Search Results (12,822)

Slaughterhouse detection of lesions compatible with bovine tuberculosis represents a key passive surveillance component in Santa Catarina, Brazil, yet subsequent trace-back investigations often fail to identify infected farms. This study developed a quantitative framework to prioritize epidemiological investigations by estimating the probability of infection associated with each farm connected to PCR-confirmed cases. Using official movement records and historical diagnostic data, we reconstructed the lifetime contact networks of slaughtered cattle presenting confirmed Mycobacterium bovis lesions (n = 502). For each sentinel animal–farm interaction (n = 1452), infection probability was estimated through a non-homogeneous Poisson process incorporating exposure duration and the time-weighted average herd size as determinants of infectious pressure. After evaluating stochastic variability through Monte Carlo simulation, a deterministic model using the mean infectious-pressure parameter was applied to classify farms into high-, medium-, and low-risk categories. Model performance was assessed using validated field diagnostic outcomes within a three-year temporal window. High-risk farms represented most validated contacts (58%) and demonstrated a relative risk of 3.48 compared with lower-risk category. These findings indicate that a standardized risk-based classification can substantially improve the prioritization of trace-back investigations, offering a practical decision-support tool to enhance bovine tuberculosis surveillance and contribute to eradication strategies in Santa Catarina. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

Evaluating Net Primary Productivity (NPP) variations driven by climatic variability and mining activities is fundamental for understanding ecological dynamics in high-intensity open-pit mining areas. Focusing on high-intensity open-pit mining areas of Qian’an, China, from 2016 to 2022, by integrating Sentinel-2, ERA-5 Land reanalysis dataset and Dynamic World V1, we employed an improved Carnegie–Ames–Stanford Approach (CASA) framework alongside the Thornthwaite Memorial algorithm to quantify actual NPP (ANPP) and potential NPP (PNPP). Additionally, the Relative Contribution Index (RCI) was utilized to explicitly isolate mining-driven NPP (MNPP) variations. The results revealed a significant downward trajectory in ANPP within the high-intensity open-pit mining area, with a cumulative reduction of 5.3 × 10⁸ gC a⁻¹. This productivity loss exhibited significant spatial heterogeneity, with the most severe degradation concentrated in core mining districts, including Malanzhuang, Caiyuan, Yangdianzi, and Muchangkou. ANPP, MNPP, and PNPP maintained relative stability overall but displayed significant interannual fluctuations during 2019–2022. RCI analysis indicated MNPP dominated ANPP in 62.67% of the study area, with mining impacts intensifying in 62.83% of the region. Driver mechanisms identified precipitation as the dominant climatic factor enhancing ANPP, whereas mining activities constituted the primary driver of ANPP reduction. Mining accounted for 61.33% of ANPP changes, significantly exceeding climatic variability’s 38.67% contribution. In conclusion, these findings provide a scientific foundation for developing ecological carbon sink systems and optimizing ecological restoration strategies. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

Although tourism is increasingly seen as a key component of sustainable regional development and economic diversification, its extraordinary expansion raises governance and environmental issues at the local level. The current study assesses the influencing factors of inbound tourism demand to Saudi Arabia, a strategic empirical study due to its rapid and ambitious transformation under Vision 2030. This national strategy is designed to cultivate diverse tourist destinations, including coastal eco-resorts, mountain nature escapes, and urban cultural hubs. The unique sustainability hurdles in each area make the Kingdom a prime location for analyzing the development of regional tourism. This research focuses on the vibrant interfaces among sustainable practices, logistical efficiency, perceptions of safety and uncertainty, and macroeconomic environments that shape the Kingdom’s competitiveness as a tourism region. The study draws several beneficial findings using balanced panel data of 16 origin countries during the period of 2009–2023 and is assessed using a dynamic panel Generalized Method of Moments model. The findings state extensive perseverance within tourism flows, such that past arrivals significantly enable simultaneous inflows. Inbound tourism is strongly and favourably influenced by destination-side factors, particularly logistical performance, human rights conditions, and Saudi Arabia’s socioeconomic prosperity. In a similar vein, the demand for outward travel is strongly reinforced by origin-country prosperity. But travel expenses attenuate, environmental pressures and political risk reduce arrivals, and relative prices and pandemic uncertainty play a negligible role. The findings highlight the need to upgrade the country’s logistics infrastructure, enhance rights protection and governance, integrate sustainable practices, and capitalise on prosperity to make Saudi Arabia a desirable travel destination by Vision 2030. A key contribution of this study is to demonstrate how infrastructure, environmental stewardship, and institutional quality shape a region’s tourism attractiveness. The study illustrates how sustainability must be incorporated into regional-specific strategies to balance economic goals with ecological and social imperatives, providing a framework for other countries interested in sustainable tourism. Full article

