Search Results (55,567)

With increasing environmental pollution and a high incidence of respiratory infections, pulmonary nodules (PN) are being detected more frequently. Although most are benign, they are often accompanied by chronic inflammation and localized fibrosis, which may predispose patients to progression toward idiopathic pulmonary fibrosis (IPF). However, the biological relationship between benign pulmonary nodules (BPNs) and IPF remains poorly understood. Therefore, this study aims to investigate the shared molecular mechanisms and identify potential biomarkers linking BPN and IPF, with the goal of elucidating the pathogenic transition from BPN to IPF. In this study, microarray data from GEO datasets were systematically analyzed to explore shared molecular mechanisms, immune infiltration characteristics, and potential early intervention strategies linking BPN and IPF. Differential expression analysis, protein–protein interaction (PPI) networks, weighted gene co-expression network analysis (WGCNA), and integrative machine learning approaches identified MME and ANKRD23 as key hub genes associated with the transition from BPN to IPF. Both genes demonstrated strong diagnostic performance, with Area Under the Curve (AUC) values exceeding 0.7, and were significantly correlated with immune cell infiltration, particularly effector memory CD8⁺ T cells. Functional enrichment and gene set enrichment analyses indicated that these genes were mainly involved in immune-related processes in BPN, while in IPF, ANKRD23 was linked to cytoskeletal organization and genomic stability, and MME was enriched in profibrotic pathways such as TGF-β signaling. The diagnostic value of these biomarkers was further validated in a bleomycin-induced IPF mouse model using quantitative polymerase chain reaction (qPCR). In addition, drug–gene interaction prediction and molecular docking analyses highlighted several naturally derived compounds with favorable binding affinity and anti-inflammatory properties, among which folic acid, curcumin, and arbutin emerged as promising candidates for safe early intervention. Collectively, these findings identify MME and ANKRD23 as potential biomarkers for early identification of BPN patients at risk of developing IPF and provide a theoretical basis for early diagnosis and targeted preventive strategies. Full article

Against the backdrop of China’s “dual carbon” goals, investigating whether market-based environmental regulations can effectively induce technological upgrading is critical for achieving a sustainable low-carbon transition. This study adopts a staggered difference-in-differences (DID) approach within a two-way fixed-effects framework, supplemented by propensity score matching (PSM-DID), to identify the causal impact of the carbon emission trading (CET) pilot policy. The research utilizes a comprehensive panel dataset of A-share listed companies in heavy-polluting industries from 2010 to 2024, incorporating IPC-matched green patent application data to provide a granular assessment of corporate innovation performance. The empirical findings reveal a structural divergence: while the CET policy promotes green innovation in state-owned enterprises (SOEs), it exhibits a potential “crowding-out” effect on private enterprises, a relationship further explained by the mechanisms of carbon information disclosure and financing constraints. These results suggest that the “Porter Effect” in emerging markets is highly conditional on institutional resource endowments, implying that policymakers must complement market incentives with differentiated financial support and enhanced transparency standards to foster a more equitable innovation ecosystem. Full article

Nitric oxide (NO) is a gaseous biocompatible radical molecule with demonstrated biomedical and antimicrobial benefits. Developing adaptable, long-lasting delivery systems for NO has become an essential goal for both combating resistant bacterial growth and providing sustained medical benefits. Silsesquioxane (SQ)-based organogels were chosen and synthesized as robust, tunable NO-release platforms. These highly stable SQ gel frameworks, composed of silicon–oxygen backbones with variable R groups, exhibited high porosity and surface area and offered chemical versatility, enabling control over NO loading and release. 3-Mercaptopropyl groups were utilized as sulfur-based NO-releasing substituents (-RSNOs), with additional R groups capable of altering accessibility to RSNO sites through hydrophobicity and steric hindrance. The NO release profile, rate, and duration of the functionalized gels were also tailored by adjusting the number of RSNO sites in the elastomeric system, thereby enabling a customizable release profile. This combination of NO-releasing silsesquioxanes with silicone elastomers yields composite materials that are integratable into biomedical applications, offering NO release up to 40 days within modeled physiological conditions in PBS buffer. Full article

