Search Results (9,008)

Plant phenology is a sensitive indicator of ecosystem responses to climate change, yet its dynamics and drivers in subtropical montane forests remain poorly understood. Based on the continuous phenological monitoring of 12 dominant tree species from 2008 to 2022 in a mid-subtropical evergreen broad-leaved forest on Ailao Mountains, China, this study analyzed phenological shifts and their climatic drivers. The results show that, (1) unlike the widely reported trends in northern mid-to-high latitudes, spring phenophases (budburst and leaf-out) did not exhibit significant advancing trends, while autumn phenophases (leaf coloration and fall) remained stable; (2) water availability played a dominant role in regulating spring phenology, with both budburst and leaf-out showing significant negative correlations with winter-spring precipitation, and responses varied significantly across hydrological year types; and (3) the life form strongly influenced phenological strategies, with evergreen species exhibiting earlier spring phenology than deciduous species. This study highlights that in seasonally humid subtropical montane forests, water availability exerts a stronger control on phenology than temperature. Our findings underscore the necessity of incorporating precipitation variability and functional trait differences into assessments of forest phenology and ecosystem functioning under future climate change, providing a scientific basis for the conservation and adaptive management of subtropical forests. Full article

Non-destructive prediction of harvest timing is increasingly important in greenhouse melon cultivation, yet image-based methods alone often fail to reflect environmental factors affecting fruit development. Likewise, environmental or fertigation data alone cannot capture fruit-level variation. This gap calls for a multimodal approach integrating both sources of information. This study presents a fusion model combining RGB images with environmental and fertigation data to predict optimal harvest timing for melons. A YOLOv8n-based model detected fruits and estimated diameters under marker and no-marker conditions, while an LSTM processed time-series variables including temperature, humidity, CO₂, light intensity, irrigation, and electrical conductivity. The extracted features were fused through a late-fusion strategy, followed by an MLP for predicting diameter, biomass, and harvest date. The marker condition improved detection accuracy; however, the no-marker condition also achieved sufficiently high performance for field application. Diameter and weight showed a strong correlation (R² > 0.9), and the fusion model accurately predicted the actual harvest date of August 28, 2025. These results demonstrate the practicality of multimodal fusion for reliable, non-destructive harvest prediction and highlight its potential to bridge the gap between controlled experiments and real-world smart farming environments. Full article

To enhance the climate adaptability and diagnostic precision of university sustainability frameworks, this study presents a critical advancement to the PICSOU (Performance Indicators for Core Sustainability Objectives of Universities) framework’s Indoor Environmental Quality (IEQ) module. The research employs a comparative approach across two distinct climate zones: the campus of Chengdu Jincheng College in a humid subtropical climate (CDJCC; Köppen Cwa) with natural ventilation, and the campus of Tallinn University of Technology in a temperate climate (TalTech; Köppen Dfb) with mechanical ventilation. A key innovation at CDJCC was the deployment of a novel, integrated sensor that combines a Frequency-Modulated Continuous Wave (FMCW) radar module for real-time occupancy detection with standard IEQ sensor suite (CO₂, PM_2.5, temperature, humidity), enabling unprecedented analysis of occupant-IEQ dynamics. At TalTech, comprehensive IEQ monitoring was conducted using standard sensors. Results demonstrated that mechanical ventilation (TalTech) effectively decouples indoor conditions from external fluctuations. In contrast, natural ventilation (CDJCC) exhibits strong seasonal coupling, reflected by a Seasonal Ventilation Efficacy Coefficient (${\lambda }_{\mathit{season}}$), indicating that seasonal differences in effective ventilation are present but vary by indoor space type under occupied conditions. Consistent with this stronger indoor–outdoor linkage, PM_2.5 infiltration was also pronounced in naturally ventilated spaces, as evidenced by a high infiltration factor ($I/O$ ratio) that remained consistently elevated. This work conclusively validates a conditional, climate-resilient workflow for PICSOU’s IEQ category, integrating these empirical coefficients to transform its IEQ assessment into a dynamic and actionable tool for optimizing campus sustainability strategies globally. Full article

