Search Results (808)

This study systematically investigates the heterogeneity of the Chang 6 reservoir in the Wuqi–Dingbian region of the Ordos Basin through integrated petrographic analysis using scanning electron microscopy (SEM), thin-section petrography, and mercury intrusion porosimetry. The results reveal that this feldspathic sandstone reservoir exhibits significant compositional and textural variations controlled by depositional environments. Dingbian samples displayed elevated feldspar (avg. 42.3%), lithic fragments (18.1%), and carbonate cementation (15.7%), accompanied by intense mechanical compaction and cementation processes. Pore systems in Dingbian were dominated by residual intergranular pores (58–62% of total porosity) and secondary dissolution pores. In contrast, Wuqi reservoirs demonstrated superior pore connectivity through well-developed intergranular pores (65–72%), grain boundary pores, and microfracture networks. Pore throat characterization revealed distinct architectural patterns: Wuqi exhibited broad bimodal/multimodal distributions (0.1–50 μm) with 35–40% macro-throat (>10 μm) contribution to flow capacity, while Dingbian showed narrow unimodal distributions (1–10 μm) with <15% macro-throat participation. These microstructural divergences fundamentally governed contrasting production behaviors. Wuqi wells achieved higher initial flow rates (15–20 m³/d) with 60–70% water cut, yet maintained stable production through effective displacement systems enabled by dominant macropores. Conversely, Dingbian wells produced lower yields (5–8 m³/d) with 75–85% water cut, experiencing rapid 30–40% initial declines that transitioned to prolonged low-rate production phases. This petrophysical framework provides critical insights for optimized development strategies in heterogeneous tight sandstone reservoirs, particularly regarding water management and enhanced oil recovery potential. Full article

The disposal of urban tree litter as waste has significant implications for material cycles, energy flows, and global climate change within urban ecosystems. However, the species-specific contributions of urban trees to atmospheric CO₂ emissions through soil respiration (R_S) remain poorly understood. This study investigates the effects of litter management on R_S dynamics in urban green spaces, focusing on six common species (Mangifera indica, Ficus microcarpa, Cinnamomum camphora, Bauhinia purpurea, Triadica sebifera, and Celtis sinensis) in Fuzhou, China. Three litter treatments—litter retention (CK), litter removal (RL), and litter doubling (DL)—were established to monitor monthly R_S fluctuations. Results indicate that DL significantly increased R_S rates, while RL reduced them. The increase in R_S due to litter addition was more pronounced than the decrease caused by litter removal for most species. R_S rates exhibited a unimodal seasonal pattern, peaking in summer. Furthermore, litter treatments influenced the temperature sensitivity coefficient (Q₁₀), with F. microcarpa showing the highest average Q₁₀ (4.16) and M. indica the lowest (1.88). This study underscores the critical role of litter input in modulating R_S in urban green spaces and highlights the joint but asymmetric effects of soil temperature and moisture on R_S dynamics. Full article

Trees play a critical role in climate regulation, biodiversity, and carbon storage as they cover approximately 30% of the global land area. Nowadays, Machine Learning (ML)is key to automating large-scale tree species classification based on active and passive sensing systems, with a recent trend favoring data fusion approaches for higher accuracy. The use of 3D Deep Learning (DL) models has improved tree species classification by capturing structural and geometric data directly from point clouds. We propose a fully Multimodal Tree Species Classification Network (MMTSCNet) that processes Light Detection and Ranging (LiDAR) point clouds, Full-Waveform (FWF) data, derived features, and bidirectional, color-coded depth images in their native data formats without any modality transformation. We conduct several experiments as well as an ablation study to assess the impact of data fusion. Classification performance on the combination of Airborne Laser Scanning (ALS) data with FWF data scored the highest, achieving an Overall Accuracy (OA) of nearly 97%, a Mean Average F1-score (MAF) of nearly 97%, and a Kappa Coefficient of 0.96. Results for the other data subsets show that the ALS data in combination with or even without FWF data produced the best results, which was closely followed by the UAV-borne Laser Scanning (ULS) data. Additionally, it is evident that the inclusion of FWF data provided significant benefits to the classification performance, resulting in an increase in the MAF of +4.66% for the ALS data, +4.69% for the ULS data under leaf-on conditions, and +2.59% for the ULS data under leaf-off conditions. The proposed model is also compared to a state-of-the-art unimodal 3D-DL model (PointNet++) as well as a feature-based unimodal DL architecture (DSTCN). The MMTSCNet architecture outperformed the other models by several percentage points, depending on the characteristics of the input data. Full article

