Search Results (46,912)

Assessing zoo animal welfare can involve generic evaluations or targeted, species-specific protocols. While the latter offer greater precision, their development is often limited by species diversity and the lack of validated indicators. The Animal Welfare Monitor^® (AWM) protocol addresses these challenges by adapting Welfare Quality^® principles to zoological contexts. Its core innovation is a four-level hierarchical structure (base, order, family, species) linking broad taxonomic knowledge to species-level protocols. This enables tailored questionnaires for each species, including data-deficient taxa, by leveraging information from related groups. Questionnaires, covering housing, nutrition, health, and behaviour, are complemented by behavioural observations. AWM currently covers 87 species (69 mammals, 15 birds, 2 amphibians, 1 reptile) and constitutes a substantial database of species-specific welfare assessment protocols embedded within a single, standardised methodological framework. Between 2021 and 2025, 14 zoos conducted over 1000 assessments and 15,000 behavioural observations, demonstrating the protocol’s feasibility in routine operations. AWM integrates data entry with visual documentation, such as photographs of enclosures or enrichment, which add context, enhance decision-making, and strengthen long-term records. While refinements such as group-level assessment remain, AWM offers a scalable, flexible tool combining scientific rigour with operational applicability, supporting positive welfare outcomes across diverse zoological institutions. Full article

This exploratory eye-tracking study investigates how the emotional content of food bank advertisements influences food donor perception and visual attention. It does so by addressing a gap in the literature on eye-tracking applications in food donation contexts and social neuroscience. Visual attention represents a fundamental behavioural precursor to decision-making, yet its role in charitable communications remains underexplored. The objective of this research was to investigate how the content of food bank advertisements is associated with the way that potential food donors perceive food bank posters on a cognitive level. This study adopted a social neuroscience approach, using the methodology of eye-tracking to examine the visual attention patterns that form while viewing food bank posters. Participants (N = 96) viewed four posters varying in their emotional appeal, i.e., positive, neutral, negative and cognitive dissonance, while their eye movements were being recorded. Results revealed the robust attentional prioritisation of generic pictorial content over specific organisational logos or abstract symbols across all metrics and posters with large effect sizes (r = 0.69–0.87). It was found that pictures captured participants’ attention three to seven times faster than logos and also received two to seven times more fixations. The poster carrying a negative appeal elicited the strongest pictorial advantage, consistent with the negativity bias in attention allocation. Exploratory analysis found no significant correlation between participants’ past charitable behaviour and visual attention patterns, thus suggesting that the Picture Superiority Effect operates universally, regardless of individual past charitable behaviours. This is the first eye-tracking study examining donor-facing food bank communications in Poland, contributing to social neuroscience approaches in prosocial behaviour research. Findings suggest charitable organisations should prioritise emotionally engaging pictures’ inclusion over logo prominence in their visual communications messaging. Full article

Continuous-variable quantum communication (CVQC) relies on finite-window estimation of phase space moments, making receiver decisions sensitive to finite measurement resolution, calibration uncertainty, and confidence-calibrated tolerances. This paper develops a receiver-centric threat modeling framework for structured (including adversarial) physical-layer disturbances under finite-sample inference. We introduce an operational taxonomy, reconnaissance, exploratory, and denial-of-service, defined by statistical visibility relative to acceptance regions rather than by assumed physical mechanisms. Using an effective estimator space Gaussian model ${\widehat{\mathbf{r}}}^{\prime }=G\widehat{\mathbf{r}}+\xi$ with additive covariance N, we show how distinct mechanisms can be observationally equivalent within finite tolerances and we propose a protocol-agnostic scalar severity coordinate $\Delta E$ based on the covariance trace. We derive ${\chi }^2$-based missed-detection expressions and a soft detectability boundary scaling as $1/\sqrt{n}$, and we corroborate the predicted $P_{\mathrm{miss}}\left(\nu \right)$ behavior via Monte Carlo simulations across representative block sizes. The resulting framework clarifies the delimitation from conventional CV-QKD excess noise parameterization and provides a structured basis for monitoring-layer design and comparative threat-taxonomy mapping. Full article