Canagliflozin (CFZ), a sodium–glucose cotransporter 2 (SGLT2) inhibitor, is extensively utilized in the management of type 2 diabetes. Among its various polymorphic forms, the hemi-hydrate (Hemi-CFZ) has been selected as the active pharmaceutical ingredient (API) for CFZ tablets due to its superior solubility. However, during the production, storage, and transportation of CFZ tablets, Hemi-CFZ can undergo transformations into anhydrous (An-CFZ) and monohydrate (Mono-CFZ) forms under the influence of environmental factors such as temperature, humidity, and pressure, which may adversely impact the bioavailability and clinical efficacy of CFZ tablets. Therefore, it is imperative to develop rapid, accurate, non-destructive, and non-contact methods for quantifying An-CFZ and Mono-CFZ content in CFZ tablets to control polymorphic impurity levels and ensure product quality. This research evaluated the feasibility and reliability of using near-infrared spectroscopy (NIR) combined with partial least squares regression (PLSR) for simultaneous quantitative analysis of An-CFZ and Mono-CFZ in CFZ tablets, elucidating the quantifying mechanisms of the quantitative analysis model. Orthogonal experiments were designed to investigate the effects of different pretreatment methods and ant colony optimization (ACO) algorithms on the performance of quantitative models. An optimal PLSR model for simultaneous quantification of An-CFZ and Mono-CFZ in CFZ tablets was established and validated over a concentration range of 0.0000 to 10.0000 w/w%. The resulting model, Y_{An-CFZ/Mono-CFZ} = 0.0207 + 0.9919 X, achieved an R² value of 0.9919. By analyzing the relationship between the NIR spectral signals selected by the ACO algorithm and the molecular structure information of An-CFZ and Mono-CFZ, we demonstrated the feasibility and reliability of the NIR-PLSR approach for quantifying these polymorphic forms. Additionally, the mechanism of PLSR quantitative analysis was further explained through the variance contribution rates of latent variables (LVs), the correlations between LVs loadings and tablets composition, and the relationships between LV scores and An-CFZ/Mono-CFZ content. This study not only provides a robust method and theoretical foundation for monitoring An-CFZ and Mono-CFZ content in CFZ tablets throughout production, processing, storage, and transportation, but also offers a reliable methodological reference for the simultaneous quantitative analysis and quality control of multiple polymorphic impurities in other similar drugs. Full article

This systematic review maps and compares experimental strategies for estimating residual expansion in concrete elements affected by internal expansive reactions (IER), with emphasis on cores extracted from in-service structures. It adopts an operational taxonomy distinguishing achieved expansion (deformation already occurred, inferred through DRI/SDT or back-analysis), potential expansion (upper limit under free conditions), and residual expansion (remaining portion estimated under controlled temperature, T, and relative humidity, RH), in addition to the free vs. restrained condition and the diagnostic vs. prognostic purpose. Seventy-eight papers were included (PRISMA), of which 14 tested cores. The limited number of core-based studies is itself a key outcome of the review, revealing that most residual expansion assessments rely on adaptations of laboratory ASR/DEF protocols rather than on standardized methods specifically developed for concrete cores extracted from in-service structures. ASR predominated, with emphasis on accelerated free tests ASTM/CSA/CPT (often at 38 °C and high RH) for reactivity characterization, and on Laboratoire Central des Ponts et Chaussées (LCPC) No. 44 and No. 67 protocols or Concrete Prism Test (CPT) adaptations to estimate residual expansion in cores. Significant heterogeneity was observed in temperature, humidity, test media, specimen dimensions, and alkali leaching treatment, as well as discrepancies between free and restrained conditions, limiting comparability and lab-to-field transferability. A minimum reporting checklist is proposed (type of IER; element history; restraint condition; T/RH/medium; anti-leaching strategy; schedule; instrumentation; uncertainty; decision criteria; raw data) and priority gaps are highlighted: standardization of core protocols, leaching control, greater use of simulated restraint, and integration of DRI/SDT–expansion curves to anchor risk estimates and guide rehabilitation decisions in real structures. Full article

Extreme environments such as low pressure, high temperature, and intense radiation pose severe challenges for humidity sensors, causing conventional hygroscopic materials to exhibit sluggish responses, drift, and instability. In response, recent research has adopted multi-level strategies involving material modification, structural engineering, and packaging optimization to enhance the adaptability of humidity-sensitive materials in extreme environments. This review examines humidity sensing from an environmental perspective, integrating sensing mechanisms, material classifications, and application scenarios. The performance, advantages, and limitations of six major categories of humidity-sensitive materials, including carbon-based, metal oxides, conductive and insulating polymers, two-dimensional (2D) materials, and composites, are systematically summarized under extreme conditions. Finally, emerging development trends are discussed, highlighting a shift from material-driven to system-driven approaches. Future progress will rely on multidisciplinary integration, including interface engineering, multiscale structural design, and intelligent algorithms, to achieve higher accuracy, stability, and durability in extreme-environment humidity sensing. Full article

