Search Results (37,051)

Scrap steel is known to influence the fluid flow characteristics of the melt pool in converter steelmaking. However, few studies have considered the effects of the scrap steel charging structure. In this study, a physical model of a 1:8.8 steel–scrap–gas three-phase flow converter was established to investigate the effects of scrap steel state, distribution, material type and shape on the fluid flow characteristics of the converter melt pool. The velocity distribution within the molten pool was measured using particle image velocimetry, while mixing time under various operating conditions was determined using the stimulus–response method. Considering the melting behaviour of scrap steel and the gas utilisation rate comprehensively, the results indicate that when scrap steel is arranged in a uniform position at the bottom of the converter—comprising 90% medium scrap in rectangular scrap and 10% heavy scrap in thin-plate form—and the gas flow rate is 750 m³/h, the overall dynamic conditions of the melt pool are optimal. At this time, the mixing time is 68.2 s (a reduction of up to 45.4%), average velocity is 0.117 m/s (a maximum increase of 207.9%) and turbulent energy dissipation rate is 0.0266 m²/s³ (a maximum increase of 141.8%). Finally, a relationship was established between stirring power and mixing time at different scrap steel charging structures, providing a methodological reference and data support for optimising the charging structure of scrap steel and efficiently using scrap steel in converters. Full article

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔE_int ≈ −22.6 to −31.1 kcal·mol⁻¹), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔE_gap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity. Full article

Businesses increasingly rely on algorithmic systems and machine learning models to make operational decisions about customers, employees, and counterparties. These “algorithmic operations” can improve efficiency but also concentrate liability in a small number of technically complex, drifting models. Algorithmic operations liability (AOL) risk arises when these systems generate legally cognizable harm. We develop a simple taxonomy of AOL risk sources: model error and bias, data quality failures, distribution shift and concept drift, miscalibration, machine learning operations (MLOps) and integration failures, governance gaps, and ecosystem-level externalities. Building on this taxonomy, we outline a simple analysis of AOL risk pricing using some basic actuarial building blocks: (i) a confusion-matrix-based expected-loss model for false positives and false negatives; (ii) drift-adjusted error rates and stress scenarios; and (iii) credibility-weighted rates when insureds have limited experience data. We then introduce capital and loss surcharges that incorporate distributional uncertainty and tail risk. Finally, we link the framework to AOL risk controls by identifying governance, documentation, model-monitoring, and MLOps practices that both reduce loss frequency and severity and serve as underwriting prerequisites. Full article

As former chemical sites, especially retired pesticide plants, the redevelopment of “brownfield” land imposes urgent demands for detailed environmental investigation and remediation. Addressing the current limitations in pollution characterization, which often remain confined to two-dimensional representations and lack research on the vertical migration mechanisms of heavy metals and their integration with three-dimensional remediation and management strategies, this study focuses on a typical retired pesticide plant site in Southeastern Zhejiang, China. Through systematic analysis of 916 soil borehole samples collected from 92 sampling points, the study integrates three-dimensional visualization technology and three-dimensional ordinary kriging interpolation to establish a high-precision three-dimensional characterization system covering stratigraphy, pollution plumes, and composite risks. The findings reveal that the As and Ni pollution plumes have volumes of 5.35 × 10⁴ m³ and 2.78 × 10⁵ m³, respectively. Furthermore, As and Ni exhibit significant vertical migration capabilities within sandy and silty soil layers, while elements such as Hg, Cd, and Pb are primarily concentrated in the surface fill layer. By combining three-dimensional risk modeling based on the single-factor pollution index, Nemerow comprehensive index, and potential ecological risk index, the study precisely classifies the site into four graded zones: safe use zone, basically safe use zone, low-risk control zone, and high-risk control zone. This approach enables the visualization and quantification of pollution levels. The research constructs a comprehensive methodological framework that extends from three-dimensional pollution characterization to zonal management decision-making, providing scientific evidence and technical support for the precise remediation and sustainable redevelopment of similar retired industrial sites. Full article

