Search Results (3,781)

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

This study investigated the effects of microwave (MW) pre-treatment (45 kJ total irradiated microwave energy) and magnetic nanoparticles (MPs) on the anaerobic digestion (AD) of meat-processing sludge, integrating biokinetic modeling with dielectric parameter measurements. Five different sludge variants were examined: native (non-treated control); MP-only control; microwave pre-treated sludge, and MW + MP combination with the nanoparticles either retained in the fermentation medium or removed prior to anaerobic digestion. Cumulative biomethane production was evaluated using the modified Gompertz, Logistic, and Weibull models, and key kinetic parameters (maximum achievable methane yield, maximum rate of product formation, and λ-values) were compared across the different treatments. The results revealed that the highest production rate, along with the highest biomethane potential, could be achieved when combining MW treatment with magnetic nanoparticles which were retained in the fermentation medium during AD. Based on the biokinetic analysis, this combined method increased biomethane potential by 52% to 390 mL CH₄/gVS and maximum methane production rate by 85% to 37 mL CH₄/gVS/day compared to the untreated control. The measurement of relative permittivity (${\epsilon }^{\prime }$) exhibited progressive changes during digestion, and the maximum rate of change in ${\epsilon }^{\prime }$ strongly correlated with the maximum methane production rate across all samples (R² > 0.98). These results highlight the potential of microwave–metal oxide nanoparticle pre-treatment for process enhancement and to demonstrate the suitability of dielectric parameter measurement as a rapid, non-invasive indicator of biochemical activity during anaerobic digestion. Full article

Despite the immense potential of digital intelligence technologies to enhance corporate profitability, manufacturing enterprises often face the “digital–green paradox”, which indicates that while companies invest in digital and intelligent transformation, their energy consumption increases rather than promoting green transition. To provide reasonable transformation solutions for manufacturers still caught in this paradox, this paper adopts a single-case study approach from a product lifecycle perspective. Focusing on FAW-Volkswagen—a manufacturing enterprise demonstrating outstanding performance in digital-intelligent green transformation—this study conducts an in-depth investigation into the stage characteristics and underlying mechanisms. The results show that the following: (1) The digital-intelligent green transformation of manufacturing enterprises is an iterative process evolving from “green design, low-carbon production, intelligent service to enterprise spiral value-added”, with distinct digital-intelligent empowerment models at each stage. (2) By leveraging digital-intelligent technologies, manufacturing enterprises can build a multi-tiered “internal-external dual circulation” green development system encompassing the “enterprise—industrial chain—full ecosystem,” driving comprehensive green upgrades across the entire industry and ecosystem. This paper reveals the intrinsic mechanisms through which digital-intelligent technologies facilitate manufacturing enterprises’ green transformation. It expands and enriches the research context and theoretical implications of product lifecycle management, offering management insights and strategic references for other enterprises pursuing green transformation and upgrading pathways in the digital-intelligent economy era. Full article

Intensive broiler chicken farming is one of the most important livestock sectors globally. However, intensive production systems raise concerns about farm sustainability, as well as ensuring animal welfare and product quality. For this reason, identifying novel, high-value-added feed ingredients is crucial. Winery by-products (WBPs) are a valuable source of bioactive compounds and can be utilized as functional feed ingredients. This study evaluated the effects of dietary supplementation with grape seed meal and grape pomace meal in diets for broilers up to 42 days of age. Three dietary treatments were formulated—grape seed meal (3% and 6%), grape pomace meal (3% and 6%), and a combination (3% seed meal + 3% pomace meal)—along with a standard diet (control). The proximal composition (moisture, protein, fatty acid profile, fats, ash), antioxidant parameters (ROS, GSH, NO, POV), free radical scavenging activity (DPPH and ABTS^•+), and total phenolic content of the meat and physical characteristics (color) were assessed. While proximal composition of meat was not significantly influenced by the dietary treatment, some parameters, such as total phenolic content, PUFA levels, and antioxidant and free radical scavenging activity, were improved. These results demonstrate enhanced favorable traits improving chicken meat quality and confirm the potential of WBPs as functional feed ingredients, promoting a more sustainable production model aligned with the principles of the circular economy. Full article

