Search Results (1,577)

The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg²/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources. Full article

Backgruond: Chronic pain is a prevalent and disabling condition, affecting 20–30% of the global population, which requires multidisciplinary approaches integrating non-pharmacological therapies and promoting patient engagement in self-management. Objective: To describe the structure, content, outcomes, and lessons learned from multicomponent workshops for chronic non-cancer pain using non-pharmacological therapies. Methods: A quasi-experimental before–after study was conducted in patients attending a chronic pain workshop at San Juan de Dios Hospital (Bormujos, Seville, Spain) between November 2021 and May 2024, with a 3-month follow-up, Validated scales and an ad hoc patient survey were administered at baseline, immediately post-workshop, and at 3-month follow-up. Furthermore, comparative analysis was conducted 4 months before and after the intervention for emergency visits and consultations, medication consumption, and employment status. Analyses employed Chi-square or Fisher’s exact tests (categorical variables); student’s t-tests or Mann–Whitney U (between-group); paired t-tests or Wilcoxon (within-group pre–post); and effect sizes (Cohen’s d, Rosenthal’s r). Significance was set at p < 0.05. Results: 197 patients completed the workshop; 178 (90.4%) were women, mean age: 55.0; 114 (57.9%) had fibromyalgia. Reductions were observed in: pain (scale 0–10) (baseline: 7.0; end of workshop: 5.0; 3 months: 5.0; p < 0.001); anxiety (13.0; 9.0; 11.0; p < 0.001); and depression (11.4; 7.2; 6.8; p < 0.001) (scales 0–21). Increases were noted in: well-being (scale 0–10) (4.0; 6.0; 5.0; p < 0.001); quality of life (scale 0–1) (0.399; 0.581; 0.556; p < 0.001); health status (scale 0–100) (40.0; 60.0; 60.0; p < 0.001); self-esteem (scale 9–36) (23.5; 27.1; 26.6; p < 0.001); and resilience (scale 6–30) (17.0; 18.0; 18.0; p = 0.002, p < 0.001). PROMs were completed by 189 patients at the end of the workshop and 110 at 3 months: pain decreased (end of workshop: 76.7%; 3 months: 80.7%); medication decreased (80.5%; 78.1%); and habits improved (87.2%; 87.6%). 40 patients (37.4%) reduced emergency visits and scheduled consultations. Overall satisfaction: 9.7. Conclusions: The workshop enhanced patients’ self-management and produced improvements in pain, quality of life, emotional well-being, and self-esteem, with effects maintained at 3 months. Full article

Background: Mozambique introduced the 10-valent pneumococcal conjugate vaccine (PCV10) in 2013 using a three-dose primary series with no booster dose (3p+0) and later switched to the PCV13 using a schedule of two primary doses with one booster (2p+1). We aimed to describe the burden and serotype distribution of pneumococcal meningitis in children under 5 years of age in Mozambique over an eleven-year period starting with the year of PCV10 introduction, and assess the impact of the PCV vaccine and schedule changes. Methods: We analysed meningitis surveillance data in Mozambique from March 2013 through to December 2023. Cerebrospinal fluid (CSF) samples were collected from eligible children in three referral hospitals (Maputo Central Hospital [south], Beira Central Hospital [central], and Nampula Central Hospital [north]). Culture and polymerase chain reaction assay (qPCR) were performed on each sample. S. pneumoniae-positive samples were subsequently serotyped using multiplex assay. We estimated annual incidence rates for pneumococcal meningitis in children under 5 years old following the PCVs’ introduction (2013–2023). The impact of the product switch and schedule change from PCV10/3p+0 to PCV13/2p+1 on the burden and serotype distribution of pneumococcal meningitis was assessed. Results: Of the 4075 CSF samples tested, 7.4% (301/4075) were positive for S. pneumoniae, 2.5% (103/4075) for H. influenzae, and 1.0% (42/4075) for N. meningitidis. Pneumococcal meningitis incidence in children under five reduced from 44.7 cases per 100,000 in 2013 to 4.6 cases per 100,000 in 2023, an 89.7% reduction. In the PCV13/2p+1 period (2020–2023), pneumococcal meningitis incidence was 51.2% lower than the PCV10/3p+0 period (2013–2017) (IRR 0.49, 95% CI 0.4–0.6; p < 0.001). PCV10-serotype pneumococcal meningitis incidence among children under five decreased by 65.6% in the PCV13/2p+1 period (IRR 0.34, 95% CI 0.2–0.6; p < 0.001). We detected zero cases of pneumococcal meningitis due to the PCV13-serotype in 2020–2023, whereas non-PCV10/13-serotypes increased by 76% (IRR 1.76, 95% CI 1.2–2.6; p = 0.004). The case–fatality proportion decreased by 71.9% (95% CI 62.9–84.8%) in the PCV13/2p+1 period. Conclusions: Since the introduction of PCVs in Mozambique, the burden of pneumococcal meningitis and deaths in children under 5 years of age has substantially decreased, as well as the prevalence of PCV13-serotypes. Higher valency PCVs are needed due to the increased prevalence of non-PCV10/13-serotypes. Funding: Gavi, The Vaccine Alliance, reference number: MOZ-HSS-2-INS; WHO Reference: 2014405143-0, creation DFC to support HIB & Surveillance System. Full article

