Deep Venous Thrombosis in Patients Recovered from COVID-19: A Long-Term Sequel.

The rapid increase in the use of computerized systems in supply chains has revealed a significant number of cybersecurity weaknesses, particularly where large enterprises interact with their small-scale vendors. This paper examines the gaps and weaknesses in cybersecurity integration across different supply chain partners. Through qualitative analysis of existing literature, case studies, and industry frameworks, the research identifies the limitations of traditional compliance-based approaches, such as the fundamental restraints faced by small vendors, limited budgets, technical expertise, and cybersecurity awareness. The study proposes a tiered, partnership-oriented framework that reframes cybersecurity integration from punitive compliance requirements to collaborative support mechanisms for large enterprises and small-scale vendors. Key findings propose that successful integration requires (1) risk-based vendor categorization, (2) proportionate security requirements, (3) shared resource models, and (4) continuous relationship management. The paper concludes that the ability to withstand, recover from, or adapt to cyber-attacks and system failures in modern supply chains depends on transforming vendor relationships from transactional compliance to strategic partnerships, with large firms assuming greater responsibility for capability building across their extended digital ecosystem. Practical recommendations include developing scalable assessment tools, creating cybersecurity knowledge sharing platforms, and establishing clear governance structures that balance security requirements with vendor sustainability.

ABSTRACT Photovoltaic (PV) systems are an invaluable green power solution to the energy requirements in the world yet as they are placed outside, they are subject to numerous issues and environmental challenges that may lead to loss of efficiency in the systems and even system failures. Thus, early and accurate fault diagnosis is necessary to minimize downtime and costs of maintenance. This paper proposes an automated PV-based fault diagnosis system with the aid of a pre-trained image-based InceptionV3 convolutional neural network, which can find faults in panels. The model was trained and tested on a diverse dataset of 885 images to classify six categories of conditions, including physical degradation, electrical degradation, bird droppings, dust, snow coverage, and fault-free panels. Using transfer learning and extensive data augmentation, the model achieved a good overall test-based accuracy of 85.71%. The comprehensive analysis of the performance showed that it was highly suitable for detecting Dusty, Clean, and Bird-drop classes with high F1-scores. However, the model struggled with the lower recall Physical-Damage class, indicating that it was not fully reliable in detecting subtle physical defects such as cracks. The results, complemented by ROC curves and confusion matrices, determine the extent to which the InceptionV3 architecture is appropriate for this task and highlight some diagnostic limitations. This article is important in providing more reliable and cost-effective solar energy production by creating a clear and methodologically rigorous standard for automated PV fault detection.

To characterize clinical reasoning in prioritization and test whether errors are linked to experience or are universal, by examining how information congruence and informativeness influence nurses' prioritization and diagnostic reasoning, and by identifying cognitive mechanisms underlying systematic errors under clinical uncertainty. A concurrent embedded mixed-methods study was conducted with 130 nurses from two university hospitals. Using a think-aloud protocol, participants reasoned through four experimentally controlled clinical scenarios in which information congruence (data aligned vs. misaligned with the most common diagnosis) and informativeness (amount of data) were manipulated. Prioritization accuracy (correct vs. incorrect priority) was the primary outcome. Qualitative analysis identified cognitive biases, which were entered into a logistic regression model to quantify their association with accuracy. Accuracy collapsed when nurses faced incongruent clinical data, falling from 49.3 % in congruent scenarios to 18.4 % in incongruent ones (31-point drop; 95 % CI 20-42 %; p<0.001). This decrement was independent of age, experience, educational level, and ward type. Qualitative analysis showed that most nurses (71.4 %) actively dismissed critical conflicting cues. Confirmation bias (OR=0.048, p=0.015) and information bias (OR=0.082, p=0.010), were strong significant predictors of incorrect prioritization. Nursing prioritization errors are systematic cognitive failures rather than random mistakes or simple knowledge deficits. The core vulnerability appears to be metacognitive: an impaired ability to detect and resolve conflict between an activated mental model and new, incongruent information. Interventions to reduce diagnostic and prioritization errors should explicitly train cognitive and metacognitive skills for managing incongruence and flexibly updating clinical representations.

