Search Results (1,138)

In urban underground space construction using shield tunnelling, the geological conditions ahead of the tunnel face are often uncertain. Without timely and accurate classification of the ground type, mismatches in operational parameters, uncontrolled costs, and schedule risks are likely to occur. Using observations from an earth pressure balance (EPB) project on an urban railway, a data-driven classification framework is developed that integrates shield tunnelling operating measurements with physically derived quantities to discriminate among soft soil, hard rock, and mixed strata. Principal component analysis (PCA) is performed on the training set, followed by a systematic comparison of tree-based classifiers and hyperparameter optimization strategies to explore the attainable performance. Under unified evaluation criteria, a categorical bosting (CatBoost) model optimized by a Nevergrad combination strategy (NGOpt) attains the highest test accuracy of 0.9625, with macro-averaged precision and macro-averaged recall of 0.9715 and 0.9716, respectively. To mitigate optimism from single-point estimates, stratified bootstrap intervals are reported for the test set. A Monte Carlo experiment applies independent perturbations to the PCA-transformed features, producing low label-flip rates across the three classes, with only minor changes in probability calibration metrics, which suggests consistent decisions under sensor noise and sampling bias. Overall, within the scope of the considered EPB project, the study delivers a compact workflow that demonstrates the feasibility of uncertainty-aware ground-type classification and provides a methodological reference for developing decision-support tools in underground tunnel construction. Full article

The ramifications of climate change, which are projected to lead to increased drought, desertification, and water scarcity, are expected to have a significant impact on the agricultural sector of Türkiye, particularly in the Mediterranean coastal regions. This study presents an extensive evaluation of potential agricultural drought conditions in southwestern Türkiye, using a high-resolution, convection-permitting (0.025°) modeling approach. We employ a single, physically consistent model chain, dynamically downscaling the CMIP6 MPI-ESM-HR Earth System Model with the COSMO-CLM regional climate model at a convection-permitting (CP) resolution (0.025°) under IPCC Shared Socioeconomic Pathways SSP3-7.0, reflecting a high-emission scenario with regional socioeconomic challenges. Southwestern Türkiye, situated at the intersection of the Mediterranean and continental climates, hosts rare climatic and ecological conditions that sustain a highly productive and diverse agricultural system. This region forms the backbone of Türkiye’s agricultural economy but is increasingly vulnerable to climate variability and fluctuations that threaten its agricultural stability and resilience. Our study employs a novel approach that utilizes multivariate assessment of agricultural drought in the Mediterranean Region by integrating precipitation, soil moisture, and temperature variables from 2.5 km resolution climate simulations. Agricultural drought conditions were evaluated using the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSI), and the Standardized Temperature Index (STI), derived by normalizing respective climate variables from climate simulations spanning from 1995 to 2014 for the historical period, from 2040 to 2049 and from 2070 to 2079 for future projections. CP climate simulations (CPCSs) exhibit a modest warm and dry bias during all seasons but slightly wetter conditions during summer when compared with station observations. Correlations between indices indicate that soil moisture variations in the future will become more sensitive to changes in temperature rather than precipitation. Results from this specific model chain reveal that the probability of compound events where precipitation and soil moisture deficits coincide with anomalously high temperatures will rise for all threshold levels under the SSP3-7.0 scenario towards the end of the century. For the most severe conditions (|Z| > 1.2), the compound likelihood increases to about 3%, highlighting the enhanced occurrence of rare events in a changing climate. These findings, conditional on the model and scenario used, provide a high-resolution, physically grounded perspective on the potential intensification of agricultural drought regimes. Full article