Background/Objectives: Codon usage bias affects gene expression and translation efficiency across species. The effective number of codons (ENC) and GC content influence codon preference, often displaying unimodal or bimodal distributions. This study investigates the correlation between ENC and GC rankings across species and how their relationship affects codon usage distributions. Methods: I analyzed nuclear-encoded genes from 17 species representing six kingdoms: one bacteria (Escherichia coli), three fungi (Saccharomyces cerevisiae, Neurospora crassa, and Schizosaccharomyces pombe), one archaea (Methanococcus aeolicus), three protists (Rickettsia hoogstraalii, Dictyostelium discoideum, and Plasmodium falciparum),), three plants (Musa acuminata, Oryza sativa, and Arabidopsis thaliana), and six animals (Anopheles gambiae, Apis mellifera, Polistes canadensis, Mus musculus, Homo sapiens, and Takifugu rubripes). Genes in all 17 species were ranked by GC content and ENC, and correlations were assessed. I examined how adding or subtracting these rankings influenced their overall distribution in a new method that I call Two-Rank Order Normalization or TRON. The equation, TRON = SUM(ABS((GC rank₁:GC rank_N) − (ENC rank₁:ENC rank_N))/(N²/3), where (GC rank₁:GC rank_N) is a rank-order series of GC rank, (ENC rank₁:ENC rank_N) is a rank-order series ENC rank, sorted by the rank-order series GC rank. The denominator of TRON, N²/3, is the normalization factor because it is the expected value of the sum of the absolute value of GC rank–ENC rank for all genes if GC rank and ENC rank are not correlated. Results: ENC and GC rankings are positively correlated (i.e., ENC increases as GC increases) in AT-rich species such as honeybees (R² = 0.60, slope = 0.78) and wasps (R² = 0.52, slope = 0.72) and negatively correlated (i.e., ENC decreases as GC increases) in GC-rich species such as humans (R² = 0.38, slope = −0.61) and rice (R² = 0.59, slope = −0.77). Second, the GC rank–ENC rank distributions change from unimodal to bimodal as GC content increases in the 17 species. Third, the GC rank+ENC rank distributions change from bimodal to unimodal as GC content increases in the 17 species. Fourth, the slopes of the correlations (GC versus ENC) in all 17 species are negatively correlated with TRON (R² = 0.98) (see Graphic Abstract). Conclusions: The correlation between ENC rank and GC rank differs among species, shaping codon usage distributions in opposite ways depending on whether a species’ nuclear-encoded genes are AT-rich or GC-rich. Understanding these patterns might provide insights into translation efficiency, epigenetics mediated by CpG DNA methylation, epitranscriptomics of RNA modifications, RNA secondary structures, evolutionary pressures, and potential applications in genetic engineering and biotechnology. Full article

This study focused on T. strauchii in Sayram Lake, Xinjiang. In August 2023, a total of 768 samples were collected to investigate its morphological, age, and growth characteristics. T. strauchii has an elongated body with a slightly raised area behind the head. Its head is flat, the body is slender, the back contour is arc-shaped, the trunk is thick and round, and the tail is short. Principal component analysis (PCA) showed that the cumulative contribution rate of the first three principal components was 53.80%, which reflects the morphological characteristics of the species. Moreover, gender characteristics are not prominently manifested in external morphology. Discriminant analysis showed an accuracy rate of 51.80%, indicating that the accuracy of gender discrimination relying solely on external morphology is limited. The species’ age distribution ranges between 1 and 7 years old, with the dominant age around 3 years old, and age structure showing a unimodal distribution. The relationship between body length and body weight is W = 7.432 × 10⁻⁶L^3.037(R² = 0.995, n = 768). The exponent 3.037 indicates a growth pattern with priority given to body mass growth because it is greater than 3. The von Bertalanffy growth equation was selected to describe the growth of T. strauchii. The body length growth equation is L_t =139.346 [1 − e^{−0.267(t+1.639)}], and the body mass growth equation is W_t = 27.79 [1 − e^{−0.267(t+1.639)}]^3.073. The inflection point age (t_i) is 2.563, the growth coefficient (k) is 0.267, and the growth characteristic index(φ) is 3.715. The growth rate decreases with age, and the growth inflection point ages of males and females differ. The research findings provide basic data for population assessment, resource protection, and rational fishing in fishery resource management. This highlights the ecological adaptability of T. strauchii and emphasizes the importance of comprehensively considering multiple factors in fishery management. Full article

