Search Results (2,784)

Municipal solid waste challenges (MSW) and concerns about fossil fuel dependence motivate efforts to recover energy from waste, including refuse-derived fuel (RDF). Techno-economic assessment (TEA) evaluates the feasibility of systems by quantifying investment performance. However, most RDF-TEA studies typically rely on isolated sensitivity analyses. That provides limited insight into interaction effects in emerging markets. This study maps the multivariable feasibility of RDF production from MSW in Ghana under realistic economic conditions. Using a pilot-calibrated case study, the assessment integrates discounted cash flow analysis with response surface methodology–design of experiment (RSM-DoE). A central composite design evaluates interaction effects among operational and economic variables for a system capacity of 2875 tonnes RDF/year. The results indicate economic viability with a net present value (NPV) of USD 892,556.44, a payback period (PBP) of 6.61 years and a levelised production cost (LPC) of USD 18.96/tonne. The RSM models show high explanatory power (R², R²adj, R²pred > 90%). Sensitivity results demonstrate that support mechanisms can significantly reduce LPC and PBP while preserving investment viability. The study quantifies the feasibility thresholds and the support instruments within the RDF design levers. It further provides a transferable framework for assessing deployment and upscaling in emerging markets. The findings highlight the need for structured pricing mechanisms and regulatory support for the long-term sustainability of RDF as an AF. Full article

Extremes of the Edgeworth box concern corner allocations and their relationship to the contract curve in a two-good, two-agent exchange economy. In the standard pure-exchange setting with well-behaved preferences, the contract curve comprises all Pareto-efficient allocations, including interior tangencies and boundary corners, where no mutually beneficial trade remains. When money is introduced as a numéraire (a medium of exchange only), real feasibility and preferences are unchanged, so the contract curve remains the benchmark for efficiency. When money provides liquidity services (is valued for holding), agents may rationally abstain from trade even near interior tangencies; short-run outcomes can therefore include inaction at corners. This entry defines these objects, outlines the efficiency conditions at boundaries, and summarizes how monetary interpretations affect short-run behavior in general equilibrium and monetary economics. The Edgeworth geometry remains a real-exchange depiction; when we discuss money as a store of value, we use it as a short-run, reduced-form outside option that proxies intertemporal motives. This does not “fix” the box; it clarifies why no-trade at or near corners can be individually rational when liquidity is valued. Full article

Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications. Full article

D-tagatose is a high-value, low-calorie sweetener that can be produced from dairy lactose via a two-step enzymatic route: lactose hydrolysis to galactose followed by galactose isomerization to tagatose. Here, we combined genomics, in silico structural analysis, and biochemical assays to evaluate the lactose-to-tagatose conversion potential of an Antarctic isolate, L47, identified as Ewingella americana (NCBI accession SAMN54554459). Genome mining revealed one L-arabinose isomerase gene (araA) and three β-galactosidase genes (bgaA, bglY, lacZ), an uncommon combination in a single bacterium. Recombinant AraA was produced in Escherichia coli and biochemically characterized, showing Mn²⁺ dependence and measurable D-galactose isomerization, reaching ~18% tagatose from 100 mM galactose after 48 h under the tested conditions. In contrast, the β-galactosidases were predominantly recovered as insoluble aggregates in E. coli; therefore, β-galactosidase activity was assessed using washed inclusion-body preparations. Under these conditions, BgaA displayed the most consistent o-NPG hydrolyzing activity, whereas BglY and LacZ did not yield reproducible activity. Overall, our results identify BgaA as the most tractable lactose-hydrolyzing candidate from L47 in the current workflow and indicate that AraA performance is the principal bottleneck toward an efficient lactose-to-tagatose process, motivating future optimization at the enzyme and process levels. Full article