Regulatory agencies worldwide have implemented stringent measures to monitor and reduce Salmonella contamination in poultry products. Rapid quantitative detection methods enable producers to identify contamination early, implement corrective actions, and enhance food safety. This study aimed to develop and optimize a surface plasmon resonance (SPR) biosensor for the quantitative detection of Salmonella Typhimurium in ground chicken. The sensor surface was functionalized with a well-characterized monoclonal antibody specific to Salmonella flagellin, and an SPR workflow was established for quantitative analysis. Ground chicken samples were inoculated with four S. Typhimurium strains at contamination levels ranging from −0.5 to 3.5 Log CFU/g and enriched at 42 °C for 10 or 12 h prior to SPR analysis. Contamination levels were confirmed using the Most Probable Number (MPN) method. Linear regression analysis indicated that optimal quantification was achieved after 10 h of enrichment (R² ≥ 0.86), whereas extended enrichment (12 h) did not improve performance. The limit of quantification (LOQ) was below 1 CFU/g. A strong positive correlation (R² ≥ 0.85) was observed between SPR and MPN results, demonstrating consistency between the two methods. These findings highlight SPR as a rapid, reliable, and cost-effective alternative to conventional methods for Salmonella quantification. By delivering accurate results within a single day, SPR enhances testing efficiency and supports the production of safer poultry products, thereby reducing public health risks associated with Salmonella contamination. Full article

Abductive reasoning—the search for plausible explanations—has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single “best” explanation remains. This reductionist framing fails on two critical fronts. First, it overlooks how human reasoners naturally sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. Second, when applied to complex investigations in legal or scientific domains, it forces destructive competition between hypotheses that later prove compatible or even synergistic, as demonstrated by historical cases in physics, astronomy, and geology. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allowing them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. For immediate decisions, it models expert cognitive processes; for extended investigations, it transforms competition into “co-opetition” where competing hypotheses strengthen each other. Case studies span historical mysteries (Ludwig II of Bavaria, the “Monster of Florence”), literary demonstrations (Murder on the Orient Express), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems. Full article

Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize “good-put”: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station’s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems. Full article

Lightning-ignited wildfires represent a dominant natural disturbance agent in the Greater Khingan Mountains of northeastern China; however, the relationship between their occurrence and lightning characteristics remains insufficiently quantified. This study analyzed cloud-to-ground (CG) lightning data (2019–2024) and 417 lightning-ignited wildfires (2019–2024) using a full-waveform lightning detection network and spatial matching based on the Minimum Distance Method. Lightning activity shows pronounced spatiotemporal clustering, with more than 93% of flashes occurring in summer and a diurnal peak at 15:00. About 74.6% of wildfires ignited within 1 km of a lightning strike, and the holdover time exhibited clear seasonality, peaking in August (≈317 h). Negative CG (−CG) flashes dominated ignition events (56.5% multiple-stroke, average multiplicity = 2.60), and igniting flashes were concentrated within the −10 to −30 kA peak-current range, suggesting a key threshold for ignition. Vegetation type strongly influenced ignition efficiency: cold temperate and temperate coniferous forests recorded the highest lightning and fire counts, while alpine grasslands and sedge meadows showed the highest lightning ignition efficiency (LIE). These findings clarify how lightning electrical properties and vegetation conditions jointly determine ignition probability and provide a scientific basis for improving lightning-ignited wildfire risk monitoring and early-warning systems in boreal forest regions. Full article

Damage to structural members or joints can change the load transfer path of the structure. Additionally, structures may experience severe damage or even collapse due to the impact of aftershocks. To investigate the effects of beam-column joint damage, link damage, and aftershocks on the seismic performance of K-shaped eccentrically braced steel frame (K-EBF) structures, incremental dynamic analysis, fragility analysis, and collapse resistance evaluation were conducted using examples of 12-story and 18-story K-EBF structures. The results showed that considering beam-column joint damage, link damage, and aftershocks compared to not considering them, and the maximum inter-story drift ratio (θ_max) of the 12-story and 18-story K-EBF structures increased by 11.1% and 20.1%, respectively, under fortification earthquakes, and by 30.0% and 56.7%, respectively, under rare earthquakes. The failure probability of the severe damage limit state of the 12-story and 18-story K-EBF structures increased by 1.0% and 3.0%, respectively, under fortification earthquakes, and by 15.3% and 24.0%, respectively, under rare earthquakes. Additionally, the minimum collapse margin ratios (CMR_{P = 10%}) of the two structures decrease by 27.8% and 32.3%, respectively. The influence of aftershocks on the structural seismic response tends to intensify as the intensity of ground motion increases, and the beam-column joint damage and link damage further increases the failure probability of different damage limit states, leading to a decrease in the minimum collapse resistance coefficient of the structure. Therefore, in the seismic performance analysis of K-EBF structures, the effects of beam-column joint damage, link damage, and aftershocks should be fully considered to accurately reflect the response of structures under seismic actions. Overall, the impact of link damage, as well as aftershocks, on the structural collapse resistance is greater than that of beam-column joint damage. Full article

