Search Results (29,890)

Pig behavior statistics can reflect their health status. Conventional approaches depend on manual observation to derive behavioral information from video recordings, a process that demands substantial time and human effort. To overcome these limitations in indoor intensive farming environments, this study introduces an effective approach for recognizing pig behaviors, employing an enhanced YOLOv8n architecture. The approach utilizes advanced object detection algorithms to automatically identify pig behaviors, including stand, lie, eat, fight, and tail-bite, from overhead video footage of the enclosure. First, images of daily pig behaviors are collected using cameras to build a pig behavior dataset. To boost detection accuracy, the SE attention mechanism is embedded within the feature extraction backbone of the YOLOv8n network to enhance its representational capacity, strengthening the model’s capacity to grasp overarching contextual information and improve the expressiveness of extracted features. The GIoU loss function is employed during training to reduce computational cost and accelerate model convergence. Moreover, integrating Ghost convolution into the backbone significantly reduces both computational complexity and the total number of parameters. The experimental findings reveal that the optimized YOLOv8n model contains just 1.71 million parameters, marking a 42.93% reduction relative to the baseline model. Its floating-point operations total 5.0 billion, indicating a 38.27% decrease, while the mean average precision (mAP@50) reaches 96.8%, surpassing the original by 2.6 percentage points. Compared with other widely used YOLO-based object detection frameworks, the proposed approach achieves notably higher accuracy while requiring significantly lower computational resources and model complexity. Full article

(1) Background: As security breaches rise, the “Security by Design” approach is imperative for software organizations. (2) Problem: A significant gap remains between declared security priorities and actual developer behavior. This gap widens in hybrid environments, where social mechanisms that reinforce security norms weaken. (3) Objective: This research investigates the organizational mechanisms translating security priorities into secure coding behavior and proposes a framework to maintain them in distributed teams. (4) Methods: We surveyed 244 software developers across international sites of a large IT enterprise. Using validated measures, we tested a mediation model linking priorities, climate, and behavior, with remote work as a moderator. (5) Results: Organizational Security Climate mediates the relationship between priorities and behavior. Crucially, remote work significantly weakens this mediation, showing that “hybrid friction” disrupts the transmission of security norms. (6) Conclusions: We created a framework for building a security climate in hybrid teams by introducing explicit mechanisms, such as traceable leadership signals and structured network hubs. This ensures clear DevSecOps integration and consistent security implementation across all locations. Full article

Improving energy efficiency is critical for tackling environmental issues and achieving sustainable development. Understanding how digital technology affects energy efficiency and its underlying mechanisms can deepen our comprehension of the economic consequences of digital innovation. This study adopts a dictionary-based method to identify digital technology patents from a large-scale patent dataset and employs a comprehensive evaluation approach incorporating both subjective and objective weights to measure digital technology advancement. Building on this framework, the research uses city-level data from China and applies panel data models alongside mediation effect models as core analytical tools to investigate the impact mechanisms and effects of digital technology on energy efficiency. Key findings reveal that digital technology has developed rapidly, exhibiting distinct phase-specific characteristics, especially after 2010, though notable regional disparities remain. Robust tests confirm that digital technology significantly enhances energy efficiency. Nonlinear regression results indicate that the marginal effect of digital technology changes dynamically across different stages of energy efficiency development. Heterogeneity tests demonstrate that the impact of digital technology on energy efficiency exhibits typical heterogeneous characteristics. Mechanism analysis shows that digital technology enhances energy efficiency primarily through two pathways: green technology innovation and industrial structure upgrading. Further analysis suggests that regional convergence in energy efficiency is objectively present, and digital technology actively accelerates this convergence process. These findings offer practical insights to guide policymakers in designing and implementing digital technology-driven strategies aimed at enhancing energy efficiency. Full article

