Search Results (4,674)

Clarifying the complex interaction between climate risks and the new energy industry chain is of key significance to advancing the energy transition and strengthening industrial chain robustness. This research pairwise-matches the climate physical risk and the climate transition risk with the entire range of the new energy industry chain segments, comprehensively examining the pairwise interactive relationships. By applying the MF-ADCCA series of methods, it was revealed that there are prevalent asymmetric cross-correlated multifractal characteristics between climate risks and the new energy industry. The long-term memory under the upward trend of the market is distinctly stronger than that under the downward trend. Given that this correlation can indirectly reflect market efficiency differences, this paper constructs the Hurst Volatility Sensitivity Index (HVI) and the Hurst Asymmetry Index (HAI) and further proposes the Unified Market Efficiency Index (UMEI). Its innovative advantage resides in the balanced integration of volatility efficiency and structural symmetry, in turn enabling a comprehensive assessment of the new energy market efficiency under climate risk perturbations. Static analysis reveals that the overall market efficiency of the new energy industry under the climate transition risk is generally higher than that under the climate physical risk, and the market efficiency of mature upstream and midstream new energy segments is significantly superior to that of the downstream. Dynamic evolution characteristics indicate that market efficiency has typical time-varying traits, the evolution of which is often driven by significant policies or extreme events. The climate transition risk tends to trigger aperiodic structural adjustments, while the climate physical risk mostly induces periodic efficiency fluctuations. This study furnishes solid evidence for the new energy market in coping with climate risks. Full article

The maturity of cannabis flowers at harvest critically influences cannabinoid yield and product quality. However, conventional assessment methods rely on subjective visual inspection of trichomes and stigmas, making them inherently inconsistent. This research presents an automated framework integrating computer vision and deep learning to objectively evaluate cannabis flower maturity. High-resolution macro images were acquired using low-cost smartphone-based systems under greenhouse and laboratory conditions. A two-stage pipeline was implemented: a fine-tuned Faster R-CNN model detected trichomes (Precision: 0.815; Recall: 0.802), while a YOLOv8 classifier categorized them into clear, milky, or amber classes (Accuracy: 98.6%). In parallel, a YOLOv8 segmentation model delineated stigmas (AP50: 52.2%) to compute color ratios as maturity indicators. Features were aggregated at the flower level and correlated with HPLC-measured cannabinoid concentrations. A dataset of over 14,000 images was collected across multiple imaging sessions to support training, evaluation, and correlation experiments. Results demonstrated that stigma coloration—detectable with low-end devices—provides a robust visual indicator of peak chemical maturity, with the green-to-orange transition aligning with maximum cannabinoid concentration. This work offers a scalable, cost-effective solution for real-time maturity assessment in cannabis cultivation, contributing to improved harvest timing and quality control. Full article

(1) Background: Eating disorder risk factors in children are early maturation, body dissatisfaction, dieting, stress and physical inactivity. The COVID-19 pandemic has further exacerbated these factors due to isolation, online classes and reduced physical activity, all of which have increased children’s risk of developing eating disorders. The aim of the research was to examine the frequency of eating disorders among school-aged children in the Republic of Serbia during the COVID-19 pandemic, as well as the association of these disorders with socio-demographic characteristics, lifestyle habits, and levels of depression, anxiety and stress. (2) Methods: The research was conducted as a descriptive cross-sectional study on a sample of students from the fifth grade of elementary school to the fourth year of secondary school. The research was conducted from May to August in 2023. using the EAT-26 questionnaire. Before the research, the approval of the ethics committee of the Jagodina Health Center (No. 1238/28.04.2023.) was obtained, where the research was conducted. (3) Results: The results show that 5.8% of students exhibited eating disorder symptoms during the COVID-19 pandemic (EAT-26 ≥ 20). Statistically significant differences were observed in girls with an eating disorder, who had a significantly lower body weight compared to the others (p < 0.05). Students with symptoms of depression, anxiety and stress showed eating disorders significantly more often. Also, elementary school students and boys with an eating disorder visited a nutritionist and played sports more often. (4) Conclusions: Research has shown that during the COVID-19 pandemic, a smaller percentage of students showed symptoms of eating disorders, with girls being more sensitive. Disorders were significantly associated with the presence of depression, anxiety and stress. The obtained results indicate the importance of monitoring children’s psychological and nutritional health, as well as the need for preventive and intervention measures in crisis conditions. Full article

