Search Results (8,968)

Cyberworthiness extends the concept of cybersecurity by evaluating whether systems and networks can perform their intended functions securely while maintaining protection against cyber threats. In corporate environments, cyberworthiness aims to ensure security, operational resilience, and trustworthiness across interconnected business processes and digital infrastructures. Modern organisations increasingly rely on complex cyber–physical and information systems, where vulnerabilities in software, networks, and devices can introduce significant operational and security risks. Cyberworthiness, therefore, encompasses security controls, risk management practices, and compliance with recognised cybersecurity standards and governance frameworks. It supports the assessment of information technology components and their exposure to both known and emerging cyber attacks, enabling organisations to evaluate system robustness and operational continuity. While cyberworthiness has historical foundations in system assurance and dependability, it also provides a conceptual basis for contemporary cyber resilience strategies. This paper discusses the concept of cyberworthiness in corporate organisations and identifies potential pathways for its practical implementation. It analyses existing cybersecurity standards and governance frameworks to support structured cyberworthiness assessment. This study presents a structured comparative review of fifteen cyberworthiness-relevant standards, supported by a Source Quality Appraisal Framework, a Framework Selection Guide specifying when each standard should be preferred and where conflicts arise, and a five-dimensional Cyberworthiness Assessment Readiness Model (CARM), a directional self-assessment instrument. The Efficient Automatic Safety and Security Assurance (EASSA) concept is proposed as a direction for future research, not a validated deployed system. Ensuring cyberworthiness remains challenging due to automation limitations in all reviewed standards, evolving threat landscapes, and governance complexity, requiring organisations to adopt integrated and measurable approaches to safeguard their digital assets and operational systems. Full article

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We developed the framework using a targeted narrative synthesis of contemporary governance guidance and documented early implementation experiences, purposively selected to represent major strands of current practice and debate. A structured expert panel consultation (n = 9) was subsequently conducted to assess the face validity and content validity of the proposed framework domains. Results: We propose the Public Health Responsible AI Capability (PH-RAIC) framework, which adapts principles of transparency, accountability, fairness, ethics, and safety to institutional realities faced by public health agencies. PH-RAIC identifies four interdependent capability domains: (1) strategic governance and alignment; (2) data and infrastructure stewardship; (3) participatory design, equity, and public engagement; and (4) lifecycle oversight, learning, and decommissioning. All four domains achieved Content Validity Index (CVI) values ≥ 0.85 in the expert panel consultation. The framework is presented as a conceptual, meso-level model that has undergone preliminary expert validation but requires further empirical testing in real-world agency settings. Conclusions: PH-RAIC links these domains to example practices, diagnostic questions, and illustrative measurement indicators to help agencies navigate efficiency–equity trade-offs and strengthen legitimacy and accountability in AI-enabled public health systems. It offers a validated conceptual basis for future empirical testing and operational readiness tools. Full article

Background: The growing use of big data and algorithmic personalisation in political communication has intensified concerns about surveillance, privacy, and manipulation. Although previous research has examined these issues among the general public, much less is known about how journalism students—future professionals who have grown up in data-fied environments—perceive them. This study investigates the extent to which these students feel ‘spied on’ by digital platforms and online media, how such perceptions influence their trust in media, platforms and political actors, and what attitudes they hold regarding the ethical use of data in journalism. (2) Methods: Based on a survey of 222 journalism students, the research analyses perceptions of digital surveillance, awareness of political microtargeting, and attitudes toward the ethical use of audience data in journalism practice. A qualitative component, through focus groups, complements the survey by exploring ethical reflections on algorithmic tracking and journalistic responsibility. (3) Results: The findings reveal a widespread distrust of social networks and political actors and a more moderate scepticism toward the news media. Students express strong ethical concerns about data use and algorithmic personalisation, particularly in political communication and in relation to their future professional roles. (4) Conclusions: The study suggests that journalism students show critical awareness of algorithmic personalisation. Their perceptions highlight the need for academic training in transparency, consent, and accountability in data-driven practices. Full article

