Search Results (1,105)

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

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains challenging due to their dense distribution and small size, along with complex background. Therefore, this study introduces a Feature Enhancement and Transformer Network (FETNet) by integrating the Feature Enhancement Module (FEM) and Transformer module (EdgeViT). Correspondently, they can strengthen both global and local features and enable accurate segmentation of small shrubs in complex backgrounds. The ablation experiments demonstrated that incorporation of FEM and EdgeViT can improve the overall segmentation accuracy, with 1.19% improvement of the Mean Intersection Over Union (MIOU). Comparison experiments show that FETNet outperforms the two leading models of FCN8s and SegNet, with the MIOU improvements of 7.2% and 0.96%, respectively. The spatial details of the extracted results indicated that FETNet is able to accurately extract dense, small shrubs while effectively suppressing interference from roads and building shadows in spatial details. The proposed FETNet enables precise shrub extraction in arid areas and can support ecological assessment and land management. 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

The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F₁ generations. In the following years, the F₁ hybrids were advanced to the BC₂F₁ and BC₂F₂ generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC₂F₂ lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC₂F₂ plants were subsequently advanced to homozygous BC₂F₃ lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security. Full article

The mountainous rivers of Central Asia host diverse ichthyofauna threatened by increasing anthropogenic pressures, particularly water pollution, abstraction, and hydropower development. This study provides valuable morphometric and ecological data for Schizothorax eurycephalus (snow trout) and Triplophysa ferganaensis (stone loach) in the Shakhimardan River basin, Uzbekistan. S. eurycephalus exhibited positive allometric growth, while T. ferganaensis showed negative near-isometric growth. The mean Fulton’s Condition Factor was 1.0 for S. eurycephalus and 0.7 for T. ferganaensis, with site-specific variations. Strong correlations among morphometric parameters, particularly length–height relationships, support non-invasive monitoring techniques. Dietary analysis revealed S. eurycephalus was predominantly herbivorous, with around 70% algae consumption. Early sexual maturity was observed in S. eurycephalus males, whereas T. ferganaensis showed no clear maturity signs, but swollen bellies suggested ongoing or recent reproductive activity. These baseline morphometric and ecological data establish a solid foundation for future ecological assessments, conservation strategies, and the design and monitoring of mitigation measures to address anthropogenic impacts in this vulnerable region. Full article

A shallow quaternary sedimentary aquifer within the river alluvial deposits of eastern Uzbekistan is increasingly vulnerable to the impacts of climate change and anthropogenic activities. Despite its essential role in supplying water for domestic, agricultural, and industrial purposes, the aquifer system remains poorly understood. This study employed a three-dimensional MODFLOW-based groundwater flow model to assess climate change impacts on water budget components under the SSP5-8.5 scenario for 2020–2099. Model calibration yielded RMSE values between 0.25 and 0.51 m, indicating satisfactory performance. Simulations revealed that lateral inflows from upstream and side-valley alluvial deposits contribute over 84% of total inflow, while direct recharge from precipitation (averaging 120 mm/year, 24.7% of annual rainfall) and riverbed leakage together account for only 11.4%. Recharge occurs predominantly from November to April, with no recharge from June to August. Under future scenarios, winter recharge may increase by up to 22.7%, while summer recharge could decline by up to 100%. Groundwater storage is projected to decrease by 7.3% to 58.3% compared to 2010–2020, indicating the aquifer’s vulnerability to prolonged dry periods. These findings emphasize the urgent need for adaptive water management strategies and long-term monitoring to ensure sustainable groundwater use under changing climate conditions. Full article

Water is essential for ecological balance, environmental sustainability, and food security, particularly in arid regions where effective water management increasingly depends on farmer cooperation. The Samarkand region of Uzbekistan, known for its favorable climate and leading role in grape production, is facing rising drought conditions. This study explores the factors influencing grape farmers’ willingness to collaborate on water management in the districts of Ishtikhan, Payarik, and Kushrabot, which together produce 75–80% of the region’s grapes. A quantitative survey of 384 grape-producing households was conducted across 19 county citizens’ gatherings (38.7% of such gatherings), and structural equation modeling was employed to analyze a framework consisting of four dimensions: norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices. The results indicate that norms and environmental concerns positively influence collaboration, suggesting a collective orientation toward sustainability. In contrast, economic barriers such as high costs and limited financial capacity significantly hinder cooperative behavior. Furthermore, a strong individual intention to adopt sustainable practices was associated with a greater likelihood of collaboration. These findings highlight the critical drivers and constraints shaping collective water use in agriculture and suggest that targeted policy measures and community-led efforts are vital for promoting sustainable water governance in drought-prone regions. Full article

