Search Results (5,273)

Sea surface roughness (SSR) retrieval is a frontier topic in the field of ocean remote sensing, and SSR retrieval based on multi angle, passive, visible spectrum remote sensing images has been proven to have potential applications. Traditional multi angle retrieval models ignored the nonlinear relationship between radiation and digital signals, resulting in low accuracy in SSR retrieval using visible spectrum remote sensing images. Therefore, we analyze the transmission characteristics of signals and random noise in sea surface imaging, establish signals and noise transmission models for typical sea surface imaging visible spectrum remote sensing systems using Complementary Metal Oxide Semiconductor (CMOS) and Time Delay Integration-Charge Coupled Device (TDI-CCD) sensors, and propose a model for SSR retrieval using multi angle passive visible spectrum remote sensing images. The proposed model can effectively suppress the noise behavior in the imaging link and improve the accuracy of SSR retrieval. Simulation experiments show that when simulating the retrieval of multi angle visible spectrum images obtained using CMOS or TDI-CCD imaging systems with four SSR levels of 0.02, 0.03, 0.04, and 0.05, the proposed model relative errors using two angles are decreased by 4.0%, 2.7%, 2.3%, and 2.0% and 6.5%, 4.3%, 3.7%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 7.0%, 6.7%, 7.8%, and 9.0% and 9.5%, 8.3%, 9.0%, and 10.2%. When using more fitting data, the relative errors of the model were decreased by 5.0%, 2.7%, 2.5%, and 2.0% and 7.0%, 5.0%, 4.3%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 8.5%, 7.0%, 8.0%, and 9.4%, and 10.0%, 8.7%, 9.3%, and 10.0%. Full article

Background: Benzodiazepine poisoning is a frequent cause of emergency department (ED) visits, often related to suicide attempts. Flumazenil is the only specific antidote, but its continuous infusion protocol remains controversial because of its uncertain outcome benefits and increased risk of adverse events. This study aimed to evaluate the effect of continuous flumazenil infusion on the time to recovery of consciousness and secondary outcomes in patients with benzodiazepine poisoning stratified by hospitalization status. Methods: A retrospective cohort study was conducted at a tertiary hospital in Seoul, Korea, including adults treated for benzodiazepine poisoning in the ED between April 2019 and March 2024. The primary outcome being the time from arrival at the ED to regaining consciousness. Multivariate regression identified independent predictors of delayed recovery. Results: Among the 370 patients, 52.4% were hospitalized. Flumazenil infusion was administered in 46.8% of the patients, more often in hospitalized patients. In this group, flumazenil infusion significantly reduced the median time to regain consciousness (13.7 vs. 19.4 h, p = 0.006) but did not affect the overall hospital stay. In nonhospitalized patients, flumazenil infusion did not shorten the awakening time or prolong the ED stay. Adverse events, mainly agitation, were more frequent with flumazenil infusion. Conclusions: Continuous infusion of flumazenil accelerates the recovery of consciousness only in hospitalized patients who are severely affected by benzodiazepine poisoning but with increased adverse events and no reduction in hospital stay. Individualized patient selection and evidence-based protocols are needed for optimal and safe antidote use. Full article

Coordinated adaptive signal control is a proven strategy for improving traffic efficiency and minimizing vehicular delays. First, we develop a Queue Evolution and Delay Model (QEDM) that establishes the relationship between detector-measured queue lengths and model parameters. QEDM accurately characterizes residual queue dynamics (accumulation and dissipation), significantly enhancing delay estimation accuracy under oversaturated conditions. Secondly, we propose a novel intersection-level signal optimization method that addresses key practical challenges: (1) pedestrian stages, overlap phases; (2) coupling effects between signal cycle and queue length; and (3) stochastic vehicle arrivals in undersaturated conditions. Unlike conventional approaches, this method proactively shortens signal cycles to reduce queues while avoiding suboptimal solutions that artificially “dilute” delays by extending cycles. Thirdly, we introduce an adaptive coordination control framework that maintains arterial-level green-band progression while maximizing intersection-level adaptive optimization flexibility. To bridge theory and practice, we design a cloud–edge–terminal collaborative deployment architecture for scalable signal control implementation and validate the framework through a hardware-in-the-loop simulation platform. Case studies in real-world scenarios demonstrate that the proposed method outperforms existing benchmarks in delay estimation accuracy, average vehicle delay, and travel time in coordinated directions. Additionally, we analyze the influence of coordination constraint update intervals on system performance, providing actionable insights for adaptive control systems. Full article

