Search Results (2,007)

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these issues, this study introduces a simple correction scheme (G3) for determining drying SWCCs using the centrifuge method based on high matric suction calibration points. The performance of the proposed G3 method was systematically evaluated against a modified method considering soil shrinkage (G1) and the conventional uncorrected method (G2). Results revealed significant soil linear shrinkage post-centrifugation, accompanied by a reduction in total soil porosity and an increase in soil bulk density. SWCCs from all methods exhibited strong consistency at low matric suction ranges but diverged markedly at high matric suction segments. High matric suction data dominated the SWCC fitting. The G1 method achieved the highest fitting accuracy, while the G3 method performed the worst yet maintained acceptable reliability. The G2 method yielded optimal SWCC for simulating saturated soil water content, field capacity, and permanent wilting point. Conversely, Hydrus-1D simulations revealed superior performance of the G3 method in simulating farmland soil moisture dynamics during the dehumidification process. Values of R² across methods followed G3 > G1 > G2, while mean absolute error, mean absolute percentage error, and root mean square error exhibited the opposite trend. These findings highlight that the previous modified approaches are more suitable for low and medium matric suction ranges. The proposed correction method enhances drying SWCC performance across the full matric suction range, offering a practical refinement for the centrifuge method. This advancement could enhance the reliability in soil hydraulic characterization and contribute to a better understanding of the hydraulic–mechanical–chemical behavior in soils. Full article

Background: Neoadjuvant chemotherapy (NAC) is a standard preoperative intervention for early-stage breast cancer (BC). Dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) has emerged as a critical tool for evaluating treatment response and pathological complete response (pCR) following NAC. Computational modeling offers a robust framework to simulate tumor growth dynamics and therapy response, leveraging patient-specific data to enhance predictive accuracy. Despite this potential, integrating imaging data with computational models for personalized treatment prediction remains underexplored. This case study presents a proof-of-concept prognostic tool that bridges oncology, radiology, and computational modeling by simulating BC behavior and predicting individualized NAC outcomes. Methods: CE-MRI scans, clinical assessments, and blood samples from three retrospective NAC patients were analyzed. Tumor growth was modeled using a system of partial differential equations (PDEs) within a reaction–diffusion mass transfer framework, incorporating patient-specific CE-MRI data. Tumor volumes measured pre- and post-treatment were compared with model predictions. A 20% error margin was applied to assess computational accuracy. Results: All cases were classified as true positive (TP), demonstrating the model’s capacity to predict tumor volume changes within the defined threshold, achieving 100% precision and sensitivity. Absolute differences between predicted and observed tumor volumes ranged from 0.07 to 0.33 cm³. Virtual biomarkers were employed to quantify novel metrics: the biological conversion coefficient ranged from 4 × 10⁻⁷ to 6 × 10⁻⁶ s^-1, while the pharmacodynamic efficiency coefficient ranged from 1 × 10⁻⁷ to 4 × 10⁻⁴ s^-1, reflecting intrinsic tumor biology and treatment effects, respectively. Conclusions: This approach demonstrates the feasibility of integrating CE-MRI and computational modeling to generate patient-specific treatment predictions. Preliminary model training on retrospective cohorts with matched BC subtypes and therapy regimens enabled accurate prediction of NAC outcomes. Future work will focus on model refinement, cohort expansion, and enhanced statistical validation to support broader clinical translation. Full article

This article presents the design, implementation, and comparative evaluation of two Unmanned Aerial Vehicles (UAV)-based architectures for automated road data acquisition and processing. The first system uses Arduino Portenta H7 boards to perform real-time inference at the edge, reducing connectivity dependency. The second employs ESP32-CAM modules that transmit raw data for remote server-side processing. We experimentally validated and compared both systems in terms of power consumption, latency, and detection accuracy. Results show that the Portenta-based system consumes 36.2% less energy and achieves lower end-to-end latency (10,114 ms vs. 11,032 ms), making it suitable for connectivity-constrained scenarios. Conversely, the ESP32-CAM system is simpler to deploy and allows rapid model updates at the server. These findings provide a reference for Intelligent Transportation System (ITS) deployments requiring scalable, energy-efficient, and flexible road monitoring solutions. Full article

