Search Results (40,723)

Reliability is a fundamental aspect of modern structural engineering due to the inherent randomness of materials, loads, and environmental conditions. However, as system complexity increases, a substantial computational cost is typically required to evaluate the failure probability, often involving 10⁵–10⁶ limit state function evaluations in a conventional Monte Carlo simulation. To address this challenge, this study presents an active-learning-driven deep neural network (ALDNN) meta model algorithm to improve both efficiency and accuracy in reliability analysis. To substantially reduce the computational costs, a multi-phase active learning framework incorporating weighted sampling and adaptive threshold-based candidate filtering is implemented by iteratively selecting more important points and adaptively training deep neural networks. Thresholds for candidate sample points and training datasets are gradually adjusted based on feedback from estimated responses. The proposed method reduces the number of true limit state evaluations to the order of 10² in the benchmark problems considered, while maintaining high accuracy. Its performance is assessed using widely referenced benchmark problems, and finite-element-method-based implicit examples for frame structures are further employed to verify applicability. The results demonstrate the high efficiency, accuracy, and scalability of the ALDNN meta model as system complexity increases. Full article

Geopolymer concrete, with its waste-reutilization property and reduction in carbon footprint relative to conventional concrete, combined with high-mechanical properties, has gained very broad recognition during the last few years. In real-life applications, however, there exists some variation in its mechanical properties, which has a direct impact on the structural safety and reliability. Thus, there is a strong need to thoroughly explore this variability in performance and its implications for the structure. This paper has used response surface methodology to explore the influence of three factors, namely fly ash to binder ratio, aggregate to binder ratio, and water to binder ratio (W/B). Mix proportions had been developed to 13 mixes, and 260 specimens were tested in compressive strength, splitting tensile strength, elastic modulus, and slump. There were statistical properties calculated. The findings have shown that the effect of W/B on the coefficient of variation (COV) of compressive and splitting tensile strength is a significant one. W/B below 0.45 indicates that the COV of compressive and splitting tensile strengths is kept at a low level of 0.05–0.08. Nevertheless, at W/B above 0.48, the COV is so high as above 0.15. Through statistical testing, the compressive strength was found to be normally distributed (p = 0.0585, μ = 0.9800, σ = 0.572) and this is consistent with the normal distribution general structure of ordinary Portland cement concrete and the splitting tensile strength was found to be of a Weibull distribution (p = 0.6673, μ = 0.9427, σ = 0.1678), which reflects the standard deviation of the strength pattern and brittle nature of the material when it is subjected to a tensile load. Full article

Optimizing layout schemes for multi-branch river hubs is complex due to the need to balance conflicting goals—safety, ecology, and economy—under significant uncertainty. To address these challenges, this study proposes a comprehensive evaluation method integrating a dynamic weighting mechanism and a two-dimensional cloud model. First, we constructed an evaluation index system covering engineering safety and benefits. A multi-agent game theory approach was employed for combination weighting to reconcile the diverse interests of government, environmental, and community agents. Furthermore, a dynamic mechanism was introduced to adjust indicator importance across three key stages: dam site selection, hub layout, and detail optimization. Subsequently, the schemes’ uncertainty and risk status were quantified using a two-dimensional cloud model within a “probability-loss” framework. The methodology was validated using the Ganjiang River Hub Project. The results demonstrate that the method effectively captures the evolutionary path of decision-making priorities, transitioning from “safety-first” in early stages to “benefit-maximization” later. This study provides robust, stage-aware, and visual decision support for complex hydraulic engineering layouts, ensuring a scientific trade-off between risk control and comprehensive benefits. Full article