Hydrogen energy has emerged as a strategic pathway for decarbonization, industrial transformation, and energy security across major economies. This study does not directly evaluate ex post policy outcomes. Instead, it develops a Four-Dimensional Governance Framework to assess the structural effectiveness and implementation-oriented capacity embedded within national hydrogen policy frameworks. The analysis examines G20 countries through four dimensions, namely policy objectives, policy intensity, policy tools, and policy subjects. Using the entropy weighted TOPSIS method, the study compares the relative coherence of hydrogen governance architectures across countries. The results show that countries such as the United States, the United Kingdom, Germany, France, Canada and Japan consistently rank among the leading group in the comparative evaluation, while other countries occupy intermediate or lower positions according to the composite index results. Policy subjects and policy objectives receive relatively higher weights in the empirical analysis, indicating their stronger contribution to cross-national differentiation within the constructed index. The study provides a structured basis for comparing hydrogen governance frameworks and offers a replicable method for future research linking policy design to implementation evidence. Full article

This paper introduces the concept of the Point of No Return as a physically grounded safety indicator for rear-end vehicle conflicts, addressing fundamental limitations of the widely used time-to-collision metric. Unlike purely kinematic approaches, the proposed formulation incorporates braking capability and reaction constraints, enabling a direct assessment of whether a collision can still be avoided. To illustrate the applicability of the concept, a vision-based framework using a single camera is developed based on dashcam data, combining YOLO-based object detection, Kalman-filter tracking, and geometric distance estimation derived from bounding-box features and camera projection models. The estimated distance is further processed to obtain relative motion, allowing a unified analysis of time to collision and the Point of No Return within the same evaluation pipeline. Experimental results on real-world driving sequences show that the Point of No Return consistently precedes critical conditions identified by time to collision and provides a more stable and physically interpretable characterization of the transition toward collision inevitability. The results also highlight the sensitivity of the proposed indicator to braking capability, while showing lower sensitivity to variations in relative speed. Overall, this study demonstrates the relevance of the Point of No Return as a complementary indicator for collision risk assessment, offering a physically meaningful basis for decision-making in driver assistance systems and improving the interpretation of critical traffic situations. The proposed approach supports sustainable urban mobility by enabling earlier and more reliable intervention strategies, contributing to improved traffic safety, smoother traffic flow, and reduced environmental impact. Full article

The annual occupational personal ultraviolet radiation (UVR) exposure of outdoor workers is vital for several purposes, including non-melanoma skin cancer risk assessment and the recognition of UVR-related pathologies as occupational diseases. Estimations of annual personal exposure (PE) are based on measurements, which are influenced by the measuring period respectively by the start and end time of the measurements, and PEs gained from different periods may differ noticeably. Therefore, we present a method that recalculates PE measurements to any other period (time and duration) during the day, and which is also applicable for measured ambient UVR to determine the relative personal UVR exposure (ERTA). The application shows the necessity of considering not only duration but especially time, as noon hours contribute differently than morning and evening hours. The uncertainties of recalculations are within ±5% if the measuring or target periods last at least 5 h and noon hours are covered. Furthermore, we propose a method to calculate annual PE using ERTA. The application for Austria shows that depending on the work-time model (working hours, working times) and date of holidays, annual PE may differ by up to 30%. Additionally, interannual variability of 16% within a ten-year period suggests avoiding a single year for consideration. Full article

