Search Results (5,767)

Purpose or Objective: To evaluate the feasibility and clinical utility of integrating sequential PSMA-PET imaging into an offline–online adaptive workflow for response-based dominant intraprostatic lesion (DIL)-boosting high-risk prostate cancer treated with stereotactic ablative radiotherapy (SABR). Materials and Methods: As part of a prospective trial, patients were treated on MR- or CBCT-guided adaptive radiotherapy (ART) systems with prostate/pelvic node 5-fraction SABR (36.25 Gy/25 Gy) with DIL boost (50 Gy). Whereas traditional DIL boost volumes delineate full pre-therapy imaging-defined disease (GTVinitial), this study serially refined DIL boost volumes based on treatment response defined by PSMA-PET scans after neoadjuvant androgen deprivation therapy (nADT, GTVmb1) and fraction 3 SABR (GTVmb2). DIL delineation employed PET-PSMA fusion to CT/MR simulation and was guided by a rule-based %SUVmax threshold approach. Comparisons of GTV volumes and OAR dosimetry were performed between plans using GTVinitial versus GTVmb1/GTVmb2 for DIL boost, for each of the initial cohorts of five patients from the initially treated cohorts. Results: Five patients treated on MR-Linac (n = 3) or CBCT-based ART (n = 2) were analyzed. Three patients exhibited complete imaging response after nADT, omitting GTVmb boosts. Offline GTVmb refinements based on PSMA-PET were seamlessly integrated into ART workflows without introducing additional treatment time. DIL GTV volumes significantly decreased (p = 0.03) from an initial mean of 11.4 cc (GTVinitial) to 4.1 cc (GTVmb1) and 3.0 cc (GTVmb2). Dosimetric analysis showed meaningful reductions in OAR doses: rectal wall D0.035 cc decreased by up to 12 Gy, while bladder wall D0.035 cc and V18.3 Gy reduced from 52.3 Gy and 52.3 cc (Plan_initial) to 42.9 Gy and 24.9 cc (Plan_mb2), respectively. Urethra doses remained stable, with minor reductions. Sigmoid and femoral head doses remained within acceptable limits. Online adaptation efficiently addressed daily anatomical variations, enabling simulation-free plan re-optimization. Conclusion: PSMA-PET-guided adaptive microboosting for HRPCa SABR is feasible and effective. Standard MR-Linac and CBCT systems offer practical alternatives to BgRT platforms, enabling biology-driven dose personalization and potentially reducing toxicity. Full article

Emotion recognition from speech is essential for human–computer interaction (HCI) and affective computing, with applications in virtual assistants, healthcare, and education. Although deep learning has made significant advancements in Automatic Speech Emotion Recognition (ASER), the challenge still exists in the task given variation in speakers, subtle emotional expressions, and environmental noise. Practical deployment in this context depends on a strong, fast, scalable recognition system. This work introduces a new framework combining DenseNet121, especially fine-tuned for the crowd-sourced emotional multimodal actors dataset (CREMA-D), with the convolutional block attention module (CBAM). While DenseNet121’s effective feature propagation captures rich, hierarchical patterns in the speech data, CBAM improves the focus of the model on emotionally significant elements by applying both spatial and channel-wise attention. Furthermore, enhancing the input spectrograms and strengthening resistance against environmental noise is an advanced preprocessing pipeline including log-Mel spectrogram transformation and normalization. The proposed model demonstrates superior performance. To make sure the evaluation is strong even if there is a class imbalance, we point out important metrics like an Unweighted Average Recall (UAR) of 71.01% and an F1 score of 71.25%. The model also gets a test accuracy of 71.26% and a precision of 71.30%. These results establish the model as a promising solution for real-world speech emotion detection, highlighting its strong generalization capabilities, computational efficiency, and focus on emotion-specific features compared to recent work. The improvements demonstrate practical flexibility, enabling the integration of established image recognition techniques and allowing for substantial adaptability in various application contexts. Full article

