Search Results (1,696)

This study investigates the multifaceted impact of digitalization on economic performance across the 27 European Union member states from 2017 to 2023. Using a comprehensive panel dataset, the analysis moves beyond aggregate metrics to dissect how specific digital levers contribute to trade performance and national income. A two-way fixed effects (FEs) regression model is employed to rigorously control for unobserved country-specific heterogeneity and common time-based shocks, with diagnostic tests confirming the suitability of this specification. The results reveal a complex and often counter-intuitive set of relationships. One key finding is a statistically significant negative association between the EU’s headline Digital Economy and Society Index (DESI) and goods exports, a paradox that emerges in the model once specific business-level digital tools are accounted for. This suggests that composite indices can be misleading for granular policy analysis. The marginal benefit of cloud adoption diminishes significantly in countries with higher levels of public investment in Research and Development (R&D), indicating a substitution rather than a complementary relationship between these two innovation channels. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

This study examines the establishment of China’s national supercomputing centers as an exogenous policy shock. Utilizing data from Chinese manufacturing enterprises listed between 2003 and 2023, it applies a multi-period difference-in-differences (DID) model to assess the impact of computing infrastructure on innovation within Chinese manufacturing enterprises. Results indicate that computing infrastructure significantly enhances manufacturing innovation, a finding that is robust across various tests. This effect is positively moderated by the internal R&D investment of enterprises and the external market share. Heterogeneity analysis reveals that the enhancement effect of computing infrastructure on innovation is more pronounced in non-state-owned enterprises, those located in the eastern region, and those with low ownership concentration. Furthermore, computing infrastructure not only boosts the quantity of innovation but also enhances its quality. This paper offers micro-level evidence for emerging countries to advance sustainable development, transformation, and upgrading of the manufacturing sector through computing infrastructure. Full article

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

In metal fused deposition modeling (FDM), performance is governed by feedstock formulation, most critically the metal solid loading, while binder selection is constrained by environmental impacts and limited recyclability. This study investigates the development and performance of highly filled 17-4PH stainless steel (17-4PH) feedstocks formulated with a low-molecular-weight polymer binder system, specifically designed for FDM in metal additive manufacturing (AM). The binder system, composed of low-cost, recyclable paraffin wax and stearic acid, was used to prepare feedstocks containing 93.0–96.0 wt.% metal powder. Rheological analysis indicated that intermediate powder loadings (95.0–95.5 wt.%) yielded optimal shear-thinning behavior, essential for stable extrusion during printing. Printing trials identified 95.5 wt.% as the critical powder loading, delivering superior print fidelity and structural integrity relative to both under-filled (93.0–94.5 wt.%) and overfilled formulations. Green part characterization revealed increased density and flexural modulus with rising powder content, while thermal debinding and sintering trials indicated enhanced thermal stability and dimensional retention at higher loadings. The as-sintered specimens from the 95.5 wt.% feedstock achieved a relative density (RD) of 96.5% and significantly improved mechanical performance, including an ultimate tensile strength (UTS) of 758 MPa and 5.2% elongation, clearly outperforming the 95.0 wt.% variant. Tribocorrosion testing further validated these improvements, with the higher-density samples showing a lower coefficient of friction and a reduced wear coefficient of 2.1 × 10⁻⁵ mm³·(N·m)⁻¹ in 3.5% NaCl solution. Full article

The fast-paced lifestyle of modern people has changed their dietary structure and increased the prevalence of obesity, of which a high-fat diet is the main cause. Therefore, this study investigates whether reducing fat intake can improve obesity and physical health. We induced an obese model with a 60 kcal% fat diet (HFD) for 12 weeks, followed by an intervention with a 4.9 kcal% fat diet (regular chow diet, RD) for 20 weeks. We found that after 20 weeks of RD, various indicators were significantly reduced compared with the HFD group, including dietary intake (3.26 ± 0.38 g, p < 0.01), Lee index (385.24 ± 14.22, p < 0.0001), blood glucose (8.75 ± 2.44 mmol/L, p < 0.01), blood lipids (TC: 2.60 ± 0.63 mmol/L, p < 0.001; TG: 0.72 ± 0.08 mmol/L, p < 0.001; and LDL-C: 0.57 ± 0.30 mmol/L, p < 0.0001), and inflammatory status (IL-6: 32.70 ± 7.55 pg/mL, p < 0.05). In addition, increasing dietary intake also indirectly increased fiber intake, which could promote intestinal microbiota diversity. Changing the diet of obese mice from HFD to RD still maintained the abundance of the probiotics Akkermansia, Parabacteroides, Alloprevotella, and Porphyromonadaceae, among which fiber intake played an important role. Therefore, we found that only reducing dietary fat intake was effective for weight loss, and dietary fiber intake helped maintain a healthy intestinal microbiota balance. Full article

