Search Results (1,233)

Fruit shape is determined by patterns of cell division and expansion during early development, yet the upstream transcription factors coordinating cell wall dynamics and cytoskeletal organization remain largely unknown. Here, we report that SlbHLH113, a bHLH transcription factor, positively regulates tomato fruit elongation. Overexpression (OE) of SlbHLH113 produced elongated fruits with increased length/width ratio, whereas RNAi lines exhibited flattened fruits. Histological analysis revealed that SlbHLH113 alters columella cell polarity—promoting elongated cell morphology without affecting cell area—and reduces columella–placenta width and locule width, without altering pericarp thickness. Transcriptomic profiling identified 87 differentially expressed genes in OE lines, with enrichment in cell wall-related processes. Notably, a pectate lyase gene (PL5) and an expansin gene (EXT90) were down-regulated, while genes involved in oriented cellulose deposition (COBRA4) and ethylene signaling were up-regulated. Importantly, SlbHLH113 physically interacts with the microtubule-associated protein SlIQD21a, as demonstrated by yeast two-hybrid and luciferase complementation assays. Finally, SlbHLH113 did not affect major nutrient contents in red-ripe fruits. Collectively, our findings identify SlbHLH113 as a novel regulator of tomato fruit shape that might act through cell polarity control, cell wall remodeling, and interaction with a microtubule-associated protein, offering a potential target for improving fruit morphology without compromising nutritional quality. Full article

Community-based drug rehabilitation (CBDR) in China involves multiple types of frontline workers, yet little empirical research has examined how these workers carry out their respective roles and collaborate in practice. This study explored the roles, collaborative practices, and role boundaries of drug rehabilitation social workers (DRSWs) and community drug control officers (CDCOs) in CBDR in Guangzhou, China. Semi-structured in-depth interviews were conducted with 23 DRSWs and 9 CDCOs across two sequential phases, and data were analyzed using reflexive thematic analysis. The findings revealed that DRSWs primarily performed professional rehabilitation services alongside administrative assistance, while CDCOs focused on administrative management, support for enforcement-related procedures, and upward resource advocacy. Five areas of collaboration were identified, characterized by a spontaneous complementary division of labor. However, role boundary ambiguity was also observed at three interconnected levels: DRSWs’ administrative workload had expanded beyond an assisting capacity, some CDCOs described care-giving practices that approached the professional domain of social work, and workers reported that persons with drug use histories (PWUDs) often had difficulty distinguishing between the two roles. These findings highlight the need for clearer role definitions and institutionalized coordination mechanisms in CBDR. Full article

Forecasting and intelligent operation of HVAC systems in public buildings are crucial for energy conservation, carbon reduction, and enhancing occupant comfort. This area has seen significant progress. However, for intermittent HVAC systems, noise reduction techniques applied to periodic load sequences may distort the load during start–stop transitions. To tackle this challenge, an innovative hybrid prediction method, GCMWSG-HOA-LightGBM, is proposed in this study. The GCMWSG filter utilizes period division and boundary protection mechanisms to avoid cross-period coupling errors in traditional filters, preserving the authenticity of cooling load start–stop transitions. Additionally, the HOA is employed to automatically optimize the hyperparameters of the LightGBM model, enhancing both parameter tuning efficiency and prediction accuracy. The proposed model is validated using operational data from an office building in Foshan, Guangdong Province, and its performance is compared with several hybrid models: HOA-LightGBM, BWO-LightGBM, AO-LightGBM, TPE-LightGBM, HOA-RF, BWO-RF, AO-RF and TPE-RF. The results show that the GCMWSG-HOA-LightGBM model outperforms all comparison models across all metrics, achieving R², MAPE, and CVRMSE values of 92.22%, 7.05%, and 9.46%, respectively, highlighting its accuracy and stability. The findings provide an efficient solution for intelligent load prediction of HVAC systems with intermittent and periodic characteristics, offering valuable engineering insights and theoretical guidance. Full article