This paper compares two common dispatch policies—Load-Following (LF) and Cycle-Charging (CC)—for a photovoltaic Battery Energy Storage System (PV–BESS) microgrid (MG) with a 12 kW diesel generator, using a full-year of real 15 min PV and load data from an industrial use case in Germany. A forward time-step simulation enforces the battery State-of-Energy (SoE) window (total basis [20, 100] %, DoD = 80%) and computes curtailment, generator use, and unmet energy. Feasible designs satisfy a Loss of Power Supply Probability (LPSP) ≤ 0.03. Economic evaluation follows an Equivalent Annual Cost (EUAC) model with PV and BESS Capital Expenditure/Operation and Maintenance (CAPEX/O&M) (cycle life dependent on DoD and 15-year calendar life), generator costs, and fuel via SFC and diesel price. A value of lost load (VOLL) can be applied to unserved energy, with an optional curtailment penalty. Across the design space, a clear cost valley appears toward moderate storage and modest PV, with the baseline optimum at ≈56 kWp PV and 200 kWh BESS (DoD = 80%). Both policies meet the reliability target (in our runs LPSP ≈ 0), and their SoE trajectories are nearly identical; CC only lifts the SoE slightly after generator-ON events by using headroom to charge, while LF supplies just the residual deficit. Sensitivity analyses show that the optimum is most affected by diesel price and discount rate, with smaller shifts for ±10% changes in SFC. The study provides a transparent, reproducible workflow—grounded in real data—for controller selection and capacity planning. Full article

Introduction: Postpartum family planning (PPFP) is a critical strategy for improving maternal and child health by preventing unintended pregnancies and optimizing birth spacing. However, PPFP uptake remains suboptimal in Ethiopia, where sociocultural barriers, limited health information, and inadequate counseling impede progress. Mobile health (mHealth) interventions have shown promise in overcoming these challenges by delivering targeted health information directly to individuals. This study aimed to evaluate the effect of an mHealth intervention on uptake and the intention to use PPFP among postpartum women in South Ethiopia. Methods: We conducted a cluster-randomized controlled trial in randomly selected health facilities in South Ethiopia. Pregnant women from primary hospitals and health centers were selected from registers and family folders. Data were collected using face-to-face and mobile interviews and analyzed using a generalized linear mixed model (GLMM) to account for the clustering. Results: The mHealth intervention significantly increased PPFP uptake (OR = 2.89, 95% CI: 1.55–5.37) and the intention to use PPFP (AOR = 2.05, 95% CI: 1.24–3.46) compared to standard care. The predicted probability of using PPFP was 85% in the intervention group. Women who discussed family planning with their partners (AOR = 2.10, 95% CI: 1.30–3.35) had a higher probability of using PPFP, and those exposed to media (AOR = 1.58, 95% CI: 1.07–2.32) had an increased likelihood of planning to use PPFP. Conversely, limited autonomy in decision-making and delays in postnatal care attendance were associated with reduced uptake and intention to use PPFP. Conclusions: The mHealth intervention improved uptake of PPFP and increased intention to use PPFP among postpartum women in South Ethiopia. PPFP uptake was higher in the intervention group (85%) than in the control group (68%). Partner involvement, decision-making autonomy, and media exposure emerged as significant facilitators of PPFP adoption. Scaling up mHealth interventions could address unmet family planning needs, but integration with broader strategies that address sociocultural barriers and enhanced counseling is essential. Interventions must be contextually tailored and grounded in behavioral theory (HBM, TPB, and TAM) to maximize effectiveness. Future research should examine the long-term sustainability and adaptability of mHealth approaches across diverse contexts. Full article

