Search Results (2,249)

This study examines the pigments and materials used in Girolamo Romanino’s Musicians fresco (1537–1538), located in the Duomo Vecchio in Brescia, with the aim of identifying and analyzing the artist’s colour palette. Ten samples of the pictorial layer and mortar were collected from two frescoes and characterized using microscopic and spectroscopic techniques. Confocal laser scanning microscopy (CLSM) was used to define the best positions where single-point, spectroscopic techniques could be applied. Raman spectroscopy and micro-Fourier transform Infrared spectroscopy (micro-FTIR) were used to detect pigments and organic binders, respectively. X-ray powder diffraction (XRPD) provided additional insights into the mineral composition of the pigmenting layers, in combination with environmental scanning electron microscopy equipped with energy-dispersive spectroscopy (ESEM-EDS). The analysis revealed the use of traditional fresco pigments, including calcite, carbon black, ochres, and copper-based pigments. Smalt, manganese earths, and gold were also identified, reflecting Romanino’s approach to colour and material selection. Additionally, the detection of modern pigments such as titanium white and baryte points to restoration interventions, shedding light on the fresco’s conservation history. This research provides one of the most comprehensive analyses of pigments in Romanino’s works, contributing to a deeper understanding of his artistic practices and contemporary fresco techniques. Full article

Research has well documented the far-reaching benefits of providing educational opportunities for individuals who are incarcerated, applicable to the students themselves and society. Given the many benefits, it is encouraging that access to U.S. Pell Grants for incarcerated students was restored in July 2023—the first time in nearly 30 years that need-based federal postsecondary financial aid was available to individuals in U.S. prisons. Although Pell Restoration enables an increasing number of colleges and universities to provide higher-education-in-prison (HEP) programs, this funding guarantees nothing about the quality and rigor of programming. In fact, relatively little is known about the nature, scope, and quality of HEP programs within the United States, and it is both timely and important to deeply examine these topics. The present study is a critical qualitative case study of three college-in-prison programs in the southern United States. To interrogate the nature and quality of the programs, this study explores the experiences and practices of program faculty and directors, drawing from research and scholarship in education and the behavioral sciences to examine two key areas: faculty training and the educational experiences made available to students. Multiple forms of data were collected, and two main findings emerged: (a) faculty training is piecemeal and limited, and (b) the educational experiences made available in the three programs are simultaneously empowering and disempowering. Using Ladson-Billings’s concept of the education debt (including its historical, moral, and economic underpinnings), this study highlights that the three college-in-prison programs—like many HEP programs across the United States—both contribute to and challenge the education debt. Full article

Power outages pose considerable risks to the reliability of electric grids, affecting both consumers and utilities through service disruptions and potential economic losses. This study analyzes a historical outage dataset from a Regional Transmission Organization (RTO) to reveal key patterns and trends that suggest outage management strategies. By integrating exploratory data analysis, predictive modeling, and a Large Language Model (LLM)-based interface integration, as well as data visualization techniques, we identify and present critical drivers of outage duration and frequency. A random forest regressor trained on features including planned duration, facility name, outage owner, priority, season, and equipment type proved highly effective for predicting outage duration with high accuracy. This predictive framework underscores the practical value of incorporating planning information and seasonal context in anticipating outage timelines. The findings of this study not only deepen the understanding of temporal and spatial outage dynamics but also provide valuable insights for utility companies and researchers. Utility companies can use these results to better predict outage durations, allocate resources more effectively, and improve service restoration time. Researchers can leverage this analysis to enhance future models and methodologies for studying outage patterns, ensuring that artificial intelligence (AI)-driven methods can contribute to improving management strategies. The broader impact of this study is to ensure that the insights gained can be applied to strengthen the reliability and resilience of power grids or energy systems in general. Full article

Background/Objectives: Sleep is pivotal for recovery, immunity, and energy restoration; however, sleep problems exist in elite athletes. Nutrition and supplementation strategies can play both a positive and negative role in sleep quality and quantity. Elite athletes experience unique psychological and physiological demands above non-elite athletes and may require different nutrition strategies to promote sleep. Nutrient interventions and their effect on sleep in elite athletes is an emerging area, with further research warranted. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews and Joanna Brigg’s Institute Reviewer’s Manual for Scoping Reviews were utilised to assess the available evidence on nutrition strategies used to promote sleep in elite athlete cohorts, and we tried to identify the interventions that could be best researched in the future. NUtrition QUality Evaluation Strengthening Tools (NUQUEST) was used to enhance rigour and assess risk of bias in studies. The Paper to Podium (P2P) Matrix was used to offer practitioners practical recommendations. Results: 12 studies met the inclusion criteria for nutrition interventions or exposures to promote sleep in elite athletes. The median participant group size was 19 and study designs were considered together to ascertain potential sleep promoting strategies. Kiwifruit, Tart Cherry Juice and high dairy intake, limited to females, have demonstrated the highest potential to promote sleep in elite athletes, despite limited sample sizes. A-lactalbumin, carbohydrate pre-bed, casein, tryptophan, probiotic and meeting energy demands showed varying results on sleep quality in elite athletes. Conclusions: Kiwifruit, Tart Cherry Juice and dairy consumption offer potential nutritional interventions to promote sleep in elite athletic populations, while protein-based interventions may have a ceiling effect on sleep quality when elite athletes are already consuming >2.5 g·kg⁻¹ body mass (BM) or are already meeting their sleep duration needs. Full article

