Search Results (3,465)

Pistachio (Pistacia vera L.) holds significant importance due to its diverse applications and nutritional benefits. The nuts are rich in essential amino acids, antioxidants, fiber, healthy fats, and minerals, making them highly valuable for human nutrition. However, pistachios are significantly challenged by salinity stress, which negatively affects their growth and metabolism. Understanding the impact of salinity stress on pistachios is crucial for developing effective strategies to enhance their tolerance, improve growth, and ensure sustainable production in saline environments. To investigate the effects of salinity on energy metabolism and amino acid composition, we monitored key metabolites and free amino acid levels in UCB-1 pistachio leaves at 7- and 21-day salt stress treatments using Liquid Chromatography–Mass Spectrometry (LC-MS) and Ultra Performance Liquid Chromatography (UPLC). Our findings revealed that salinity affected nearly all analyzed metabolites, with varied patterns observed at different time points. Notably, all free amino acids except threonine accumulated significantly in response to salt stress. Meanwhile, reductions in 3PGA, Fru1,6bP, and Glu6P+Fru6P (glycolysis and Calvin cycle intermediates) suggest a decrease in photosynthetic activity, which may ultimately impact respiration rates. These results demonstrate that salinity stress affects both amino acid metabolism and central carbon metabolism, with the magnitude and pattern of these changes depending on the duration of exposure. The observed metabolic adjustments likely represent an adaptive response, enabling the plant to partially mitigate the detrimental effects of salt stress. Full article

DNA direct reactive impurities (DDRIs) can react with nucleophilic sites of DNA, leading to mutations. The control strategies outlined in International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 are based on the known compound structure of DDRIs. Non-target screening of DDRIs in drugs is still challenging due to the diversity of the species and the poor stability. In this study, a derivatization reagent including a reactive group and report group was designed to screen DDRIs. Based on the electrophilic theory of chemical carcinogenesis, an amine reagent was used as a reactive group to interact with DDRIs. Two derivatization reagents, p-methoxyaniline and p-methoxybenzoyl-β-alaninamide, were employed, each containing different chromatographic modification groups to mitigate matrix effects. The derivatization products were analyzed by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS). Non-target screening for DDRIs was achieved by product ions filtering of the report group. Full article

Generating non-standard fonts, such as running script (e.g., XingShu), poses significant challenges due to their high stroke continuity, structural flexibility, and stylistic diversity, which traditional component-based prior knowledge methods struggle to model effectively. While diffusion models excel at capturing continuous feature spaces and stroke variations through iterative denoising, they face critical limitations: (1) style leakage, where large stylistic differences lead to inconsistent outputs due to noise interference; (2) structural distortion, caused by the absence of explicit structural guidance, resulting in broken strokes or deformed glyphs; and (3) style confusion, where similar font styles are inadequately distinguished, producing ambiguous results. To address these issues, we propose a novel skeleton-guided diffusion model with three key innovations: (1) a skeleton-constrained style rendering module that enforces semantic alignment and balanced energy constraints to amplify critical skeletal features, mitigating style leakage and ensuring stylistic consistency; (2) a cross-scale skeleton preservation module that integrates multi-scale glyph skeleton information through cross-dimensional interactions, effectively modeling macro-level layouts and micro-level stroke details to prevent structural distortions; (3) a contrastive style refinement module that leverages skeleton decomposition and recombination strategies, coupled with contrastive learning on positive and negative samples, to establish robust style representations and disambiguate similar styles. Extensive experiments on diverse font datasets demonstrate that our approach significantly improves the generation quality, achieving superior style fidelity, structural integrity, and style differentiation compared to state-of-the-art diffusion-based font generation methods. Full article

Endovascular repair of thoracoabdominal aortic aneurysms (TAAAs) requires multidisciplinary expertise to minimize mortality and disabling complications. Despite satisfactory outcomes with this approach being common knowledge, extensive aortic coverage occurring in fenestrated/branched endovascular aortic repair (F/B-EVAR) carries a non-negligible risk of spinal cord ischemia (SCI). Recently, many authors have proposed different endovascular strategies to mitigate the risk of SCI; however, the real effectiveness of these maneuvers is not universally recognized due to a lack of standardized protocols among individual centers. Several adjuncts have been proposed to obtain staged occlusion of segmental aortic branches to promote spinal cord preconditioning. These strategies include proximal thoracic aortic repair (PTAR), temporary aneurysm sac perfusion (TASP), and minimally invasive staged segmental artery coil embolization (MIS²ACE). The present paper aims to provide an overview of the most advanced staging strategies used in high-volume aortic centers, pointing out that it takes meticulous preoperative planning to face every clinical scenario. Full article