Recently, developments in hyperspectral image (HSI) classification have brought increasing attention to the challenges of the open-set problem. However, current open-set methods generally overlook the intra-class multimodal structure, making it difficult to comprehensively capture the global data distribution, which in turn reduces their ability to distinguish known from unknown classes. To address this, we propose a novel global distribution-aware network (GDAN) that jointly performs pixel-wise HSI classification and trustworthy uncertainty-aware identification of unknown class. First, a generative adversarial network (GAN) is employed as the backbone, enhanced with a self-attention (SA) module to capture long-range dependencies across the extensive spectral bands of hyperspectral data. Second, an interpretable open-set HSI classification framework is designed, combining GAN with Markov Chain Monte Carlo (MCMC) to model global distribution by exploring intra-class multimodal structures and estimate predictive uncertainty. In this framework, the traditionally fixed discriminator weights are reformulated as probability distributions, and posterior inference is conducted using MCMC within a Bayesian framework. Finally, accurate categories and predictive uncertainty of ground objects can be obtained through posterior sampling, while samples with high uncertainty are assigned to the unknown class, thus enabling accurate open-set HSI classification. Extensive experiments on three benchmark HSI datasets demonstrate the superiority of the proposed GDAN for open-set HSI classification, yielding overall classification accuracies of 94.6%, 92.6%, and 94.8% in the 200-sample scenario. Full article

Subway stations are enclosed spaces with high passenger density and complex evacuation conditions. Fires in such environments can escalate rapidly and cause severe consequences. This study proposes a dynamic risk assessment model grounded in dual symmetries. The first symmetry is a balanced “Human–Machine–Environment–Management” analytical structure. The second is a coherent model transformation from a Fault Tree (FT) to a Bayesian Network (BN). Shuanggang Station on Nanchang Metro Line 1 serves as a case study. This work establishes a comprehensive evaluation system based on 4 first-level indicators of man–machine–environment–management, 9 secondary indicators, and 27 tertiary indicators. FT analysis identified 117 minimal cuts and 14 minimal paths, pinpointing core risk nodes such as flammable materials and oxidizers, electrical equipment overheating, and fire management deficiencies. The model was then symmetrically converted into a BN using GeNle Academic 4.1 software to support dynamic probability inference. The results show that prevention measures at Shuanggang Station reduce the fire occurrence probability from 0.000249 to 0.00007 (a 71.9% reduction). The probability importance of rescue escape routes is 0.00223. This indicates that the accessibility of rescue routes constitutes a highly sensitive hazard. The symmetric framework and modeling approach offer a scientific basis for targeted fire prevention, control, and evacuation management in the Nanchang Metro and similar stations. The findings support improvements in the safety and resilience of metro operations. Full article

Lightning strikes are a major cause of wind turbine (WT) damage, with approximately 80% of cloud-to-ground lightning strikes exhibiting a multi-stroke characteristic. Therefore, studying the transient overvoltages induced by multiple lightning strokes is essential for the effective lightning protection of offshore WTs. Firstly, a multiple-stroke lightning current model representative of Guangdong Province, China, is established based on data from the lightning location system and rocket-triggered lightning experiments. Simulations are then employed to analyze the transient overvoltage of a Guangdong offshore wind farm under multiple lightning strikes. Simulation results indicate that when a WT is subjected to a two-stroke lightning flash, with current amplitudes corresponding to a cumulative probability density of approximately 1%, the surge arrester A1 must be configured with four parallel columns to ensure the insulation safety of the equipment without sustaining damage. Additionally, adequate electrical clearance must be maintained between the power cable and the tower wall, or alternatively, a high-strength insulating material may be applied over the cable armor to prevent flashover. Moreover, it is observed that the front time of the impulse current flowing through the surge arrester is approximately 2 μs, significantly shorter than the front time specified in IEC 60099-4 for the repetitive charge transfer capability test of ZnO varistors. Hence, it is essential to consider local lightning intensity and distribution characteristics when studying the transient overvoltages in offshore wind farms, optimizing surge arrester configurations, and assessing the impulse withstand performance of ZnO varistors, in order to ensure the safe and stable operation of offshore WTs. Full article

