Search Results (233)

The basic/helix-loop-helix (bHLH) transcription factors are crucial regulators of plant development and stress responses. In this study, we conducted a genome-wide analysis of the bHLH family in Rosa roxburghii, an economically important fruit crop. A total of 89 non-redundant RrbHLHs were identified and unevenly distributed across the seven chromosomes. Phylogenetic analysis classified them into 23 subfamilies and 7 Arabidopsis subfamilies were absent, indicating lineage-specific evolutionary trajectories. Conserved motif and gene structure analyses showed that members within the same subfamily generally shared similar architectures, yet subfamily-specific variations were evident, suggesting potential functional diversification. Notably, key residues involved in DNA-binding and dimerization were highly conserved within the bHLH domain. Promoter analysis identified multiple cis-acting elements related to hormone response, stress adaptation, and tissue-specific regulation, hinting at broad regulatory roles. Expression profiling across fruit developmental stages and in response to GA₃ treatment revealed dynamic expression patterns. Furthermore, 21 duplicated gene pairs (17 segmental and 4 tandem duplicated pairs) were identified, with most evolving under purifying selection. Detailed analysis of these pairs revealed that segmental duplication, coupled with structural variations such as exon indels, dissolution/joining, and exonization/pseudoexonization, substantially contributed to their functional divergence during evolution. Our results provide a basis for understanding the evolution and potential functions of the RrbHLHs. Full article

The agricultural industry faces increasing environmental degradation due to the intensive use of conventional chemical fertilizers, leading to water pollution and alterations in soil composition. In addition, root-knot and cyst nematodes are major constraints to cucumber production, causing severe root damage and yield losses worldwide, underscoring the need for sustainable alternatives to conventional fertilization and pest management. Under greenhouse conditions, a four-month cultivation trial evaluated vegetable oil-, micronutrient-, and activated flavonoid-based biostimulants, applying Key Eco Oil^® (Miami, USA) via soil drench (every 15 days) combined with foliar sprays of CropBioLife^® (Victoria, Australia) and KeyPlex 120^® (Miami, USA) (every 7 days). Results showed reduced parasitic nematodes by 66% in soil and decreased gall formation by 41% in roots. Chlorophyll fluorescence and infrared gas analysis revealed higher oxygen-evolving complex efficiency (38%), increased PSII electron transport, improved the fluorescence decrease ratio, also known as the vitality index (Rfd), and higher CO₂ assimilation compared to conventional treatments. Processed cucumbers showed higher sugar and nearly double ascorbic acid content, with improved flesh consistency and color. Therefore, the application of these bioactive products significantly reduced nematode infestation while enhancing plant growth and physiological performance, underscoring their potential as sustainable tools for crop cultivation and protection. These results provide evidence that sustainable bioactive biostimulants improve plant resilience, productivity, and nutritional quality, offering also an environmentally sound approach to pest management. Full article

This study introduces the Hybrid Hydrological Forecast System (HHFS), a dual-stage, data-driven framework for monthly streamflow forecasting at the Santa Branca outlet in the upper Paraíba do Sul River Basin, Brazil. The system combines two nonlinear regressors, Multi-Layer Perceptron (MLP) and extreme Gradient Boosting (XGB), calibrated through a structured four-step evolutionary procedure in GA₁ (hydrological weighting, dual-regime Ridge fusion, rolling bias correction, and monthly mean–variance adjustment) and a hydro-adaptive probabilistic optimization in GA₂. SHAP-based analysis provides physical interpretability of the learned relations. The regressive stage (GA₁) generates a bias-corrected and climatologically consistent central forecast. After the full four-step optimization, GA₁ achieves robust generalization skill during the independent test period (2020–2023), yielding NSE = 0.77 ± 0.05, KGE = 0.85 ± 0.05, R² = 0.77 ± 0.05, and RMSE = 20.2 ± 3.1 m³ s⁻¹, representing a major improvement over raw MLP/XGB outputs (NSE ≈ 0.5). Time-series, scatter, and seasonal diagnostics confirm accurate reproduction of wet- and dry-season dynamics, absence of low-frequency drift, and preservation of seasonal variance. The probabilistic stage (GA₂) constructs a hydro-adaptive prediction interval whose width (max-min streamflow) and asymmetry evolve with seasonal hydrological regimes. The optimized configuration achieves comparative coverage COV = 0.86 ± 0.00, hit rate p = 0.96 ± 0.04, and relative width r = 2.40 ± 0.15, correctly expanding uncertainty during wet-season peaks and contracting during dry-season recessions. SHAP analysis reveals a coherent predictor hierarchy dominated by streamflow persistence, precipitation structure, temperature extremes, and evapotranspiration, jointly explaining most of the predictive variance. By combining regressive precision, probabilistic realism, and interpretability within a unified evolutionary architecture, the HHFS provides a transparent, physically grounded, and operationally robust tool for reservoir management, drought monitoring, and hydro-climatic early-warning systems in data-limited regions. Full article

