Search Results (344)

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

This study presents an integrated spatiotemporal assessment of drought conditions in the Thung Kula Ronghai region of Northeastern Thailand from 2001 to 2023. Multiple satellite-derived drought indices, including SPI, SPEI, RDI, and AI, together with NDVI anomalies, were used to detect seasonal and long-term drought dynamics affecting rainfed Hom Mali rice production. The results show that dry season droughts now affect up to 17 percent of the region’s agricultural land in some years, while severe drought zones persist across more than 2.5 million hectares over the 20-year period. In the most recent 5 years, approximately 50 percent of cultivated areas experienced moderate to severe drought conditions. The RDI showed the strongest correlation with NDVI anomalies (r = 0.22), indicating its relative value for assessing vegetation response to moisture deficits. The combined index approach delineated high-risk sub-regions, particularly in central Thung Kula Ronghai and lower Surin, where drought frequency and severity have intensified. These findings underscore the region’s increasing exposure to dry-season water stress and highlight the need for site-specific irrigation development and adaptive cropping strategies. The methodological framework demonstrated here provides a practical basis for improving drought monitoring and early warning systems to support the resilience of Thailand’s high-value rice production under changing climate conditions. Full article

Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022–2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January–June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha⁻¹), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R² of 0.802 (RMSE = 983.8 kg ha⁻¹), the protein model an R² of 0.786 (RMSE = 1.71%), and the RFV model an R² of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures—particularly in March and May—and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management—prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity—can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions. Full article

It is vital to stabilize pillar dams in underground reservoirs in coal mine goafs to protect groundwater resources and quarry safety, practice green mining, and protect the ecological environment. Considering the actual occurrence of coal pillar dams in underground reservoirs, acoustic emission (AE) mechanical tests were performed on dry, naturally absorbed, and soaked coal samples. According to the mechanical analysis, Quantitative analysis revealed that dry samples exhibited the highest mechanical parameters (peak strength: 12.3 ± 0.8 MPa; elastic modulus: 1.45 ± 0.12 GPa), followed by natural absorption (peak strength: 9.7 ± 0.6 MPa; elastic modulus: 1.02 ± 0.09 GPa), and soaked absorption showed the lowest values (peak strength: 7.2 ± 0.5 MPa; elastic modulus: 0.78 ± 0.07 GPa). The rate of mechanical deterioration increased by ~25% per 1% increase in moisture content. It was identified that the internal crack development presented a macrofracture surface initiating at the sample center and expanding radially outward, and gradually expanding to the edges by adopting AE seismic source localization and the K-means clustering algorithm. Soaked absorption was easier to produce shear cracks than natural absorption, and a higher water content increased the likelihood. The b-value of the AE damage evaluation index based on crack development was negatively correlated with the rock damage state, and the S-value was positively correlated, and both effectively characterized it. The research results can offer reference and guidance for the support design, monitoring, and warning of coal pillar dams in underground reservoirs. (The samples were tested under two moisture conditions: (1) ‘Soaked absorption’—samples fully saturated by immersion in water for 24 h, and (2) ‘Natural absorption’—samples equilibrated at 50% relative humidity and 25 °C for 7 days). Full article

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

Soil bacterial community composition and diversity can be an important bioindicator for assessing ecosystem stability, and photovoltaic (PV) shading is a key factor influencing soil bacterial communities in rocky desertification areas; however, how the composition and diversity of soil bacterial communities change with PV operation duration remains unclear. Focusing on the experimental demonstration site of Shilin ecological photovoltaic (PV) power plant in Yunnan Province, we compared soil properties under PV arrays and non-PV control areas with different operation durations (7 and 13 years). The results showed that long-term PV operation significantly increased soil TN and TK content compared to CK, while increasing Ure and ALP activities, but inhibiting CAT activity and decreasing soil moisture, pH, SOC, and TP. High-throughput sequencing revealed stable dominant bacterial phyla (e.g., Aspergillus, Acidobacteriota) and beneficial genera (e.g., RB41, Sphingomonas), with an increase in relative abundance of Bacillota-like phyla but a decrease in Acidobacterium. The α-diversity (ACE, Chao1 index) and β-diversity of soil bacteria greatly increased with years of PV operation, reaching a maximum in the 13-year PV operation area. Correlation analyses showed that differences in soil bacterial communities in regions with different years of PV operation were mainly influenced by differences in PH and enzyme activities. Full article