Visual harvest maturity is a key visual phenotype for orchard management and harvesting decisions, yet chestnut fruits in natural orchards often exhibit weak color contrast, subtle texture variation, blurred boundaries, and frequent occlusion under complex illumination. This study addresses RGB-based visual harvest maturity recognition and proposes AHM-YOLO, an improved instance segmentation model built upon YOLOv11n-seg. The proposed model enhances maturity-related feature representation by strengthening color- and edge-sensitive cues, stabilizing spatial dependencies under occlusion and illumination variation, and improving cross-scale semantic consistency in dense orchard scenes. A chestnut dataset collected from a typical orchard in Shandong Province is annotated into three visual harvest maturity stages (unripe, semi-ripe, and ripe). To ensure reliable evaluation, the dataset is partitioned at the acquisition unit level, and all experiments are conducted using multi-seed repeated runs. Experimental results show that AHM-YOLO achieves 84.3% Mask mAP50 and 72.2% Mask mAP50–95, demonstrating consistent improvements over the baseline model in complex orchard environments. Full article

The scientific activity and outputs of the neurosciences are fascinating and, for the most part, uncontroversial. However, there have been sustained claims that neuroscientific findings represent a powerful challenge to historic, orthodox Christian teaching concerning human ontology. While philosophers had long debated the “mind/brain” problem, the rise of “eliminative materialism” (in the specific form of “neurophilosophy”) in the last quarter of the 20th century evoked various responses to the proposition that a mature neuroscience would forever banish familiar “folk science” entities like beliefs and desires as well as immaterial souls or minds. These would all be shown to play no role in a thoroughly materialistic, mature, neuroscientific understanding of ourselves. One aspect of the response to such claims within Christian scholarship was a turn to non-reductive physicalism and theological monism prompting a reassessment of biblical teaching concerning human ontology, seeking a position that would be consistent both with neuroscience (or its alleged implications) and Christian teaching. The aim of this paper is to review neuroscientific, philosophical and theological developments in order to establish where theological anthropology currently stands. In part this requires an assessment of contemporary neuroscience (including the subfield of “consciousness studies”) because while the science continues to generate intriguing hypotheses and data, it has fallen some way short of the eliminative materialists’ hopes of forty years ago. Additionally, important methodological criticisms of the science have emerged concerning such issues as reproducibility and participant selection. This may have contributed to the twenty-first century resurgence of interest in the sort of dualism long a key component of theological orthodoxy, as well as highlighting the need for a reassertion of theological values, methods and perspectives. The apparent non-elimination of theology indicates a need to rebalance theological and neuroscientific perspectives in developing our understanding of the person. Full article

Graphene-based sensors and biosensors are attractive candidates for chemical and biological threat detection due to their high surface sensitivity, rapid transduction, and low-power operation, yet real-world deployment remains constrained by cross-sensitivity, interface instability in biosensing, and limited validation under operational conditions. This review consolidates key requirements for Chemical, Biological, Radiological, and Nuclear (CBRN) detection and proposes a structured roadmap to guide the transition from laboratory demonstrations to field-relevant sensing systems. The roadmap is explicitly modular and non-linear, integrating (i) qualitative research planning and gap analysis, (ii) computational screening via molecular docking as a hypothesis-generation tool with well-defined limitations, (iii) graphene electrode fabrication and functionalization using pulsed laser deposition (PLD) to enable tunable thickness/defect engineering and strong interface control, (iv) multiscale characterization combining laboratory methods with in situ/portable diagnostics, and (v) field-oriented performance evaluation focused on response time, stability, selectivity against industrial interferents, and false-positive/false-negative behavior. Iterative feedback loops connect all modules, enabling progressive refinement of material processing, recognition chemistry, and device architecture. By framing success in terms of technology-maturity progression and operational metrics, this roadmap provides a practical, defense-relevant framework for developing deployable graphene-based CBRN sensing platforms. Full article