At present, although there are a variety of assessment systems to rate the financing ability of enterprises, these systems suffer from the problems of outdated indicators and subjective weighting methods. In this paper, the impact of ESG performance on financing ability is taken as an evaluation index and combined with 13 other indexes to construct a new TOPSIS assessment system. Cooperative game theory in the form of the entropy weight method and a BP neural network is used to avoid the subjectivity of weighting. After establishing the evaluation model, we selected cross-sectional data from 4590 listed companies on the Shanghai and Shenzhen stock exchanges in 2023 to train the evaluation model and explore the impact of various indicators on financing capabilities. The results show the following: (1) Total revenue and total assets of main board companies are the main factors affecting financing ability. (2) Total revenue growth rate, total revenue, and R&D costs of Science and Technology Innovation Board Market (STAR Market) companies are the main factors affecting the financing ability. (3) Growth Enterprise Market (GEM) companies’ total revenue and R&D costs are the main factors affecting financing ability. This study uses data from 2023. In practical applications, it is recommended to use the latest data for evaluation and analysis, and to update the weights every six months. Full article

Objective: We aimed to determine the incidence and screening outcomes of retinopathy of prematurity (ROP) in preterm infants managed at a tertiary neonatal intensive care unit (NICU) and to identify associated risk factors. Material and Methods: Medical records of 454 premature infants who underwent ROP screening between April 2016 and August 2021 were retrospectively analyzed. Infants with birth weight (BW) ≤ 1500 g or ≤32 weeks of gestational age and those with BW > 1500 g or GA > 32 weeks who had an unstable clinical course were included. All of them were born in the same center. Demographic characteristics, potential risk factors for ROP, ocular examination findings, and treatment requirement were recorded. Results: During the five-year study period, ROP was observed in 75 (16.6%) of a total of 454 premature infants with a mean gestational age (GA) of 30.19 ± 2.49 weeks and a mean BW of 2025.15 ± 614.46 g in the NICU. Of these patients, 67 (14.8%) had stage I disease and 8 (1.8%) had stage II disease. Advanced-stage ROP was not detected in any of the cases. The median GA of patients diagnosed with ROP was 29 weeks (22–35) and the median BW was 2100 g (500–3750), which were significantly lower than those without ROP (p < 0.001). When multivariate logistic regression analysis was evaluated with the Wald method, the accuracy rate of the model examining the combined effect of GA, intraventricular hemorrhage (IVH), respiratory distress syndrome (RDS), patent ductus arteriosus (PDA), necrotizing enterocolitis (NEC), and surfactant treatment was 85.9%. In this model, gestational age (OR: 0.712, p < 0.001), IVH (OR: 2.915, p = 0.010), RDS (OR: 2.129, p = 0.004), NEC (OR: 3.679, p < 0.001), PDA (OR: 2.434, p = 0.021), and surfactant treatment (OR: 2.271, p = 0.002) were found to be independent risk factors for ROP development. Conclusions: Small GA and low BW are the main risk factors for the development of ROP. The incidence of ROP was found to be lower than similar studies conducted in our country. While severe ROP cases have been reported in more mature infants in Turkey, our study found no treatment-requiring ROP cases, likely reflecting the higher mean GA and BW characteristics of our cohort. Full article

Purpose: The construction industry is now experiencing a thorough transformation through digital technologies, especially with building information modeling (BIM). Despite significant BIM advantages, most construction projects suffer from low BIM performance due to the fragmented BIM use mode. To facilitate lifecycle-integrated BIM implementation, this study demonstrates that introducing BIM lifecycle service providers (BLSPs) is feasible and offers significant improvements in terms of BIM benefits. Hence, this study proposes a customized framework to select BLSPs. Approach: This study utilized both qualitative and quantitative methods. It first adopted semi-structured interviews as part of the qualitative method to deduce the initial criteria for BLSPs’ selection. 30 interviews were conducted iteratively with managers proficient and experienced in selecting BLSPs, through which 25 initial criteria were identified. Then, as the basis of the applied quantitative method, a questionnaire survey was used to evaluate these criteria by determining the critical ones, identifying the latent factor groupings, and assigning criteria weights. Subsequently, an assessment framework was established. Finally, the study was in favor of eight construction projects, highlighting the practicality and validity of the framework. Findings: The results depicted that project BIM service capability is a primary factor for BLSPs’ selection. Within this factor, several specialized criteria need to be considered, such as “boundary spanning competence of the BIM manager” and “BIM service plans with lifecycle cognition.” Meanwhile, “past innovative BIM service practices” and “BIM research and development (R&D)” that originate in corporate innovation capacity were emphasized when selecting BLSPs. Furthermore, for holistic assessment and recognizing the peculiarities of digital BIM service, the study found that criteria like “Privacy and security” and “Backup system” are required, which demonstrate BIM service reliability. Originality/value: This study expands on the conventional partner selection frameworks in the construction sector and thus defines and validates a tailored one for BLSPs’ selection. Moreover, drawing such a reference solution from the framework, the study enables the selection of appropriate BLSPs for clients. Full article