In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen’s Slope Estimation and the Mann–Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low–low (L-L) clusters in the north and high–high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150–200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management. Full article

The microbial community is one of the key indicators for evaluating the health of alpine marsh wetlands, and understanding the composition and health of alpine wetland communities provides a scientific rationale for conservation and restoration efforts. Taking the alpine marsh wetlands in Qilian Mountain National Park, Qinghai Province, as the research object, 27 soil samples (0–30 cm depth) were collected in July 2024 from three types of wetlands: non-degraded (ND), low-level degraded (LD), and heavily degraded (HD). Using high-throughput sequencing, PICRUSt2 functional prediction, nonmetric multidimensional scaling (NMDS), and redundancy analysis (RDA), we analyzed the bacterial community structure and functional characteristics as well as the soil physicochemical properties across different degradation levels and soil depths. Pearson correlation analysis and RDA were used to identify key soil indicators influencing microbial community characteristics. The results showed that (1) compared to ND, the relative abundance of Acidobacteriota increased from 12.3% to 23.7%, and that of Pseudomonadota increased from 28.5% to 35.1% in HD wetlands. Meanwhile, the Shannon index rose from 5.31 in ND to 6.52 in HD, indicating significantly increased microbial community diversity and complexity with wetland degradation (p < 0.05). (2) Vertically, the six major primary metabolic functions gradually weakened with increasing soil depth in all three types of wetlands, the relative abundance of Proteobacteria decreased from 0 to 30 cm, and the α-diversity indices of soil bacteria also declined with depth. (3) Compared to ND, LD and HD showed significantly lower soil moisture content, organic matter, and total organic carbon (p < 0.05), while total potassium and pH increased significantly (p < 0.05). With increasing depth, total nitrogen significantly decreased across all degradation types (p < 0.05). Bacterial diversity, as measured by the Shannon and Simpson indices, showed a significant correlation with several soil properties (moisture, organic matter, total nitrogen, total potassium, cation exchange capacity, and total organic carbon; p < 0.05). Furthermore, pH emerged as a primary environmental driver shaping microbial community structure across different soil depths. These findings offer technical guidance and a theoretical framework for comprehending the degradation and restoration dynamics of alpine marsh wetland ecosystems in the Qilian Mountains. Full article

In the present study, groups of 10 adult males from wild-type strain Drosophila melanogaster Canton-S and corresponding mutant tab2^201Y were continuously observed using automatic digitization. Data based on instantaneous movement and cumulated movement positions were obtained for micro-areas providing space for resources (food and moisture) and for activity (edge area and intermediate area [between edge and center-diffusion areas]) within the observation arena for 24 h. The results confirmed the natural tendency of local aggregation among individuals within the observation arena (14 cm × 14 cm) at a relatively low density of ten individuals. For Canton-S, temporal cooccurring patterns among different parameters were observed as time progressed, with two primary patterns identified in the resource supply areas: single peak and double peaks. The single peak was observed with maximum speed and I-index, indicating minimum degree of isolated individuals from groups, during the transition from the photoperiod to the scotoperiod. The double peaks occurred before (mid-to-late photoperiod) and after (end of scotoperiod) the single peak, co-occurring temporally with a number of parameters including duration rates, stop number (total occurrence of pauses), stop time (total duration of pauses), mean crowding (MC), and social space index (SSI), indicating local aggregations for feeding in accordance with maximum durations in resource supply areas. Temporally cooccurring trends in parameters were also found with the stop number and SSI in micro-areas associated with activity, indicating that short pauses were needed to keep balance between attraction and repulsion between nearby individuals. Overall, the measured parameters varied depending on the micro-area, light phase, and strain. In particular, behavioral differences were observed for tab2^201Y, including an increase in speed, especially in the areas related to activity during the scotoperiod. Between strains, behavioral differences in the measured parameters were observed less for tab2^201Y than Canton-S. Full article

