Search Results (5,361)

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

Mean flow velocities and the corresponding turbulence fluctuation velocities were measured within the suction port of a standard twin-screw compressor using LDV and PIV optical techniques. Time-resolved velocity measurements were carried out over a time window of 1° at a rotor speed of 1000 rpm, a pressure ratio of 1, and an air temperature of 55 °C. Detailed LDV measurements revealed a very stable and slow inflow, with almost no influence from rotor movements except near the rotors, where a more complex flow formed in the suction port. The axial velocity near the rotors exhibited wavy profiles, while the horizontal velocity showed a rotational flow motion around the centre of the port. The turbulence results showed uniform distributions and were independent of the rotors’ motion, even near the rotors. PIV measurements confirmed that there is no rotor movement influence on the inflow structure and revealed complex flow structures, with a crossflow dominated by a main flow stream and two counter-rotating vortices in the X-Y plane; in the Y-Z plane, the presence of a strong horizonal stream was observed away from the suction port, which turned downward vertically near the entrance of the port. The corresponding turbulence results in both planes showed uniform distributions independent of rotor motions that were similar in all directions. Full article

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R${}^2$ of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R${}^2$ of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

This paper considers non-singular black holes. It discusses the observation of particles falling onto ordinary and non-singular black holes from the perspective of a distant observer. It is demonstrated that, during a stage in the evolution of non-singular black holes, powerful energy fluxes can be emitted. Distant observers may interpret these fluxes as white holes. Full article

A novel Ag(I) coordination polymer, [Ag₂(bipy)(btca)]_n, (SCP 1) was synthesized using 4,4′-bipyridyl (bipy) and 1,2,4,5-benzene-tetracarboxylic acid (H₄BTC). Characterization by FT-IR, ¹H/¹³C NMR, and single-crystal X-ray diffraction confirmed its 3D network structure. The structure of SCP 1 consists of two chains arranged in …ABAB… fashion. Chain A is one-dimensional, containing [Ag(4,4′-bipy)]_n chain, while chain B is free, containing uncoordinated 1,2,4,5-benzene tetracarboxylate and water molecules. The stacking and argentophilic interactions extend the chain A of [Ag(4,4′-bipy)]_n into a two-dimensional layer. In contrast, chain B of uncoordinated 1,2,4,5-benzene tetracarboxylate and water molecules form a 1-D chain through extensive hydrogen bonds between water molecules and BTC ions and between water molecules themselves. Chains A and B are connected through extensive hydrogen bonds, generating a three-dimensional network structure. This Silver I supramolecular coordination polymer (SCP 1) demonstrated high catalytic activity as a recyclable heterogeneous catalyst for the synthesis of 5-substituted 1H-tetrazoles via [3+2] cycloaddition of NaN₃ and terminal nitriles under solvent-free conditions in a Q-tube pressure reactor (yields: 94–99%). A mechanistic proposal involving cooperative Lewis acidic Ag(I) sites and Brønsted acidic -COOH groups facilitates the cycloaddition and protonation steps. SCP 1 catalyst exhibits reusability up to 4 cycles without significant loss of activity. The structural stability of the SCP 1 catalyst was assessed based on PXRD and FTIR analyses of the catalyst after usage, confirming its integrity during the recycling process. Full article

Rapidly pinpointing the origin of accidental chemical gas releases is essential for effective response. Prior vision pipelines—such as 3D CNNs, CNN–LSTMs, and Transformer-based ViViT models—can improve accuracy but often scale poorly as the temporal window grows or winds meander. We cast recursive backtracking of concentration fields as a finite-horizon, multi-step spatiotemporal sequence modelling problem and introduce Recursive Backtracking with Visual Mamba (RBVM), a Visual Mamba-inspired, directionally gated state-space backbone. Each block applies causal, depthwise sweeps along ${\mathrm{H}}^{\pm }$, ${\mathrm{W}}^{\pm }$, and ${\mathrm{T}}^{\pm }$ and then fuses them via a learned upwind gate; a lightweight MLP follows. Pre-norm LayerNorm and small LayerScale on both branches, together with a layer-indexed, depth-weighted DropPath, yield stable stacking at our chosen depth, while a 3D-Conv stem and head keep the model compact. Computation and parameter growth scale linearly with the sequence extent and the number of directions. Across a synthetic diffusion corpus and a held-out NBC_RAMS field set, RBVM consistently improves Exact and hit $\le 1$ over strong 3D CNN, CNN–LSTM, and ViViT baselines, while using fewer parameters. Finally, we show that, without retraining, a physics-motivated two-peak subtraction on the oldest reconstructed frame enables zero-shot dual-source localization. We believe RBVM provides a compact, linear-time, directionally causal backbone for inverse inference on transported fields—useful not only for gas–release source localization in CBRN response but more broadly for spatiotemporal backtracking tasks in environmental monitoring and urban analytics. Full article