Background: Oxidative stress (OS) plays an important role in oral lichen planus (OLP) development; however, the precise functions of the genes associated with OS (OSRGs) remain unclear. This study aimed to identify and characterize OS-linked molecular markers in OLP. Methods: Data were obtained from the GSE38616 and GSE211630 datasets, along with 467 OSRGs. Candidate genes were identified by cross-referencing differentially expressed genes with OSRGs. Biomarkers were then selected through a protein–protein interaction network analysis using Cytoscape. Functional enrichment analysis, regulatory network mapping, therapeutic compound prediction, molecular docking simulations, and RNA modification profiling were also performed. Single-cell RNA sequencing was used to characterize biomarker distribution among the distinct cell populations. Gene expression was validated using quantitative real-time PCR (qRT-PCR). Results: Five genes emerged as key biomarkers: TGFB1, KLF4, TNF, NQO1, and MMP9. Functional enrichment analysis revealed that these markers are involved in immune regulatory pathways between lymphoid and nonlymphoid cellular compartments. Network analysis identified hsa-miR-449a and hsa-miR-449b-5p as potential regulators of NQO1 and KLF4. Pharmaceutical screening identified several potential therapeutic compounds, such as meropenem anhydrous and hydroxyurea, which exhibit targeted binding affinity for key biomarkers. Docking simulations indicated robust binding interactions (binding energies < −5 kcal/mol) for most compound–biomarker combinations, excluding the KLF4–hydroxyurea pairing. In addition, putative m6A methylation sites were identified in the TNF, KLF4, and TGFB1 transcripts. Single-cell analysis identified T lymphocytes as the primary cell type of interest, with TGFB1 expression increasing progressively during T-cell maturation. Validation by qRT-PCR confirmed the transcriptomic results, demonstrating elevated expression of TGFB1, TNF, and MMP9, along with reduced NQO1 expression in OLP tissues. Conclusions: TGFB1, KLF4, TNF, NQO1, and MMP9 were identified as potential OS-associated biomarkers in OLP. These findings provide insights into disease mechanisms and reveal potential therapeutic targets. Full article

Post-quantum cryptography (PQC) is, and should be, currently dominating the field of cybersecurity, with many works designing and evaluating the transition of communications security to quantum-safe solutions. As the security level and implementations of post-quantum algorithms become more mature, the research on their application to realistic conditions changes accordingly, especially their application to widely adopted network architectures and corresponding protocols such as the Public Key Infrastructure (PKI). In this survey, we identified articles presenting ways of integrating PQC algorithms to PKI and classified related work according to the employed methods and benchmarking choices. The main results from many evaluations converge to similar conclusions on the performance of the most popular PC digital signature algorithms; however, modeling choices concerning architecture variants, hardware and measurement metrics vary. The diversity of the results and experimental setups makes comparison difficult and arrival at an objective conclusion regarding PKI requirements almost impossible. Ultimately, this review reveals a fragmented landscape of benchmarking practices for post-quantum PKI systems. The absence of standardized evaluation frameworks and common test environments limits the comparability and reproducibility of the findings. We aim to provide reference implementations, which are essential to guide the transition of PKI infrastructures toward robust, scalable, and quantum-resistant deployments. Full article

Multi-level models are core artifacts of Model-Based Systems Engineering (MBSE) for cross-disciplinary collaboration and staged evolution, yet assessing their maturity in the conceptual design phase remains difficult. This paper proposes a systematic, model-centric maturity assessment method for instrumentation conceptual design. By tailoring ISO/IEC 25010 to instrumentation characteristics, we establish a seven-dimensional quality attribute framework (functional suitability, performance efficiency, interaction capability, reliability, maintainability, flexibility, and structural completeness) and an L0–L4 maturity scale for multi-level MBSE models. The indicators are structured using a Quality Attribute Utility Tree. CRITIC derives the objective weights by jointly considering the score dispersion and inter-indicator correlation, and Dempster–Shafer evidence theory is used to map the indicator values and expert ratings onto basic belief assignments and fuses the multi-source evidence to the output maturity levels with explicit confidence and uncertainty. A case study of an automatic dosing instrument for solid foam drainage agents at a high-pressure gas wellhead yields an overall maturity of $L_1$ (Structured), with $BetP\left(L_1\right)= 0$.424, and an overall unknown mass of 0.186. The results highlight reliability and performance efficiency as the main bottlenecks and support targeted model refinement and resource allocation in early-stage design. Full article