This study examined how pedagogical strategies can support student-coaches’ (SCs) empowerment and promote preservice teachers’ (PSTs) professional learning within Sport Education (SE) seasons. Sixty-seven tenth-grade students (aged 15–18) participated in SE units taught by three PSTs (two males, one female, aged 22–25) enrolled in a master’s degree program in Teaching of Physical Education in Primary and Secondary Education in northern Portugal. Data were collected through participant observation, informal and focus group interviews, and PSTs’ reflective diaries within a Collaborative Action Research (CAR) framework and analyzed thematically. Three CAR cycles addressed key challenges: (1) encouraging SCs to assume responsibility for their role, (2) fostering inclusive and supportive team interactions, (3) strengthening SCs’ sport-specific and instructional knowledge. Guided by a facilitator, PSTs implemented strategies including pre-lesson meetings, structured communication routines, task-modification and feedback cards, accountability systems, and visual identification of SCs. Findings suggest that SCs’ empowerment was progressively constructed through interconnected psychological, relational and pedagogical processes, supported by structured mediation and iterative reflection. Simultaneously, engagement in CAR cycles enabled PSTs to develop adaptive instructional decision-making and mediation strategies. The study highlights how empowerment in SE is shaped through relational and pedagogical conditions and illustrates how CAR can foster reciprocal learning between SCs and PSTs in authentic teacher education contexts. Full article

In relation to the most commonly described ampullary ectopic pregnancies in contemporary gynecological practice, rare localizations of ectopic pregnancies represent a diagnostic and therapeutic challenge whose clinical significance far exceeds their frequency. In contrast to tubal ectopic pregnancy, these implantation localizations are characterized by specific anatomical relationships and early trophoblastic invasion into highly vascularized tissues, which is why classical diagnostic algorithms and therapeutic patterns are often not applicable in clinical practice. Clinical uncertainty is further increased by the fact that a large proportion of these pregnancies in early gestation cannot be precisely mapped and initially present as pregnancies of unknown location. This narrative review integrates contemporary evidence and guidelines of relevant professional societies with the aim of highlighting patterns of diagnostic errors, systemic weaknesses of existing approaches, and key points for safe clinical decision-making. Special emphasis is placed on the role of disciplined transvaginal ultrasound evaluation, terminological precision, and timely recognition of high-risk and nonspecific implantations. Analysis of the available literature indicates that therapeutic decisions must be individualized and guided by the implantation site and assessment of hemorrhagic risk, rather than gestational age or absolute β-hCG values. Understanding these principles represents the basis for reducing serious complications and for the development of future diagnostic and therapeutic algorithms, thereby improving treatment outcomes. Full article

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization. Full article

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time–temperature-dependent quality degradation with energy consumption in refrigerated warehouses. Unlike traditional approaches, energy is modeled as an endogenous function of warehouse filling level and warehouse temperature, allowing the interaction between inventory volume, energy efficiency, and quality preservation to be captured. The model is formulated under three coordination policies—Lot-for-Lot, traditional agreement, and consignment stock—and solved under joint decision making. Numerical results for chilled and frozen products show that neglecting energy and quality costs can lead to sub-optimal policies with total cost penalties exceeding 300% compared to the proposed integrated optimization. Results further indicate that a consignment stock agreement can reduce total system costs by up to 9% relative to traditional policies, while the optimal lot size is highly sensitive to energy prices, product value, and warehouse temperature. These findings highlight the critical role of jointly optimizing inventory, energy, and quality decisions in cold supply chains and provide actionable insights for designing more sustainable and energy-efficient production inventory systems. Full article

The rapid development of a new-type power system dominated by renewable energy has introduced growing complexity and variability into grid topology and dynamics, posing significant challenges for transmission interface power flow control. Traditional regulation methods based on operator experience and deterministic optimization often fail to achieve real-time optimality under such dynamic conditions. Leveraging its strong capability for autonomous learning and feature perception, deep reinforcement learning (DRL) offers a promising approach for addressing these challenges. This paper proposes a safe DRL-based control framework for online transmission interface power flow regulation. A Safe Soft Actor–Critic (SSAC) agent is developed, embedding power system security constraints directly into the decision process to ensure operational safety. A secure EMS-interactive training platform with containerized parallel learning is established to accelerate model convergence and improve adaptability to changing operating conditions. The developed SSAC agent is deployed in the Jiangsu Power Grid Energy Management System (EMS) for validation. Simulation and field test results demonstrate that the proposed method can generate control strategies online within milliseconds, achieving a 99.3% interface overload mitigation rate and 3.32% network loss reduction, outperforming conventional sensitivity-based optimization methods in both timeliness and economic efficiency. These results demonstrate strong real-time computational capability and compatibility with EMS-based dispatch workflows, indicating promising practical deployment potential for transmission interface control in renewable-dominated power systems. Full article

