Search Results (2,469)

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. Full article

In edge-assisted low-latency video analytics, a critical challenge is balancing on-device inference latency against the high bandwidth costs and network delays of offloading. Ineffectively managing this trade-off degrades performance and hinders critical applications like autonomous systems. Existing solutions often rely on static partitioning or greedy algorithms that optimize for a single frame. These myopic approaches adapt poorly to dynamic network and workload conditions, leading to high long-term costs and significant frame drops. This paper introduces a novel partitioning technique driven by a Deep Reinforcement Learning (DRL) agent on a local device that learns to dynamically partition a video analytics Deep Neural Network (DNN). The agent learns a farsighted policy to dynamically select the optimal DNN split point for each frame by observing the holistic system state. By optimizing for a cumulative long-term reward, our method significantly outperforms competitor methods, demonstrably reducing overall system cost and latency while nearly eliminating frame drops in our real-world testbed evaluation. The primary limitation is the initial offline training phase required by the DRL agent. Future work will focus on extending this dynamic partitioning framework to multi-device and multi-edge environments. Full article

Air pollution is associated with many adverse health outcomes, including kidney diseases. Kidney diseases, especially chronic kidney disease, are a significant public health issue globally. The burden of kidney disease is expected to rise due to population aging and the growing prevalence of diabetes and hypertension. End-stage kidney disease is associated with significant healthcare costs, morbidity, and mortality. Long-term exposure to air pollution was associated with increased risk for chronic kidney disease progression to kidney replacement therapy. Evidence on the effect of short-term exposure to air pollution on renal function is rather limited. Kidney transplant patients are likely to be even more susceptible to detrimental effects of air pollutants. Exposure to air pollution results in a higher risk for delayed graft function, acute rejection, and mortality. In this review we would like to summarize the state of knowledge on the influence of air pollution on outcomes in end-stage kidney failure and kidney transplantation. Full article

In this paper, a novel multi-mode resonator-based ultra-wideband bandpass filter topology is proposed, analyzed, and experimentally validated. The filter comprises a short shunt-stepped impedance resonator and shunt-open stubs. Thus, it can be easily implemented using microstrip technology, offering a simple and cost-effective alternative to multilayer and high-temperature superconductor thin-film-based bandpass filters. S-parameter expressions for the proposed filter are derived using even- and odd-mode methods. To validate theoretical results, a filter prototype operating at the center frequency ($f_o$) of 6.85 GHz is designed, fabricated, and experimentally tested. The measured 3 dB fractional bandwidth (FBW) of the filter exceeds 176%, and the selectivity factor (SF) reaches 0.87. Additionally, the filter outperforms most existing designs in the literature in terms of insertion loss (IL) and return loss (RL). Finally, a figure of merit (FoM) is proposed to measure the trade-off among key performance parameters (i.e., FBW, IL, RL, SF, $f_o$, and group delay flatness), and confirms that the proposed bandpass filter exhibits the best FoM compared to the state of the art. Full article

As the potential of e-aircraft in short-range routes becomes more prominent, the question of how to rationally plan airport electric infrastructure and efficiently produce it has become a key issue in the aviation industry’s efforts to move towards electrification. In this paper, we propose and construct a three-layer optimization model for determining the size of airport electric infrastructure, which is solved collaboratively at the three levels of strategic, tactical, and operational layers, in order to construct an optimization algorithm to minimize the construction and operation costs of electric infrastructure, and at the same time to ensure that flights are not delayed by the influence of electric power supply. Specifically, Stage-1 considers infrastructure sizes; Stage-2 assigns a binary charge–swap decision per turnaround under no-delay policy; Stage-3 schedules power under time-of-use tariffs and outputs a feasible day plan and daily cost. In order to verify the effectiveness of this paper’s algorithm, this paper conducts case studies and algorithm validation on actual flight data. The results show that the proposed model can significantly reduce the overall airport operating costs while ensuring normal flight operations. Full article