Accurate interpretation of missense variants in cancer-associated genes remains a critical challenge in precision oncology, as most sequence-based predictors lack mechanistic explanations. Receptor tyrosine kinases like FGFR2 exemplify this problem: their function depends on conformational dynamics, yet most variants remain classified as variants of uncertain significance (VUS). In this paper we present DyVarMap, an interpretable structural-learning framework that integrates AlphaFold2-based ensemble generation with physics-driven refinement, manifold learning, and supervised classification using five biophysically motivated geometric features. Applied to FGFR2, the framework generates diverse conformational ensembles, identifies metastable states through nonlinear dimensionality reduction, and classifies pathogenicity while providing mechanistic attributions via SHAP analysis. External validation on ten kinase-domain variants yields an AUROC of 0.77 with superior calibration (Brier score = 0.108) compared to PolyPhen-2 (0.125) and AlphaMissense (0.132). Feature importance analysis consistently identifies K659–E565 salt-bridge distance and DFG motif dihedral angles as top predictors, directly linking predictions to known activation mechanisms. Case studies of borderline variants (A628T, E608K, L618F) demonstrate the framework’s ability to provide structurally coherent mechanistic explanations. DyVarMap bridges the gap between static structure prediction and dynamics-aware functional assessment, generating testable hypotheses for experimental validation and demonstrating the value of incorporating conformational dynamics into variant effect prediction for precision oncology. Full article

To address the challenges of insufficient efficiency and accuracy in traditional detection models caused by minute target sizes, low signal-to-noise ratios (SNRs), and feature volatility in Satellite Laser Ranging (SLR) images, this paper proposes an efficient, lightweight, and high-precision detection model. The core motivation of this study is to fundamentally enhance the model’s capabilities in feature extraction, fusion, and localization for minute and blurred targets through a specifically designed network architecture and loss function, without significantly increasing the computational burden. To achieve this goal, we first design a DMS-Conv module. By employing dense sampling and channel function separation strategies, this module effectively expands the receptive field while avoiding the high computational overhead and sampling artifacts associated with traditional multi-scale methods, thereby significantly improving feature representation for faint targets. Secondly, to optimize information flow within the feature pyramid, we propose a Lightweight Upsampling Module (LUM). Integrating depthwise separable convolutions with a channel reshuffling mechanism, this module replaces traditional transposed convolutions at a minimal computational cost, facilitating more efficient multi-scale feature fusion. Finally, addressing the stringent requirements for small target localization accuracy, we introduce the MPD-IoU Loss. By incorporating the diagonal distance of bounding boxes as a geometric penalty term, this loss function provides finer and more direct spatial alignment constraints for model training, effectively boosting localization precision. Experimental results on a self-constructed real-world SLR observation dataset demonstrate that the proposed model achieves an ${\mathrm{mAP}}_{50:95}$ of 47.13% and an F1-score of 88.24%, with only 2.57 M parameters and 6.7 GFLOPs. Outperforming various mainstream lightweight detectors in the comprehensive performance of precision and recall, these results validate that our method effectively resolves the small target detection challenges in SLR scenarios while maintaining a lightweight design, exhibiting superior performance and practical value. Full article

Automatically generated chains-of-thought (gCoTs) have become common as large language models adopt deliberative behaviors. Prior work emphasizes fidelity to internal processes, leaving explanatory properties underexplored. Our central hypothesis is that these traces, produced by highly capable reasoning models, are not arbitrary by-products of decoding but exhibit stable and practically valuable textual properties beyond answer fidelity. We apply a multidimensional text-evaluation framework that quantifies four axes—structural coherence, logical–factual consistency, linguistic clarity, and coverage/informativeness—that are standard dimensions for assessing textual quality, and use it to evaluate five reasoning models on the GSM8K arithmetic word-problem benchmark (~1.3 k–1.4 k items) with reproducible, normalized metrics. Logical verification shows near-ceiling self-consistency, measured by the Aggregate Consistency Score (ACS ≈ 0.95–1.00), and high final-answer entailment, measured by Final Answer Soundness (FAS0 ≈ 0.85–1.00); when sound, justifications are compact, with Justification Set Size (JSS ≈ 0.51–0.57) and moderate redundancy, measured by the Redundant Constraint Ratio (RCR ≈ 0.62–0.70). Results also show consistent coherence and clarity; from gCoT to answer implication is stricter than from question to gCoT support, indicating chains anchored to the prompt. We find no systematic trade-off between clarity and informativeness (within-model slopes ≈ 0). In addition to these automatic and logic-based metrics, we include an exploratory expert rating of a subset (four raters; 50 items × five models) to contextualize model differences; these human judgments are not intended to support dataset-wide generalization. Overall, gCoTs display explanatory value beyond fidelity, primarily supported by the automated and logic-based analyses, motivating hybrid evaluation (automatic + exploratory human) to map convergence/divergence zones for user-facing applications. Full article

