Search Results (1,515)

Recent phytochemical investigations have demonstrated that Luvunga scandens is a rich source of structurally diverse secondary metabolites; however, its potential antioxidant-active constituents and their underlying mechanisms remain largely unexplored. In this study, an NMR-guided fractionation strategy applied to the rhizomes and leaves of L. scandens led to the isolation of ten limonoids, including three new compounds, Luvungas B–D (3, 4, and 8). Their structures and absolute configurations were determined through extensive spectroscopic analysis, X-ray diffraction, and ECD calculations. Based on the isolated analogues, a biosynthetic pathway is proposed, featuring the metabolic bifurcation of a key acyclic intermediate into the isoobacunoic acid and propellane-type lineages. Biological evaluation revealed that 8 inhibits RSL3-induced ferroptosis in HepaRG liver cells with an EC₅₀ of 16.1 µM. Mechanistic studies demonstrated that, unlike classical antioxidants, compound 8 mitigates lipid peroxidation without exhibiting direct radical-scavenging or iron-chelating activities. These findings suggest that 8 suppresses ferroptosis via non-canonical mechanisms. Full article

Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and material consumption. To address this challenge, this study proposes an artificial neural network (ANN) predictive model with 5-fold cross-validation to estimate compressive strength performance and to develop mix design recommendations based on actual and predicted results. A total of 55 experimental samples were prepared and grouped into 11 batches, with the EPS volume replacement levels ranging from 0% to 50% at 5% increments. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R²), and scatter index (SI), with graphical representations like predicted vs. actual plots, response plots, and residual plots, and the results were benchmarked against a multiple linear regression (MLR) model. Among the tested configurations, the 4-5-1 ANN model demonstrated the highest predictive accuracy. Furthermore, a Shapley (SHAP) analysis was conducted to interpret the model behavior and determine the relative importance of the input variables. The findings reveal that EPS content had the greatest influence on compressive strength prediction, followed by slump value, then gravel content, and finally concrete density. Full article

Chemical investigation of the marine sponge-derived Streptomyces xiamenensis 1310KO-148 afforded six polycyclic tetramate macrolactams (PTMs), including three known compounds (1–3) and three previously undescribed chlorohydrin-containing analogues, chlokamycins B–D (4–6). Their planar structures were elucidated by extensive analysis of 1D and 2D NMR spectra and HR-ESIMS data, while the relative configurations were assigned using NOESY correlations. The absolute configurations were further confirmed by electronic circular dichroism (ECD) calculations. Compounds 3–6 exhibited significant cytotoxic activity against 14 human cancer cell lines (GI₅₀ = 2.68–24.92 μM) and antibacterial activity against Staphylococcus aureus (MIC = 16.00–32.00 μg/mL) and Micrococcus luteus (MIC = 4.00–32.00 μg/mL) among six tested bacterial strains. Full article

This paper will present a constant-force vibration isolator (CFVI), in which the isolated load is supported by two pulley-roller mechanisms, while the dynamic stiffness is modified by a cam mechanism with the piecewise profile redefined by the user. As a result, this model can generate the constant force-displacement response within the working region, thereby obtaining quasi-zero stiffness in this range. Because of the piecewise configuration of the cam, the system motion governed by the piecewise dynamic equation under base motion excitation will be analyzed and established. The approximate solution of the piecewise dynamic equation is derived by using the average method, from which the relative amplitude–frequency relation and the absolute amplitude transmissibility of the CFVI will be obtained. The effects of the key working parameters involving the damping coefficient, critical position, and excited amplitude on the dynamic behavior and isolation effectiveness of the CFVI are considered through numerical simulations. The simulation result reveals that the dynamic response of the CFVI offers two branches: resonance and isolation. The former is significantly affected by the working parameters, whereas the latter is weakly influenced. Furthermore, the isolation effectiveness of the CFVI will be compared with that of its linear counterpart and the quasi-zero stiffness vibration isolation model using a semicircle cam (QZSI). The results demonstrate that the CFVI outperforms the other models for base motion excitations. Full article

