Search Results (3,209)

Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications. Full article

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but
its sustainability benefits depend on reliable, constraint-aware nutrient dosing under
delayed inflow effects. Aquaponics involves coupling hydroponics with aquaculture but
is difficult to control because the greenhouse/crop state at the current time step (𝑡) must
anticipate water-quality changes that arrive at the next time step (𝑡 + 1), under hard EC–
pH and dose constraints. We propose the Analysis System for Nutrient Requirements in
Hydroponics (ASNRH), a two-module, constraint-aware framework that directly
regresses next-step elemental supplementation (N, P, K; mg·L−1). First, the Fish-farm Byproduct
Prediction Module (FBPM) uses a lightweight GRU forecaster to predict inflow
chemistry at 𝑡 + 1 (e.g., NH4+/NO2−/NO3−, alkalinity) from standard aquaculture sensors.
Second, the Nutrient Requirement Prediction Module (NRPM) encodes the current
hydroponic and crop state at t in parallel with the FBPM inflow at 𝑡 + 1 via a dual-branch
architecture and fuses both representations to produce non-negative dose
recommendations while penalizing forecasted EC/pH violations and excessive actuation
volatility. The data pipeline assumes low-cost greenhouse and aquaculture sensors with
chronological, leakage-free splits. A protocol-first simulation evaluates ASNRH against
time-series and rule-based baselines using accuracy metrics (MAE/RMSE/R2), EC/pH
violation rates, and robustness under missingness/noise; ablations isolate the
contributions of the inflow branch, constraint-aware losses, and lightweight physics
priors. The framework targets deployability in decoupled or coupled aquaponics by
structurally resolving 𝑡 vs. 𝑡 + 1 asynchrony and internalizing domain constraints
during learning; procedures are specified to support reproducibility and subsequent field
trials. By operationalizing anticipatory dosing from reused aquaculture byproducts under
EC/pH feasibility constraints, ASNRH is designed to support sustainability goals such as
reduced nutrient wastage and fewer corrective water exchanges in coupled or decoupled
aquaponics. Full article

Near-field ionospheric total electron content records before and after the 15 January 2022 eruption of the Hunga-Tonga Hunga-Ha’apai submarine volcano were studied using GNSS total electron content data. The data started showing positive departures from the afternoon smooth decreasing trend ~1 h before the eruption. This anomaly is localized around the volcano, i.e., it decays as we go away from the volcano. The signature resembles the one that preceded the 2011 Tohoku-oki earthquake. However, a detailed investigation of ionospheric anomalies from a dense GNSS array in New Zealand showed a large-scale traveling ionospheric disturbance propagating toward Tonga, excited by a moderate geomagnetic storm on the previous day. This suggests that the anomaly around the volcano immediately before the eruption was caused by the arrival of this disturbance at Tonga just at the eruption time. Full article

Global container shipping faces increasing pressure to reduce fuel consumption and greenhouse gas (GHG) emissions while still meeting strict port schedules under highly uncertain terminal operations and met-ocean conditions. However, most existing voyage-planning approaches either ignore real port operation variability or treat fuel optimization and just-in-time (JIT) arrival as separate problems, limiting their applicability in actual operations. This study presents a data-driven just-in-time voyage optimization framework that integrates port-side uncertainty and marine environmental dynamics into the routing process. A dwell-time prediction model based on Gradient Boosting was developed using port throughput and meteorological–oceanographic variables, achieving a validation accuracy of R² = 0.84 and providing a data-driven required time of arrival (RTA) estimate. A Transformer encoder model was constructed to forecast fuel consumption from multivariate navigation and environmental data, and the model achieved a segment-level predictive performance with an R² value of approximately 0.99. These predictive modules were embedded into a Deep Q-Network (DQN) routing model capable of optimizing headings and speed profiles under spatially varying ocean conditions. Experiments were conducted on three container-carrier routes in which the historical AIS trajectories served as operational benchmark routes. Compared with these AIS-based baselines, the optimized routes reduced fuel consumption and CO₂ emissions by approximately 26% to 69%, while driving the JIT arrival deviation close to zero. The proposed framework provides a unified approach that links port operations, fuel dynamics, and ocean-aware route planning, offering practical benefits for smart and autonomous ship navigation. Full article