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding. Full article

Numerous thermal practical applications utilize shell and tube heat exchanger appliances to transfer heat energy between hot and cold working fluids. Incorporating metal foam to the outer periphery of inner tube improves the heat transfer process from hot water in the tube side to cold water in the shell side and consequently improves heat exchanger performance. In this study, the integration of use of a porous material together with designing a robust adaptive controller could efficiently regulate the outlet cold water temperature to the desired value. This is achieved with respect to the time required for cold water to reach the desired temperature (settling time) and the amount of hot water volume flow during a certain time span. A barrier function-based adaptive sliding mode controller (BF-based adaptive SMC) is proposed, which requires only the information of temperature measurement of cold water. The stability of BF-based adaptive SMC is proved utilizing Lyapunov function analysis. The effectiveness of proposed controller is verified via numerical results, which showed that the proposed controller could achieve considerable accuracy of cold water temperature using suitable design parameters. In addition, the robustness of controller against variation in inlet temperature is also verified. Another improvement to performance of heat exchanger system is achieved by adding the metal foam of aluminum material on inner pipe perimeter with wide range of metal foam to outer inner pipe diameters ratio ($1\le s\le 1.8$). The results showed that the settling time is significantly reduced which enables outlet cold water to reach the required temperature faster. With respect of the case of non-adding metal foam on inner pipe outer circumference, when $s=1.2$, the settling time and hot water temperature are reduced by $1/2$ and $17.3\%$, respectively, while for $s=1.8$, they are decreased by $\le 1/20$ and $35.3\%$ correspondingly. Accordingly, the required volume flow for hot water is reduced considerably. Full article

Background/Objectives: this study describes the development of a novel three-dimensional electrogoniometer for the quantitative assessment of knee mobility and stability during gait. The primary objective is to determine whether real-time measurements obtained during dynamic activity provide more clinically relevant information than traditional static assessments. Methods: the device employs angular position encoders to capture knee joint kinematics—specifically flexion, extension, rotation, and tibial translation—during locomotion. Data are transmitted in real time to an Android-based application, enabling immediate graphical visualization. A descriptive observational study was conducted involving healthy participants and individuals with anterior cruciate ligament (ACL) injuries to evaluate the device’s performance. Results: results showed that the electrogoniometer captured knee flexion-extension with a range of up to 90°, compared to 45° typically recorded using conventional systems. The device also demonstrated enhanced sensitivity in detecting variations in tibial translation during gait cycles. Conclusions: this electrogoniometer provides a practical tool for clinical assessment of knee function, enabling real-time monitoring of joint behavior during gait. By capturing functional mobility and stability more accurately than static methods, it may enhance diagnostic precision and support more effective rehabilitation planning in orthopedic settings. Full article

High-resolution remote sensing images suffer from large intra-class variance, high inter-class similarity, and significant scale variations, leading to incomplete segmentation and imprecise boundaries. To address these challenges, Transformer-based methods, despite their strong global modeling capability, often suffer from feature confusion, weak detail representation, and high computational cost. Moreover, existing multi-scale fusion mechanisms are prone to semantic misalignment across levels, hindering effective information integration and reducing boundary clarity. To address these issues, a Category-Guided Transformer (CIGFormer) is proposed. Specifically, the Category-Information-Guided Transformer Module (CIGTM) integrates global and local branches: the global branch combines window-based self-attention (WSAM) and window adaptive pooling self-attention (WAPSAM), using class predictions to enhance global context modeling and reduce intra-class and inter-class confusion; the local branch extracts multi-scale structural features to refine semantic representation and boundaries. In addition, an Adaptive Wavelet Fusion Module (AWFM) is designed, which leverages wavelet decomposition and channel-spatial joint attention for dynamic multi-scale fusion while preserving structural details. Extensive experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate that CIGFormer, with only 21.50 M parameters, achieves outstanding performance in small object recognition, boundary refinement, and complex scene parsing, showing strong potential for practical applications. Full article