The dairy cattle sector is a critical source of anthropogenic greenhouse gas (GHG) emissions and must transition to low-carbon farming to meet global climate goals. However, a systematic synthesis of the evolution and future trajectories of GHG emissions research in this field is still lacking. This study aims to address this gap by conducting a bibliometric analysis to elucidate the research evolution, hotspots, and future trends in GHG emissions from dairy cattle farming. The results showed a steady linear increase in publications (R² = 0.80), with the highest average annual growth rate of approximately 45.9% (2009–2014). The United States (91), Italy (68), the Netherlands (58), Germany (51), and Ireland (45) were the most productive countries, accounting for 60.2% of the global total. Both institutional (0.0347) and author (0.0069) collaboration densities in the global network are low, indicating a lack of a tightly integrated collaborative framework. The research hotspots evolved from foundational themes (e.g., agriculture, grasslands; 2005–2010) to environmental pressures and mitigation (2010–2020). A recent thematic shift (2020–2025) is evident towards specific mitigation strategies like rumen fermentation, sustainability, and fertility, indicating a field oriented toward low-carbon, high-efficiency transformation. The analysis underscores the critical perspective provided by life cycle assessment for this transition. This study provides a comprehensive map of the research landscape, highlighting future priorities. Grounded in a holistic life cycle assessment framework, future work should integrate technology, management, and policy to steer the global dairy industry towards a sustainable future that balances environmental health with economic viability. Full article

In the article we investigated the effectiveness of a synergistic system designed to reduce the fire hazard of flexible polyurethane (PUR) foams. The examined system consisted of a carbon-based filler graphene (G), carbon nanotubes (CNTs), or expanded graphite (EG) combined with melamine polyphosphate (MPP). The investigated polyurethane foams (PUR) were synthesized at room temperature via a polycondensation reaction between a polyol and an isocyanate, with an OH: NCO molar ratio of 2:1. Both the carbon fillers and melamine polyphosphate were homogeneously dispersed within the polyol component. Thermogravimetric analysis (TGA), cone calorimetry, and microcalorimetry were used to evaluate the influence of the fillers on the thermal stability and flammability of the PUR foams. The toxicity of the gaseous products was assessed using a coupled TG-gas analysis system, while the optical density of the evolved gases was determined using a Smoke Density Chamber (SDC). The obtained results demonstrated that the applied synergistic carbon-phosphorus filler system significantly reduced the fire hazard of the tested PUR foams. In particular, the EG5-MPP system enabled the formation of self-extinguishing materials. Full article

As a non-invasive diagnostic technique, evolved gas analysis (EGA) holds significant value in assessing the insulation conditions of critical equipment such as power cables. Current analytical methods face two major challenges: insulation materials may undergo multiple aging mechanisms simultaneously, leading to interfering characteristic gases; and traditional approaches lack the multi-label recognition capability to address concurrent fault patterns when processing mixed-gas data. These limitations hinder the accuracy and comprehensiveness of insulation condition assessment, underscoring the urgent need for intelligent analytical methods. This study proposes a deep convolutional neural network (DCNN)-based multi-label classification framework to accurately identify the gas generation characteristics of five typical power cable insulation materials—ethylene propylene diene monomer (EPDM), ethylene-vinyl acetate copolymer (EVA), silicone rubber (SR), polyamide (PA), and cross-linked polyethylene (XLPE)—under fault conditions. The method leverages concentration data of six characteristic gases (CO₂, C₂H₄, C₂H₆, CH₄, CO, and H₂), integrating modern data analysis and deep learning techniques, including logarithmic transformation, Z-score normalization, multi-scale convolution, residual connections, channel attention mechanisms, and weighted binary cross-entropy loss functions, to enable simultaneous prediction of multiple degradation states or concurrent fault pattern combinations. By constructing a gas dataset covering diverse materials and operating conditions and conducting comparative experiments to validate the proposed DCNN model’s performance, the results demonstrate that the model can effectively learn material-specific gas generation patterns and accurately identify complex label co-occurrence scenarios. This approach provides technical support for improving the accuracy of insulation condition assessment in power cable equipment. Full article