Clinical evaluation of large language models (LLMs) currently relies on static datasets and isolated scenarios that fail to capture the cascading effects of healthcare decisions. We propose the Clinical Environment Simulator (CES), a framework that evaluates clinical LLMs within digital hospital environments where every decision dynamically alters future states. The CES would use a parallel simulation architecture: a 'hospital engine' that tracks bed availability, staff workloads and equipment status in real time, and a 'patient engine' that simulates disease progression and treatment responses based on LLM interventions. Unlike current benchmarks, the CES framework requires clinical LLMs to execute decisions through realistic electronic health record interfaces, while managing trade-offs between individual patient optimization and system-wide efficiency. The CES enables three critical evaluations absent from current benchmarks: temporal reasoning under evolving constraints, where delayed diagnostics can lead to patient deterioration; resource-aware decision-making, where aggressive workups for one patient may exhaust capacity needed by others; and operational resilience, through adversarial testing with simultaneous emergencies and system failures. By scoring LLM performance on both clinical outcomes and operational metrics, the CES represents a shift toward evaluating clinical LLMs as a dynamic and integrated component of healthcare delivery systems.

Abstract In response to the issue of low diagnostic accuracy in composite material hydraulic presses operating under harsh conditions, such as high temperatures and noise levels, this paper presents a novel, dual-branch, 'spatiotemporal collaborative' deep learning diagnostic model. This model innovatively integrates sensor signals with material dynamic process parameters. A convolutional neural network (CNN) extracts spatial correlations from ten-channel hydraulic signals, while an Long Short-Term Memory (LSTM) branch captures the temporal evolution of the signals and the real-time physical quantities of the composite material. These heterogeneous features are then fused via a cross-attention mechanism, which prioritises critical fault characteristics and achieves deep decoupling between process fluctuations and actual system failures. Experimental results demonstrate 99.1% accuracy in identifying three core failure modes: malfunctions of the hydraulic pump, cooler and valve. Further comparative experiments and ablation studies validate that incorporating dynamic process parameters reduces the model's false positive rate (FPR) to 0.5%, proving its exceptional robustness and reliability in complex industrial forming environments. This advancement provides a high-precision solution for intelligent maintenance in automotive lightweight production lines.

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources.

Chronic inflammation is a fundamental driver of cancer development, linking persistent immune activation to genomic instability, microenvironmental remodeling, and malignant transformation. In inflammation-associated malignancies such as colitis-associated cancer (CAC) and metabolic dysfunction-associated fatty liver disease-related hepatocellular carcinoma (MAFLD-HCC), sustained inflammatory signaling integrates tissue injury with oncogenic pathways to promote tumor initiation and progression. Importantly, these cancers arise through prolonged preneoplastic stages, during which dysregulated immune and inflammatory responses not only drive malignant transformation but also create opportunities for cancer prevention and early disease interception. CAC and MAFLD-HCC share convergent mechanisms, including IL-6/STAT3 and NF-κB activation, cytokine-driven survival signaling, and cooperation with genetic alterations in APC, KRAS, and TP53. A critical but often underappreciated dimension of this process is the failure of endogenous immunoregulatory systems that normally restrain excessive inflammation. Among these, the negative regulator of immune cells TIPE2 plays an important role in limiting inflammatory signaling, and its reduced activity contributes to the persistence of a tumor-promoting inflammatory microenvironment that supports both disease initiation and progression. This review synthesizes the shared inflammatory, immunologic, and microenvironmental mechanisms underpinning CAC and MAFLD-HCC, with a particular emphasis on how impaired immune regulation drives the transition from chronic inflammation to cancer. We further highlight therapeutic and preventive strategies targeting inflammation-driven pathways, underscoring the dual relevance of immune modulation for cancer prevention in chronic inflammatory disease and for the treatment of established malignancy.

