Search Results (1,503)

Antiviral substances are considered emerging contaminants. Once released into the environment, they may affect organisms through complex and often still-unknown mechanisms. This study focuses on a class of antiviral substances with potential use in treating COVID-19 patients, aiming to identify specific structural characteristics that significantly contribute to their ecotoxicity. An empirical approach called quantitative structure–activity relationship (QSAR) was used for this purpose. The study examined 13 antiviral substances: atazanavir, daclatasvir, darunavir, emtricitabine, favipiravir, lopinavir, nirmatrelvir, oseltamivir, remdesivir, ribavirin, ritonavir, and sofosbuvir. The ecotoxicity of these antivirals was assessed using three tests: the Aliivibrio fischeri test, the Chlorella sp. test, and the Pseudomonas putida test. These three microorganisms represent different trophic levels in aquatic and soil ecosystems. Ecotoxicity was expressed as EC₂₀ and EC₅₀, and these values served as the dependent variables in the QSAR models. A large set of numerical descriptors calculated from the molecular structures of the antivirals was used as an independent variable. EC₂₀-based QSAR models offer insight into the effects of antivirals under sub-lethal exposure conditions. The results indicated that sub-lethal exposure in Aliivibrio fischeri was associated with favorable electronic properties and compact structures that promote cellular accumulation, while long-range fragments reduced toxicity. In Chlorella sp., sub-lethal exposure was driven by optimal molecular size, chain length, and specific electronic groups enabling cell penetration and biochemical inhibition. For sub-lethal exposure in P. putida, lipophilicity and reactive group geometry enhanced toxicity, while high short-range polarity mitigated it by limiting membrane permeability. Acute toxicity patterns showed similar trade-offs: strong electronic reactivity increased potency, but steric bulk, long-range polarity, or unfavorable mass distribution frequently restricted bioavailability and reduced toxic effects. Overall, the models demonstrated that antiviral toxicity results from a balance of electronic activity, structural accessibility, and physicochemical constraints, providing a mechanistic basis for predicting the environmental risk of selected antiviral substances. Full article

Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical application of co-array domain signal processing for underwater acoustic coprime arrays. We propose a novel array configuration based on coprime minimum disordered pairs, enabling the formation of continuously connected co-arrays without interpolating. To address the challenge of limited snapshots in underwater environments, DOA estimation can be achieved by utilizing traditional multipath matching pursuit (MMP) algorithms under the proposed continuous co-array implementation scheme. In practical applications, physical array element failures are inevitable, and faulty elements can create holes in the originally continuous co-array. While interpolation techniques can mitigate small gaps, their performance deteriorates significantly in the presence of large holes or uneven data distribution. To overcome these limitations, we introduce a sparse signal recovery (SSR) method using a fragment array data validation technique for sparse DOA estimation with an underwater acoustic coprime array. Based on the designed continuous array expansion scheme, the resulting continuous co-array is used to map the positions of element failures, revealing the gaps in the co-array. A validation model is established for partially continuous sub-arrays within the discontinuous co-array, enabling signal direction estimation based on the fragmented array validation. Both simulation and sea trial results confirm that the proposed approach maximizes the utilization of co-array elements without relying on interpolation or prediction, offering a robust solution for scenarios involving sensor failures. Full article

Artificial intelligence transforms lubricant research by linking molecular modeling, diagnostics, and industrial operations into predictive systems. In this regard, machine learning methods such as Bayesian optimization and neural-based Quantitative Structure–Property/Tribological Relationship (QSPR/QSTR) modeling help to accelerate additive design and formulation development. Moreover, deep learning and hybrid physics–AI frameworks are now capable to predict key lubricant properties such as viscosity, oxidation stability, and wear resistance directly from molecular or spectral data, reducing the need for long-duration field trials like fleet or engine endurance tests. With respect to condition monitoring, convolutional neural networks automate wear debris classification, multimodal sensor fusion enables real-time oil health tracking, and digital twins provide predictive maintenance by forecasting lubricant degradation and optimizing drain intervals. AI-assisted blending and process control platforms extend these advantages into manufacturing, reducing waste and improving reproducibility. This article sheds light on recent progress in AI-driven formulation, monitoring, and maintenance, thus identifying major barriers to adoption such as fragmented datasets, limited model transferability, and low explainability. Moreover, it discusses how standardized data infrastructures, physics-informed learning, and secure federated approaches can advance the industry toward adaptive, sustainable lubricant development under the principles of Industry 5.0. Full article

