Search Results (59,803)

To address the problems of weak geometric features, low signal response amplitude, and insufficient spatial resolvability of near-surface defects in metal substrates, a high-resolution spatiotemporal-coded eddy-current array probe is proposed. The probe adopts an array topology with time-multiplexed excitation and adjacent differential reception, achieving a balance between high common-mode rejection ratio and high-density spatial sampling. First, a theoretical electromagnetic coupling model between the probe and the metal substrate is established, and finite-element simulations are conducted to investigate the evolution of the skin effect, eddy-current density distribution, and differential impedance response over an excitation frequency range of 1–10 MHz. Subsequently, a 64-channel M-DECA probe and an experimental testing platform are developed, and frequency-sweeping experiments are carried out under different excitation conditions. Experimental results indicate that, under a 50 kHz excitation frequency, the array eddy-current response achieves an optimal trade-off between signal amplitude and spatial geometric consistency. Furthermore, based on the pixel-to-physical coordinate mapping relationship, the lateral equivalent diameters of near-surface defects with different characteristic scales are quantitatively characterized, with relative errors of 6.35%, 4.29%, 3.98%, 3.50%, and 5.80%, respectively. Regression-based quantitative analysis reveals a power-law relationship between defect area and the amplitude of the differential eddy-current array response, with a coefficient of determination R² = 0.9034 for the bipolar peak-to-peak feature. The proposed M-DECA probe enables high-resolution imaging and quantitative characterization of near-surface defects in metal substrates, providing an effective solution for electromagnetic detection of near-surface, low-contrast defects. Full article

Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal and NIR imaging. In contrast with standard deep learning approaches, our model receives ontology-based semantic supervision (via per-dataset OWL ontologies), enabling knowledge injection via SPARQL-driven reasoning during training. This structured knowledge layer not only improves multimodal feature correspondence but also restricts label consistency for improving generalization performance, particularly in early disease diagnosis. We tested our proposed method on a comprehensive set of five benchmarks (PlantVillage, PlantDoc, Figshare, Mendeley, and Kaggle Date Palm) together with domain-specific ontologies. An ablation study validates the effectiveness of incorporating ontology supervision, consistently improving the performance across Accuracy, Precision, Recall, F1-Score and AUC. We achieve state-of-the-art performance over five widely recognized baselines (PlantXViT, Multi-ViT, ERCP-Net, andResNet), with our model DoST-DPD achieving the highest Accuracy of 99.3% and AUC of 98.2% on the PlantVillage dataset. In addition, ontology-driven attention maps and semantic consistency contributed to high interpretability and robustness in multiple crop and imaging modalities. Results: This work presents a scalable roadmap for ontology-integrated AI systems in agriculture and illustrates how structured semantic reasoning can directly benefit multimodal plant disease detection systems. The proposed model demonstrates competitive performance across multiple datasets and highlights the unique advantage of integrating ontology-guided supervision in multimodal crop disease detection. Full article

The zoonotic potential of bat coronaviruses, especially HKU5, is a significant issue because of their capacity to utilize human angiotensin-converting enzyme 2 (ACE2) as a receptor for cellular entry. This study offers structural insights into the binding kinetics of HKU5 (Bat Merbecovirus HKU5) receptor-binding domain (RBD) spike protein with human ACE2 through a multiscale computational method. This study employed structural modeling, 300-nanosecond (ns) molecular dynamics (MD) simulations, alanine-scanning mutagenesis, and computational peptide design to investigate ACE2 recognition by the HKU5 RBD and its interactions with peptides. The root mean square deviation (RMSD) investigation of HKU5–ACE2 complexes indicated that HKU5 exhibited greater flexibility than SARS-CoV-2, with RMSD values reaching a maximum of 1.2 nm. Free energy analysis, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), indicated a more robust binding affinity of HKU5 to ACE2 (ΔG_Total = −21.61 kcal/mol) in contrast to SARS-CoV-2 (ΔG_Total = −5.82 kcal/mol), implying that HKU5 binding with ACE2 had higher efficiency. Additionally, a peptide was designed from the ACE2 interface, resulting in the development of 380 single-site mutants by mutational alterations. The four most promising mutant peptides were selected for 300-nanosecond (ns) MD simulations, subsequently undergoing quantum chemical calculations (DFT) to evaluate their electronic characteristics. MM/GBSA of −37.83 kcal/mol indicated that mutant-1 exhibits the most favorable binding with HKU5, hence potentially inhibiting ACE2 interaction. Mutant-1 formed hydrogen bonds involving Glu74, Ser202, Ser204, and Asn152 residues of HKU5. Finally, QM/MM calculations on the peptide–HKU5 complexes showed the most favorable ΔE_interaction of −170.47 (Hartree) for mutant-1 peptide. These findings offer a thorough comprehension of receptor-binding dynamics and are crucial for evaluating the zoonotic risk associated with HKU5-CoV and guiding the design of receptor-targeted antiviral treatments. Full article

