Search Results (7,579)

The green transition in agriculture is a key issue for achieving sustainable development. Based on panel data from 30 Chinese provinces covering the period from 2011 to 2022, this paper examines the relationship between environmental enforcement and agricultural green total factor productivity (AGTFP), with a focus on analyzing the moderating effects of land transfer and agricultural insurance, as well as their synergistic threshold characteristics. The study employs two-way fixed-effects models, moderating effect models, and Hansen threshold regression methods for empirical analysis. The baseline regression results show a significant positive association between environmental enforcement and AGTFP. This conclusion remains robust after various tests, including truncation, replacement of core explanatory variables, difference GMM, and instrumental variables. The decomposition test shows that this positive correlation is mainly reflected through the channel of technological progress, rather than the improvement in technical efficiency. Heterogeneity analysis indicates that the positive association is more pronounced in regions with high GDP, strong law enforcement capacity, and in northern regions. Moderation analysis reveals that both the land transfer rate and insurance depth positively moderate the relationship between environmental enforcement and AGTFP, and the two exhibit a synergistic effect. However, this synergistic effect exhibits nonlinear characteristics and may weaken or even reverse at extreme value intervals. A threshold model further reveals an asymmetric complementary relationship between the two institutional conditions. The moderating effect of land transfer is activated only after insurance depth crosses a threshold value, while the moderating effect of insurance depth is most effective during the small-scale farming stage. These findings suggest that environmental regulation policies should be advanced in coordination with land transfer and agricultural insurance systems, with a focus on institutional alignment and coordination. Full article

Porous ceramics have shown great application potential in aerospace, electronics, and lithium-ion battery thermal management due to their low density, high specific strength, and excellent thermal insulation. Material Jetting (MJ), a high-precision 3D printing technology, enables the fabrication of porous ceramics with tailored pore structures, but the synergistic effects of pore structure parameters (configuration, porosity, and number of periods) on their thermal insulation performance remain insufficiently explored. This study systematically investigates the thermal insulation behavior of zirconia porous ceramics fabricated by MJ through experimental tests and numerical simulations. Three typical lattice configurations (Octet, Schwarz, and Gyroid) were selected, and samples with varying porosities (40%, 50%, 60%) and numbers of periods (1, 2, 3) were prepared. The results indicate that the Octet configuration (60% porosity, 3 periods) exhibits the optimal thermal insulation performance, with a minimum cold-end temperature of 58.5 °C (experiment) and 59.21 °C (simulation), attributed to its strut-based structure that forms a more tortuous heat conduction path. For the Gyroid configuration, thermal insulation performance improves with increasing porosity (reducing solid conduction dominance under non-forced convection) and decreases with decreasing number of periods (due to inhomogeneous pore distribution extending heat transfer paths). Notably, the trend of porosity affecting thermal insulation is opposite to that of compressive performance. Numerical simulation results are consistent with experimental data in both values and trends, verifying the reliability of the model. This work clarifies the key factors regulating the thermal insulation of MJ-fabricated porous ceramics and provides practical structural design guidelines for applications such as lithium-ion battery thermal runaway management. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

The electrification of public transportation requires reliable and efficient technologies for energy transfer. Trolleybus systems represent a promising solution, as they combine high energy efficiency with reduced battery requirements. However, a central technical challenge is the precise and automatic positioning of the flexible current collector poles that connect to the overhead line. During positioning through motor actuation, the current collector shoe is caused to oscillate by external disturbances and the movement itself. To reduce oscillations, the current collectors need to be damped actively by respective actuation. This task critically depends on accurate and fast motor speed estimation for real-time control of the actuating motors. Since motor speed is not measured directly in the system, it has to be estimated from the encoder-based motor position, which introduces sensitivity to measurement noise and requires filtering. This work investigates four practical estimation approaches in the context of trolleybus applications. These include discrete-time numerical differentiation combined with FIR and IIR filtering and a modern algebraic differentiation approach. These estimation methods are evaluated under identical experimental conditions and predefined filter specifications focusing on noise suppression and time delay characteristics. The most promising approaches are further validated in closed-loop operation with respect to measurement noise-induced variations in the control input and motor speed tracking accuracy. The results demonstrate that algebraic differentiation achieves a favorable balance between noise suppression, latency, and filter order for the considered current collector system. It therefore provides a suitable basis for real-time deployment in the investigated current collector positioning control and for future active oscillation damping strategies. Full article

Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. Full article

Proper handling during harvesting and subsequent postharvest management is essential to reduce losses in fruits and vegetables, particularly because these products remain metabolically active after harvest. Physiological processes such as respiration, transpiration, ethylene production, softening, physiological disorders, and postharvest diseases determine quality deterioration, shelf life, and marketability. However, these processes do not affect all commodities in the same way; for example, climacteric fruits are strongly influenced by ethylene during ripening, whereas non-climacteric fruits generally show lower ethylene production and different postharvest behavior. In Mexico, postharvest management is especially relevant because fruit and vegetable producers differ widely in terms of production scale, infrastructure, access to technology, financing capacity, and market destination. Producers with limited access to technology require practical and low-cost alternatives, while more technologically advanced producers may use specialized systems but still experience postharvest losses due to physiological deterioration, handling conditions, logistics, and market constraints. Therefore, this review summarizes the main postharvest physiological processes affecting fruits and vegetables and discusses their implications for knowledge transfer, technology adoption, and sustainability among local producers in Mexico. The review highlights that reducing postharvest losses requires commodity-specific management, continuous technical support, low-cost and locally adaptable technologies, and coordinated participation among researchers, extension personnel, producers, government institutions, industry, and market actors. Strengthening postharvest knowledge transfer to small and local producers is essential to reduce losses, improve marketability, and promote more sustainable fruit and vegetable systems in Mexico. Full article

Extended Reality (XR), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is increasingly used to support experiential learning in Initial Teacher Education (ITE). This systematic review aimed to examine how XR technologies are integrated into university-based ITE programmes and their reported educational outcomes. Following PRISMA 2020 guidelines, a multi-source search was conducted across major databases (e.g., Scopus, Web of Science) and the grey literature (last search: January 2026). Eligible studies included empirical research on XR in ITE published between 2016 and 2026; non-empirical and non-ITE studies were excluded. Risk of bias was assessed using established appraisal criteria, and results were synthesised using a narrative thematic approach. A total of 32 studies were included. Findings indicate that XR is primarily used for classroom management training, microteaching, and reflective practice. Across studies, immersive simulations were associated with improvements in teacher self-efficacy, classroom management skills, and reflective decision-making. However, accessibility and inclusion strategies remain underdeveloped, and evidence of transfer to real classroom practice is still limited. Overall, XR functions most effectively as a preparatory tool that complements practicum-based training. Full article

The conversion of bio-waste into functional energy materials provides a robust platform for addressing both environmental and energy challenges. In this paper, discarded absorbent pads are transformed into carbon-rich frameworks, which is followed by the fabrication of composites through the incorporation of Cu₄SnS₄ (CSS) for dual electrochemical applications. Integrating CSS into the waste-derived carbon matrix induces strong synergistic effects, improving electrical conductivity, increasing active-site availability, and accelerating charge-transfer kinetics. Comprehensive physicochemical analyses confirmed the successful formation of a well-integrated heterostructure composite with favorable structural and surface characteristics. Electrochemical evaluations further demonstrated that CSS-modified carbon exhibits superior bifunctional performance. In a two-electrode configuration, the composite delivers an energy density of 12.08 Wh kg⁻¹ at a power density of 250 W kg⁻¹ along with excellent cycling stability in supercapacitor applications. As an electrocatalyst, it achieves a low overpotential of 268 mV at −10 mA cm⁻² and a small Tafel slope of 75 mV dec⁻¹, reflecting efficient reaction kinetics. The strong durability observed in both systems underscores the structural integrity and long-term operational stability of the material. Overall, this paper advances a sustainable waste-to-resource strategy for fabricating multifunctional carbon-based composites, offering a promising platform for integrated energy-storage and hydrogen-generation technologies. Full article