Multilayered geological structures are common in geotechnical engineering, where cooling shrinkage induces polygonal cracks in interlayers, compromising rock mass strength, permeability, and long-term stability. Existing thermo-mechanical studies on layered rock cracking insufficiently address the combined effects of weak interlayer geometry or interface-regulated mechanisms. To address this gap, based on meso-damage mechanics and thermodynamics, this study adopts a thermo-mechanical coupling simulation method considering rock heterogeneity, innovatively focusing on the understudied stress transfer effect at strong–weak interlayer interfaces. Systematic investigations were conducted on the initiation, propagation, and saturation of polygonal cracks in plate-like layered rocks under surface cooling, analyzing the influences of weak interlayer thickness, number, and position, and comparing surface vs. inner interlayer behaviors. Results showed that stress transfer interruption at weak–strong layer interfaces can inhibit crack propagation. Inter weak interlayers produce significantly more cracks and fragments than surface weak interlayers, with a stratified crack length distribution, and the maximum fragment area increases exponentially with weak interlayer thickness. Crack development is strongly influenced by weak interlayer thickness, with thinner layers dominated by non-penetrating cracks and thicker layers tending to develop penetrating lattice-like cracks. The inter layer stress and crack distribution exhibit fractal characteristics, with crack density decreasing layer by layer and no new cracks forming after saturation. This study clarifies the regulatory mechanism of weak interlayer features and surface cooling on crack evolution, quantifies interface effects and fractal characteristics, and provides a theoretical basis for instability prediction of layered rock structures in low-temperature geotechnical engineering. Full article

To address the increasing vulnerability of unmanned aerial vehicle (UAV) lightweight airframe structures to high-velocity fragment impacts in complex operational environments, this study combines high-velocity impact penetration tests, quasi-static strength tests, fracture-surface microanalysis, and finite-element simulation to systematically reveal the formation mechanism of typical penetration damage and its influence on residual strength. Results show that such penetration induces damage such as adiabatic-shear local melting zones, spall cracks, and grain-boundary separation, significantly weakening static strength and shifting the fracture mode from ductility- to brittleness-dominated. A modified fracture-mechanics criterion with higher prediction accuracy than the traditional net-section criterion is proposed, and a high-precision simulation model based on explicit–quasi-static coupling is established, which well reproduces damage morphology and tensile-failure processes. Compared with conventional manned aircraft structures, UAV airframes characterized by thinner skins and higher lightweighting ratios exhibit more pronounced sensitivity to penetration-induced micro-defects, making rapid residual-strength assessment essential for operational recovery and field-level repair decision-making. The research reveals the damage mechanism and provides an engineering-applicable residual-strength assessment method, offering a reliable theoretical basis and simulation tool for rapid UAV damage evaluation and fast-turnaround repair planning for civil and industrial UAV platforms. Full article

Oversized rock fragments (boulders) produced during bench blasting adversely affect the efficiency of mining downstream processes such as loading, hauling, and crushing, thus leading to regularly requiring costly secondary breakage and the use of mechanized rock breakers. This study presents a probabilistic framework for forecasting boulder size in surface mining operations by employing Gaussian Process Regression (GPR), benchmarked against the Kuznetsov–Cunningham–Ouchterlony (KCO) empirical fragmentation model and a Multivariate Regression Analysis (MVRA) equation. The research study has analyzed blasting datasets, comprising Geological Strength Index (GSI), number of holes (NH), hole depth (HD), maximum charge per delay (MCPD), total explosive mass (TEM), and boulder size determined by Split-Desktop image analysis. Eight Gaussian Process Regression kernels—squared exponential, rational quadratic, matern with ν = 3/2, and matern with ν = 5/2, both with and without automatic relevance determination (ARD)—were assessed. The GPR model with the ARD matern 3/2 kernel attained superior validation performance of R² = 0.9016 and RMSE = 4.2482, outperforming the KCO and MVRA models, which displayed significant prediction errors for boulder size. In addition, the sensitivity analysis results demonstrated that GSI and HD were the most influential parameters on boulder size, followed by NH, MCPD, and TEM, accordingly. The findings indicate that GPR, especially when employing ARD matern kernels, precisely estimates the boulder size, and thus can serve as a viable method for optimizing blast design and facilitate efficient boulder management in surface mining operations. Full article

Multi-object tracking (MOT) in vineyard environments remains challenging due to frequent and long-term occlusions caused by dense foliage, overlapping grape clusters, and complex plant structures. These characteristics often result in identity switches and fragmented trajectories when using conventional tracking methods. This paper proposes OATSAM-Track, an occlusion-aware multi-object tracking framework designed for vineyard fruit monitoring. The framework integrates lightweight MobileSAM-assisted instance segmentation to estimate target visibility and occlusion severity. Occlusion-state reasoning is further incorporated into temporal association, appearance memory updating, and identity recovery. An adaptive temporal memory mechanism selectively updates appearance features according to predicted occlusion states, reducing identity drift under partial and severe occlusions. To facilitate occlusion-aware evaluation, an extended vineyard multi-object tracking dataset (GrapeOcclusionMOTS) with SAM-refined instance masks and fine-grained occlusion annotations is constructed. The experimental results demonstrate that OATSAM-Track improves identity consistency and tracking robustness compared to representative baseline trackers, particularly under medium and severe occlusion scenarios. These results indicate that explicit occlusion modeling is beneficial for reliable fruit monitoring in precision agriculture. Full article