Background: Sarcopenia is highly prevalent in older adults and in individuals with impaired kidney function, where it is associated with adverse clinical outcomes. A creatinine–cystatin C–based sarcopenic index has been proposed as a surrogate marker of muscle status; however, its association with sarcopenia as defined by the EWGSOP2 framework, particularly in the context of renal dysfunction, remains uncertain. Methods: Older adults were classified according to EWGSOP2 criteria into probable, confirmed, and severe sarcopenia. Associations between the sarcopenic index and sarcopenia phenotypes were examined using group comparisons and multivariable logistic regression analyses in the overall cohort and in a subgroup of participants with an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Results: The sarcopenic index was not independently associated with probable, confirmed, or severe sarcopenia. In contrast, age emerged as the strongest independent correlate of probable sarcopenia (OR 1.12; 95% CI 1.05–1.19, p = 0.001), while body mass index was independently associated with confirmed sarcopenia (OR 0.91; 95% CI 0.86–0.96, p < 0.001). Similar patterns were observed in participants with reduced kidney function. Conclusions: Within the present analytical framework, the sarcopenic index did not show a meaningful association with EWGSOP2-defined probable sarcopenia, the most uniformly assessable EWGSOP2 stage, in older adults, including those with reduced kidney function. Exploratory analyses of more advanced sarcopenia stages did not reveal additional associative information. These findings should be interpreted within a descriptive and associative framework rather than a formal assessment of diagnostic or clinical decision-making performance. Full article

Unmanned aerial vehicles (UAVs) are pivotal for 6G ubiquity, yet their open line-of-sight channels increase vulnerability to interception, posing new challenges for covert communication. This paper proposes a joint optimization scheme for multi-UAV relay-assisted covert communication system with the maximum channel capacity relay selection (MCRS) criterion. Distinct from conventional single-UAV approaches, this scheme uniquely couples UAV geometric positions with the time-varying characteristics of the wireless channels, exploiting spatial diversity from UAV relays to mitigate small-scale fading in dense urban environment, and jointly optimizes the transmit power and UAVs’ altitude. Specifically, we first designed an optimal relay selection strategy and derived analytical expressions for detection error and outage probabilities over altitude-dependent Nakagami-m fading channels. Furthermore, we maximized the effective covert rate by jointly optimizing the UAVs’ hovering altitude and adaptive transmit power of source and relays, subject to covert constraints. Extensive numerical results demonstrate a near-perfect match between the derived theoretical expressions and Monte Carlo simulations and validate the accuracy of our theoretical model. Compared against conventional single-UAV and multi-fixed-altitude UAV benchmark schemes, simulations demonstrate that the joint optimization scheme with relay selection proposed significantly enhances the covert performance of UAV-assisted communication systems. Full article

This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications. Full article

Background/Objectives: Studies evaluating the impact of diet quality on nutrition- and muscle-related outcomes in cancer are limited. This study aimed to understand the diet quality of people with lung cancer and its cross-sectional associations with malnutrition, low muscle mass and (probable)-sarcopenia. Methods: Three-day food records were collected from 47 adults (mean ± SD age 70.6 ± 8.6 years; 58% male) with lung cancer prior to, or within one week, of curative-intent (chemo)radiotherapy. Dietary Guidelines Index (DGI-2013) and Mediterranean Diet Score (MDS) estimated diet quality, reflecting established healthy eating patterns. Malnutrition was determined using Patient Generated Subjective Global Assessment (PG-SGA). Low muscle mass was estimated using diagnostic third lumbar vertebra computed tomography (CT) images. (Probable)-sarcopenia was determined using the revised European Working Group for Sarcopenia in Older People definition, including low muscle (grip) strength, muscle mass and impaired function. Multivariate adjusted logistic regression analyses estimated odds ratios (OR) and 95% confidence intervals (CI) for associations between diet quality and outcomes. Results: Prevalence of malnutrition, low muscle mass and (probable)-sarcopenia were 36.2%, 50.0% and 13.6%, respectively. Mean ± SD DGI-2013 score was 53.0 ± 13.0. Adherence to the DGI-2013 was not significantly associated with malnutrition (OR, 0.67 [95%CI 0.35, 1.28]), low muscle mass (0.90 [95%CI 0.47, 1.70]) or (probable)-sarcopenia (0.73 [95%CI 0.29, 1.80]). Mean ± SD MDS was 3.6 ± 1.5. Adherence to the MDS was not significantly associated with malnutrition (0.75 [95%CI 0.37, 1.49]), low muscle mass (0.98 [95%CI 0.51, 1.88]) or (probable)-sarcopenia (1.82 [95%CI 0.72, 4.85]). Conclusions: Diet quality was not associated with malnutrition, low muscle mass or (probable)-sarcopenia. Given that overall diet quality was poor, it remains unclear whether high diet quality may be associated with nutritional status or muscle-related outcomes. Further research is needed to determine whether diet quality should be considered in nutritional interventions during lung cancer treatment. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

