Search Results (865)

The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed several challenges, contributions remain fragmented and difficult for non-technical users to apply. This work addresses the following research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems. Full article

This paper proposes a robust convolutional neural network (CNN) architecture for human activity recognition (HAR) using smartphone accelerometer data, evaluated on the WISDM dataset. We introduce two novel pooling mechanisms—Pooling A (Extrema Contrast Pooling (ECP)) and Pooling B (Center Minus Variation (CMV))—that enhance feature discrimination and noise robustness. ECP emphasizes sharp signal transitions through a nonlinear penalty based on the squared range between extrema, while CMV Pooling penalizes local variability by subtracting the standard deviation, improving resilience to noise. Input data are normalized to the [0, 1] range to ensure bounded and interpretable pooled outputs. The proposed framework is evaluated in two separate configurations: (1) a 1D CNN applied to raw tri-axial sensor streams with the proposed pooling layers, and (2) a histogram-based image encoding pipeline that transforms segment-level sensor redundancy into RGB representations for a 2D CNN with fully connected layers. Ablation studies show that histogram encoding provides the largest improvement, while the combination of ECP and CMV further enhances classification performance. Across six activity classes, the 2D CNN system achieves up to 96.84% weighted classification accuracy, outperforming baseline models and traditional average pooling. Under Gaussian, salt-and-pepper, and mixed noise conditions, the proposed pooling layers consistently reduce performance degradation, demonstrating improved stability in real-world sensing environments. These results highlight the benefits of redundancy-aware pooling and histogram-based representations for accurate and robust mobile HAR systems. Full article

Tillage practices critically influence soil’s physical properties, which are fundamental to sustainable agriculture in temperate climates. This review evaluates how conventional tillage (CvT; e.g., moldboard and chisel plowing), reduced tillage (RT), and conservation tillage (CT), particularly no-tillage (NT), affect six key indicators: bulk density (BD), saturated hydraulic conductivity (Ks), wet aggregate stability (WAS), penetration resistance (PR), available water capacity (AWC), and soil organic carbon (SOC). Special emphasis is placed on differentiating topsoil and subsoil responses to inform climate-resilient land management. A total of 70 peer-reviewed studies published between 1991 and 2025 were analyzed. Data were extracted for BD, Ks, WAS, PR, AWC, and SOC across tillage systems. Depths were standardized into topsoil (0–10 cm) and composite (>10 cm) categories. Descriptive statistics were used to synthesize cross-study trends. NT showed lower mean BD in the topsoil (1.32 ± 0.08 Mg/m³) compared with moldboard plow (1.33 ± 0.09) and chisel tillage (1.39 ± 0.12); however, the effects of tillage on BD were not statistically significant, while BD was higher at composite depths under NT (1.56 ± 0.09 Mg/m³), indicating subsoil compaction. Ks improved under NT, reaching 4.2 mm/h with residue retention. WAS rose by 33.4%, and SOC increased by 25% under CT systems. PR tended to be elevated in deeper layers under NT. Overall, CT, particularly NT, improves surface soil’s physical health and SOC accumulation in temperate agroecosystems; however, persistent subsoil compaction highlights the need for depth-targeted management strategies, such as controlled traffic, periodic subsoil alleviation, or deep-rooted cover crops, to sustain long-term soil functionality and climate-resilient production systems. Full article

Background/Objectives: Ulcerative colitis (UC) is an inflammatory bowel disease and a major cause of ulcers and chronic inflammation in the colon and rectum. Recurring symptoms include abdominal pain, rectal bleeding, and diarrhoea, and patients with UC are at a higher risk of developing comorbidities such as colorectal cancer and poor mental health. In UC, the decreased diversity and changed metabolic profile of gut microbiota, along with a diminished mucus layer, leads to disruption of the underlying epithelial barrier, with an ensuing excessive and detrimental inflammatory response. Treatment options currently rely on drugs that reduce the inflammation, but less emphasis has been placed on improving the resilience of the epithelial barrier. Macrolide antibiotics exhibit epithelial barrier-enhancing capacities unrelated to their antibacterial properties. Methods: We investigated two novel barriolides, macrolides with reduced antibacterial effects in common bacterial strains. Gut epithelial cell barrier resistance in the Caco-2 cell line, with and without co-culture with mucus-producing HT-29 cells, was increased when treated with barriolides. Using ^AXGut-on-Chip technology with inflammatory cytokine-stimulated Caco-2/HT-29 co-cultures, the effectiveness of the barriolides was confirmed. Lastly, we reveal the barrier-enhancing and inflammation-reducing effects of the barriolides in a dextran-sulphate sodium (DSS)-induced colitis mouse model. Results: We show the predictive power of the novel ^AXGut-on-Chip system and the effectiveness of the novel barriolides. Indications include reduced inflammatory response, increased epithelial barrier and decreased overall clinical score. Conclusions: The results of this study indicate the notion that barriolides could be used as a treatment option for UC. Full article

