Search Results (2,666)

The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load. Full article

In this study, the seismic performance of reinforced concrete (RC) L-shaped columns, strengthened with 100 mm and 150 mm wing walls, was determined using quasi-static tests. A total of nine L-shaped column specimens were designed and tested under cyclic loading. This study found that strengthening with wing walls increased the lateral stiffness and horizontal load bearing capacity of L-shaped columns. Notably, such improvement was found to be more significant under higher axial compression ratios, exhibiting maximum increases of 254% and 194% in load bearing capacity, in the positive and negative loading directions, respectively. Additionally, ductility was influenced by the wing wall length and axial compression ratios. Under a low axial compression ratio, the ductility coefficient first increased and then decreased with an increase in the wall length. Conversely, under a high axial compression ratio, ductility was consistently improved with increasing wall length. Furthermore, finite element (FE) models were established, and they successfully validated the experimental results, such as load–displacement responses, hysteresis behavior, skeleton curves and ultimate bearing capacity. The numerical results further strengthened the significant effect of the wing wall addition on the seismic performance of the L-shaped columns. Based on the results, a lateral capacity calculation formula is developed, providing a reliable method for assessing the seismic performance of the strengthened L-shaped columns. Therefore, the findings of this study present theoretical insights and practical guidance for the seismic retrofitting of existing RC structures with special-shaped columns. Full article

This paper introduces cognitive adaptivity as a novel framework for addressing human factors in cybersecurity during the Industry 5.0–6.0 transition, with a focus on hard-to-abate industries where digital transformation intersects sustainability constraints. While the integration of IoT, automation, digital twins, and artificial intelligence expands industrial efficiency, it simultaneously exposes organizations to increasingly sophisticated social engineering and AI-powered attack vectors. Traditional resilience-based models, centered on recovery to baseline, prove insufficient in these dynamic socio-technical ecosystems. We propose cognitive adaptivity as an advancement beyond resilience and antifragility, defined by three interrelated dimensions: learning, anticipation, and human–AI co-evolution. Through an in-depth case study of the ceramic value chain, this research develops a conceptual model demonstrating how organizations can embed trust calibration, behavioral evolution, sustainability integration, and systemic antifragility into their cybersecurity strategies. The findings highlight that effective protection in Industry 6.0 environments requires continuous behavioral adaptation and collaborative intelligence rather than static controls. This study contributes to cybersecurity literature by positioning cognitive adaptivity as a socio-technical capability that redefines the human–AI interface in industrial security. Practically, it shows how organizations in hard-to-abate sectors can align cybersecurity governance with sustainability imperatives and regulatory frameworks such as the CSRD, turning security from a compliance burden into a strategic enabler of resilience, competitiveness, and responsible digital transformation. Full article

While weighted multi-model approaches are widely used to improve predictive capability, hydrological models (HMs) and their weighted combinations that perform well under past conditions may not guarantee robustness under future climate scenarios. Furthermore, the extent to which weighting schemes influence the propagation of runoff projection uncertainty remains insufficiently explored. Therefore, this study evaluates the capacity of strategies that weight monthly scale HMs to narrow runoff projection uncertainty. Since standard approaches rely only on historical simulation skill and offer static weighting, this study introduces a refined framework, the Uncertainty Optimizing Multi-Model Ensemble (UO-MME), which dynamically considers the trade-offs between calibration performance and projection uncertainty. In performing the uncertainty decomposition, a total of 140 ensemble runoff projections, generated through a modelling chain comprising five GCMs, two emission scenarios, two downscaling methods, and seven HMs, were analyzed for Beydag and Tahtali watersheds in Türkiye. Results indicate that standard techniques, such as Bayesian model averaging, ordered weighted averaging, and Granger–Ramanathan averaging, led to either marginal reductions or noticeable increases in projection uncertainty, depending on the case and projection period. Conversely, the UO-MME achieved average reductions in projection uncertainty of around 30% across the two watersheds by balancing the influences of climate signals produced by GCMs that are reflected in the projections through HMs while maintaining high simulation accuracy, as indicated by Nash–Sutcliffe efficiency values exceeding 0.75. Although not designed to eliminate inherently irreducible uncertainty, the UO-MME framework helps temper the inflation of noisy GCM signals in runoff responses, providing more balanced hydrological projections for water resources planning. Full article

