Search Results (10,420)

Municipal sludge generation has increased rapidly with urbanization, creating significant challenges for sustainable waste management. This review proposes a system-oriented framework for sludge resource utilization by linking sludge characteristics, conversion technologies, environmental risks, and product applications. Major treatment pathways, including anaerobic digestion, pyrolysis, ozonation, and hydrothermal carbonization, are critically compared, with emphasis on their inherent trade-offs between resource recovery, energy consumption, and contaminant control. Particular attention is given to emerging contaminants, such as microplastics, per- and polyfluoroalkyl substances (PFAS), and antibiotic resistance genes, where the distinction between pollutant removal and actual risk reduction remains insufficiently addressed. The review highlights that no single technology can achieve optimal performance under all conditions, and integrated treatment trains are generally required for sustainable sludge management. Among these pathways, pyrolysis shows considerable potential for applications requiring enhanced contaminant control and value-added biochar production due to its ability to promote organic contaminant degradation, heavy metal immobilization, and carbon storage. However, the feasibility of pyrolysis and other technologies depends strongly on site-specific factors, including sludge properties, energy availability, economic conditions, and regulatory requirements. Future research should focus on integrated process optimization, comprehensive pollutant fate assessment, and standardized evaluation frameworks to advance sludge management toward a circular economy. Full article

Accurate prediction of pile base resistance is essential for the safe and economical design of deep foundations, particularly in soft soils where load-transfer mechanisms are highly nonlinear and uncertain. This study develops a comparative, probabilistic, and interpretable machine learning framework for predicting pile unit base resistance using five input variables: applied load, settlement, effective pile length, axial stiffness, and SPT value. A Gaussian Process Regression model with an automatic relevance determination (ARD) Exponential kernel achieved the best performance, with RMSE = 262.11 kPa, R² = 0.943 on an independent test set, and 95% prediction intervals with 96.46% coverage. Beyond record-level evaluation, a leave-one-pile-out validation (the first grouped validation applied to this database) showed harder generalization to entirely unseen piles, driven mainly by a per-pile level offset rather than shape mismatch (within-pile correlation = 0.975). A sequential next-stage scheme, calibrating this level from a pile’s early loading stages, then predicted its remaining segments with consistently strong agreement (Willmott’s d = 0.76–0.83), supporting practical extension of partial load tests. Interpretability was assessed using ARD, SHAP, permutation/ablation importance, and partial dependence/accumulated local effects analysis, identifying settlement as the dominant predictor. The framework combines accuracy, calibrated uncertainty, interpretability, and validated segment-level extrapolation for reliability-oriented pile assessment. Full article

Geopolitical fragmentation, intensifying geoeconomic competition, and the securitisation of economic relations are reshaping the international environment and changing the conditions under which the European Union (EU) conducts its external economic policy. As economic interdependence has become a source of both prosperity and vulnerability, the EU has begun to redefine the relationship between openness, resilience, and economic security. This article examines how these developments are reshaping the EU’s geoeconomic model and its role within an evolving global order. The study develops an analytical framework that links geopolitical pressures, external dependencies, and institutional responses to the Union’s geoeconomic adaptation. The research employs a concept-driven qualitative design combining qualitative content analysis, analytically focused comparison, and process-oriented interpretation of key EU strategic documents and the relevant academic literature. The study shows that the EU is developing a hybrid geoeconomic model that combines openness with resilience, market integration with economic security, and international cooperation with a stronger capacity to manage critical vulnerabilities. This process reflects a longer-term adjustment of the Union’s external economic strategy and its international role. The article advances current debates on geoeconomics, European integration, and global governance by explaining how geopolitical fragmentation is reshaping the EU’s external action in a more contested international environment. Full article

