Search Results (1,005)

High-fidelity motion cueing in driving simulators is essential for delivering a realistic and immersive user experience. However, the trade-off between motion accuracy and computational efficiency often hinders achieving this. Fixed-horizon Model Predictive Control (MPC)-based Motion Cueing Algorithm (MCA) frameworks frequently struggle to adapt to rapid dynamic changes in vehicle behaviour, resulting in suboptimal simulator responses. Their reliance on worst-case horizon tuning can result in inefficient platform usage and increased computational load, limiting computational efficiency and practical deployment. This study presents an adaptive MPC-based MCA designed to enhance the fidelity of motion platforms used in vehicle dynamic simulations. The proposed method dynamically adjusts the MPC prediction horizon to improve overall simulation performance while minimising motion sensation error. Within the simulation environment, the prediction horizon is adaptively updated at each simulated control step according to recent tracking-performance metrics, enabling responsiveness to varying vehicle dynamic models and driving scenarios. The system was developed and implemented using Python and MATLAB environments, with Long Short-Term Memory (LSTM) networks employed to enhance the adaptability and precision of prediction horizon adjustments. Due to safety constraints, the proposed framework was evaluated exclusively within a simulation environment and compared against both classical MPC-based MCA and RL MPC-based MCA. Experimental results demonstrate that the proposed adaptive framework improves workspace utilisation and substantially reduces computational load compared with the classical and RL-based MPC-based MCA approaches, while maintaining competitive motion cueing tracking performance. The adaptive system effectively enhances linear displacement (LD), ensuring better alignment of motion cues with platform constraints. While minor trade-offs were observed in root mean square error (RMSE) and correlation coefficients (CCs) for sensed angular velocity (SAV) and sensed specific force (SSF), the framework improves workspace utilisation and computational efficiency while maintaining competitive motion cueing performance. Furthermore, the adaptive LSTM-MPC framework substantially reduces computational load, achieving approximately 44.26 times faster execution compared with the classical MPC-based MCA and approximately 30.03 times faster execution compared with the RL MPC-based MCA. These findings highlight the potential of integrating deep learning (DL) with MPC to optimise the trade-off between motion cueing performance, platform utilisation, and computational efficiency in driving simulators. Full article

Vision is considered the most important of the five primary senses in birds, particularly in raptors, and the relatively large size of the avian eye reflects its importance. This study provides a morphological and histomorphometric characterisation of the eye and retina of the Common Kestrel (Falco tinnunculus), a diurnal raptor with bifoveate retinal organisation. Two adult Common Kestrels, obtained through a wildlife rehabilitation programme, were examined. Eye morphology was characterised using the ratio between corneal diameter and transverse eye diameter, while retinal morphology and layer thickness were evaluated using conventional histological procedures, and opsin expression was examined in both foveae. The eyes showed a globose morphology with a strongly protruding cornea and anterior segment, within the range reported for diurnal birds of prey. Marked regional variation in retinal morphology was observed, with the central retina displaying the greatest overall thickness (254.4 ± 27.04 µm), compared with 108.6 ± 15.58 µm in the peripheral retina. Two distinct foveae were identified: a deep convexiclivate fovea within the area centralis and a temporal fovea with a deep pit and steep walls. Both foveae showed displacement of the inner retinal layers and reduced thickness at the foveal pit. The central and temporal foveae exhibited depths of 217.66 µm and 106.38 µm, respectively. S- and L/M-opsin immunoreactivity was detected in both foveae, and the absence of rhodopsin immunoreactivity in the central foveal pit suggests that high-acuity vision in both foveae is predominantly mediated by cones. Full article

