Search Results (6,410)

Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly in the presence of sustained external disturbances and modeling errors. This paper presents a systematic modeling and control study using a three-degrees-of-freedom sagittal plane representation derived from the original six-degrees-of-freedom dynamics. Two linear tracking controllers are designed and compared: a full state feedback tracking controller and a linear quadratic servo controller with integral action. Practical performance is validated through real-time hardware in the loop simulation, where the controller runs on embedded hardware and the plant is executed on a real-time target including discrete time-sampling effects and analog input output communication noise associated with signal transmission. The results show that both controllers achieve stabilization, while the comparative HILS results reveal a trade-off rather than a uniformly superior controller. The full state feedback controller often yields lower finite-horizon position tracking errors, whereas the linear quadratic servo controller provides tighter body-pitch regulation and the more reliable removal of steady-state offset under sustained constant disturbances. These results demonstrate the feasibility of optimal servo control on cost-effective embedded platforms and indicate that controller selection should depend on the desired balance, considering tracking accuracy, disturbance rejection, convergence behavior, and actuator usage. Full article

Accurate and reliable localization is crucial for advanced autonomous driving systems. Traditional high-precision localization approaches rely on meticulously annotated high-definition (HD) maps and employ visual-geometric methods to derive accurate pose information. However, the construction, maintenance, and updating of HD maps are costly and time-consuming. In contrast, localization using publicly available navigation maps provides a low-cost and scalable alternative. Existing methods typically align BEV (Bird’s-Eye-View) features extracted from surround-view images with navigation maps to obtain localization results. Although such approaches can achieve high accuracy, they often neglect the inherent modality gap between BEV features and navigation maps, leading to localization errors. To address this issue, we propose NMLoNet: An End-to-End Intelligent Vehicle Localization Network Using Navigation Maps. The proposed method exploits road semantic elements to effectively bridge the modality gap between BEV representations and navigation maps. Specifically, a Deformable Attention Module is introduced after BEV feature extraction to capture long-range dependencies among BEV features. Furthermore, we innovatively incorporate vector map constraints to minimize the discrepancy between BEV and navigation map features. In addition, a multi-level cross-modal feature registration mechanism is designed to achieve more precise alignment between BEV and map representations. Extensive experiments on the nuScenes and Argoverse datasets demonstrate that NMLoNet achieves state-of-the-art performance, improving localization accuracy by approximately 11% under monocular settings and 24% under surround-view configurations. Moreover, the proposed network maintains robust localization performance in complex and highly dynamic driving environments. Full article

Space-based multi-target passive tracking is critical for space situational awareness, but faces severe challenges due to the limited field-of-view (FoV) and directional ambiguity of onboard sensors. These constraints often lead to target loss, poor observability, and decreased estimation accuracy. To address these issues, different fusion architectures have been explored. While centralized measurement-level fusion offers superior accuracy for estimating target states, distributed estimation-level fusion provides greater reliability for estimating the number of targets. To adaptively leverage these two complementary strengths, a dynamic hierarchical fusion method through real-time optimization of the fusion topology is proposed. Specifically, at each decision epoch, sensor nodes are dynamically partitioned into local fusion nodes (LFNs) and detection-only nodes (DONs). Each LFN receives measurements from selected DONs and executes an iterated-correction Gaussian-mixture probability hypothesis density filter. Subsequently, LFNs share and fuse their estimates using the intensity-dependent arithmetic average fusion. This dynamic process is achieved by applying a sensor management scheme based on partially observable Markov decision process (POMDP). To ensure accurate cardinality estimation, the reward function in POMDP utilizes the posterior expected number of targets. The resultant optimization is efficiently solved using a binary particle swarm optimization algorithm. Numerical and hardware-in-the-loop simulations demonstrate the effectiveness of the proposed method in balancing the accuracy of target number and state estimation. Full article

Accurate pedestrian trajectory prediction and intention estimation are crucial for autonomous systems and intelligent transportation applications. However, existing methods often address these two highly correlated tasks in isolation and rely on static or heuristic interaction modeling, leading to insufficient adaptability and limited generalization capability in dynamic traffic scenarios. To this end, this paper proposes MTG-TPNet, a Multi-task dynamic Graph Transformer network for joint Trajectory Prediction and intention estimation. The research framework integrates three key innovations: First, a dynamic graph neural network enhanced with motion features, whose graph topology can be adaptively learned end-to-end based on semantic and motion contexts to accurately capture evolving interactions. Second, a multi-granularity attention mechanism that collaboratively fuses geometric proximity, semantic similarity, and physical hard constraints to achieve fine-grained modeling of spatiotemporal dependencies. Third, a dynamic correlation loss based on Bayesian uncertainty, which balances multi-task learning in an adaptive manner and encourages beneficial interactions across tasks. Extensive experiments on the publicly available PIE and ETH/UCY datasets demonstrate that MTG-TPNet achieves state-of-the-art performance. On the PIE dataset, the proposed model significantly outperforms the best baseline model in trajectory prediction metrics, achieving an Average Displacement Error (ADE) of 0.21 and a Final Displacement Error (FDE) of 0.29. This represents a 27.6% reduction in ADE while maintaining stability in intention estimation. Systematic ablation studies validate the effectiveness of each proposed module, with the model retaining an average performance of 69.3%. Furthermore, cross-dataset evaluations confirm its superior generalization capability. This study provides a powerful unified framework for robust pedestrian behavior understanding in complex urban traffic scenarios. Full article