This article presents a methodology for assessing the ecosystem external costs linked to land-use changes caused by utility-scale photovoltaic systems using a regionalized life cycle approach. The core scientific challenge is to integrate a typically non-site-specific method—life cycle assessment—with a site-specific evaluation of ecosystem services affected by land-use changes. The methodology does not model specific agricultural practices. The approach is applied to three configurations of solar-tracking photovoltaic plants installed on arable land: ground-mounted photovoltaics, elevated agrivoltaics, and spaced agrivoltaics. For each configuration, the external costs or benefits per megawatt-hour (MWh) produced are estimated, allowing a comparative life cycle analysis. The findings show that the elevated agrivoltaic system is the only configuration resulting in a net loss of ecosystem service value, albeit marginal (−0.2 EUR/MWh). In contrast, the ground-mounted system yields a net benefit (approximately 1 EUR/MWh), followed by spaced agrivoltaics (0.1 EUR/MWh). These outcomes are mainly driven by the construction and operational phases, while the impacts from component production, transport, and end-of-life stages are significantly lower. The methodology offers a replicable framework for integrating the monetary evaluation of ecosystem services into life cycle assessments of land-intensive renewable energy systems. Full article

The built environment is responsible for 40% of global energy demand, and, in line with urbanisation and population growth, this demand is expected to increase steadily. Urban areas are mostly composed of materials that can absorb energy from solar radiation and dissipate the accumulated energy in the form of heat. This study integrates a UAV-based Zenmuse XT S IR camera and handheld FLIR C5 thermal camera with ENVI-met microclimate simulation, providing quantitative insights for sustainable urban planning. From the 24 h experiment results, the characteristics of building surface materials are profiled for lowering energy use for internal thermal control during the operation stage of buildings. This study shows that building surface materials with the lowest solar reflectance and highest specific heat capacity reached a peak surface temperature of 73.5 °C in Jakarta (tropical hot climate) and 44.3 °C in Xiamen (subtropical late winter climate). In contrast, materials with the highest solar reflectance and lowest specific heat only reach a peak surface temperature of 58.1 °C in Jakarta and 27.9 °C in Xiamen. The peak surface temperature occurs at 2 PM in the afternoon. Moreover, we demonstrate the capability of an infrared drone to identify the peak surface temperatures of 55.8 °C at 2 PM in the study area in Xiamen. In addition, the ENVI-met validated model shows satisfactory correlation values of R > 0.9 and R2 > 0.8. This result demonstrates UAV-IR and ENVI-met simulation integration as a scalable method for city-level UHI diagnostics and monitoring. Full article

Hot-fluid injection in thermal-enhanced oil recovery (thermal-EOR, TEOR) imposes temperature-driven volumetric strains that can substantially alter in situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic (thermo-hydro-mechanical) effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study aims to examine the influence of sustained hot-fluid injection on stress redistribution, hydraulic-fracture deformation, and fracture stability in thermal-EOR by accounting for coupled thermal, hydraulic, and mechanical interactions. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection, with temperature contrasts of ΔT = 0–100 °C and reduced pumping rate. Results show that the hydraulic-fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75–100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions adjacent to the fracture plane and with markedly elevated shear stresses that may promote natural-fracture activation. These findings show that temperature effects can directly influence injectivity, fracture containment, and the risk of unintended fracture or natural-fracture activation, underscoring the importance of temperature-aware geomechanical planning and injection-strategy design in field operations. Incorporating these effects into project design can help operators anticipate injectivity decline, improve fracture containment, and reduce geomechanical uncertainty during long-term hot-fluid injection. Full article

Greenwashing by unvalidated environmental labelling is increasingly common and highly problematic due to the potential to mislead consumers. This is especially concerning for products that pose health risks, including alcohol. As environmental sustainability becomes more important to consumers, it is vital to assess changes in the use of potentially misleading claims over time. Among the first studies of its kind globally, this study aimed to (i) develop a typology of environmental claims displayed on alcohol products in Australia, (ii) examine the prevalence of these claims to establish baseline data for ongoing tracking, and (iii) assess the provision of recycling information. Four claim categories were identified: sustainability, planet friendly, bio-related and carbon-related. Claims featured on 8% of the 5982 sampled products, with considerable variation between alcohol categories. Sustainability claims were the most prevalent (5%). Recycling information appeared on 72% of products. The results suggest ambiguous environmental claims are present although not yet widespread. In contrast, recycling information is much more common although not universal. These findings highlight the need to consider restrictions on unsubstantiated environmental claims on alcohol products that can mislead consumers. Further, a nationally standardised mandatory recycling label should be introduced to assist consumers in reducing their environmental impacts. Full article

