Search Results (318)

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

Background: Physical inactivity among Saudi Arabian adolescents is a critical public health concern due to its contribution to the rising prevalence of overweight, obesity, and non-communicable diseases. Despite this, the motivational profiles and perceived barriers that differentiate athletic from non-athletic adolescents remain understudied in the Saudi literature, particularly within the school Physical Education (PE) context. Grounded in Self-Determination Theory (SDT), the present study examined the factors preventing and motivating Saudi adolescents to engage in physical activity (PA) and discusses findings in terms of their implications for PE teachers and school-based intervention. Method: A cross-sectional study was conducted with 124 male high school students in Riyadh (mean age: 16.79 ± 0.66 years). Participants were categorized as either athletes (n = 70) or non-athletes (n = 54) based on pre-defined engagement criteria: athletes were required to report vigorous-intensity sport participation on three or more days per week for a minimum of 60 min per session. Anthropometric measurements, lifestyle behaviors (diet, screen time, sleep), motivations, and barriers were assessed using the validated Arab Teens Lifestyle Study (ATLS) questionnaire. Independent samples t-tests and chi-square tests were used to compare between-group differences; effect sizes are reported. Result: Non-athletes had a significantly higher mean BMI (29.40 ± 6.77 kg/m²) and waist circumference (98.65 ± 21.63 cm) compared to athletes (BMI: 22.19 ± 4.44 kg/m²; waist: 78.84 ± 9.51 cm; both p < 0.001). No significant differences were observed in screen time, sleep duration, or dietary habits. The primary motivations for PA among athletes were health benefits (27.1%), recreation (25.7%), and competition (20.0%), reflecting an autonomous motivational profile consistent with SDT. Among non-athletes, the predominant barriers were the lack of suitable facilities (25.9%) and the absence of an exercise partner (22.2%); reflecting unmet SDT needs for competence and relatedness respectively, while only 9.3% cited having a lack of time. Conclusions: Non-athletic participants face a significant health disadvantage characterized by higher rates of overweight and central obesity. In contrast to global trends, where academic commitments dominate barriers to PA, the principal barriers in this population are environmental and social, reflecting unmet psychological needs that PE teachers are uniquely positioned to address. Rather than focusing solely on infrastructure, PE promoters should implement need-supportive teaching practices, including competence-building tasks and cooperative peer structures, to foster the intrinsic motivational profile observed in the athletes and promote long-term PA adherence among non-athletic students, in alignment with the health objectives of Saudi Vision 2030. Full article

Driven by climate change and population growth, coastal flood risk is rising, making high-precision Digital Elevation Models (DEMs) essential for inundation simulation and risk assessment. Although global open-source DEMs are increasingly available, their regional applicability and uncertainty still require quantitative evaluation. Taking Lianyungang, a coastal city in eastern China, as the study area, this study used ICESat-2 ATL08 laser altimetry as the reference to assess the vertical accuracy of eight mainstream open-source DEMs: the ASTER GDEM, FABDEM, AW3D30 DEM, SRTM DEM, MERIT DEM, NASA DEM, Copernicus DEM, and TanDEM-X DEM. The effects of slope, aspect, and land cover on DEM errors were analyzed, and the Height Above Nearest Drainage (HAND) model was used to evaluate how DEM vertical accuracy and spatial resolution affect flood inundation simulation. The results show that the FABDEM has the highest accuracy (RMSE = 1.24 m; NMAD = 0.49 m), followed by the Copernicus DEM GLO-30 (RMSE = 1.56 m; NMAD = 0.65 m), whereas the ASTER GDEM performs worst (RMSE = 5.36 m; NMAD = 3.69 m). The SRTM DEM systematically underestimates ICESat-2 elevations, with mean and median errors of −1.85 m and −1.80 m, mainly due to acquisition time differences and land-use changes in Lianyungang. DEM errors generally increase with slope, are higher on west-facing slopes, and are larger over water bodies than over cropland and impervious surfaces. HAND simulations show that DEM-derived inundation differences are greatest under low-threshold conditions. At the 1 m HAND threshold, the MERIT DEM produces the largest inundation area (4370.28 km²), while the ASTER GDEM produces the smallest area (3330.53 km²); these differences decrease as the threshold increases. Overall, the FABDEM provides the most accurate flood inundation representation in Lianyungang, while the Copernicus DEM GLO-30 is a reliable alternative. Full article