Nowadays, the potential of hydrokinetic turbines as a sustainable alternative to complement traditional hydropower is widely recognized. This study presents a comprehensive numerical analysis of twin straight-bladed Darrieus hydrokinetic turbines, characterizing their hydrodynamic interactions and performance characteristics. The influence of turbine configuration spacing and flow parameters on efficiency and wake dynamics are investigated. The employed 3D computational approach combines the overset mesh technique, used to capture the unsteady flow around the turbines, with the URANS k-ω Shear Stress Transport (SST) turbulence model. Results show that turbine spacing improves power coefficients and overall efficiency, albeit at the cost of slower wake recovery. A noticeable performance increase is observed when the turbines are spaced between 1.5 and 2 diameters apart, which is predicted to reach up to 40% regarding the single turbine. Furthermore, the effect of flow interaction between the turbines is examined by analyzing the influence of turbine spacing on flow structures as well as pressure and skin friction coefficients on the blades. The performed analysis reveals that vortex detachment is delayed in the twin-turbine configuration compared to the isolated case, which partially explains the observed performance enhancement. The insights gained from this work are expected to contribute to the advancement of renewable hydrokinetic energy technologies. Full article

This paper outlines the process of developing a low-cost system for home appliance control via real-time hand gesture classification using Computer Vision and a custom lightweight machine learning model. This system strives to enable those with speech or hearing disabilities to interface with smart home devices in real time using hand gestures, such as is possible with voice-activated ‘smart assistants’ currently available. The system runs on a Raspberry Pi 5 to enable future IoT integration and reduce costs. The system also uses the official camera module v2 and 7-inch touchscreen. Frame preprocessing uses MediaPipe to assign hand coordinates, and NumPy tools to normalise them. A machine learning model then predicts the gesture. The model, a feed-forward network consisting of five fully connected layers, was built using Keras 3 and compiled with TensorFlow Lite. Training data utilised the HaGRIDv2 dataset, modified to consist of 15 one-handed gestures from its original of 23 one- and two-handed gestures. When used to train the model, validation metrics of 0.90 accuracy and 0.31 loss were returned. The system can control both analogue and digital hardware via GPIO pins and, when recognising a gesture, averages 20.4 frames per second with no observable delay. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) poses a significant challenge, costing annually approximately USD 1.2 billion to the U.S. swine industry due to production losses associated with, but not limited to, reproductive failure, abortion, and high pre-weaning mortality among piglets. PRRSV is endemic, with thirty percent of the U.S. breeding herd experiencing outbreaks annually. The shedding status of animals on a farm is typically assessed using serum or processing fluids from piglets, but tongue tips from deceased animals are emerging as a potential alternative specimen to support farm stability assessment. This study explored the impact of various processing and testing strategies on tongue tips to enhance the sensitivity and specificity of PRRSV detection in sow herds. We collected tongue tips from 20 dead piglets across seven sow farms, testing different pooling strategies (individual testing, and pools of n = 5 or n = 20) and laboratory processing methods (tongue tip fluid—TTF, versus tongue tissue homogenate—TTH). Additionally, we simulated storage and shipping conditions, comparing frozen samples to refrigerated ones tested at intervals of 1, 4, and 7 days post collection. RT-PCR testing revealed higher sensitivity and lower cycle threshold (Ct) values for TTF compared to TTH, suggesting that tongue tips are better tested as TTF rather than TTH for PRRSV detection. Pooling samples reduced diagnostic accuracy. Frozen samples had lower absolute Ct values, and Ct values increased by 0.2 Ct values each day post collection when the sample was kept refrigerated, emphasizing the importance of minimizing shipping delays. Tongue tips are a practical, easy-to-collect specimen that target potentially infected animals (dead piglets), offering valuable insights into swine herd health, but sample processing approaches significantly influence diagnostic outcomes. If tongue tips are used by veterinarians to assess viral presence on a farm, testing the TTF instead of TTH should be prioritized. Storage and shipment conditions should be considered to optimize laboratory results. Full article

Progressive Fibrosing Interstitial Lung Disease (PF-ILD) is a severe phenotype of Interstitial Lung Disease (ILD) with a poor prognosis, typically requiring prolonged clinical observation and multiple CT examinations for diagnosis. Such requirements delay early detection and treatment initiation. To enable earlier identification of PF-ILD, we propose ILD-Slider, a parameter-efficient and lightweight deep learning framework that enables accurate PF-ILD identification from a limited number of CT slices. ILD-Slider introduces anatomy-based position markers (PMs) to guide the selection of representative slices (RSs). A PM extractor, trained via a multi-class classification model, achieves high PM detection accuracy despite severe class imbalance by leveraging a peak slice mining (PSM)-based strategy. Using the PM extractor, we automatically select three, five, or nine RSs per case, substantially reducing computational cost while maintaining diagnostic accuracy. The selected RSs are then processed by a slice-level 3D Adapter (Slider) for PF-ILD identification. Experiments on 613 cases from The University of Osaka Hospital (UOH) and the National Hospital Organization Osaka Toneyama Medical Center (OTMC) demonstrate the effectiveness of ILD-Slider, achieving an AUPRC of 0.790 (AUROC 0.847) using only five automatically extracted RSs. ILD-Slider further validates the feasibility of diagnosing PF-ILD from non-contiguous slices, which is particularly valuable for real-world and public datasets where contiguous volumes are often unavailable. These results highlight ILD-Slider as a practical and efficient solution for early PF-ILD identification. Full article