The first measurements of the magnetic dipole hyperfine structure constants A in singly ionized thulium revealed substantial discrepancies with the corresponding theoretical calculations. Subsequent measurements expanded the very limited available dataset and demonstrated that two of the previously reported experimental A values were incorrect, thereby motivating new theoretical calculations. In this work, we employ the configuration interaction method to calculate the A constants for several low-lying levels in Tm ii, with the random-phase-approximation corrections also taken into account. Our results show good agreement with the new experimental data and provide reliable predictions for additional states where measurements are not yet available. Full article

Objectives: This study aims to explore the facilitators and barriers in the process of using AI agents for disease management in older COPD patients. Methods: Based on the normalization process theory, a descriptive qualitative study was used to conduct semi-structured interviews with 28 older patients with COPD recruited from June to August 2025 in a Class A tertiary hospital in Wuxi, Jiangsu Province. Results: A total of 28 interviews were conducted. Four themes (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring), nine subthemes (recognition of intelligent technology;supported by policy discourse and the background of national-level projects; the creation of a family atmosphere; recommendations from HCPs; relief and social connection; new “doctor”–patient relationship and communication; eliminate the burden and return to life; benefit and value perception; right self-decision by AI) in facilitators and nine subthemes (privacy conflicts and trust deficiency; blurred boundaries of human–machine responsibility and authority; non-high-quality services are chosen reluctantly; technical anxiety; lack of motivation for continued engagement; extra burden; limitations of the physical environment; human–machine dialogue frustration; a sense of uncertainty about the future of AI) in barriers were extracted. Conclusions: This study identified key factors influencing the use of AI agents in chronic disease management in older patients with COPD. The results provide directions for improving the implementation and sustainable use of AI health technologies. Full article

The purpose of this study is to examine how employees’ artificial intelligence awareness (AIA) influences adaptive performance in the workplace through the mediating roles of eustress and task crafting within the Job Demands–Resources (JD-R) Theory. Data were collected from 372 full-time employees working in five-star hotels and analyzed using PLS-SEM with WarpPLS. The findings reveal that employees’ AI awareness significantly enhances adaptive performance both directly and indirectly. AI awareness also positively predicts eustress and task crafting, suggesting that informed employees experience motivating stress and actively reshape their tasks to optimize work processes. Moreover, both eustress and task crafting serve as significant mediators, amplifying the effect of AI awareness on adaptive performance. These results underscore the value of cultivating AI knowledge among employees to foster proactive behaviors and positive stress responses, ultimately supporting adaptability in dynamic work environments. The study contributes to JD-R Theory by integrating AI-related awareness as a personal resource driving employee adaptation. Full article

Planned Missing Designs (PMDs) allow for different sets or patterns of variables to be collected from sample units. While the typical motivation for PMDs is to manage respondent burden, they can also reduce data collection costs and provide flexibility in meeting reliability requirements for key survey outputs. Almost all PMD applications involve data being Missing Completely At Random (MCAR). That is, the pattern of variables to be collected from a sample unit is determined prior to collecting any variables from the unit. Here we generalise this approach by considering designing PMDs that allow data to be Missing At Random (MAR). That is, the set of variables collected from a unit is allowed to depend upon the value of some of the variables collected from the unit. At the design stage no data have been observed and so we consider the expected information function associated with maximum likelihood estimation for any specified PMD. We show how the missing information principle can be used to determine the loss of information arising from the use of a PMD. This paper considers the multinomial distribution in detail and conducts an empirical evaluation to illustrate potential efficiency gains associated with MCAR and MAR PMDs. Full article

The rapid proliferation of misinformation on social media underscores the urgent need for scalable digital-literacy instruction. This study presents the design and evaluation of a Generative AI-enhanced serious game system that integrates Large Language Models (LLMs) to drive adaptive non-player characters (NPCs). Unlike traditional scripted interactions, the system employs role-based prompt engineering to align real-time AI dialogue with the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) framework, enabling dynamic scaffolding and authentic misinformation scenarios. A mixed-method experiment with 60 undergraduate students compared this AI-driven approach to traditional instruction using a 40-item digital-literacy pre/post test, the Intrinsic Motivation Inventory (IMI), and open-ended reflections. Results indicated that while both groups improved significantly, the game-based group achieved larger gains in credibility-evaluation performance and reported higher perceived competence, interest, and effort. Qualitative analysis highlighted the HCI trade-off between the high pedagogical value of adaptive AI guidance and technical constraints such as system latency. The findings demonstrate that Generative AI can be effectively operationalized as a dynamic interface layer in serious games to strengthen critical reasoning. This study provides practical guidelines for architecting AI-NPC interactions and advances the theoretical understanding of AI-supported educational informatics. Full article