Background/Objectives: Personalized treatment approaches are increasingly recognized as essential in the management of anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV), given the substantial heterogeneity in disease severity and patient characteristics. Kidney biopsy has the potential to serve as an effective tool for personalized treatment decision-making in patients with AAV. This study aimed to investigate the association of kidney biopsy with intensive immunosuppressive therapy and clinical outcomes in patients with AAV and kidney impairment. Methods: In this retrospective study, propensity score overlap weighting was applied to compare intensive immunosuppressive therapy and clinical outcomes (ESKD, death, combined ESKD and death, and infectious complications) between patients with AAV who underwent kidney biopsy and those who did not. Results: Out of 74 patients with AAV, 38 underwent kidney biopsy. Overlap weight analysis revealed that kidney biopsy was significantly associated with intensive immunosuppressive therapy (risk difference [RD], 28.9%; 95% confidence interval [CI], 0.017 to 0.562). Kidney biopsy was not associated with combined ESKD and death (RD, −0.2%; 95% CI, −0.302 to 0.298), death (RD, −3.8%; 95% CI, −0.264 to 0.189), ESKD (RD, −7.3%; 95% CI, −0.353 to 0.207), and infectious complications (RD, −25.9%; 95% CI, −0.537 to 0.020). Conclusions: In this observational cohort, kidney biopsy was associated with intensification of immunosuppressive therapy. However, after adjustment using overlap weighting, no statistically significant difference in clinical outcomes was detected, and the reduced effective sample size limited statistical power. These findings should be interpreted cautiously, as causal inference regarding the prognostic impact of kidney biopsy remains limited. Full article

Background: Cardiothoracic surgery is among the most technologically advanced and resource-intensive medical specialties, placing it at the intersection of rapid digital innovation and growing demands for environmental sustainability. Addressing these parallel pressures requires integrated strategies that reconcile clinical excellence with ecological responsibility. Methods: This narrative review synthesizes PubMed-indexed literature published over the past two decades, supplemented by relevant policy documents and guidelines. The review examines digital transformation and sustainability initiatives in cardiothoracic surgery through the lens of the twin transformation framework, which conceptualizes digitalization and sustainability as interdependent and mutually reinforcing processes. Results: Key domains of digital transformation include artificial intelligence and big data-driven decision-making, robotic and minimally invasive surgical techniques, digital twins and simulation-based training, telemedicine and remote monitoring, and interoperable electronic health records. Sustainability-related themes encompass the substantial environmental burden of operating rooms, green surgical practices, sustainable procurement, and hospital-level decarbonization strategies. Emerging evidence suggests that aligning digital technologies with sustainability objectives can improve clinical outcomes, enhance operational efficiency, and reduce environmental impact. However, current evidence is largely derived from pilot studies and single-center experiences. Conclusions: Twin transformation offers a coherent and forward-looking framework for the future evolution of cardiothoracic surgery, demonstrating that digital innovation and sustainability can be synergistic rather than competing goals. While significant challenges remain—including high implementation costs, limited long-term data, and fragmented regulatory frameworks—integrating digital health technologies with sustainable practices represents a promising pathway toward high-quality, efficient, and environmentally responsible cardiothoracic care. Full article

Concrete compressive strength is traditionally modeled as a function of mixture composition, with cement dosage often assumed to produce proportional strength gains. However, such interpretations are typically correlational and do not quantify the causal effectiveness of cement additions under varying mixture conditions. This study introduces an interpretable causal machine learning (ICML) framework to estimate the marginal causal effect of cement dosage on compressive strength using an R-learner-based approach. Cement content is treated as a continuous intervention variable, and heterogeneous treatment effects are estimated conditionally on mixture composition and curing age. The estimated average marginal effect of cement dosage is 0.136 MPa per kg/m³ (95% bootstrap confidence interval: [0.1055, 0.1433]). However, substantial heterogeneity is observed, with individual marginal effects ranging from −0.027 to 0.370 MPa (5th–95th percentile). Near-zero and, in limited regimes, negative marginal effects emerge under high water content and unfavorable mixture conditions, indicating inefficient cement utilization. Robustness checks across alternative cross-fitting schemes and trimming procedures confirm the stability of the estimated causal effects. Unlike conventional machine learning models that explain predicted strength values, the proposed framework applies explainability directly to the estimated causal effect function. Local SHAP-based explanations reveal the mixture configurations under which cement additions are effective or inefficient. By explicitly identifying mixture conditions under which cement additions are effective or inefficient, the proposed framework supports more rational cement use, reducing unnecessary material consumption, lowering construction costs, and easing the decision-making burden on designers in practical concrete mix design. Full article

