Search Results (126)

Many filter-feeding invertebrates consume microplastics (MP) under laboratory conditions, but little is known about newly settled, field-collected juveniles. To address this information gap, we collected 3439 juvenile invertebrates in the Indian River Lagoon (IRL), FL, USA. Previous studies suggest that the IRL is a MP hotspot. A total of 70% of IRL adult oysters (Crassostrea virginica) contained MP (mean: 2.3 MP/individual), and MP number and MP length were positively correlated with animal size. We predicted that juvenile C. virginica and other sessile invertebrates would contain MP with a positive correlation to animal size. Five species were examined; 51% were C. virginica (mean shell length ± SD: 6.3 ± 4.7 mm). Overall, 117 (3.4%) animals contained potential MP (fibers: 90.7%). Of these particles that matched FTIR databases with a score of 70% or greater, 51% were plastic and 49% were anthropogenically modified particles. No correlations to animal size were found for particle presence (logistic regressions: p ≥ 0.20 for all species) or particle length (linear regressions: p ≥ 0.23 for all species). Thus, even though found in a MP hotspot, our extrapolated results suggest few juveniles (<1%) contained MP. This information is important for understanding the relationship between MP and the life histories of filter-feeding animals, especially for species considered biological indicators of MP. Full article

The long-term coexistence of human-driven vehicles (HVs) and autonomous vehicles (AVs) in mixed traffic presents significant challenges for lane-change interactions on freeways. To address this, we propose a closed-loop decision-making framework, centered on Social Value Orientation (SVO), that covers the entire process from recognition to fallback execution. First, we use maximum-entropy inverse reinforcement learning (MaxEnt-IRL) to infer driver SVO parameters (${\theta }_{SVO}$) from the NGSIM dataset, quantifying the trade-off between selfish and cooperative behaviors as learnable weights. These parameters are then incorporated into a Transformer-based predictor via conditional embeddings, enabling the model to generate personalized trajectories from identical historical data. Furthermore, within a receding-horizon, game-theoretic framework, we combine preference-weighted payoffs with this conditional predictor and introduce a dynamic lane-change abort mechanism. This mechanism triggers a fallback maneuver, generated by an APF + MPC controller, if the expected return of continuing the lane change drops below that of aborting. Simulations across 1000 adversarial scenarios show that our method markedly improves the lane-change success rate and cruising efficiency compared to the IDM + MOBIL baseline. It also significantly reduces forced merges and hazardous events when encountering aggressive or selfish blocking vehicles, demonstrating the safety and robustness benefits of our preference-aware model and abort mechanism. Full article

Background: Restless legs syndrome (RLS) is a prevalent neurological sleep disorder that often impairs sleep maintenance. This single-arm, open-label study evaluated the efficacy, safety, and tolerability of extended-duration tonic motor activation (XD-TOMAC) in adults with RLS who experience frequent awakenings with symptoms. Methods: The study comprised three stages: Stage 1 (2 weeks of no intervention), Stage 2 (8 weeks XD-TOMAC), and Stage 3 (2 weeks of no intervention). XD-TOMAC consisted of bilateral high-frequency peroneal nerve stimulation programmed to 180 min duration and administered nightly at bedtime. Nineteen adults with moderate–severe RLS were enrolled, each reporting at least three nights per week of RLS symptoms causing increased awakenings or interfering with returning to sleep after waking. Results: The intent-to-treat analysis population included all patients who began Stage 2 (n = 15). After 8 weeks of XD-TOMAC, the mean change in International RLS Study Group Rating Scale (IRLS) score was −10.6 points (p < 0.001), and the mean change in Medical Outcomes Study Sleep Problems Index II (MOS-II) was −29.5 points (p < 0.001). The mean change in the number of nocturnal awakenings was −1.1 per night (p = 0.009), and the mean change in sleep efficiency was +8.5% (p = 0.001). The mean change in time awake with RLS symptoms after sleep onset was −28.1 min (p = 0.009). Each of these improvements was sustained at the end of Stage 3 (p < 0.01). There were no serious or severe device-related adverse events. Conclusions: Compared with prior 30 min TOMAC studies, XD-TOMAC demonstrated greater efficacy and similar tolerability, supporting its potential as a nonpharmacological therapy for RLS patients whose symptoms frequently disrupt sleep. Full article