The sustainable enhancement of regional science and technology industrial competitiveness is an important objective of artificial intelligence (AI) policy. However, how different AI policy instruments can be combined to achieve this goal remains insufficiently understood. This study aims to address this issue by identifying the configurational pathways through which combinations of AI policy instruments contribute to the sustainable enhancement of regional science and technology industrial competitiveness. Based on a policy instrument framework, we analyze AI policies issued by provincial-level governments in China and apply fuzzy-set qualitative comparative analysis (fsQCA), which is appropriate for examining the combined effects of multiple policy instruments. The results show that no single policy instrument is sufficient to produce high regional science and technology industrial competitiveness. Instead, sustained competitiveness is achieved through multiple equivalent configurations of policy instruments. Three driving pathways are identified—(supply and demand)-environmental resonance, demand-driven (supply-environmental) assurance, and supply–demand complementarity—covering five specific configurations. The variation across configurations indicates that effective AI policy mixes are contingent on regional resource endowments and development conditions. Technology R&D support, talent cultivation and collaboration, and application demonstration and promotion emerge as the most recurrent core conditions across configurations. Accordingly, local governments should develop coordinated AI policy mixes, align differentiated policy pathways with regional conditions, and prioritize technology R&D support, talent cultivation and collaboration, and application demonstration and promotion to sustain long-term regional competitiveness. Full article

Due to urbanization and industrialization, there are significant regional differences in carbon emissions, making it increasingly urgent and necessary to conduct an in-depth examination of carbon emission trends from energy consumption across various sectors at the provincial level. Taking Hebei Province, a major carbon-emitting province in China, as a case study, we analyzed carbon emissions from three perspectives: historical emissions, influencing factors, and scenario projections. First, we established a carbon emission inventory for energy consumption. Second, using the integrated LMDI-SD-MC framework, we constructed four subsystems economy, society, energy, and technology and employed three scenarios for forecasting. The results show that: (1) Carbon emissions in Hebei Province from 2003 to 2021 exhibited increased trend year by year, with the share of coal and coke decreasing and the share of natural gas increasing. The industry, residential, and transportation sectors accounted for more than 95% of total carbon emissions. (2) In terms of influencing factors, energy intensity and the level of economic development contributed the most significantly, with contribution rates of −75.97% and 195.97%, respectively. (3) Among the scenario projections, the low-carbon development scenario is the most suitable for Hebei Province, enabling the province to achieve its “Dual Carbon” goals as scheduled. Under the baseline development scenario, the peak is reached in 2040. Under the rapid development scenario, carbon emissions will reach 1130.86 10⁶ tons by 2060. (4) Uncertainty analysis using Monte Carlo simulation for all three scenarios showed errors within ±10%, indicating that the model results are robust and interpretable. This study provides a provincial level emission reduction perspective for China to achieve its “Dual Carbon” goals and sustainable development. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing Agri-Food 4.0 supply chain resilience. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

To promote sustainable biomass recycling and support food security, Tenebrio molitor (TM) larvae can serve as an eco-friendly source of food and feed. This study compared the survival, growth performance, and nutritional composition of TM larvae fed five diets. The control (CON) diet contained distillers’ dried grains with solubles (DDGS) and wheat bran (WB), while the experimental diets included 10–40% lignocellulose-rich organic products from rewetted peatlands (LPRP) replacing WB, with DDGS adjusted to maintain equivalent protein levels (about 21%). A total of 2500 larvae were divided into five replicates per treatment (100 larvae each). Survival exceeded 90% across all groups. Larvae fed the CON diet had a higher final body weight than those on the 30% and 40% LPRP diets (p < 0.05), with no significant differences among the CON and 10% and 20% LPRP groups. The feed conversion ratio (fresh matter) was significantly lower in the CON and 10% LPRP groups than in the other groups (p < 0.05). Larvae fed the 10% LPRP diet showed slightly higher crude protein content (55.8%) compared to the control group (54.8%) and the other treatment groups, whereas those fed the 30% LPRP diet had the highest numerical total amino acid content. Taken together, these results indicate that incorporating 10% LPRP with DDGS and WB provides the best overall balance between growth performance and nutritional quality for TM larvae, supporting sustainable production and circular economy goals. Full article

