Search Results (369)

Technological decoupling, geopolitical tensions, and carbon neutrality pressures have created systemic risks, making supply chain security a global concern. Digital-driven new quality productivity (NQP), as a key driver of supply chain upgrading, plays a crucial role in restructuring modern supply chain systems and enhancing resilience. Based on data from Chinese supply chain data from listed companies (2012–2023), this study integrates enterprise-level NQP and applies complex network methods and the Hadamard product model to analyze how NQP regulates supply chain resilience. The results show that NQP affects network resilience through three nonlinear coupling mechanisms: strengthening defense at fixed points, promoting recovery through chain reinforcement, and enhancing sustainability via network expansion. Its impact is stage-dependent—showing partial vulnerability during early technology diffusion but significantly improving overall resilience at maturity, with structural imbalance remaining a potential risk. This study provides theoretical and practical insights for optimizing supply chain structures and improving risk prevention and collaborative capabilities. Full article

Background/Objectives: The neonatal and infant periods represent a critical window for brain development, characterized by rapid and heterogeneous processes such as myelination and cortical maturation. Accurate assessment of these changes is essential for understanding normative trajectories and detecting early abnormalities. While conventional MRI provides valuable insights, automated classification remains challenging due to overlapping developmental stages and sex-specific variability. Methods: We propose SEPoolConvNeXt, a novel deep learning framework designed for fine-grained classification of neonatal brain development using T1- and T2-weighted MRI sequences. The dataset comprised 29,516 images organized into four subgroups (T1 Male, T1 Female, T2 Male, T2 Female), each stratified into 14 age-based classes (0–10 days to 12 months). The architecture integrates residual connections, grouped convolutions, and channel attention mechanisms, balancing computational efficiency with discriminative power. Model performance was compared with 19 widely used pre-trained CNNs under identical experimental settings. Results: SEPoolConvNeXt consistently achieved test accuracies above 95%, substantially outperforming pre-trained CNN baselines (average ~70.7%). On the T1 Female dataset, early stages achieved near-perfect recognition, with slight declines at 11–12 months due to intra-class variability. The T1 Male dataset reached >98% overall accuracy, with challenges in intermediate months (2–3 and 8–9). The T2 Female dataset yielded accuracies between 99.47% and 100%, including categories with perfect F1-scores, whereas the T2 Male dataset maintained strong but slightly lower performance (>93%), especially in later infancy. Combined evaluations across T1 + T2 Female and T1 Male + Female datasets confirmed robust generalization, with most subgroups exceeding 98–99% accuracy. The results demonstrate that domain-specific architectural design enables superior sensitivity to subtle developmental transitions compared with generic transfer learning approaches. The lightweight nature of SEPoolConvNeXt (~9.4 M parameters) further supports reproducibility and clinical applicability. Conclusions: SEPoolConvNeXt provides a robust, efficient, and biologically aligned framework for neonatal brain maturation assessment. By integrating sex- and age-specific developmental trajectories, the model establishes a strong foundation for AI-assisted neurodevelopmental evaluation and holds promise for clinical translation, particularly in monitoring high-risk groups such as preterm infants. Full article

This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model with seven control variables (including firm systematic risk, asset turnover ratio, and ownership concentration) to quantify ESG’s marginal effect on stock returns. Annual regressions (2017–2022) reveal distinct ESG coefficient shifts: insignificant negative coefficients in 2017–2018, significantly positive coefficients in 2019–2020, and significantly negative coefficients in 2021–2022. Heterogeneity analysis across five non-financial industries (Utilities, Properties, Conglomerates, Industrials, Commerce) shows industry-specific ESG effects. Portfolio performance tests using 2023 data (stocks divided into eight ESG groups) indicate that portfolios with medium ESG scores outperform high/low ESG portfolios and the traditional mean-variance model in risk-adjusted returns (Sharpe ratio) and volatility control, avoiding poor governance risks (low ESG) and excessive ESG resource allocation issues (high ESG). Overall, policy shocks and institutional maturation transformed the market from ESG indifference to ESG-motivated pricing within a decade, offering insights for stakeholders in emerging ESG markets. Full article