Family Allotment Gardens (FAGs) represent key components of urban cooling and air-purification systems. However, research has mainly focused on their social roles and on their contributions to food production. This study quantified the capacity of FAGs in Warsaw (Poland) to provide two key ecosystem services at distances up to 300 m from their boundaries: air-pollution filtration and microclimate regulation. Measurements of particulate matter (PM1, PM2.5, PM10), air temperature and relative humidity were conducted along transects inside and outside three allotment complexes in autumn 2023, a period characterised by increased traffic emissions and elevated particulate levels. The results show a moderate but significant reduction in PM concentrations inside gardens (by about 2 µg/m³; r = 0.22–0.29) and slightly higher humidity (by 2.1%; r = −0.34). The cooling effect was weak (<0.3 °C; r = 0.06), indicating a limited spatial range under autumn conditions, though selected transects exhibited stronger local effects. The results confirm that FAGs can contribute to air purification and local climate regulation, but their effectiveness depends on vegetation structure and urban context. Strengthening their role requires integration with green-infrastructure planning and emission-reduction practices within gardens. FAGs, beyond their recreational and productive value, should be recognised as active components of urban adaptation strategies. Full article

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article

The quality and yield of grafted tomato seedlings are significantly influenced by the selection of high-quality and robust rootstocks. The effectiveness of these rootstocks is dependent on various environmental factors and genetic traits. One of the most critical factors in cultivation is light, as its intensity plays a vital role in seedling growth, overall development, metabolic processes, the efficiency of the photosynthetic system, and other essential plant functions. The aim of this study was to investigate the changes in the photosynthetic system activity and the growth of tomato rootstocks depending on the light intensity. The study was conducted at the Institute of Horticulture, Lithuanian Center for Agricultural and Forestry Sciences, focusing on four tomato rootstock varieties grown in a controlled environment. The plants were grown at a temperature of +23/19 °C and a relative humidity of 55–60%, under different levels of illumination (high-pressure sodium lamps), PPFD: 150, 250 and 350 ± 10 µmol m⁻² s⁻¹. The results indicated that optimal growth and biomass accumulation occurred at around 250 µmol m⁻² s⁻¹, with the most significant growth observed in the rootstocks ‘Auroch’ and ‘Goldrake’. Higher light intensities, specifically at 350 µmol m⁻² s⁻¹, did not consistently enhance growth and could even lead to a reduction in leaf area and overall growth in some cultivars such as ‘Auroch’ and ‘TOR23901’. Although photosynthetic parameters improved with increased light intensity up to 350 µmol m⁻² s⁻¹, these enhancements did not translate into additional growth benefits. Full article

Polystigma amygdalinum the causal agent of Red Leaf Blotch (RLB), is responsible for one of the most important foliar diseases affecting almond [Prunus dulcis (Miller) D.A. Webb] in the Mediterranean Basin and the Middle East. The study is aimed at improving knowledge on RLB epidemiology and the role of environmental conditions in disease development. Field monitoring was conducted from 2022 to 2025 in three almond orchards located in Apulia (southern Italy) and characterized by different microclimatic conditions. A total of 39 cultivars, including Apulian local germplasm and international cultivars (‘Belona’, ‘Genco’, ‘Guara’, ‘Ferragnès’, ‘Filippo Ceo’, ‘Lauranne^® Avijor’, ‘Soleta’, and ‘Supernova’), were evaluated. Symptoms occurred from late spring to summer, resulting particularly severe on ‘Guara’ and ‘Lauranne^® Avijor’, whereas ‘Belona’, ‘Ferragnès’, ‘Genco’, and ‘Supernova’ exhibited the highest tolerance. To our knowledge, this is also the first report of RLB tolerance by ‘Filippo Ceo’, ‘Ficarazza’, ‘Centopezze’, and ‘Rachele piccola’ representing potential genetic resources for breeding programs. Moreover, these findings reinforced previous observations proving that RLB was less severe on medium-late and late cultivars. Disease incidence varied significantly among sites and years and was strongly associated with increased rainfall, higher relative humidity, and mild temperatures recorded in November, influencing disease occurrence in the following growing season. P. amygdalinum was consistently detected by qPCR in all RLB-affected tissues and, in some cases, from mixed early RLB + Pseudomonas-like symptoms. From some leaves with early RLB symptoms, P. amygdalinum was also successfully isolated in pure culture. Overall, our results provide clear evidence that P. amygdalinum is the sole fungal pathogen consistently associated with typical RLB symptoms in Apulia (southern Italy) and highlight important cultivar-dependent differences. Its frequent molecular detection in leaves showing atypical or mixed symptoms suggests unresolved epidemiological aspects requiring further investigation. Full article

