Search Results (12,464)

This study investigates the Enzyme Biofilm Method (EBM), a biological wastewater treatment technology previously developed by the authors. EBM employs microbial-derived hydrolytic enzyme groups in the initial treatment stage to break down high-molecular-weight organic matter—such as starch, proteins, and fats—into low-molecular-weight compounds. These compounds enhance the growth of native microorganisms, promoting biofilm formation on carriers and improving treatment efficiency. Over the past decade, EBM has been practically applied in food factory wastewater facilities handling high organic loads. The enzyme groups used in EBM are derived from cultures of Bacillus mojavensis, Saccharomyces cariocanus, and Lacticaseibacillus paracasei. To clarify the system’s mechanism and ensure its practical viability, this study focused on starch—a prevalent and recalcitrant component of food wastewater—using two evaluation approaches. Verification 1: Field testing at a starch factory showed that adding enzyme groups to the equalization tank effectively reduced biological oxygen demand (BOD) through starch degradation. Verification 2: Laboratory experiments confirmed that the enzyme groups possess both amylase and maltase activities, sequentially breaking down starch into glucose. The resulting glucose supports microbial growth, facilitating biofilm formation and BOD reduction. These findings confirm EBM’s potential as a sustainable and effective solution for treating high-strength food industry wastewater. Full article

With the increasing depth of coal resource extraction, the creep characteristics of gangue backfill in deep backfill mining are crucial for the long-term deformation of rock strata. Existing research predominantly focuses on the instantaneous deformation response of either the backfill alone or the strata movement, lacking systematic studies that reflect the long-term time-dependent deformation characteristics of the strata-backfill system. This study addresses gangue–roof composite specimens with varying gangue particle sizes. Utilizing physical similarity ratio theory, graded loading confined compression creep experiments were designed and conducted to investigate the effects of gangue particle size and moisture content on the creep behavior of the gangue–roof composites. A fractional-order creep constitutive model for the gangue–roof composite was established, and its parameters were identified. The results indicate the following: (1) The creep of the gangue–roof composite exhibits two-stage characteristics (initial and steady-state). Instantaneous strain decreases with increasing particle size but increases with higher moisture content. Specimens reached their maximum instantaneous strain under the fourth-level loading, with values of 0.358 at a gangue particle size of 10 mm and 0.492 at a moisture content of 4.51%. (2) The fractional-order creep model demonstrated a goodness-of-fit exceeding 0.98. The elastic modulus and fractional-order coefficient showed nonlinear growth with increasing particle size, revealing the mechanism of viscoplastic attenuation in the gangue–roof composite. The findings provide theoretical support for predicting the time-dependent deformation of roofs in deep backfill mining. Full article

Accurate prediction of chlorophyll content in Brassica napus L. (rapeseed) is essential for monitoring plant nutritional status and precision agricultural management. The current study focuses on single cultivars, limiting general applicability. This study used unmanned aerial vehicle (UAV)-based RGB and multispectral imagery to evaluate six rapeseed cultivars chlorophyll content across mixed-growth stages, including seedling, bolting, and initial flowering stages. The ExG-ExR threshold segmentation was applied to remove background interference. Subsequently, color and spectral indices were extracted from segmented images and ranked according to their correlations with measured chlorophyll content. Partial Least Squares Regression (PLSR), Multiple Linear Regression (MLR), and Support Vector Regression (SVR) models were independently established using subsets of the top-ranked features. Model performance was assessed by comparing prediction accuracy (R² and RMSE). Results demonstrated significant accuracy improvements following background removal, especially for the SVR model. Compared to data without background removal, accuracy increased notably with background removal by 8.0% (R²p improved from 0.683 to 0.763) for color indices and 3.1% (R²p from 0.835 to 0.866) for spectral indices. Additionally, stepwise fusion of spectral and color indices further improved prediction accuracy. Optimal results were obtained by fusing the top seven color features ranked by correlation with chlorophyll content, achieving an R²p of 0.878 and an RMSE of 52.187 μg/g. These findings highlight the effectiveness of background removal and feature fusion in enhancing chlorophyll prediction accuracy. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

Fibrillins (FBNs) are indispensable for plant growth and development, orchestrating multiple physiological processes. However, the precise functional role of FBNs in cotton fiber development remains uncharacterized. This study reports a genome-wide characterization of the FBN gene family in cotton. A total of 28 GhFBN genes were identified in upland cotton, with systematic analyses of their phylogenetic relationships, protein motifs, gene structures, and hormone-responsive cis-regulatory elements. Expression profiling of GhFBN1A during fiber development revealed stage-specific activity across the developmental continuum. Transcriptomic analyses following hormone treatments demonstrated upregulation of GhFBN family members, implicating their involvement in hormone-mediated regulatory networks governing fiber cell development. Collectively, this work presents a detailed molecular characterization of cotton GhFBNs and establishes a theoretical foundation for exploring their potential applications in cotton breeding programs aimed at improving fiber quality. Full article