This study describes the characterization of a novel thermostable α-amylase from a Bacillus licheniformis 104.K strain isolated from the Kashkadarya region of Uzbekistan. Phylogenetic analysis revealed that the thermostable α-amylase belongs to glycoside hydrolase family 13 subfamily 5 (GH13_5) and shares high sequence similarity with known α-amylases. Our results demonstrate that the recombinant α-amylase exhibits optimal activity at pH 6.0 and 90 °C, retaining full activity after 30 min at 60 °C. The addition of CaCl₂ significantly enhanced thermostability, with the enzyme retaining more than 95% of its initial activity at 70 °C after 30 min. Our findings indicate that α-amylase from B. licheniformis 104.K is a functional, thermostable enzyme with potential industrial applications. This study highlights the commercial significance of thermostable amylases and the need to identify novel, cost-effective, and sustainable sources. The results of this study will contribute to the fields of enzyme applications, stabilizing additives, and genetic engineering of thermostable genes. Full article

Icariin (ICA) is widely recognized for its health benefits. In this work, we examined the intermolecular interactions between ICA and proteinase K (PK) via multi-spectroscopic techniques and molecular simulations. The experimental findings revealed that ICA quenched the fluorescence emission of PK by forming a noncovalent complex. Both hydrogen bonding and van der Waals interactions are essential for the complex’s formation. Then Förster resonance energy transfer (FRET), competitive experiments, and synchronous fluorescence spectroscopy were adopted to verify the formation of the complex. Molecular docking studies demonstrated that ICA could spontaneously bind to PK by hydrogen bonding and hydrophobic interactions, which is consistent with the spectroscopic results. The PK-ICA complex’s dynamic stability was evaluated using a 50 ns molecular dynamics (MD) simulation. The simulation results revealed no significant structural deformation or positional changes throughout the entire simulation period. The complex appears to be rather stable, as seen by the average root-mean-square deviation (RMSD) fluctuations for the host protein in the PK-ICA complex of 1.08 Å and 3.09 Å. These outcomes of molecular simulations suggest that ICA interacts spontaneously and tightly with PK, consistent with the spectroscopic findings. The approach employed in this research presents a pragmatic and advantageous method for examining protein–ligand interactions, as evidenced by the concordance between empirical and theoretical findings. Full article

Cancer presents significant challenges owing to its complex molecular pathways and resistance to therapy. Natural metabolites have significant medicinal potential by regulating the sensing and signaling pathways associated with cancer development. Recognizing their interactions within the tumor microenvironment may unveil innovative techniques for inhibiting malignant activities and improve therapy success. This article highlights studies regarding ovarian cancer metabolism, signaling mechanisms, and therapeutic natural substances. This study summarizes clinical and experimental results to emphasise the synergistic effects of alkaloids, flavonoids, and terpenoids in improving therapeutic effectiveness and alleviating drug resistance. Bioactive compounds are essential in regulating ovarian cancer metabolism and signaling pathways, affecting glycolysis, lipid metabolism, and the survival of tumor cells. This review examines metabolic programming and essential pathways, including glycolysis, TCA cycle, lipid metabolism, PI3K/AKT/mTOR, AMPK, and MAPK, emphasizing their therapeutic significance. The integration of metabolic treatments with medicines based on natural compounds has significant potential for enhancing treatment effectiveness and mitigating therapeutic resistance. Ovarian cancer needs an integrated strategy that includes metabolic reprogramming, signaling modulation, and drugs derived from natural products. Natural chemicals provide intriguing approaches to address chemotherapy resistance and improve treatment efficacy. Further research is required to enhance these methodologies and evaluate their practical applicability for improved patient outcomes. Full article

Camellia oleifera is one of the four largest woody oil species in the world, with more than 5 million hectares planted in China alone. Reducing bud damage and improving harvesting net rate and efficiency have become the key challenges to mechanized harvesting of Camellia oleifera fruits. This paper presents a novel comb-spiral impact harvesting device primarily composed of four parts, which are lifting mechanism, picking mechanism, rotating mechanism, and tracked chassis. The workspace of the four-degree-of-freedom lifting mechanism was simulated, and the harvesting reachable area was maximized using MATLAB R2021a software. The picking mechanism, which includes dozens of spirally arranged impact pillars, achieves high harvesting efficiency through impacting, brushing, and dragging, while maintaining a low bud shedding rate. The rotary mechanism provides effective harvesting actions, and the tracked chassis guarantees free movement of the equipment. Simulation experiments and field validation experiments indicate that optimal performance can be achieved when the brushing speed is set to 21.45 r/min, the picking finger speed is set to 341.27 r/min, and the picking device tilt angle is set to 1.0°. With these parameters, the harvesting quantity of Camellia oleifera fruits is 119.75 kg/h, fruit shedding rate 92.30%, and bud shedding rate as low as 9.16%. This new model for fruit shedding and the comb-spiral impact harvesting principle shows promise as a mechanized harvesting solution for nut-like fruits. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