In the context of dual-carbon goals, Park-Level Integrated Energy Systems (PIES) are pivotal for enhancing renewable energy integration and promoting clean, efficient energy use. However, the inherent non-linearity from multi-energy coupling and the high dimensionality of operational data present substantial challenges for conventional scheduling optimization methods. To overcome these obstacles, this paper introduces a novel multi-objective scheduling framework for PIES leveraging deep reinforcement learning. We innovatively formulate the scheduling task as a Markov Decision Process (MDP) and employ the Trust Region Policy Optimization (TRPO) algorithm, which is adept at handling continuous action spaces. The state and action spaces are meticulously designed according to system constraints and user demands. A comprehensive reward function is then established to concurrently pursue three objectives: minimum operating cost, minimum carbon emissions, and maximum exergy efficiency. Through comparative analyses against other AI-based algorithms, our results demonstrate that the proposed method significantly lowers operating costs and carbon footprint while enhancing overall exergy efficiency. This validates the model’s effectiveness and superiority in addressing the complex multi-objective scheduling challenges inherent in modern energy systems. Full article

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems. Full article

Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize “good-put”: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station’s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems. Full article

With increasing quality demands in pharmaceutical and cosmetic packaging, this work presents a unified inspection platform for transparent ampoules that synergistically integrates stress measurement, dimensional measurement, and surface defect detection. Key innovations include an integrated system architecture, a shared-resource task scheduling mechanism, and an optimized deployment strategy tailored for production-like conditions. Non-contact residual stress measurement is achieved using the photoelastic method, while telecentric imaging combined with subpixel contour extraction enables accurate dimensional assessment. A YOLOv8-based deep learning model efficiently identifies multiple surface defect types, enhancing detection performance without increasing hardware complexity. Experimental validation under laboratory conditions simulating production lines demonstrates a stress measurement error of ±3 nm, dimensional accuracy of ±0.2 mm, and defect detection mAP@0.5 of 90.3%. The platform meets industrial inspection requirements and shows strong scalability and engineering potential. Future work will focus on real-time operation and exploring stress–defect coupling for intelligent quality prediction. Full article

Accurate short-term electricity load forecasting (STELF) is essential for grid scheduling and low-carbon smart grids. However, load exhibits multi-timescale periodicity and non-stationary fluctuations, making STELF highly challenging for existing models. To address this challenge, an Autoformer–Transformer residual fusion network (ATRFN) is proposed in this paper. A dynamic weighting mechanism is applied to combine the outputs of Autoformer and Transformer through residual connections. In this way, lightweight result-level fusion is enabled without modifications to either architecture. In experimental validations on real-world load datasets, the proposed ATRFN model achieves notable performance gains over single STELF models. For univariate STELF, the ATRFN model reduces forecasting errors by 11.94% in mean squared error (MSE), 10.51% in mean absolute error (MAE), and 7.99% in mean absolute percentage error (MAPE) compared with the best single model. In multivariate experiments, it further decreases errors by at least 5.22% in MSE, 2.77% in MAE, and 2.85% in MAPE, demonstrating consistent improvements in predictive accuracy. Full article