Multifractal analysis has been used in the exploration of soil grain size distributions (GSDs) in environmental and agricultural research. However, multifractal studies regarding the GSDs of sediments in braided delta-front are currently scarce. Open-source software designed for the realization of this technique has not yet been programmed. In this paper, the multifractal parameters of 61 GSDs from braided delta-front in the Paleogene Enping Formation in Huilu Low Uplift, Pearl River Mouth basin, are calculated and compared with traditional parameters. Multifractal generalized dimension spectrum curves are sigmoidal and decrease monotonically. Multifractal singularity spectrum curves are asymmetric, convex, and right-hook unimodal. The entropy dimension and singularity spectrum width ranges of silt-mudstones and gravelly sandstones are wider than those of fine and medium-coarse sandstones. The symmetry degree scopes from different lithologies are concentrated in distinguishing intervals. With the increase of grain sizes, the symmetry degree decreases overall. Both the symmetry degree and mean of GSDs are effective to distinguish the different lithologies from various depositional environments. A flexible and easy-to-use MATLAB (2021b)^® GUI (graphic user interface) package, MfGSD (Multifractal of GSD, V1.0), is provided to perform multifractal analysis on sediment GSDs. After raw GSDs imported into MfGSD, multifractal parameters are batch calculated and graphed in the interface. Then, all multifractal parameters can be exported to an Excel file, including entropy dimension, singularity spectrum, correlation dimension, symmetry degree of multifractal spectrum, etc. MfGSD is effective, and the multifractal parameters outputted from MfGSD are helpful to distinguish depositional environments of GSDs. MfGSD is open-source software that can be used to explore GSDs from various kinds of depositional environments, including water or wind deposits. Full article

As an important component of terrestrial ecosystems, mountain vegetation serves as an indicator of climate change. Due to the sensitivity of the Tibetan Plateau Mountains (TPM) to climate change and their ecological fragility, their vegetation dynamics (greenness and greening) have become a hot spot issue in global environmental change. Topography is a relatively stable environmental factor that shapes vegetation by creating localized microenvironments. However, existing research primarily focuses on the effects of climate change and human activities on vegetation dynamics. Therefore, a more comprehensive understanding of the dependence of vegetation dynamics on topography is needed. To elucidate the relationship between topography and the spatial heterogeneity of vegetation dynamics, we conducted this study using the recently released high-precision Sensor-Independent Leaf Area Index product. Through long-term trend analyses and joint comparisons of multiple topographic variables, this study elucidates key patterns: (1) North-facing slopes exhibit higher vegetation greenness and stronger greening trends than south-facing slopes, whereas east- and west-facing slopes show comparable greenness but stronger greening on west-facing slopes. (2) Vegetation greenness and greening increase with slope steepness. (3) With increasing elevation, greenness decreases progressively, while greening follows a unimodal pattern—initially increasing, then decreasing, and nearing zero at high altitudes. These findings underscore the pivotal role of topography in regulating vegetation responses to climate change. This study provides new insights into the interplay between topography and vegetation dynamics, advancing our understanding of ecological processes on the TPM and informing strategies for ecosystem management under global warming. Full article

The peach fruit fly (Bactrocera zonata), a significant polyphagous pest, poses a considerable threat to fruit crops across its expanding range. Although climate change significantly impacts pest populations, its effects on B. zonata remain understudied. This research examined B. zonata population dynamics across two distinct Egyptian ecological zones (Sohag and Ismailia Governorates) from 2013–2023 using pheromone traps and climate data. Results revealed significant spatial and temporal variations in abundance patterns. Both regions displayed a unimodal distribution, with Sohag exhibiting a distinct peak during September to November, whereas Ismailia showed a broader peak period spanning from August to December. Temperature significantly influenced population levels while precipitation showed no significant effect. Similarly, our results indicated increasing population trends in both regions despite no significant long-term temperature changes. These findings suggest that factors beyond temperature alone, such as host fruit availability, regional environmental variations, and potentially evolving agricultural practices, drive B. zonata population growth, highlighting the need for comprehensive, climate-responsive pest management strategies that account for regional variations. Full article