Urban school environments often face significant thermal discomfort due to extensive paved surfaces, limited vegetation, and outdated building designs. This study examines how green spaces can mitigate temperature extremes and improve thermal comfort at two secondary schools in Coimbra, Portugal: Escola Secundária José Falcão (ESJF) and Escola Secundária D. Dinis (ESDD). Using a mixed-methods approach that combined school community surveys with on-site microclimatic measurements, we integrated user feedback on comfort with data on temperature and humidity variations across different indoor and outdoor spaces. Results revealed that tree-shaded areas consistently maintained lower air temperatures and higher relative humidity than unshaded zones, which experienced intense heat accumulation—up to a 5 °C difference. At ESJF, the older infrastructure and large asphalt surfaces led to severe heat retention, with east-facing classrooms recording the highest indoor temperatures. ESDD’s pavilion-style layout and existing green spaces provided comparatively better thermal conditions, although insufficient vegetation maintenance and limited shade reduced their effectiveness. The findings demonstrate a clear correspondence between the school community’s perceptions of thermal comfort and the measured microclimatic data. Vegetation—particularly deciduous trees—plays a critical role in cooling the school microclimate through shading and evapotranspiration. Strategic interventions such as expanding tree cover in high-exposure areas, installing green roofs and walls, and carefully selecting species can significantly reduce temperature extremes and improve outdoor usability. In addition, fostering environmental education and participatory co-design programs can encourage sustainable behaviors within the school community, underlining the importance of inclusive, nature-based solutions for climate adaptation. This research highlights that integrating green infrastructure in school design and management is a cost-effective strategy for thermal regulation. Green spaces, when co-designed with community involvement, not only enhance climate resilience and student well-being but also contribute to broader sustainable urban development goals. Full article

With accelerating urbanization and global climate warming, Urban Heat Islands (UHIs) pose serious threats to urban development. Existing UHI research mainly focuses on inland regions, lacking systematic understanding of coastal city heat island mechanisms. We selected eight Chinese coastal cities with different backgrounds, quantitatively assessed urban heat island intensity based on summer 2023 Landsat 8 remote sensing data, established block-LCZ spatial analysis units, and employed a combination of machine learning models and causal inference methods to systematically analyze the regional differentiation characteristics of Urban Heat Island Intensity (UHII) and the influence mechanisms of multi-dimensional driving factors within land–sea interaction contexts. The results revealed the following: (1) UHII in the study area presents obvious spatial differentiation, with the highest value occurring in Hong Kong (2.63 °C). Northern cities generally had higher values than southern ones. (2) Different Local Climate Zone (LCZ) types show significant differences in thermal contributions, with LCZ2 (compact midrise) blocks presenting the highest UHII values in most cities, while LCZ G (water) and LCZ A (dense trees) blocks exhibit stable cooling effects. Nighttime light (NTL) and distance to sea (DS) are dominant factors affecting UHII, with NTL marginal effect curves generally presenting hump-shaped characteristics, while DS shows different response patterns across cities. (3) Causal inference reveals true causal driving mechanisms beyond correlations, finding that causal effects of key factors exhibit significant spatial heterogeneity. The research findings provide a new cognitive framework for understanding the formation mechanisms of thermal environments in Chinese coastal cities and offer a quantitative basis for formulating regionalized UHI mitigation strategies. Full article

To explore strategies for the safe utilization of farmland co-contaminated with cadmium (Cd) and lead (Pb), this field study systematically evaluated the impacts of humic acid (HA) and potassium fulvate (PF) at different application rates (0, 1500, 3000, and 4500 kg·ha⁻¹) on the growth, yield, and translocation of Cd and Pb within the soil–plant system of maize (Zea mays L.). The results showed that while HA and PF did not significantly alter total soil Cd and Pb concentrations, they markedly reduced their bioavailable fractions. This mitigation of heavy metal phytotoxicity significantly promoted maize growth and yield, with the high-dose HA treatment increasing yield by a maximum of 32.9%. Both amendments dose-dependently decreased Cd and Pb concentrations, bioconcentration factors (BCF), and translocation factors (TF) in all maize tissues, particularly in the grains. At equivalent application rates, PF was slightly more effective than HA in reducing heavy metal concentrations in the grains. Notably, a significant positive correlation was observed between Cd and Pb concentrations across all plant parts, confirming a synergistic accumulation and translocation mechanism. This synergy provides a physiological explanation for the broad-spectrum immobilization efficacy of these humic substances. In conclusion, applying HA and PF presents a dual-benefit strategy for increasing yield and reducing risks in Cd- and Pb-contaminated farmlands. This study proposes a differentiated application approach: PF is the preferred option when ensuring food-grade safety is the primary goal, whereas high-dose HA is more advantageous for maximizing yield in soils with low-to-moderate contamination risk. Full article