The construction industry is under mounting pressure to enhance its sustainability performance. Increasing project complexity and risk require real-time data collection, monitoring, and assistance in decision making via the Internet of Things (IoT). IoT has emerged as a critical enabling technology to overcome these hurdles. This study provides a bibliometric and thematic overview of IoT applications in sustainable construction project management to identify research trends, key themes, and practical implications for project managers. We used a structured screening process to analyze peer-reviewed journal papers, conference articles, and book chapters listed in the Scopus database. We identified 77 publications published between 2019 and 2025. Using VOSviewer_1.6.20_exe, we analyzed publication trends, source influences, geographical dispersion, and keyword co-occurrence patterns. Since 2023, research output and citation impact have increased dramatically, with sustainability, project management, and IoT serving as the main conceptual foundations recorded. Real-time monitoring, wireless sensor networks, safety improvement, BIM and digital twin integration, and resource and energy optimization are the five main application domains recognized using thematic synthesis. This shows a marked transition from standalone sensing applications to integrated, intelligent, and predictive systems that enable data-driven decision making throughout the construction lifecycle. This review highlights the ongoing difficulties associated with data quality, sensor dependability, system interoperability, and energy limitations. IoT is progressing from a support technology to a core operational and managerial infrastructure for sustainable construction, with major consequences for project management and future research. Full article

This study examined associations between two strip-cropping innovations, cereal–legume (Mbili-Mbili) and legume–legume (doubled-up legume, DUL), and intra-household decision-making, labor allocation, and control over production benefits among smallholder farmers in Babati, northern Tanzania. Household survey data were collected from 157 households using a multi-stage cluster sampling approach, capturing variation by gender, age groups, and household characteristics. Across technologies, households were predominantly male-headed (91.7%), with men comprising 71.3% of respondents and managing 66.9% of trial plots. Decision-making on production, marketing, and income use was predominantly led by men, with joint decision-making accounting for approximately 24–32% of income-related decisions. Women contributed a larger share of field labor across both systems, providing 17.7% more labor than men under Mbili-Mbili and 29.7% more under DUL. Economically, Mbili-Mbili was associated with higher average net benefits (US$731 ha⁻¹) and benefit–cost ratios (2.5) than DUL (US$213 ha⁻¹; BCR = 0.7). More than half of Mbili-Mbili participants (53.3%) reported modifying the trial design, compared with 18.4% of DUL participants; Mbili-Mbili farmers trained more non-project farmers on average (4.0 vs. 0.9) and allocated larger areas for expansion (0.5 vs. 0.3 ha). Exploratory analysis suggested descriptive associations between productivity and economic outcomes and selected household characteristics, including labor availability and education. Overall, Mbili-Mbili exhibited stronger economic performance but higher labor requirements, with women contributing disproportionately to field operations under both technologies. These findings highlight the need for gender-responsive design, labor-saving options, and inclusive decision-making arrangements to support equitable and sustainable adoption of diversified strip-cropping innovations. Full article

The deployment of green hydrogen production is increasingly considered a strategic opportunity for energy-exporting countries. However, beyond technological and environmental aspects, large-scale industrial projects may generate complex and uncertain social and economic impacts at the regional level. This study investigates the potential social implications of introducing a green hydrogen production plant in the Department of Paysandú, Uruguay, using a System Dynamics modeling approach. It proposes an initial system model designed to establish a foundational Modeling and Simulation framework. The model explicitly represents feedback mechanisms linking public finance, education, labor competencies, productivity, and social behavior impact, allowing the exploration of long-term socio-economic trajectories under alternative institutional and policy conditions. It is used as an exploratory decision-support tool to assess conditional pathways, trade-offs, and risks. Results indicate that positive social outcomes, such as human capital accumulation and regional income growth, are possible but not automatic; they depend critically on governance capacity, fiscal sustainability, labor market coordination, and social acceptance, and may be attenuated or delayed under adverse scenarios. While this framework provides a strategic engineering lens on the social dimension, it represents a first step toward a comprehensive decision-making tool. The study analyzes a complex system by integrating energy, production, economic, social, and environmental aspects from strategic engineering lens and contributes to the literature by integrating social dimension and institutional constraints into a Modeling and Simulation framework applied to green hydrogen industrialization, offering insights into policy design under uncertainty in emerging energy-export contexts. Full article

In this paper, a novel approach for fault detection in the stator windings of induction motors is presented. The procedure is based on spectral analysis of the current signal. However, due to the specific target application, short duration signals (0.2 s) are utilized, which results in poor spectral resolution. To address this issue, a statistical methodology is developed to minimize uncertainty in decision-making. To construct a health indicator (HI), a statistical analysis is performed to identify spectral components that are both informative and robust. For the selected fault-related frequencies, the HI was created. Using confidence intervals and statistical testing, a fault detection scheme was proposed. The method was validated on an experimental dataset, including both healthy and faulty conditions. The method has been tested on current signals with five levels of fault severity and seven load conditions. Experimental studies on a dedicated test rig demonstrated the high efficiency of the proposed approach for such specific constraints. Full article