Background/Objectives: Nutrition is essential to outcomes in critically ill children; however, optimal timing, route, and composition of feeding remain uncertain. Prior studies demonstrate considerable variability in study design, patient populations, and outcome measures, limiting comparability. This review synthesizes international pediatric intensive care unit (PICU) nutrition studies evaluating timing, route, and content of nutritional interventions and summarizes associated clinical outcomes and nutritional adequacy. Methods: A comprehensive scoping review was conducted using the PICOS framework. PubMed and Embase databases were searched for studies published between 2015 and 2025 enrolling critically ill children ≤21 years old admitted to PICUs. Eligible studies assessed timing (early vs. late enteral nutrition), nutritional composition, or feeding route (enteral vs. parenteral). Screening and full-text review were performed independently by two reviewers using Covidence, with discrepancies resolved by a third reviewer. Quality assessment used STROBE. The protocol was registered with PROSPERO. Results: Of 652 identified records, 30 studies met inclusion criteria. Studies were conducted primarily in the United States (27%), with additional contributions from Spain and Brazil (10% each) and several other countries. Study designs included randomized controlled trials (27%) and observational studies (73%). Interventions examined feeding route (14%), nutritional content (38%), and timing (48%). Frequently reported outcomes included feeding intolerance or adverse events, duration of mechanical ventilation, time to nutrition goals, PICU length of stay, mortality, and nutritional adequacy. Conclusions: The contemporary PICU nutrition literature demonstrates persistent heterogeneity in practice and outcomes. This review identifies ongoing gaps in timing, delivery, and adequacy of nutritional support. Full article

Generative artificial intelligence (AI) has demonstrated significant potential in education, yet empirical research on its application in “hardware-driven” interdisciplinary design courses remains scarce. This study employed a randomized crossover experimental design in an IoT Hardware and Design Innovation course at Hunan University. Twelve industrial design undergraduates with no prior IoT background alternated between AI-assisted (ChatGPT-4o) and traditional learning resource conditions across six short-cycle tasks. The crossover design enabled each participant to serve as both experimental and control subjects, yielding 72 observation-level data points. Grounded in Cognitive Load Theory, the study examined three dimensions: process efficacy, difficulty structure, and switching adaptation costs. Results indicated that AI significantly improved perceived task completion efficiency, self-reported goal attainment, and learning experience, yet self-assessed knowledge transfer did not differ significantly between conditions. AI reduced the total number of reported difficulties but altered the difficulty-type distribution: resource-retrieval difficulties decreased while information-verification difficulties increased—a phenomenon we term “difficulty restructuring”. Furthermore, switching from AI back to traditional resources incurred significantly higher adaptation costs than the reverse transition, revealing emerging dependency risks. These findings suggest that generative AI may function more as a “difficulty restructurer” than a “difficulty eliminator” in hardware-driven design education, providing exploratory empirical evidence for incorporating verification literacy into future course design and calling for calibrated scaffold fading that may help mitigate emerging dependency risks. Full article

Many RRT*-based sampling path planning algorithms consider neighboring nodes around a newly added node. The neighbor radius parameter determines which nodes are included. The performance of RRT*-based algorithms can vary significantly with . This variation can weaken generalization across environments. This paper quantitatively analyzes the effect of on performance in sampling-based path planning for mobile robots in a warehouse environment. We evaluate RRT*-based algorithms by varying . We then select the heuristic chosen for each algorithm and compare the algorithms under the same conditions. Experiments are conducted in a warehouse environment with a fixed start position and five goal positions. Performance is evaluated using planning time, path length, and cumulative change in turning angle. Lower values indicate better performance for all three metrics. Based on the experimental results, we derive a heuristic value of for each case. We also identify algorithm characteristics in computational efficiency and path quality under the heuristically chosen parameter settings. The final goal of this study is to provide quantitative evidence for selecting in warehouse applications. We also present guidelines for parameter setting and algorithm selection for RRT*-based sampling path planning. Full article