Background/Objectives: Antimicrobial consumption (AMC) surveillance and antimicrobial stewardship (AMS) constitute effective strategies to combat the increasing antimicrobial resistance rates worldwide. Post-Soviet countries (Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan) implemented various elements of AMC surveillance and AMS to different extents. The limited quantity and quality of data from post-Soviet countries make it difficult to assess health system performance; therefore, this region is a blind spot in global AMR monitoring. This systematic review assesses and characterises AMC surveillance and AMS implementation in post-Soviet countries. Methods: Evidence was compiled via a search in PubMed, Google Scholar, Embase, CyberLeninka, and Scopus. The eligibility criteria included AMC surveillance- and AMS-related papers in human health within defined regions and timelines. Some literature from the official websites of international and national health organisations was included in the search. Results: As a result of the searches, screening, and critical appraisal, three peer-reviewed publications and 31 documents were selected for analysis. Eleven out of fifteen countries with updated national action plans for combating antimicrobial resistance have defined AMC surveillance and AMS as strategic objectives. All 15 examined countries submitted antimicrobial consumption data to international networks and reported the existence of approved laws and regulations on antibiotic sales. However, disparities exist in the complexity of monitoring systems and AMS implementation between high-income and low-income countries in the region. Conclusions: This review provides key insights into the existing AMC surveillance and AMS implementation in former Soviet countries. Although the approach of this review lacks quantitative comparability, it provides a comprehensive qualitative framework for national-level AMC surveillance and AMS system assessment. Full article

Xylosalsola chiwensis (Popov) Akhani & Roalson is listed in the Red Data Book of Kazakhstan as a rare species with a limited distribution, occurring in small populations in Kazakhstan, Uzbekistan, and Turkmenistan. The aim of this study is to deepen the understanding of the ecological conditions of its habitats, the floristic composition of its associated plant communities, the species’ morphological and anatomical characteristics, and its molecular phylogeny, as well as to identify the main threats to its survival. The ecological conditions of the X. chiwensis habitats include coastal sandy plains and the slopes of chinks and denudation plains with gray–brown desert soils and bozyngens on the Mangyshlak Peninsula and the Ustyurt Plateau at altitudes ranging from −3 to 270 m above sea level. The species is capable of surviving in arid conditions (less than 100 mm of annual precipitation) and under extreme temperatures (air temperatures exceeding 45 °C and soil surface temperatures above 65 °C). In X. chiwensis communities, we recorded 53 species of vascular plants. Anthropogenic factors associated with livestock grazing, industrial disturbances, and off-road vehicle traffic along an unregulated network of dirt roads have been identified as contributing to population decline and the potential extinction of the species under conditions of unsustainable land use. The morphometric traits of X. chiwensis could be used for taxonomic analysis and for identifying diagnostic morphological characteristics to distinguish between species of Xylosalsola. The most taxonomically valuable characteristics include the fruit diameter (with wings) and the cone-shaped structure length, as they differ consistently between species and exhibit relatively low variability. Anatomical adaptations to arid conditions were observed, including a well-developed hypodermis, which is indicative of a water-conserving strategy. The moderate photosynthetic activity, reflected by a thinner palisade mesophyll layer, may be associated with reduced photosynthetic intensity, which is compensated for through structural mechanisms for water conservation. The flow cytometry analysis revealed a genome size of 2.483 ± 0.191 pg (2n/4x = 18), and the phylogenetic analysis confirmed the placement of X. chiwensis within the tribe Salsoleae of the subfamily Salsoloideae, supporting its taxonomic distinctness. To support the conservation of this rare species, measures are proposed to expand the area of the Ustyurt Nature Reserve through the establishment of cluster sites. Full article

The escalating impacts and occurrence of wildfires threaten the public, economies, and global ecosystems. Physiologically declining or dead trees are a great portion of the fires because these trees are prone to higher ignition and have lower moisture content. To prevent wildfires, hazardous vegetation needs to be removed, and the vegetation should be identified early on. This work proposes a real-time fire risk tree detection framework using UAV images, which is based on lightweight object detection. The model uses the MobileNetV3-Small spine, which is optimized for edge deployment, combined with an SSD head. This configuration results in a highly optimized and fast UAV-based inference pipeline. The dataset used in this study comprises over 3000 annotated RGB UAV images of trees in healthy, partially dead, and fully dead conditions, collected from mixed real-world forest scenes and public drone imagery repositories. Thorough evaluation shows that the proposed model outperforms conventional SSD and recent YOLOs on Precision (94.1%), Recall (93.7%), mAP (90.7%), F1 (91.0%) while being light-weight (8.7 MB) and fast (62.5 FPS on Jetson Xavier NX). These findings strongly support the model’s effectiveness for large-scale continuous forest monitoring to detect health degradations and mitigate wildfire risks proactively. The framework UAV-based environmental monitoring systems differentiates itself by incorporating a balance between detection accuracy, speed, and resource efficiency as fundamental principles. Full article