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, $t_k^{\mathrm{*}}$ at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

The Blue Economy has emerged as a vital framework for achieving sustainable economic diversification, environmental stewardship, and social resilience, particularly in regions facing ecological pressures such as the Gulf Cooperation Council (GCC). Despite its increasing recognition in national strategies, including Saudi Vision 2030 and the UAE’s Blue Economy Strategy 2031, scholarly research in the GCC remains fragmented and uneven. This study provides the first comprehensive bibliometric and thematic review of Blue Economy research in the region, analyzing publications produced between 2000 and 2025. The analysis reveals four dominant thematic clusters: fisheries and food security, governance and coastal policy, climate resilience and ecosystem restoration, and blue finance and economic diversification. At the same time, it identifies persistent gaps in social equity, gender inclusivity, traditional ecological knowledge, and regional coordination. By situating GCC research within broader global debates, the study underscores both the strengths and limitations of the current knowledge base. The findings contribute to academic debate and policy development by offering a conceptual framework that highlights inclusive governance, innovative financing, and nature-based solutions as key pillars for future research and practice. In doing so, the study provides a roadmap for advancing the Blue Economy agenda in the GCC and beyond. Full article

(1) Introduction: The clinical success of dental implants depends on rapid osseointegration, which can be impaired by hydrocarbon contamination and biological aging of titanium surfaces. Chairside plasma surface treatment has emerged as a practical method to restore surface hydrophilicity and enhance early bone–implant integration. (2) Materials and Methods: This retrospective cohort study evaluated 73 plasma-treated implants placed in 47 patients from June 2023 to October 2024. Non-thermal atmospheric pressure plasma was applied immediately before placement using the ACTILINK™ Reborn system. Implant stability was assessed baseline, weekly for the first four weeks, and again at week 8 using resonance frequency analysis (ISQ). Subgroup analyses were conducted according to initial ISQ, jaw location, implant length/diameter, and final insertion torque. (3) Results: All implants healed uneventfully without a stability dip. Mean ISQ increased from 78.97 ± 5.52 at placement to 83.74 ± 4.36 at week 8 (p < 0.001). Implants with lower initial stability demonstrated the greatest relative gains, while those with very high initial stability showed minimal changes. Mandibular and shorter implants demonstrated higher stability gains compared to maxillary and longer fixtures. (4) Conclusions: Chairside plasma surface treatment was associated with progressive ISQ increases during the 8-week healing period. The greatest gains occurred in implants with lower initial stability, while very stable implants showed little change. Stability improvements were also greater in mandibular sites, shorter fixtures, and those with higher insertion torque. These findings are limited to short-term ISQ outcomes and require validation in prospective controlled trials with standardized protocols. Full article

Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

As urban areas exert profound pressures on the natural environment, driving significant resource consumption and waste production, designers need to rethink the way urban projects are being developed. Therefore, this article advances the operationalization of the Circular Urban Metabolism (CUM) framework by analyzing a design case study: the Promenade Nardal in Paris. While CUM integrates the systemic material flow analysis of Urban Metabolism with the restorative strategies of the Circular Economy, it remains limited in its spatial articulation and applicability at the scale of urban design. Through a mixed-methods approach combining Life Cycle Assessment, spatial analysis, and qualitative inquiry, the article compares two circular design strategies, associated with the reuse of paving stones and the recycling of glass to produce Misapor, with conventional alternatives. Results demonstrate that stone reuse reduced CO₂ emissions, energy demand, and water use, while Misapor showed energy and water advantages but slightly higher CO₂ emissions due to production and transport. Beyond quantitative metrics, the study reveals the distinct design efforts and institutional frictions induced by circular practices, especially reuse, which requires adaptive aesthetics, labor-intensive design, and negotiation with technical norms. By spatializing material flows and foregrounding design agency, the article refines CUM as a situated and scalable framework, highlighting the need for context-sensitive, materially differentiated strategies in circular urban design. Full article