The formation and evolution of galaxies and other astrophysical objects have become of great interest, especially since the launch of the James Webb Space Telescope in 2021. The mass, size, and density of objects in the early universe appear to be drastically different from those predicted by the standard cosmology—the $\mathsf{\Lambda }$CDM model. This work shows that the mass–size–density evolution is not surprising when we use the CCC+TL cosmology, which is based on the concepts of covarying coupling constants in an expanding universe and the tired light effect contributing to the observed redshift. This model is consistent with supernovae Pantheon+ data, the angular size of the cosmic dawn galaxies, BAO, CMB sound horizon, galaxy formation time scales, time dilation, galaxy rotation curves, etc., and does not have the coincidence problem. The effective radii $r_e$ of the objects are larger in the new model by $r_e\propto {\left(1+z\right)}^{0.93}$. Thus, the object size evolution in different studies, estimated as $r_e\propto {\left(1+z\right)}^s$ with $s=-1.0$ ± $0.3$, is modified to $r_e\propto {\left(1+z\right)}^{s+0.93}$, the dynamical mass by ${\left(1+z\right)}^{0.93},$ and number density by ${\left(1+z\right)}^{-2.80}$. The luminosity modification increases slowly with $z$ to 1.8 at $z=20$. Thus, the stellar mass increase is modest, and the luminosity and stellar density decrease are mainly due to the larger object size in the new model. Since the aging of the universe is stretched in the new model, its temporal evolution is much slower (e.g., at $z=10$, the age is about a dex longer); stars, black holes, and galaxies do not have to form at unrealistic rates. Full article

Lipid nanoparticles/liposomes (LNPs) represent a highly adaptable nanocarrier system that has gained significant traction in oncology for both therapeutic and diagnostic (theranostic) purposes. Their structural flexibility, biocompatibility, and capacity to encapsulate diverse therapeutic agents ranging from chemotherapeutics to nucleic acids and imaging tracers have enabled targeted cancer treatment with improved efficacy and reduced systemic toxicity. This review critically examines liposome-based platforms across a broad spectrum of cancers, including melanoma, lung, colorectal, liver, breast, ovarian, pancreatic, brain tumors, sarcoma, neuroblastoma, and leukemia. It outlines recent advances in ligand-mediated targeting, pH- and temperature-responsive release systems, and multifunctional LNPs capable of delivering combined therapeutic and imaging payloads. Moreover, the review discusses preclinical outcomes, current clinical trial status, and the challenges hindering clinical translation. By integrating recent innovations and emphasizing translational potential, this work highlights the pivotal role of LNPs in advancing precision cancer therapeutics and diagnostics. Full article

Background: Urban disasters expose persistent gaps in the operational picture and timely decision-making for response teams, which require user-centred systems that connect analysis to action. This study proposes and formatively validates an integrated framework that couples digital twins and augmented reality for humanitarian logistics. Methods: A mixed methods design combined a structured literature synthesis with a practitioner survey across architecture, engineering, planning, BIM, and construction to assess perceived value and adoption conditions. Results: Findings indicate that practitioners prioritised digital twins for enhancing situational awareness (71.4%) and augmented reality for providing real-time information overlays (64.3%). A majority judged that integrating these technologies would yield substantial improvements in disaster response (67.9%), despite implementation challenges. Conclusions: The framework links live state estimation and short-horizon simulation to role-specific, in-scene AR cues, with the aim of reducing decision latency and improving coordination. Adoption depends primarily on human and organisational factors, including user accessibility, preparation needs, and clear governance. These results suggest a viable pathway to operationalise the bridge between analysis and field action and outline priorities for pilot evaluation. Full article