Microbially induced carbonate precipitation (MICP), as an eco-friendly biomineralization technology, has opened up an innovative path for the green and low-carbon development of the mining industry. Unlike conventional methods, its in situ solidification minimizes environmental disturbances and reduces carbon emissions during construction. This article reviews the research on MICP technology in various scenarios within the mining industry, summarizes the key factors influencing the application of MICP, and proposes a future research direction to fill the gap of the lack of systematic guidance for the application of MICP in this field. Specifically, it elaborates on the solidification mechanism of MICP and its current application in the solidification and storage of tailings, heavy metal immobilization, waste resource utilization, carbon sequestration, and field-scale deployment, establishing a technical foundation for broader implementation in the mining sector. Key influencing factors that affect the solidification effect of MICP are discussed, along with critical engineering challenges such as the attenuation of microbial activity and the low uniformity of calcium carbonate precipitation under extreme conditions. Proposed solutions include environmentally responsive self-healing technologies (the stimulus-responsive properties of the carriers extend the survival window of microorganisms), a one-phase low-pH injection method (when the pH = 5, the delay time for CaCO₃ to appear is 1.5 h), and the incorporation of auxiliary additives (the auxiliary additives provided more adsorption sites for microorganisms). Future research should focus on in situ real-time monitoring of systems integrated with deep learning, systematic mineralization evaluation standard system, and urea-free mineralization pathways under special conditions. Through interdisciplinary collaboration, MICP offers significant potential for integrated scientific and engineering solutions in mine waste solidification and sustainable resource utilization. Full article

Aiming at the problem of poor autonomy and weak time performance of reentry trajectory planning for Reusable Launch Vehicle (RLV), an online reentry trajectory planning and guidance method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. In view of the advantage that the drag acceleration can be quickly measured by the airborne inertial navigation equipment, the reference profile adopts the design of the drag acceleration–velocity profile in the reentry corridor. In order to prevent the problem of trajectory angle jump caused by the unsmooth turning point of the section, the section form adopts the form of four multiple functions to ensure the smooth connection of the turning point. Secondly, considering the advantages of the TD3 dual Critic network structure and delay update mechanism to suppress strategy overestimation, the TD3 algorithm framework is used to train multiple strategy networks offline and output profile parameters. Finally, considering the reentry uncertainty and the guidance error caused by the limitation of the bank angle reversal amplitude during lateral guidance, the networks are invoked online many times to solve the profile parameters in real time and update the profile periodically to ensure the rapidity and autonomy of the guidance command generation. The TD3 strategy networks are trained offline and invoked online many times so that the cumulative error in the previous guidance period can be eliminated when the algorithm is called again each time, and the online rapid generation and update of the reentry trajectory is realized, which effectively improves the accuracy and computational efficiency of the landing point. Full article