With the rapid development of multimodal prompt learning in unsupervised domains, prompt tuning has demonstrated significant potential for dense counting tasks. However, existing supervised methods heavily rely on annotated data, limiting their generalization capabilities. Additionally, unimodal prompt designs fail to fully leverage the complementary advantages of multimodal data, compromising the accuracy and robustness of counting systems. To address these challenges, we propose DE-CLIP, an unsupervised dense counting method based on multimodal deep shared prompts and cross-modal alignment ranking. DE-CLIP constructs hierarchically ordered textual prompts and optimizes the image encoder via cross-modal alignment ranking loss, which enforces rank-aware embedding learning by aligning visual patches with incrementally scaled textual descriptions, thereby enhancing the model’s numerical perception. The text encoder recursively injects visual information across transformer layers, achieving the progressive fusion of textual and visual prompts to improve multimodal representation. Simultaneously, the image encoder interacts deeply with textual prompts at each transformer layer, strengthening the synergy between visual features and textual semantics. A multimodal collaborative fusion module further enables bidirectional interaction between modalities via self-attention and cross-attention mechanisms, enhancing the model’s capability to comprehend and process complex scenes. The experimental results demonstrate that DE-CLIP significantly outperforms the existing supervised and unsupervised methods across multiple dense counting benchmarks, achieving superior recognition accuracy and generalization ability. This validates its exceptional performance and broad applicability in unsupervised settings. Full article

Recently, various biocompatible and biodegradable materials have garnered significant attention as cosmetic fillers for skin rejuvenation. Among these, poly ε-caprolactone (PCL), poly L-lactic acid (PLLA), poly D,L-lactic acid (PDLLA), and polydioxanone (PDO) microspheres have been developed and commercialized as a dermal filler. However, its irregularly hydrophobic microspheres pose hydration challenges, often causing syringe needle blockages and side effects such as delayed onset nodules and papules after the procedure. In this study, we synthesized a polyethylene glycol-poly D,L-lactic acid (mPEG-PDLLA) copolymer to address the limitations of conventional polymer fillers. Comprehensive characterization of the copolymer was performed using nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, and differential scanning calorimetry. The mPEG-PDLLA copolymers demonstrated a unimodal size distribution of approximately 121 ± 20 nm in an aqueous solution. The in vitro cytotoxicity and collagen genesis of mPEG-PDLLA copolymers were evaluated using human dermal fibroblast cells. In this study, angiogenesis was observed over time in hairless mice injected with mPEG-PDLLA copolymers, confirming its potential role in enhancing collagen synthesis. To assess the inflammatory response, the expression levels of the genes MMP1 and IL-1β were analyzed. Additionally, gene expression levels such as transforming growth factor-β and collagen types I and III were compared with Rejuran^® in animal studies. The newly developed collagen-stimulating PEGylated PDLLA may be a safe and effective option for skin rejuvenation. Full article

To investigate the mechanism of ground hydraulic fracturing technology in preventing mine earthquakes induced by hard–thick roof (HTR) breakage in coal mines, this study established a Timoshenko beam model on a Winkler foundation incorporating the elastoplasticity and strain-softening behavior of coal–rock masses. The following conclusions were drawn: (1) The periodic breaking step distance of a 15.8 m thick HTR on the 61,304 Workface of Tangjiahui coal mine was calculated as 23 m, with an impact load of 15,308 kN on the hydraulic support, differing from measured data by 4.5% and 4.8%, respectively. (2) During periodic breakage, both the bending moment and elastic deformation energy density of the HTR exhibit a unimodal distribution, peaking 1.0–6.5 m ahead of cantilever endpoint O, while their zero points are 40–41 m ahead, defining the breaking position and advanced influence area. (3) The PBSD has a cubic relationship with the peak values of bending moment and elastic deformation energy density, and the exponential relationship with the impact load on the hydraulic support is $F_{ZJ}=5185.2e^{0.00431L_p}$. (4) Theoretical and measured comparisons indicate that reducing PBSD is an effective way to control impact load. The hard–thick roof ground hydraulic fracturing technology (HTRGFT) weakens HTR strength, shortens PBSD, effectively controls impact load, and helps prevent mine earthquakes. Full article

Vegetation plays a crucial role in nature-based carbon neutrality solutions, exhibiting a strong correlation with climatic factors. This study employed a modified CASA (Carnegie–Ames–Stanford Approach) model to estimate Net Primary Productivity (NPP) across Cambodia, as well as Baise, Guilin and Fenyang from China representing diverse climatic zones—from 2000 to 2020. Spatiotemporal NPP patterns and their underlying mechanisms were investigated using Theil–Sen median analysis, the Mann–Kendall test and land use change matrices. The results indicate that: (1) Mean annual NPP from 2000 to 2020 was 753.68 gC·m⁻²·a⁻¹, 960.58 gC·m⁻²·a⁻¹, 768.11 gC·m⁻²·a⁻¹ and 334.20 gC·m⁻²·a⁻¹ for Cambodia, Baise, Guilin and Fenyang, respectively. While Cambodia showed a non-significant downward trend, the other regions exhibited upward trends. (2) Cambodia’s NPP demonstrated elevated values in the eastern and southwestern regions. Baise and Guilin exhibited higher NPP values at the periphery with lower central values, while Fenyang displayed a northwest–southeast gradient in NPP. (3) Forestland and cultivated land dominated the study areas with a unimodal relationship between elevation and vegetation NPP. (4) Temperature primarily influenced the NPP of Cambodia, Baise and Guilin, and precipitation was the dominant factor in Fenyang. Cambodia (tropical area) and Baise/Guilin (subtropical area), benefiting from favorable hydrothermal conditions, maintained high annual NPP. Fenyang (temperate area), with less favorable conditions, showed a strong positive correlation between precipitation and NPP. Positive correlations existed between NDVI (Normalized Difference Vegetation Index) and annual mean NPP across all study regions. (5) Annual mean NPP in the study area initially increased with elevation but declined beyond a certain altitude. These findings enhance understanding of vegetation carbon cycling across diverse climatic zones, informing accurate regional carbon sink assessments. Full article