Global warming has become a significant public health concern, with intensive livestock farming as a major contributor. To mitigate greenhouse gas emissions, strategies such as manipulating the ruminal environment with dietary additives are essential. This study evaluated Moringa oleifera, a globally widespread tree with antioxidant, multivitamin, protein-rich, and anti-inflammatory properties, as a feed additive. Rumen fluid was collected from three Pelibuey sheep, homogenized, and subjected to an in vitro fermentation study for 48 h with three alfalfa/moringa ratio treatments: T0 Control (100:0), T1 Low (85:15), and T2 High (70:30). Total RNA was extracted, followed by high-definition sequencing of the metatranscriptome. The sequencing yielded approximately 456 million sequences. A total of 117 phyla were identified and approximately 1300 genera were mapped. Predominant phylum differed by treatment: T0, Firmicutes; T1, Proteobacteria; and T2 with Synergistetes, at least one sample per treatment. Archaea were nearly absent in T1, which explains a statistically significant decrease in methane production. In the Gene Set Enrichment Analysis (GSEA), it was observed that one of the metabolic pathways with a statistically significant difference (p-value < 0.05) was that of methane, specifically in the low moringa treatment (T1) compared to the control (T0). From the functional analysis, differentially expressed enzymes were identified, some of which are involved in the methane metabolic pathway, such as formate dehydrogenase (EC 1.17.1.9) and glycine hydroxymethyltransferase (EC 2.1.2.1), which are intermediates in methane formation. These results suggest that 15% Moringa oleifera supplementation alters ruminal microbiota, reduces archaeal activity, and suppresses methane-related pathways. These findings provide molecular evidence supporting the potential of M. oleifera as a methane mitigation strategy in ruminant nutrition. Full article

In contemporary spatial planning, territorial resilience is rapidly gaining relevance, referring to a territory’s capacity to withstand, adapt to, recover from, and transform in response to environmental, social, and economic pressures. However, several constraints limit its operationalisation in planning. A key element to addressing this gap is to investigate where and which interventions are most urgently needed to tackle the impact of hazards on territories. This can be achieved by understanding and localising the vulnerabilities of territorial systems, thereby enabling the definition of appropriate mitigation and adaptation measures. This paper presents the application of R3C-GeoResilience, an open-source GIS tool and its methodological framework, which allows mapping territorial vulnerabilities across different geographical contexts and spatial scales. The methodology is applied to the Italian case of the Union of Bassa Romagna (UBR), aiming to build capacity for local practitioners to implement resilience thinking in decision-making processes. Findings underscore the potential of R3C-GeoResilience to enhance evidence-based planning and policymaking, supporting adaptive and transformative strategies to address territorial vulnerabilities. The application of the research demonstrates the replicability and adaptability of the methodological framework for integrating participatory vulnerability mapping into local governance and urban planning strategies, thereby enhancing the resilience of territories. Full article

As Virtual Try-On (VTON) technology matures, 2D VTON methods based on diffusion models can now rapidly generate diverse and high-quality try-on results. However, with rising user demands for realism and immersion, many applications are shifting towards 3D VTON, which offers superior geometric and spatial consistency. Existing 3D VTON approaches commonly face challenges such as barriers to practical deployment, substantial memory requirements, and cross-view inconsistencies. To address these issues, we propose an efficient 3D VTON framework with robust multi-view consistency, whose core design is to decouple the monolithic 3D editing task into a four-stage cascade as follows: (1) We first reconstruct an initial 3D scene using 3D Gaussian Splatting, integrating the SMPL-X model at this stage as a strong geometric prior. By computing a normal-map loss and a geometric consistency loss, we ensure the structural stability of the initial human model across different views. (2) We employ the lightweight CatVTON to generate 2D try-on images, that provide visual guidance for the subsequent personalized fine-tuning tasks. (3) To accurately represent garment details from all angles, we partition the 2D dataset into three subsets—front, side, and back—and train a dedicated LoRA module for each subset on a pre-trained diffusion model. This strategy effectively mitigates the issue of blurred details that can occur when a single model attempts to learn global features. (4) An iterative optimization process then uses the generated 2D VTON images and specialized LoRA modules to edit the 3DGS scene, achieving 360-degree free-viewpoint VTON results. All our experiments were conducted on a single consumer-grade GPU with 24 GB of memory, a significant reduction from the 32 GB or more typically required by previous studies under similar data and parameter settings. Our method balances quality and memory requirement, significantly lowering the adoption barrier for 3D VTON technology. Full article