High-pressure water jet technology is widely utilized for cleaning marine artificial structures due to its portability, efficiency, and environmental friendliness, yet traditional jets underperform in submerged environments. Gas-assisted water jet technology has predominantly been applied to rock breaking—where vertical forces are prioritized—with insufficient research into flow regime evolution, limiting its utility for cleaning applications. This study introduces a supercavitating high-pressure water jet aimed at improving underwater cleaning efficiency while lowering economic costs. Employing ANSYS Fluent—with the RNG k-ε turbulence model and mixture model—validated via high-speed camera experiments, we explored the flow regime evolution of both unconstrained and semi-constrained impinging jets. The key findings of this paper are as follows: The cavity evolves with a periodic “necking-bubbling” pattern, whose intensity correlates positively with gas outlet velocity and supply rate; moderate gas supply—with 120 L/min identified as optimal through orthogonal analysis—effectively delays water jet breakup. For semi-constrained jets, the wall-adjacent gas flow also exhibits “necking-bubbling”; small-angle impact (30° versus 60°) reduces near-wall shear vortices, enhancing gas cavity stability on the target plate. This study bridges the gap between gas-assisted jet technology and underwater cleaning requirements, offering theoretical insights and optimized parameters for efficient, low-cost marine structure cleaning. It thereby supports the sustainable exploitation of marine resources and the stable operation of key marine facilities. Full article

Due to their simplicity and ease of visualization, lattice models can be useful to illustrate basic concepts in thermodynamics. The recipe to obtain classical thermodynamic expressions from lattice models is usually based on invoking the thermodynamic limit, and the ideal gas law can easily be obtained as the density of non-interacting particles vanishes. We present a lattice-based analysis that shows that, when a gas consisting of non-interacting particles evolves towards mechanical equilibrium with the environment, the ideal gas law can be obtained with no recourse to unnecessary assumptions regarding the size or particle density of the lattice. We also present a statistical mechanical analysis that considers a quantized volume and reproduces the process obtained for the discrete lattice model. We show how the alternative use of a well-known and accessible model (the non-interacting lattice gas) can give microscopic insights into thermal systems and the assumptions that underlie the laws used to describe them, including local vs. global equilibrium, irreversible processes, and the sometimes subtle difference between physical assumptions and mathematically convenient approximations. Full article

China has abundant deep coalbed methane (CBM) resources; however, high temperature, stress, and reservoir pressure complicate the gas adsorption–desorption–diffusion–seepage processes, severely restricting the development of deep CBM. Through experimental research on adsorption, desorption, diffusion, and seepage behaviors of various coal samples, the control mechanisms of deep coal reservoir properties on CBM production in the Linxing–Shenfu region have been revealed. The results indicate that CBM adsorption and desorption characteristics are jointly controlled by coal rank, ash yield, temperature. and pressure. Among the above conditions, coal rank and pressure exhibit positive effects, while ash yield and temperature show inhibitory effects. Analysis of desorption efficiency based on the Langmuir model further identifies sensitive desorption and rapid desorption stages as key phases for enhancing productivity. Moreover, the gas diffusion mechanism is dynamically evolving, with Knudsen diffusion and Fick diffusion being the main modes during high ground pressure stages, gradually transitioning to the coexistence of Knudsen, transition, and Fick diffusions as pressure decreases. Concurrently, gas–water seepage experiments demonstrate that increasing temperature will reduce the irreducible water saturation and enhance the relative permeability of the gas. Since irreducible water saturation is negatively correlated with relative permeability of gas, the relative permeability of the gas phase, cross-point saturation, and the range of the two-phase co-seepage zone all significantly increases with the increase in temperature. The findings systematically elucidate the regulatory mechanisms of deep coal reservoir properties in the process of “adsorption–desorption–diffusion–seepage,” providing critical theoretical support for optimizing development strategies and enhancing the efficiency of deep CBM development. Full article

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible. Full article

This study investigates cross-market dependence and dynamic hedging performance between the U.S. equity market and major commodity assets across distinct crisis regimes. Using daily data for the S&P 500 index and four key commodities (WTI crude oil, gold, wheat, and natural gas), we examine how market linkages evolve during systemic disruptions by applying Multifractal Detrended Cross-Correlation Analysis (MFCCA) and the q-dependent detrended correlation coefficient. Hedging performance is assessed using optimal hedge ratios estimated under two multivariate GARCH frameworks: the Asymmetric Dynamic Conditional Correlation (ADCC-GARCH) and the Generalized Orthogonal GARCH (GO-GARCH) model. The findings reveal strong multiscale and time-varying dependencies that intensify during high-volatility periods, reducing the benefits of conventional portfolio diversification. Hedging effectiveness proves to be regime dependent and strongly influenced by nonlinear cross-market interactions. The GO-GARCH model captures volatility spillovers and asymmetric co-movements more effectively, delivering superior hedging results compared with ADCC, especially during episodes of extreme market stress. Among the analysed commodities, crude oil and gold offer the most reliable hedging properties, whereas wheat and natural gas show unstable performance due to supply side shocks. These results emphasize the need for flexible, dynamically adjusted risk-management strategies during crisis environments. Full article