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. Full article

Background/Objectives: Interleukin-6 (IL-6), a pleiotropic cytokine involved in immune regulation, is consistently detected in human semen, even in the absence of overt infection. Its contribution to sperm dysfunction, oxidative stress, and inflammation remains incompletely understood. This study evaluated the associations between seminal IL-6 concentrations and markers of semen quality, oxidative stress, nuclear integrity, and genital tract inflammation in infertile men. Methods: A cohort of 204 infertile men was assessed. Seminal IL-6 was quantified by electrochemiluminescence immunoassay. Semen parameters, malondialdehyde (MDA), catalase (CAT) activity, sperm DNA fragmentation index (DFI), sperm chromatin decondensation index (SDI), leukocytospermia, and bacteriospermia were measured. Analyses included correlation testing, IL-6 threshold stratification (<30, 30–60, 60–100, ≥100 pg/mL), and multivariate regression. Results: IL-6 was detectable in all samples (median: 31.52 pg/mL; range: 1.5–5000 pg/mL). Higher IL-6 levels were significantly associated with reduced sperm concentration, progressive motility, and vitality, and with increased DFI, SDI, MDA, leukocyte counts, and bacteriospermia (p < 0.001). In multivariate models, IL-6 independently predicted reduced progressive motility (β = −0.005; p = 0.032) and elevated leukocyte count (β = 0.0018; p < 0.0001). Logistic regression further showed that IL-6 increased the odds of DFI ≥ 30%, SDI ≥ 30%, and bacteriospermia (p < 0.05). Conclusions: Seminal IL-6 emerges as a sensitive biomarker of immuno-oxidative stress and sperm dysfunction in infertile men. Its integration into clinical evaluation may improve the assessment of inflammatory and oxidative contributors to male infertility. Full article

Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applied to estimate forest biomass, these approaches often rely on complex data acquisition and processing workflows that limit their scalability for large-area assessments. To improve the efficiency, this study evaluates the potential of annual multi-sensor satellite embeddings derived from the AlphaEarth Foundations model for forest biomass prediction. Using field inventory data from 89 forest plots at the Yunhe Forestry Station in Zhejiang Province, China, we assessed and compared the performance of four machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MLPNN), and Gaussian Process Regression (GPR). Model evaluation was conducted using repeated 5-fold cross-validation. The results show that SVR achieved the highest predictive accuracy in broad-leaved and mixed forests, whereas RF performed best in coniferous forests. When all forest types were modeled together, predictive performance was consistently limited across algorithms, indicating substantial heterogeneity (e.g., structure, environment, and topography) among forest types. Spatial prediction maps across Yunhe Forestry Station revealed ecologically coherent patterns, with higher biomass values concentrated in intact forests with less human disturbance and lower biomass primarily occurring in fragmented forests and near urban regions. Overall, this study highlights the potential of embedding-based remote sensing for regional forest biomass estimation and suggests its utility for large-scale forest monitoring and management. Full article

High-entropy alloys (HEAs) present a fundamental design paradox: their exceptional properties arise from complex, high-dimensional composition–process–microstructure–property (CPMP) relationships, yet the knowledge needed to navigate this space is fragmented across a vast and unstructured literature. Large language models (LLMs) offer a transformative interface to this complexity. By extracting structured facts from text, they can convert dispersed and heterogeneous evidence (i.e., findings scattered across many studies and reported with inconsistent test protocols or characterization standards) into queryable knowledge graphs. Through code generation and tool composition, they can automate simulation pipelines, surrogate model construction, and inverse design workflows. This review analyzes how LLMs can augment key stages of HEA research—from intelligent literature mining and multimodal data integration (using LLMs to automatically extract and structure data from texts and to combine information across text, images, and other data sources) to model-driven design and closed-loop experimentation—illustrated by emerging case studies. We propose concrete evaluation protocols that measure direct scientific utility, including knowledge-graph completeness, workflow setup efficiency, and experimental validation hit rates. We also confront practical limitations: data sparsity and noise, model hallucination, domain bias (where models may exhibit superior predictive performance for specific, well-represented alloy systems over others due to imbalances in training data), and the imperative for reproducible infrastructure. We argue that domain-specialized LLMs, embedded within grounded, verifiable research systems, can not only accelerate HEA discovery but also standardize the representation, sharing, and reuse of community knowledge. Full article