In the context of global sustainable development, the construction of green mining facilities has emerged as a pivotal strategy for advancing sustainable mining practices. As a substantial mineral resource base in China, the Qinghai-Xizang Plateau (QXP) is of significant concern due to its importance for mineral exploitation. However, the natural conditions of the region, such as freezing temperatures, low oxygen levels, frequent freeze–thaw cycles, and fragile ecology, pose substantial challenges to mining activities, making green mine construction an inevitable choice for mining development on the QXP. This study uses SWOT analysis to macroscopically evaluate the strengths, weaknesses, opportunities, and threats of green mine construction on the QXP. This study adopts SWOT analysis to sort out, from a macro and systematic perspective, the internal resource endowments, technical reserves, external policy and market opportunities, as well as multiple challenges such as ecological vulnerability, harsh climate, regulation, and public opinion in the construction of green mining on the QXP. Furthermore, four typical cases, namely the Julong Copper Mine, Zhaxikang Lead–Zinc Mine, Zaozigou Gold Mine, and Duolong Copper Mine, are selected for analysis, and their differentiated paths in ecological restoration, digital mines, tailings disposal, and community-benefit sharing are summarized. International comparisons reveal the similarities and differences in policies, technologies, and other aspects between the QXP and other high-altitude regions. The study holds that it is necessary to promote the coordinated development of resource exploitation and ecological protection in green mining on the QXP through technological innovation, policy optimization, community collaboration, and the construction of a full-life-cycle environmental-monitoring system. At the same time, it points out the limitations of the current research in quantitative analysis and future research directions. Full article

Power outages in non-faulted zones caused by system failures significantly reduce the reliability of distribution networks. To address this issue, this paper proposes a fault self-healing technique based on the integration of soft open points (SOPs) and network reconfiguration. A mathematical model for power restoration is developed. The model incorporates SOP operational constraints and the stochastic output of photovoltaic (PV) distributed generation. And this formulation enables the determination of the optimal network reconfiguration strategy and enhances the restoration capability. The study first analyzes the operational principles of SOPs and formulates corresponding constraints based on their voltage support and power flow regulation capabilities. The stochastic nature of PV power output is then modeled and integrated into the restoration model to enhance its practical applicability. This restoration model is further reformulated as a second-order cone programming (SOCP) problem to enable efficient computation of the optimal network configuration. The proposed method is simulated and validated in MATLAB R2019a. Results demonstrate that combining the SOP with the reconfiguration strategy achieves a 100% load restoration rate. This represents a significant improvement compared to traditional network reconfiguration methods. Furthermore, the second-order cone programming (SOCP) transformation ensures computational efficiency. The proposed approach effectively enhances both the fault recovery capability and operational reliability of distribution networks with high penetration of renewable energy. Full article

This study proposes an image-enhancement method to address the challenges of low visibility and color distortion in images captured during yellow sandstorms for an image sensor based outdoor surveillance system. The technique combines traditional image processing with deep learning to improve image quality while preserving color consistency during transformation. Conventional methods can partially improve color representation and reduce blurriness in sand–dust environments. However, they are limited in their ability to restore fine details and sharp object boundaries effectively. In contrast, the proposed method incorporates Retinex-based processing into the training phase, enabling enhanced clarity and sharpness in the restored images. The proposed framework comprises three main steps. First, a cycle-consistent generative adversarial network (CycleGAN) is trained with unpaired images to generate synthetically paired data. Second, CycleGAN is retrained using these generated images along with clear images obtained through multiscale image decomposition, allowing the model to transform dust-interfered images into clear ones. Finally, color preservation is achieved by selecting the A and B chrominance channels from the small-scale model to maintain the original color characteristics. The experimental results confirmed that the proposed method effectively restores image color and removes sand–dust-related interference, thereby providing enhanced visual quality under sandstorm conditions. Specifically, it outperformed algorithm-based dust removal methods such as Sand-Dust Image Enhancement (SDIE), Chromatic Variance Consistency Gamma and Correction-Based Dehazing (CVCGCBD), and Rank-One Prior (ROP+), as well as machine learning-based methods including Fusion strategy and Two-in-One Low-Visibility Enhancement Network (TOENet), achieving a Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score of 17.238, which demonstrates improved perceptual quality, and an Local Phase Coherence-Sharpness Index (LPC-SI) value of 0.973, indicating enhanced sharpness. Both metrics showed superior performance compared to conventional methods. When applied to Closed-Circuit Television (CCTV) systems, the proposed method is expected to mitigate the adverse effects of color distortion and image blurring caused by sand–dust, thereby effectively improving visual clarity in practical surveillance applications. Full article

As global economic integration deepens, import and export trade plays an increasingly vital role in China’s economy. To enhance regulatory efficiency and achieve scientific, transparent credit supervision, this study proposes a trade credit risk evaluation model based on interpretable machine learning, incorporating loss preferences. Key risk features are identified through a comprehensive interpretability framework combining SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), forming an optimal feature subset. Using Light Gradient Boosting Machine (LightGBM) as the base model, a weight adjustment strategy is introduced to reduce costly misclassification of high-risk enterprises, effectively improving their recognition rate. However, this adjustment leads to a decline in overall accuracy. To address this trade-off, a Bagging ensemble framework is applied, which restores and slightly improves accuracy while maintaining low misclassification costs. Experimental results demonstrate that the interpretability framework improves transparency and business applicability, the weight adjustment strategy enhances high-risk enterprise detection, and Bagging balances the overall classification performance. The proposed method ensures reliable identification of high-risk enterprises while preserving overall model robustness, thereby providing strong practical value for enterprise credit risk assessment and decision-making. Full article