Cold recycled mixtures with bitumen emulsion (CRME) serving in seasonally frozen regions are susceptible to mechanical deterioration under repeated freeze-thaw (F-T) cycles, which is primarily manifested as interfacial damage and crack propagation. However, the micro-mechanical processes associated with the transmission and dissipation of frost-heaving stresses induced by water-ice phase transition within the interfacial transition zone (ITZ) between reclaimed asphalt pavement (RAP) and asphalt mortar remain to be further characterized. In this study, a numerical simulation approach coupling frost heave effects with the phase transition of water-ice particles was developed based on X-ray computed tomography (CT) and the discrete element method (DEM), and the micro-mechanical parameters of the RAP-asphalt mortar ITZ were determined through laboratory experiments. Combined with acoustic emission (AE) monitoring, the damage evolution characteristics of cold recycled mixtures and the associated interfacial damage mechanisms under freeze-thaw action were systematically investigated. The results indicate that the optimal micro-parameters of the RAP-asphalt mortar ITZ can be taken as approximately 85% of those of virgin asphalt mortar. After 20 freeze-thaw cycles, the number of shear cracks and tensile cracks in ITZ on RAP surface reached 493 and 92, respectively, which were much higher than 11 and five on the surface of new aggregate. ITZ was the main control weak area of freeze-thaw damage. Compared with the unfrozen specimens, the minimum effective contact number of mortar decreased by 1.63%, 4.52% and 8.52% respectively after 5, 10 and 20 freeze-thaw cycles, and the total effective contact number decreased from 75,842 to 69,383. Freeze-thaw cycles significantly reduce the strain energy storage capacity of CRME: the maximum energy storage capacity of the adhesive spring decreased from 2.15 J in the non-freeze-thaw state to 1.28 J in 10 cycles (a decrease of 40.47%) and 1.16 J in 20 cycles (a decrease of 46.05%), and the damage mode changed from brittle fracture to interface-controlled energy dissipation. The proposed water-ice phase transition-based DEM framework provides a reliable numerical tool for investigating freeze-thaw damage mechanisms and supporting durability-oriented design of cold recycled pavement materials. Full article

Polyethylene wax (PEW) often carries a broad carbon-number distribution (CND) that limits its use in grades with controlled melting point, viscosity, and hardness. This study examines how multistage molecular distillation reshapes PEW composition and how the resulting compositional change is transmitted to fraction properties. An industrial PEW was processed through a wiped-film light-removal step and three short-path distillation stages, yielding five fractions whose normalized yields were 4.03% (PEW-40), 12.16% (PEW-70), 22.26% (PEW-80), 30.33% (PEW-90) and 30.71% (PEW-105). The peak carbon number migrated stepwise from C42 in the feedstock to C21, C37, C42, C50 and C68 in the fractions, and basic wax properties, DSC transition temperatures and TGA mass-loss temperatures moved consistently with the compositional shift. Weighted mean carbon number correlated with drop melting point (R² = 0.938), kinematic viscosity at 100 °C (R² = 0.951), penetration (R² = 0.780) and oil content (R² = 0.798), and the second-heating melting peak correlated with the 50% mass-loss temperature (R² = 0.992). These results support a three-level correspondence connecting distillation operation, CND reshaping and fraction properties, and position molecular distillation as a grade-oriented physical process for by-product PEW. Full article

To promote the value-added circular utilization of coastal driftwood, a single salt-exposed driftwood specimen was thermally converted into porous carbon materials by slow pyrolysis. Highly porous carbons are attractive for applications such as adsorption and catalyst support because accessible micro- and mesopores provide a large interfacial area. Prior to carbonization, the thermochemical characteristics of the driftwood were evaluated by proximate analysis, elemental analysis, calorific-value determination, and thermogravimetric analysis (TGA). Pyrolysis was conducted at 400, 500, 600, 700, and 800 °C with residence times of 0, 30, and 60 min at a heating rate of 10 °C/min. The crude biochar products were subsequently washed with reverse-osmosis water. Carbonization temperature was the principal process variable governing pore development. The condition producing the maximum measured porosity was 800 °C with a 60 min residence time, for which the crude biochar yield was 22.98 wt%. The instrument-reported BET surface area and total pore volume of DW-800-60 were 777.5 m²/g and 0.47 cm³/g, respectively. Re-evaluation of the same N₂ isotherm using the Rouquerol consistency criteria gave a physically consistent BET estimate of approximately 944.0 m²/g over P/P₀ = 0.0051–0.0597. Gas adsorption indicated a predominantly microporous structure with an additional mesoporous contribution, whereas the scanning electron microscope (SEM) showed inherited micrometer-scale wood channels. DW-800-60 contained 87.6 wt% carbon. Because the botanical species and mineral-salt composition were not identified and practical adsorption or catalytic performance were not tested, the results should be regarded as specific to the investigated specimen and as a basis for future application-oriented evaluation. Full article