The urban thermal environment poses a significant challenge to public health and sustainable urban development. Conventional pre-defined classification schemes, such as the Local Climate Zone (LCZ) system, often fail to capture the highly heterogeneous structure of complex urban areas, thus limiting their applicability. This study introduces a novel framework for urban thermal environment analysis, leveraging multi-source data and eXplainable Artificial Intelligence to investigate the driving mechanisms of Land Surface Temperature (LST) across various urban form types. Focusing on the area within Beijing’s 5th Ring Road, this study employs a K-Means clustering algorithm to classify urban blocks into nine distinct types based on their building morphology. Subsequently, an eXtreme Gradient Boosting (XGBoost) model, coupled with the SHapley Additive exPlanations (SHAP) method, is utilized to analyze the non-linear impacts of ten selected driving factors on LST. The findings reveal that: (1) The Compact Mid-rise type exhibits the highest annual average LST at 296.59 K, with a substantial difference of 11.29 K observed between the hottest and coldest block types. (2) SHAP analysis identifies the Normalized Difference Built-up Index (NDBI) as the most significant warming factor across all types, while the Sky View Factor (SVF) plays a crucial cooling role in high-rise areas. Conversely, road density (RD) shows a negative correlation with LST in Open Low-rise areas. (3) The influence of urban form is twofold: increased building height (BH) can induce warming by trapping heat while simultaneously providing a cooling effect through shading. (4) The impact of land use functional zones on LST is significantly modulated by urban form, with temperature differences of up to 2 K observed between different functional zones within compact block types. The analytical framework proposed herein holds significant theoretical and practical implications for achieving fine-grained thermal environment governance and fostering sustainable development in the context of global urbanization. Full article

Metabolic illnesses such as obesity, type 2 diabetes and hyperuricemia are becoming more common, driving intensified research into nutritional interventions through targeted dietary modifications as a primary preventive strategy. The apparent fluctuation in blood glucose value is modulated by the digestive behavior of starch. Moreover, polyphenols—historically considered to be anti-nutrients due to their inhibition of digestive enzymes and sometimes astringent taste—can be used to significantly improve the functional properties of starch. This can be achieved primarily through α-amylase inhibition and the modulation of other enzyme activities, alongside the antioxidant and anti-inflammatory effects of polyphenols. Depending on their fine structure, starches are digested at different rates: rapidly digestible starch (RDS) spikes blood glucose; slowly digestible starch (SDS) smooths postprandial blood glucose peaks; resistant starch (RS) feeds gut microbes. The fine structure of starches, such as straight-chain starches, can form complexes with polyphenols through their ‘empty V-type’ structures under controlled processing conditions. Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and in vitro digestion modeling analyses have revealed that the formation of starch–polyphenol complexes primarily occurs due to certain interactions (hydrophobic interactions and hydrogen bonding) which lead to stabilized structures, including V-type encapsulation; this significantly increases the content of RSs and slows down enzymatic digestion rates. These complexes lower the GI values of foods via molecular barrier effects, while synergistically boosting antioxidant and anti-inflammatory activities; their anti-digestive capabilities were found to be superior even to those of ordinary starch–lipid compounds. However, limitations persist in the research and application of starch–polyphenol complexes: human bioavailability validation; incomplete mechanistic understanding of multicomponent interactions; industrial scalability challenges due to polyphenol instability. Full article