Industrialised construction (IC) represents a foundational strategy for overcoming entrenched productivity constraints and supply shortfalls in the housing sector. By enabling the mass production and mass customisation of advanced kit-of-parts systems, IC supports more efficient, predictable, scalable, and sustainable building delivery through integrated, standardised, and digitally enabled processes. However, adoption remains uneven due to fragmentation across regulatory, organisational, and technological systems. This paper presents a systematic literature review and thematic synthesis of the literature published between 2000 and 2025 to examine performance outcomes, adoption trends, digital integration maturity, and emerging platform-based design for manufacture and assembly (P-DfMA) approaches, and the main drivers. The review shows that significant performance gains are achievable, including notable reductions in construction time and cost variability, along with substantial reductions in material waste, together with measurable improvements in quality, safety, and delivery predictability. However, widespread uptake of IC remains constrained. This is largely driven by regulatory misalignment, rigid and bespoke procurement and delivery models, inconsistent and unstable supply chain capacity, and the lack of standardised components and integrated digital workflows. Building on these insights, this paper examines the key enablers required for sector-wide transformation toward an ecosystem that supports standardised kit-of-parts solutions, digitally driven design-to-production workflows, and aligned policy and procurement frameworks that are capable of delivering scalable and repeatable industrialised housing. The findings provide a consolidated evidence base and identify the key enablers for policymakers, industry stakeholders, and researchers working to move from project-centred delivery models to platform-based, digitally integrated, and industrialised construction systems. We searched Scopus, Web of Science, ScienceDirect, and Google Scholar, complemented by targeted industry and policy repositories; the searches were last updated on 1 December 2025. After screening, 117 sources were included. The review was not registered, and no review protocol was prepared. Full article

Cloud storage continues to experience recurring provider-side breaches, raising concerns about the confidentiality and recoverability of user data. This study addresses this challenge by introducing an Artificial Intelligence (AI)-driven hybrid architecture for secure, reconstruction-resistant multi-cloud storage. The system applies telemetry-guided fragmentation, where fragment sizes are dynamically predicted from real-time bandwidth, latency, memory availability and disk I/O, eliminating the predictability of fixed-size fragmentation. All payloads are compressed, encrypted with AES-128 and dispersed across independent cloud providers, while two encrypted fragments are retained within a VeraCrypt-protected local vault to enforce a distributed trust threshold that prevents cloud-only reconstruction. Synthetic telemetry was first used to evaluate model feasibility and scalability, followed by hybrid telemetry integrating real Microsoft system traces and Cisco network metrics to validate generalization under realistic variability. Across all evaluations, XGBoost and Random Forest achieved the highest predictive accuracy, while Neural Network and Linear Regression models provided moderate performance. Security validation confirmed that partial-access and cloud-only attack scenarios cannot yield reconstruction without the local vault fragments and the encryption key. These findings demonstrate that telemetry-driven adaptive fragmentation enhances predictive reliability and establishes a resilient, zero-trust framework for secure multi-cloud storage. Full article

Background: Early local recurrence (ELR) in musculoskeletal sarcoma is associated with poor oncologic outcomes, yet the relative impact of tumor biology versus system-level factors remains insufficiently understood. This multicenter real-world study within the Swiss Sarcoma Network evaluated whether the initial care pathway influences the risk and timing of ELR. Methods: Patients with histologically confirmed sarcoma and documented local recurrence were classified according to initial management within a Comprehensive Care Pathway (CCP) or a Fragmented Care Pathway (FCP). ELR was defined as recurrence within 12 months after index surgery. Associations were analyzed using restricted Cox proportional hazards models and Firth-penalized logistic regression, adjusting for key clinicopathologic factors. Follow-up was calculated from index surgery to death or administrative censoring (median 88.2 months; interquartile range, 54.9–141.6). Results: Among 158 patients with local recurrence, 96 (60.8%) were treated within CCP, and 62 (39.2%) entered through FCP. ELR occurred in 53 patients (33.5%) and was more frequent in the FCP cohort. Fragmented care was independently associated with ELR in both time-to-event analysis (hazard ratio 2.00, 95% CI 1.14–3.51) and penalized logistic regression (odds ratio 2.83, 95% CI 1.09–6.94). Unplanned (“whoops”) procedures and incomplete resection margins were substantially more common in FCP and independently predicted ELR. Tumor grade also contributed to risk, but the magnitude of the pathway effect was comparable. ELR was associated with higher rates of synchronous metastases and inferior survival compared with late local recurrence. Adjuvant therapy did not independently reduce ELR risk after adjustment for surgical quality. Conclusions: These findings indicate that ELR in musculoskeletal sarcoma is strongly influenced by modifiable system-level factors. Early referral, multidisciplinary evaluation, and expert margin-oriented surgery are critical to reducing early recurrence and improving patient outcomes. Full article

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article