At a spallation neutron source, neutron pulses of varying energies are generated, and the detection of neutrons by instrument detectors is recorded as time-of-flight from the emission of the neutron pulse to its arrival at specific detector pixels with high time resolution. The flight path of neutrons from the moderator to the sample and then to the detector must be precisely calibrated at the detector-pixel level using standard powders, so the neutron events from all pixels can be time-focused to produce high-resolution diffraction patterns. Modern time-of-flight neutron diffractometers at spallation neutron sources are equipped with two-dimensional detectors with millimeter-scale pixelations. The number of pixels in a diffraction instrument can reach millions, which makes a single-pixel-level calibration process time-consuming or even impossible with conventional refinement or fitting approaches. Here we present a machine-learning-aided calibration process using a train-and-predict approach, in which machine learning models are trained on the relationship between an individual pixel time-of-flight diffraction pattern and its diffraction constant. These models use a portion of the available pixels for training, and a good model then predicts the diffraction constants precisely and rapidly for large sets of pixel diffraction patterns. Full article

The present study has applied a probabilistic oil spill modeling framework to assess the potential risks associated with offshore oil spills in the Foz do Amazonas sedimentary basin, a region of exceptional ecological importance and increasing geopolitical and socio-environmental relevance. By integrating a large ensemble of simulations with validated hydrodynamic, atmospheric and wave-driven forcings, the analysis of said simulations has provided a robust and seasonally resolved assessment of oil drift and beaching patterns along the Guianas and the Brazilian Equatorial Margin. The model has presented a total of 47,500 simulations performed on 95 drilling sites located across the basin, using the Lagrangian Spill, Transport and Fate Model (STFM) and incorporating a six-year oceanographic and meteorological variability. The simulations have included ocean current fields provided by HYCOM, wind forcing provided by GFS and Stokes drift provided by ERA5. Model performance has been evaluated by comparisons with satellite-tracked surface drifters using normalized cumulative Lagrangian separation metrics and skill scores. Mean skill scores have reached 0.98 after 5 days and 0.95 after 10 days, remaining above 0.85 up to 20 days, indicating high reliability for short to intermediate forecasting horizons and suitability for probabilistic applications. Probabilistic simulations have revealed a pronounced seasonal effect, governed by the annual migration of the Intertropical Convergence Zone (ITCZ). During the JFMA period, shoreline impact probabilities have exceeded 40–50% along extensive portions of the French Guiana and Amapá state (Brazil) coastlines, with oil reaching the coast typically within 10–20 days. In contrast, during the JASO period, beaching probabilities have decreased to below 15%, accompanied by a substantial reduction in impact along the coastline and higher variability in arrival times. Although coastal exposure has been markedly reduced during JASO, a residual probability of approximately 2% of oil intrusion into the Amazonas river mouth has persisted. Full article

In intensive breeding systems, goats reared for meat production are often housed in group pens at high stocking densities. This study aimed to investigate the correlation between expressed behaviours and stocking density, and to compare the time budget of these confined goats with that of pasture-based goats. A detailed ethogram of 19 mutually exclusive behavioural activities was developed. Behavioural observations were conducted continuously over 72 h on group pens selected for their variation in stocking density and homogeneity in breed, age, body condition and acclimation period since arrival. Using the scan-sampling method (96 scans per goat daily), data were collected from 42 goats. The time budget, expressed as the mean frequency (%) ± standard deviation for each behavioural activity, was calculated. The associations between time budget and stocking density were assessed via bivariate analysis, with the strength and direction of relationships quantified using Pearson’s correlation coefficient (r). Results indicated that self-grooming and Bipedal stance/Climbing were positively correlated with increased space allowance (i.e., lower stocking density), suggesting their potential utility as positive welfare indicators for housed fattening goats in group pens. Furthermore, the time budget differed notably from pasture-based patterns, primarily characterized by resting (53.09% ± 2.72%), eating (16.05% ± 2.88%), and moving (2.30% ± 0.75%). Full article