Background: Heart failure (HF) affects over 64 million people globally, imposing substantial morbidity, mortality, and economic burdens. Despite advances in guideline-directed therapies, adherence remains suboptimal due to low health literacy and complex regimens. ChatGPT, an advanced large language model by OpenAI, offers conversational capabilities that could enhance HF education, management, and research. This systematic review synthesizes evidence on ChatGPT’s applications in HF, evaluating its accuracy in patient education and question-answering, enhancing readability, and clinical documentation/symptom extraction. Methods: Following PRISMA guidelines, we searched PubMed, Embase, and Cochrane up to July 2025 using the terms “ChatGPT” and “heart failure”. Inclusion: Studies on ChatGPT (3.5 or 4) in HF contexts, such as in education, readability and symptom extraction. Exclusion: Non-HF or non-ChatGPT AI. Data extraction covered design, objectives, methods, and outcomes. Thematic synthesis was applied. Results: From 59 records, 7 observational studies were included. Themes included patient education/question-answering (n = 5), readability enhancement (n = 2), and clinical documentation/symptom extraction (n = 1). Accuracy ranged 78–98%, with high reproducibility; readability improved to 6th–7th grade levels; and symptom extraction achieved up to 95% F1 score, outperforming traditional machine learning baselines. Conclusions: ChatGPT shows promise in HF care but requires further randomized validation for outcomes and bias mitigation. Full article

As a key zero-carbon energy carrier, the accurate measurement of liquid hydrogen flow in its industrial chain is crucial. However, the ultra-low temperature, ultra-low density and other properties of liquid hydrogen can introduce calibration errors. To enhance the measurement accuracy and reliability of liquid hydrogen flow, this study investigates the heat and mass transfer within a 1 m³ non-vented storage tank during the calibration process of a liquid hydrogen flow standard device that integrates combined dynamic and static gravimetric methods. The vertical tank configuration was selected to minimize the vapor–liquid interface area, thereby suppressing boil-off gas generation and enhancing pressure stability, which is critical for measurement accuracy. Building upon research on cryogenic flow standard devices as well as tank experiments and simulations, this study employs computational fluid dynamics (CFD) with Fluent 2024 software to numerically simulate liquid hydrogen flow within a non-vented tank. The thermophysical properties of hydrogen, crucial for the accuracy of the phase-change simulation, were implemented using high-fidelity real-fluid data from the NIST Standard Reference Database, as the ideal gas law is invalid under the cryogenic conditions studied. Specifically, the Lee model was enhanced via User-Defined Functions (UDFs) to accurately simulate the key phase-change processes, involving coupled flash evaporation and condensation during liquid hydrogen refueling. The simulation results demonstrated good agreement with NASA experimental data. This study systematically examined the effects of key parameters, including inlet flow conditions and inlet liquid temperature, on the flow characteristics of liquid hydrogen entering the tank and the subsequent heat and mass transfer behavior within the tank. The results indicated that an increase in mass flow rate elevates tank pressure and reduces filling time. Conversely, a decrease in the inlet liquid hydrogen temperature significantly intensifies heat and mass transfer during the initial refueling stage. These findings provide important theoretical support for a deeper understanding of the complex physical mechanisms of liquid hydrogen flow calibration in non-vented tanks and for optimizing calibration accuracy. Full article

This scoping review systematically maps the rapidly evolving application of Artificial Intelligence (AI) in Autism Spectrum Disorder (ASD) diagnostics, specifically focusing on computational behavioral phenotyping. Recognizing that observable traits like speech and movement are critical for early, timely intervention, the study synthesizes AI’s use across eight key behavioral modalities. These include voice biomarkers, conversational dynamics, linguistic analysis, movement analysis, activity recognition, facial gestures, visual attention, and multimodal approaches. The review analyzed 158 studies published between 2015 and 2025, revealing that modern Machine Learning and Deep Learning techniques demonstrate highly promising diagnostic performance in controlled environments, with reported accuracies of up to 99%. Despite this significant capability, the review identifies critical challenges that impede clinical implementation and generalizability. These persistent limitations include pervasive issues with dataset heterogeneity, gender bias in samples, and small overall sample sizes. By detailing the current landscape of observable data types, computational methodologies, and available datasets, this work establishes a comprehensive overview of AI’s current strengths and fundamental weaknesses in ASD diagnosis. The article concludes by providing actionable recommendations aimed at guiding future research toward developing diagnostic solutions that are more inclusive, generalizable, and ultimately applicable in clinical settings. Full article

This study investigates the static mechanical behavior of a novel eco-friendly high-performance concrete (HPC) reinforced with fibers under different moisture conditions, reflecting the humidity variations commonly encountered in engineering practice. Three saturation levels—natural, dry, and water saturated—were considered. The optimal dosages of basalt and glass fibers were first identified through tests in the natural state, and empirical relationships between fiber volume fraction, compressive strength, and fracture energy were established. Comparative experiments were then conducted at the optimal dosages under varying saturation conditions. Results show that basalt fiber provides superior compressive strength, exceeding that of glass fiber by 0.86% in the dry state and 10.66% in the saturated state. Conversely, glass fiber exhibits a greater enhancement in flexural strength, with improvements of 14.91% and 3.38% over basalt fiber under dry and saturated conditions, respectively. Although preliminary models were proposed to correlate fiber volume fraction with strength in dry and saturated environments, their predictive accuracy proved limited. Overall, the findings highlight the distinct reinforcing effects of basalt and glass fibers on HPC under different moisture conditions, offering guidance for the design and application of fiber-reinforced recycled concrete in humid service environments. Full article