Grounded in Self-Determination Theory (SDT), this study tested the hypothesis that body image perception delineates distinct motivational pathways, linking the perceived interpersonal style of exercise professionals to basic psychological needs, motivation quality, and long-term exercise persistence intentions. A sample of 821 regular exercisers was divided into two groups based on body image: “Satisfied” (n = 276) and “Dissatisfied due to Overweight” (n = 545). Participants completed validated measures of perceived interpersonal behaviors (supportive/thwarting), basic psychological need satisfaction/frustration, motivational regulation, and exercise persistence intention. A clear divergent pattern emerged, strongly supporting the main hypothesis. The “Satisfied” group reported a positive pathway: perceiving more need-supportive behaviors from instructors was associated with greater satisfaction of autonomy, competence, and relatedness, which in turn correlated with more self-determined motivation and stronger persistence intentions. Conversely, the “Dissatisfied” group reported a negative pathway: perceiving more need-thwarting behaviors was associated with greater need frustration, which correlated with more non-self-determined motivation and weaker persistence intentions. Measurement invariance confirmed these pathways are comparable across groups. The findings highlight that body image perception is a key correlate of distinct motivational experiences in exercise settings. Crucially, they underscore the significant association between the professional’s perceived interpersonal style and these pathways. Fostering need-supportive environments that enhance autonomy, competence, and relatedness is associated with more adaptive motivation and adherence, offering a valuable framework for practitioners aiming to support clients, particularly those with body image concerns. Full article

3D printing offers great potential for rapid and cost-effective fabrication of microfluidic lab-on-a-chip systems. Through a comparative approach, we implemented staggered herringbone mixer (SHM), Tesla mixer, and split and recombine mixer (SAR), along with a basic unperturbed channel into one chip and performed comparative mixing efficiency experiments. We also introduced a phaseguide-based, T-shaped stop structure at the Y-shaped inlets for bubble-free and parallel filling. The structures were analyzed with two poorly mixable dye solutions at flow rates ranging from 1 µL/min to 200 µL/min. The mixing efficiency was evaluated using optical gray value analysis and compared against diffusion-based mixing. The fluid-aligning phaseguides in the 3D-printed system were shown to work. Among the three different mixing structures tested, SHM exhibited the best mixing efficiency at all tested flow rates. Uniformly designed SHM structures contain a region of poor mixing between the two zones of turbulence. In a non-uniform design, fluid breakers were placed between two SHM units to redirect poorly mixed fluids to the edges, resulting in 100% mixing efficiency across all measured flow rates. These results, especially SHM with fluid breakers, support the development of cost-effective injection-molded lab-on-a-chip systems with improved mixing functionalities at close range instead of simple long-length meandric systems. Full article

In this study, we examined the enhancement of erythritol production by the Yarrowia divulgata strain 1485. Although erythritol fermentation has been thoroughly investigated in earlier studies, the influence of inoculum ratio has not been comprehensively addressed. Therefore, this parameter was selected as the focus of the present work. Since industrial-scale erythritol production is typically carried out using more efficient fungal strains, further improvements in economic viability are primarily expected through integration with other biotechnological processes, allowing the simultaneous generation of multiple valuable products. To this end, the erythritol fermentation was coupled with microbial pigment production, and the potential recovery of additional compounds—such as biodetergents and cosmetic ingredients—were also explored. Based on the results, the fermentation with a 15% inoculation rate appears to be the most effective, producing 67.9 ± 6.0 g/L of erythritol, and 61.81 ± 0.02 mg/L of pigment was successfully extracted at the end of the pigment fermentation. The cells seem capable of increasing the skin’s moisturizing effect according to our preliminary tests when glass bead cell disruption is used, and the emulsifier has also proven to be effective, maintaining an emulsification index (EI) above 50% even after 24 h. When performing a kinetic model, we found that the measured data matched the model predictions and confirmed optimal inoculation size (15%), providing a solid basis for subsequent techno-economic analysis. The integration of the two basic fermentations (erythritol and pigment) is therefore considered successful, and the Yarrowia divulgata strain appears to have great biotechnological potential. Full article

Ongoing conflicts in sub-Saharan Africa negatively affect the population’s mental health and weaken health care systems. Collaboration among stakeholders is recommended to strengthen mental health services in post-conflict settings, despite limited evidence on context-specific strategies. This paper aimed to identify strategies for collaboration between traditional and biomedical services to improve mental health care. An adapted nominal group technique was employed during a one-day stakeholder workshop. Fourteen participants representing traditional and biomedical mental health services discussed and prioritised strategies based on importance and feasibility to reach consensus. Five collaborative care strategies were prioritised based on stakeholder consensus regarding importance and feasibility: (1) collaborative learning, (2) formalising coordination, (3) capacity building, (4) joint intervention programs, and (5) regulatory support. Key mechanisms for implementing these strategies were also identified, including piloting integrated interventions, appointing a dedicated focal person to coordinate, providing basic psychosocial counselling skills, reducing harmful practices, and strengthening supportive supervision. Mutual learning was identified as a crucial cross-cutting component of all approaches. The conclusion was that implementing these prioritised strategies could improve mental health care. Further research is needed to evaluate the effectiveness of these strategies in enhancing collaborative care and improving mental health outcomes for individuals. Full article