Seepage forces due to the flow of water inside embankment hydraulic structures, such as dams or levees, result in internal erosion or piping. This will result in a reduction in soil strength, causing the failure of hydraulic structures. Stabilization of the soil is one of the most effective approaches to avoid such catastrophic failure and prevent significant loss of life and property. The objective of this research is to stabilize sandy soil against internal erosion using fly ash (FA) alone and fly ash mixed with alkali-activated binder (NaOH). Although fly ash is commonly used for clay soil, its reactivity with alkali activators like NaOH makes it a potential candidate for stabilizing non-cohesive sandy soils when combined with alkaline solutions. A well-graded sandy soil was selected and mixed with fly ash alone and fly ash with sodium hydroxide at different percentages. Compaction curves were determined for each percentage, and specimens from the mix were remolded at 98% relative compaction and optimum moisture content corresponding to the compaction curve value. The hole erosion test (HET) was employed to evaluate internal erosion parameters. During the hole erosion test, seepage conditions were simulated by applying a controlled water flow through remolded specimens to replicate erosion caused by internal seepage forces. Additionally, the internal erosion parameters were evaluated at different curing times (2 days, 7 days, and 28 days were selected to capture short-term, intermediate, and long-term effects of chemical reactions on soil stabilization). Parameters such as the friction factor, coefficient of soil erosion, and critical shear stress were obtained, and the erosion rate index (I_HET) was determined. It was found that using FA–NaOH significantly reduced internal erosion and increased the erosion rate index and the critical shear of the soil. The addition of 10% fly ash mixed with activated-alkali binder at 7 days curing time stabilized the soil against erosion. At this percentage, the erosion rate index equal to 5.3 and soil was categorized as: “very slow erosion”. However, mixing the sand with fly ash alone has a small or insignificant effect on the internal erosion of the soil, especially at higher percentages of fly ash. The optimum percentage of fly ash alone to improve the soil resistance to internal erosion was found to be 5% at 28 days of curing time where the soil rated as “moderately slow”. Full article

Vegetation plays an important role in carbon sequestration in terrestrial ecosystems and is affected by climate change and human activities. As a major factor affecting vegetation growth, the role of soil moisture in the impacts of climate change on vegetation is not well understood. Therefore, the effects of climate change on net primary productivity (NPP) may be underestimated. In this study, we analyzed the spatial distribution of NPP and land use degree comprehensive index (LDCI) in China from 2001 to 2020. The actual and relative contributions of climate change and human activities to NPP variation were explored. The findings indicated that NPP trended upward in 73.12%, 66.78%, and 81.34% of woodland, grassland, and cropland areas, respectively. Most of the woodland and grassland showed a decreasing trend in LDCI, while 48.63% of the cropland showed an increasing trend. The positive joint effects of climate change and human activities increased the NPP of woodlands, grasslands, and croplands by 42.83%, 53.49%, and 45.22%, respectively. Human activities (55.04%) contributed more to NPP than did climate change (44.96%). Analyzing the response of NPP (woodlands, grasslands, and croplands) to climate change and human activities in China is conducive to taking more targeted measures for different land use types to increase carbon sinks in terrestrial ecosystems. Full article

The accurate determination of discrete element parameters is crucial for ensuring reliable results in simulating the critical post-harvest stages of rice grain (processing, transportation, and storage) with different moisture contents. To determine the discrete element parameters, a physical model of rice grain was constructed by the multi-sphere (MS) modeling approach. Using the repose angle as the evaluation index, the discrete element parameters of rice grain were calibrated and optimized through the Plackett–Burman (PB) test, the steepest climbing test, and the Box–Behnken (BB) test using EDEM software. A moisture content–significance discrete element parameters model was further developed based on a moisture content–repose angle model ($R^2$ = 0.992) and a repose angle–significance discrete element parameters model ($R^2$ = 0.970). The calibration results showed that the relative error between the simulated and actual repose angle did not exceed 3.52%. Meanwhile, the cylinder lifting method and unloading mass flow rate verification were performed. And the results showed that the relative errors of the repose angle and mass flow rate of rice grain did not exceed 2.09% and 7.72%, respectively. The study provides a general and reliable method for determining the parameters of discrete element method simulation for rice grain with different moisture contents. Full article