This study proposes a process model for sharing ambiguous category concepts in virtual reality (VR) using an active inference framework. The model executes a dual-layer Bayesian update after observing both self and partner actions and predicts actions that minimize free energy. To incorporate agreement-seeking with others into active inference, we added disagreement in category judgments as a risk term in the free energy, weighted by gaze synchrony measured using Dynamic Time Warping (DTW), which is assumed to reflect joint attention. To validate the model, an object classification task in VR including ambiguous items was created. The experiment was conducted first under a bot avatar condition, in which ambiguous category judgments were always incorrect, and then under a human–human pair condition. This design allowed verification of the collaborative learning process by which human pairs reached agreement from the same degree of ambiguity. Analysis of experimental data from 14 participants showed that the model achieved high prediction accuracy for observed values as learning progressed. Introducing gaze synchrony weighting (${\gamma }_0\ge 0.5$) further improved prediction accuracy, yielding optimal performance. This approach provides a new framework for modeling socially shared cognition using active inference in human–robot interaction contexts. Full article

To align with disaster monitoring and sustainable risk assessment, the low-carbon transition of fisheries necessitates comprehensive carbon emission management throughout the supply chain. As China advances supply-side structural reform, transitioning from traditional to low-carbon fisheries is vital for the green development of the industry and its associated sectors. This study employs input–output models and LMDI decomposition to examine the trends and drivers of embodied carbon emissions within China’s fishery production system from 2010 to 2019. By constructing a cross-sectoral full-emission accounting system, the research calculates total direct and indirect emissions, exploring how accounting scopes influence regional responsibility and reduction strategies. Empirical results indicate that while China’s aquatic trade and processing have steadily developed, the sector remains dominated by low-value-added primary products. This structure highlights vast potential for deep processing development amidst shifting global dietary habits. Factor decomposition reveals that economic and technological development are the primary drivers of carbon emissions. Notably, technological progress within fisheries emerges as the most significant factor, playing a pivotal role in both driving and potentially mitigating emissions. Consequently, to effectively lower carbon intensity, the study concludes that restructuring the fishery industry is crucial. Promoting low-carbon development and enhancing the R&D of green technologies are essential strategies to navigate the dual challenges of industrial upgrading and environmental protection. Full article

Background: Renal and cardiac dysfunction are major determinants of adverse outcomes following transcatheter aortic valve replacement (TAVR). The ratio of blood urea nitrogen to left ventricular ejection fraction (BUN/EF) integrates renal and cardiac status into a single physiological index. This study aimed to evaluate the prognostic value of both baseline and temporal (48–72 h) BUN/EF ratios for predicting mortality after TAVR. Methods: A total of 429 patients (mean age 76 ± 8 years; 51% female) who underwent TAVR for severe aortic stenosis between 2017 and 2025 were retrospectively analyzed. The primary endpoint was long-term all-cause mortality; in-hospital mortality was secondary. Receiver operating characteristic (ROC) curves, Cox regression, and reclassification metrics (NRI, IDI) assessed prognostic performance. Restricted cubic spline (RCS) analysis explored non-linear associations. Results: During a median follow-up of 733 days, overall and in-hospital mortality rates were 37.8% and 7.9%, respectively. Both baseline and 48–72 h BUN/EF ratios were independently associated with mortality (HR = 3.46 and 3.79 per 1 SD increase; both p < 0.001). The temporal ratio showed superior discrimination for in-hospital mortality (AUC = 0.826 vs. 0.743, p = 0.007). Adding baseline BUN/EF to EuroSCORE II significantly improved model performance (AUC 0.712 vs. 0.668, p = 0.031; NRI = 0.33; IDI = 0.067). RCS analysis revealed a linear relationship for baseline and a steep, non-linear association for temporal ratios with mortality risk. Conclusions: The 48–72 h BUN/EF ratio is a robust dynamic biomarker that predicts early mortality after TAVR, while baseline BUN/EF identifies patients at long-term risk. Integrating this simple bedside index into risk algorithms may refine postoperative monitoring and improve outcome prediction in TAVR populations. Full article