Performance differences between female youth handball players selected for national teams and non-selected peers are often linked to strength, speed, and power. This study aimed to compare the conditioning capacities and body composition of national and non-national youth handball players. The sample included 36 female players (17.13 ± 1.75 years), 18 national and 18 position-matched non-national players. Anthropometry, sprint and change in direction ability, vertical jump, upper- and lower-body strength, aerobic capacity, and body composition were assessed using standard tests and bioimpedance analysis. For normally distributed variables, an independent-samples t-test was applied, while for variables that did not meet the normality assumptions, the Mann–Whitney U test was used. Cohen’s d was used to assess effect size. National team players showed significantly greater jump height (p < 0.001, d = 1.408), higher relative peak power (p < 0.001, d = 1.644), and faster 20 m sprint times (p = 0.004, d = −1.037). No significant differences were found in body composition or the other measured parameters, although a moderate Yo-Yo IRL1 effect size suggests a potential practical advantage in aerobic capacity for national team players. These results indicate that explosive power and linear speed are key discriminators for youth national-team selection. Full article

This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) estimation that incorporates uncertainties in both TRLs and Integration Readiness Levels (IRLs). The qualitative component uses expert judgment and visual heat maps to identify subsystem-specific maturity gaps, particularly in automation, digitalization, and sustainability. The quantitative component explicitly separates three methodological layers often treated implicitly in prior research: (i) the probabilistic model representing uncertainties in TRL and IRL, (ii) the uncertainty-propagation problem linking these variables to system-level readiness, and (iii) the Monte Carlo algorithm employed to solve this problem. This structure enables the derivation of SRL distributions that reflect uncertainty more realistically than deterministic approaches, allowing statistical analysis of different characteristics of these distributions and exploratory sensitivity analysis. Results show that the Pods4Rail system is positioned between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While hardware-related subsystems such as the Transport Unit and Rail Carrier Unit exhibit relatively higher maturity, planning, logistics, and operational management functionalities remain at early development stages. By combining interpretative insight with statistical rigor, the proposed framework offers a transparent and reproducible approach to early-phase readiness assessment. Its transferability makes it suitable for other innovative mobility systems facing similar challenges of incomplete information, uncertain integration pathways, and high conceptual complexity. Full article

Reconfigurable intelligent surfaces (RISs) represent a paradigm shift in wireless communications, offering unprecedented control over electromagnetic wave propagation for next-generation 6G networks. This paper presents a comprehensive framework for high-precision indoor localization exploiting cooperative multi-RIS deployments. We introduce the adaptive multi-stage hybrid localization (AMSHL) algorithm, a novel approach that strategically combines fingerprinting-based and geometric time-difference-of-arrival (TDoA) methods through condition-aware adaptive fusion. The proposed framework employs a 4-RIS cooperative architecture with strategically positioned panels on room walls, enabling comprehensive spatial coverage and favorable geometric diversity. AMSHL incorporates five key innovations: (1) a hybrid fingerprint database combining received signal strength indicator (RSSI) and TDoA features for enhanced location distinctiveness; (2) a multi-stage cascaded refinement process progressing from coarse fingerprinting initialization through to iterative geometric optimization; (3) an adaptive fusion mechanism that dynamically adjusts algorithm weights based on real-time channel quality assessment including signal-to-noise ratio (SNR) and geometric dilution of precision (GDOP); (4) a robust iteratively reweighted least squares (IRLS) solver with Huber M-estimation for outlier mitigation; and (5) Bayesian regularization incorporating fingerprinting estimates as informative priors. Comprehensive Monte Carlo simulations at 3.5 GHz carrier frequency with 400 MHz bandwidth demonstrate that AMSHL achieves a median localization error of 0.661 m, root-mean-squared error (RMSE) of 1.54 m, and mean-squared error (MSE) of 2.38 m², with 87.5% probability of sub-2m accuracy, representing a 4.9× improvement over conventional hybrid fingerprinting in median error and a 7.1× reduction in MSE (from 16.83 m² to 2.38 m²). An optional sigmoid-based fusion variant (AMSHL-S) further improves sub-2m accuracy to 89.4% by eliminating discrete switching artifacts. Furthermore, we provide theoretical analysis including Cramér–Rao lower bound (CRLB) derivation with an empirical MSE comparison to quantify the gap between practical algorithm performance and theoretical bounds (MSE-to-CRLB ratio of approximately $4.0\times {10}^4$), as well as a computational complexity assessment. All reported metrics have been cross-validated for internal consistency across formulas, tables, and textual descriptions; improvement factors and error statistics are verified against primary simulation outputs to ensure reproducibility. The complete simulation framework is made publicly available to facilitate reproducible research in RIS-aided positioning systems. Full article