The construction sector remains a major contributor to global energy consumption and greenhouse gas emissions. Heat loss through building envelopes plays a key role, especially in regions with long heating seasons. Hollow building blocks are widely used due to their low cost and structural simplicity, but their inadequate thermal insulation requires additional layers of insulation, increasing costs and complicating installation. The production of cement and traditional insulation materials is associated with a high carbon footprint and disposal issues, which conflict with sustainable development principles and decarbonization goals. In contrast to previous reviews that primarily address bio-based insulation in general building envelopes or focus on bioaggregates in concrete mixes, this paper specifically targets the application of biomaterials in hollow building blocks. It emphasizes how bio-based loose-fill and bound fillers interact with the peculiar thermo-fluid behavior of hollow cavities, including natural convection, conduction and radiation. The effects on thermal performance (thermal conductivity, U-value of walls) are analyzed, along with selected aspects of mechanical strength and durability. Gaps in long-term data on biodegradation are identified. Recommendations for selecting strategies depending on climate and design are offered, as well as directions for future research, including numerical modeling of thermal conditions. The results highlight the potential of biomodified blocks for creating energy-efficient and environmentally friendly wall systems. Full article

Transformers are key devices in the new electricity system, and the entire life cycle is associated with a considerable resource consumption and carbon footprint (CF). Understanding CF is essential for accelerating the low-carbon transition of the industry. Therefore, a systematic CF model for transformers is constructed in this study based on life cycle assessment (LCA). The results indicate that the operation stage is the overwhelmingly dominant phase for CF of transformer, with electricity acting as the main carbon source. The CF at the raw-material stage mainly originates from steel and copper. Through analysis, eight key impact factors were identified, leading to the formulation of three-dimensional carbon reduction pathways. It was observed that a 10% reduction in total losses of a transformer results in an approximate 10% decline in CF. At the same time, the transition of the electricity grid to clean energy helps reduce CF during operation. In addition, the effectiveness of a multi-factor carbon reduction pathway was examined. The results showed that, under this integrated pathway, the CF across all transformer rated voltages could be reduced by 9.75%. Based on this, a system pathway centered on enhancing operational energy efficiency is proposed, supported by green materials and processes, and coordinated through smart operation and maintenance, and circular recycling. This provides quantitative evidence and decision support for the green transition of transformers, contributing to the broader goals of sustainability development in electricity system. Full article

Background: This study investigates the impact of anatomically constrained preprocessing and deep learning architecture selection on benign versus malignant breast lesion classification in contrast-enhanced mammography (CEM), with the goal of improving robustness and clinical reliability across heterogeneous data sources. Methods: In this retrospective multicenter study, CEM images from 300 patients (314 lesions) were combined with 1003 publicly available CEM images, yielding a total of 1120 breast cases. Automatic breast segmentation was performed using the LIBRA framework to generate breast-mask images. Eleven deep learning models, including classical convolutional neural networks, attention-based networks, hybrid convolutional neural networks (CNNs), Transformer architectures, and mammography-specific models, were trained and evaluated using both original DICOM images and breast-mask inputs. Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, AUROC, and AUPRC on cross-validation and independent test sets. Hyperparameter optimization was conducted for the best-performing architecture. Results: Models trained on breast-mask images consistently outperformed those trained on original DICOM images across all architectures and metrics, with AUROC improvements ranging from +0.06 to +0.21. Among all models, ResNet50 trained on breast-mask images achieved the best performance (AUROC = 0.931; AUPRC = 0.933; balanced accuracy = 0.834), further improved after optimization (balanced accuracy = 0.886; sensitivity = 0.842; specificity = 0.930). Classical CNN architectures demonstrated performance comparable to or exceeding that of more complex hybrid CNN–Transformer models when anatomically focused preprocessing and rigorous optimization were applied. Conclusions: Anatomically constrained preprocessing through breast-mask segmentation substantially enhances deep learning performance and stability in CEM-based breast lesion classification. These findings indicate that input representation quality and training optimization are critical determinants of clinically relevant performance, often outweighing architectural complexity, and may support more reliable AI-assisted decision support in CEM workflows. Full article