Flooding is one of the most common and destructive natural disasters worldwide, and projections indicate that its intensity will increase across various climate regions during this century. South Sudan is particularly vulnerable due to a combination of factors, including hydrological releases from Lake Victoria, local rainfall patterns, and wetland retention dynamics. These factors raise important questions regarding the hydrological connectivity between Lake Victoria and the Nile system. This study examined how upstream hydrological conditions impact flood dynamics in South Sudan’s flood-prone regions, specifically in the states of Jonglei and Unity along the River Nile. To statistically estimate flood propagation lag time from Lake Victoria to the Sudd wetland, we used Cyclone Global Navigation Satellite System (CYGNSS) remote sensing data and water-level altimetry from both Lake Victoria and the River Nile at Mangalla. The analytical methods included moving block bootstrap (MBB) cross-correlation and Gaussian process (GP) modeling. Furthermore, we validated the event-based propagation and inundation patterns using flood event reports from the Displacement Tracking Matrix (DTM). The findings indicate that the statistical propagation signals took approximately 106 days during the wet season (95% confidence interval [CI]: 60–150 days) and 134 days during the dry season (95% CI: 75–195 days) for the downstream water level response to reach the River Nile at Mangalla, and 3–4 weeks to reach the adjacent floodplains downstream. Residual stationarity diagnostics showed augmented Dickey–Fuller (ADF) statistics below −7 across the analyzed propagation pathways, indicating statistically stationary lag-adjusted residual behavior. Consistent temporal correspondence between inferred flood arrival windows and independently reported DTM flood-impact periods provides cautious support for the hydrological plausibility of the estimated propagation structure. Full article

Accurate nanoscale displacement readout is essential for optical inertial sensors, precision positioning, and micro-vibration characterization. In this work, we develop a free-space optical heterodyne interferometric readout system for low-frequency nanoscale displacement sensing and establish an SNR-guided adaptive demodulation framework. Two complementary demodulation strategies are integrated: Bessel-function-based frequency-domain sideband extraction for small-amplitude low-SNR motion and IQ quadrature phase tracking for larger-amplitude displacement. The experimentally demonstrated framework maps the applicability regimes of the two methods and enables wavelength-referenced displacement readout over a range from sub-nanometer narrowband detection to 250 nm under the present experimental conditions. The implemented system achieves a repeated-measurement repeatability of 0.40 nm under a 10 Hz excitation condition, and spectral SNR analysis is consistent with time-domain statistical evaluation. Finally, the readout system is applied to a quartz pendulum inertial structure, demonstrating its potential for photonic displacement sensing and optical inertial sensor characterization. Full article

In the context of the influx of about 1 million displaced Rohingya people from Myanmar into the Cox’s Bazar District of Bangladesh in 2017, it is critical to examine their impacts on the sustainability of the social–ecological system in host Bangladesh. The specific objectives of the study are to assess the nature of intergroup conflicts between the resettled and host communities, the emerging threats posed by resettlement to social–ecological sustainability, and the adaptation and resilience of both communities. A Case Study approach was adopted in the Rohingya resettlement area of Ukhia Upazila of Cox’s Bazar District, Bangladesh. Primary data were collected through Key Informant Interviews, Focus Group Discussions, and oral history conversations. The findings reveal that the average population density in the Rohingya refugee camps is 20 m² per person, whereas the international guideline for refugee camp population density is 30–45 m²/person. The sudden Rohingya population influx has resulted in considerable land cover change, livelihood competition, and deteriorated security conditions. Between 2015 and 2023, a rapid decline in the extent of dense forest was observed—from 93 sq km to 63 sq km. The sense of land loss among the host community created a resentment towards the resettled Rohingyas that turned into social conflicts and unrest. Despite these damages, socioeconomic evolution, the implementation of adaptive measures, and successful restoration programs by the relevant institutions have revealed some degree of community resilience. An inclusive development planning strategy is recommended to sustain livelihood opportunities for both communities and local social–ecological systems. Full article

Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. Full article

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of existing bridges to determine their load-carrying capacity. This paper presents one of the first documented applications, to the authors’ knowledge, of low-cost sensing technology for capturing bridge displacements with sub-millimeter tracking precision during acceptance load testing. The study explores the use of modern remote sensing methods based on digital image correlation (DIC) to assess vertical displacements of a truss railway bridge span under moving loads. Video data were recorded using a standard smartphone under nighttime conditions with artificial lighting, demonstrating a highly accessible and cost-effective measurement approach. The collected data were processed using the DES Vision System and compared with results obtained from traditional measurement techniques, such as accelerometers, enabling an evaluation of the accuracy and precision of the DIC method. The findings show that smartphone-based video recordings can provide displacement measurements with millimeter- to sub-millimeter-level tracking precision. Additionally, a numerical finite element method (FEM) model was developed to support interpretation of the structural response under moving loads. Full article

Accurate and unobtrusive measurement of upper-limb kinematics is critical for advancing wearable sensing technologies used in industrial ergonomics, human–machine interaction, and real-time biomechanics monitoring. This study evaluates the performance of two soft, flexible wearable sensors—BendLabs biaxial angular displacement sensors and StretchSense capacitive stretch sensors—for quantifying wrist and elbow motions during simulated dynamic industrial tasks. Wrist flexion–extension and radial–ulnar deviation were measured using BendLabs sensors mounted on the dorsal hand, while elbow flexion–extension was captured using StretchSense sensors positioned along the elbow joint. A multi-camera optical motion capture system served as the reference standard. Sensor data were preprocessed using baseline correction, smoothing, denoising, and normalized cross-correlation techniques to support temporal alignment with motion-capture recordings. Across all activities, the BendLabs sensors demonstrated moderate agreement with motion capture for wrist kinematics, with generally better performance for radial–ulnar deviation than for flexion–extension. StretchSense sensors demonstrated stronger agreement with motion capture for elbow flexion–extension, with performance that was generally consistent across task types. These findings support the feasibility of soft wearable sensors for capturing upper-limb kinematics during simulated occupational tasks and highlight their potential for integration into ergonomic assessment, occupational monitoring systems, and future industrial wearable platforms. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

Surface quality in turning is still evaluated mainly by post-process profilometry, which limits the use of sensor feedback during machining. This article examines whether perpendicular vibration displacement can be used as a practical indirect indicator of surface roughness in the turning of aluminum alloys. The study is based on 204 synchronized segment-level vibration–roughness observation pairs collected during 408 s of turning. The vibration meter operated in displacement mode, continuously measuring vibration while the SD logger stored one perpendicular displacement p-p reading every 2 s; R_a and R_z were then associated with the corresponding machined segment. The analysis combined descriptive time-domain statistics, low-frequency FFT/STFT descriptors of process-state evolution, phase segmentation, correlation analysis, and linear regression. Very strong within-dataset relationships were obtained between perpendicular vibration displacement and surface roughness, with R² = 0.992 for R_a and R² = 0.988 for R_z. Entry, steady-state, and exit phases showed different variability levels, and the steady-state segment provided the most stable basis for roughness estimation. Because the logger sampling interval was 2 s, the spectral results should be interpreted as low-frequency process-state descriptors rather than as direct chatter measurements. Within this scope, the results support the use of perpendicular displacement sensing as a low-cost feasibility approach for in-process roughness indication. Broader transfer to CNC production, other alloys, and higher-bandwidth monitoring requires additional validation. Full article

Detecting hail from remote sensing observations remains challenging because hail develops rapidly and its signatures may appear at different levels within a storm. Ground-based radar and geostationary meteorological satellites are the two primary observing systems for this task, yet their observations are often spatially misaligned. Satellite measurements mainly characterize the thermal structure near the cloud top, whereas radar observations capture the lower-level precipitation core. This mismatch is further exacerbated by satellite parallax, namely the apparent horizontal shift of high cloud tops caused by the oblique viewing geometry of a geostationary satellite, together with the vertical tilt of convective storms. Existing joint methods generally combine satellite cloud-top information with radar precipitation information directly, without explicitly correcting the spatial displacement, which limits detection accuracy. To address this issue, we propose HailDeformer, a deep learning framework that first aligns satellite and radar features through a bidirectional deformable cross-attention module equipped with a position-wise confidence gate and optimized with smoothness, contrastive alignment, and observation-structure consistency losses, and then refines the fused representation using an inter-scale attention module and a wavelet-guided refinement module. Experiments on a four-region dataset from China show that HailDeformer consistently outperforms Direct Fusion, Manual Weighting, Cross-Attention Fusion, and Optical Flow Alignment, achieving a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.916, an F1 score of 0.864, a Critical Success Index (CSI) of 0.760, and the lowest False Alarm Ratio (FAR) of 0.149. Ablation studies further confirm that all proposed modules and associated constraints contribute to the overall performance, with the alignment module providing the largest improvement. Additional evaluations demonstrate that HailDeformer remains effective throughout storm evolution and under challenging observational conditions. Full article