Heat stress is a major challenge for broiler production worldwide and is expected to intensify with more frequent heatwaves. This study focuses on mechanically ventilated broiler houses in South Korea, where heatwaves have become increasingly frequent. Three regression meta-models were developed to predict the indoor temperature–humidity index (THI) directly from weather forecast data, using simulated results from a validated building energy simulation (BES) model. A TRNSYS-based BES model was validated against field measurements from four rearing cycles in a commercial broiler house (RMSE 1.31–2.16; MAPE < 2.00%). Using 3072 simulation cases that combined multiple sites, thermal-transmittance levels, cooling conditions, building sizes, and broiler body weights, three regression meta-model approaches were evaluated: a condition-specific regression meta-model for each condition set, a unified regression meta-model with categorical predictors, and a single variable meta-model using only external THI as a predictor. All three showed strong predictive performance, and the unified regression meta-model achieved R² = 0.978, RMSE = 0.817, and MAPE = 0.829, providing the best balance between accuracy and simplicity. This unified model offers a practical tool to link weather forecasts with broiler-house design and environmental-control decisions for heat-stress risk management. Full article

The origin of life is commonly discussed within two competing conceptual frameworks: the metabolism-first and information-first hypotheses. While each emphasizes a different defining property of early life, modern biochemistry reveals a fundamental interdependence between metabolic processes and genetic information transfer, leading to a persistent chicken-and-egg problem. In this study, we investigate a prebiotically plausible reaction system that enables the concurrent formation of molecular precursors associated with both frameworks. Under simulated Hadean hydrothermal conditions, acetylene, ammonia, cyanide, and carbon monoxide were reacted in aqueous solution in the presence of transition metal sulfides. Using gas chromatography–mass spectrometry combined with stable isotope labeling, we demonstrate the simultaneous formation of the nucleobase uracil and the amino acids alanine and aspartic acid. Isotopic incorporation patterns allow reconstruction of the underlying reaction pathways and confirm the contribution of all starting materials to product formation. While amino acids are produced continuously over the observed period in significantly higher yields than uracil, uracil formation exhibits a pronounced time-dependent maximum after three days. Variations in pH, reaction time, and metal sulfide catalysts modulate product yields but do not prevent the parallel emergence of both molecular classes. These findings support a scenario in which proto-metabolic chemistry and molecular precursors of genetic information could have arisen simultaneously within a shared geochemical setting. The results provide experimental support for a coupled origin of metabolism and transcriptional building blocks, offering a potential resolution to the dichotomy between metabolism-first and information-first models of early life. Full article

Three-dimensional (3D) epicuticular wax coverage on plant surfaces contributes to multifunctional surface properties, such as enhanced water repellence, reduced pathogen adherence, modified optical properties, and reduced insect adhesion. The diversity in wax projection morphology, size, abundance, and spatial arrangement among plant species results in a broad spectrum of anti-adhesive effects, reflecting both phylogenetic history and ecological function. This study presents a numerical model consisting of 3D tubular-shaped structures randomly deposited on a substrate and forming a highly porous layer. The simulations based on this model demonstrate a strong reduction in adhesion to the contacting insect adhesive pad. It is found that a structure formed by sufficiently long tubes, where the length is enough to support the tubes in space and build a porous 3D structure with a very low density, at relatively weak attraction to the underlying substrate, leads to the weakest adhesion. The model is constructed on the basis of our recent works combining discrete and continuous approaches in biological modeling. It mainly exploits the technique of the movable digital automata, allowing modeling of numerous numerically elastic cylinders that can be moved in 3D space, elastically collide with one another and with boundaries, and build self-consistent surface structures, which can be used to mimic nano- or microscale surface coverages of real plants. Full article