Abstract Analog circuits are fundamental to a wide range of industrial systems, where their evaluation is essential for ensuring operational reliability and preventing system failures. However, diagnostic methodologies for analog circuits are markedly less developed than those for their digital counterparts, primarily due to the inherent difficulty of detecting soft faults within analog environments. One particularly challenging category of faults involves simultaneous degradations across multiple components that do not result in a hard failure of the circuit. Indeed, there is a notable lack of studies addressing the detection of simultaneous soft faults in analog circuits. This study proposes a method for identifying this type of soft fault occurrence in analog circuits by combining Machine Learning (ML) techniques, specifically Random Forests and Artificial Neural Networks, with an Evolutionary Algorithm (EA) based on Particle Swarm Optimization (PSO). The proposed approach is validated on a second-order Sallen-Key band-pass filter, a circuit in which soft fault classification is particularly challenging. Furthermore, the study highlights the performance improvements achieved through the proposed combined method in detecting and classifying simultaneous soft faults. This study demonstrates that an iterative process combining ML and EA techniques enables accurate fault prediction in electronic circuits. Moreover, the integration of these strategies can enhance the performance of classification problems that are traditionally addressed using either ML or EA in isolation. The effectiveness of the proposed method is evaluated using several statistical metrics, including the Matthews Correlation Coefficient (MCC), F1-score, and others.

Private housing project failures in Malaysia have emerged as a significant concern with profound implications for industrial project management efficiency and economic stability. This study develops a comprehensive strategic framework aimed at enhancing industrial project management practices and strengthening policy resilience, drawing critical lessons from the systemic failures observed in Malaysia’s private housing sector. Using a mixed-methods approach that integrates quantitative insights from 195 industry stakeholders and qualitative findings from 13 expert interviews, the framework synthesizes principles from project management theory, stakeholder theory, and institutional governance. It addresses five interrelated dimensions: strategic planning, resource optimization, proactive risk governance, multi-stakeholder engagement, and regulatory modernization. The findings reveal that failures in private housing projects are underpinned by fragmented planning, financial vulnerabilities, weak regulatory enforcement, and inadequate stakeholder coordination, equally relevant to broader industrial and infrastructure contexts. By proposing integrative strategies such as the adoption of digital project monitoring systems, standardized compliance protocols, and collaborative stakeholder platforms, this study contributes to enhancing operational efficiencies and fostering a more resilient policy environment. Ultimately, the framework offers actionable pathways not only to mitigate project abandonment risks but also to advance sustainable industrial practices, safeguard economic interests, and support Malaysia’s transition towards a more robust and circular construction economy.

The purpose of the study is to develop methods for optimal estimation of unknown non-random factors affecting the quality of production equipment by the criterion of maximum likelihood, as well as random factors by the criterion of minimum standard error based on the processing of information related to claims received using the mathematical theory of random point processes and the theory of statistical solutions. The research method consists in applying the well-known hypothesis about the distribution of operating time for failure of technical systems in the form of an exponential distribution depending on the failure rate function. The fact is used that the corresponding distribution of the number of failures is distributed according to the Poisson law with the same function of failure rates. It is assumed that the intensity function depends not only on time, but also on a set of unknown non-random parameters, or on random parameters. It is emphasized that such factors may reflect the generalized state of the technical system, and information about this may be contained in the facts of product claims. The task of optimal estimation of the parameters on which the failure rate function depends is set. Since in this formulation of the problem, only the facts of filing claims, as well as the times of their presentation, are available for processing, the maximum likelihood function method is used for optimal estimation of nonrandom parameters, and the optimal Kalman filter is used for random parameters. The problem of optimal estimation of unknown parameters from a multiplicatively separable failure rate function, i.e. one that is representable as a product of a separate function of time and a vector function of unknown parameters, is considered. It is shown that for such a function, the optimal estimation problem is reduced to the problem of estimating a single scalar parameter that scales the time function. The well-known Kalman algorithm for continuous parameters is applied to the case of the observed process in the form of the number of claims’ events and the time of their occurrence. Examples of evaluation of both unknown and random factors are given for unified real data on tissue defects, and confirm the operability of the algorithms and their applicability for the simplest assessments of the condition of production equipment. The new results include the formulation of the problem of studying a failure intensity function that depends on a set of unknown nonrandom parameters, the application of the maximum likelihood method and Kalman algorithm for optimal estimation of these parameters, and the proof that for a separable failure intensity function, the optimal estimation reduces to the estimation of a scalar quantity that scales the time-dependent intensity function. The conclusion states that examples of assessment of factors affecting the function of the failure rate confirm the operability of the algorithm and its applicability for the simplest assessments of the condition of production equipment. A separate task is to develop analytical expressions for the failure rate function that depends on parameters, as well as methods for comparing estimates obtained by different methods. Solving these tasks will make it possible to develop methods for clarifying the condition of production equipment.

