Grids

Computing grids are infrastructures that provide almost infinite computing capacities, they are now used in all fields, from the study of pandemics to the monitoring of rocket trajectories and the study of meteorological and climatic phenomena. They have a distributed and heterogeneous architecture that gives them unlimited computing performance. They are made up of several computing nodes that are subject to failures like frank failures. A frank failure in a computing grid is an abnormal and unexpected interruption of a node. Many frank failure tolerance protocols have been proposed in the literature but none of these protocols integrate the anticipation of frank failures. The objective of this article is to propose a model based on the PDEVS formalism of frank failure tolerance in a computing grid that allows the anticipation of frank failures. The proposed model relies on the temperature variation of electronic components and on the state of the hard disk through the values provided by SMART data to predict a probable frank failure of a node. The results of the simulations on the different scenarios that we have carried out show that our results provide better performances than those proposed in the literature when the number of nodes to tolerate is greater than 200.

Resilience as a protective factor in the development of post-traumatic stress disorder in patients hospitalised for COVID-19

Introduction and objective: Both the illness itself and hospitalisation due to COVID-19 are potentially traumatic experiences, especially when the course of the disease is severe. Resilience has been identified as a crucial factor in the process of successfully coping with traumatic events and may protect against the development of symptoms of post-traumatic stress disorder. The aim of this study was to determine the role of resilience in the prevention of post-traumatic stress disorder symptoms in patients requiring hospitalisation due to COVID-19. Materials and methods: A total of 138 patients (59 women, 79 men; mean age: 52.34 years; standard deviation, SD = 12.46) hospitalised at the University Hospital in Krakow were included in the study, which was conducted 7–8 months after their COVID-19 diagnosis. The PTSD Checklist for DSM-5 (PCL-5) and the Resilience Measurement Scale (SPP-25) were administered, and stepwise multiple regression analysis was used in the predictive models. Results: A total of 31 participants (22.5%) met the criteria for probable post-traumatic stress disorder symptoms. The study participants were characterised by a moderate level of resilience (5th sten), with women exhibiting lower levels than men. Higher levels of resilience were associated with lower severity of PTSD symptoms. A significant predictor of post-traumatic stress disorder symptoms was “Tolerance for failure and treating life as a challenge”. Conclusions: Resilience has an impact on the severity of symptoms of post-traumatic stress disorder. Specifically, the dimension of “Tolerance” within resilience appears to be an important protective factor against post-traumatic stress disorder symptoms after hospitalisation for COVID-19. Patients hospitalised with severe course of COVID-19 exhibit a moderate level of resilience, with men demonstrating higher resilience levels than women.

Failure’s virtues: the seeding of an emerging entrepreneurial ecosystem in a peripheral region

ABSTRACT A key strategy of governments in economically-lagging regions is to provide financial support to innovative start-ups. Yet, if such businesses fail, governments are criticized for ‘wasting tax-payers money’. This paper challenges this narrative. It provides a case study of Consilient Technologies, located in St John’s, Newfoundland and Labrador, Canada’s most economically under-developed province. The company had developed technology for the emerging cellular-phone market. It received significant funding from the Federal and Provincial governments. It recruited talent who would have left the province to seek employment and attracted others back to the province. It provided its workforce with the opportunity to acquire new competences, experience and knowledge along with entrepreneurial learning. Following its closure, its employees either moved to other technology firms in the emerging local entrepreneurial ecosystem or started their own businesses. The company also had significant demonstration effects for aspiring technology entrepreneurs. The study demonstrates to policy-makers that businesses that they support but which subsequently fail can nevertheless have a positive impact on the ecosystem. Accordingly, they need to have need to a tolerance for failure.