This paper undertakes an in-depth exploration into the issue of quantization errors that occur during color gamut conversion within LED full-color display systems. To commence, a CIE-xyY colorimetric framework, which is customized to the unique characteristics of LED, is constructed. This framework serves as the bedrock for formulating the principles governing the operation of LED color gamuts. Subsequently, the conversions among diverse color spaces are scrutinized with great meticulousness. The core emphasis then shifts to dissecting how discrete control systems, in conjunction with quantization errors at low grayscale levels, precipitate the distortion of color gamut boundaries during the conversion process. The Laplacian operator is deployed to furnish a geometric comprehension of the distortion points, thereby delineating the topological discrepancies between the target and actual points. The quantitative analysis precisely delineates the correlation between quantization precision and the quantity of distortion points. The research endeavors to disclose the intricate relationships among quantization, color spaces, and colorimetric fidelity. This paper is conducive to the prospective calibration and rectification of LED display systems, furnishing a theoretical underpinning for the further enhancement of color reproduction in LED displays. Consequently, LED monitors can be rendered capable of satisfying the stringent accuracy requisites of advanced imaging and media. Full article

The optimization of the micro-motion rotary mechanism aims to obtain the maximum rotation angle in a certain space and increase the compensation range of the micro-motion mechanism. Aiming to address the disadvantages of a small movement stroke, low positioning accuracy, and limited research on the sub-arc-second level of precision micro-drive mechanism, a micro-drive mechanism was designed in this study and structural optimization was performed to obtain the maximum output angle. Additionally, the performance of the optimized mechanism was investigated. First, based on the principle of a flexure hinge guide and conversion, a micro-drive rotary mechanism that could transform the linear motion of piezoelectric ceramics into rotating motion accurately without parasitic motion and non-motion direction force was designed. Second, its structural optimization was achieved using the particle swarm optimization algorithm. Third, analyses of the drive performance and kinematics of the system were conducted. Finally, a performance test platform for the micro-drive rotary mechanism was built, its positioning performance and dynamic characteristics were verified experimentally, and the maximum rotary displacements and positioning error of the system were calculated. This research has certain reference value for studies of ultra-precision positioning. Full article

With the deepening of unconventional oil and gas resource development, the gas–liquid two-phase flow phenomenon in horizontal gas wells is becoming increasingly complex. Accurate and efficient prediction of the flow state has become key to optimizing production. While traditional numerical simulation methods are highly accurate, their long calculation times make them unsuitable for real-time applications. Conversely, purely data-driven methods struggle with accuracy under sparse data conditions. This paper proposes a deep operator network method (PI-DeepONet) that integrates physical prior knowledge—specifically the drift-flux model—to rapidly predict two-phase flow parameters. By jointly training the network with both data loss and physical loss, the model’s accuracy and generalization are significantly enhanced. Comparing the results with the OLGA numerical simulator verifies the model’s high performance. The average relative error of the PI-DeepONet on the test set is less than 1%, with the error of some physical quantities controlled within 0.2%. Critically, the single prediction time is less than 0.1 s, achieving a calculation speed nearly 50,000 times higher than the traditional numerical simulation method. The model significantly improves prediction speed while ensuring accuracy, making it ideal for real-time simulation and rapid response requirements in horizontal wells. This study provides a new path for intelligent diagnosis and prediction of underground working conditions and demonstrates broad engineering application potential. Full article

The radius-to-width ratio has an obvious impact on the flow structure within curved channels, which most natural rivers possess, but there are currently few studies on the influence of the radius-to-width ratio of a tributary (R/B) on the hydrodynamic characteristics of a confluent channel. In order to contribute to this field of research, this study employed the RNG k-ε turbulence model, which has good applicability and accuracy for confluence, to investigate the effects of the R/B and flow ratios (q*) on the hydraulic characteristics of confluence. The results reveal that the numerical model can effectively simulate the velocity distribution in the confluence. The values of the key errors are all relatively small (e.g., the value of Mean RMSE is 0.05), and the flow patterns near the bed and water surfaces are different. The maximum velocity zone (MVZ) and the scale of the separation zone (SZ) increase as R/B increases; conversely, the MVZ and the scale of the SZ decrease as the q* increases. Upstream of the confluence, turbulent kinetic energy (TKE) increases and decreases as R/B and q* increase, respectively, while TKE downstream of the confluence hardly changes. Furthermore, the size of the SF decreases as R/B increases. The value of $\overline{S_w}$ peaks downstream of the confluence, increases with the increase in the R/B, and decreases with the increase in the q*. The results of this study will contribute to a better understanding of the hydrodynamic characteristics of confluence and provide valuable insights for the management and ecological restoration of confluent channels. Full article