The escalating development of Maritime Autonomous Surface Ships (MASS) has imposed rigorous demands on the precision, continuity, and resilience of onboard integrated navigation systems. However, in complicated marine settings, data from the Global Navigation Satellite System (GNSS) and Doppler Velocity Log (DVL) are frequently corrupted by multipath effects and non-line-of-sight (NLOS) interference. These disturbances introduce anomalous observations that violate Gaussian noise assumptions, thereby severely deteriorating the robustness and estimation quality of traditional sliding-window factor graph optimization (SW-FGO). To mitigate this problem, this study introduces a novel integrated navigation strategy termed gradient-adaptive factor graph optimization (GA-FGO). By designing a gradient-adaptive robust objective function within the factor graph structure, the proposed method dynamically re-weights constraints from the inertial navigation system (INS), GNSS, and DVL. This mechanism adequately suppresses the influence of measurement outliers at the optimization level. Furthermore, a unified solution framework utilizing iterative reweighted least squares (IRLS) and the Gauss–Newton method is established to simultaneously perform adaptive weight updates and state estimation. Validation was based on offline field data benchmarked against the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and standard SW-FGO. The simulation results demonstrated that the GA-FGO algorithm achieves superior positioning accuracy and estimation stability under realistic measurement conditions. Full article

Multi-agent inverse reinforcement learning (MA-IRL) infers the underlying reward functions or objectives of multiple agents by observing their behavioral data, thereby providing insights into collaboration, competition, or mixed interaction strategies among agents, and addressing the symmetrical ambiguity problem where multiple rewards may correspond to the same strategy. However, most existing algorithms mainly focus on solving cooperative and non-cooperative tasks among homogeneous multi-agent systems, making it difficult to adapt to the dynamic topologies and heterogeneous behavioral strategies of multi-agent systems in real-world applications. This makes it difficult for the algorithm to adapt to scenarios with locally sparse interactions and dynamic heterogeneity, such as autonomous driving, drone swarms, and robot clusters. To address this problem, this study proposes a dynamic heterogeneous multi-agent inverse reinforcement learning framework (GAMF-DHIRL) based on a graph attention mean field (GAMF) to infer the potential reward functions of agents. In GAMF-DHIRL, we introduce a graph attention mean field theory based on adversarial maximum entropy inverse reinforcement learning to dynamically model dependencies between agents and adaptively adjust the influence weights of neighboring nodes through attention mechanisms. Specifically, the GAMF module uses a dynamic adjacency matrix to capture the time-varying characteristics of the interactions among agents. Meanwhile, the typed mean-field approximation reduces computational complexity. Experiments demonstrate that the proposed method can efficiently recover reward functions of heterogeneous agents in collaborative tasks and adversarial environments, and it outperforms traditional MA-IRL methods. Full article