Conceptualizing the role of virtual service agents in service failure recovery: Guiding insights

The hospitality industry employs artificial intelligence (AI) to positively influence customer psychological reactions and behavior. Our research addresses the challenges posed by AI service failure and explores the role of virtual service agents (VSAs) during the service failure recovery (SFR) process. In this study, we conducted a scoping review of 87 high-quality literature items published between 2000 and June 2024 to investigate how AI service failures contribute to adverse customer psychological reactions and behavior. This paper conceptualizes VSAs’ anthropomorphic attributes that may play important roles during the SFR process by influencing customer comfort and service failure tolerance. Moreover, we present how the severity of service failure and the competence of VSAs moderate the relationship between VSA anthropomorphic attributes and customer service failure tolerance. Theoretical and practical implications are discussed to provide additional insights.

Distributed optimization for multi-commodity urban traffic control

A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed-gradient projection algorithm. The intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.

Percolation of conditional dependency clusters based on edge-coupled interdependent networks

Considering the existence of multiple edge dependencies in realistic interdependent networks, we propose a model of edge-coupled interdependent networks with conditional dependency clusters (EINCDCs). In this model, the edges in network A depend on the edges in dependency clusters of size m in network B. If the failure rate of edges within the dependency clusters in network B exceeds the failure tolerance α, the corresponding edges in network A that depend on those clusters in network B will fail accordingly. By adopting the self-consistent probabilities approach, a theoretical analytical framework is established to quantitatively address this model. Specifically, we study the robustness of the system verified with numerical simulations in the effect of the cluster size and failure tolerance under random attacks on systems composed of two networks A and B constructed with Random Regular (RR), Erdös-Rényi (ER) and Scale Free (SF) models. Our results show that both networks A and B undergo a first-order or hybrid phase transition when the dependency cluster size does not exceed 2. However, when the cluster size of dependency clusters exceeds 2, a non-monotonic behavior is observed. In particular, when the failure tolerance is in the range from 0 to 0.5, the robustness of the system weakens with the growing in the number of dependency clusters of size 2. While, this tendency reverses when the failure tolerance is in the range from 0.5 to 1. Moreover, we observe that due to the asymmetric interdependency between the two networks, network B always undergoes first-order phase transition, whereas network A could exhibit different types of phase transitions, which depends on the size of dependency clusters. In addition, the failure tolerance may have opposite effects on the two networks with the growing of dependency cluster sizes. The conclusions of the study may provide useful implications and enrich the understanding in the robustness of edge-coupled interdependent networks.

Does Failure Tolerance of the Board of Directors Affect Investment Efficiency?

Based on the perspective of failure tolerance, this paper measures the boards failure tolerance by whether the companys performance failing to meet the boards expectations leads to the mandatory replacement of managers. On this basis, the paper selects data from A-share listed companies in China from 2010 to 2022 to study the impact of board failure tolerance on corporate investment efficiency. The research finds that board failure tolerance can significantly improve corporate investment efficiency, both inhibiting over-investment and alleviating under-investment.

Does Failure Tolerance of the Board of Directors Affect Investment Efficiency?

Does Failure Tolerance of the Board of Directors Affect Investment Efficiency?

Effects of human-robot interaction type on customer tolerance of humanoid robot service failure

ABSTRACT The impact of robot service failure on customer retention is well-recognized, with a growing focus on effective robot recovery strategies. Despite this, the formation of human-robot relationships during interactions is not well understood. This research explores the impact of human-robot interaction types on customer tolerance toward humanoid robot service failures. Employing three scenario-based experimental studies, we demonstrate that customers generate the highest tolerance for robot service failures within companionate relationships, followed by assistive relationships, and instrumental relationships. In addition, this research confirms the mediating role of psychological distance and identifies the moderating role of service context in the relationship between the human-robot interaction relationships and customer tolerance through psychological distance. The findings offer insights for enhancing marketing strategies and the development of human-robot interaction paradigms.