High-latitude coastal regions are highly sensitive to climate change, yet their geomorphology is obscured by sea ice, landfast ice and seasonal snow, restricting the applicability of optical remote sensing for fine coastal classification. To address this limitation, we develop an interpretable coastal classification framework integrating ICESat-2 photon-counting LiDAR and explainable machine learning. Multi-dimensional morphometric features describing cross-shore geometry, vertical relief and local slope variability are extracted from ICESat-2 ATL03 along-track profiles to train a CatBoost classifier, with five-fold cross-validation and sample weighting to mitigate class imbalance. Introducing SHAP-based interpretability into ICESat-2-driven coastal geomorphic classification enables the identification of morphometric controls on coastal-type differentiation. Validated in the Bering Sea with 447 profiles and a 75%/25% stratified split, the framework achieved an overall accuracy of 86.6%, a macro-average recall of 89.4% and a Kappa coefficient of 0.84. SHAP analysis identifies that coastal width is the most influential feature for model-based classification of coastal geomorphic types, while slope and local steepness variability serve as important predictive indicators for distinguishing rocky and sedimentary coasts. This framework links data-driven classification to geomorphic processes and provides a potentially generalisable approach for fine-scale coastal mapping in high-latitude environments. Full article

Over the past 20 years, the iron-activated persulfate systems have been widely used for removing pharmaceuticals and personal care products (PPCPs) from water. However, slow Fe(III)/Fe(II) redox cycling and precipitation of iron, unless in very acidic conditions, were the main limitations. Thus, two ligand-assisted Fe(III)/persulfate systems, Fe(III)-acetohydroxamic acid (AHA)/peroxydisulfate (PDS) and Fe(III)-nitrilotriacetic acid (NTA)/peroxymonosulfate (PMS), were comparatively investigated for the degradation of atenolol (ATL) in this study. The experimental results showed that the Fe(III)-NTA/PMS system worked much better than the AHA system. However, the cost of PMS is higher than that of PDS, which should be considered. The primary advantage of the NTA system was its ability to overcome the pH limitations. It worked well over a wide pH range (3.0–10.0), whereas the AHA system could only be used in a narrower pH window (pH 2.4 to 6.5). The investigation of radicals that contributed to ATL degradation revealed that sulfate radicals (SO₄^•−) were dominant in the NTA system, while hydroxyl radicals (^•OH) and SO₄^•− were the primary and secondary radicals in the AHA system. These results provided useful insight into the comparative behavior of two ligand-assisted Fe(III)/persulfate systems for ATL degradation, with the Fe(III)-NTA/PMS system showing clear potential under neutral or near-neutral conditions, while Fe(III)-AHA/PDS may still represent a lower-cost option under acidic conditions. Full article

This research work is aimed at developing and manufacturing thermoplastic-composite parts for a Type V hydrogen storage tank based on a patented design. A 57% fibre volume fraction of a carbon fibre and polyamide 11 (PA11) thermoplastic matrix was used in an automated tape layup (ATL) process to manufacture a laser-assisted in situ-consolidated composite part for a hydrogen storage vessel. A series of mechanical and thermal tests were performed to optimize the process parameters for composite manufacturing. Based on the optimized process parameters, a scaled-up demonstrator composite part was manufactured and demoulded using pressurized air. Full article

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R² = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R² = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

Digital elevation models (DEMs) are foundational for critical tasks such as flood inundation simulation, disaster risk assessment, and ecosystem monitoring in coastal zones, yet their vertical accuracy is significantly compromised by complex terrain and surface characteristics. This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) across the subtropical coastal region of Southeast China using ICESat-2 ATL08 data as a reference. By integrating an eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP), we successfully decoupled systematic biases from random noise. The results show that NASADEM achieved the lowest RMSE (7.775 m), followed by COP30 and AW3D30. While the Terrain Ruggedness Index (TRI) and categorically encoded Land Cover were identified as the universally dominant error drivers across all datasets, explainable analysis revealed distinct secondary mechanisms: X-band COP30 is notably susceptible to canopy height, exhibiting significant positive bias in forests exceeding 15 m; C-band NASADEM shows a systematic bias related to topographic position, typically overestimating ridges and underestimating valleys; and optical AW3D30 is significantly affected by stereo-matching errors. Furthermore, the analysis quantified a systematic error component of ~40%. These findings provide a data-driven basis for DEM selection and highlight that accuracy improvements should prioritize vegetation removal for radar DEMs and enhanced stereo-matching for optical models. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article

Leishmaniasis remains an important neglected tropical disease, and current treatments are limited by toxicity, resistance, and low bioavailability. In this study, novel guanidine derivatives were evaluated through an integrated approach, combining in silico physicochemical profiling with in vitro biological assays using Leishmania (Viannia) braziliensis, the etiological agent of American Tegumentary Leishmaniasis (ATL). Most compounds exhibited favorable drug-like properties, though variations in lipophilicity and solubility influenced biological performance. Among the tested molecules, FURL-G5 emerged as the most promising candidate, showing potent activity against promastigote forms and low cytotoxicity in murine macrophages, resulting in high selectivity indices (SI > 10), comparable to those of LQOF-G1, a compound with previously established leishmanicidal effects. These compounds were also tested on intracellular amastigotes, drastically reducing the infection rate of macrophages. The integration of an in silico approach and biological validation enabled rational compound prioritization and supports the early-stage development of these scaffolds. Overall, this study reinforces the potential of guanidine-based compounds as leads for innovative ATL drug discovery and demonstrates the value of multidisciplinary strategies for identifying selective and safe therapeutic candidates. Full article

Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-threshold denoising methods and improve adaptability across varying density conditions, this study proposes a dual-stage denoising framework for ICESat-2 ATL03 photon data. In the first stage, local photon densities are estimated within a reliable radius, log-transformed, and stratified into multiple levels. Adaptive thresholds are then applied at each level to suppress low-density noise while minimizing over-filtering in sparse regions. In the second stage, residual feedback-driven adaptive fitting strategy is applied along the ground track, where polynomial fitting was performed in sliding windows, with the window size dynamically adjusted based on residuals to refine local structures and eliminate outliers. The experiment was conducted in South Holland and Friesland, across 84 ICESat-2 tracks, where quantitative evaluations under varying day/night and beam conditions confirmed the effectiveness of the proposed framework. For denoising, the proposed method achieved high denoising accuracy, with F1-scores exceeding 0.97 in most cases, outperforming previous methods. Furthermore, building heights derived from footprint buffering and elevation differencing are validated against airborne LiDAR, yielding coefficient of determination (R²) values of 0.7235 and 0.9487 for the two regions, with root mean square error (RMSE) values of 1.5045 m and 1.8849 m, respectively. This study confirms the effectiveness and robustness of the proposed dual-stage framework, demonstrating its strong capability for both noise suppression in ICESat-2 ATL03 photon data and the subsequent accurate estimation of building heights. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), is capable of acquiring large-scale terrain and forest structural information through photon-counting LiDAR. However, photon point clouds exhibit significant noise variability due to diurnal changes and variations in beam intensity, which undermines the accuracy and stability of terrain and canopy height retrievals in forested regions. To address the limited adaptability of existing methods under daytime/nighttime and strong/weak beam conditions, this study proposes a multi-stage processing framework integrating photon denoising, classification, and quasi-full-waveform reconstruction. First, local statistical features combined with adaptive parameter optimization were employed, applying Gaussian and exponential fitting to denoise daytime strong and weak beams and enhance the signal-to-noise ratio (SNR). Subsequently, an improved random sample consensus (RANSAC) algorithm was introduced to remove residual noise and classify photons under both diurnal and beam-intensity variations. Finally, a radial basis function (RBF) interpolation was used to reconstruct quasi-full-waveform curves for terrain and canopy heights. Compared with the ATL08 product (terrain root mean square error (RMSE): 2.65 m for daytime strong beams and 5.77 m for daytime weak beams), the proposed method reduced RMSE by 0.53 m and 1.30 m, respectively, demonstrating enhanced stability and robustness under low-SNR conditions. For canopy height estimation, all beam types showed high consistency with airborne LiDAR measurements, with the highest correlation achieved for nighttime strong beams (R = 0.90), accompanied by the lowest RMSE (4.82 m) and mean absolute error (MAE = 2.97 m). In comparison, ATL08 canopy height errors for nighttime strong beams were higher (RMSE = 5.67 m; MAE = 4.16 m). Notably, significant improvements were observed for weak beams relative to ATL08. These results indicate that the proposed framework effectively denoises and classifies photon point clouds under diverse daytime/nighttime and strong/weak beam conditions, providing a robust methodological reference for high-precision terrain and forest canopy height estimation in forested regions. Full article

Background/Objectives: Repetitive Transcranial Magnetic Stimulation (rTMS) can enhance post-stroke aphasia recovery. The right Inferior Frontal Gyrus is the most common target in rTMS studies for inhibitory stimulation. However, lexicosemantic processes involve a large brain network including the Anterior Temporal Lobe (ATL). We hypothesize that rTMS targeting the ATL will improve lexicosemantic impairments in people with post-stroke aphasia. Methods: In a Single-Case Experimental Design, three people with post-stroke aphasia with lexicosemantic impairments performed Picture and Auditory Naming and Semantic Decision tasks five times a week for one or two weeks to establish baseline scores. Then, each participant received continuous inhibitory Theta Burst Stimulation targeting the right ATL, five times a week for two weeks. After each rTMS session, participants performed all linguistic tasks. A follow-up measurement was performed one month after the end of the study. Results: All participants showed significant improvement in the Picture Naming task, while only P1 improved in Auditory Naming accuracy. In the Semantic Decision task, only P2 showed improvement in both accuracy and RT, while P1 showed improvement in RT alone and P3 showed no improvement. Conclusions: The results suggest that ATL could be a potential target for future brain stimulation studies in aphasia involving lexicosemantic impairments. RTMS targeting the ATL may modulate the connected ventral semantic stream, leading to improvements in lexical access. This preliminary study highlights the possibility of selecting the cortical target for rTMS based on the clinical profile of the participant, an approach that will need further investigation in larger sham-controlled studies. Full article