Precision livestock farming (PLF) integrates sensing technologies, data acquisition (DAQ) systems, and machine learning (ML) frameworks to continuously monitor individual animals and support welfare assessment through physiological and behavioral observations. Advances in infrared thermography, radar sensing, vision-based systems, acoustic monitoring, and wearable technologies have substantially expanded the ability to collect high-resolution data describing animal responses to internal and external stimuli. However, despite considerable technological progress, a persistent gap remains between sensing performance demonstrated under controlled experimental conditions and reliable deployment within commercial livestock environments. This gap is characterized by environmental variability, unrestricted animal movement, and operational constraints within commercial environments. Using a structured review methodology, this review examines sensing modalities, embedded DAQ architectures, communication strategies, ML methodologies, data privacy, farmer adoption, and an illustrative engineering workflow through the lens of welfare-relevant physiological characteristics. Emphasis placed on the distinction between direct sensor measurements and the biological processes they represent. Sensor outputs do not directly quantify welfare, stressors, or management outcomes; rather, they provide measurements of physiological and behavioral responses that require appropriate biological context for meaningful interpretation. As a result, welfare assessment does not depend solely on the ability to acquire data, but also on the ability to accurately relate those data to underlying physiological mechanisms. Within this framework, ML serves as a critical bridge between measurement and interpretation by enabling the analysis of complex, multimodal datasets. Future advancement of welfare-oriented PLF systems will require stronger alignment among sensing methodologies, physiological understanding, and practical deployment realities to generate meaningful, scalable, and biologically grounded welfare assessments. Full article

Java is one of the most widely used object-oriented programming languages, making accurate and efficient defect localization essential for improving software quality and reliability. Conventional static analysis techniques primarily rely on predefined rules and localized syntactic matching, which may limit their ability to capture complex structural and semantic relationships among program entities. To address these limitations, this paper proposes JDQuery, a query-driven defect localization framework for Java source code based on a code knowledge graph. The framework parses Java source code into abstract syntax trees (ASTs), extracts software entities and their semantic relationships according to a formalized domain ontology, and constructs a unified code knowledge graph that integrates syntactic and semantic information. Based on the structural characteristics of Java defects, defect patterns are translated into Cypher queries, enabling flexible defect localization through graph pattern matching. Experiments on multiple open-source Java projects, including both injected defects and native real-world defects, demonstrate that JDQuery achieves precision values of 97.20% and 92.87% on two projects of different code sizes. A comparative evaluation with PMD further shows that JDQuery achieves substantially higher recall while maintaining comparable precision for the evaluated defects. Efficiency experiments demonstrate that JDQuery maintains millisecond-level query latency even when processing large-scale Java projects. Full article

Coarse root systems govern tree anchorage, yet remain among the least documented components of tree architecture: excavation is irreversible, and established 3D methods rely on specialist scanners and lengthy post-processing. We evaluated whether a consumer smartphone records exposed coarse root architecture metrically, and which traits agree most closely with manual measurement. Four fully exposed Scots pine (Pinus sylvestris L.) root systems in northwestern Poland were scanned with an iPhone 17 Pro running Scaniverse, at about 30 min of acquisition and 5 h of processing per tree. Clouds were registered, cleaned and oriented to magnetic north in CloudCompare; of eight architectural metrics, four were validated against manual references at 95 cross-sections on 44 roots, and four were exploratory. Visible root length (root-mean-square error, RMSE, 22.2 cm, 8.4%), azimuth (RMSE 3.58°, mean absolute error 2.47°) and depth (RMSE 3.18 cm, 14.9%) agreed most closely with the reference; 70 of 77 first-order roots were detected with no false positives. Diameter was the weakest metric and the only one dependent on the operator (RMSE 0.46 and 0.29 cm for two operators on the same clouds). Smartphone LiDAR thus turns an irreversible excavation into a permanent, measurable record of the traits relevant to anchorage, provided that centimetre-level diameters are not required. Full article

The growing need to ensure the safety, resilience, and sustainability of existing building structures has accelerated the adoption of artificial intelligence (AI) for structural condition assessment and rehabilitation. This critical narrative review synthesizes 82 retained sources, including 33 application-oriented sources, through a transparent, structured literature search and study-selection process; it is not a formal systematic review or meta-analysis. To organize this fragmented evidence base, the review introduces the Data-to-Decision (D2D) Continuum, a unifying conceptual framework that traces eight engineering stages from data acquisition through damage detection, localization, quantification, condition and performance assessment, prognosis, reliability and risk assessment, to rehabilitation decision support. Classical machine learning, deep and temporal models, physics-guided and probabilistic approaches, and emerging foundation models are examined according to the engineering output required at each stage. The strongest evidence concerns bounded defect detection and localization, whereas uncertainty-aware prognosis, risk-informed rehabilitation selection, multi-site validation, and governed deployment remain markedly less mature. By integrating existing monitoring, digital-twin, life-cycle risk, and maintenance-decision concepts into an interface-centered evidence chain, the D2D framework clarifies what must be validated before an AI output can responsibly influence an intervention. Full article