Nosy Be in the northwestern Madagascar hosts one of the largest known seasonal feeding aggregations of whale sharks. However, the environmental drivers influencing whale shark surface feeding behaviour in this area remain poorly understood. This study investigates the relationship between environmental variability and surface feeding strategies of immature whale sharks at Nosy Be. Boat-based surveys were conducted in November 2018, 2019, 2022, and 2023, resulting in the photo-identification of 88 individuals and the recording of 85 surface feeding events. The influence of environmental factors on feeding behaviour was assessed using multicollinearity among the environmental covariates and three-level step approach: permanova, multinomial logistic regression, marginal effects, and Cochran’s Q, to evaluate whether environmental conditions discriminate feeding-behaviour categories and to quantify how individual covariates relate to behavioural composition under a multi-step framework. Results showed that there is not a strong enough predictive signal for behaviour based on environmental variables; however, thanks to the marginal effects, it is possible to better assess the probability of a certain type of eating behaviour in the presence of an increase in one of the environmental variables, for example, chlorophyll-a appears to be the most interesting, because its increase is associated with a greater probability of some behaviours instead the others. These preliminary observations provide the first insights to evaluate environmental influences on immature whale shark surface feeding behaviour in Nosy Be, highlighting that it is therefore necessary to deepen and increase data collection to have long and significant series of data, integrated also with data on the preys subject to feeding behaviour and to evaluate which other unobserved aspects, perhaps linked precisely to the consistency and quality of the prey, could allow us to predict feeding behaviour. Improving the understanding of these relationships is essential for predicting whale shark habitat use and for supporting conservation and management strategies in a region increasingly affected by climate variability and anthropogenic pressures. Full article

This paper continues the development of information-geometric models for data analysis and physics by focusing on their formulation and interpretation through variational principles. Building on the geometric framework introduced previously, we investigate how fundamental variational structures—such as information-theoretic functionals—naturally encode the laws of nature. In the first manuscript, we showed that a wide class of physical problems can be expressed as constrained variational problems on spaces of probability distributions, leading to geodesic flows, gradient dynamics, and generalized Hamiltonian formulations on statistical manifolds. In this second part, we extend the variational formalism by utilizing an extended metric, clarifying the geometric origin of the dynamical equations commonly used in modern physics and providing a coherent interpretation of physical laws in terms of information optimization. By emphasizing variational foundations, this paper strengthens the conceptual and mathematical links between information geometry, data analysis, and physics, and it provides a flexible framework for extending geometric methods to complex, high-dimensional, and dynamical systems. Full article

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance ($p<0.001$), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%. Full article

User behavior anomaly detection plays a vital role in network security for identifying malicious access and abnormal activities in high-dimensional internet user behavior data. Although Transformer architectures have been widely adopted in anomaly detection tasks, and their integration with Variational Autoencoders (VAEs) has often been used to further improve detection accuracy, existing integration methods have failed to effectively balance global feature dependency modeling and generative data distribution learning. This results in limited capability in identifying complex anomalous patterns. To address this issue, this paper proposes DF-TransVAE, a novel deeply integrated framework that advances the integration of a Transformer and a VAE for supervised anomaly detection. The framework first fuses global contextual representations from the Transformer encoder with original input features, then maps the fused representation into the latent space via the VAE encoder. A cross-attention mechanism is introduced as the core of deep integration, enabling dynamic, bidirectional interaction between the fused features and latent variables to enhance information fusion. Lastly, a fully connected classifier equipped with residual connections outputs anomaly probabilities for supervised binary classification. Experimental results on two public datasets demonstrate that the proposed framework achieves better performance than existing deep learning methods in terms of accuracy, precision, recall, and F1-score, particularly in detecting complex anomalous patterns. Our results indicate that the deep integration mechanism we propose effectively addresses the limitations of conventional Transformer–VAE combinations. Full article