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

This study evaluated serum neurofilament light chain (NfL) as a biomarker for spinal cord diseases in dogs, including 46 healthy dogs and 76 with conditions, such as intervertebral disc herniation (IVDH), syringomyelia (SM), fibrocartilaginous embolism (FCE), and acute non-compressive nucleus pulposus extrusion (ANNPE). There was a significant difference in serum NfL levels between healthy dogs (12.55 pg/mL) and those with spinal cord diseases (91.10 pg/mL; p < 0.0001). The NfL level in dogs with SM (50.7 pg/mL) was significantly lower than that in dogs with IVDH (99.3 pg/mL; p = 0.012) and those with other diseases, including FCE and ANNPE (241.0 pg/mL; p = 0.002). The area under the curve for differentiating between dogs with spinal cord diseases and healthy dogs was 0.91, with an optimal NfL cutoff value of 30.31 pg/mL (sensitivity of 80.68%; specificity of 91.30%). For dogs with IVDH treated solely with medication, the serum NfL levels in the Poor and Static group (180.0 pg/mL) were significantly higher than those in the Partial and Good group (81.30 pg/mL) (p = 0.03). Serum NfL is a promising biomarker for neuroaxonal injury, aiding in differentiating SM from other spinal cord diseases and evaluating treatment response. Full article

To investigate the failure characteristics and high-strain-rate mechanical response of polyvinyl alcohol-modified alkali-activated materials (PAAMs) under static and dynamic impact loads, quasi-static and uniaxial impact compression tests were performed on AAMs with varying PVA content. These tests employed a universal testing machine and an 80 mm diameter split Hopkinson pressure bar (SHPB). Digital image correlation (DIC) was then utilized to study the surface strain field of the composite material, and the crack propagation process during sample failure was analyzed. The experimental results demonstrate that the compressive strength of AAMs diminishes with higher PVA content, while the flexural strength initially increases before decreasing. It is suggested that the optimal PVA content should not exceed 5%. When the strain rate varies from 25.22 to 130.08 s⁻¹, the dynamic compressive strength, dissipated energy, and dynamic compressive increase factor (DCIF) of the samples all exhibit significant strain rate effects. Furthermore, the logarithmic function model effectively fits the dynamic strength evolution pattern of AAMs. DIC observations reveal that, under high strain rates, the crack mode of the samples gradually transitions from tensile failure to a combined tensile–shear multi-crack pattern. Furthermore, the crack propagation rate rises as the strain rate increases, which demonstrates the toughening effect of PVA on AAMs. Full article

The On-Load Tap Changer (OLTC) in power transformers is a critical component responsible for regulating the output voltage, and the early detection of OLTC faults is essential for maintaining power grid stability. In this paper, we propose a one-class deep learning anomaly detection model named “U-AttentionFlow” based on acoustic signals from the OLTC operation. The proposed model is trained exclusively on normal operating data to accurately model normal patterns and identify anomalies when new signals deviate from the learned patterns. To enhance the ability of the model to focus on significant features, we integrate the squeeze-and-excitation (SE) block and Convolutional Block Attention Module (CBAM) into the network architecture. Furthermore, static positional encoding and multihead self-attention (MHSA) are employed to effectively learn the temporal characteristics of time-series acoustic signals. We also adopted a U-Flow-style invertible multiscale coupling structure, which integrates features across multiple scales while ensuring the invertibility of the model. Experimental validation was conducted using acoustic data collected under realistic voltage and load conditions from actual ECOTAP VPD OLTC equipment, resulting in an anomaly detection accuracy of 99.15%. These results demonstrate the outstanding performance and practical applicability of the U-AttentionFlow model for OLTC anomaly detection. Full article

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

Traditional energy harvesting systems, such as photovoltaics and wind power, often rely on external environmental conditions and are typically associated with contact-based vibration wear and bulky structures. This study introduces light-fueled self-vibration to propose a self-harvesting system, consisting of liquid crystal elastomer fibers, two resistors, and two piezoelectric cantilever beams arranged symmetrically. Based on the photothermal temperature evolution, we derive the governing equations of the liquid crystal elastomer fiber–piezoelectric beam system. Two distinct states, namely a self-harvesting state and a static state, are revealed through numerical simulations. The self-oscillation results from light-induced cyclic contraction of the liquid crystal elastomer fibers, driving beam bending, stress generation in the piezoelectric layer, and voltage output. Additionally, the effects of various system parameters on amplitude, frequency, voltage, and power are analyzed in detail. Unlike traditional vibration energy harvesters, this light-fueled self-harvesting system features a compact structure, flexible installation, and ensures continuous and stable energy output. Furthermore, by coupling the light-responsive LCE fibers with piezoelectric transduction, the system provides a non-contact actuation mechanism that enhances durability and broadens potential application scenarios. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