Occupational Burnout and Psychological Resilience Levels Among Physiotherapists

Abstract This study sets out to determine those factors that may increase the risk of occupational burnout among physiotherapists. Fifty-six physiotherapists (39 women and 14 men aged 25-69) with various specializations (paediatric, adult, or both), work models (individual, team, or both), and workplaces (public or private) completed the Oldenburg Burnout Inventory (OLBI) to assess their burnout levels and the Resilience Scale (SPP-25) in order to have their psychological resilience evaluated. Pearson correlation coefficients showed a significant inverse relationship between psychological resilience and the risk of burnout. Among environmental factors, private clinical practice had a protective effect against burnout. Regression analysis revealed that tolerance for failure and viewing life as a challenge (a subscale of SPP-25) were key predictors of burnout. Higher tolerance for failure and viewing life as a challenge corresponded with lower scores on the exhaustion and disengagement scales. The study showed that the higher the psychological resilience, regardless of specialization, work model, or workplace, the lower the risk of burnout. This indicates the need for greater attention to employees’ personality traits, which can significantly impact work quality.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Treatment options for patients with pilonidal sinus disease: PITSTOP, a mixed-methods evaluation.

There is no consensus on optimal management of pilonidal disease. Surgical practice is varied, and existing literature is mainly single-centre cohort studies of varied disease severity, interventions and outcome assessments. A prospective cohort study to determine: • disease severity and intervention relationship • most valued outcomes and treatment preference by patients • recommendations for policy and future research. Observational cohort study with nested mixed-methods case study. Discrete choice experiment. Clinician survey. Three-stage Delphi survey for patients and clinicians. Inter-rater reliability of classification system. Thirty-one National Health Service trusts. Patients aged > 16 years referred for elective surgical treatment of pilonidal disease. Surgery. Pain postoperative days 1 and 7, time to healing and return to normal activities, complications, recurrence. Outcomes compared between major and minor procedures using regression modelling, propensity score-based approaches and augmented inverse probability weighting to account for measured potential confounding features. Clinician survey: There was significant heterogeneity in surgeon practice preference. Limited training opportunities may impede efforts to improve practice. Cohort study: Over half of patients (60%; N = 667) had a major procedure. For these procedures, pain was greater on day 1 and day 7 (mean difference day 1 pain 1.58 points, 95% confidence interval 1.14 to 2.01 points, n = 536; mean difference day 7 pain 1.53 points, 95% confidence interval 1.12 to 1.95 points, n = 512). There were higher complication rates (adjusted risk difference 17.5%, 95% confidence interval 9.1 to 25.9%, n = 579), lower recurrence (adjusted risk difference -10.1%, 95% confidence interval -18.1 to -2.1%, n = 575), and longer time to healing (>34 days estimated difference) and time to return to normal activities (difference 25.9 days, 95% confidence interval 18.4 to 33.4 days). Mixed-methods analysis: Patient decision-making was influenced by prior experience of disease and anticipated recovery time. The burden involved in wound care and the gap between expected and actual time for recovery were the principal reasons given for decision regret. Discrete choice experiment: The strongest predictors of patient treatment choice were risk of infection/persistence (attribute importance 70%), and shorter recovery time (attribute importance 30%). Patients were willing to trade off these attributes. Those aged over 30 years had a higher risk tolerance (22.35-34.67%) for treatment failure if they could experience rapid recovery. There was no strong evidence that younger patients were willing to accept higher risk of treatment failure in exchange for a faster recovery. Patients were uniform in rejecting excision-and-leave-open because of the protracted nursing care it entailed. Wysocki classification analysis: There was acceptable inter-rater agreement (κ = 0.52, 95% confidence interval 0.42 to 0.61). Consensus exercise: Five research and practice priorities were identified. The top research priority was that a comparative trial should broadly group interventions. The top practice priority was that any interventions should be less disruptive than the disease itself. Incomplete recruitment and follow-up data were an issue, particularly given the multiple interventions. Assumptions were made regarding risk adjustment. Results suggest the burden of pilonidal surgery is greater than reported previously. This can be mitigated with better selection of intervention according to disease type and patient desired goals. Results indicate a framework for future higher-quality trials that stratify disease and utilise broad groupings of common interventions with development of a patient-centred core outcome set. This trial is registered as ISRCTN95551898. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/17/02) and is published in full in Health Technology Assessment; Vol. 28, No. 33. See the NIHR Funding and Awards website for further award information.