Analyzing large datasets of animal vocalizations requires efficient bioacoustic methods for categorizing the vocalization types. This study evaluates the effectiveness of different vocalization clustering methods, comparing fully automated and semi-automated methods against the gold standard of manual expert annotations. Effective methods achieve good clustering performance whilst minimizing human effort. We release a new dataset of 1454 zebra finch vocalizations manually clustered by experts, on which we evaluate (i) fully automated clustering using off-the-shelf methods based on sound embeddings and (ii) a semi-automated workflow relying on refining the embedding-derived clusters. Clustering performance is assessed using the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results indicate that while fully automated methods provide a useful baseline, they generally fall short of human-level consistency. In contrast, the semi-automated workflow achieved agreement scores comparable to inter-expert reliability, approaching the levels of expert manual clustering. This demonstrates that refining embedding-derived clusters reduces annotation time while maintaining gold standard accuracy. We conclude that semi-automated workflows offer an optimal strategy for bioacoustics, enabling the scalable analysis of large datasets without compromising the precision required for robust behavioral insights. Full article

The paper presents a comparative experimental study of heat-transfer behavior in three alloys considered candidate materials for nuclear reactors: the austenitic stainless steel 316L, Zircaloy-4 (currently used in CANDU reactors), and an ODS alloy with a ferritic matrix. The investigation was conducted across five temperature intervals, each sample being subjected to a thermal shock through short-term overheating to the upper limit of its respective interval. The variation of thermal diffusivity in the three alloys was determined as a function of both measurement temperature and applied thermal shock, and trends in heat-transfer behavior were compared across the five temperature ranges. The experimental results show that up to 400 °C, Zircaloy-4 exhibits the highest thermal diffusivity, followed by the ODS alloy, with the lowest values measured for 316L steel. At approximately 450 °C, the ratio between 316L and the ODS alloy reverses. Beyond this point, increasing the temperature up to 900 °C is accompanied by a continuous rise in thermal diffusivity for both 316L stainless steel and Zircaloy-4. In contrast, for the ODS steel, increasing temperature leads to a continuous decrease in thermal diffusivity, reaching a minimum near the Curie point. The novelty of the study lies in the comparative assessment of the influence of temperature on the heat-transfer process in three alloys relevant to nuclear energy, covering the operating temperature ranges of CANDU and ALFRED reactors, as well as potential accidental overheating up to 900 °C. A particular feature of the work is the prior application of a short-duration overheating step produced using solar energy. The results are relevant not only for nuclear reactors but also for other high-temperature applications in corrosive environments. Full article

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

This study evaluates whether an AI-built DeFi website case can match professionally developed DeFi platforms in digital marketing performance, innovation-related strategic behavior, and entrepreneurial impact. Using a multi-method design, we compare five established DeFi websites (Aave, Lido, Curve, MakerDAO, Uniswap) against one AI-built interface (Nexus Protocol). The analysis is designed as a five-platform benchmarking study of established professional DeFi websites, complemented by one AI-built case (Nexus Protocol) used as an illustrative comparison rather than a representative class of AI-built interface. The objectives are to (i) test differences in traffic composition and acquisition strategies, (ii) quantify how engagement signals predict authority and branded traffic, (iii) examine cognitive processing and trust-cue attention via eye tracking, and (iv) model emergent engagement and authority dynamics using agent-based simulation (ABM). Web analytics (March–October 2025) show significant variation in traffic composition across professional platforms (ANOVA F = 3.41, p = 0.0205), while regression models indicate that time on site and pages per visit positively predict Authority Score (R² = 0.61) and Branded Traffic (R² = 0.55), with bounce rate exerting an adverse effect. PCA and k-means clustering identify three strategic archetypes (innovation-driven, balanced-growth, efficiency-focused). Eye-tracking results show that professional interfaces generate tighter fixation clusters and shorter scan paths, indicating higher cognitive efficiency. In contrast, fixation on key UI elements and trust cues is comparable across interface types. ABM outputs further suggest that reduced engagement depth in the AI-built interface yields weaker long-run branded-traffic and authority trajectories. Overall, the study provides an integrated evaluation framework and evidence-based implications for AI-driven interface design in high-trust fintech environments. Full article