The optimization of thermal performance in buildings is essential for sustainable urban development, yet the high cost and complexity of traditional thermal conductivity measurement methods limit broader research and educational applications. This study developed and validated a low-cost, replicable prototype that determines the thermal conductivity of roof tiles and composites using the Lee Disc method automated with Arduino-based acquisition. Standardized samples of ceramic, fiber–cement, galvanized steel, and steel coated with a castor oil-based polyurethane composite reinforced with miriti fiber (Mauritia flexuosa) were analyzed. The experimental setup incorporated integrated digital thermocouples and strict thermal insulation procedures to ensure measurement precision and reproducibility. Results showed that applying the biocompatible composite layer to metal tiles reduced thermal conductivity by up to 53%, reaching values as low as 0.2004 W·m⁻¹·K⁻¹—well below those of ceramic (0.4290 W·m⁻¹·K⁻¹) and fiber–cement (0.3095 W·m⁻¹·K⁻¹) tiles. The system demonstrated high accuracy (coefficient of variation < 5%) and operational stability across all replicates. These findings confirm the feasibility of open-source, low-cost instrumentation for advanced thermal characterization of building materials. The approach expands access to experimental research, promotes sustainable insulation technologies, and offers practical applications for both scientific studies and engineering education in resource-limited environments. Full article

This paper presents the convergence analysis of a newly proposed algorithm for approximating solutions to split equality variational inequality and fixed point problems in real Hilbert spaces. We establish that, under reasonably mild conditions, specifically when the involved mappings are quasimonotone, uniformly continuous, and quasi-nonexpansive, the sequences generated by the algorithm converge strongly to a solution of the problem. Furthermore, we provide several numerical experiments to demonstrate the practical effectiveness of the proposed method and compare its performance with that of existing algorithms. Full article

This paper presents a detailed, measurement-based assessment of interharmonic, harmonic, and supraharmonic emissions from a Battery Energy Storage System (BESS) supporting electric vehicle (EV) fast charging. In contrast to prior literature, which is largely simulation-based and often neglects interharmonic and even harmonic components, this study provides real-world data under dynamic operating conditions. Emission limits are established in accordance with relevant international standards, with the observed deviations from standard practices highlighted in existing studies. The operation of the BESS-assisted fast-charging system is classified into five distinct operating stages, and the variations in spectral emissions across these stages are analyzed. A comparative evaluation with a grid-fed fast charger reveals the influence of BESS integration on power quality. Notably, the analysis shows a significant increase in even harmonics during EV charging events. This component is identified as the limiting factor in the network’s harmonic hosting capacity, underscoring the need to account for even harmonics in future grid compatibility assessments. These findings provide valuable insights for grid operators, EV infrastructure planners, and standardization bodies aiming to ensure compliance with power quality standards in evolving charging scenarios. Full article

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects multiple organs and systems with a broad and heterogeneous spectrum of clinical manifestations. National disease-specific datasets and registries are crucial for clinical research since they can provide real-world and long-term data about clinical aspects, biomarkers, and treatments. Registries collect data from actual patients over time, outside the controlled environment of randomized controlled trials. This can help enhance the understanding of the natural history of a disease, provide information about how treatments work in everyday settings and elucidate potential variations in care and outcomes across different geographic areas. Here, we present a protocol for the creation of a standardized national disease-specific dataset for patients with SLE—the Systemic Lupus Erythematous Network (LUNET) Registry—which will facilitate data sharing, cross-comparison, and interoperability among centers. The LUNET registry is intended to serve as a comprehensive primary data source, capturing real-world longitudinal clinical information and the heterogeneity of patient presentations that are often underrepresented in traditional clinical trials. Ultimately, the LUNET registry will help to optimize SLE management in routine clinical practice by enabling the compilation of real-world evidence to inform clinical decision-making and health policy. Full article

Sex determination is a fundamental biological process governing animal reproduction. Although substantial progress has been made in elucidating its genetic basis, the genetic architecture underlying complex sex determination systems remains poorly understood. In this study, we identify sex-associated insertion–deletion (indel) variants, screen candidate genes, and compare sex-associated variation across populations with different genetic backgrounds in the Fujian oyster (Magallana angulata). Based on whole-genome resequencing data of a culture strain (designated FL), a total of 299,774 high-quality indels were identified. By integrating genome-wide association analysis (GWAS), fixation index (F_ST) analysis, and sex-biased genotype frequency comparisons, 77 overlapping sex-associated indels were identified, predominantly clustered within a 1.8 Mb (8.3–10.1 Mb) region on chromosome 9. Principal component analysis (PCA) based on the sex-associated markers and their subsets consistently separated male and female individuals in the FL strain. For two representative sex-associated indels, PCR-based genotyping methods were developed and validated. Functional annotation identified putative candidate genes for sex determination, including PKD1L1, 5-HTRL, SCP, and CCKRa. Comparative analysis of variants within PKD1L1 across wild, farmed, and selectively bred populations revealed a progressive enrichment of male-linked alleles in domesticated and selectively bred groups, particularly in male individuals. This study provides direct evidence that sex in the Fujian oyster is genetically determined and reveals that domestication and artificial selection may drive the emergence of major sex-determining loci, offering important insights into the genetic basis of sex determination in the Fujian oyster, and establishing a theoretical and practical foundation for molecular marker-assisted breeding of monosex lines for this species. Full article