This study investigates the acceptance of an AI-powered decision-support chatbot among professionals in a marketing and sales context, addressing a gap in technology acceptance research by examining data-intensive decision environments that remain underexplored. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes an extended model incorporating Behavioral Intention, Performance Expectancy, Effort Expectancy, Social Influence, Output Quality, Time Saving, Source Trustworthiness, Cognitive Load, and Chatbot Self-Efficacy. An experimental study was conducted with 106 professionals using a chatbot-enhanced business analytics platform to complete marketing KPI analysis tasks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Behavioral Intention to use decision-support chatbots is significantly influenced by Performance Expectancy, Effort Expectancy, and Social Influence. Performance Expectancy is strongly driven by Output Quality, Time Saving, and Source Trustworthiness, while Effort Expectancy is significantly shaped by reduced Cognitive Load and higher Chatbot Self-Efficacy. The findings suggest that chatbot acceptance in professional decision-making depends not only on usability and performance beliefs but also on cognitive relief, trust in information sources, and efficiency gains, highlighting important implications for both theory and the design of AI-based decision-support systems. Full article

During coal mining, parallel voids ahead of an advancing working face often trigger intense dynamic loading and structural instability, posing significant risks to operational safety. Using the 43,201 working face of the Shiyangou Coal Mine as a case study, this research investigates the mechanisms of surrounding rock instability and proposes an integrated synergistic control strategy. Based on voussoir beam theory, a mechanical model of the roof structure—incorporating the nonlinear coupling between the gangue and immediate roof—was developed to establish the critical thresholds for the rotational instability of key blocks. Analytical results indicate that the limit breaking distance for “Key Block B” in the main roof is 24.49 m, which defines the primary zone for advanced reinforcement and hazard prevention. Furthermore, applying short-arm beam theory, this study clarifies how pre-split roof cutting disrupts the transmission of advance abutment pressure, identifying 8° as the optimal cutting angle. Building on these insights, a multi-faceted control system was implemented, combining hydraulic fracturing for pressure relief, pumpable backfill pillars, and an artificial false roof (utilizing a suspended I-beam structure 1.2 m above the floor). Field monitoring confirms that this collaborative approach effectively stabilizes the surrounding rock, ensuring the safe and continuous passage of the working face through parallel void areas. Full article

The increasing emphasis on sustainable governance and Environmental, Social, And Governance practices has heightened the need for banks to strengthen internal mechanisms that support sustainable finance, particularly in emerging market contexts. The aim of this study is to examine the roles of capacity building, green competencies, and financial technology in shaping sustainable finance in small-capital banks. Survey data were collected from employees of small-capital banks classified under Indonesia’s capital-based bank grouping KBMI 1 and listed on the Indonesia Stock Exchange, and were analyzed using partial least squares structural equation modeling. The results show that capacity building has a significant positive effect on both green innovation and sustainable finance, highlighting the importance of organizational learning and human capital development. Green competencies positively influence green innovation but do not have a direct effect on sustainable finance. Financial technology has a significant positive effect on sustainable finance, whereas its effect on green innovation is not supported. In addition, green innovation does not directly influence sustainable finance and does not mediate the relationships between internal organizational drivers and sustainable finance. These findings demonstrate that sustainable finance in small-capital banks is advanced through Environmental, Social, And Governance-oriented governance mechanisms that prioritize internal organizational readiness and digital enablement. By clarifying how internal organizational capabilities translate Environmental, Social, And Governance practices into sustainable financial outcomes, this study contributes to the sustainability literature by providing context-specific evidence on governance pathways for advancing sustainable finance in emerging market banking systems. Full article

Place-based approaches have gained significant attention as a means of addressing entrenched disadvantage through collaborative, locally responsive service delivery, yet implementation has yielded mixed results and the systemic factors that facilitate or impede inter-organisational collaboration remain inadequately understood. This study applied participatory systems mapping as part of a systemic inquiry to identify leverage points for place-based integrated service delivery in the City of Casey, an outer-metropolitan municipality in Melbourne, Australia. Twenty-one representatives from the Casey Futures Partnership engaged in group model building workshops, co-producing a causal loop diagram containing 33 factors and 104 directional connections. The resulting map was analysed using a blended analytical approach combining network metrics with the Action Scales Model. Funding availability and criteria emerged as the most central factor within the system, while belief-level factors, including territorial behaviour and resource and collaboration mindset, were found to be substantially shaped by upstream structural conditions. Factors combining network influence with deeper system positioning and amenability to local action included awareness of community needs and priorities, trust and willingness to collaborate from funders, inter-organisational communication, and advocacy effectiveness. The findings support multi-level place-based approaches that address underlying beliefs and structural conditions alongside operational improvements. Full article