This study examines the multidimensional nature of poverty and its underlying local determinants within the Suri Sadar Sub-Division of Birbhum District, Eastern India, an area marked by sharp ecological and socio-economic contrasts. Adopting a mixed-method approach, the research integrates primary household survey data (2024-25) with secondary spatial datasets to construct a comprehensive analytical framework. The extent and intensity of multidimensional poverty were measured using the Alkire–Foster (AF) method, while the determinants were identified through a Binary Logistic Regression model. Findings reveal that multidimensional poverty in the region is deeply rooted in the intersection of human, environmental, and spatial factors rather than mere income deprivation. Approximately 26.8 per cent of households were found to be multidimensionally poor, with the western plateau blocks, i.e., Rajnagar, Khoyrasole, and Md. Bazar, showing the highest deprivation levels. Spatial poverty drivers include education, agriculture, and gender equality improvements. Policy implications emphasise the need for geographically tailored, multi-sectoral interventions that focus on human capability, investing in infrastructure, and promoting gender-inclusive development. By elucidating the localized dynamics of poverty, this research contributes to the broader discourse on spatial inequality and sustainable development in rural Eastern India, offering actionable insights for evidence-based regional planning and targeted poverty alleviation. Full article

This paper investigates an adaptive pilot-assisted channel estimation framework for orthogonal frequency-division multiplexing (OFDM)-based high-speed railway (HSR) communications over non-stationary wideband channels. Within this framework, a channel-aware adaptive pilot insertion (CA-API) mechanism is combined with an linear minimum mean square error (LMMSE) shrinkage technique to adjust pilot density based on temporal channel variations. Using the refined pilot-domain observations, three time-domain channel estimators namely piecewise cubic Hermite interpolation (PCHIP), autoregressive (AR), and Gaussian process regression (GPR), are comparatively evaluated under measurement-based HSR channel models. Simulation results across Remote Area (RA), Closer Area (CEA), and Close Area (CA) conditions demonstrate that the benefit of adaptive pilot scheduling is strongly scenario-dependent. In RA and CEA, the CA-API scheme reduces overhead while maintaining channel reconstruction accuracy close to that of the fixed-pilot baseline, with average overhead reductions of 38% and 30%, respectively. Under the more dispersive CA condition, the adaptive mechanism tends to increase pilot density to preserve reliable channel tracking. Among the evaluated algorithms, GPR delivers the highest estimation accuracy, AR provides a balanced trade-off between accuracy and implementation complexity, and PCHIP is less accurate but remains attractive because of its low complexity. This study provides practical insights into the joint design of adaptive pilot scheduling and channel estimation for HSR wireless communication systems. Full article

Background: Splenic flexure cancers represent a small but anatomically complex subset of colorectal malignancies, characterised by variable vascular supply and lymphatic drainage. This has led to ongoing uncertainty regarding the optimal extent of resection and level of vascular ligation. With increasing adoption of robotic surgery, a range of operative approaches has been described, although these remain inconsistently reported and poorly synthesised. Aim: We aimed to map operative heterogeneity in robotic splenic flexure cancer surgery, focusing on resection extent, vascular ligation, reconstruction strategy, and technical configuration. Methods: A structured scoping review was performed in accordance with PRISMA-ScR guidelines. MEDLINE (PubMed) and Embase (Ovid) were searched from inception to January 2026. Studies reporting robotic resection for primary splenic flexure colon cancer with extractable operative detail were included. Data were synthesised descriptively and grouped into three tiers based on study design: comparative cohorts, case series, and technical reports. Results: Sixteen studies comprising 97 robotic resections were included. Three were comparative cohort studies; two were case series, and eleven were single-case technical reports. Comparative studies consistently described a flexure-directed segmental resection with preservation of the inferior mesenteric artery and division of the left colic artery. In contrast, there was greater variability in management of the middle colic branches and inferior mesenteric vein. Reconstruction strategy differed across tiers, with extracorporeal anastomosis more commonly reported in comparative cohorts, while intracorporeal techniques were frequently described in technical series. Anastomotic configuration, specimen extraction, and use of indocyanine green fluorescence varied widely. Terminology relating to complete mesocolic excision and D2/D3 lymphadenectomy was inconsistent and often poorly defined. Conclusions: Robotic splenic flexure surgery shows broad agreement in overall resection strategy but considerable variability in vascular control, reconstruction, and technical execution. This likely reflects differences in reporting rather than clear differences in oncological intent. A more consistent and structured operative description is needed to allow meaningful comparison and to support the development of evidence-based approaches in this technically demanding area. Full article