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

The performance of polymer nanocomposites is governed primarily by the structure and properties of the matrix–filler interphase. This study presents a quantitative Raman spectroscopy analysis of interphase evolution in epoxy nanocomposites reinforced with two-dimensional WS₂, whose surface chemistry was systematically tuned via grafting of aminoacetic acid (AA) at concentrations of 2.5, 5.0, and 7.5 wt.%. By tracking peak shifts, linewidths, intensity ratios, and integrated areas of the characteristic WS₂ phonon modes (2LA(M) + E₂g¹, A₁g, and defect-related bands), we establish a non-linear, concentration-dependent interfacial response. Minor spectral variations at 2.5 wt.% AA indicate limited interfacial interaction. At 5.0 wt.% AA, suppression of the A₁g mode and significant band broadening reflect increased structural disorder. At 7.5 wt.% AA, coordinated red shifts (~−1.8 cm⁻¹) and the appearance of an additional band near 432.8 cm⁻¹ suggest the development of a strain-mediated interfacial state. Overall, increasing AA concentration leads to a non-linear evolution of the WS₂–epoxy interface, as reflected in peak positions, linewidths and intensity ratios. These Raman-derived descriptors correlate directly with enhanced mechanical properties (flexural and tensile strength) and thermal stability (Vicat softening point) of the composites. The results demonstrate that effective interfacial coupling requires a critical surface coverage and that Raman spectroscopy serves as a powerful tool for non-destructively probing and optimizing interphase architecture in TMD/polymer systems. Full article

Because dust-emission processes driven by local, small-scale winds (e.g., terrain-induced winds) are difficult to accurately capture with mesoscale or larger-scale predictive models, this study employed a CFD-Lagrangian particle-tracking approach to numerically simulate near-surface dust release and transport under different atmospheric stability conditions in the same local flow field. The novelty of this work was the integration of MOST-based stable/neutral/unstable inflow construction with Lagrangian particle tracking, enabling a consistent comparison of stability effects within one framework. This framework is useful for assessing local blowing-sand impacts on short-range receptors. A near-surface source term was specified for PM10-class mineral dust, and particles were emitted using a vertically exponential allocation. Simulations were conducted over a kilometer-scale flow domain containing an idealized cosine hill, and the low-level concentration patterns and dispersion-height variations in the resulting dust cloud were analyzed. Compared with neutral conditions, stable stratification produced higher near-surface concentrations and a lower dispersion height, whereas unstable stratification yielded lower near-surface concentrations and a higher dispersion height; as the $\left|L\right|$ increased, the unstable cases gradually approached the neutral state. The influence of reference wind speed exhibited clear stability dependence: under stable conditions, stronger winds intensified the buoyancy-related suppression of dust dispersion, while under unstable conditions, stronger winds inhibited the vertical spreading of the dust cloud. In addition, reduced air density representative of plateau environments resulted in lower dust-cloud concentrations and higher dispersion heights. These findings highlight the coupled effects of stratification and wind speed on near-field dust dispersion and provide a reference for assessing local dust emissions over complex terrain. Full article