The paper focuses on materials characterization and in vivo biocompatibility tests of Ti–6Al–7Nb–0.3REE wt.% alloys (REEs—Y, Ce, La) for use as a promising material to produce personalized medical implants and shed light on possible toxicity effects of REE alloy microdoping. All alloys were produced by the electric arc melting method and characterized by scanning electron microscopy (SEM), optical microscopy (OM), energy-dispersive X-ray spectroscopy analysis (EDX), X-ray diffraction (XRD), true density analysis, micro- and nanoindentation methods, and reducing/oxidation melting techniques. True density of alloys increased in the following order: Ti−6Al−7Nb−0.3Y (4.4563 ± 0.1075 g/cm³) < Ti−6Al−7Nb−0.3Ce (4.7255 ± 0.2853 g/cm³) < Ti−6Al−7Nb−0.3La (4.8019 ± 0.0111 g/cm³). XRD analysis indicated that Ti–6Al–7Nb–0.3Y alloy consisted of single α–Ti phase in comparison with Ti–6Al–7Nb–0.3La (α–Ti to β–Ti = 82 to 18) and Ti–6Al–7Nb–0.3Ce (α–Ti to β–Ti = 90.5 to 9.5). The single-phase Ti–6Al–7Nb–0.3Y alloy had the finest α–Ti phase crystallites (22.32 nm); the larger α–Ti crystallites in the dual-phase Ti–6Al–7Nb–0.3Ce and Ti–6Al–7Nb–0.3La (30.77 nm and 29.83 nm, respectively) suggested the presence of the β–Ti phase (23.34 nm and 25.61 nm, respectively). REE microdoping of alloys changed the lattice volume (∆V): α–Ti phase—0.269% for Ti–6Al–7Nb–0.3Y, 1.799% for Ti–6Al–7Nb–0.3Ce, 0.595% for Ti–6Al–7Nb–0.3La; and β–Ti phase—0.334% for Ti–6Al–7Nb–0.3Ce, 0.670% for Ti–6Al–7Nb–0.3La. Nanohardness (H) and elastic modulus (E) increased in the following order: Ti−6Al−7Nb−0.3La (4.01 GPa and 135 GPa, respectively) < Ti−6Al−7Nb−0.3Y (4.39 GPa and 137 GPa, respectively) < Ti−6Al−7Nb−0.3Ce (4.67 GPa and 146 GPa, respectively). In vivo tests were conducted using 46 sexually mature male Wistar rats by means of skin implantation of samples with d = 11 mm and h = 1 mm. Our research shows that Ti–6Al–7Nb–0.3La alloy (Group 2) and Ti–6Al–7Nb–0.3Ce alloy (Group 3) induced sustained hepatotoxic and nephrotoxic effects. Ti–6Al–7Nb–0.3Y alloy induced a slight local inflammatory response; however, serum biochemical analysis suggested this effect was compensated. Full article

Biodiverse composts obtained through composting are widely used in regenerative agriculture due to their ability to improve soil quality, crop growth, and productivity, primarily by promoting beneficial microorganisms. These composts result from the decomposition of mixtures containing nitrogenous and plant biomass. During plant biomass preparation, litter serves as a source of beneficial microorganisms, which transition from endophytes to decomposers. This study tested the hypothesis that the type of litter influences the composition of bacterial and fungal communities in biodiverse composts, thereby affecting species abundance and diversity. To this end, litter from the tree species Handroanthus impetiginosus (Angiosperm—AC) and Pinus elliottii (Gymnosperm—GC) was evaluated in compost preparation, also investigating the impact of litter type on the concentration of macronutrients, chemical parameters (such as organic carbon, cation exchange capacity—CEC; carbon/nitrogen ratio—C/N; organic matter—OM; pH, and humic substances fractions, including humic and fulvic acids), and microbiological quality (assessed by Microbial Biomass Carbon—MBC). The microbial composition of composts prepared with both AC and GC litter was more influenced by the composting method than by plant origin, with bacterial genera such as Thermobacillus (representing 1.27% and 1.23% of the genera present in AC and GC, respectively) and thermotolerant species, adapted to the high temperatures of the thermophilic phase, being notably present. GC litter favored a higher abundance of bacterial (pi = 0.027) and fungal species (pi = 0.042), despite the antimicrobial properties of P. elliottii. In contrast, AC compost accumulated higher levels of macronutrients and OM (39.5%), reflecting the efficacy of specific fungi in decomposition, particularly species from the phyla Chytridiomycota and Zoopagomycota, identified exclusively in this compost. MBC analysis indicated that composts reach optimal efficiency and nutritional quality between 60 and 90 days of maturation, suggesting that this period is the most suitable for leveraging the resident microbiota and producing high-quality composts for agricultural use. Full article

Technological progress and green, low-carbon growth are vital for sustainable economic development. Since supply chains are a major source of corporate carbon emissions and they face coordination challenges exceeding firm-level digitalization, China’s SCIAPP policy emphasizing cross-organizational green collaboration for low-carbon transformation applies to them. This study, using panel data from A-share listed companies (2013–2022), employs a difference-in-differences method to analyze how supply chain digitalization influences corporate carbon emissions within the framework of the Supply Chain Innovation and Application Pilot Program (SCIAPP). The results show that supply chain digitalization significantly lowers emissions, and the findings are robust to endogeneity tests and other robustness checks. Heterogeneity analysis indicates that firms with higher governance standards and advanced digital maturity gain the most in emission reductions, especially state-owned enterprises and manufacturing companies. Mechanism tests suggest that improvements in supply chain efficiency and increased corporate innovation drive this effect. Theoretically, the research extends the digitalization–emission relationship from individual firms to entire supply chains, proposing and confirming a dual-channel framework (efficiency and innovation) that combines transaction-cost and resource-based views. Methodologically, treating the implementation of the SCIAPP as a quasi-natural experiment yields strong causal evidence beyond mere correlations. The study highlights the importance of the SCIAPP in achieving dual carbon targets and tackling global climate challenges, providing empirical insights to help enterprises reduce emissions and promote high-quality, efficient development. Full article