Digital Elevation Model (DEM) resolution plays a critical role in hydraulic flood modelling by influencing inundation accuracy, spatial precision and computational efficiency. However, limited studies have simultaneously evaluated both inundation accuracy and computational performance across multiple DEM resolutions in event-based urban flood modelling. This study aims to evaluate the impact of DEM spatial resolution on the performance of HEC-RAS 1D and 2D models in simulating an event-based urban flood that occurred on 3 March 2025. A 1 m LiDAR-derived DEM was resampled to 2 m, 5 m, 8 m, 10 m, 20 m, 25 m, and 30 m resolutions to assess the effects of terrain generalization on hydraulic response. Simulated inundation extents were validated against observed flood areas derived from aerial imagery, and computation time was recorded for each scenario. Results reveal a clear trade-off between spatial accuracy and computational demand. In the 1D simulations, deviation from observed inundation increased from 0.76 ha at 1 m to 2.50 ha at 30 m, while computation time remained relatively stable. The 2D simulations were more sensitive to DEM resolution, with deviation increasing from 0.33 ha to 3.12 ha and longer runtimes at finer resolutions. Among the evaluated scenarios, the 10 m DEM provided the most balanced performance in both 1D and 2D models. For rapid assessment and operational flood management, where computational efficiency and timely decision-making are critical, a 1D modelling approach combined with a 10 × 10 m DEM is recommended as a practical and efficient solution. Full article

The digital economy (DE) is increasingly associated with higher carbon productivity (CP) and is widely regarded as an important factor in efforts to achieve the dual-carbon goals. However, the formulation of differentiated policies is constrained by a limited understanding of the multi-factor collaborative mechanisms and their asymmetric configurational pathways. This study combines the GMDH algorithm with the fsQCA approach to explore the multiple sufficient paths for high carbon productivity. Through feature selection and nonlinear modeling, the GMDH algorithm identifies five key variables associated with CP: the industrial robot permeability, software business development, digital innovation input, the usage depth of digital finance, and mobile communication facilities. The fsQCA method reveals that three configurational pathways consistent with higher levels of CP: the “innovation and finance-driven model” represented by Sichuan and Hunan, the “innovation-assisted digital industrialization model” represented by Henan and Hebei, and the “industry digitalization first developing model” represented by Jiangxi, Guangdong, Zhejiang, and Shanghai. Considering the uneven regional development across China, this study further categorizes provinces into four regional development types: innovation and finance-driven, digital industry empowerment, industrial digitalization leadership, and potential cultivation. Correspondingly, tailored policy recommendations are proposed for each region, providing practical insights consistent with the observed configurational patterns for improving CP in the context of DE development. Full article

This study examines the determinants of bilateral trade asymmetries between Türkiye and its three main EU partners—Germany, Bulgaria, and Romania—over 2002–2024. Within the gravity framework, bilateral symmetry in trade data implies that reported exports should equal partner imports (Xᵢⱼ = Mⱼᵢ). Deviations from this condition reflect systematic distortions caused by valuation practices, institutional gaps, and crisis-induced disruptions. This study employs a fixed-effects panel framework to identify the structural and contextual determinants of mirror−data asymmetries in Türkiye–EU trade. Using HS2−level mirror statistics from TÜİK and Eurostat, three asymmetry measures—the Bilateral Asymmetry Index (BAI), Absolute Logarithmic Difference (ALD), and Relative Symmetry Index (RSI)—are estimated through a fixed-effects panel model. Results show that a one−unit improvement in logistics performance (LPI) reduces asymmetry by approximately 0.17 points (p < 0.01). Maritime connectivity (LSCI) shows a small but statistically significant positive coefficient, while exchange rate volatility remains insignificant. The effects of global crises are heterogeneous: the 2008 financial crisis significantly increases asymmetry (+0.07, p < 0.01), whereas COVID−19 is associated with a reduction in asymmetry (−0.04, p < 0.01). The interaction between LPI and crisis periods is negative and significant (−0.03, p < 0.05), confirming that a stronger logistics capacity buffers crisis-induced reporting gaps. Country-specific results reveal that Romania drives much of the variation (within−R² = 0.26), while Germany remains largely insulated from crisis effects. The findings highlight that deviations from bilateral symmetry are driven by structural and institutional factors rather than random error. Policy recommendations stress harmonized customs valuation, digital logistics integration, and enhanced Türkiye–EU statistical coordination to strengthen trade data reliability and crisis resilience. Full article