Asynchronous consensus for multi-agent systems and its application to Federated Learning

Federated Learning (FL) improves the performance of the training phase of machine learning procedures by distributing the model training to a set of clients and recombining the final models in a server. All clients share the same model, each with a subset of the complete dataset, addressing size issues or privacy concerns. However, having a central server generates a bottleneck and weakens the failure tolerance in truly distributed environments.This work follows the line of applying consensus for FL as a no-centralized approach. Moreover, the paper presents a fully distributed consensus in MAS (multi-agent system) modeling and a new asynchronous consensus in MAS (multi-agent system). The paper also includes some descriptions and tests for implementing such learning algorithms in an actual agent platform, along with simulation results obtained in a case study about electrical production in Australian wind farms.

Failure tolerant functionally graded fiber-reinforced UHTCs exposed to hypersonic flows

A functionally graded ZrB2 ceramic containing increasing content of short carbon fiber, 0–60 vol%, was tested in a supersonic arc-jet wind tunnel by exposure to flow in simulated air. The composite, that exhibited increased failure tolerance due to the combined toughening effect of the fiber bridging and the residual stresses accumulated between scales of different composition, successfully survived 2120 K for 300 sec, whereas scale spalling occurred upon reaching 2930 K. Overall, in both conditions, carbon fiber remained protected and with unchanged morphology, providing the necessary structural stability for surviving the shocks encountered by the hottest components of new generation aerospace vehicles.

Strategy planning and business performance: the moderating roles of market sensing and tolerance for failure

PurposeThis study aims to examine the relevance of strategic marketing planning in this agile era and its effect on firms’ international performance and explores conditions under which the influence of planning changes.Design/methodology/approachBased on contingency theory, a conceptual model is tested based on survey data from internationalizing firms. Data were analyzed using partial least squares -structural equation modeling.FindingsMarketing strategy planning is (still) associated with enhanced performance, and depends on external and internal contingencies. While the planning−performance relationship is amplified by market sensing (external contingency), surprisingly, it is decreased in presence of high tolerance for failure (internal contingency).Practical implicationsFindings seek to transform marketing planning in international business practice by requiring that its implementation receives the attention of senior management.Originality/valueMarketing strategy planning should not be deemphasized. While planning appears to be undergoing an identity crisis, practitioners’ attention to marketing planning is warranted.

Penerapan TCP IP Fiber Optic Menggunakan Topologi Mesh pada Aplikasi Cisco Packet Traser

The application of TCP/IP over fiber optic networks with mesh topology has become an interesting topic in the development of modern network infrastructure. This research aims to explore and apply the basic concepts of TCP/IP via fiber optic technology using a mesh topology in the Cisco Packet Tracer simulation. This research method involves building a virtual computer network consisting of several nodes connected via fiber optic cables. The aim of this research is to understand the reliability, efficiency and performance of networks implemented through fiber optic technology and mesh topology. In the initial stage, a literature study was carried out to understand the basic principles of TCP/IP, fiber optic technology, and mesh topology. Next, using Cisco Packet Tracer, a virtual computer network is built using appropriate hardware and software to simulate a fiber optic-based network with a mesh topology. Experiments were conducted to test network performance in terms of data transfer speed, latency, and connection reliability. Experimental results show that the application of TCP/IP over a fiber optic network with a mesh topology is able to provide reliable and efficient performance. The main advantages of using fiber optic technology are high data transfer speeds and better connection reliability compared to other transmission media. In addition, mesh topology provides high flexibility and redundancy in the network, thereby increasing failure tolerance and increasing network scalability.