2-(Methylthio) benzothiazole (MTBT) is widely used in the industrial and pharmaceutical fields, but limited research has been conducted on its aquatic toxicity. In this study, we established a zebrafish model to systematically evaluate its developmental and functional toxicity, focusing on the cardiovascular systems of larvae. The results showed that MTBT significantly reduced heart rate, caused pericardial edema and deformity, delayed cardiac maturation, decreased stroke volume and cardiac output, and led to vascular structural defects. Mechanistically, MTBT upregulated the expression of the core target PTGS2, activated the apoptotic pathway, and mediated cardiovascular toxicity. This study is the first to systematically confirm the cardiovascular toxicity of MTBT, supplementing its toxicological database and providing a scientific basis for the establishment of environmental safety thresholds and risk management. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Arrhythmias significantly contribute to morbidity and mortality in patients with congenital heart disease (CHD). While postoperative factors predisposing to arrhythmias are well-established, early electrophysiological alterations in pediatric CHD remain poorly understood. This review summarizes current knowledge on postnatal cardiac maturation, conduction-system development, and electrophysiological abnormalities in pediatric patients with and without CHD. Importantly, arrhythmia prevalence, mechanisms, and clinical relevance are systematically discussed across three pediatric groups, including healthy children and patients with unrepaired and repaired CHD. Understanding developmental arrhythmogenic mechanisms may facilitate early risk stratification, guide clinical management decisions, and improve long-term outcomes for pediatric patients with CHD. This review discusses the complex interplay between cardiac maturation, congenital defects, and arrhythmogenesis. It also outlines future directions that include noninvasive monitoring, selective intraoperative mapping, animal model studies, and standardized data collection to improve early risk stratification and long-term outcomes in children with CHD. Full article

This article proposes a scientifically grounded approach to risk assessment for infrastructural and functional systems that underpin the development of digitally transformed regional territories under conditions of high threat dynamics and sociotechnical instability. The core methodology is based on modeling of multifactorial threats through the application of fuzzy set theory and logic–linguistic analysis, enabling consideration of parameter uncertainty, fragmented expert input, and the lack of a unified risk landscape within complex infrastructure environments. A special emphasis is placed on components of technogenic, informational, and mobile infrastructure that ensure regional viability across planning, response, and recovery phases. The results confirm the relevance of the approach for assessing infrastructure resilience risks in regional spatial–functional systems, which demonstrates the potential integration into sustainable development strategies at the level of regional governance, cross-sectoral planning, and cultural reevaluation of the role of analytics as an ethically grounded practice for cultivating trust, transparency, and professional maturity. Full article

Accurate prediction of crop phenological stages is essential for effective crop management. Such a prediction provides the timing of phenological stages, thus aiding in scheduling management practices, understanding the potential risks of adverse weather at critical phenological stages, and adjusting sowing dates. Temperature is the dominant climatic factor affecting maize (Zea mays L.) development, with photoperiod serving as a secondary influence. This study used maize field data with recorded flowering and maturity dates to evaluate the stability of phenological stage predictions obtained using the calendar days method, thermal functions, and photothermal functions. These methods were used to calculate the number of days, accumulated temperature, and accumulated photothermal units from sowing to flowering and from flowering to maturity. Results showed that thermal functions produced the most stable predictions, with the lowest average coefficient of variation (CV) being 8.37%. The thermal functions were further categorized as empirical linear, empirical nonlinear, and process-based. Within each category, the functions with the lowest average CVs were growing degree days (GDD_8,34; 9.12%), thermal leaf unit (GTI; 7.74%), and agricultural production system simulator (APSIM; 8.26%), respectively. Among them, GTI had the lowest CV, indicating its superior stability in predicting maize phenological stages. These results provide a basis for selecting thermal models in maize phenology research and can support improved decision-making in crop scheduling and management. Full article

As deepfake technology matures, its risks in spreading false information and threatening personal and societal security are escalating. Despite significant accuracy improvements in existing detection models, their inherent opacity limits their practical application in high-risk areas such as forensic investigations and news verification. To address this gap in trust, explainability has become a key research focus. This paper provides a systematic review of explainable deepfake detection methods, categorizing them into three main approaches: forensic analysis, which identifies physical or algorithmic manipulation traces; model-centric methods, which enhance transparency through post hoc explanations or pre-designed processes; and multimodal and natural language explanations, which translate results into human-understandable reports. The paper also examines evaluation frameworks, datasets, and current challenges, underscoring the necessity for trustworthy, reliable, and interpretable detection technologies in combating digital misinformation. Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. Full article