A well-structured urban park system (UPS) is crucial for optimizing urban spatial layout and improving the quality of the human living environment. In response to the tendency of current planning to prioritize quantitative indicators while overlooking the relational structure arising from the collective spatial configuration of parks, this study introduces Social Network Analysis (SNA) to evaluate the spatial structure of Shanghai’s park system by constructing a service-coverage overlap network. The findings reveal the following: (1) Parks with high degree centrality are concentrated in high-density urban core areas due to service overlap, whereas large suburban parks with high betweenness centrality function as critical bridging hubs, reflecting a polycentric structure. (2) There is a discernible discrepancy between these emergent network tiers and the statutory park hierarchy, highlighting a tension between bottom-up spatial patterns and top-down planning frameworks. (3) Stability simulations indicate a dual character of the system, where the network topology is vulnerable to attacks yet functionally resilient to failures due to spatial redundancy, suggesting that a decline in service quality may precede the loss of basic accessibility. This study demonstrates the value of SNA in diagnosing park system structure, identifying key nodes, and assessing system resilience. The insights advocate for planning approaches that transcend rigid hierarchical frameworks, integrate the actual functional roles of parks, and protect structural hubs, thereby enhancing systemic resilience and promoting equitable service provision. Full article

The fast adoption of electric vehicles (EVs) and the integration of renewable distributed generators (DGs) provide significant operational issues for radial distribution networks (RDNs), notably in terms of power losses, voltage variations, and system stability. This paper investigates the optimal placement and sizing of EV charging stations (EVCSs) and DGs under varying EV hosting factors (EV-HFs). An impedance matrix-based load flow method is developed, and a derived analytical formula for power loss calculation is proposed to improve computational efficiency. A weighted multi-objective function is developed to reduce active power losses and voltage variations while optimizing the voltage stability index and the yearly cost savings from energy loss. The optimization is performed using a deterministic heuristic procedure that incrementally adjusts the location and size of EVCSs and DGs until no further improvement in the fitness function is achieved. This stepwise approach provides fast convergence with low computational effort compared to population-based metaheuristics. The methodology is used on the IEEE 33-bus system under different loading conditions and EV-HFs. The results reveal that for 40% and 60% EV-HFs, active power losses decreased by about 57% compared with the basic case, while the minimum bus voltage improved from 0.9148 pu to 0.9654 pu and 0.9641 pu. The economic analysis demonstrates annual savings of up to USD 473,550, with a payback period between 7 and 8 years. These findings emphasize the need of integrated EVCS and DG planning in improving future distribution systems’ technical and economic performance. Full article

This paper investigates the tensile behavior of cotton ring-spun yarn through experimental testing, numerical simulation, and theoretical calculation. Firstly, scanning electron microscope testing of the microscopic geometric morphologies of yarns was performed for the development of basic finite element (FE) models. Then, the influences of tensile speed and yarn length on the tensile properties of yarn were studied using tensile experiments. Numerical simulations were further performed to investigate the effects of yarn diameter, twist angle, and friction between fibers on the tensile modulus of yarn. Finally, a modified ‘rule-of-mixtures’ equation was proposed to effectively calculate the tensile modulus of yarn through incorporating the friction correction factor. The experimental results show that the tensile modulus and strength of tested yarn are significantly affected by the yarn structure and are not sensitive to the yarn length and tensile speed. Furthermore, the tensile moduli of yarns obtained from the numerical simulations show a good fitting accuracy with those obtained from experimental tests when the friction coefficient is set to 0.5 in the FE models. The simulation results show that the twist angle and friction coefficient are two key factors affecting the tensile modulus of yarn. The modified ‘rule-of-mixtures’ equation presents better accuracy for the calculation of the tensile modulus of yarn compared with the traditional ‘rule-of-mixtures’ equation, which can be used to replace the FE modeling and simulation and reduce the computational cost. This work will provide a deeper understanding of the mechanical properties of cotton ring-spun yarns and enhance their application in the textile industry. Full article