This paper presents an MAUT-based decision-support framework, developed within the NAVGREEN project, to enable the evaluation of alternative fuels and technologies in shipping decarbonization pathways toward zero-emission targets. The framework integrates stakeholder-derived weights elicited through the Analytic Hierarchy Process (AHP) and systematically evaluates alternatives across five criteria: cost, technological maturity, safety and regulatory compatibility, carbon footprint, and social acceptability. Alternatives are mapped into a common utility space through criterion-specific utility functions and aggregated into a composite utility score, enabling transparent and reproducible comparison of single and combined solutions. To strengthen applicability beyond a single illustrative application, the study incorporates a structured scenario and sensitivity analyses (policy stringency, infrastructure constraints, conservative regulatory environments, and weight and parameter perturbations) to assess ranking stability under plausible future conditions. A case study on an Ultramax bulk carrier is used solely to demonstrate the operability and workflow of the method, rather than to empirically validate technology choices across all ship types. Optional AI-assisted elicitation may be used as a supporting aid to harmonize indicative inputs when data are incomplete; however, validation of AI-generated estimates is outside the scope of the present study and is identified as future work. Full article

Compared with conventional financing approaches, supply chain financing demonstrates superior adaptability in risk management, greater cost-effectiveness in financial control, and enhanced efficiency in approval processes, owing to its deep integration with industrial chains. This investigation explores the intrinsic relationship between digital innovation and corporate supply chain financing. To ensure the rigor and reliability of the research conclusions, we adopt an empirical research method based on the OLS econometric regression model to systematically examine the relationship between digital innovation and supply chain financing. Our findings reveal that digital innovation positively influences corporate operations and information disclosure quality, thereby facilitating supply chain financing acquisition. Specifically, digital innovation enhances both Tobin’s Q and information transparency, which consequently improves firms’ access to supply chain financing. Furthermore, we observe pronounced heterogeneity in digital innovation’s impact on supply chain financing accessibility, with more pronounced effects observed in state-owned enterprises, mature firms, and regions with less developed legal frameworks. From the perspective of theoretical contributions, this study enriches the application scenario of signal transmission theory. We verify that operational improvement driven by digital innovation can serve as an effective signal to alleviate information asymmetry in supply chain financing. Meanwhile, we supplement the research on information asymmetry theory by providing a digital solution to mitigate information frictions between supply chain partners. In terms of practical contributions, we provide actionable insights for firms. Specifically, our findings guide firms to leverage digital innovation to improve supply chain financing accessibility. Additionally, these findings offer references for supply chain stakeholders and relevant authorities to optimize financing support mechanisms. Full article

To address fragile feature representation in sparse regions and detail loss in occluded scenes caused by uneven sampling density in 3D point cloud semantic segmentation on the SemanticKITTI dataset, this article proposes an innovative framework that integrates density-adaptive feature enhancement with lightweight spectral fine-tuning, which involves frequency-domain transformations (e.g., Fast Fourier Transform) applied to point cloud features to optimize computational efficiency and enhance robustness in sparse regions, which involves frequency-domain transformations to optimize features efficiently. The method begins by accurately calculating each point’s local neighborhood density using KD tree radius search, subsequently injecting this as an additional feature channel to enable the network’s adaptation to density variations. A density-aware loss function is then employed, dynamically adjusting the classification loss weights—by approximately 40% in low-density areas—to strongly penalize misclassifications and enhance feature robustness from sparse points. Additionally, a multi-view projection fusion mechanism is introduced that projects point clouds onto multiple 2D views, capturing detailed information via mature 2D models, with the primary focus on semantic segmentation tasks using the SemanticKITTI dataset to ensure task specificity. This information is then fused with the original 3D features through backprojection, thereby complementing geometric relationships and texture details to effectively alleviate occlusion artifacts. Experiments on the SemanticKITTI dataset for semantic segmentation show significant performance improvements over the baseline, achieving Precision 0.91, Recall 0.89, and F1-Score 0.90. In low-density regions, the F1-Score improved from 0.73 to 0.80. Ablation studies highlight the contributions of density feature injection, multi-view fusion, and density-aware loss, enhancing F1-Score by 3.8%, 2.5%, and 5.0%, respectively. This framework offers an effective approach for accurate and robust point cloud analysis through optimized density techniques and spectral domain fine-tuning. Full article