Air pollution from ship operations can pose a significant challenge for coastal cities, particularly where ports are closely integrated into the urban fabric. This study examines the influence of ship docking on PM_2.5 concentrations in Chios, Greece, a medium size island city where the port directly borders densely populated neighbourhoods. Calibrated PurpleAir sensors were installed at urban and suburban sites to measure PM_2.5, with data analysed alongside ship call records and meteorological observations. An event-based concentration enhancement metric (%ΔC) was estimated to compare PM_2.5 during docking with the preceding 3 h background for 170 ship arrivals in February and August 2022. The results showed that under prevailing northerly winds in August, PM_2.5 at the downwind urban site increased on average by 5.0 µg m⁻³ (48%), whereas winter increments were smaller (6.1%) due to higher background variability. When both seasons and all wind directions were pooled, the urban site exhibited a mean enhancement of 1.7 µg m⁻³ (19%), while impacts at the suburban site remained minor (3%). Median-based uncertainty analysis confirmed robust enhancements under northerly winds only. Wind direction and wind speed were the primary controls on %ΔC, whereas ship engine power and time at berth had limited influence. The results suggest that ship-related PM_2.5 impacts are detectable but remain spatially and temporally limited in coastal urban environments, including medium-sized islands characterised by relatively low shipping activity. Full article

Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, even when the signal-to-noise ratio (SNR) is moderate to high. Specifically, P-wave coda energy can obscure S-wave onsets analysis, and shear wave splitting can generate ambiguous arrivals. In this study, we propose a novel multi-channel arrival picking framework based on Constrained Dynamic Time Warping (CDTW) for phase identification and simultaneous P- and S-wave arrival estimation. The DTW algorithm aligns microseismic signals that may be out of sync due to differences in timing or wave velocity by warping the time axis to minimize cumulative distance. Time delay constraints are imposed to ensure physically plausible alignments and improve computational efficiency. Furthermore, we introduce a Multivariate CDTW approach to jointly process the three-component (3C) data, leveraging inter-component and inter-receiver arrival consistency across the entire downhole array. The method is validated against the Short-Term Average/Long-Term Average (STA/LTA) and waveform cross-correlation techniques using field data from a shale gas hydraulic fracturing. Results demonstrate that the proposed algorithm significantly enhances arrival time accuracy and inter-receiver consistency, particularly in scenarios involving P-wave coda interference and shear wave splitting. Full article

The performance of true random number generators (TRNGs) fundamentally depends on the quality of their entropy sources (ESs). However, many FPGA-friendly designs still rely on a single mechanism and struggle to achieve both high throughput and low resource cost. To address this challenge, we propose the exclusive OR (XOR) Self-Feedback Ring Oscillator (XORSFRO), an XORNOT-style TRNG that integrates two cross-connected XOR gates with a short inverter delay chain and clocked sampling. A unified timing model is developed to describe how arrival-time skew and gate inertial delay lead to cancellation, narrow-pulse generation, and inversion events, thereby enabling effective entropy extraction. Experimental results on Xilinx Spartan-6 and Artix-7 FPGAs demonstrate that XORSFRO maintains stable operation across standard process–voltage–temperature (PVT) variations, while achieving higher throughput and lower hardware overhead compared with recent FPGA-based TRNGs. The generated bitstreams pass both the NIST SP 800-22 and NIST SP 800-90B test suites without post-processing. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

Inland LNG-fuelled liner shipping is emerging as a significant trend, yet limited refueling infrastructure presents operational challenges. The complexity of inland navigation requires frequent speed adjustments to meet scheduled arrivals, which directly affects fuel consumption and refueling strategies. Additionally, imbalances in domestic and foreign trade container flows further increase operating costs for liner shipping companies. Given estimated weekly demands, considering navigational restrictions such as water depth and bridge clearance, as well as streamflow velocity, port time windows, empty container repositioning, port selection, speed adjustment, and uncertain fuel consumption, two novel models based on empty container arc variables and node variables are formulated, aiming to maximize voyage profit. These models are extended from divisible demand to indivisible demand cases. The explicit expression for the maximum fuel consumption under the worst-case speed deviation is derived, and an external linear approximation algorithm is proposed to linearize the nonlinear models while controlling approximation errors. Furthermore, the NP-hardness of the problem, the strict equivalence of the two modeling approaches, and the solution properties are proved. A case study of LNG-fuelled liner shipping on the Yangtze River shows the following: (1) for divisible demand, both models achieve optimal solutions within seconds, while for indivisible demand, the node-variable model outperforms the arc-variable model; (2) tactical strategies should be flexibly adjusted based on seasonal water depth, fuel prices, carbon taxes, speed deviations, and expected lock passage times; and (3) increasing fuel prices and carbon taxes generally reduce port calls and sailing speeds, suggesting that stricter fuel price and carbon tax policies can support the transition to green shipping. This study provides both theoretical guidance and managerial insights, supporting shipping companies in optimizing operations and promoting the development of sustainable inland shipping. Full article