Driven by technologies such as the Internet of Things and artificial intelligence, smart buildings have developed rapidly, and the demand for processing massive amounts of data has risen sharply. Traditional cloud computing is confronted with challenges such as high network latency and large bandwidth pressure. Although edge computing can effectively reduce latency, it has problems such as resource limitations and difficulties with cluster collaboration. Therefore, cloud–edge collaboration has become an inevitable choice to meet the real-time and reliability requirements of smart buildings. In view of the problems with the existing task scheduling methods in the smart building scenario, such as ignoring container compatibility constraints, the difficulty in balancing global optimization and real-time performance, and the difficulty in adapting to the dynamic environments, this paper proposes a two-stage cloud-edge collaborative dynamic task scheduling mechanism. Firstly, a task scheduling system model supporting container compatibility was constructed, aiming to minimize system latency and energy consumption while ensuring the real-time requirements of tasks were met. Secondly, for this task-scheduling problem, a hierarchical and progressive solution was designed: In the first stage, a Resource-Aware Cost-Driven Greedy algorithm (RACDG) was proposed to enable edge nodes to quickly generate the initial task offloading decision. In the second stage, for the tasks that need to be offloaded in the initial decision-making, a Proximal Policy Optimization algorithm based on Action Masks (AMPPO) is proposed to achieve global dynamic scheduling. Finally, in the simulation experiments, the comparison with other classical algorithms shows that the algorithm proposed in this paper can reduce the system delay by 26–63.7%, reduce energy consumption by 21.7–66.9%, and still maintain a task completion rate of more than 91.3% under high-load conditions. It has good scheduling robustness and application potential. It provides an effective solution for the cloud–edge collaborative task scheduling of smart buildings. Full article

This study aimed to evaluate the biomechanical effects of implant length, mandibular morphology, graft application, loading timing, and force direction on peri-implant stress distribution using finite element analysis (FEA). Five mandibular models representing normal, atrophic, and graft-augmented conditions were constructed. Each model was analyzed with 6 mm and 12 mm Straumann Standard implants under two loading types, vertical (200 N) and oblique (100 N at 30°), across three loading protocols (immediate, early, and delayed). Stress analysis was conducted using von Mises and principal stress criteria, focusing on cortical and trabecular bone, the implant–abutment complex, and the mandibular canal. Under vertical loading, increasing the implant length from 6 mm to 12 mm reduced the maximum tensile stresses in trabecular bone from 0.930 MPa to 0.475 MPa (an approximate 49% decrease). However, oblique loading caused a substantial increase in stresses in all regions, with trabecular compressive stress reaching up to −19.102 MPa and cortical tensile stress up to 179.798 MPa in the atrophic mandible. Graft application significantly reduced peri-implant stresses; for example, maximum compressive stress in the cortical bone decreased from −227.051 MPa in the atrophic model to −13.395 MPa in the grafted model under similar loading conditions. Although the graft donor site was not explicitly modeled, the graft material (Bio-Oss) was anatomically positioned in the posterior mandible to simulate buccolingual augmentation and its biomechanical effects. Stress concentrations around the mandibular canal remained below the 6 MPa threshold for neurovascular injury in all scenarios, indicating a biomechanically safe outcome. These findings indicate that oblique loading and reduced bone volume may compromise implant survival, whereas graft application plays a critical role in mitigating stress levels and enhancing biomechanical stability. The study also emphasizes the importance of considering force direction and bone quality in clinical planning, and highlights the novelty of combining graft simulation with FEA to assess its protective role beyond implant length alone. Full article

Background: Hearing loss is known to cause structural and functional abnormalities in the central auditory pathways. Interventions with hearing aids that amplify acoustic signals have been developed to combat hearing loss. However, little is known about how such devices may affect the brain and mitigate the progression of hearing loss. We hypothesized that timely intervention that amplifies acoustic signals would delay further progression of hearing loss by maintaining central auditory activity and neural structure. Method: To that end, we provided eight weeks of acoustic stimulation tailored to compensate for subject-specific patterns of frequency loss in two mouse models of progressive hearing loss. We evaluated the effects of sound amplification on endbulb of Held anatomy at different ages of intervention in mice with early-onset (DBA/2) and late-onset (C57Bl/6) hearing loss. Results: We observed in both strains that endbulbs undergo rapid and progressive atrophy in untreated control subjects exposed to a baseline, unamplified, sound environment. In contrast, endbulb atrophy was significantly slowed in treated mice (p < 0.05). Conclusions: These data provide a possible explanation for how the brain benefits from sound amplification via hearing aid devices. Full article