This article explores urban ‘societal resilience’ during the global pandemic of 2020–2021. This health crisis involved a complex interweaving of social, cultural, political, and economic processes which involved both top-down measures undertaken by nation-state governments and bottom-up actions by local residents. In a research study undertaken in two European cities—Stuttgart and London—we focussed on two migrant minorities and the involvement by ‘experts’ and ‘non-experts’ in the meso-level where these top-down measures and bottom-up actions met. Our study provided a grounded understanding of ‘layered resilience’ where resiliency develops through the disjunctive order of communication patterns, public service delivery, institutionalized dialogue, narratives, and values. Through distinguishing between resiliency and resilience, we seek to illustrate the ‘elastic’ character of urban modes of integration. Our study suggests the need for more empirically grounded investigations into the continuity and difference between adaptation and adjustment, normality and normalcy, and resilience and resiliency. It also highlights the importance of context-specific and path-dependent notions of resilience and resiliency. Full article

A major danger that significantly raises the possibility of deep coal mining accidents is the delayed water influx from the bottom plate, which is brought on by the activation of tiny faults brought on by mining at the working face of the restricted aquifer. This study develops 17 numerical models utilizing FLAC3D simulation software 6.00.69 to clarify the activation and water inburst mechanisms of minor faults influenced by various parameters, incorporating fluid–solid coupling effects in coal seam mining. The developmental patterns of the stress field, displacement field, plastic zone, and seepage field of the floor rock layer were systematically examined in relation to four primary factors: aquifer water pressure, minor fault angle, fracture zone width, and the distance from the coal seam to the aquifer. The results of the study show that the upper and lower plates of the minor fault experience discontinuous deformation as a result of mining operations. The continuity of the rock layers below is broken by the higher plate’s deformation, which is significantly larger than that of the lower plate. The activation and water flow into small faults are influenced by many elements in diverse ways. Increasing the distance between the coal seam and the aquifer will make the water conduction pathway more resilient. This will reduce the amount of water that flows in. On the other hand, higher aquifer water pressure, a larger fracture zone, and a fault that is tilted will all help smaller faults become active and create channels for water to flow into. The gray relational analysis method was used to find out how sensitive something is. The sensitivities of each factor to water influence were ranked from high to low as follows: distance between the aquifer and coal seam (correlation coefficient 0.766), aquifer water pressure (0.756), width of the fracture zone (0.710), and angle of the minor fault (0.673). This study statistically elucidates the inherent mechanism of delayed water instillation in minor faults influenced by many circumstances, offering a theoretical foundation for the accurate prediction and targeted mitigation of mine water hazards. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

Frontline workers across health, emergency, and social care sectors are repeatedly exposed to distressing events and chronic stressors as part of their occupational roles. Unlike single-event trauma, these cumulative exposures accrue over time, generating persistent psychological and physiological strain. Traditional diagnostic frameworks, particularly post-traumatic stress disorder (PTSD), were not designed to capture the layered and ongoing nature of this occupational trauma. This commentary introduces the concept of Persistent Traumatic Stress Exposure (PTSE), a framework that reframes trauma among frontline workers as an exposure arising from organisational and systemic conditions rather than solely an individual disorder. It aims to reorient understanding, responsibility, and intervention from a purely clinical lens toward systems, cultures, and organisational duties of care. PTSE is presented as an integrative paradigm informed by contemporary theory and evidence on trauma, moral injury, organisational stress, and trauma-informed systems. The framework synthesises findings from health, emergency, and social care settings, illustrating how repeated exposure, ethical conflict, and institutional pressures contribute to cumulative psychological harm. PTSE highlights that psychological injury may build across shifts, careers, and lifetimes, requiring preventive, real-time, and sustained responses. The framework emphasises that effective support is dependent on both organisational readiness, the structural conditions that enable trauma-informed work, and organisational preparedness, the practical capability to enact safe, predictable, and stigma-free responses to trauma exposure. PTSE challenges prevailing stigma by framing trauma as a predictable occupational hazard rather than a personal weakness. It aligns with modern occupational health perspectives by advocating for systems that strengthen psychological safety, leadership capability and access to support. By adopting PTSE, organisations can shift from reactive treatment models toward proactive cultural and structural protection, honouring the lived realities of frontline workers and promoting long-term wellbeing and resilience. Full article

In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is created at the interface by applying opposite in-plane, transverse electrical biases to the two segments. An efficient tight-binding theoretical formulation is developed to calculate electron states in the structure. In a reference configuration, we obtain energy eigenvalues and probability distributions that feature interface-confined electron eigenstates induced by the topological discontinuity. Moreover, to elucidate the implications of interface confinement for electron transport, a modified configuration is introduced to transform the eigenstates into transport-active, quasi-localized ones. We show that such states result in Fano “anti-resonances” in transmission spectra. The resilience of these quasi-localized states and their associated Fano fingerprints is examined with respect to fluctuations. Finally, a proof-of-concept band-stop electron energy filter is presented, highlighting the potential of this confinement mechanism and, more broadly, valley-dependent topological physics in designing nanoscale devices in gapless single-layer graphene. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article