Carbon dioxide (CO₂) capture using alkanolamines remains the most mature technology, yet faces challenges including solvent loss, high regeneration energy and equipment corrosion. Ionic liquids (ILs) are proposed as alternatives, but their high viscosity and production costs hinder industrial use. Thus, blending ILs with amines offers a promising approach. This work presents new experimental data for aqueous blends of 1-butyl-3-methylimidazolium hydrogen sulfate, $\left[\mathrm{B}\mathrm{m}\mathrm{i}{\mathrm{m}}^{+}\right]\left[\mathrm{H}\mathrm{S}{\mathrm{O}}_4^{-}\right],$ with 2-amino-2-methyl-1-propanol (AMP) and 3-(methylamino)propylamine (MAPA) and for choline glycine, $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, with AMP, modeled using the modified Kent–Eisenberg approach. It was shown that substituting a portion of the amine with $\left[\mathrm{B}\mathrm{m}\mathrm{i}{\mathrm{m}}^{+}\right]\left[\mathrm{H}\mathrm{S}{\mathrm{O}}_4^{-}\right]$ reduces CO₂ uptake per mole of amine due to the lower solution’s basicity, despite the added sites for physical absorption. In contrast, the replacement of an amine portion with $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$ enhances both physical and chemical interactions, leading to increased CO₂ solubility per mole of amine. Finally, replacing a small portion of water with $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$ does not significantly alter the bulk CO₂ solubility (moles of CO₂ per kg of solvent) but lowers the solvent’s vapor pressure. Given the non-toxic nature of $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, the resulting solvent poses no added environmental risk. Model predictions agree well with experimental data (deviations of 2.0–11.6%) and indicate low unreacted amine content at CO₂ partial pressures of 1–10 kPa for carbamate-forming amines, i.e., $\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, and MAPA. Consequently, at higher CO₂ partial pressures, the solubility increases due to carbamate hydrolysis and molecular CO₂ dissolution. 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

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

Anterior cruciate ligament (ACL) injuries in skeletally immature patients pose unique clinical and surgical challenges due to the presence of open physes and ongoing growth. In recent years, multiple surgical strategies have been developed to restore knee stability while minimizing the risk of growth disturbances. However, clinical decision-making remains complex due to the lack of consensus regarding the optimal timing, technique, and graft selection for this population. This narrative review outlines the current clinical and radiological tools used to assess skeletal maturity and explores how maturity status informs surgical approach, with particular emphasis on physeal-sparing, hybrid, and transphyseal techniques. We summarize postoperative complications—including growth disturbances and graft failure—while highlighting current guideline recommendations and ongoing controversies. Lastly, we propose a multimodal model for skeletal maturity assessment to support individualized treatment strategies and emphasize the need for standardized protocols and high-quality research to improve long-term outcomes in pediatric ACL reconstruction. Full article

This review presents a critical analysis of Japan’s hydrogen strategy, focusing on the broader context of its decarbonization efforts. Japan aims to achieve carbon neutrality by 2050, with intermediate targets including 3 million tons of hydrogen use by 2030 and 20 million tons by 2050. Unlike countries with abundant domestic renewables, Japan’s approach emphasizes hydrogen imports and advanced storage technologies, driven by limited local renewable capacity. This review not only synthesizes policy and project-level developments but also critically evaluates Japan’s hydrogen roadmap by examining its alignment with global trends, technology maturity, and infrastructure scalability. The review integrates recent policy updates, infrastructure developments, and pilot project results, providing insights into value chain modeling, cost reduction strategies, and demand forecasting. Three policy conclusions emerge. First, Japan’s geography justifies an import-reliant pathway, but it heightens exposure to price, standards, and supply-chain risk; diversification across LH₂ and ammonia with robust certification and offtake mechanisms is essential. Second, near-term deployment is most credible in industrial feedstocks (steel, ammonia, methanol) and the maritime sector, while refueling rollout lags materially behind plan and should be recalibrated. Third, cost competitiveness hinges less on electrolyzer CAPEX than on electricity price, liquefaction, transport; policy should prioritize bankable offtake, grid-connected renewables and transmission, and targeted CAPEX support for import terminals, bunkering, and cracking. Japan’s experience offers a pathway in the global hydrogen transition, particularly for countries facing similar geographic and energy limitations. By analyzing both the progress and the limitations of Japan’s hydrogen roadmap, this study contributes to understanding diverse national strategies in the rapidly changing state of implementation of clean energy. Full article