This study examined whether the Ringelmann and Köhler effects emerge in adolescent rowing by assessing how crew size influences performance, physiological responses and perceived exertion in youth rowers aged 14–17 years. A total of 136 competitive rowers (mean age = 15.79 ± 1.14 years) completed four three-minute maximal-effort trials on a rowing ergometer under four conditions: individual trials, two-person crews, four-person crews and eight-person crews. Objective performance indicators, including stroke rate, heart rate and perceived exertion (Borg scale), were recorded. Repeated-measures ANOVA indicated that objective performance indicators (distance and power output) remained largely stable across conditions and age groups, although some isolated and non-systematic differences with large intra-subject effect sizes emerged in the younger category (14–15 years), particularly in the two-person crew condition. In contrast, the stroke rate differed consistently across crew sizes, with higher values observed in the eight-person crew condition in both age groups. Cardiovascular responses showed minimal and transient variation between conditions. Perceived exertion differed markedly by age, with older rowers (16–17 years) reporting significantly higher effort during individual trials compared with crew-based conditions, without corresponding gains in objective performance. Overall, although crew size influenced the regulation and perception of effort, the findings do not provide support for a consistent expression of either the Ringelmann or Köhler effects in adolescent rowing, as no systematic performance losses or motivational gains among weaker crew members were evident. These results suggest that developmental differences in self-regulation and effort perception may play a more prominent role than crew size alone in shaping performance responses, with practical implications for training design and crew configuration in youth rowing. Full article

Expanded and intensified agriculture is a major driver of habitat loss for endangered species such as the Oriental White Stork (Ciconia boyciana), making wildlife-friendly farming an increasingly important approach for reconciling biodiversity conservation with agricultural development. Building on a 2018 feasibility study in the Sanjiang Plain, this research employs a choice experiment to examine how preferences for Oriental White Stork-friendly farming have evolved among urban consumers and residents of stork habitats under expanding green consumption and increasing experience with environmentally friendly farming. The results reveal pronounced preference heterogeneity and persistent cognitive separation between wildlife conservation and agricultural production, particularly among urban consumers, despite a stable group being willing to pay a premium for stork-friendly products. Rural residents’ decisions remain largely economically driven, though younger farmers with prior experience in environmentally friendly practices show more positive attitudes. Significant urban–rural differences suggest policy complementarities, whereby price-oriented incentives may encourage price-sensitive farmers to adopt green agriculture, while intrinsically motivated farmers require support through an Oriental White Stork-oriented value chain. Overall, the findings demonstrate that Wildlife-Friendly Farming cannot be effectively promoted through a one-size-fits-all approach; instead, stratified, group-specific policy and market mechanisms are essential for aligning producer incentives with consumer demand and supporting the long-term viability of biodiversity-friendly agricultural systems. Full article

Background: CrossFit^® is a high-intensity functional training modality with increasing popularity, yet limited evidence describes the general profile of its practitioners. Objective: To characterize CrossFit^® athletes based on their training habits, injury prevalence, and nutritional supplement use, with specific consideration given to sex and age. Methods: An online questionnaire was completed by 358 practitioners (182 women; mean age 35.6 ± 9.1 years) from various Spanish regions. Descriptive and comparative analyses (χ² and ANOVA; p < 0.05) were conducted for training patterns, injury history, and supplement consumption. Results: Over half of the sample had practiced CrossFit^® for more than three years, typically training 3–4 days per week in one-hour sessions. Participants primarily reported social and health-related motivations and identified as non-competitive. Overall, 42.2% experienced at least one CrossFit^®-related injury, most frequently affecting the shoulder (15.6%) and lumbar spine (10.1%), largely attributed to repetitive overload. Supplement use was widespread (81.8%), with creatine (60.3%) and protein (49.4%) being the most commonly consumed. Conclusions: CrossFit^® practitioners train consistently, value the social environment, and show an injury pattern similar to that of other strength-based disciplines. Supplement consumption is highly prevalent across groups. Coaches and health professionals should prioritize injury-prevention strategies, promote safe load progression, and guide responsible supplement use. Full article