Modular construction (MC) is frequently promoted as a path to circular economy (CE) outcomes in built environments, yet circular adoption and performance remain uneven. This study investigates how systemic barriers shape the implementation of circular strategies in MC. A systematic literature review combined with bibliometric mapping and systems-oriented synthesis was conducted using 124 Web of Science records published between 2011 and August 2025. Bibliographic coupling, co-citation, and keyword co-occurrence analyses were used to characterise the field’s intellectual structure, while 30 studies were selected for thematic coding and systems mapping. Ten recurrent barriers were identified and consolidated into six clusters: technical, financial, regulatory, stakeholder and organisational, quality assurance, and institutional and knowledge-based challenges. Their relative severity was assessed across four MC-relevant circular strategies: reuse, repurposing, design for disassembly, and multifunctionality. Systems mapping revealed three reinforcing feedback dynamics involving financial, stakeholder, and supply-chain pressures, knowledge and quality assurance constraints, and regulatory and design lock-in effects that stabilise conventional delivery and constrain circular implementation. Despite being underrepresented in the literature, multifunctionality emerges as a cross-cutting leverage point for enabling adaptable modular systems. The study synthesises five implementation pathways, including adaptable multifunctional design, interoperable interfaces, digital traceability, collaborative life-cycle integration, and policy alignment, and outlines systems-derived leverage points to guide future research and practice in circular modular construction. Full article

Artificial intelligence decision systems are increasingly deployed in safety-, policy-, and human-sensitive settings where actions must satisfy feasibility constraints under incomplete information. Existing post-deployment monitoring approaches can detect observable failures, distribution shifts, or performance degradation, but they cannot, by themselves, determine whether feasibility can be guaranteed when safety-relevant latent states remain indistinguishable at decision time. This paper develops a formal framework for reliable deployment and monitoring of AI decision systems under fixed observation structures. We model deployment through latent states, observations, observation-consistent state sets, state-wise feasibility constraints, and observation-based policies. The framework characterizes when feasibility-guaranteed deployment is structurally possible, when it requires intervention, and when it is impossible without modifying the information structure. We prove that every task falls into one of three regimes: deployable and automatically measurable systems, non-automatically deployable but remediable systems, and hard non-deployable systems. We further introduce an operator-assisted review and rollback mechanism for remediable cases and show that additional data or monitoring alone is insufficient unless such measurements refine feasibility-relevant latent-state ambiguity. Empirical examples and a digital health case study illustrate how the framework supports practical deployment assessment, monitoring design, and human-in-the-loop safeguards for AI systems operating under partial observability. Full article

Asymmetry is a prevalent phenomenon in low-power and lossy networks (LLNs) due to resource constraints and unstable links. The routing protocol for the low power and lossy network (RPL), standardized by the Internet Engineering Task Force (IETF), is specifically designed for LLNs with characteristics of resource constraints, lossy links, and complex communication environments. However, its performance is fundamentally limited by node capabilities and unstable links, a contradiction exacerbated by the stringent QoS demands of emerging applications like IIoT or precision agriculture. Consequently, new RPL routing technologies based on the digital twin-enabled computing power network, called RPL-DTCP, were designed to improve network QoS and support practical applications. First, a low-power and lossy network architecture based on twin-enabled computing network was proposed, considering LLN requirements and computing twin services. Second, in response to the requirements of the digital twin, computing power network and LLNs for low synchronization latency, high data accuracy, efficient computing resource utilization, and energy conservation, several routing metrics were designed, including the data processing model, model deployment rate, end-to-end delay, node remaining energy, and ETX. Then an initial matrix and a comprehensive objective function were formulated to comprehensively evaluate these metrics. Third, to solve the multi-objective optimization problem, an enhanced whale optimization algorithm (E-WOA) was developed. E-WOA improved upon the standard version by using improved Tent chaotic mapping for population initialization, nonlinear adaptive convergence factor, and Cauchy variation mutation operator for solution perturbation, thereby enhancing its global search capability and convergence speed, enabling it to effectively identify the optimal routing path. Simulations confirmed that RPL-DTCP outperforms benchmark algorithms, achieving significant reductions in end-to-end delay, higher packet delivery ratios, extended network lifetime, etc. These findings demonstrate that RPL-DTCP effectively addresses the resource-performance contradiction in LLNs, providing a reliable and efficient routing framework for emerging compute-intensive IoT applications. Full article