Accurate power system load forecasting is the core prerequisite for guaranteeing the safe and stable operation of power grids and supporting the efficient scheduling of power systems. To improve the accuracy of load forecasting and portray the non-stationarity and multi-scale characteristics of the load sequence, this paper proposes a short-term load forecasting method based on ICEEMDAN decomposition-LSTM feature extraction-hybrid cosine attention mechanism iTransformer. Firstly, the original load sequence is decomposed using the Improved Complete Ensemble Empirical Modal Decomposition (ICEEMDAN) to extract the intrinsic modal function (IMF) and residuals (Res), and the multidimensional input feature set is constructed by combining exogenous variables such as meteorology. Secondly, multi-source features were extracted using the Long Short-Term Memory (LSTM) network to capture the complex nonlinear correlations and long-term dependencies. Finally, the extracted features are input into the iTransformer model that introduces the hybrid cosine attention mechanism. The hidden feature representation is obtained through the encoder layer modeling, and the linear mapping in the output layer generates the load forecast value. The results show that the prediction method proposed in this paper achieves better performance and can effectively improve the accuracy of short-term load prediction, which provides an effective technical support for the short-term scheduling and flexible operation of the power system. Full article

In U-shaped automated container terminals (U-shaped ACTs), automated guided vehicles (AGVs) need to frequently interact with yard cranes (YCs), and separate scheduling of the two devices will affect terminal efficiency. Therefore, this study explores the coordinated scheduling problem between the two devices. To solve this problem, a high-precision simulation model of the U-shaped ACTs is established, which incorporates real operational logic. Second, an Improved Non-dominated Sorting Genetic Algorithm II based on Proximal Policy Optimization (INSGAII-PPO) is proposed. The algorithm uses PPO to realize dynamic genetic operator selection and makes related improvements, which improve the multi-objective optimization ability of NSGAII, and solve the collaborative scheduling problem by combining simulation. Finally, a hybrid weighted Technique for Order Preference by Similarity to Ideal Solution with preferences is proposed to select the final solution. The experimental results show that the scheme obtained by INSGAII-PPO exhibits better convergence and diversity, and offers significant advantages compared with the comparison algorithms. Moreover, the energy consumption and waiting time of the final solution selected by the proposed method are reduced by 3.42% and 4.87% on average. The proposed method has the capability of providing a theoretical reference for the AGVs and YCs collaborative scheduling of U-shaped ACTs. Full article

Background/Objectives: 18F-fluorocholine is a positron emission tomography (PET) tracer earlier found to be a marker of macrophage content in carotid plaques. We aimed to assess the feasibility of 18F-choline PET-MRI to non-invasively localize vulnerable coronary plaques, using optical coherence tomography (OCT) as a reference standard. Methods: Patients with recent myocardial infarction who were scheduled for a secondary angiography of a non-culprit vessel underwent 18F-fluorocholine coronary PET-MRI. Subsequently, OCT was performed during the secondary angiography. Maximum target-to-background (TBRmax) values of 18F-fluorocholine uptake were determined in two vessel sections that contained either vulnerable or stable plaques as defined by OCT. The OCT-based definition of a vulnerable plaque was a fibrous cap thickness < 70 µm. To enhance the detectability of coronary plaques using PET, three different motion-correction strategies were used: multigate respiratory gating motion correction (MRG-MOCO), extended MR-based motion correction (eMR-MOCO), and extended MR-based motion correction with ECG gating (eMR-MOCO-ECG). Results: Fifteen patients were included in this study. One patient needed to be excluded due to extravasation of the tracer. In another patient, no region with only a stable plaque could be identified. TBRmax values were as follows for three different reconstructions in vulnerable versus stable plaques: MRG-MOCO: mean TBRmax 1.45 vs. 1.35, p = 0.52 (n = 13); eMR-MOCO: mean TBRmax 1.47 vs. 1.27, p = 0.26 (n = 11); eMR-MOCO-ECG: mean TBRmax 1.49 vs. 1.26, p = 0.21 (n = 11). Conclusions: 18F-fluorocholine uptake in vulnerable atherosclerotic plaques in coronary arteries was not significantly different from uptake in stable plaques, even though advanced motion-correction methods were applied. That may be caused by multiple factors, such as small coronary plaque size, tracer biology, or remaining cardiac motion. Full article