This policy paper discusses the quality of Buddhist Marriage Guidance, which is currently not optimal in equipping prospective brides and grooms with an understanding of Dhamma principles and practical skills for fostering harmonious families. The main policy issue is the severe inconsistency in implementation and curriculum, triggered by weak binding national regulations and a policy focus that is too administrative rather than substantive program outcomes. Through descriptive qualitative methods, in which the author analyzes this issue using public policy theory approaches (such as Standardization Theory, Implementation Failure, and Result-Oriented Administration) and supported by an USG (Urgency, Seriousness, Growth) analysis for problem priority, it is found that the root of the problem lies in the failure of the oversight system to address the high autonomy of the Assembly and resistance to standardization. The USG analysis identifies five main problems, with three of the top priorities: Curriculum Inconsistency, Unstandardized Facilitators, and Cross-Sector Synchronization (Scores 13-14). Therefore, this policy paper recommends the issuance of a Decree of the Director General of Buddhist Community Guidance on the Establishment and Management of a Centralized Digital Platform. This decision was chosen based on William N. Dunn's scoring, which showed that the digital solution achieved the highest score (22) because it was considered the most efficient and capable of ensuring equal access to the Minimum Core Curriculum to all Buddhists, fundamentally shifting the focus from fulfilling administrative requirements to achieving substantive results in the form of increasing program efficacy and the quality of Buddhist families in Indonesia.

Mob violence is an emerging threat to the rule of law in Pakistan, often fueled by religious incitement, blasphemy allegations, and perceived failures in the legal system. This study aims to examine the social, legal, and procedural barriers to preventing mob violence in Pakistan from a criminal justice perspective. Drawing on qualitative interviews with 44 elite respondents (judges, lawyers, and police officials), this research explores the underlying causal factors that contribute to mob violence in Pakistan and the systemic limitations of the criminal justice system in addressing this issue. Thematic and cross-referenced analyses reveal an intersection of drivers, including widespread distrust in legal institutions, weak law enforcement capacity, a collective moral justification mindset, and the paradoxical role of religious and tribal leaders as both instigators and mediators. The findings indicate how procedural delays, lack of accountability, and technological deficiencies within the criminal justice system contribute to normalizing extrajudicial actions. The study also illustrates the strategic role played by community influencers in both escalating and de-escalating mob violence, revealing a power imbalance that further undermines formal justice mechanisms. Finally, the study highlights the need for a multidimensional reform approach that includes strengthening police response capacity, regulating religious rhetoric, and accelerating judicial proceedings in order to reinforce Pakistan's criminal justice system.

ABSTRACT When Chiung Yao (瓊瑤 1938–2024), the ‘Queen of Romance’ who shaped the emotional landscape of generations across East Asia, staged her death as a public protest, she authored a final, telling paradox. This article argues that Chiung Yao’s ‘labour of decline’ constitutes a radical confrontation with the cultural and biopolitical narrative of ageing as decline. Her struggle – spanning the clinical, domestic, and public spheres – diagnoses the systemic failures of late capitalism. Intervening in celebrity ageing studies, this study shifts the focus from the representation of ageing and repositions the end-of-life stage as the most intense frontier of decline, offering a structural critique grounded in East Asian Modernity. To trace this paradoxical trajectory, the analysis first demonstrates how Chiung Yao mobilised her celebrity persona as biopolitical resistance against the medical gaze. It then excavates the unwaged social reproductive labour and moral injury that revealed a deeper crisis of care, before showing how this resistance was ultimately captured by the logic of communicative capitalism. Ultimately, Chiung Yao’s case reveals the bleak efficacy of a system that extracts value even from our final acts of dissent, transforming a demand for dignity into a marketable spectacle.