Sodium chloride (NaCl) serves multiple functions in meat product processing, including flavor enhancement, preservation, and texture regulation. However, excessive sodium intake significantly increases the risks of chronic diseases, including cardiovascular disease, hypertension, and gastric cancer. Globally, sodium intake among the general population consistently exceeds the daily upper limit recommended by the World Health Organization (less than 2000 mg sodium per day, equivalent to <5 g salt per day). In certain regions, processed meat products are an important source of dietary sodium intake. Consequently, the development of low-sodium meat products has emerged as a critical priority in both the food industry and public health. This review reviews and summarizes the multifunctional roles of sodium chloride in meat products and the underlying mechanisms of these functions, and evaluates mainstream sodium reduction strategies, namely direct sodium reduction, physical modification, salt substitutes, flavor enhancement, odor-induced saltiness enhancement (OISE), and non-thermal processing. The analysis indicates that individual strategies exhibit limitations in terms of sensory quality, safety, or cost. Future efforts should focus on achieving effective sodium reduction through the synergistic application of multiple strategies, without compromising product quality or safety. This review further proposes a product-type-oriented strategy matrix, and multi-strategy synergy combined with AI optimization which is put forward as a promising potential pathway for the industrialization of sodium reduction in meat products, which remains to be validated by further research. Full article

To examine the effects of brief mindfulness meditation on attention network function in university students with problematic short-video use, participants were screened using the SPAI and an adapted IAT and assigned to an intervention or control group. The intervention group received 4 weeks of brief mindfulness meditation, whereas the control group received no training. Both groups completed the Attention Network Test before and after the intervention. MANCOVA examined group differences in alerting, orienting, and executive control while controlling for baseline performance, age, and sex. Follow-up ANCOVAs were exploratory and Holm-adjusted. The overall multivariate group effect was not significant. Exploratory analyses showed no significant between-group differences in alerting or orienting, whereas the intervention group showed a significantly lower executive control effect than the control group after Holm adjustment. Descriptive RT and ACC patterns across the five ANT conditions did not indicate a consistent improvement in task performance. Taken together, brief mindfulness meditation did not produce an overall improvement in attention network function. However, exploratory findings suggest that it may be associated with a lower conflict-processing cost in executive control. This finding should be interpreted cautiously and requires further verification. Full article

Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models. Full article

While research has investigated the mental health burdens of refugee and asylum seekers with diverse sexual orientation and gender identity and expression (SOGIE), far less attention has been given to what supports their mental health, particularly within the Canadian context. In response to an identified need from a local community organization, this scoping review aimed to identify current approaches that support SOGIE asylum seekers’ mental health in Canada. Using the PRISMA-ScR guidelines, searches took place on 7 and 8 March 2026 across peer-reviewed and grey literature databases indexed in MEDLINE, EMBASE, PsycINFO, CINAHL Plus, LGBTQ+ Source, Scopus, Web of Science, SocIndex, Sociological Abstracts, ERIC, Global Health, ProQuest Dissertations and Theses Citation Index, EBSCO Open Dissertations, ProQuest’s Canadian Research Index, Government of Canada Library, Canadian Council for Refugees Member Organizations, Newcomer Research Library, UNHCR Canada, Egale, and Google. Title, abstract, and full-text screening were completed by two team members, and seven studies met the eligibility criteria outlined. The analysis of four peer-reviewed studies and three grey literature documents suggests that SOGIE asylum seeker mental health should involve emotional and psychological support during the refugee determination process, as well as address social determinants of health. Additionally, the available literature indicated that there must be an awareness of how stigma and mistrust, intersectional challenges, and structural barriers impact how mental health supports are provided to and received by SOGIE asylum seekers in Canada. While having seven included documents primarily based in Ontario and Quebec is a limitation in the evidence, this also provides evidence for the need to conduct further research about SOGIE asylum seeker mental health in other areas of Canada. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article