Tropical cyclones typically weaken rapidly during poleward propagation due to decreasing sea surface temperatures and increasing vertical wind shear. Super Typhoon Oscar (1995) deviated from this pattern by maintaining Category-5 intensity at an anomalously high latitude. This study investigates the oceanic mechanisms driving this resilience by integrating satellite SST data with atmospheric (ERA5) and oceanic (HYCOM) reanalysis products. Our analysis shows that the storm track intersected a persistent marine heatwave (MHW) characterized by a deep thermal anomaly extending to approximately 150 m. This elevated heat content formed a strong stratification barrier at the base of the mixed layer (~32 m) that prevented the typical entrainment of cold thermocline water. Instead, storm-induced turbulence mixed warm subsurface water upward to effectively mitigate the negative cold-wake feedback. This process sustained extreme upward enthalpy fluxes exceeding 210 W m⁻² and generated a regime of thermodynamic compensation that enabled the storm to maintain its structure despite an unfavorable atmospheric environment with moderate-to-strong vertical wind shear (15–20 m s⁻¹). These results indicate that the three-dimensional ocean structure acts as a more reliable predictor of typhoon intensity than SST alone in regions affected by MHWs. As MHWs deepen under climate warming, this cold-wake mitigation mechanism is likely to become a significant factor influencing future high-latitude cyclone hazards. Full article

Strengthening their supply chain resilience constitutes a strategic priority for Chinese enterprises to respond to evolving globalization patterns and sustain long-term competitiveness in an increasingly sustainability-oriented market. This research systematically measures enterprise supply chain resilience by analyzing panel data from Chinese listed firms (2010–2022) through the tri-dimensional constructs of resistance capacity, recovery resilience, and adaptive creativity. Regression analyses demonstrate that digital transformation significantly improves enterprise supply chain resilience, exhibiting dual-threshold characteristics in nonlinear relationships. Mediation tests reveal that information sharing and resource integration capabilities serve as the critical transmission channels. Digital transformation demonstrates strong predictive validity for both the resistance capacity and adaptive creativity of supply chains. The seemingly paradoxical findings on enterprise operational efficiency highlight the need for a more layered and dynamic understanding of its underlying mechanisms. The positive impact of digital transformation on supply chain resilience demonstrates heterogeneity across state-owned versus non-state-owned enterprises, regions, and industry types. The findings offer actionable insights for orchestrating digital transformation initiatives and designing tiered supply chain resilience frameworks that support enterprises’ sustainability goals. Full article

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive industry. To overcome these analytical limitations, this study implements a multilayer network framework for examining global automotive supply chains spanning 2017 to 2023. The research particularly emphasizes the identification of critical risk sources through both static and dynamic analytical perspectives. The static analysis employs multilayer degree and strength centralities to illuminate the pivotal roles that countries such as China, the United States, and Germany play within these multilayer automotive supply networks. Conversely, the dynamic risk propagation model uncovers significant cascade effects; a disruption in a major upstream supplier can propagate through intermediary layers, ultimately impacting over 85% of countries in the finished automotive layer within a short temporal threshold. Furthermore, this study investigates how individual nations’ anti-risk capabilities influence the overall resilience of multilayer automotive supply networks. These insights offer valuable guidance for policymakers, enabling strategic topological modifications during disruption events and enhanced protection of the most vulnerable risk sources. Full article

The merging of the Internet of Things (IoT) and Artificial Intelligence (AI) advances has intensified challenges related to data authenticity and security. These advancements necessitate a multi-layered security approach to ensure the security, reliability, and integrity of critical infrastructure and intelligent surveillance systems. This paper proposes a two-layered security approach that combines a discrete cosine transform least significant bit 2 (DCT-LSB-2) with artificial neural networks (ANNs) for data forensic validation and mitigating deepfakes. The proposed model encodes validation codes within the LSBs of cover images captured by an IoT camera on the sender side, leveraging the DCT approach to enhance the resilience against steganalysis. On the receiver side, a reverse DCT-LSB-2 process decodes the embedded validation code, which is subjected to authenticity verification by a pre-trained ANN model. The ANN validates the integrity of the decoded code and ensures that only device-originated, untampered images are accepted. The proposed framework achieved an average SSIM of 0.9927 across the entire investigated embedding capacity, ranging from 0 to 1.988 bpp. DCT-LSB-2 showed a stable Peak Signal-to-Noise Ratio (average 42.44 dB) under various evaluated payloads ranging from 0 to 100 kB. The proposed model achieved a resilient and robust multi-layered data forensic validation system. Full article