This paper evaluates Urban Heat Island (UHI) mitigation strategies in Florence’s historical centre, characterized by relevant cultural heritage value and significant tourist fluxes but increasingly susceptible to heatwaves. The research work focused on the evaluation of both current microclimate conditions and mitigation solutions for UHI-related issues, using ENVI-met microclimate modelling software as a simulation tool. Different models, featuring a 2 m grid resolution and detailed material properties, were produced to assess outdoor air temperature (Ta), mean radiant temperature (MRT), and Universal Thermal Climate Index (UTCI), chosen as reference parameters for human thermal sensation. Diversified conditions induced by the peculiarities of the urban layout were highlighted, with current Ta up to 32 °C and MRT exceeding 55 °C in paved open areas. Site-specific measures and their expected effectiveness were hence analyzed. De-paving and greening yield modest local cooling (Ta reduction up to −0.25 °C, MRT up to −1.75 °C), while tree installation ensures that MRT decreases by −7.50 °C to −12.00 °C. Most effectively, suspended shading fabrics preventing direct radiation can act on Ta (−0.09 °C to −0.25 °C) and provide substantial MRT reductions (−7.50 °C to −17.00 °C), significantly improving thermal comfort. The findings emphasize the potentialities of site-specific, reversible interventions in historic centres to combine climate adaptation and heritage preservation. Full article

This study investigates underground heat sources and develops effective strategies for mitigating heat hazards in coal mines, with a focus on the design and optimization of cooling systems. Using the 3107 fully mechanized mining face of Menkeqing Coal Mine as a case study, geological survey data and in situ measurements were combined to evaluate the severity of thermal hazards. Thermodynamic and heat transfer models were applied to quantify heat dissipation from multiple sources. Computational fluid dynamics (CFD) simulations, based on data-driven modeling and geometric reconstruction, tested different equipment layouts and spacing configurations to identify optimal cooling schemes. Field implementation of the designed cooling system confirmed its effectiveness, offering practical guidance for improving heat hazard control and cooling system optimization in deep coal mines. Full article

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article

Fusarium root rot (FRR) is a major disease affecting common bean (Phaseolus vulgaris L.) production worldwide. In Egypt, FRR has recently become more prevalent, threatening crop yields. Fusarium species are considered the primary causal agents of this disease. To identify the pathogens associated with FRR and evaluate host resistance, Fusarium isolates were obtained from diseased common bean plants collected in different Egyptian governorates. Morphological identification, pathogenicity assays on six cultivars (Alpha, Samantha, Giza 6, Giza 12, Cambo, and Nebraska), and molecular identification using TEF-1α gene sequencing were conducted. Thirteen isolates exhibited pathogenicity, and five isolates (FP33, FP24, FP26, FP21, and FP11) were classified as highly aggressive. Isolate FP33 caused the highest disease severity, reaching 90% on Giza 6 and 80% on Cambo, while Nebraska showed the highest resistance (30% disease severity). Similarly, FP24 led to 85% and 75% severity on Giza 6 and Cambo, respectively. Nebraska and Giza 12 showed the greatest resistance, while Giza 6 and Cambo were most susceptible. Molecular analysis identified FP33 and FP24 as F. equiseti, FP26 and FP21 as F. oxysporum, and FP11 as F. solani. The study demonstrates the genetic and pathogenic variability among Fusarium isolates causing root rot in common bean. Nebraska and Giza 12 were identified as the most resistant cultivars, while Giza 6 and Cambo were highly susceptible. These findings highlight the importance of selecting resistant cultivars and implementing integrated disease management strategies to mitigate FRR in Egypt. The results also contribute valuable data for breeding programs aimed at developing durable resistance. The integration of morphological, molecular, and pathogenicity data provides a framework for future epidemiological studies and sustainable disease management strategies. Full article

With the increasing penetration of wind and photovoltaic (PV) power in modern power systems, accurate power forecasting has become crucial for ensuring grid stability and optimizing dispatch strategies. This study focuses on multiple wind farms and PV plants, where three deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and a hybrid Transformer–BiLSTM model—are constructed and systematically compared to enhance forecasting accuracy and dynamic responsiveness. First, the predictive performance of each model across different power stations is analyzed. The results reveal that the LSTM model suffers from systematic bias and lag effects in extreme value ranges, while Bi-LSTM demonstrates advantages in mitigating time-lag issues and improving dynamic fitting, achieving on average a 24% improvement in accuracy for wind farms and a 20% improvement for PV plants compared with LSTM. Moreover, the Transformer–BiLSTM model significantly strengthens the ability to capture complex temporal dependencies and extreme power fluctuations. Experimental results indicate that the Transformer–BiLSTM consistently delivers higher forecasting accuracy and stability across all test sites, effectively reducing extreme-value errors and prediction delays. Compared with Bi-LSTM, its average accuracy improves by 19% in wind farms and 35% in PV plants. Finally, this paper discusses the limitations of the current models in terms of multi-source data fusion, outlier handling, and computational efficiency, and outlines directions for future research. The findings provide strong technical support for renewable energy power forecasting, thereby facilitating efficient scheduling and risk management in smart grids. Full article