Background: The effectiveness of prostate-specific antigen (PSA)-based early detection of prostate cancer remains controversial and implementation-dependent. Screening policy changes have substantially altered PSA testing behavior in the United States, yet longitudinal evidence from non-organized European settings is limited. We assessed 12-year changes in awareness and utilization of PSA-based early detection and identified subgroups requiring targeted counseling. Methods: Two cross-sectional survey waves were conducted in 2009 (Study Phase 1) and 2021 (Study Phase 2) among men recruited via general practitioner practices in urban and rural regions of Germany. The survey was developed and reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). Identical questionnaires were used across phases. Endpoints were awareness of PSA-based early detection and prior PSA testing. Univariable and multivariable logistic regression evaluated independent associations with sociodemographic and behavioral factors. To assess sensitivity to compositional differences between survey waves, post-stratified weighted analyses re-aligning Study Phase 2 to the Study Phase 1 distribution of age category, educational attainment, and smoking status were conducted. Results: The analytic cohort comprised 890 men (Study Phase 1, n = 755; Study Phase 2, n = 135). Compared with Study Phase 1, Study Phase 2 participants more frequently were non-smokers (63.0% vs. 48.5%, p < 0.001) and had a university degree (38.5% vs. 30.5%, p = 0.002). In primary multivariable analyses, higher educational attainment (OR 1.71, 95% CI 1.24–2.36) and paternity (OR 1.94, 95% CI 1.25–3.01) were independently associated with greater awareness, whereas increasing age (OR 1.39, 95% CI 1.29–1.50) and higher educational attainment (OR 1.63, 95% CI 1.19–2.24) were independently associated with utilization. Study phase was not independently associated with either endpoint in primary models. In post-stratified sensitivity analyses, study phase was positively associated with utilization, indicating sensitivity of temporal contrasts to population composition. Conclusions: In primary multivariable analyses, we did not detect statistically significant temporal differences in awareness or utilization of PSA-based early detection within this German non-organized setting. The emergence of a study phase effect in weighted sensitivity analyses suggests that apparent time trends may be influenced by compositional differences between survey waves. Persistent social gradients, particularly related to educational attainment, underscore the importance of targeted, evidence-based counseling in opportunistic early detection systems. Larger, prospectively designed studies are needed to distinguish true temporal change from sampling-related effects. Full article

Despite consensus in the mathematics education literature regarding the mutually dependent components of math proficiency, as well as the importance of their development, most elementary-aged students in the United States demonstrate a lack of proficiency in math according to national assessment data. Whole number knowledge, which includes skills in computation and word-problem solving, is understood to be a critical foundation for the development of later math skills. This study used a multiple-baseline experimental design to evaluate the impacts of an evidence-based mathematics intervention, Accelerating Mathematics Performance with Practice Strategies (AMPPS), on third- through fifth-grade students’ skills with complex computation, as well as on their word-problem-solving performance. Furthermore, we evaluated effects on students’ math self-concept. Five students identified to have difficulties in math received AMPPS in a one-on-one, in-person format. The results of the study were mixed. For example, when using visual analyses as our primary analytic method, these analyses did not show robust intervention effects on students’ computation skills but did show at least some improvement for most students’ word-problem-solving skills. Additionally, supplemental analyses comparing student growth to national and school-based norms suggested that all participants seemed to benefit from the intervention, but these analyses were not intended to examine experimental causality. Despite study limitations and a lower than optimal number of AMPPS sessions (dosage) provided to students, the present study offers several directions for future research, as well as possible implications for practitioners regarding intervention selection, intensity, and evaluation. The findings will also be discussed in the context of conducting systematic replication studies, which are essential for understanding the generality of a given phenomenon (e.g., an effect of a school-based intervention) across a wide range of situations and conditions. Full article

Background: Fast excitatory transmission in the central nervous system is carried out by AMPA-type glutamate receptors. Neuronal hyperexcitability and epilepsy have been associated with the dysregulation of AMPA receptor function. Modulation of the gating kinetics of AMPA receptor function has been proposed to be a desirable target for therapy, especially when the modulation is transmembrane AMPA receptor regulatory protein (TARP)-dependent and AMPA receptor subunit composition-dependent. Methods: Eight dibenzobarrelene-based heterocycles were characterized for their effects on the human embryonic kidney cells expressing homomeric GluA1 and heteromeric GluA1/2 AMPA receptors, either alone or co-expressed with the TARPγ8 auxiliary subunit, using whole-cell patch-clamp electrophysiological recordings, and the current amplitude and kinetics of desensitization and deactivation were measured after rapid glutamate application. Results: Each chemical evaluated suppressed glutamate-induced currents via AMPA receptors and augmented both desensitization and deactivation, indicating a negative allosteric modulatory effect. The co-expression of TARPγ8 diminished, but did not eradicate, the inhibition and acceleration induced by the compounds. The observations indicate that the chemicals diminish agonist-bound open states and facilitate transitions to non-conducting states while maintaining effectiveness. Conclusions: The present study describes a specific kinetic mechanism by which dibenzobarrelene derivatives impair the function of the AMPA receptor and its dependence on auxiliary proteins. The present study provides a mechanistic understanding of AMPA receptor gating modulation and establishes a pharmacological framework for future investigations in more physiologically relevant systems. Full article

