Search Results (19,653)

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

A functional Huangjiu-based liqueur (called by Lujiu in China), a type of Chinese rice wine, was developed by incorporating Chinese gall leaven, as a medicinal–edible homologous ingredient, into the fermentation process to enhance its bioactivity. The physicochemical properties and enzymatic activities were investigated and found that supplementation with 2% (v/v) Chinese gall leaven optimized fermentation efficiency and substrate utilization. The co-fermentation significantly elevated the concentrations of bioactive compounds and improved antioxidant capacity, particularly free radical scavenging activity. Compared to traditional Chinese rice wine, the supplemented variant exhibited markedly higher levels of malic acid and phenolic acids. GC-MS analysis identified 85 and 84 volatile flavor compounds in the two supplemented variants, respectively, exceeding the 70 compounds detected in traditional Huangjiu. GC-IMS further revealed significant enrichment of key alcohols (e.g., 3-methyl-1-butanol, 2-methyl-1-propanol) and aldehydes (e.g., propanal, acetaldehyde) in the supplemented group. Microbial community analysis indicated distinct shifts, with increased relative abundances of Pediococcus, Lactiplantibacillus, Aspergillus, and Saccharomyces in the Chinese gall leaven-supplemented fermentation. These results suggest that the native microflora and enzymatic systems of Chinese gall leaven could enhance microbial metabolism and fermentation efficiency, thus contributing to the unique characteristics of rice wine and providing a novel strategy for functional Huangjiu-based liqueur production. Full article

Cenchrus fungigraminus (Juncao) is a high-yielding, fast-growing forage crop with considerable potential for livestock feed; however, optimizing its processing is essential for cost reduction and quality enhancement. This study comprised three components: (1) a comprehensive analysis of 25 on-farm silage samples from five locations in Southwest China using Grey Relational Analysis (GRA); (2) an assessment of the effects of three cutting heights (low: 100–150 cm; mid: 150–200 cm; high: 200–250 cm) on silage quality; and (3) a comparison of silage quality between natural fermentation and microbial inoculant treatments using mature Juncao (250–300 cm). The results showed that: (1) in the on-farm silage samples, carbon supplementation was significantly positively correlated with total digestible nutrients (TDN), relative feed value (RFV), ether extract (EE), and sensory evaluation (p < 0.05), and the GRA identified the top-ranked treatments, including J2, J3, J6, X6, and J5; (2) in the cutting height trials, fiber content increased significantly with cutting height (p < 0.05), while crude protein (CP) and TDN decreased significantly (p < 0.05). The 200–250 cm group exhibited optimal fermentation quality, characterized by the highest total volatile fatty acids (total VFA) and lactic acid concentrations, alongside the lowest pH and ammonia nitrogen/total nitrogen ratios (NH₃-N/TN); (3) in the inoculant comparison, the natural fermentation group demonstrated significantly higher degradation rates of acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) compared to the microbial inoculant group, while also maintaining a lower pH, higher total VFA and lactic acid. Consequently, for on-farm production, carbon supplementation is recommended to improve silage quality. Although cutting Juncao below 200 cm provides higher nutritional value, a height of 200–250 cm is advised to ensure optimal fermentation characteristics. Furthermore, natural fermentation proves superior to microbial inoculant treatment for mature Juncao. Together, these measures offer an effective strategy for producing high-quality Juncao silage. Full article

The oxidation of flexible polyurethane (PUR) foams significantly impacts product durability, vehicle indoor air quality, and volatile organic compound (VOC) emissions. This study investigates oxidation kinetics and VOC emissions (65–155 °C) from foams with indices between 70 and 115 (molar ratio of NCO to NCO-reactive groups × 100), where a higher index represents greater hard segment (methylene diphenyl diisocyanate) and lower soft segment (polyether polyol) content. Using a flow-through setup with PTFE chambers and Tenax thermodesorption tubes and dinitrophenylhydrazine (DNPH) cartridges, VOCs from initial analyte loading, hydroperoxide degradation, and autoxidation were distinguished, providing robust kinetic data unaffected by diffusion interference. A higher index accelerated soft segment degradation, increasing oxidation rates and VOC emissions. The activation energy of 1,2-propanediol-1-acetate-2-formate increased from 87 kJ/mol in low-index to 108 kJ/mol in high-index formulations. VOC emissions from high-index foams were tripled for acetaldehyde during long-term aging at 65 °C. While most emissions followed Arrhenius behavior, formaldehyde and acrolein deviated above 100 °C, with higher hard-segment content extending their Arrhenius range. These findings link PUR composition to degradation behavior and emissions, enabling formulation improvements. The results advance methods for evaluating raw material contributions and the performance of antioxidants under realistic aging conditions. Full article