When a fuel briquette is pressed using solar electricity in summer and burned for heating in winter, the briquette functions as a seasonal energy store—without batteries, self-discharge, or capital investment in storage infrastructure. This paper quantifies such “indirect energy storage” at an operating briquette production facility in Sumy, Ukraine, using 2024 operational data from a 34 kW hybrid solar power plant integrated into the production process without battery storage under continental climate conditions (50°55′ N) and full-scale military conflict. The objective was to determine the contribution of the solar power plant (SPP) to energy supply, analyse the structure of electricity consumption, and quantify the mechanism of indirect accumulation of renewable energy through transformation into solid biofuels. The study tested two hypotheses: (H1) that integration of a solar power plant into industrial daytime operation (6:00–22:00) achieves a self-consumption rate close to 100%, displacing grid electricity without curtailment or storage losses; and (H2) that the solar fraction embedded in produced briquettes constitutes a quantifiable mechanism of indirect seasonal energy storage despite a temporal mismatch between solar peaks (summer) and product demand (winter). Methods included statistical analysis of monthly and intraday operational data; Pearson correlation analysis between solar generation and production cycles; energy audit of production processes; decomposition of specific consumption into pressing and packaging components; and a simple economic assessment (NPV, IRR, LCOE, payback) with sensitivity analysis. Annual production reached 1222.975 t of briquettes. Total specific electricity consumption (including two short packaging campaigns in June and July only) was 141.3 ± 12.6 kWh/t (CV = 8.9%). After deducting 4962 kWh of dedicated packaging electricity (2.9% of annual consumption), the specific consumption for briquette pressing alone was 136.7 ± 5.0 kWh/t (CV = 3.7%)—within the European benchmark range of 80–150 kWh/t for wood densification, with tight monthly variation indicating a stable, well-tuned pressing operation throughout the year. The SPP supplied 18.3% of total annual electricity, peaking at 33.06% in May and averaging 29.95% from March to August. Intraday analysis of 530 five-minute intervals confirmed a 100% self-consumption rate across all seasons (H1 supported). A total of 223.4 t of briquettes containing accumulated solar energy were produced during the spring–summer period. A weak negative correlation (r = −0.28) between monthly SPP generation and briquette production was observed but did not reach statistical significance (p = 0.385); this descriptive—rather than causal—relationship is consistent with the expected temporal shift between summer surpluses and winter demand, and is itself a signature of indirect rather than direct energy coupling (H2 supported in a descriptive sense). The compound efficiency along the solar-to-stored-fuel chain was estimated at approximately 68%, providing a quantitative indicator for the indirect-storage concept. Economic analysis yielded a simple payback period of about 3 years, NPV (20 yr, 12%) ≈ 1.15 million UAH, IRR ≈ 33%, and LCOE ≈ 3.28 UAH/kWh—61% below the prevailing industrial tariff of 8.45 UAH/kWh—with sensitivity analysis showing positive NPV across ±20% variation in electricity price and ±15% in CAPEX. To the best of the authors’ knowledge, this is the first empirical quantification of biomass-solar integration as a seasonal energy buffer operating without battery storage. The solar energy accumulated in briquettes is sufficient to heat 56–74 households for a full winter season. Regional scaling of the present configuration—under explicit assumptions of comparable facility sizes and operating regimes—could in principle provide fuel for 15,000–20,000 households (8–12% of regional heating needs during energy crises). These findings are directly relevant to post-conflict energy recovery and to regions where attacks on energy infrastructure have left solid biofuels as the primary available heating source. Full article