Fine-Grained Visual Classification (FGVC) involves distinguishing highly similar subordinate categories within the same basic-level class, presenting significant challenges due to subtle inter-class variations and substantial intra-class diversity. While Vision Transformer (ViT)-based approaches have demonstrated potential in this domain, they remain limited by two key issues: (1) the progressive loss of gradient-based edge and texture signals during hierarchical token aggregation and (2) insufficient extraction of discriminative fine-grained features. To overcome these limitations, we propose a Gradient-Aware Token Injection Transformer, a novel framework that explicitly incorporates gradient magnitude and orientation into token embeddings. This multi-modal feature fusion mechanism enhances the model’s capacity to preserve and leverage critical fine-grained visual cues. Extensive experiments on four standard FGVC benchmarks demonstrate the superiority of our approach, achieving 92.9% top-1 accuracy on CUB-200-2011, 90.5% on iNaturalist 2018, 93.2% on NABirds, and 95.3% on Stanford Cars, thereby validating its effectiveness and robustness. Full article

Ethanol stability is an important indicator for evaluating the quality and heat-processing suitability of raw milk. Traditional ethanol stability testing relies on destructive laboratory procedures, which are not suitable for large-scale industrial use. In contrast, parameters such as protein, fat, lactose and other basic compositional indicators are already measured routinely in dairy plants through sensor-based or spectroscopic systems. This provides the basis for developing a non-destructive soft sensing approach for ethanol stability. In this study, a soft sensing model was developed to predict ethanol stability from commonly monitored raw-milk intake indicators. An autoencoder was used to examine feature correlations and select variables with stronger relevance to ethanol stability. TabNet was then applied to build the classification model, and a TabDDPM-based data generation method was introduced to address class imbalance in the dataset. The proposed model was trained and tested using three years of industrial raw-milk intake data from a dairy company. It achieved an accuracy of 92.57% and a recall of 90.26% for identifying ethanol-unstable samples. These results demonstrate the model’s strong potential for practical engineering applications in real-world dairy quality monitoring. Full article

Limitations associated with traditional automation approaches within manufacturing have driven the pursuit of more flexible and intelligent robot guidance methods. One promising development in this area is the integration of external multitarget six degrees of freedom (6 DoF) distributed large-volume metrology (DLVM) into the control loop. Although multiple standards exist across dimensional metrology, motion tracking, indoor positioning, robot guidance, and machine tool accuracy, there is no harmonised, technology-agnostic standard that fully encompasses the unique challenges of 6 DoF DLVM systems for dynamic applications. This work identifies key gaps in the current standards’ landscape and presents a technology-agnostic candidate test methodology intended to support future standardisation of dynamic DLVM performance evaluation. The method provides a metrologically grounded spatial reference path and a temporal alignment strategy so that position and orientation errors can be reported in the intrinsic coordinates of the path. The paper covers the basic principle of the test, artefact construction, synchronisation strategies, preliminary error modelling, and a baseline uncertainty approach, and reports representative results from initial prototype trials on a multi-nodal distance-camera DLVM system. The prototype results demonstrate feasibility and highlight temporal sampling and traceable timing as current limiting factors for fully deconvolving latency and pose error; these aspects are therefore positioned as instrumentation requirements and the focus of ongoing work. Full article

Food supply veterinarians, those who service the dairy, swine, poultry, small ruminant, and beef cattle industries, benefit society by protecting animal and public health and ensuring a safe, wholesome food supply. However, there are not enough entering the workforce to meet current and future demands. Non-formal learning environments can be used as a recruitment tool to provide participants with positive interactions and hands-on experiences. To build awareness of food supply veterinary medicine (FSVM) in youth, we developed an immersion program designed to provide high school students with hands-on experiences with food animal species. Day camps were held during the summers of 2022 and 2023, each coordinated with multiple partners at different locations in central Ohio. Year One camp utilized registration and post-test surveys and Year Two utilized matching pre- and post-test for analysis. Over the two programs, 110 participants engaged in hands-on experiences, including: outbreak investigations, measuring clinical parameters, performing diagnostics, and basic veterinarian procedures. Pre- and post-test evaluations were performed to measure changes in participants’ attitudes and perceptions, and a McNemars test was used to evaluate Year Two data. In Year One, we saw positive shifts in those interested in FSVM careers. In Year Two, we saw positive shifts in knowledge of FVSM careers, with biosecurity knowledge increasing. Outreach activities like day camps can be replicated in other locations to increase interest in FSVM careers. Full article