The planned inclusion of the steel and electrolytic aluminum sectors into China’s Carbon Emission Allowance (CEA) market—initially limited to thermal power since 2021—will expand its coverage to approximately 70% of national carbon emissions, significantly influencing carbon pricing. This study employs a Variational Mode Decomposition–Support Vector Regression (VMD-SVR) model to forecast carbon price fluctuations under three post-expansion scenarios. The results indicate that, in addition to quota allocations, factors such as sectoral emission scales, the CSI 300 Power Index, and the Shanghai Energy Price Index substantially affect price trends. While market expansion induces a short-term price increase, it also stabilizes prices by reducing volatility. Furthermore, different quota allocation methods yield distinct outcomes: equal allocation facilitates a smoother market transition, whereas benchmarking provides stronger incentives for emissions reductions. Full article

Biochar obtained from digestate is a promising material in the context of digestate management. However, it is important to note that the properties of the resulting material are largely dependent on the parameters of the pyrolysis process, with temperature being a particularly significant factor. The objective of this study was to evaluate the impacts of the digestate pyrolysis temperature on the chemical structure, thermal stability, and thermal decomposition characteristics of biochar produced at temperatures of 400, 500, 600, and 800 °C in an inert nitrogen atmosphere. Material characterization was performed using a range of analytical techniques, including elemental analysis, FTIR spectroscopy, thermogravimetric analysis (TGA/DTG), and coupled TGA–FTIR analysis, in order to identify volatile products released during the heating process. The results demonstrated that elevating the pyrolysis temperature results in progressive carbonization and aromatization of the carbon structure. Concurrently, functional groups containing oxygen and hydrogen were eliminated, as evidenced by declines in the H/C and O/C atomic ratios. FTIR analysis confirmed the disappearance of aliphatic and hydroxyl bands, as well as the dominance of aromatic structures and mineral components in biochar subjected to high-temperature treatment. The TGA results demonstrated an enhancement in thermal stability with increasing pyrolysis temperature. Concurrently, the TGA–FTIR analysis revealed a substantial decline in the emission of volatile decomposition products from biochar obtained at temperatures ≥600 °C. Overall, the pyrolysis temperature of digestate determines the utilization potential of the resulting biochar; in particular, low-temperature biochar can be used as a soil amendment and methane fermentation stimulant, while high-temperature biochar can be used for contaminant immobilization in soil and long-term carbon sequestration. Full article

Systemic risk propagation in modern financial markets is characterized by non-linear contagion and rapid topological evolution, rendering traditional static monitoring methods ineffective. Existing Graph Neural Networks (GNNs) often struggle to capture “structural breaks” during crises due to their reliance on static adjacency assumptions and isotropic aggregation. To address these challenges, this study proposes the Temporal Attentive Graph Networks (TAGN), a dynamic framework designed for extreme volatility prediction and financial surveillance. TAGN constructs an incremental multi-scale graph by fusing high-frequency trading data, supply chain linkages, and institutional co-holdings to model heterogeneous risk transmission channels. Technically, it employs a deeply coupled GAT-GRU architecture, where the Graph Attention Network (GAT) dynamically assigns weights to contagion sources, and the Gated Recurrent Unit (GRU) memorizes the trajectory of structural evolution. Extensive experiments on the S&P 500 dataset (2018–2024) demonstrate that TAGN significantly outperforms state-of-the-art baselines, including WinGNN and PatchTST, achieving an AUC of 0.890 and a Precision at 50 of 61.5%. Notably, a risk early-warning index derived from TAGN exhibits a 1–2 week lead time over the VIX index during major market stress events, such as the Silicon Valley Bank collapse. This research facilitates a paradigm shift from historical statistical estimation to dynamic network-aware sensing, offering interpretable tools for RegTech applications. Full article