Background/Objectives: Chronic limb-threatening ischemia (CLTI) is a severe form of peripheral artery disease characterized by ischemic rest pain or ulcer necrosis. In Europe, spinal cord stimulation (SCS) can be offered to CLTI patients with chronic pain to improve mobility and prolong limb preservation. We evaluated the long-term, real-world outcomes of SCS therapy in patients with CLTI. Methods: In this observational study, medical chart review data from consecutive CLTI patients treated with SCS were analyzed. Results: Fifty-three patients (56.6% Fontaine Stage III, 39.6% Fontaine Stage IV, 3.8% Fontaine Stage IIb) had a single-stage SCS implant procedure between 2013 and 2022. Two years after SCS therapy activation, claudication pain intensity had significantly improved; the overall numerical rating scale pain score decreased from 9.4 ± 0.9 at baseline to 3.7 ± 3.2 (p < 0.0001). In addition, walking distance increased by more than 350 m (from 70 ± 87 to 429 ± 320 m, p < 0.0001), and pre-existing skin lesions stabilized in ten patients (63%). The probability of limb survival in Fontaine’s stage IIb/III and Fontaine’s stage IV patients at 12 months was 90% and 70%, respectively (log-rank p-value = 0.04). Finally, significant associations were found between the occurrence of an amputation after SCS and Fontaine Stage (p = 0.01), active smoking (p = 0.02), hypertension (p = 0.04), and prior minor amputation (p = 0.02). No major complications were reported. Conclusions: Our real-world experience suggests that SCS for CLTI patients provides significant and durable improvements in ischemic pain and functional outcomes. SCS may also help reduce the natural risk of major amputation, especially when implemented at early CLTI stages. Full article

UAV-Assisted Mobile Ad Hoc Networks (UAMANETs) provide flexible communication support in dynamic and infrastructure-limited environments. This paper studies a representative UAMANET architecture in which a subset of UAVs forms stable task clusters with ground nodes while simultaneously acting as relays in an airborne backbone network. To characterize the network capacity under contention-based medium access and multi-hop routing, we introduce Aggregate Multi-hop Information Efficiency (AMIE), a capacity-oriented metric that jointly accounts for MAC-layer contention, multi-hop routing, and end-to-end transmission reliability. Based on an IEEE 802.11p access model, we extend Bianchi’s CSMA/CA analytical framework to UAMANETs, enabling a quantitative characterization of how spectrum resource allocation affects AMIE through link activation probability, transmission interruption, and end-to-end hop count. Building on the derived analytical insights, we further develop a soft centralized resource management framework, in which an existing MSF-PSO algorithm is employed as a numerical solver to optimize resource allocation under implicit MAC-layer coupling constraints. Numerical results demonstrate that, compared with conventional IEEE 802.11p spectrum resource settings, the proposed framework can achieve substantial AMIE improvements under representative network configurations. Full article

Background and Objectives: High-risk resected cutaneous melanoma carries a substantial risk of recurrence, and additional host-related prognostic biomarkers are needed beyond conventional tumor-centered factors. Red blood cell distribution width (RDW) reflects systemic inflammation and physiological stress and may provide incremental prognostic information. Materials and Methods: In this retrospective cohort study, 164 patients with stage II–III cutaneous melanoma who underwent curative-intent surgical resection were analyzed. A receiver operating characteristic (ROC) curve analysis determined the optimal RDW cut-off for relapse-free survival (RFS), which was 14.2%. Patients were categorized into low and high RDW groups accordingly. Survival probabilities were estimated using the Kaplan–Meier method and compared with the log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to evaluate associations between RDW status, clinicopathological variables, and RFS. Results: During a median follow-up of 58.3 months, patients with high RDW had significantly shorter RFS compared with those with low RDW. In univariate analysis, elevated RDW was associated with an increased risk of recurrence (HR 2.79, 95% CI 1.39–5.58; p = 0.004). After adjustment for key prognostic factors (e.g., stage, Breslow, age, adjuvant therapy), high RDW remained an independent predictor of inferior RFS (HR 2.74, 95% CI 1.37–5.47; p = 0.004). Stage III disease also independently predicted worse RFS (HR 4.67, 95% CI 2.04–10.68; p < 0.001). Conclusions: Baseline RDW independently predicts RFS in high-risk resected stage II–III cutaneous melanoma and may enhance prognostic stratification using a simple, widely available biomarker. Full article