Effective operation of autonomous maritime systems requires sensor architectures tailored to mission-specific requirements, as key performance criteria like accuracy and energy consumption vary significantly by operational context. Against this background, this study develops a dual-stage, multi-criteria procedure to evaluate and assess individual sensors accounting for scenario-based requirements, using the TOPSIS algorithm as its core method. The first stage individually assesses sensors against scenario-specific requirements to generate context-aware weighting factors (${\alpha }_i^{\left(s\right)}$). In the second stage, these factors are used to evaluate the overall performance of seven predefined sensor suites across five distinct operational scenarios (e.g., ‘Coastal Surveillance’ or ‘Protection of Critical Infrastructure’). The procedure is complemented by an architectural robustness assessment that systematically captures the impact of component failures. This flexible approach serves as a generic decision framework for designing unmanned maritime systems across different mission profiles. By integrating key performance metrics and failure scenarios within a context of prioritized operational requirements, the dual-stage multi-criteria procedure enables more than just selecting an optimal configuration. It reveals the fundamental architectural design principles. Our results demonstrate that for precision-focused tasks such as ‘Coastal Surveillance’, specialized sensor suites combining electro-optical and laser rangefinder achieves the highest performance score (0.84). Conversely, for scenarios with balanced requirements like ‘Protection of Critical Infrastructure’, architectures based on functional complementarity (e.g., electro-optical and Radar, score (0.64)) prove most effective. A key finding is that maximizing sensor quantity does not guarantee optimal performance, as targeted, mission-specific configurations often outperform fully integrated systems. The significance of this study lies in providing a systematic framework that shifts the design paradigm from a ‘more is better’ approach to an intelligent, context-aware composition, enabling the development of truly robust and efficient sensor architectures for autonomous maritime systems. Full article

Background: The aim of this study was to compare four recently introduced LLMs (ChatGPT-5, Grok 4, Gemini 2.5 Flash, and Claude Sonnet-4). Experienced endodontists evaluated the accuracy, completeness, and readability of the responses given to open-ended questions about iatrogenic events in endodontics. Methods: Twenty-five open-ended questions related to iatrogenic events in endodontics were prepared. The responses of the four LLMs were evaluated by two specialist endodontists using a Likert scale for accuracy and completeness, and the Flesch Reading Ease Score (FRES), Flesch–Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Simplified Measure of Gobbledygook (SMOG), and Coleman–Liau Index (CLI) for readability. Results: The accuracy score of ChatGPT-5’s responses to open-ended questions (4.56 ± 0.65) was found to be significantly higher than those of Gemini 2.5 Flash (3.64 ± 0.95) and Claude Sonnet-4 (3.44 ± 1.19) (p = 0.009, and p = 0.002, respectively). Similarly, the completeness score of ChatGPT-5 (2.88 ± 0.33) was higher than those of Claude Sonnet-4, Gemini 2.5 Flash, and Grok 4 (p < 0.001, p = 0.002, and p = 0.007, respectively). In terms of readability measures, ChatGPT-5 and Gemini 2.5 Flash achieved better FRESs than Claude Sonnet-4 (p = 0.003, and p < 0.001, respectively). Conversely, FKGL scores were higher for Claude Sonnet-4 and Grok 4 compared to ChatGPT-5 (p < 0.001, and p = 0.008, respectively). Correlation analyses revealed a strong positive association (r_s = 0.77; p < 0.001) between accuracy and completeness, a weak negative correlation (r_s = −0.19; p = 0.047) between completeness and FKGL, and a strong negative correlation between (r_s = −0.88; p < 0.001) FKGL and FRES. Additionally, ChatGPT-5 demonstrated lower GFI and CLI scores than the other models, while its SMOG scores were lower than those of Gemini 2.5 Flash and Grok 4 (p = 0.001, and p < 0.001, respectively). Conclusions: Although differences were observed between the LLMs in terms of the accuracy and completeness of the responses, ChatGPT-5 showed the best performance. Even with high scores of accuracy (excellent) and completeness (comprehensive), it must not be forgotten that incorrect information can lead to serious outcomes in healthcare services. Therefore, the readability of responses is of critical importance, and when selecting a model, readability should be evaluated together with content quality. Full article

This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids. Full article