We analyzed elevational species richness gradients (“decline”, “increase”, “unimodal”, or “bimodal”) in the Himalayan range using data from 157 publications covering both plants and animals. Our study tested the hypothesis that unimodal gradients, explainable by the geometric mid-domain effect, dominate in the mountains, while decreasing or increasing gradients result from studies that only examined limited sections of the full altitudinal range. Multivariate canonical correspondence analysis was applied to associate gradient shapes with altitude ranges, geographic locations, and the taxa studied. Our results show that, across taxa, most Himalayan altitudinal gradients exhibit a unimodal shape, with diversity peaks at approximately 2500 m a.s.l. for plants and 2200 m a.s.l. for animals. The gradient shapes were primarily influenced by three interrelated predictors: vertical range, maximum elevation, and mean elevation. Studies from the world’s highest mountain range suggest that surveys encompassing substantial portions of the elevational range tend to produce hump-shaped gradients, while incomplete sampling leads to declining or increasing species richness patterns. Full article

Background/Objectives: Schizophrenia is a severe psychiatric disorder characterized by deficits in perception and advanced cognitive functions. Prior studies have reported abnormal lateralization in cortical morphology and functional connectivity in schizophrenia. However, it remains unclear whether schizophrenia affects hemispheric asymmetry in the hierarchical organization of functional connectome. Methods: Here, we apply a gradient mapping framework to the hemispheric functional connectome to estimate the first three gradients, which characterize unimodal-to-transmodal, visual-to-somatomotor, and somatomotor/default mode-to-multiple demand hierarchy axes. We then assess between-group differences in intra- and inter-hemispheric asymmetries of these three functional gradients. Results: We find that, compared to healthy controls, patients with schizophrenia exhibit significantly altered hemispheric asymmetry in functional gradient across multiple networks, including the dorsal attention, ventral attention, visual, and control networks. Region-level analyses further reveal that patients with schizophrenia show significantly abnormal hemispheric gradient asymmetries in several cortical regions in the dorsal prefrontal gyrus, medial superior frontal gyrus, and somatomotor areas. Lastly, we find that hemispheric asymmetries in functional gradients can differentiate between patients and healthy controls and predict the severity of positive symptoms in schizophrenia. Conclusions: Collectively, these findings suggest that schizophrenia is associated with altered hemispheric asymmetry in functional hierarchy, providing novel perspectives for understanding the atypical brain lateralization in schizophrenia. Full article

Occupational health risk prediction of miners is a core issue to ensure the safety of high-risk operations. Current risk assessment methodologies face critical limitations, as conventional unimodal prediction systems frequently demonstrate limited efficacy in capturing the multifactorial nature of occupational health deterioration. This study presents a novel stacked ensemble architecture employing dual-phase algorithmic optimization to address these muti-parametric interactions. The proposed framework implements a hierarchical modeling paradigm: (1) a primary predictive layer employing heterogeneous base learners (Random Forest and Logistic Regression classifiers) to establish foundational decision boundaries, and (2) a meta-modeling stratum utilizing regularized logistic regression with hyperparameter optimization via grid search-assisted k-fold cross-validation. Empirical validation through comparative analysis reveals the enhanced ensemble achieves a mean accuracy of 90%. Receiver operating characteristic analysis confirms superior discriminative capacity (AUC = 0.89), surpassing conventional ensemble methods by 23.3 percentile points. The model’s capacity to quantify nonlinear exposure–response relationships while maintaining computational tractability suggests significant utility in occupational health surveillance systems. These findings substantiate that the proposed dual-layer optimization framework substantially advances predictive capabilities in occupational health epidemiology, particularly in addressing the complex synergies between environmental hazards and physiological responses in confined industrial environments. Full article