Potato late blight is a devastating disease and threatening global potato production, necessitating accurate early prediction for effective management and yield enhancement.This paper presents the PLB-GPT, a novel generative pre-trained transformer-based model built on GPT-2 architecture, designed to forecast late blight outbreaks using meteorological data. Our method is trained and evaluated on a real-world dataset encompassing temperature, humidity, atmospheric pressure, and other climatic variables from diverse regions of China; PLB-GPT demonstrates state-of-the-art performance. The framework of PLB-GPT employs advanced fine-tuning strategies, including Linear Probing, Full Fine-Tuning, and a novel two-stage method, effectively applied across different time windows (1-day, 3-day, 5-day, 7-day). The model achieves an accuracy of 0.8746, a precision of 0.8915, and an F1 score of 0.8472 in the 5-day prediction window, surpassing baseline methods such as CARAH, ARIMA, LSTM, and Informer. These results highlight PLB-GPT as a robust tool for early disease outbreak prediction, with significant implications for agricultural disease management. Full article

Chilli (Capsicum annuum) is a popular spice and vegetable crop of significant economic importance that is cultivated worldwide in warm and humid climatic zones. Although chilli is a thermophilic crop, its quality and yield potential are significantly affected due to various abiotic factors, including extremely fluctuating temperatures beyond the optimum temperatures (18–30 °C). Global warming and anthropogenic activities lead to adverse climatic changes, imposing severe stress on growth, development, and productivity. High temperatures above 43–45 °C adversely affect chilli crops, especially during the reproductive stages, by causing immature fruit dropping, poor seed vigour, reduced number of flowers, flower abscission, aborted reproductive organs, reduced fruit set, and significant yield loss by 50%. Therefore, to reduce quantitative and qualitative losses, heat management is necessary from April to June in Pakistan, when the temperature rises beyond 40 °C. For heat management, the hybridisation of heat-resilient and high-yielding genotypes to develop heat-tolerant high-yielding hybrids appears to be a rational approach. These genetically improved hybrids inherit such characteristics that assist in maintaining vigorous growth, fruit quality, and stable yield without significant yield losses even under heat-stressed conditions. Hence, the thermotolerant chilli hybrids developed through hybridisation help to satisfy the escalating demand for chilli and guarantee the financial stability of farmers. Full article

Non-invasive and intelligent technologies have been utilized to monitor agricultural systems in real time, facilitating expedient decision-making and the reduction in animal stress in diverse climatic conditions. The objective of this study was to develop a deep learning supervised model to classify Nelore cattle (Bos indicus) into two groups: those in comfort and those under thermal stress. Thirty cattle, aged between 18 and 20 months, were evaluated between June and December 2023, resulting in 676 samples collected across four daily periods (6:00, 12:00, 18:00, and 24:00). Biotic variables included rectal temperature (RT) and respiratory rate (RR), while abiotic variables included air temperature (AT) and relative humidity (RH). The neural network model exhibited an accuracy and recall of 72% but a low specificity of 42%. These metrics indicate that while the model is effective in detecting stress situations, it faces challenges in correctly identifying animals in thermal comfort, likely due to class imbalance and the need for additional input features to capture environmental adaptability. Consequently, it can be posited that supervised learning models are valuable tools for precision livestock farming, provided that discriminatory limitations are mitigated by refining input characteristics and data balancing. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

Low-concentration trichloroethylene (TCE) and tetrachloroethylene (PCE) indoors pose a significant threat to human health due to their potent carcinogenic properties. However, existing research has predominantly focused on high-concentration scenarios in industrial settings, offering limited guidance for indoor air purification. This study investigated the adsorption mechanisms and performance regulation of coconut shell activated carbon for TCE/PCE through experimental analysis, molecular simulations, and dynamic modeling. Experimental results demonstrated that PCE, characterized by its non-polar nature and high boiling point, exhibited a substantially higher adsorption capacity than TCE. Increased humidity induced competitive adsorption between water molecules and pollutants, reducing the adsorption capacity of PCE by approximately 30%. Molecular simulations validated that water molecules occupied the active sites of oxygen-containing functional groups and pores, impeding the diffusion of TCE/PCE, while the non-polar surface of activated carbon preferentially adsorbs PCE. A dynamic prediction model developed in this study accurately forecasted breakthrough curves under varying pollutant concentrations, temperatures, humidities, and air velocities and quantified the service life of activated carbon. Response surface methodology revealed that controlling inlet concentrations (TCE < 7 ppb, PCE < 30 ppb), air velocity (<1 m/s), humidity (<50%), and temperature (<25 °C) can extend the service life of activated carbon to 3–5 months. Full article