This study aimed to forecast monthly PM_2.5 concentrations in Zabol, one of the world’s most dust-prone regions, using four time series models: SARIMA, SARIMAX enhanced with Fourier terms (selected based on spectral peak analysis), TBATS, and a novel hybrid ensemble. Spectral analysis identified a dominant annual cycle (frequency 0.083), which justified the inclusion of two Fourier harmonics in the SARIMAX model. Results demonstrated that the hybrid model, which optimally combined forecasts from the three individual models (with weights ω₂ = 0.628 for SARIMAX, ω₃ = 0.263 for TBATS, and ω₁ = 0.109 for SARIMA), outperformed all others across all evaluation metrics, achieving the lowest AIC (1835.04), BIC (1842.08), RMSE (9.42 μg/m³), and MAE (7.43 μg/m³). It was also the only model exhibiting no significant residual autocorrelation (Ljung–Box p-value = 0.882). Forecast uncertainty bands were constant across the prediction horizon, with widths of approximately ±11.39 μg/m³ for the 80% confidence interval and ±22.25 μg/m³ for the 95% confidence interval, reflecting fixed absolute uncertainty in the multi-step forecasts. The proposed hybrid framework provides a robust foundation for early warning systems and public health management in dust-affected arid regions. Full article

With the rapid advancement of Autonomous Mobile Robots (AMRs) in industrial automation and intelligent logistics, achieving efficient and safe path planning in dynamic environments has become a critical challenge. These environments require robots to perceive complex scenarios and adapt their motion strategies accordingly, often under real-time constraints. Existing methods frequently struggle to balance efficiency, responsiveness, and safety, especially in the presence of continuously changing dynamic obstacles. While Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) have each shown promise in this domain, they also face limitations when applied individually—such as high computational demands or insufficient environmental exploration. To address these challenges, we propose a hybrid path planning framework that integrates an optimized DRL algorithm with MPC. We replace the Actor’s output with a learnable noisy linear layer whose mean and scale parameters are optimized jointly with the policy via backpropagation, thereby enhancing exploration while preserving training stability. TD3 produces stepwise control commands that evolve into a short-horizon reference trajectory, while MPC refines this trajectory through constraint-aware optimization to ensure timely obstacle avoidance. This complementary process combines TD3′s learning-based adaptability with MPC’s reliable local feasibility. Extensive experiments conducted in environments with varying obstacle dynamics and densities demonstrate that the proposed method significantly improves obstacle avoidance success rate, trajectory smoothness, and path accuracy compared to traditional MPC, standalone DRL, and other hybrid approaches, offering a robust and efficient solution for autonomous navigation in complex scenarios. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

Surface drifter trajectory prediction is essential for applications in environmental management, maritime safety, and climate studies. This survey paper reviews research from the past two decades, and systematically classifies the evolution of methodologies into six successive generations, including numerical models, data assimilation, statistical and probabilistic approaches, machine learning, deep learning, and hybrid or AI-based data assimilation (1st–5.5th Generation). To our knowledge, this is the first systematic generational classification of trajectory prediction methods. Each generation revealed distinct strengths and limitations. Numerical models ensured physical consistency but suffered from accumulated forecast errors in observation-sparse regions. Data assimilation improved short-term accuracy as observing networks expanded, while machine learning and deep learning enhanced short-range forecasts but faced challenges such as error accumulation and insufficient physical constraints in longer horizons. More recently, hybrid frameworks and AI-based data assimilation have emerged, combining physical models with deep learning and traditional statistical techniques, thereby opening new possibilities for accuracy improvements. By comparing methodologies across generations, this survey provides a roadmap that helps researchers and practitioners select appropriate approaches depending on observation density, forecast lead time, and application objectives. Finally, this paper highlights that future systems should shift focus from deterministic tracks toward credible uncertainty estimates, region-aware designs, and physically consistent prediction frameworks. Full article

This contribution aims to shed light on two hermeneutical perspectives of the twentieth century which, although developed in different contexts and through distinct languages, share not only certain conceptual affinities but are both grounded in a relational ontological framework. The first is the notion of reading (notion de lecture) elaborated by the French thinker Simone Weil (1909–1943), particularly during her time in Marseille (1940–1942); the second is the idea of reading as “performance” or “execution” (esecuzione) proposed by the Italian philosopher Luigi Pareyson (1918–1991) within the framework of his aesthetic theory of formatività (1954). The aim of this study is, first, to outline the essential features of both perspectives and resonances and, subsequently, to highlight their points of convergence and original features. The goal, however, is not to propose a systematic comparison between the two authors, but rather to show the theoretical fruitfulness of a dialogue between Weil’s and Pareyson’s reflections on aesthetics and hermeneutics, from which a profile of “renewed thought” in a broad sense can emerge, opening up to a fruitful inter- and trans-disciplinary dialogue rooted in the search for truth as a shared horizon. Full article