Background: Collegiate gymnastics imposes high repetitive loads on the lower extremities, particularly the Achilles and patellar tendons, yet longitudinal data describing tendon adaptation across a competitive season remain limited. Objectives: To examine seasonal changes in Achilles and patellar tendon morphology (thickness, cross-sectional area [CSA], echogenicity, vascularity, and symmetry) across a twelve-month competitive cycle in Division I female gymnasts and to explore relationships with pain. Methods: This longitudinal observational study included twenty-five Division I female gymnasts (age: 20.0 ± 1.6 years; height: 159.5 ± 6.2 cm; weight: 57.8 ± 5.7 kg). Bilateral ultrasound assessments of the Achilles and patellar tendons were performed at three time points (post-summer, preseason, and postseason). Tendon thickness, CSA, echogenicity, and vascularity were evaluated using standardized imaging protocols. Symmetry indices were calculated, and pain was assessed using validated scales. Normality was assessed using appropriate statistical tests. Parametric data were expressed as mean ± standard deviation (SD), and non-parametric data as median and interquartile range. Paired comparisons were conducted using paired t-tests or Wilcoxon signed-rank tests, with Holm correction applied for multiple comparisons. Results: Achilles tendon thickness increased from summer to postseason (p < 0.05), with no significant changes in CSA after adjustment. Echogenicity and vascularity remained unchanged. Patellar tendon morphology was largely stable; however, left proximal thickness decreased from summer to preseason and remained reduced at postseason (p < 0.01), with no other consistent regional changes. Pain prevalence increased modestly across the season without a clear lateralized pattern or association with symmetry indices. Conclusions: Achilles tendon thickness appears to be a sensitive marker of seasonal adaptation in female collegiate gymnasts, whereas patellar tendon morphology remains stable. These findings support the use of longitudinal ultrasound monitoring for athlete screening and load management. Full article

China hosts a large number of closed coal mines containing abundant residual resources. Driven by resource recycling, mine safety, environmental protection, and the dual-carbon goals, research on gas resources in closed coal mines has expanded rapidly. In some closed mines, substantial unmined coal resources remain with high gas content, making in situ gas resources a key focus of investigation. Given the Xinzhuangzi closed coal mine as a case study, this study analyzed the distribution of coal resources based on the monitoring results of coal extraction and remaining reserves, and the distribution of gas content based on regression equation. Furthermore, it applied a volumetric calculation method to estimate the gas resources of any a certain unit, all units, and summarize the gas resources across different coal seams, structural divisions, mining levels and the entire coal mine, thereby characterizing the in situ gas resources. The results indicated that the area below −412 m in the closed Xinzhuangzi coal mine was favorable for in situ gas resource development, containing 20,061.1 × 10⁴ t of coal resource and 2250.32 × 10⁶ m³ of gas resources, with a gas resource abundance of 1.96 × 10⁸ m³/km². C13, B11b, B4, B7a, B6, and B8 were favorable targets for in situ gas resources, each containing over 100 × 10⁶ m³ of gas resources, and these seams were thick and stable. Levels 6 and 7 were favorable zones for in situ gas resources, each containing abundant coal resources with high gas content, holding 644.94 × 10⁶ and 1407.77 × 10⁶ m³ of gas resources, respectively. These findings provided not only a scientific basis for the future evaluation and development of gas resources in this coal mine, but also important references for the study of in situ gas resources in other abandoned mines. And, several suggestions were given about the development prospects of gas resources. Full article

Predicting garlic prices is difficult because the crop behaves as both an agricultural commodity and a speculative asset. Unlike staple grains, which follow more predictable seasonal supply cycles, garlic can be stored for over a year, its production is geographically concentrated, and its demand remains inelastic. This industry structure makes it susceptible to speculative hoarding, where even minor harvest deficits may trigger sharp price spikes. A typical example is the “Suan Ni Hen” (crazy garlic) phenomenon in the Chinese market: during the 2009–2010 and 2016 periods, speculative capital repeatedly exploited expectations of harvest reduction to engage in large-scale hoarding. According to data released by China’s National Development and Reform Commission (NDRC) at the end of October 2016, national wholesale garlic prices surged by 90% year-on-year, with purchase prices in some major producing areas doubling or multiplying within a short period. Such short-term price bubbles, together with severe volatility and abrupt regime shifts, can make standard forecasting models unreliable in this uncertain environment. Existing methods, ranging from traditional seasonal algorithms to deep learning networks, often overlook the need to decouple the local trend-weekly-seasonal baseline from the dynamic effects of multi-source external signals. This paper proposes GPF-EVMoLE, a compositional multi-step forecasting framework built on an explicit division of labor. The framework first extracts an interpretable local trend and weekly-seasonal baseline through an ETS decomposition module. Two specialized components then process the residual signal: a temporal fusion Transformer-style variable selection network (VSN) uses multi-source external features to identify informative macroeconomic and environmental signals at each forecasting step, while a Mixture of Linear Experts (MoLE) models phase-wise regime shifts within the residual series. Together, these modules adaptively integrate heterogeneous information. This study evaluates the framework on a custom daily evaluation dataset containing 17,685 records across six major producing regions in three provinces. At 7-day and 14-day forecasting horizons, GPF-EVMoLE consistently outperforms eight representative statistical, machine learning, and deep learning baselines across MAE, RMSE, and MAPE metrics. Ablation studies verify the necessity of each component, showing that structural separation of the forecasting tasks helps overcome the limitations of monolithic models and provides an accurate and interpretable solution for complex agricultural markets. Full article