Resina Draconis (RD), a traditional Chinese medicine, has been widely used in treating diabetic foot ulcers. However, its specific mechanisms of action remain incompletely understood. First, network pharmacology combined with GEO clinical sample data mining was employed to systematically analyze the therapeutic targets of RD in promoting diabetic wound healing. Second, an AGEs-induced RAW264.7 cell model was utilized to investigate the regulatory effects of RD and its primary active components on the AGE-RAGE signaling pathway, along with their anti-inflammatory and antioxidant activities. Finally, a diabetic wound mouse model was established to validate the efficacy of RD and further explore its underlying molecular mechanisms. Integrated analysis of network pharmacology and GEO database mining identified 492 potential therapeutic targets of RD in diabetic wound healing, primarily involving the AGE-RAGE pathway. In vitro, RD (6.25 μg/mL) significantly suppressed AGE-induced inflammatory factors and ROS production while downregulating AGE-triggered RAGE protein overexpression. In vivo, RD hydrogel accelerated diabetic wound healing by modulating the AGE-RAGE axis and regulating macrophage polarization. RD effectively promotes diabetic wound healing through synergistic regulation of the AGE-RAGE pathway, oxidative stress suppression, and macrophage polarization modulation, providing a novel therapeutic strategy for diabetic wound management. Full article

Astrocyte-derived extracellular vesicles (ADEVs) have emerged as promising neuroprotective agents for ischemic stroke. In this study, we evaluated the therapeutic potential of hypoxia-conditioned ADEVs (HxEVs) administered intracerebroventricularly in a rat model of transient middle cerebral artery occlusion (tMCAO). Serial magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI) was performed at 1, 7, 14, and 21 days post-stroke. HxEV treatment produced a significant reduction in infarct volume from day 1, sustained through day 21, and was accompanied by improvements in motor and sensory recovery. DTI analyses showed progressive normalization of fractional anisotropy (FA) and radial diffusivity (RD), particularly in the corpus callosum and striatum, reflecting microstructural repair. In contrast, mean diffusivity (MD) was less sensitive to these treatment effects. Regional differences in therapeutic response were evident, with earlier and more sustained recovery in the corpus callosum than in other brain regions. Histological findings confirmed greater preservation of dendrites and axons in HxEV-treated animals, supporting the role of these vesicles in accelerating post-stroke neurorepair. Together, these results demonstrate that hypoxia-conditioned ADEVs promote both structural and functional recovery after ischemic stroke. They also highlight the value of DTI-derived biomarkers as non-invasive tools to monitor neurorepair. The identification of region-specific therapeutic effects and the validation of reliable imaging markers provide a strong foundation for future research and development. Full article

High-density communities, characterized by concentrated populations and compact built environments, often exacerbate issues such as green space fragmentation, uneven distribution, and intensified urban heat island effects. Investigating the spatiotemporal heterogeneity and evolutionary characteristics of landscape patterns driven by population density (POP), road density (RD), street-level GDP (GDP_S), and nighttime light intensity (NTL) in Wuhan’s high-density communities using a geographically weighted regression (GWR) model is essential for informing sustainable urban planning strategies. The results showed that ED, PD, and SHDI exhibit consistent annual declines averaging 1.53%, 0.97% and 0.59%, respectively, while AI increased steadily at 0.11% per year. This indicates that human intervention has surpassed natural succession and become the dominant force in shaping landscape patterns. Among them, POP and RD are the direct driving factors for landscape pattern changes, while GDP_S and NTL indirectly affect landscape patterns through economic structural adjustments and land use changes, forming differentiated spatial patterns in high-density communities. In terms of relationships, the GWR model performs better than ordinary least squares regression (OLS) by adjusting R² and residual Moran’s I, significantly improving its explanatory power. This study demonstrates the effectiveness of the GWR model in revealing the spatiotemporal heterogeneity between socioeconomic factors and landscape patterns, providing a transferable analytical framework for high-density cities. It thereby offers empirical and methodological support for addressing regional ecological constraints and advancing sustainable urban renewal. Full article

This paper investigates the use of reinforcement learning (RL) algorithms to learn adaptive hedging strategies for derivatives under realistic market conditions, incorporating permanent market impact, execution slippage, and transaction costs. Market frictions arising from trading have been explored in the optimal trade execution literature; however, their influence on derivative hedging strategies remains comparatively understudied within RL contexts. Traditional hedging methods have typically assumed frictionless markets with only transaction costs. We illustrate that the dynamic decision problem posed by hedging with frictions can be modelled effectively with RL, demonstrating efficacy across various market frictions to minimize hedging losses. The results include a comparative analysis of the performance of three RL models across simulated price paths, demonstrating their varying effectiveness and adaptability in these friction-intensive environments. We find that RL agents, specifically TD3 and SAC, can outperform traditional delta hedging strategies in both simplistic and complex, illiquid environments highlighted by 2/3rd reductions in expected hedging losses and over 50% reductions in 5th percentile conditional value at risk (CVaR). These findings demonstrate that DRL agents can serve as a valuable risk management tool for financial institutions, especially given their adaptability to different market conditions and securities. Full article