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

Background/Objectives: Aberrant expression of high-mobility group protein B1 (HMGB1) has been linked to cancer development and progression. Methods: To better comprehend the role of HMGB1 expression in cancer, a tissue microarray containing 14,966 samples from 134 different tumor entities and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. Results: Strong HMGB1 staining occurred in almost all normal cell types and in most cancers. Of 11,808 evaluable cancers, only 7.8% showed complete absence of HMGB1 staining (HMGB1 deficiency) while 9.9% showed 1+, 25.0% showed 2+, and 57.2% showed 3+ HMGB1 positivity. Absence of HMGB1 staining mostly occurred in pheochromocytoma (90.0%), seminoma (72.4%), gastrointestinal stromal tumor (28.6%), adrenal cortical carcinoma (25.0%), and Hodgkin’s lymphoma (25.0%). Low HMGB1 staining was linked to poor histologic grade (p < 0.0001), advanced pT stage (p < 0.0001), high UICC stage (p < 0.0001), and distant metastasis (p = 0.0413) in clear cell renal cell carcinoma, invasive tumor growth in urothelial carcinoma (pTa vs. pT2–4, p < 0.0001), mismatch repair deficiency (p = 0.0167) in colorectal cancers, and advanced pT stage in invasive breast carcinoma of no special type (p = 0.0038). Strong HMGB1 staining was linked to nodal metastases in high-grade serous ovarian carcinomas (p = 0.0213) and colorectal adenocarcinomas (p = 0.0137), as well as to poor histological grade in squamous cell carcinomas (p = 0.0010). Conclusions: HMGB1 deficiency and reduced HMGB1 expression occur in a broad range of different tumor entities. Low rather than strong HMGB1 staining is often linked to an aggressive tumor phenotype. Whether HMGB1 deficiency renders cells susceptible to specific drugs remains to be determined. Full article

The kinetics of insulin aggregation and fibril formation were studied in vitro using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy. Our investigation centered on the protein’s morphological and structural changes to better understand the transient molecular configurations that occur during the lag phase. SEM images showed that, already at early incubation stages, a network of disordered pseudo-filaments, ranging in length between 200 and 500 nanometers, develops on the surface of large aggregates. At later stages, fibrils catalyzed by protein aggregates were observed. Principal Component Analysis (PCA) of the FTIR data identified signatures of intramolecular β-sheet secondary structures forming during the lag phase and at the onset of the exponential growth phase. These absorption bands are linked to secondary nucleation mechanisms due to their transient nature. This interpretation is further supported by a chemical equilibrium model, which yielded a reliable secondary nucleation rate constant, K₂, on the order of 10⁴ M⁻² s⁻¹. Full article

Due to small population size, Population Viability Analysis (PVA) of endangered species often pools all individuals into a single matrix to decrease variation in estimation of transition rates. These pooled populations may mask significant environmental variation among populations, affecting estimates. Using 10 years of population data (2000–2010) on the endangered plant Jacquemontia reclinata in Southeastern Florida, USA, I parameterized a stage-structured matrix model and calculated annual growth rates (lambdas)and elasticity for each year using stochastic matrix models. The metapopulation model incorporating actual dynamics of the two largest populations showed a lower occupancy rate and higher risk of extinction at an earlier time compared to a model that used the average of all natural populations. Analyses were consistent that incorporating population variation versus average dynamics in modeling J. reclinata demography results in more variation and greater extinction risk. Local variation may be due to both weather (including minimum winter temperature and total annual precipitation) and local disturbance dynamics in these urban preserves. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Gastrointestinal (GI) cancers represent a significant global health burden, with high morbidity and mortality often linked to late-stage detection and metastatic disease. The progression of these malignancies is critically driven by angiogenesis, the formation of new blood vessels, and the surrounding dynamic tumor microenvironment (TME), a complex ecosystem comprising various cell types and non-cellular components. This comprehensive review, based on a systematic search of the PubMed database, synthesizes the existing literature to define the intertwined roles of angiogenesis and the TME in GI tumorigenesis. The TME’s influence creates conditions favorable for tumor growth, invasion, and metastasis, but sometimes induces resistance to current therapies. Available therapeutic strategies for inhibiting angiogenesis involve antibodies and oral tyrosine kinase inhibitors, while immune modulation within the tumor microenvironment is mainly achieved through checkpoint inhibitor antibodies and chemotherapy. Creative emerging strategies encompassing cellular therapies, bispecific antibodies, and new targets such as CD40, DLL4, and Ang2, amongst others, are focused on inhibiting proangiogenic pathways more profoundly, reversing resistance to prior drugs, and modulating the TME to enhance therapeutic efficacy. A deeper understanding of the complex interactions between components of the TME is crucial for addressing the unmet need for novel and effective therapeutic interventions against aggressive GI cancers. Full article