Alcohol (ethanol, an intoxicating agent in all alcoholic beverages) is the most widely consumed beverage in the United States and is a leading risk-factor for cerebrovascular diseases. Although neurons, microglia, and astrocytes have been moderately studied for their responsiveness to ethanol, the brain vasculature is minimally explored and is emerging as a key player in the interplay between neuroinflammation, cerebrovascular disease, and alcohol use disorder (AUD). The blood–brain barrier (BBB), a critical regulator of brain homeostasis, relies on the coordinated function of various cellular and molecular components to maintain its immune-privileged status. Emerging evidence indicates that chronic ethanol exposure disrupts BBB function, contributes to neurovascular dysfunction, and increases brain permeability to peripheral immune factors. This review introduces the endothelial cells (ECs) that make up the BBB and provides a brief overview of the junction proteins and transporters that assist with EC function and EC interactions with other cells of the neurovascular unit, including pericytes, smooth muscle cells, and perivascular macrophages and glial cells. In addition, this review highlights ethanol’s effects on ECs and the cells that interact with them. Lastly, given the mounting evidence on gender differences in AUD and the supporting sex differences in ethanol consumption in preclinical models, this review discusses the discovered sex differences in EC-specific genes and identifies open questions such as the influence of sex-dependent differences, genetic factors, and their interactions with ethanol on EC function. Taken together, a deeper understanding of how ethanol disrupts EC structure and function will advance therapeutic strategies to mitigate neuroinflammation and related pathologies associated with chronic ethanol exposure. Full article

Timber price dynamics are most often analysed using trends, seasonality, and classical measures of volatility, which describe the magnitude of fluctuations but only to a limited extent capture the temporal structure of the price-generating process. The aim of this study is to identify the structural complexity and long-term memory of quarterly prices of WC0 pine timber in the regional timber market in Poland. The analysis is based on nominal net prices (PLN/m³) from 16 forest districts of the Regional Directorate of State Forests in Kraków over the period 2005–2024, with reference to nationally averaged timber prices. Long-term dependence is assessed using the Hurst exponent estimated by detrended fluctuation analysis (DFA) applied to log returns, while the geometric complexity of price trajectories is characterised by the fractal dimension and additionally validated using the Higuchi estimator. Cross-sectional results reveal substantial spatial heterogeneity in scaling properties, indicating the coexistence of persistent (trend-following) and corrective (anti-persistent) dynamics across forest districts. Rolling-window analysis (40 quarters) demonstrates temporal variability in price dynamics, with particularly pronounced shifts observed in 2019–2021. Cluster analysis based on time-varying Hurst exponent values identifies two groups of forest districts with distinct persistence trajectories, corresponding to more trend-dominated and corrective price dynamics. In contrast, national-level prices generally exhibit higher persistence than local prices, reflecting the effects of price aggregation. Overall, the results show that fractal analysis uncovers persistent spatial and temporal differences in timber price structures that remain invisible when relying solely on variance-based measures, with direct implications for the choice of planning horizons and timber sale strategies in regional markets. Full article

NiTi Shape Memory Alloys (SMAs) display notable thermomechanical properties such as superelasticity and the elastocaloric effect, which makes them of interest for emerging solid-state cooling and thermal management applications. It is recognized that a considerable amount of work has been recently conducted to improve the understanding of the uniaxial tensile and compressive response of Ni-Ti SMAs; however, there has been limited work on the response to bending, which is an important operational mode in the practical designs of devices. This work consists of an experimental study of the thermomechanical response of Ni-Ti cantilever beams to cyclic bending. Nitinol samples (100 mm × 20 mm × 1 mm) were shape-set at 550 °C for 30 min and tested at 1800 rpm. The sample surface temperature change was monitored with infrared thermography data and analyzed with the Profile Mono Segment and Area Rectangle methods. The findings show that there was a measurable elastocaloric temperature change of approximately 4–5 °C, and temperature change increased by 21–25% as bending deflection increased from 31 mm to 33 mm. This was further shown to be nonlinear with the applied strain amplitude, reinforcing the strong coupling between mechanical and thermal response. The results demonstrate that Ni-Ti cantilever beams have significant potential for compact, sustainable solid-state cooling and energy storage applications, with thermal energy transfer strongly dependent on strain and energy transfer optimization. Full article

The photocycloaddition (PCA) of chalcones represents an important reaction pathway for accessing substituted cyclobutanes, which is a molecular framework with utility in synthetic chemistry, materials science, and medicine. In the past, our group has demonstrated the utility of the large cavity of γ-CD as a container for encapsulating two photo reactants for directing the PCA of several classes of aryl alkenes with high stereo- and regioselectivity: the cavitand-mediated photodimerization (CMP) approach. The CMP of chalcones reported in this work further demonstrates the effectiveness of this approach as high yields of dimers were observed in the photoreactions, while they were non-reactive in the solid state and yielded only the isomerization product in homogeneous media. The γ-CD CMP of chalcones yielded predominantly dimerized products in very good to high yields (>70%), composed of a mixture of three dimers in different proportions with syn HH as the major product. Computational analysis of the ground state complex structures revealed a strong correlation between the stability of the complex and predominance of the stereoisomer in the mixture. Further insights were deduced from temperature-dependence studies, which showed a shift in dimer selectivity tending towards a single stereoisomer. Full article