Botrytis cinerea, the causal agent of grey mold in grapevine, remains one of the most economically important pathogens in viticulture and a key target for sustainable biocontrol strategies. This study evaluated the antagonistic potential of seven Trichoderma isolates (T1–T7), collected from the rhizosphere of grapevine in Morocco, using a combination of in vitro and in planta assays designed to capture multiple direct and indirect modes of action. The isolates exhibited variable levels of antagonism through competition, volatile organic compounds, extracellular metabolites, and elicitation responses. Preliminary in planta assays on detached grape berries further demonstrated that all selected isolates reduced lesion development, with preventive applications yielding the strongest protection. Overall, the study highlights the complementary and strain-specific mechanisms underlying Trichoderma & B. cinerea interactions and underscores the importance of isolate selection and application timing for the development of effective and environmentally friendly grey mold management strategies. These findings provide a mechanistic basis for the future evaluation of promising isolates under vineyard conditions. Full article

Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about delay distributions (e.g., fixed probability distributions or scenario sets). However, due to the suddenness and multi-source nature of flight delays, their true distribution is difficult to accurately characterize, limiting the effectiveness of these methods in real-world uncertain conditions. By constructing a Wasserstein-metric ambiguity set, the proposed model captures distributional uncertainty without assuming fixed probabilities, thereby handling delay risks more robustly. The study incorporated chance constraints to bound extreme delay probabilities and reformulated the model as a tractable mixed-integer program. Experiments on real airline data demonstrate that DRAR outperforms traditional benchmarks, reducing propagation delays by 4–6%, volatility by 7–9%, and extreme delay risks by up to 15.7%. Thus, the model provides a practical tool for aviation decision-makers: airlines can leverage it to optimize aircraft scheduling and routing, systematically mitigate delay propagation risk, control the probability of extreme delays, and consequently reduce indirect operational costs arising from crew overtime and airport scheduling conflicts, thereby enhancing overall resource efficiency and operational resilience. These results validate DRAR as an effective tool for controlling tail risks and ensuring sustainable operations in uncertain aviation environments. Full article

The spreading process constitutes a pivotal stage in green tea manufacturing. This study integrated GC-E-Nose with targeted metabolomics to comprehensively elucidate the dynamic changes in sensory characteristics and aroma substances of green tea across varying spreading degrees. Our findings demonstrated that spreading degree significantly modulated green tea’s aroma profile, with lighter degree particularly promoting the development of desirable floral aroma. GC-MS/MS quantification identified 70 volatile compounds, among which 38 exhibited spreading-dependent differential accumulation (VIP > 1.0, p < 0.05). Five key odorants, including indole, β-ionone, nerolidol, cis-jasmone, and β-damascenone, were highlighted as essential contributors to the floral aroma. Molecular docking simulations indicated stronger binding affinities between these five odorants and the olfactory receptor OR1D2 (<−6 kcal/mol), primarily via hydrogen bonding and hydrophobic interactions. These findings indicate that modulating the spreading degree is an effective processing strategy to enhance the development of floral aroma in green tea, offering valuable insights for precision-driven optimization of tea processing protocols. Full article

Pulse crops, having the capacity for biological nitrogen (N) fixation, rarely receive N fertilizers, but information is scarce on N balance for pulse crops or pulse crop-spring wheat (Triticum aestivum L.) rotations. The objective of the study was to evaluate N balance based on N inputs and outputs and soil N sequestration rate for pulse crops and pulse crop-spring wheat rotations from 2021 to 2024 in the US northern Great Plains. Pulse crops (chickpea [Cicer arietinum L.], lentil [Lens culinaris Medik.], and pea [Pisum sativum L.]) were rotated with spring wheat to form four crop rotations (chickpea–spring wheat, lentil-spring wheat, pea–spring wheat, and spring wheat–spring wheat). Total N input from N fertilization, biological N fixation, soil N mineralization, crop seed, and precipitation was 9–27% greater for pea than for other crops and greater for pea–spring wheat than chickpea–spring wheat and continuous spring wheat. Total N output from grain N removal, ammonia volatilization, denitrification, plant senescence, leaching, surface runoff, and gaseous emissions was 20–62% greater for spring wheat than pulse crops. Nitrogen sequestration rate at 0–15 cm was 89% greater for spring wheat than lentil and 106–107% greater for pea-spring wheat and spring wheat–spring wheat than lentil–spring wheat. Nitrogen balance was 215–356% greater for chickpea and pea than lentil and spring wheat and 114–118% greater for chickpea–spring wheat and pea–spring wheat than lentil–spring wheat. Greater N input increased N surplus for pea or pea-spring wheat, and greater N output increased N deficit for spring wheat or spring-spring wheat compared to lentil or lentil–spring wheat, indicating that pea alone or in rotation with spring wheat reduced N loss to the environment by increasing soil N storage compared to continuous spring wheat. Full article