After a series of successful reintroductions, the Eurasian beaver (Castor fiber) is expanding its range throughout Europe. Timely monitoring of beaver activity contributes to early detection of environmental impacts and aids in mitigating human–wildlife conflicts and other threats. However, the signs of beaver presence are difficult to detect in some environments, e.g., densely vegetated river banks or in areas with considerable water level variability. In these cases, new technologies can offer opportunities for easier and faster monitoring. In the current study, we provide a characterisation of the wood-gnawing activity of a newly established beaver population in Northern Bulgaria, using a traditional transect method. In addition, we test the application of unmanned aerial vehicles (UAVs) to detect and map the signs of beaver activity. The overall gnawing-activity characteristics of newly established Castor fiber populations in Bulgaria follow the pattern documented in earlier studies: the affected trees were mainly willow and poplar, located at less than 10 m from the riverbank, with a diameter mostly under 30 cm. However, there were considerable differences in the tree size and distance from the water between the two studied habitats—the Danube River and its tributaries. No dams were recorded, probably due to the rivers’ sizes. We found no significant difference in the detection rates of the UAV with and without canopy cover. Overall, the UAV-based transects were reliable for the detection of the species’ presence, but not for quantification of its activity patterns, due to the low detection rates, in comparison with ground-level transects. We believe that the method is promising because it is cost- and time-saving but could be improved using cameras with better resolution and by involving machine learning algorithms. The drone detection method could help identify the areas with the densest populations of the species, where Natura 2000 protected zones could then be established. Full article

The rapid advancement of Generative Artificial Intelligence (GenAI) has enhanced fashion design creativity by introducing aesthetics beyond conventional norms. With its unique and novel aesthetics, AI-generated clothing has sparked widespread discussion on social media. However, little is known about how consumers respond when these virtual designs are transformed into wearable physical products. This study examines factors influencing Chinese consumers’ acceptance of physicalized AI-generated clothing (PAGC), which is a sustainable fashion category that improves design efficiency and enables small-scale experimental production. Grounded in the Theory of Consumption Values (TCV), eight variables across four value dimensions—functional, social, emotional, and epistemic—were identified, along with demographic characteristics. Using a non-probability voluntary sampling method, 661 valid responses from Chinese consumers were collected and analyzed through a multinomial logistic regression model. The study found that perceived algorithmic creativity, perceived novelty, and social identity are the three most influential factors on acceptance. Consumers with higher education, lower income, or fashion- and technology-related backgrounds were more likely to accept PAGC. By situating PAGC within the context of sustainable fashion innovation, this study enhances understanding of Chinese consumers’ decision-making and offers managerial insights for fashion brands striving to balance creativity and social responsibility in the GenAI era. Full article

Underground mining often causes surface displacement and deformation above and around mined-out areas, and mining-induced subsidence has become a growing concern for ground stability worldwide. Given the proximity between the studied mine and a neighboring operation, potential mutual influences during extraction were examined to ensure the safety of surface structures. This study analyzes the stability of the overlying strata by combining theoretical prediction and numerical simulation, considering the cumulative effects of adjacent mining activities. The main findings are as follows: (1) The probability integration method was used to predict surface deformation and subsidence caused by underground mining, providing deformation data for the 4# shaft, 4# return air shaft, 5# return air shaft, and surrounding ground surface. (2) A three-dimensional geomechanical model was built using FLAC3D finite-difference software based on actual topographical and geological data to assess the impact of mining on overburden stability. Results show that the surrounding rock remained primarily in the elastic stage, with a maximum surface subsidence of 47.7 mm, confirming the structural stability of the 4# and 5# shafts. (3) Analyzing stress redistribution during deep ore extraction in both mining zones reveals that stress disturbances were mainly confined to the excavation areas, with a maximum local stress concentration of 83.34 MPa at stope corners. The combined mining activities resulted in an overall subsidence of approximately 48.7 mm, which decreased gradually outward from the center. This research presents an integrated theoretical and numerical framework that combines probability integration theory with FLAC3D simulation to assess the cumulative deformation and stress interactions of neighboring underground mines. The proposed method offers a practical and transferable tool for evaluating regional mine stability and surface deformation risks in multi-mine districts. Full article