Reliable air pollution monitoring remains a major challenge in Sub-Saharan Africa (SSA), limiting the assessment of population exposure and the development of effective mitigation strategies. Recent advances in low-cost (LC) sensors offer promising opportunities, but their deployment in low-infrastructure settings still faces significant technical and logistical challenges. This study presents the experience gained from deploying LC sensor networks in Burkina Faso, Niger, and the Republic of Guinea, focusing on the practical challenges of installing and maintaining these systems under demanding conditions. In Burkina Faso, an LC station was co-located with a reference-grade instrument, enabling field calibration. In Niger, factory-calibrated LC sensors were deployed across urban, semi-urban, and rural settings, while in Guinea they were installed in a remote area. Several practical issues and challenges emerged, including unstable power supplies, limited internet connectivity, safety, and logistical constraints. Careful planning and involvement of local expertise proved essential for the long-term sustainability of LC sensors. Knowledge transfer to local partners supported ongoing maintenance and strengthened data ownership. Overall, this study demonstrated that the reliability of LC air quality networks in SSA depends not only on technology, but also on adaptive strategies, robust calibration, and strong local engagement, offering practical guidance for future scalable and sustainable implementations in resource-limited settings. Full article

Introduction: River fragmentation caused by hydropower infrastructure remains a primary threat to aquatic biodiversity, creating a critical need for fish passage solutions that can adapt to high environmental variability. Although adaptive management (AM) has the potential to significantly improve longitudinal connectivity and ecological resilience, its application in real-world fishway operations is currently limited. Objective: This study aims to present and validate a flexible AM framework designed to optimize fish passage by integrating low-cost monitoring systems with automated data processing and predictive modeling. Methodology: The proposed system combines a sensor network for real-time water-level and environmental monitoring with biological performance data obtained through Passive Integrated Transponder (PIT) technology. These data were processed locally using edge computing. Over a two-year period, weekly aggregated data were used to develop Random Forest models to identify the primary drivers of fish movement. Results: The final model successfully identified five key drivers: luminosity, water temperature, and three nested hydraulic parameters at the fishway’s upstream section. Validation at a vertical-slot fishway in Vadocondes (Duero River, Spain) showed that retrospective optimization—specifically adjusting sluice-gate regulation—could increase downstream water levels and reduce drops at the first cross wall. This adjustment demonstrated a substantial increase in predicted fish passage without requiring changes to the hydropower plant’s core operation. Conclusions: The framework is highly flexible and transferable to other regulated river systems. However, its success is contingent upon the definition of clear ecological objectives and the seamless integration of monitoring results into the day-to-day operation of river infrastructure. Full article

Recent advances in satellite technology have significantly progressed, yet acquiring high-quality images with meaningful labels for Earth observation missions remains a costly and time-intensive process. Furthermore, captured scenes frequently exhibit defects such as misaligned color channels, extensive cloud cover, or repetitive patterns in similar environments. Fortunately, the evolution of generative artificial intelligence offers a solution by enabling the creation of realistic synthetic scenes, simulating the characteristics of any targeted imager, and thereby mitigating the scarcity of authentic data. This paper demonstrates the feasibility of transferring knowledge from specialized AI-generated datasets to Earth observation missions. Leveraging a novel dataset of Spanish map tiles, Pix2Pix, CUT, and ControlNet models were implemented to synthesize satellite imagery. To analyze structural and topological generalizability, identical U-Net instances were trained on the resulting collections for building, road, and water segmentation tasks, and subsequently tested on independent authentic imagery. The results reveal a clear decoupling between visual realism and functional utility. Incorporating synthetic samples into hybridized training datasets successfully surpassed the limitations of using real data alone, increasing maximum Dice scores by 0.9% (to 54.1% for buildings), 2.3% (to 38.6% for roads), and 4.1% (to 46.5% for waterbodies). This systematic validation establishes structural-guided synthetic data augmentation as a robust, adaptable strategy for Earth observation applications across diverse sensors and geometric objectives. Full article