This paper presents the development, modeling, and validation of a scaled UAV-VTOL low-cost prototype equipped with a jet propulsion system with vertical take-off and landing capabilities. The prototype is designed as an experimental testbed for reusable rocket control strategies, with a particular focus on thrust vectoring and landing stabilization. The study begins with the evolution of the CAD, followed by a guide for the correct assembly of the device. The development of the electronic system included the integration of an ARM Cortex-M7 microcontroller, inertial sensors, and a LIDAR-based altitude measurement system; this was enhanced by a Kalman estimator to mitigate the sensor’s noise. A series of experimental tests were conducted to characterize the key subsystems. Actuator characterization improved the linearized nozzle control model, ensuring predictable thrust redirection. The test bench results confirmed the EDF’s thrust curve and its ability to sustain controlled flight, despite minor losses due to battery discharge variations. Furthermore, state-space modeling aided the development of controllers for altitude stabilization and attitude control, with simulations proving the feasibility of maintaining stable flight conditions. Experimental validation confirmed that the prototype provides a practical platform for future research in reusable rocket dynamics and autonomous landing algorithms. Full article

To ensure the safety and quality of grain during storage requires distributed monitoring of temperature and relative humidity within the bulk material, where hundreds of sensors may be needed. Conventional multi-channel systems are often constrained by the limited number of sensors connectable to a single acquisition unit, high hardware cost, and poor scalability. To address these challenges, this study proposes a novel design method for a multi-channel temperature and relative humidity acquisition system (MTRHAS). The system integrates sequential sampling control and a time-division multiplexing mechanism, enabling efficient data acquisition from multiple sensors while reducing hardware requirements and cost. This system employs sequential sampling control using a single complex programmable logic device (CPLD), and uses multiple CPLDs for multi-channel sensor expansion with a shared address and data bus for communication with a microcontroller unit (MCU). A prototype was developed using two CPLDs and one MCU, achieving data collection from 80 sensors. To validate the approach, a simulated grain silo experiment was conducted, with nine sensors deployed to monitor temperature and relative humidity during aeration. Calibration ensured sensor accuracy, and real-time monitoring results revealed that the system effectively captured spatial and temporal variation patterns of intergranular air conditions. Compared with conventional designs, the proposed system shortens the sampling cycle, decreases the number of acquisition units required, and enhances scalability through the shared bus architecture. These findings demonstrate that the MTRHAS provides an efficient and practical solution for large-scale monitoring of grain storage environments. Full article

Mosses are key players in semi-arid ecosystems; however, the functional roles of mosses on hydrologic buffering and water quality have hardly been assessed. In the present study, the water storage, saturation dynamics, and ion release experiment of a set of four moss species (Hypnum lacunosum, Homalothecium lutescens, Dicranum scoparium, and Tortella tortuosa) was performed by a more simplified immersion and drainage procedure with water chemistry analyses. All species reached a sorption equilibrium between 10 and 20 min, with pleurocarpous taxa retaining 20–35% more water than acrocarpous species and possessing water-holding capacities (WHCs) between 300% and 700% of dry weight. Species-specific differences in water chemistry (pH, EC, and TDS) were observed: Tortella tortuosa presented the greatest ionic flux, and Hypnum lacunosum presented little variation in pH and electrical conductivity. These findings imply that the mosses operate as micro-scale buffers regulating both water quantity and water quality, and thereby the soil stability, infiltration, and drought resilience. The combined hydrological and biogeochemical view offers a novel understanding of bryophyte ecohydrology and highlights the significance of mosses in the practice of watershed management and climate-change mitigation. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article