Electricity distribution systems face increasing challenges due to demand growth, regulatory requirements, and the integration of distributed generation. In this context, distribution companies must make strategic and well-supported investment decisions, particularly in asset reinforcement actions such as reconductoring. This paper presents a multi-stage methodology to optimize reconductoring investments under load forecasting uncertainties. The approach combines a decomposition strategy with Monte Carlo simulation to capture demand variability. By discretizing a lognormal probability density function and selecting the largest loads in the network, the methodology balances computational feasibility with modeling accuracy. The optimization model employs exhaustive search techniques independently for each network branch, ensuring precise and consistent investment decisions. Tests conducted on the IEEE 123-bus feeder consider both operational and regulatory constraints from the Brazilian context. Results show that uncertainty-aware planning leads to a narrow investment range—between USD 55,108 and USD 66,504—highlighting the necessity of reconductoring regardless of demand scenarios. A comparative analysis of representative cases reveals consistent interventions, changes in conductor selection, and schedule adjustments based on load conditions. The proposed methodology enables flexible, cost-effective, and regulation-compliant investment planning, offering valuable insights for utilities seeking to enhance network reliability and performance while managing demand uncertainties. Full article

Background: Central precocious puberty (CPP), defined as the onset of secondary sexual characteristics before age 8 in girls, is increasingly prevalent worldwide. CPP is often caused by early activation of the HPG axis, leading to accelerated growth and bone maturation. However, the diagnostic accuracy of standard bone age (BA) methods remains uncertain in this context. Objective: To compare the diagnostic accuracy of the Greulich–Pyle atlas (GPA) and Tanner–Whitehouse 3 (TW3) methods in estimating skeletal age in girls with CPP and to assess the predictive value of serum hormone levels for estimating chronological age (CA). Methods: An observational, cross-sectional diagnostic study was conducted, involving n = 109 girls aged 6–12 years with confirmed CPP (Ethics Committee approval: CHUC_2023_86; 13 July 2023). Left posteroanterior hand–wrist (PA–HW) radiographs were assessed using the GPA and TW3 methods. Anthropometric measurements were recorded, and serum concentrations of estradiol, LH, FSH, DHEA-S, cortisol, TSH, and free T4 were obtained. Comparisons between CA and BA estimates were conducted using repeated-measures ANOVA, and ANCOVA was applied to examine the hormonal predictors of CA. Results: Both GPA and TW3 overestimated CA between 7 and 12 years, with the GPA showing larger deviations (up to 4.8 months). The TW3 method provided more accurate estimations, particularly at advanced pubertal stages. Estradiol (η²_p = 0.188–0.197), LH (η²_p = 0.061–0.068), and FSH (η²_p = 0.008–0.023) emerged as the strongest endocrine predictors of CA, significantly enhancing the explanatory power of both radiological methods. Conclusions: The TW3 method demonstrated superior diagnostic accuracy over GPA in girls with CPP, especially between 7 and 12 years. Integrating estradiol, LH, and FSH into BA assessment significantly improved the accuracy, supporting a more individualized and physiologically grounded diagnostic approach. Full article

Acacia mearnsii De Wild. has been introduced to over 150 countries for its economic value. However, it easily escapes from plantations and establishes monospecific stands across plains, hills, valleys, and riparian habitats, including protected areas such as national parks and forest reserves. Due to its negative ecological impact, A. mearnsii has been listed among the world’s 100 worst invasive alien species. This species exhibits rapid stem growth in its sapling stage and reaches reproductive maturity early. It produces a large quantity of long-lived seeds, establishing a substantial seed bank. A. mearnsii can grow in different environmental conditions and tolerates various adverse conditions, such as low temperatures and drought. Its invasive populations are unlikely to be seriously damaged by herbivores and pathogens. Additionally, A. mearnsii exhibits allelopathic activity, though its ecological significance remains unclear. These characteristics of A. mearnsii may contribute to its expansion in introduced ranges. The presence of A. mearnsii affects abiotic processes in ecosystems by reducing water availability, increasing the risk of soil erosion and flooding, altering soil chemical composition, and obstructing solar light irradiation. The invasion negatively affects biotic processes as well, reducing the diversity and abundance of native plants and arthropods, including protective species. Eradicating invasive populations of A. mearnsii requires an integrated, long-term management approach based on an understanding of its invasive mechanisms. Early detection of invasive populations and the promotion of public awareness about their impact are also important. More attention must be given to its invasive traits because it easily escapes from cultivation. Full article