Advances in digital pathology and artificial intelligence (AI) are significantly transforming the approach to analyzing the tumor microenvironment (TME) in gastrointestinal cancers (GICs). The TME consists of tumor cells, stromal components, and immune cells. It plays a key role in disease progression, treatment response, and patient prognosis. This review discusses the most important TME biomarkers, such as tumor budding (TB), tumor-infiltrating lymphocytes (TILs), and tertiary lymphoid structures (TLSs), with emphasis on their prognostic and predictive significance. Traditional histopathological assessment of these parameters is limited by subjectivity, intraobserver variability, and time-consuming nature. In this context, AI-based tools enable automated, quantitative, and more reproducible analysis of entire histological sections. Deep learning models allow the accurate detection and classification of structures and also analysis of their spatial organization. They provide new biological insights unavailable in routine diagnostics. The integration of imaging data with molecular and clinical information leads to the development of personalized medicine. Despite numerous advantages, the implementation of AI in clinical practice continues to face challenges related to standardization, data availability, and model interpretability. Full article

The rapid digitalization of district heating systems (DHSs) has driven the large-scale deployment of thermal Internet of Things (TIoT) sensors, which generate massive real-time operational data. Traditional centralized computing architectures struggle to process massive concurrent data. Furthermore, they fail to balance the stringent low-latency demands of real-time control tasks with the low-energy constraints of battery-powered terminal devices. To solve the complex problem of minimizing the weighted sum of system latency and energy consumption, we propose an Improved Mayfly Algorithm (IMA). The algorithm integrates five targeted structural enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset mechanism, and simulated annealing (SA)-driven local search, to efficiently navigate the high-dimensional rugged decision space and mitigate premature convergence. Extensive simulation results show that the proposed collaborative architecture achieves the lowest total system cost compared with traditional isolated computing paradigms (local-only, edge-only, and cloud-only). Notably, the proposed IMA reduces the total baseline weighted cost by 17.2% compared with the standard MA. Furthermore, under maximum practical industrial workloads (750 concurrent tasks, representing a highly complex 2250-dimensional MINLP space), the IMA maintains strong scalability and dominance, outperforming the second-best algorithm (BWO) by 15.8%. This research provides a low-latency, energy-efficient scheduling solution for TIoT-enabled DHS, and offers technical support for the intelligent and low-carbon transformation of urban energy infrastructure. Full article

A dual-mode variable gain amplifier (VGA) with a wide-dynamic-range is proposed in this paper. The VGA is designed in a 0.18 μm CMOS process, and it has a body-driven variable load cell and binary gain array structure to implement both the digitally stepped programmable gain amplifier (PGA) mode and the analog-controlled VGA mode. This design removes additional digital conversion modules when integrated into an automatic gain control (AGC) loop, which simplifies the whole system architecture significantly. The design is also able to address several limitations of conventional VGAs, such as a single control mode, low AGC compatibility, and a narrow gain range. The simulation results after post-layout indicate that at PGA mode, the design has an ultra-wide gain band of −0.03 to 126.9 dB with a constant gain step of 1 dB. And in VGA mode, it allows smooth, continuous gain adjustment over a large range of −25.3 dB to 187.4 dB. The bandwidth of −3 dB is more than 45 MHz in both modes. The whole VGA uses 1.026 mW and has a core size of 0.011 mm². The output 1-dB compression point (OP1dB) was −1.57 dBm at minimum gain in the PGA mode and −4.02 dBm in the VGA mode. Besides, PVT analysis, Monte Carlo simulations and AGC system-level verification are evident enough to prove that the suggested VGA has high immunity to PVT (Process, Voltage, Temperature) variations, stable processes and high practicality in engineering applications. Full article