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power ($R^2\approx 0.88$) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions. Full article

Accurate prediction and management of train dwell times are essential for achieving efficient and sustainable public transport operations. This study evaluates established statistical dwell-time models within the context of Victoria’s regional railway network, contrasting their predictions with empirical data from video-based observations. Historically, these models—rooted in linear and non-linear regression analyses—have been designed for urban settings in peak periods. However, their applicability to regional railways, characterised by lower service frequencies with unique infrastructure and operational constraints, has been underexplored. The models were assessed for their ability to predict both passenger flow time and total dwell time under regional operating conditions. Results show that while passenger flow time can be predicted with moderate accuracy (best model R² ≈ 0.65), total dwell time models perform considerably worse (best model R² ≈ 0.25), largely due to unmodelled operational delays. The analysis identifies door operation cycles and conductor procedures as the primary operational variables influencing variability in total dwell time. Additionally, variations in passenger behaviour between peak and off-peak periods affect model performance. The findings underscore the need to incorporate local operational and behavioural factors into dwell-time models to enhance their predictive reliability for regional rail contexts. This study provides an empirical foundation for refining dwell time modelling approaches, supporting policymakers and operators in improving scheduling efficiency and overall service sustainability in regional rail networks. Full article

This preliminary study investigates a direct, non-delaminated route to valorize multilayer pharmaceutical sachet offcuts (comprising paper/plastic/aluminum) as partial substitutes for wood fiber in wood-based panels. Milled offcuts were incorporated at 10, 20, and 30 wt% (control: wood only). Laboratory mats were hot-pressed at 170 °C for 9 min under a staged pressure regime. Sampling and three-point bending were performed according to EN 326-1 and EN 310, respectively, with the density held essentially constant by controlling the mat mass and press stops. Bending stiffness (MOE) was maintained at 10–20 wt% (within experimental uncertainty of the reference), while 30 wt% showed a consistent downward trend (approximately 10%). Bending strength (MOR) peaked at 10 wt% (approximately 8% higher than the reference), then declined at 20% and 30%. Representative stress–strain curves corroborated these outcomes, indicating auxiliary bonding and crack-bridging effects at low waste loadings. Hygroscopic performance improved monotonically: 24 h water absorption and thickness swelling decreased progressively with increasing substitution, attributable to the hydrophobic polymer layers and aluminum fragments interrupting capillary pathways. Process observations identified opportunities to improve press-cycle efficiency at higher waste contents, and the dispersed foil imparted a subtle decorative sheen. Overall, the results establish the technical feasibility and a practical utilization window of approximately 10–20 wt% for furniture-grade applications. Limitations include the laboratory scale, a single resin/press schedule, and the absence of internal bond, density profile, emissions, and long-term durability tests—topics prioritized for future work (including TGA/DSC, EN 317 extensions, and scale-up). Full article

Unmanned Aerial Vehicle (UAV)-aided two-way relaying systems have attracted widespread attention due to their ability to improve communication efficiency, reduce deployment costs, and enhance reliability. However, most existing systems employ the Time-Division Multiple Access (TDMA) protocol, which suffers from rigid resource allocation and fails to efficiently manage antenna resources within a time slot for multiple users. Furthermore, the reliance on simple Line-of-Sight (LoS) channel models in many studies is often inaccurate, leading to significant performance degradation. To address these issues, this paper investigates a UAV-assisted two-way relaying system based on the Probabilistic Line-of-Sight (PrLoS) model. We propose a novel two-way transmission protocol, termed the Dynamic Dual-Antenna Time-Slot Allocation Protocol (DDATSAP), to facilitate flexible antenna resource allocation for multiple user pairs. To maximize the minimum average message rate for ground users, we jointly optimize the Resource Scheduling Factor (RSF), transmit power, and UAV trajectory. Since the formulated problem is non-convex and challenging to solve directly, we propose an efficient iterative algorithm based on Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) techniques. Numerical simulation results demonstrate that the proposed scheme exhibits superior performance compared to benchmark systems. Full article