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines. Full article

Capsule-based self-healing technologies offer a promising solution to extend pavement service life without requiring external activation. The effect of the capsule content on the mechanical behaviour of self-healing asphalt mixtures still needs to be understood. This study presents a numerical evaluation of the isolated effect of incorporating capsules containing encapsulated rejuvenators, at different volume contents, on the stiffness and strength of asphalt mixtures through a three-dimensional discrete-based programme (VirtualPM3DLab), which has been shown to predict well the experimental behaviour of asphalt mixtures. Uniaxial tension–compression cyclic and monotonic tensile tests on notched specimens are carried out for three capsule contents commonly adopted in experimental investigations (0.30, 0.75, and 1.25 wt.%). The results show that the effect on the stiffness modulus progressively increases as the capsule content grows in the asphalt mixture, with a reduction ranging from 4.3% to 12.3%. At the same time, the phase angle is marginally affected. The capsule continuum equivalent Young’s modulus has minimum influence on the overall rheological response, suggesting that the most critical parameter affecting asphalt mixture stiffness is the capsule content. Finally, while the peak tensile strength shows a maximum reduction of 12.4% at the highest capsule content, the stress–strain behaviour and damage evolution of the specimens remain largely unaffected. Most damaged contacts, which mainly include aggregate–mastic and mastic–mastic contacts, are highly localised around the notch tips. Contacts involving capsules remained intact during early and intermediate loading stages and only fractured during the final damage stage, suggesting a delayed activation consistent with the design of healing systems. The findings suggest that capsules within the studied contents may have a moderate impact on the mechanical properties of asphalt mixtures, especially for high-volume contents. For this reason, contents higher than 0.75 wt.% should be applied with caution. Full article

To improve the spatial processing performance in the low frequency and long range shallow water communication system, a multi-mode joint equalization scheme is proposed, which combines modal depth function estimation, mode filtering, and multi-input equalization. This method first estimates the modal depth function of the effective modes by Singular Value Decomposition (SVD) of Cross Spectral Density Matrix (CDSM), then separates the influence of each mode on the continuous-time signal by the vertical array mode filtering without any prior information. After these pre-processings, the separated signal is only affected by the single channel mode, and the output Signal-to-Noise Ratio (SNR) is enhanced, and channel delay spread is reduced simultaneously. All the separated parts are then sent to a multi-input equalizer to compensate for the channel fading between different modes.Simulation results verify that compared with single channel equalization after beamforming and multichannel equalization, the proposed multi-mode joint equalization can obtain 3 dB and 6 dB gain, respectively. Experimental results also show that the proposed equalization can achieve lower Bit Error Rate (BER) and higher output SNR. Full article

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumour, associated with poor survival outcomes and significant clinical challenges. Conventional diagnostic methods, including MRI, CT, and histopathological analysis of tissue biopsies, are limited by their inability to reliably distinguish treatment effects from true tumour progression, often resulting in misdiagnosis and delayed intervention. Repeated tissue biopsies are also invasive and unsuitable for longitudinal monitoring. Liquid biopsy, a minimally invasive approach analysing tumour-derived material in biofluids such as blood and cerebrospinal fluid (CSF), offers a promising alternative. This review aims to evaluate current evidence on circulating biomarkers including circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), microRNAs (miRNAs), extracellular vesicles (EVs), and proteins in GBM diagnosis and monitoring, and to assess the potential role of artificial intelligence (AI) in enhancing their clinical application. Methods: A narrative synthesis of the literature was undertaken, focusing on studies that have investigated blood- and CSF-derived biomarkers in GBM patients. Key aspects evaluated included biomarker biology, detection techniques, diagnostic and prognostic value, current technical challenges, and progress towards clinical translation. Studies exploring AI and machine learning (ML) approaches for biomarker integration and analysis were also reviewed. Results: Liquid biopsy enables repeated and minimally invasive sampling of tumour-derived material, reflecting the genetic, epigenetic, proteomic, and metabolomic landscape of GBM. Although promising, its translation into routine clinical practice is hindered by the low abundance of circulating biomarkers and lack of standardised collection and analysis protocols. Evidence suggests that combining multiple biomarkers improves sensitivity and specificity compared with single-marker approaches. Emerging AI and ML tools show significant potential for improving biomarker discovery, integrating multi-omic datasets, and enhancing diagnostic and prognostic accuracy. Conclusions: Liquid biopsy represents a transformative tool for GBM management, with the capacity to overcome limitations of conventional diagnostics and provide real-time insights into tumour biology. By integrating multiple circulating biomarkers and leveraging AI-driven approaches, liquid biopsy could enhance diagnostic precision, enable dynamic disease monitoring, and improve clinical decision-making. However, large-scale validation and standardisation are required before routine clinical adoption can be achieved. Full article

Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can delay timely interventions. Deep learning has emerged as a powerful approach for extracting critical features essential to identifying and classifying violent behavior, enabling the development of accurate and scalable models across diverse domains. This study presents the Int.2D-3D-CNN architecture, which integrates a two-dimensional convolutional neural network (2D-CNN) and 3D-CNNs for video-based violence recognition. Compared to traditional 2D-CNN and 3D-CNN models, the proposed Int.2D-3D-CNN model presents improved performance on the Hockey Fight, Movie, and Violent Flows datasets. The architecture captures both static and dynamic characteristics of violent scenes by integrating spatial and temporal information. Specifically, the 2D-CNN component employs lightweight MobileNetV1 and MobileNetV2 to extract spatial features from individual frames, while a simplified 3D-CNN module with a single 3D convolution layer captures motion and temporal dependencies across sequences. Evaluation results highlight the robustness of the proposed model in accurately distinguishing violent from non-violent videos under diverse conditions. The Int.2D-3D-CNN model achieved accuracies of 98%, 100%, and 98% on the Hockey Fight, Movie, and Violent Flows datasets, respectively, indicating strong potential for violence recognition applications. Full article

Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations in cover crop outcomes across Nebraska, focusing on three critical management factors: seeding rate, termination timing, and termination-to-corn planting intervals. Using Decision Support System for Agrotechnology Transfer (DSSAT) simulations, we evaluated the effects of these practices on cover crop biomass, growth stages, and subsequent corn yield across seven sites. The results revealed that corn yield remained resilient across all sites, with no statistically significant differences (p > 0.05) across termination timings, seeding rates, or termination-to-planting intervals. A CC seeding rate analysis showed that biomass tended to increase with higher seeding densities, particularly from 200 to 250 plants m⁻², but the gains diminished beyond that, and few pairwise comparisons reached statistical significance. Termination timing had a significant effect on biomass and growth stages, with delayed termination resulting in greater biomass accumulation and advanced phenological development (e.g., Zadoks > 45), which may complicate termination efficacy. Increasing termination-to-planting intervals led to reduced biomass due to shorter growing periods, though these reductions were not associated with significant corn yield penalties. This study highlights the importance of tailoring CC management strategies to local environmental conditions and agronomic objectives. By addressing these site-specific factors, the findings offer actionable insights for farmers and land managers to optimize both ecological benefits and productivity in Nebraska’s no-till systems. Full article

This paper addresses challenges in speed tracking control for UAV systems equipped with variable-pitch propellers (VPPs), particularly those arising from communication delays and dynamic environmental disturbances. We propose a hybrid control strategy integrating an enhanced Smith predictor with fuzzy adaptive PID control. The Smith predictor compensates for time delays within the propulsion system, while the fuzzy PID controller dynamically adjusts for variations in UAV dynamics and external disturbances. Our approach incorporates real-time fluctuations in air-to-ground communication, encompassing both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Theoretical analysis and numerical simulations validate the proposed architecture, demonstrating enhanced control performance, reduced delay effects, and improved robustness for real-time applications. Full article