Organoid technology has emerged as a revolutionary tool in cancer research, offering physiologically accurate, three-dimensional models that preserve the histoarchitecture, genetic stability, and phenotypic complexity of primary tumors. These self-organizing structures, derived from adult stem cells, induced pluripotent stem cells, or patient tumor biopsies, recapitulate critical aspects of tumor heterogeneity, clonal evolution, and microenvironmental interactions. Organoids serve as powerful systems for modeling tumor progression, assessing drug sensitivity and resistance, and guiding precision oncology strategies. Recent innovations have extended organoid capabilities beyond static culture systems. Integration with microfluidic organoid-on-chip platforms, high-throughput CRISPR-based functional genomics, and AI-driven phenotypic analytics has enhanced mechanistic insight and translational relevance. Co-culture systems incorporating immune, stromal, and endothelial components now permit dynamic modeling of tumor–host interactions, immunotherapeutic responses, and metastatic behavior. Comparative analyses with conventional platforms, 2D monolayers, spheroids, and patient-derived xenografts emphasize the superior fidelity and clinical potential of organoids. Despite these advances, several challenges remain, such as protocol variability, incomplete recapitulation of systemic physiology, and limitations in scalability, standardization, and regulatory alignment. Addressing these gaps with unified workflows, synthetic matrices, vascularized and innervated co-cultures, and GMP-compliant manufacturing will be crucial for clinical integration. Proactive engagement with regulatory frameworks and ethical guidelines will be pivotal to ensuring safe, responsible, and equitable clinical translation. With the convergence of bioengineering, multi-omics, and computational modeling, organoids are poised to become indispensable tools in next-generation oncology, driving mechanistic discovery, predictive diagnostics, and personalized therapy optimization. Full article

Fiber-optic sensing systems based on a forward transmission interferometric structure can achieve high sensitivity and a wide frequency response over long distances. However, there are still shortcomings in its ability to position multi-point vibrations and detect low-frequency vibrations, which limits its usefulness. To address these challenges, we study the viability of merging long-range forward-transmission distributed vibration sensing (FTDVS) with high spatial resolution optical frequency-domain reflectometry (OFDR), forming the first reported hybrid distributed sensing method between these two methods. The probe light source is shared between the two sub-systems, which utilizes stable linear optical frequency sweeping facilitated by high-order sideband injection locking. As a result, this is a new approach for the FTDVS method, which conventionally uses fixed-frequency continuous light. The method of nearest neighbor signal replacement (NSR) is proposed to address the issue of discontinuity in phase demodulation under periodic external modulation. The experimental results demonstrate that the hybrid system can determine the position of vibration signals between 0 and 900 Hz within a sensing distance of 21 km. When the sensing distance is extended to 71 km, the FTDVS module can still function adequately for high-frequency vibration signals. This hybrid architecture offers a fresh approach to simultaneously achieving long-distance sensing and wide frequency response, making it suitable for the combined measurement of dynamic (e.g., gas leakage, pipeline excavation warning) and quasi-static (e.g., pipeline displacement) events in long-distance applications. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Vulnerability is often analysed as a static condition of residents at a location, exposed to disaster and other risks. Studies on individual aspects of mobility and vulnerability exist, but comprehensive studies or guiding frameworks are lacking. The paper’s unique contribution compared to existing vulnerability models lies in emphasising vulnerability not only at fixed places, but also during transit, movement, and temporary phases. This paper highlights the current state of research on mobility vulnerability within disaster risk contexts. Through a systematic literature review, the study discovers a lack of research analysing specific vulnerabilities during mobility. Additionally, existing vulnerability frameworks are improved by incorporating (i) disaster risk and impact scenarios, (ii) different types of movements and mobilities linked to disaster risk situations, (iii) multiple localities, modalities, and temporalities, as well as multiple risks during sequences of movement and stationary phases, (iv) daily and occasional hazards, and (v) emic and etic perspectives on vulnerability. The findings of this study aim to inform future research on risk and vulnerability, supporting more effective responses amidst the changing dynamics of disaster situations. Full article