Globally, little is known about the dispersal of microplastics (MP) and anthropogenic particles (AP) via atmospheric deposition (AD) into water bodies. Correlating AD to the large number of MP in estuaries is challenging but an important first step toward reducing this form of pollution. A previously published model of the surface waters of the Indian River Lagoon (IRL, east central coast of Florida, USA) estimated it contained 1.4 trillion microplastics. To determine if AD could produce this much plastic deposition, we deployed passive AD collectors throughout a 145 km² area at three site types with assistance from citizen scientists. We predicted that the rate of deposition of MP and AP would be greatest in residential areas, intermediate within a national park, and lowest on intertidal oyster reefs. Moreover, we predicted Florida’s wet season and individual rain events would increase deposition based on the published literature. Over 14 months, deposition averaged 1224 MP/m²/d; extrapolated, this yields 1.1 trillion MP for the lagoon-wide total deposition estimate (95% CI: 0.86–1.39 trillion MP). This value suggests that AD may represent an important pathway for MP to enter this estuary. More MP were deposited during rain events and in the wet season, with no differences among sites. Overall, our results provide important data for understanding AD of MP and AP in estuaries. Full article

This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then the worst-case disturbance policy and the asymmetric saturation optimal control signal can be obtained. Secondly, the multivariate probabilistic collocation method (MPCM) is used to evaluate the value function at designated sampling points. The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods. Furthermore, the DET mode is utilized to solve the SDP problem to reduce the computational burden on communication resources. Finally, the Lyapunov stability theorem is applied to analyze the stability of time-varying systems, and the simulation shows the feasibility of the designed method. Full article

We experimentally investigate the structural, magnetic, transport, and electronic properties of two d⁵ iridate double perovskite materials La₂BIrO₆ (B = Mg, Zn). Notably, despite similar crystallographic structure, the two compounds show distinctly different magnetic behaviors. The M = Mg compound shows an antiferromagnetic-like linear field-dependent isothermal magnetization below its transition temperature, whereas the M = Zn counterpart displays a clear hysteresis loop followed by a noticeable coercive field, indicative of ferromagnetic components arising from a non-collinear Ir spin arrangement. The local structure studies authenticate perceptible M/Ir antisite disorder in both systems, which complicates the magnetic exchange interaction scenario by introducing Ir-O-Ir superexchange pathways in addition to the nominal Ir-O-B-O-Ir super-superexchange interactions expected for an ideally ordered structure. While spin–orbit coupling (SOC) plays a crucial role in establishing insulating behavior for both these compounds, the rotational and tilting distortions of the IrO₆ (and MO₆) octahedral units further lift the ideal cubic symmetry. Finally, by measuring the Ir-L₃ edge resonant inelastic X-ray scattering (RIXS) spectra for both the compounds, giving evidence of spin–orbit-derived low-energy inter-J-state (intra t_2g) transitions (below ~1 eV), the charge transfer (O 2p → Ir 5d), and the crystal field (Ir t_2g → e_g) excitations, we put forward a qualitative argument for the interplay among effective SOC, non-cubic crystal field, and intersite hopping in these two compounds. Full article

In recent years, inverse reinforcement learning algorithms have garnered substantial attention and demonstrated remarkable success across various control domains, including autonomous driving, intelligent gaming, robotic manipulation, and automated industrial systems. Nevertheless, existing methodologies face two persistent challenges: (1) finite or non-optimal expert demonstration and (2) ambiguity in which different reward functions lead to same expert strategies. To improve and enhance the expert demonstration data and to eliminate the ambiguity caused by the symmetry of rewards, there has been a growing interest in research on developing inverse reinforcement learning based on the maximum entropy method. The unique advantage of these algorithms lies in learning rewards from expert presentations by maximizing policy entropy, matching expert expectations, and then optimizing the policy. This paper first provides a comprehensive review of the historical development of maximum entropy-based inverse reinforcement learning (ME-IRL) methodologies. Subsequently, it systematically presents the benchmark experiments and recent application breakthroughs achieved through ME-IRL. The concluding section analyzes the persistent technical challenges, proposes promising solutions, and outlines the emerging research frontiers in this rapidly evolving field. Full article