Several animal models of Alzheimer’s disease have been developed and tested for diagnostic and treatment purposes. [¹¹C]PIB is the gold-standard radiotracer for the detection of Aβ plaque deposits, a hallmark of the disease. This study aimed to evaluate the in vivo detection of Aβ plaques using [¹¹C]PIB microPET imaging across different animal models of Alzheimer’s disease. The study included 3xTg-AD transgenic mice, TgF344-AD transgenic rats and Aβ injection-based rat model. The results showed an age-related increase in [¹¹C]PIB uptake in 3xTg-AD mice, particularly in the midbrain and thalamus. In TgF344-AD rats, differences were also observed compared to WT controls, with the highest values observed in the hippocampus and cortex. In the injection-based model, inoculated rats showed greater uptake in the injection site than SHAM animals. Across all microPET studies, [¹¹C]PIB uptake was consistently higher in females than in their male counterparts. These findings support the value of transgenic and Aβ injection-based models in preclinical research on Aβ plaque deposition and highlight the importance of considering species, model type, sex, and age in experimental design. Full article

Taiwan has set an ambitious target of net-zero carbon emissions by 2050, relying heavily on offshore wind capacity of 13.1 GW by 2030 and 40–55 GW by 2050. Floating offshore wind (FOW) is expected to play a central role in meeting these targets, particularly in deep-water areas where fixed-bottom technology is technically constrained. This study combined S-curve modeling for capacity projections, learning curves for cost estimation, and input–output analysis to quantify economic and environmental impacts under three deployment scenarios. Our findings indicate that FOW development provides substantial economic benefits, particularly under the high-growth scenario. During the construction phase through 2040, total output is projected to exceed NTD 1.97 trillion, generating more than NTD 1 trillion in gross value added (GVA) and over 470,000 full-time equivalent (FTE) jobs. By 2050, operations and maintenance (O&M) output is expected to reach approximately NTD 50 billion, supporting roughly 14,200 jobs and about NTD 13.8 billion in income. Annual CO₂ reduction could reach up to 10.4 Mt by 2050 under the high-growth scenario, or about 6.86 Mt under the low-growth case, demonstrating the potential of FOW to drive industrial development while advancing national decarbonization. Full article

Objective: This in vitro study evaluated the accuracy and precision of five intraoral scanners (IOSs) by examining the interaction between gingival model color and linear measurement distances. Materials and Methods: Seven color-distinct models were scanned to obtain absolute deviation data from six linear distances between four reference points. Measurements were analyzed using Zeiss Inspect software v2025.3.3.4. Due to non-normal data distribution, all factors (Scanner, Model, Pair) and their interactions were assessed using Aligned Rank Transform (ART) ANOVA. Accuracy was defined as median absolute deviation, and precision as the coefficient of variation (CV%). Results: Statistical analysis identified significant differences in absolute deviation across all main factors and their three-way interactions (p < 0.001). The Medit i700 and Trios 5 demonstrated the lowest overall median deviation (0.09 mm), followed closely by Trios 3 (0.10 mm), with no statistically significant differences among them. The P5 model yielded lower deviations, while extreme colors increased variability. In terms of precision, values varied significantly based on specific interactions; the highest precision was recorded for the Shining scanner on the White model (A–C pair, CV: 7.33%), whereas the lowest precision was observed for the Sirios scanner on the Black model (A–D pair, CV: 158.10%). Conclusions: Within the limitations of this in vitro study, deviation values varied according to gingival color and pair distance. Gingival colors with a higher pink saturation (P5) and shorter distances yielded lower deviations, whereas extreme colors and longer distances were associated with reduced precision. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. Full article

This study examines the potential implications of the EU–Mercosur free trade agreement for the Polish horticultural sector, with particular emphasis on sustainability, trade competitiveness, and structural complementarities between the regions. Drawing on production, trade, and demographic data for the EU, Poland, and Mercosur countries, the analysis evaluates the alignment of horticultural supply and demand structures, the degree of intra-industry exchange, and the economic conditions shaping bilateral trade. The research applies the Grubel–Lloyd index and a Poisson Pseudo-Maximum Likelihood (PPML) gravity model to assess the determinants of Poland’s horticultural exports to Mercosur. The results indicate that trade remains predominantly inter-industry, reflecting substantial differences in agricultural specialisation and regulatory frameworks. At the same time, rising income levels in Mercosur, together with selected product-level complementarities, indicate emerging export opportunities for Poland. Poland’s trade with the Southern Common Market remains mainly as inter-industry, with the greatest export potential concentrated in high-value-added processed goods. Divergent sustainability standards, particularly in pesticide use, environmental regulation, and carbon-intensive transport, pose structural challenges that may affect the competitiveness and environmental footprint of expanded trade. Overall, the findings provide evidence that closer integration with Mercosur may support export diversification, but requires careful alignment with the EU’s sustainability objectives to ensure resilient and environmentally responsible development of the horticultural sector. Full article