Suckler beef cows and their calves are commonly separated when calves are between four and ten months old. This is earlier than would happen naturally and causes stress in dams and calves and reduces feed intake and immunocompetence, and thus introduces calf performance and health problems. To address these concerns, weaning by separation was gradually phased out on a single extensive suckler beef farm comprising nine separate breeding herds based on chalk downland in southern England. Over seven consecutive years, the farm’s breeding herds were converted to natural weaning, one to two herds per year. This meant yearling calves stayed with their dams until weaned off naturally and beyond the subsequent calving season. To examine the effects of yearlings being left with their dams, retrospective data were collected on the subsequent calves’ survival to one year old (‘raising success’). The dams had their previous calf either still present as a yearling (YP) when the new calf arrived or had had their previous calf removed at eight months old, so it was absent (YA). Data were retrospectively analysed on 1822 calves born to 663 dams in total over the seven years. Raising success overall was 96% for YP calves and 95% for YA. Chi-squared analysis of only one calf per cow (N = 663; YP = 382, YA = 281) confirmed that raising success was not negatively associated with yearling presence. A separate analysis compared farm data on serum total protein levels of 81 YP and 12 YA 1–10-day-old calves as measures of colostrum antibody uptake. Mann–Whitney U testing showed an insignificant trend towards higher antibody uptake in YA calves (p < 0.1). However, over 86% of calves in both groups had ‘excellent’ total protein values according to a standard classification used for dairy calves (>6.2 g/dL). The findings show for the first time and under conditions studied here that beef calves can be left with their dams without a negative effect on the survival of the subsequent calf. Concerns of sibling rivalry disturbing the bonding process and leading to competition for colostrum and milk were not confirmed. In conclusion, allowing cows to wean their calves naturally could potentially be a viable management option for similar beef suckler herds, including those used in habitat/soil restoration projects. Full article

Global air transport has become the dominant mode of long-distance travel, carrying more than four billion passengers in 2019 and projected to exceed 8 billion by 2040. Nevertheless, limited demand and economic inefficiencies often make direct connections unfeasible, forcing many passengers to rely on transfers. In such cases, synchronizing arrivals and departures at hub airports is crucial to minimizing transfer times and maximizing passenger retention. This study investigates the synchronization problem at Istanbul Airport, one of the world’s largest hubs, using metaheuristic optimization. Three algorithms—Genetic Algorithms (GA), Modified Discrete Particle Swarm Optimization (MDPSO), and Evolutionary Strategies (ES)—were applied in parallel to optimize arrival and departure schedules for a major airline. The proposed chromosome-based framework was tested through parameter tuning and validated with statistical analyses, including ANOVA and Games–Howell pairwise comparisons. The results show that MDPSO achieved strong improvements, while ES consistently outperformed both GA and MDPSO, increasing successful passenger transfers by more than 200% compared to the original schedule. These findings demonstrate the effectiveness of evolutionary metaheuristics for large-scale airline scheduling and highlight their potential for improving hub connectivity. This framework is generalizable to other hub airports and airlines, and future research could extend it by integrating hybrid metaheuristics or applying enhanced forecasting methods and more dynamic scheduling approaches. Full article

Accurate user positioning in 6G networks is essential for next-generation mobile services. However, classical approaches such as time-difference-of-arrival (TDoA) remain vulnerable to dense multipath and NLoS conditions commonly found in indoor and industrial environments. This paper proposes an AI-driven RF fingerprinting framework that leverages uplink channel state information (CSI) to achieve robust and privacy-preserving 2D localization. A lightweight convolutional neural network (CNN) extracts location-specific spectral–spatial fingerprints from CSI tensors, while a federated learning (FL) scheme enables distributed training across multiple gNBs without sharing raw channel data. The proposed integration of CSI tensor processing with FL and structured pruning is introduced as a novel solution for practical 6G edge positioning. To further reduce latency and communication costs, a structured pruning mechanism compresses the model by 40–60%, lowering the memory footprint with negligible accuracy loss. A performance evaluation in 3GPP-compliant indoor factory scenarios indicates a median positioning error below 1 m for over 90% of cases, significantly outperforming TDoA. Moreover, the compressed FL model reduces the FL communication load by ~38% and accelerates local training, establishing an efficient, secure, and deployment-ready positioning solution for 6G networks. Full article