Land use/cover change (LUCC) is a dominant driver of ecosystem service dynamics in arid inland basins. Focusing on the Yarkant River Basin (YRB), Xinjiang, we coupled the PLUS land-use simulation with the InVEST Habitat Quality Model to project 2040 land-use patterns under four policy scenarios—Natural Development (ND), Arable Protection (AP), Ecological Protection (EP), and Economic Development (ED)—and to quantify their impact on habitat quality. Model validation against the 2020 map indicated strong agreement (Kappa = 0.792; FOM = 0.342), supporting scenario inference. From 1990 to 2023, arable land expanded by 58.17% and construction land by 121.64%, while forest land declined by 37.45%; these shifts corresponded to a basin-wide decline and increasing spatial heterogeneity of habitat quality. Scenario comparisons showed the EP pathway performed best, with 32.11% of the basin classified as very high-quality habitat and only 8.36% as very low-quality. In contrast, under ED, the combined share of very low + low quality reached 11.17%, alongside greater fragmentation. Spatially, high-quality habitat concentrates in forest and grassland zones of the middle–upper basin, whereas low-quality areas cluster along the oasis–desert transition and urban peripheries. Expansion of arable and construction land emerges as the primary driver of degradation. These results underscore the need to prioritize ecological-protection strategies especially improving habitat quality in oasis regions and strengthening landscape connectivity to support spatial planning and ecological security in dryland inland river basins. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

A treatment process was developed for effluents from direct physical lithium-ion battery (LIB) recycling with a focus on the removal of organic contaminants. The high chemical oxygen demand to biological oxygen demand ratio (COD/BOD₅) of 3.9–4.6 indicates that biological treatment is not feasible. Therefore, three advanced oxidation processes were evaluated: UV/H₂O₂ oxidation, the Fenton process and electrochemical oxidation. Two target scenarios were considered, namely compliance with the limit for discharge into the sewer system (COD = 800 mg/L) and compliance with the stricter limit for direct discharge into surface waters (COD = 200 mg/L). Under the investigated conditions, UV/H₂O₂ oxidation and the Fenton process did not meet the required discharge limits and exhibited high chemical consumption. In contrast, electrochemical oxidation achieved both discharge criteria with a lower energy demand, requiring 32.8 kWh/kgCOD_removed for sewer discharge and 95.3 kWh/kgCOD_removed for direct discharge. An economic assessment further identified electrochemical oxidation as the most cost-effective option, with treatment costs of EUR 6.63/m³, compared to EUR 17.31/m³ for UV/H₂O₂ oxidation and EUR 28.66/m³ for the Fenton process. Overall, electrochemical oxidation proved to be the most efficient and environmentally favorable technology for treating wastewater from LIB recycling, enabling compliance with strict discharge regulations while minimizing the chemical and energy demand. Full article

Background: In nuclear medicine, numerous cancer types are treated via internal irradiation with radiopharmaceuticals, including low-LET (linear energy transfer) beta-emitting radionuclides like Lu-177. In most cases, such treatments lead to low-dose exposure of organ systems with β-irradiation, which induces only few isolated DSBs (double-strand breaks) in the nuclei of hit cells, the most threatening DNA damage type. That damaging effect contrasts with the clustering of DNA damage and DSBs in nuclei traversed by high-LET particles (α particles, ions, etc.). Methods: After in-solution β-irradiation for 1 h with Lu-177 leading to an absorbed dose of about 100 mGy, we investigated the spatial nano-organization of chromatin at DSB damage sites, of repair proteins and of heterochromatin marks via single-molecule localization microscopy (SMLM) in PBMCs. For evaluation, mathematical approaches were used (Ripley distance frequency statistics, DBScan clustering, persistent homology and similarity measurements). Results: We analyzed, at the nanoscale, the distribution of the DNA damage response (DDR) proteins γH2AX, 53BP1, MRE11 and pATM in the chromatin regions surrounding a DSB. Furthermore, local changes in spatial H3K9me3 heterochromatin organization were analyzed relative to γH2AX distribution. SMLM measurements of the different fluorescent molecule tags revealed characteristic clustering of the DDR markers around one or two damage foci per PBMC cell nucleus. Ripley distance histograms suggested the concentration of MRE11 molecules inside γH2AX-clusters, while 53BP1 was present throughout the entire γH2AX clusters. Persistent homology comparisons for 53BP1, MRE11 and γH2AX by Jaccard index calculation revealed significant topological similarities for each of these markers. Since the heterochromatin organization of cell nuclei determines the identity of cell nuclei and correlates to genome activity, it also influences DNA repair. Therefore, the histone H3 tri methyl mark H3K9me3 was analyzed for its topology. In contrast to typical results obtained through photon irradiation, where γH2AX and H3K9me3 markers were well separated, the results obtained here also showed a close spatial proximity (“co-localization”) in many cases (minimum distance of markers = marker size), even with the strictest co-localization distance threshold (20 nm) for γH2AX and H3K9me3. The data support the results from the literature where only one DSB induced by low-dose low LET irradiation (<100 mGy) can remain without heterochromatin relaxation for subsequent repair. Full article