This study systematically evaluated the drying kinetics of Zanthoxylum bungeanum Maxim. during microwave vacuum drying (MVD), pulsation vacuum drying (PVD) and hot-air drying (HAD) at different temperatures and analyzed the heating mechanism differences in the three technologies via numerical simulation. Drying kinetics indicated that MVD was the most efficient technique owing to its volumetric dielectric heating, whereas the PVD efficiency depended heavily on precise cyclic parameter control. As verified by simulations, a more uniform temperature field was formed in MVD, while PVD achieved focused core heating via infrared radiation. Quality analysis revealed that the dehiscence rate increased significantly with the temperature, and both MVD and PVD demonstrated superior color retention over HAD; however, MVD was the most effective for preserving volatile oils, while PVD excelled in amide preservation. It should be noted that the specific component retention advantages of PVD were balanced by its strict parameter requirements, which limits its potential for large-scale application. Comprehensive evaluation confirmed MVD’s superiority in Z. bungeanum drying, effectively retaining thermosensitive components under a vacuum pressure of −90 kPa at 60 °C. Full article

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness. Full article

Background/Objectives: Forest bathing (Shinrin-yoku) is a nature-based approach with potential preventive health relevance. This review summarizes evidence on its effects on immune function, stress physiology, and neuroprotective pathways. Methods: A narrative review of peer-reviewed studies was conducted using major scientific databases, including observational and interventional research assessing physiological or neurocognitive outcomes following forest exposure. Results: Forest bathing is associated with enhanced natural killer (NK) cell activity, modulation of inflammatory cytokine profiles, reductions in cortisol levels, and shifts toward parasympathetic autonomic dominance. Evidence also suggests a contributory role of tree-derived biogenic volatile organic compounds and phytoncides in immune and stress-regulatory effects. Emerging findings indicate potential benefits for cognitive restoration, emotional regulation, and neurotrophic signaling; however, substantial heterogeneity in study design, exposure characteristics, and outcome measures limits direct comparability and causal inference. Conclusions: Current evidence supports forest bathing as a promising, low-risk strategy for supporting immune resilience, stress regulation, and neurocognitive well-being within a preventive health framework. Preliminary findings also suggest potential benefits in chronic neurological conditions, supporting its neuroprotective role within multimodal neurorehabilitation strategies. Standardized intervention protocols, mechanistic biomarkers, and longitudinal studies are required to strengthen clinical relevance and guide evidence-based integration into public health and lifestyle medicine. Full article

Monitoring the freshness of Salmo salar within cold chain logistics is paramount for ensuring food safety. However, conventional physicochemical and microbiological assays are impeded by inherent limitations, including destructiveness and significant time latency, rendering them inadequate for the real-time, non-invasive inspection demands of modern industry. Here, we present a novel detection framework synergizing hyperspectral imaging (400–1000 nm) with the Transformer deep learning architecture. Through a rigorous comparative analysis of twelve preprocessing protocols and four feature wavelength selection algorithms (Lasso, Genetic Algorithm, Successive Projections Algorithm, and Random Frog), prediction models for Total Volatile Basic Nitrogen (TVB-N) and Total Viable Count (TVC) were established. Furthermore, the capacity of the Transformer to capture long-range spectral dependencies was systematically investigated. Experimental results demonstrate that the model integrating Savitzky-Golay (SG) smoothing with the Transformer yielded optimal performance across the full spectrum, achieving determination coefficients (R²) of 0.9716 and 0.9721 for the Prediction Sets of TVB-N and TVC, respectively. Following the extraction of 30 characteristic wavelengths via the Successive Projections Algorithm (SPA), the streamlined model retained exceptional predictive precision (R² ≥ 0.95) while enhancing computational efficiency by a factor of approximately six. This study validates the superiority of attention-mechanism-based deep learning algorithms in hyperspectral data analysis. These findings provide a theoretical foundation and technical underpinning for the development of cost-effective, high-efficiency portable multispectral sensors, thereby facilitating the intelligent transformation of the aquatic product supply chain. Full article