Chitosan, produced through deacetylation of chitin from crustacean byproducts and, increasingly, fungal biomass and insects, is attracting food-sector interest because it combines antimicrobial activity, antioxidant capacity, biodegradability, and film-forming behavior in a single polymer. This review discusses how source, molecular weight (MW), degree of deacetylation, solubility, and charge density shape its performance in food systems. The paper then follows the main technological routes now tested or used: edible films and coatings, hydrogels, cryogels, nanoparticles, microcapsules, and hybrid matrices. These formats can protect fresh produce, meat, poultry, fish, seafood, and dairy foods, while also supporting beverage clarification, emulsion control, release of natural antimicrobials or antioxidants, and freshness monitoring in active or intelligent packaging. The evidence indicates strong promise, especially where microbial growth, lipid oxidation, moisture transfer, and short shelf life remain limiting factors. Yet, wider industrial use is still slowed by water sensitivity, sensory effects, raw-material variation, cost, process scale-up, and regulatory alignment. Future work should move beyond laboratory efficacy and address reproducible production, food-specific validation, and consumer acceptance. Full article

The transition to a circular economy requires the safe management of sewage sludge through nutrient and energy recovery. However, pharmaceuticals and personal care products (PPCPs) present a significant challenge. These compounds tend to accumulate in sludge via sorption, shifting the environmental burden from the aqueous phase to the sludge. This manuscript provides a comprehensive review of the scientific literature on technical alternatives for valorizing sewage sludge and removing emerging contaminants. The study evaluates the limitations of conventional biological methods, such as anaerobic digestion and composting, which exhibit variable efficacy and are often insufficient to degrade some commonly used pharmaceuticals. On the contrary, thermal treatments (pyrolysis, gasification, and hydrothermal processes) are considered robust alternatives capable of achieving the high removal of chemical compounds. Furthermore, the article emphasizes the innovative potential of utilizing carbon-based byproducts (biochar and hydrochar) as adsorbents, catalysts, or soil amendment to enhance the removal of PPCPs within the treatment infrastructure itself. The integration of advanced thermal technologies is essential to mitigate the risks of contaminant transfer to the food chain and ensure a safe and sustainable nutrient cycle. Full article

The association between digital economy (DE) and manufacturing transformation (MT) is conditioned by regional structural characteristics, yet little is known about how this association varies within provinces that are peripheral at the national scale. This study examines Yunnan Province, China, as a dual-peripheral context, where regions are simultaneously distant from national economic cores and internally structured along a pronounced core–intermediate–periphery gradient. Using prefecture-level panel data from 16 cities over 2011–2023, the analysis shows that the positive association between DE and MT is spatially attenuated along this gradient. Furthermore, three key regional factors—transportation infrastructure, industrial agglomeration, and technological talent—correspond to distinct spatial conditioning patterns. Transportation infrastructure exhibits an extensible but spatially bounded pattern, industrial agglomeration is most strongly associated with intermediate prefectures, and technological talent displays a highly concentrated pattern within the provincial core. These differentiated patterns indicate that internally differentiated peripheral structures are associated with different forms of spatial conditioning in the observed DE–MT association, rather than producing a uniform spatial pattern. Based on these findings, region-specific strategies targeting connectivity, industrial coordination, and talent development are recommended to support inclusive and context-sensitive manufacturing transformation. The study provides an analytically transferable perspective by highlighting how different regional conditions may correspond to different spatial reaches of digital–manufacturing transformation within peripheral systems. Full article

Waste management is a vital component of modern economies, requiring not only technological solutions, but also economic and social approaches that reflect human needs while minimizing environmental harm. Within the European Union, sustainable development remains a central objective, promoting strategies in which waste is not merely disposed of, but is also recovered and reused whenever feasible. Landfill gas, primarily composed of methane, can be captured and managed in a controlled way. If left unregulated, methane emissions present serious risks to human health and contribute significantly to environmental degradation. At the same time, methane represents a valuable yet underutilized renewable energy source. In Poland, emission monitoring is conducted through the National Pollutant Release and Transfer Register, which operates as part of a broader European system. Landfill operators must report methane emissions and pay associated environmental fees. This study aimed to estimate methane emissions across Polish voivodeships from 2019 to 2023, considering both economic and social dimensions of sustainability. The analysis relied on official register data and landfill documentation, enabling evaluation of reporting accuracy and regulatory effectiveness. The findings indicate that current policies insufficiently encourage emission reductions, highlighting the need for systemic reforms, improved transparency, and clearer regulatory thresholds to drive meaningful environmental progress. Full article