The transport sector is a major contributor to urban air pollution and greenhouse gas emissions in Pakistan, posing significant challenges to sustainable development and climate commitments. This study develops the first technology-resolved, high-resolution, multi-pollutant emission inventory and scenario analysis for Pakistan’s transport sector, addressing key gaps in previous studies that lacked integrated multi-pollutant assessments, comprehensive coverage of non-road sources, and long-term scenario comparisons. The analysis integrates road and non-road transport sources within the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) modeling framework. Emissions are projected for 2024–2050 under a business-as-usual (BAU) scenario and three mitigation pathways: an Electric Vehicle Transition (EVT) emphasizing transport electrification, a Euro-VI scenario focusing on stringent fuel and vehicle emission standards, and an integrated nationally determined contribution strategy (NDC+) scenario combining electrification, regulatory improvements, and structural transport reforms. In 2024, transport-related emissions are estimated at approximately 22 kt of fine particulate matter (PM_2.5), over 300 kt of nitrogen oxides (NO_x), and nearly 39 Mt of carbon dioxide (CO₂), alongside substantial emissions of other gaseous pollutants and short-lived climate forcers. By 2050, the NDC+ scenario achieves the largest reductions relative to business-as-usual, demonstrating that coordinated electrification and emission control strategies can simultaneously reduce air pollution and greenhouse gas emissions. The results demonstrate strong synergies between climate mitigation and air quality improvement, showing that integrated strategies combining electrification with stringent emission standards can simultaneously reduce greenhouse gas emissions and major air pollutants while advancing cleaner and more sustainable mobility. This analysis provides a consistent and policy-relevant evidence base derived from best-available data and modeling tools to support Pakistan’s NDC implementation, sustainable mobility planning, and integrated air quality and climate strategies, with lessons transferable to other rapidly developing economies. Full article

Investments in urban parks are frequently justified not only in terms of their environmental and recreational amenities but also in terms of their potential to fuel appreciation in neighborhoods and corresponding increases in local fiscal revenues. There is a substantial body of literature indicating that proximity to parks and other forms of urban open spaces can be capitalized into surrounding residential property values; however, there is relatively limited research examining whether these effects plausibly translate into fiscal revenues that could offset the costs of urban park projects. This study seeks to address this question in the context of Chicago’s 606 Trail, a 2.7-mile elevated rail-to-trail conversion completed and opened on 6 June 2015, after a publicly disclosed investment of $95 million in the project at the time of opening. Using property transaction data and a difference-in-differences event-study approach, this study estimates the impact of park completion on surrounding residential property values and converts these amenity capitalization effects into corresponding municipal fiscal revenues. The main contribution of this paper is to demonstrate that amenity capitalization and fiscal self-financing are analytically separate outcomes, even though park completion may be associated with corresponding increases in surrounding residential property values. The study contributes a framework for evaluating substantial urban park investments by drawing on both urban amenity studies and municipal finance studies. Full article

This paper presents an analytical framework for the preliminary design of stringer-stiffened composite panels subjected to low-velocity impact. The formulation combines First-Order Shear Deformation Theory with a two-degree-of-freedom spring–mass model, while the super-stringer is represented as a Euler–Bernoulli beam whose bending contribution is transferred to the skin mid-surface through the parallel axis theorem. This provides a computationally efficient tool for rapid parametric assessment of stiffened configurations at the early design stage. To support laminate selection, a Specific Impact Energy Index (SIEI) is introduced to rank configurations according to their elastic energy storage efficiency relative to the product of skin and stringer thicknesses. The tool is validated against both published experimental results and a finite element dynamic explicit model, demonstrating a good approximation of the impact response. It is then applied to identify the optimum laminate configuration for a super-stringer case study within the design space considered. Full article

Background: The nasolabial angle is widely used in orthodontic diagnosis, but its composite geometric nature complicates interpretation. Whether it primarily reflects skeletal or soft-tissue influences remains unclear, as no previous study has quantitatively partitioned its angular components using multivariable regression in a large adult sample. Objective: The objective of this study is to quantify the relative angular contributions of skeletal classification and soft-tissue parameters to NLA morphology using a multivariate cephalometric model. Methods: A retrospective analysis of 504 adult lateral cephalograms was conducted. Subjects were categorized by sagittal (Class I, II, or III) and vertical skeletal patterns (hyperdivergent, normodivergent, and hypodivergent). Measured variables included the NLA, nasal inclination (N/FH), upper lip inclination (L/FH), upper lip thickness, and maxillary incisor inclination. Correlation and multivariable regression analyses were performed. Results: Although the NLA differed across sagittal classes (p = 0.001; η² = 0.047), upper lip inclination demonstrated the strongest association with the NLA (r = 0.766; R² = 0.588). In multivariable analysis, L/FH exhibited the largest standardized contribution (β = 0.752, p < 0.001), while vertical pattern and lip thickness were not independently associated. Class II showed a modest independent association (β = 0.083, p = 0.006). Conclusions: Nasolabial angle variation largely reflects upper lip orientation rather than skeletal classification alone. These findings support component-based interpretation, emphasizing separate evaluation of lip inclination during esthetic orthodontic assessment. Full article