Gardenia tea is esteemed for its delicate and fragrant aroma. Background: However, there is a scarcity of studies focused on the aromatic properties of gardenia-scented white tea, particularly regarding how these aroma profiles evolve over different storage durations. Methods: This research sought to analyze the volatile compounds present in gardenia white tea through headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) alongside multivariate analysis techniques. Results: Findings indicated that the main chemical categories found in newly white tea (NWT), aged white tea (AWT), gardenia newly white tea (GNWT), and gardenia aged white tea (GAWT) included esters, terpenoids and ketones. The multivariate analysis pinpointed 11 significant volatile compounds (such as linalool, [(Z)-non-6-enyl] acetate, and (E)-non-4-enal) and an 10 additional key volatile compounds (including linalool, [(Z)-non-6-enyl] acetate, and 1-isothiocyanato-3-(methylthio)-2-Propane) that had variable importance in projection (VIP) values exceeding 2 and odor activity values (OAVs) greater than 1. These compounds effectively distinguished the aroma profiles of GNWT from NWT and GAWT from AWT. Notably, the levels of these compounds were significantly elevated in GNWT and GAWT compared to their NWT and AWT counterparts. Additionally, three volatile compounds in GNWT and six in GAWT showed a decline in concentration relative to NWT and AWT. Conclusions: These compositional differences are suggested to clarify the aromatic distinctions between gardenia-scented white tea and its white tea base. The outcomes of this study will contribute to a deeper chemical understanding of the unique aroma of gardenia white tea, providing a theoretical basis for assessing quality and developing products based on different storage periods. Full article

The phenomenon of fast fashion has resulted in high yarn consumption and growing textile waste from both manufacturing and consumers. Rising environmental awareness and evolving legislation, including landfill restrictions, have prompted the search for sustainable recycling methods to manage textile end-of-life. This study investigates the mechanical recycling of polyamide 6.6 (PA66) yarn using a chain extender (Joncryl) and antioxidant (Irganox). Thermogravimetric analysis (TGA) confirmed that thermal stability in recycled PA66 was maintained compared to the original yarn, and the presence of Joncryl further enhanced this stability. Oxidative-onset temperature (OOT), measured by differential scanning calorimetry (DSC), supported these improvements. Gas chromatography–mass spectrometry (GC/MS) identified key degradation products, which were correlated with changes in the polymer matrix. Mechanical testing showed a 31% decrease in Young’s modulus after initial recycling, which was reversed with further processing. This behavior suggests the formation of shortened semi-crystalline chains and new linkages promoted by Joncryl. Viscosity and limiting viscosity number increased by up to 50%, depending on both additive concentrations. Overall, Joncryl and Irganox enhanced viscosity, mechanical strength, and notably thermal stability, confirming their suitability for recyclable textile-grade PA66 yarns. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

In this study, the evolved gas analysis of polypropylene (PP), mixed wood biomass (WB), cardboard (CB), and their blends was investigated using a coupled thermo-gravimetric analysis–Fourier transform infrared spectroscopy (TG–FTIR) approach. The data obtained were used to semi-quantify the yield of volatile products from the individual feedstocks and their blends. Using N₂/O₂ (80/20) as the gasifying agent, the TG–FTIR setup was operated from ambient temperature to 850 °C at heating rates of 20 and 40 °C/min. The results indicated that the C–H stretching functional group exhibited higher yields in blends with greater PP mass percentages. In the CB/WB blends, C–H stretching recorded the lowest yield, ranging from 5 to 10 a.u. Conversely, blends containing an average PP mass of 16% showed C–H yields between 20 and 25 a.u. The levels of C–H were observed to increase proportionally with the PP mass fraction in the sample. Furthermore, the evolution of gases from carbonyl functional groups was the highest in the three-component blend with equal mass percentages, with C=O yields reaching 20–25 a.u. at 20 °C/min and 35–40 a.u. at 40 °C/min. The production of carbon monoxide (CO) was also highest in the three-component blend with equal mass percentages, yielding 9–10 a.u. Among the two-component blends, the PP/CB 50/50% blend exhibited the highest CO levels, ranging from 8 to 9 a.u. Overall, higher heating rates resulted in comparatively greater yields across all functional groups, particularly for C–H volatiles. These findings underscore the significance of blend composition and thermal ramping in optimising gasification performance. The results contribute to a deeper understanding of co-gasification dynamics and support the development of targeted feedstock strategies for efficient thermochemical conversion and improved control over volatile emissions. Full article