Abstract Failure in most material systems is characterized by strain localization, where deformation concentrates within a narrow region. Recently, a class of truss-based metamaterials has been shown to undergo severe deformation without exhibiting localization 1 . The mechanisms underlying this unusual delocalized response remain unknown. Here, we employ graph theory to elucidate the origins of this behavior. Each lattice is represented as a pair of graphs—the tension and compression networks—and their topological properties are quantified using graph-theoretic metrics. We find that the onset of localization correlates strongly with connectivity measures of these graphs. Specifically, deformation delocalization arises from an asymmetry in connectivity between these networks: when the tension graph remains more connected than the compression graph, deformation spreads throughout the structure instead of localizing. Connectivity measures such as average global efficiency 2 capture this transition quantitatively. This framework provides design principles for creating materials and metamaterials that intrinsically resist failure localization.

The implementation of the European Union Deforestation Regulation (EUDR), which became valid in May 2023, brings about strict due diligence and traceability standards for major commodities, transforming the market access of the developing nations. Ethiopia has high levels of compliance risks because smallholder-controlled institutionally disjointed value chains dominate its economy, which is based on high export rates of coffee, livestock and forestry products. Although the literature on the topic analyses technical factors of related regulations, the sample of cross-sectoral governance-oriented studies that assess the effects of institutional coordination and technological potential to determine national preparedness (especially in African cases) remains limited. This paper offers a multi-sectoral assessment of the contributions that can be made to EUDR uptake in Ethiopia. It is the interplay of institutional architecture, the awareness among the stakeholders, and the presence of the digital infrastructure. With a convergent mixed method-based design, the researcher gathered primary data sources by using 42 semi-structured interviews with government authorities, exporters, cooperatives and civil society players in the major regions, and these will be combined with secondary sources on trade and policy documents. The results contain indicators of systemic failures: the level of stakeholder awareness falls significantly between federal and farm, institutional coordination is divided, technological potential to trace is exceptionally small. These difficulties lie in the further loading of larger governance bodies, which means that the issue of EUDR compliance is not as technical as it is a challenge to multi-level governance and systemic capacity. Vulnerabilities are high in terms of sectoral, with coffee regarding moderacy and lack of warning preparation; the livestock and forestry sectors are well behind

This article examines Deaf survivors’ access to domestic abuse protection in England through a socio-legal analysis combining doctrinal frameworks (European Convention on Human Rights, Equality Act 2010, the Public Sector Equality Duty, and the Domestic Abuse Act 2021) with a national mapping of provision. It demonstrates how hearing-normative system design, interpreter-dependent access, and fragmented commissioning produce patterned and foreseeable exclusion, constituting institutional legal harm. By contrasting these systemic failures with Deaf-led, BSL-first services, the article shows that rights become exercisable when accessibility is embedded by design rather than delivered through reactive adjustment. It conceptualizes this recognition–realization gap as the Deaf Legal Illusion: formal recognition without reliable, substantive access in practice. The article concludes by identifying system-level reforms necessary to make equality exercisable and accountability enforceable. Keywords: Deaf Legal Studies; domestic abuse; British Sign Language (BSL); Equality Act 2010; Public Sector Equality Duty (PSED); access to justice; institutional legal harm; socio-legal research; commissioning; interpreter-dependent systems.

Assessment of surgeon workload using the NASA-TLX during the early introduction of robot-assisted total knee arthroplasty.

Nutrient flow modeling as a tool for investigating self-regulating mechanisms and productivity in eutrophication processes in coastal lagoons.

Reprogramming mitochondrial homeostasis in renal ischemia-reperfusion injury.