Chrysanthemum contains numerous active compounds, including flavonoids and phenolic acids, with its dried capitula widely used for tea and medicinal applications. The content of functional compounds is readily influenced by environmental factors, and the use of varieties with high-level and stable bioactive compounds is essential for sustainable cultivation. However, a key challenge is identifying genotypes that consistently perform well for functional-component traits in contemporary breeding activities. This study aimed to evaluate the performance and stability of functional components in tea chrysanthemums across multiple years. Total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were investigated in 24 tea chrysanthemum accessions across three growing years of 2018, 2021, and 2022. The additive main effects and multiplicative interaction (AMMI) model analysis revealed significant genotype (G), environment (E), and genotype-by-environment interaction (GEI) effects for all functional traits across three growing years. The GEI accounted for 63.58% to 80.82% of the variation across the four components in the AMMI model. Based on the AMMI stability value (ASV) parameter, the tea chrysanthemums showing the most stable concentrations of total flavonoids, chlorogenic acid, luteoloside, and isochlorogenic acid A were identified. Based on phenotypic values and stability results, Suju-6, Hongxinju, Wangongju, and Baixiaoxiangju performed relatively well across the functional components investigated, making them promising candidates for future breeding and promotion programs. These findings provide valuable insights into the genetic basis of functional elements in tea chrysanthemum and will contribute to further genetic improvement. Full article

Chilli is a major horticultural crop in tropical and subtropical regions that contributes substantially to the global culinary and economic sectors. However, anthracnose remains one of the most destructive diseases, causing severe losses in both field and stored fruits. Current management strategies offer limited long-term effectiveness, highlighting the need for sustainable alternatives. This study developed nanoemulsions (NEs) from Garcinia atroviridis fruit extract and evaluated their biocontrol potential against Colletotrichum capsici alone and in combination with Trichoderma harzianum. Two formulations, NE4 and NE7, exhibited good thermostability without phase separation at 25 and 54 °C, with droplet sizes of 135.1 and 124.1 nm, respectively, and were non-phytotoxic to chilli seedlings. In vitro, the nanoemulsions significantly suppressed C. capsici mycelial growth (62%) compared to the crude extract. Under rain shelter conditions, NE integrated with T. harzianum (T7 and T8) was highly effective in delaying disease onset and reducing disease severity, achieving 90.07% and 88.37% relative disease reduction, respectively. These treatments also produced the highest marketable yields, comparable to the synthetic fungicide Dithane M-45^® (2 g L⁻¹). In contrast, the untreated control group exhibited an 83% yield loss. The results indicate that nanoemulsions of G. atroviridis fruit extract, particularly when combined with T. harzianum, offer a promising and sustainable biological control option for managing pre-harvest chilli anthracnose. Their incorporation into integrated pest management programmes may reduce dependence on chemical fungicides and support safer chilli production systems. Full article

Polysialic acid (PSA), a highly negatively charged glycan attached mainly to the neural cell adhesion molecule (NCAM), is emerging as a critical but underrecognized extracellular regulator of glutamatergic neurotransmission. While previous literature has focused on PSA’s developmental roles, increasing evidence indicates that PSA–NCAM also contributes to synaptic plasticity mechanisms in the mature brain. This review integrates evidence from structural biophysics, single-channel electrophysiology, and disease models to explain how PSA modulates glutamate receptor gating to control learning and memory. We synthesize findings from biochemical reconstitution, electrophysiological recordings, and in vivo studies to show that PSA can modulate α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor open probability, burst duration, and cooperative gating without affecting conductance, thereby promoting long-term potentiation. Conversely, PSA selectively suppresses GluN2B-containing extrasynaptic N-methyl D-Aspartate (NMDA) receptor activity by lowering open probability and calcium influx, maintaining an optimal balance between potentiation and depression while providing neuroprotection. Disruption of PSA–NCAM signaling in developmental and disease models, including prenatal cannabinoid exposure and neurodegeneration, produces cognitive deficits reversible by PSA restoration. Notably, much of the current evidence derives from in vitro systems, with relatively few studies conducted in vivo, and studies employing PSA mimetics mostly, which should be considered when interpreting physiological relevance. Collectively, the available evidence suggests that PSA functions as an extracellular modulator linking synaptic glycans to glutamate receptor regulation and plasticity related signaling pathways, highlighting the potential importance of extracellular glycan mechanisms in the control of synaptic function. Full article