A deeper understanding of growth–climate relationships in natural forests (NFs) and planted forests (PFs) is crucial for the prediction of climate change impacts on forest productivity. Yet, the mechanisms and divergences in climatic responses between these forest types remain debated. This study investigated P. tabulaeformis NFs and PFs in China using tree-ring chronologies to analyze their radial growth responses to climatic factors and associated temporal–spatial dynamics. The results reveal significant negative correlations between radial growth and mean temperatures (Tmean) in August of the previous year and June of the current year, and positive correlations were observed with the September standardized precipitation evapotranspiration index (SPEI) of the previous year and May precipitation (PPT) and SPEI of the current year. Compared with NFs, PFs exhibited a heightened climatic sensitivity, with stronger inhibitory effects from prior- and current-year growing-season temperatures and greater SPEI influences during the growing season. Moving window analysis demonstrated higher temporal variability and more frequent short-term correlation shifts in PF growth–climate relationships. Spatially, NFs displayed latitudinal divergence, autumn Tmean shifted from growth-suppressive in southern regions to growth-promotive in the north, and winter SPEI transitioned from positive to negative correlations along the same gradient. However, PFs showed no significant spatial patterns. Relative importance analysis highlighted water availability (PPT and SPEI) as the dominant driver of NF growth, whereas temperature, moisture, and solar radiation co-regulated PF growth. These findings provide critical insights into climate-driven growth divergences between forest types and offer scientific support for the optimization of NF conservation and PF management under accelerating climate change. Full article

Wildfires pose significant threats to tropical ecosystems, yet, fire precursors in these regions are not well understood. This study conducts a statistical analysis of fire precursors in the tropical area of Antioquia, Colombia, using remote sensing data from VIIRS and MODIS satellites. We integrated and pre-processed data on fire occurrences, the Enhanced Vegetation Index (EVI), land classifications, and various weather and soil variables to ensure compatibility for statistical analysis. Our findings reveal that reductions in EVI by 3.9% and relative humidity by 21%, coupled with increases in air temperature by 4.9 °C, soil temperature by 2.77 °C, and wind speed by 0.17 m/s, are significant precursors to fire occurrences in this tropical region. Notably, precipitation and soil moisture did not show definitive correlations with fire events, contrasting with findings in temperate regions. Spatial and temporal analyses indicate that fires are more frequent during the first three months of the year and tend to recur in specific areas, suggesting persistent environmental risks and human influences, particularly in built-up areas. The limitations of the study include reliance on a single land classification dataset from 2020, which may not account for land cover changes over the study period, and the coarse temporal resolution of EVI data that could overlook rapid vegetation changes. Despite these limitations, the results provide valuable insights for enhancing early warning systems, informing policy development, and improving resource allocation for fire risk management in tropical ecosystems. Full article

Objective: This study aimed to develop and characterize the physicochemical properties of a self-emulsion drug delivery system (SNEDDS) incorporating galangal extract (GE) and lemongrass oil (LGO). Then, to develop mouthwash powders containing GE- and LGO-loaded SNEDDS (GL-mouthwash powder) as a promising alternative for preventing and treating denture stomatitis. Methods: The solubility of GE in various vehicles was determined. Subsequently, pseudo-ternary phase diagrams of the different ingredients, oil (LGO), surfactant (Tween^® 80), and co-surfactant (Propylene glycol) were selected to develop the SNEDDS. Then, SNEDDS containing GE and LGO (GL-SNEDDS) were prepared and characterized. The optimized liquid GL-SNEDDS was transformed into GL-mouthwash powder by absorbing onto mannitol and blending with a sweetener. Subsequently, various evaluations including drug recovery, moisture content, emulsification time, stability, anti-Candida activity, and in vitro cytotoxicity were performed. Results: The developed SNEDDS formulation improved GE and LGO solubility. The optimized GL-SNEDDS exhibited a small droplet size of 148.2 ± 2.1 nm with a polydispersity index of 0.11 ± 0.03 and a zeta potential of 2.14 ± 0.11 mV. In addition, the GL-mouthwash powder demonstrated a high drug recovery of >80% with a low moisture of <10% and exhibited greater physicochemical stability under accelerated conditions. The developed GL-mouthwash powder rapidly formed a stable nanoemulsion within 2 min after reconstitution. Interestingly, GL-mouthwash powder exhibited strong anti-Candida activity with no toxicity to human fibroblast cells, which demonstrated superior biocompatibility relative to existing commercial products. Conclusions: These findings suggest that GL-mouthwash powder has potential as an alternative prevention and treatment of oral Candida infection. Full article