As a critical component of urban ecological infrastructure, urban forests play a pivotal role in regulating regional climate and mitigating the urban heat island (UHI) effect. However, existing studies have predominantly focused on single temporal snapshots or aggregate spatial scales, with limited attention to the seasonal dynamics of urban forest landscape patterns and a lack of systematic quantification of their nonlinear regulatory mechanisms. Empirical evidence from subtropical cities remains particularly scarce. In this study, Hangzhou was selected as the study area. Land Surface Temperature (LST) was retrieved using the Google Earth Engine (GEE) platform, and the Thermal Field Variance Index was employed to classify UHI intensity. Six representative forest landscape indices were selected to construct an evaluation framework. Pearson correlation analysis and the XGBoost model were further applied to quantify the relationships between landscape patterns and seasonal LST variations. The results reveal that: (1) LST in Hangzhou exhibits pronounced seasonal variability, following the order of summer > spring > autumn > winter. Areas without UHI effects dominate in spring, summer, and autumn, whereas the extent of strong UHI zones increases markedly in winter. (2) All landscape indices are significantly correlated with seasonal LST; forest ratio and forest largest patch index show negative correlations, while forest patch density, forest landscape shape index, number of patches, and landscape division index (DIVISION) are positively correlated. (3) The XGBoost model indicates that DIVISION consistently exhibits high contribution across all seasons, identifying it as a key determinant of LST variation. These findings provide a scientific basis for optimizing urban forest landscape configuration and developing effective UHI mitigation strategies. Full article

This paper proposes a sectional-chain silicon drift detector (SDD), featuring an elliptical-shaped voltage divider resistor chain, that can address the issue with traditional concentric ring SDDs, which cannot independently provide voltage division. The study replaces the conventional linear voltage divider with an elliptical structure, using its diversity geometry to improve the uniformity of the electric field distribution within the detector’s sensitive area, effectively solving the problem of distortions of edge electric fields and those between the SDD’s cathode rings in traditional structures. The relevant parameters of the elliptical resistor chain are calculated through formulas, which establish a quantitative relationship between the resistance values and the elliptical geometric dimensions, providing a theoretical basis for electric field uniformity control. A device physics model is then established using TCAD for simulation analysis to obtain key performance parameters: the electric potential and electric field distribution inside the detector, the spatial distribution of electron concentration in the detector bulk, and the electric potential gradient on the detector surface. These parameters provide a design reference for the application of high-performance SDDs in fields such as X-ray energy spectroscopy nuclear physics experiments and space radiation monitoring. Full article

Human–elephant conflict (HEC) has emerged as a major conservation and socio-economic challenge across Asia, largely driven by habitat degradation and increasing human pressure within elephant ranges. In India, expanding agriculture, mining activities, and infrastructure development have progressively altered forest landscapes, restricting elephant movement and intensifying interactions with human settlements. This study examines the relationship between landscape dynamics and HEC in the West Singhbhum district, Jharkhand, India. A three-year field investigation (2018–2020) across four forest divisions—Porahat, Chaibasa, Kolhan, and Saranda—was integrated with multi-temporal land-use and land-cover (LULC) analysis from 2000 to 2020 to evaluate habitat changes and their influence on conflict patterns. During the study period, 157 human casualties and extensive crop and property losses were recorded, indicating the severity of the conflict in the region. Landscape analysis revealed a substantial decline in dense forest cover and a reduction of large core forest areas (>500 acres), accompanied by increasing agricultural expansion and forest perforation. NDVI trends further indicated widespread deterioration in vegetation condition, reflecting declining habitat quality. These structural landscape changes have fragmented elephant habitats and displaced movement routes toward human-dominated landscapes and are thus associated with a spatial clustering of conflict events, particularly in the Chaibasa Forest Division. In contrast, the Saranda Forest Division retains relatively intact forest cores and supports more stable elephant habitat conditions. The findings demonstrate that HEC in the region is strongly linked to habitat fragmentation and declining vegetation quality rather than random elephant behaviour. Maintaining large contiguous forest blocks, restoring landscape connectivity, and implementing targeted mitigation strategies are therefore essential for sustaining elephant populations while reducing conflict with local communities. Full article