Ischemia/reperfusion (I/R) injury following stroke results in increased neuronal cell death due to mitochondrial hyperactivity. Ischemia results in loss of regulatory phosphorylations on cytochrome c oxidase (COX) and cytochrome c of the electron transport chain (ETC), priming COX for hyperactivity. During reperfusion, the ETC operates at maximal speed, resulting in hyperpolarization of the mitochondrial membrane potential (ΔΨ_m) and reactive oxygen species (ROS) production. We have shown that COX-inhibitory near-infrared light (IRL) provides neuroprotection in small and large animal models of brain I/R injury. IRL therapy is non-invasive and non-pharmacological and does not rely on blood flow. We identified specific wavelengths of IRL, 750 and 950 nm, that inhibit COX activity. To model the mitochondrial effects following neuronal I/R, SH-SY5Y cells underwent oxygen–glucose deprivation/reoxygenation (OGD/R) ± IRL applied at the time of reoxygenation. Untreated cells exhibited ΔΨ_m hyperpolarization, whereas IRL treated cells showed no significant difference compared to control. IRL treatment suppressed ROS production, decreased the level of cell death, and reduced the time to normalize mitochondrial activity to baseline levels from 4–5 to 2.5 h of reperfusion time. We show that IRL treatment is protective by limiting ΔΨ_m hyperpolarization and ROS production, and by speeding up cellular recovery. Full article

Background: Pickleball is one of the fastest growing sports, and the use of virtual reality is also fast growing. Because the physiological responses in real life (IRL) vs. virtual reality are unknown, the purpose of this research was to compare heart rate, metabolic and pulmonary measures IRL vs. mixed reality (MR) during pickleball activity. Methods: Eleven adult participants were outfitted with a portable metabolic unit, heart rate monitor, and virtual reality headsets. Participants played simulated pickleball for 5 min IRL and 5 min in MR. Dependent variables included average heart rate (HR [beats per minute (bpm)], ventilation (VE [L/min]), tidal volume (VT [L]), respiratory frequency (Rf [breaths per min]), respiratory exchange ratio (RER), percent of calories from fat (FAT%), percent of calories from carbohydrate (CHO%), energy expenditure (EE [kilocalorie (kcal]), and VO₂ (mL/kg/min). Data were analyzed using paired t-tests with significance accepted at p < 0.05. Effect size measurements were determined by interpretation of small (d = 0.2), medium (d = 0.5), and large (d = 0.8). Results: All metabolic and pulmonary variables except for FAT% were higher during IRL when compared with MR with effect sizes ranging from median to large. Conclusions: The results of this study provide evidence that playing pickleball IRL results in greater physiological responses in comparison to MR. Since MR demands less exertion and substrate use than IRL this result can be beneficial for training purposes with the added potential of reduced injury. Full article

This paper explores the feasibility of using a wireless Inductive Resonant Link (IRL) for wearable posture monitoring. The proposed system is based on magnetically coupled textile resonators and is implemented using a Single Input Multiple Output (SIMO) configuration. In particular, the setup consists of four inductively coupled resonators: one transmitting coil integrated into a textile structure and positioned on the back of the neck, and three receiving coils placed on the shoulders. The magnetic coupling between these elements varies as a function of the user’s posture, making it possible to monitor postural changes by analyzing variations in the transmission coefficients of the link. Unlike traditional sensor-based systems that require multiple components and data processing, the proposed method uses the inherent response of the inductive link to detect posture in a simple and efficient way. To validate the concept, experimental measurements of the scattering parameters were carried out using a compact and low-power vector network analyzer. The results show a consistent and measurable relationship between postural changes and variations in the transmission coefficients, demonstrating the effectiveness of the proposed system in distinguishing between different postures. The findings suggest that inductive resonant wireless links, especially when implemented with textile components, represent a promising alternative to traditional wearable sensor technologies for posture tracking. The approach offers significant advantages in terms of wearability, power consumption, and simplicity, making it suitable for applications in ergonomics, rehabilitation, occupational health, and smart clothing. Full article