Excessive fertilizer use not only harms agricultural sustainability but also leads to massive energy waste and carbon emissions. Under China’s carbon peaking and carbon neutrality goals, using social networks to spread better fertilization practices and reduce excessive application can deliver real wins for both energy savings and emission cuts. This paper examines whether farmers’ social network positions affect their fertilizer use. We analyze 14 years of data from 206 farm households in Gansu, China, using fixed effects models that incorporate degree, betweenness, and closeness centrality. Our results reveal that centrally positioned farmers substantially reduce fertilizer application: each 0.1 unit rise in standardized degree, betweenness, and closeness centrality corresponds to reductions of 1.26%, 0.84%, and 0.78%, which translate to actual reductions and carbon emission reduction of 1.06, 0.71, and 0.66 kg/mu; 9.52, 6.38, and 5.93 kg CO₂e/mu. The effects are stronger for farmers with more education, higher off-farm income, and tighter network connections. The effect of degree centrality on fertilizer reduction increased by 7.2 percentage points after 2018. Extension services should build on existing social networks and use key node farmers to drive other farmers in the village to reduce fertilizer use. It helps reduce carbon emissions from fertilizer production and promote sustainable agricultural development. Full article

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting their ability to perform informed re-planning and backtracking when routes become unsafe. This paper proposes a neuro-symbolic evacuation planning framework that integrates Lifelong Planning A* (LPA*) with ontology-driven semantic reasoning and a Bidirectional Long Short-Term Memory (BiLSTM) prediction model. The building’s spatial and semantic knowledge is represented using the Web Ontology Language (OWL) and Resource Description Framework (RDF), enabling automated inference of implicit connections and enforcement of safety policies. The BiLSTM model learns temporal patterns from ontology-consistent evacuation trajectories and provides guidance for remaining-cost estimation and early prediction of routes likely to require backtracking, which is combined with a bounded semantic heuristic to preserve admissibility and optimality guarantees. Simulation results in a multi-floor academic building show that the proposed BiLSTM-guided semantic LPA* framework reduces average evacuation time by up to 9.6%, decreases node expansions by up to 32%, and increases evacuation success rates to 96.2% compared with a purely semantic baseline. The BiLSTM model also achieves strong predictive performance, with a test AUC of 0.92 for backtracking prediction and a next-state accuracy of 87.1%. The proposed framework is designed to support explainable, policy-compliant, and incrementally adaptable evacuation guidance under rapidly evolving emergency conditions. Full article

Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article

Existing studies on greenwashing have primarily focused on post-incident supervision, with limited attention given to proactive mechanisms. This study aims to develop an early warning evaluation model for greenwashing behavior in building materials enterprises exposed to negative public opinion. The main findings are as follows: (1) Drawing on actor network theory, gray system theory, the analytic network process, and gray fuzzy comprehensive evaluation, this study constructs an early warning evaluation model for greenwashing behavior in building materials enterprises. This model comprises 5 first-level dimensions and 20 s-level indicators, integrating key stakeholders (i.e., government, negative public opinion, media, the public, and enterprise) and is validated through case analysis. (2) Government dimension: Environmental regulation intensity emerges as the most critical indicator. (3) Negative public opinion dimension: Attention is the most critical indicator. (4) Media dimension: Media visibility ranks as the most critical indicator. (5) Public dimension: Public sentiment is the most influential indicator. (6) Enterprise dimension: The environmental performance level is the most critical indicator. This study offers both theoretical and practical foundations for the early warning, monitoring, and governance of enterprise greenwashing, contributing to the advancement of sustainable development and transparent environmental communication in the building materials industry. Full article