Background: Amnestic mild cognitive impairment (aMCI) is considered a transitional state between normal aging and dementia, often without visible abnormalities on standard brain magnetic resonance (MR) images. The aim of the study was to analyze both microstructural and functional brain abnormalities using advanced MR techniques. Methods: The study included 27 patients with aMCI and an age-matched control group (CG) of 25 healthy subjects. All MR studies were performed on a 3T MR scanner (Philips, Ingenia) with a 32-channel head and neck coil using volumetric 3D T1 sequences, followed by a resting-state functional MRI (rs-fMRI) sequence. Volumetric analysis was performed using the Destrieux atlas to assess potential structural differences between groups. Seed-to-voxel functional connectivity analyses were conducted using the bilateral hippocampi and both anterior and posterior divisions of the parahippocampal gyri as seed regions. Results: Compared to healthy controls, reduced cortical thickness was observed in aMCI subjects in the temporal regions, frontal and orbitofrontal areas, limbic areas, parietal and sensorimotor cortices, as well as occipito-temporal regions. Additionally, significantly increased functional connectivity was observed between bilateral medial temporal lobe (MTL) regions and the right thalamus. Conclusions: Cortical thinning in various brain regions along with the increased functional connectivity between the MTL regions and the right thalamus may reflect potential compensatory mechanisms in response to initial subtle degenerative changes, emphasizing the importance of using both functional and structural imaging to detect early changes in aMCI patients. Full article

To address the problems of unreasonable task allocation and low target search efficiency in the collaborative search of multiple unmanned undersea vehicles (UUVs) in complex marine environments, this paper proposes a search-information-driven collaborative task planning method for multi-UUV systems, and constructs a systematic and integrated multi-UUV collaborative task planning framework. Considering the spatial characteristics of the complex underwater environment and sonar detection rules, an underwater task environment grid model and an active sonar instantaneous detection model are constructed as the environmental and detection foundation of the framework. Within the framework, the Gaussian Mixture Model (GMM) is adopted to realize dynamic division of task regions, and reasonable resource allocation among multiple UUVs is achieved by defining scientific area allocation indicators. A search information map consisting of target probability distribution and environmental uncertainty is established, and a receding horizon planning framework is introduced to balance short-term detection effectiveness and long-term search value. Furthermore, a motion-coded Grey Wolf Optimization (GWO) algorithm is proposed to generate continuous UUV paths, which avoids path discontinuity caused by discrete grids and ensures the convergence efficiency of the algorithm. Simulation results verify that compared with traditional methods, the proposed method improves the total probability benefit by 19.87% and the number of discovered targets by 18.29%, demonstrating better search performance and environmental adaptability. Full article

Existing technologies can achieve relative geometric correction and stabilization of geostationary satellite image sequences through fixed land scene matching or homonymous point adjustment. However, these methods heavily rely on fixed land areas, rendering them completely ineffective in vast ocean regions with only ship targets. Additionally, the trajectories of ship targets after processing still exhibit noticeable jitter, hindering motion information analysis. To address these issues, this paper proposes a joint image adjustment and stabilization method based on multi-target trajectories in marine environments: (1) An optimized target detection algorithm based on a multi-scale heterogeneous convolution module is introduced, which extracts background and target features through convolutions of different scales, enabling accurate detection and tracking of weak small targets in the image sequence frame by frame. (2) Curve fitting is performed on the detected positions of the same ship across multiple frames to simulate its motion trajectory under stabilized conditions. Combined with the prior assumption of uniform motion, an equal-division strategy is adopted to determine the corrected positions of the target in the image sequence. (3) The deviation correction values of multiple targets within the same frame are obtained, and based on the principle of intra-frame deviation consistency, precise image stabilization is achieved under multi-target constraints. Experiments based on Gaofen-4 satellite image sequences demonstrate that this method reduces the average position deviation of ship targets in the original images from 8.5 pixels (425 m) to 3.4 pixels (170 m), a decrease of approximately 59.41%, effectively improving the relative geometric accuracy of the image sequence and significantly eliminating target trajectory jitter. Full article