Micro- and nanoscale mass sensing is crucial for applications such as molecular detection and wearable monitoring. However, the observation of mass perturbations in flexible composite structures requires systematic theoretical evaluation. This study develops a dual-mode vibration-based mass-sensing model based on a film–substrate composite strip. By releasing and re-stretching pre-strain in the soft substrate, the ribbon can reversibly switch between a two-dimensional flat configuration (Mode 1) and a three-dimensional buckled configuration (Mode 2), leading to distinct dynamic responses. Under a finite-deformation Euler–Bernoulli beam assumption, displacement fields and kinematic relations are formulated for both configurations. An energy-based approach is employed to decompose the total energy into stretching and bending contributions, while an added-mass block is incorporated into the kinetic energy as a lumped mass. The governing equations of motion are derived using the Lagrange equations and the Hamiltonian function. Based on these results, the influence of the added mass on displacement signatures is examined, and the mode-dependent observability in the flat versus buckled states is compared, providing an analytical basis for mass sensor evaluation. Full article

Cyanobacterial blooms in lakes and reservoirs pose significant environmental and public health risks. This paper presents an effective exploration strategy to detect them from Autonomous Surface Vehicles (ASVs) equipped with probes, whose sensing trajectories are optimized by an AI-based planner that considers the 3D spatial-temporal evolution of the cyanobacteria concentration obtained by a multiphysics model. The planner, simultaneously working on the AI decision-making and robotic domains, optimizes the surface displacement of the ASV and the depth of its probe by solving a constrained multi-objective optimization problem that minimizes the mission duration and trajectory length, maximizes the possibilities of the probe to overpass areas with high concentration of cyanobacteria, and satisfies operational constraints (such as ASV velocity or acceleration limits, and obstacle avoidance). The optimization is supported by two well-known versions of the Non-Sorted Generic Algorithm (NSGA-II and NSGA-III) and by encoding the trajectories with spline curves whose number of control points can be fixed, progressively increased, or freely manipulated by the algorithm. The performance of different configurations of the planner is tested against six scenarios obtained from different simulations of the multiphysics model (which couples water dynamics and temperature, light transmission, daily vertical migration of the cyanobacteria and their growth). The statistical analysis of the planner results determines that NSGA-III working with variable-length chromosomes and NSGA-II with the progressive increment of spline points as the best configurations for maximizing cyanobacteria detection, and minimizing mission duration and trajectory length. Full article

Acoustoelasticity provides the physical sensing principle for ultrasonic stress measurement. However, most existing formulations are restricted to isotropic media, simple stress conditions, and Cartesian coordinate systems, which limits their applicability in practical sensing scenarios involving curved and anisotropic structures. In this work, a general tensorial formulation of acoustoelasticity is developed based on the theory of incremental deformation. The proposed governing equations describe the motion of incremental displacement with explicit dependence on initial stress or strain, and are applicable to materials with arbitrary symmetry and general initial stress states. Owing to its coordinate-independent tensorial nature, the formulation can be expressed in any curvilinear coordinate system. To facilitate practical ultrasonic sensing applications, the general equations are further expanded in a cylindrical coordinate system for orthotropic materials. This enables the analysis of elastic wave propagation in curved structures such as pipelines, pressure vessels, and boreholes. The formulation establishes a direct relationship between initial stress and effective elastic properties, which determine wave velocities measurable by ultrasonic sensors, such as time-of-flight and phase velocity. The proposed approach provides a rigorous theoretical foundation for ultrasonic stress sensing and nondestructive testing, particularly for curved and anisotropic structures, and supports improved accuracy in sensor-based stress evaluation. Full article