As organizations become increasingly digital, the environmental impact of digital infrastructures is gaining growing attention within ESG agendas. However, many organizations still struggle to translate digital infrastructure data into clear, measurable, and reliable ESG reporting outcomes. This study develops and empirically tests a socio-technical model explaining how organizations achieve ESG measurement and reporting readiness through sustainable computing practices. Drawing on a quantitative cross-sectional survey of 312 respondents from government, private, and educational organizations in Saudi Arabia and the GCC region, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis (MGA). The findings reveal that organizational drivers are the strongest predictors of sustainable computing practices, while organizational barriers exert significant negative effects on adoption. Sustainable computing practices play a critical mediating role by enabling organizations to transform fragmented digital data into structured and credible ESG reporting systems. Sectoral differences further highlight the influence of institutional contexts on adoption pathways. The study contributes by positioning sustainable computing as a foundational organizational capability that bridges digital transformation and ESG reporting, offering both theoretical insights and practical implications for enhancing ESG measurement and reporting readiness. Full article

Integrating on-device learning into autonomous systems requires neural network frameworks that achieve both high energy efficiency and low latency. While spiking neural networks (SNNs) provide a promising event-driven paradigm, implementing hardware-efficient learning remains a challenge due to the computational overhead of error signaling and global gradients. This paper introduces a hardware-oriented hierarchical spiking predictive coding (SPC) framework designed for end-to-end event-driven systems. The proposed architecture implements an implicit prediction error encoding mechanism through local lateral and supervisory feedback connections, eliminating the need for dedicated error-storage memory or complex inter-layer error communication. The entire framework is structured and parameterized for physical implementation, utilizing digital-aligned simulations and arithmetic operations. We evaluate the system on neuromorphic datasets using a fixed 1 ms temporal resolution to mirror real-time hardware constraints. Experimental results demonstrate that the SPC framework can effectively identify stimuli from transient event streams, achieving stable on-device learning. Our work provides a practical path toward deploying low-power, scalable hierarchical spiking networks in resource-constrained environments. Full article

The textile and clothing industry has historically exerted a significant negative impact on the environment. Excessive water consumption, chemical pollution, and soil degradation are just a few of the pressing environmental concerns linked to this sector. Addressing these issues has become a priority not only for regulatory bodies, at the National and European levels, but also for the industry itself. More recently, growing attention has turned to reducing the huge volume of waste generated by consumers’ unbridled purchase of clothing. In this context, the Circular Economy (CE) and the Digital Product Passport (DPP) have emerged as complementary approaches for improving product circularity, transparency, and traceability. However, in the textile and clothing sector, their effective implementation also depends on consumer participation in practices such as prolonged use, repair, reuse, and responsible end-of-life management. This article presents EcoProve, a gamified platform designed to encourage consumer engagement with CE practices through smart wardrobe management. The platform allows users to register garments, track usage, record maintenance and repair actions, and document sharing, donation, remaking, and recycling activities. These functionalities aim both to promote more sustainable clothing-related behaviours and to support the structured recording of use phase data relevant to DPP-oriented lifecycle information. This study reports the development and pilot validation of the platform with end users. The results suggest positive effects on environmental awareness, perceived understanding of sustainable textile-related practices, and initial self-reported changes in habits associated with clothing use and disposal. The findings support the potential of gamified digital platforms to foster consumer participation in CE systems in the textile and clothing sector while also indicating the need for broader and longer-term evaluations. Full article