Current management strategies for sorghum aphids heavily rely on indiscriminate chemical application, leading to severe environmental consequences and impacting food safety. While precision spraying offers a viable remediation for pesticide overuse, its effectiveness depends on accurately locating and classifying pests. To address the critical challenge of segmenting small, swarming aphids in complex field environments, we propose FESW-UNet, a dual-domain attention network that integrates Fourier-enhanced attention, spatial attention, and wavelet-based downsampling into a UNet backbone. We introduce an efficient multi-scale attention (EMA) module between the encoder and decoder to enhance global context perception, enabling the model to capture more accurate relationships between global and local features in the field. In the feature extraction stage, we embed a simple attention module (SimAM) to target key infestation regions while suppressing background noise, thereby enhancing pixel-level discrimination. Furthermore, we replace conventional downsampling with Haar wavelet downsampling (HWD) to reduce resolution while preserving structural edge details. Finally, a Fourier-enhanced attention module (FEAM) is added to the skip-connection layers. By using complex-valued weights to regulate frequency-domain features, FEAM effectively fuses global low-frequency structures with local high-frequency details, thereby enhancing feature representation diversity. Experiments on the Aphid Cluster Segmentation dataset demonstrate that FESW-UNet outperforms other models, achieving an mIoU of 68.76%, mPA of 78.19%, and mF1 of 79.01%. The model also demonstrated strong adaptability on the AphidSeg-Sorghum dataset, achieving an mIoU of 81.22%, mPA of 87.97%, and mF1 of 88.60%. The proposed method offers an efficient and feasible technical solution for monitoring and controlling sorghum aphids through image segmentation, demonstrating broad application potential. Full article

Shared manufacturing platforms improve the utilization of manufacturing resources by digitally matching demand with competing manufacturers and providing value-added services (VAS). Because VAS is costly and its benefits are jointly created, an appropriate cooperation mechanism between the platform and manufacturers is essential for achieving sustainable profitability. This study explores three cooperation strategies: (1) no-cooperation strategy (Model N); (2) cost-sharing strategy (Model CS); and (3) revenue-sharing (Model RS) strategy. This study establishes a shared supply chain model for each strategy, derives the equilibrium results, and compares the optimal performances. The results show that neither cost sharing nor revenue sharing guarantees a Pareto improvement: both parties benefit only when the negotiated cost-sharing ratio or revenue-sharing rate lies within a feasible range that properly balances the platform’s service cost burden and the manufacturers’ participation incentives. Additionally, equilibrium profits for both manufacturers and the sharing platform are decreasing as the value-added services (VAS) cost coefficient increases. Thus, the sharing platform should endeavor to decrease the VAS cost efficiency to reduce the VAS cost and enhance profits for all participants. These findings provide actionable guidance for selecting cooperation strategies and setting sharing parameters to achieve mutually beneficial outcomes in platform-enabled shared manufacturing. Full article

Biodegradable waste is commonly treated as a problem to be managed, but it can be a valuable resource when considered within a circular bioeconomy perspective. This article develops a practical and systems-based frame work for integrating biodegradable waste, ranging from municipal food scraps to wastewater biosolids, into valuable resources. It explores real-world strategies for transforming waste into value-added products, including composting, anaerobic digestion, biochemical conversion, and the creation of bio-based materials. The review also highlights key drivers and barriers, including technical, regulatory, and social factors, which shape the feasibility and impact of circular solutions. A visual model illustrates the full cycle, from identifying waste streams to reintegrating recovered resources. The paper also highlights case studies from Toronto, Milan and Brazil as examples of successful implementation. Overall, this paper emphasizes a pragmatic yet regenerative shift toward organic resource recovery aligned with sustainability and decarbonization goals. Full article