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle’s state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications. Full article

The selection of an appropriate formwork system represents a critical decision in the planning of reinforced concrete multi-story buildings. While this decision has traditionally been deferred to the construction phase, increasing evidence of time and cost overruns in construction projects has highlighted the necessity of addressing it during earlier stages, particularly in design and planning. Early identification and selection of the optimal formwork system enhances the likelihood of achieving significant improvements in both time efficiency and cost effectiveness. To facilitate this process, a decision-support framework based on the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods has been developed. This framework provides decision-makers with a structured and systematic approach for evaluating alternatives and selecting the most suitable formwork system for a given project. By offering an analytical foundation for the decision-making process, the framework assists designers and engineers in mitigating risks associated with delays and potential standstills during construction. The findings indicate that the proposed decision-support framework ensures both clarity and consistency in decision-making outcomes, irrespective of the analytical method employed. Consequently, it contributes to more robust planning and execution of construction projects. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article

Using the “Policy Pressure-Innovation Alignment-Performance Transformation” theory, this paper looks at how ESG ratings, green innovation, and corporate dual-organizational performance are linked. This study uses a multi-period Difference-in-Differences (DID) model in conjunction with a conditional mediation effect model to examine how ESG ratings causally influence substantive green innovation, which in turn improves corporate financial and environmental performance. Regression results show that corporate ESG ratings have a big effect on the performance of both organizations. ESG ratings have a bigger effect on financial performance, while ESG scores have a bigger effect on environmental performance. Looking at the sub-dimensions shows that policy ratings have immediate effects on environmental performance and delayed effects on financial performance. The conclusion that the internalization response of corporate environmental costs is timely, while the market revaluation has a delayed transmission effect, holds true after being tested through parallel trend analysis and synthetic DID testing. More research shows that differences in ESG ratings hurt financial performance but help environmental performance. This means that differences in ESG ratings may lead to more real green innovation activities, which have a direct effect on the environment and, in the end, lead to bigger improvements in environmental performance. The moderating effect test shows that being aware of the environment makes substantive green innovation more focused on quality by making people feel responsible for their actions. Also, environmental management leads to more corporate green patents, which has resource displacement effects and makes green patent innovations less effective. Heterogeneity analysis shows that state-owned businesses use their institutional advantages to improve the “quality-quantity” of substantive green innovation, which helps their corporate green development performance. Declining businesses push for green innovation to fix problems that are already there, but mature businesses don’t like ESG rating policies because they are stuck in their ways, which stops them from making real progress in green innovation. This paper ends with micro-level evidence and theoretical support to solve the “greenwashing” problem of ESG and come up with “harmonious coexistence” policy combinations that work for businesses. Full article

Methods: Using the most recent pre-COVID National Inpatient Sample (2016–2019), we evaluated inpatient mortality and economic impact after elective primary total hip arthroplasty (THA) across 327,123 cases (1,635,615 weighted discharges).Results: Overall inpatient mortality was 0.04%, but was higher in patients ≥ 80 years (0.15%), with weekend admissions (0.10%), and with surgical delay ≥ 1 day (0.17%). Comorbidities with the greatest mortality association included congestive heart failure and chronic kidney disease (both with markedly elevated odds), and acute in-hospital complications (e.g., pulmonary embolism) carried substantial risk. Complications also increased resource use; for example, heart failure, pulmonary edema, and acute coronary artery disease were each associated with significantly higher costs and prolonged length of stay. Conclusion: These findings provide a contemporary, pre-pandemic national baseline that quantifies high-risk subgroups and the economic footprint of adverse events, supporting targeted perioperative strategies and hospital planning for elective THA. Full article