New, highly integrated, disruptive aircraft concepts are being devised to reduce aviation’s environmental footprint, but their performance is oftentimes challenging for the aircraft designer to assess. Furthermore, these novel aircraft often introduce new risks, such as noise, that cannot be addressed quickly by available methods. Overall, in the pursuit of more environmental friendly aircraft configurations and the lack of methods to design such aircraft, aircraft-level trade-offs between noise and performance are challenging. The present study aims to close this gap by using a machine learning-based approach for one unconventional aircraft to investigate usability in the early stages of aircraft design. Based on overflight noise measurements, noise models for this aircraft are created with different approaches and base models. The single-output models show good performance, with mean absolute errors around 1 dB, good rank correlations and R² scores above 0.9. Support vector regression provides reasonably good agreement from experiments requiring only a small effort to set up; Neural Networks achieve better performance, but increased effort is required to obtain the model. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are treated as a single co-evolving decision vector. In the offline phase, a Dual-Strategy Adaptive Differential Evolution (DS-ADE) optimizer performs global joint identification using complementary exploration–exploitation behaviors and success-history inheritance, analogous to morphology-control co-evolution in biological systems. In the online phase, a Neuro-Physical Hybrid Jacobian (NPHJ) solver augments the analytical Jacobian with gradients from a Graph Kolmogorov–Arnold Network (GKAN), enabling sensorimotor-like real-time compensation on the learned physical manifold. Experiments on an ABB IRB 120 manipulator with 600 configurations (500 training, 100 testing) report a testing distance-residual RMSE of 0.62 mm, STD of 0.59 mm, and MAX of 0.83 mm. Relative to the uncalibrated baseline, RMSE is reduced by 86.75%; compared with the strongest published baseline, RMSE improves by 23.46%. Ablation results show that joint DS-ADE optimization outperforms a sequential pipeline by 32.6%, and the graph-structured KAN outperforms a parameter-matched MLP by 26.2%. Wilcoxon signed-rank tests ($p<0.001$) confirm statistical significance. Full article

Three new cyclic tetrapeptides (nectriatidels A-C, 1–3), two new curvularin analogs (6 and 7), and four known compounds (4 and 5, 8 and 9) were isolated from the marine-derived fungal-bacterial symbiont Aspergillus spelaeus GXIMD 04541/Sphingomonas echinoides GXIMD 04532, which was obtained from Mauritia arabica in shallow coastal waters. Their structures were elucidated through NMR spectroscopy and HRESIMS, and their absolute configurations were determined by Marfey’s method and quantum chemical calculations. Compounds 1–5 showed moderate amphotericin B (AmB)-potentiating activity against Candida albicans. Compounds 7 and 8 exhibited significant activities against Mycobacterium tuberculosis, with MIC values of 32 and 16 μg/mL, respectively. Additionally, compounds 7 and 8 exhibited moderate cytotoxicity against human colorectal cancer cell lines DLD-1 and SW480, with IC₅₀ values of 25~36 μM. Whole-genome sequencing of A. spelaeus revealed a 35.91 Mb assembly encoding 106 biosynthetic gene clusters (BGCs). antiSMASH analysis revealed that 79 of these BGCs (74.5%) displayed no significant similarity to known pathways in the MIBiG database, which is dominated by hybrid clusters, terpene, T1PKS, NRPS, and NRPS-like types. Genomic analysis identified the putative biosynthetic gene clusters for these metabolites and confirmed the fungal host as the predominant producer. Full article

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article

Distributed passive vibration control systems (VCSs) offer an attractive solution for improving the seismic response of multi-story buildings, particularly in seismic retrofit applications and when soil–structure interaction (SSI) effects are explicitly considered. This study presents a soil-dependent optimization framework of distributed Tuned Mass Damper (TMD) and Tuned Mass Damper Inerter (TMDI) systems applied to a ten-story building. The proposed framework determines the optimal number, tuning, damping and spatial distribution of these VCS, including non-collocated inerter configurations for TMDI layouts, while also examining different auxiliary mass ratios. Soil–structure interaction effects are explicitly incorporated by considering four soil classes (A–D) in accordance with Eurocode 8, enabling a systematic evaluation of soil-dependent vibration control effectiveness. Structural performance is evaluated using normalized performance criteria associated with peak absolute floor displacements, floor accelerations and inter-story drifts. The results indicate that distributing control devices along the height of the structure enhances seismic mitigation for both TMD and TMDI configurations, with performance improvements becoming more pronounced as the number of devices increases. Moreover, TMDI systems consistently achieve superior response reduction compared to TMDs across all soil classes, highlighting their potential as a robust, efficient, and lightweight passive vibration control solution for seismic retrofit applications involving SSI effects. Full article

Dynamic mechanical thermal analysis (DMTA) is widely used to assess the effects of process- and environment-induced changes in polymer matrix composites, with the glass transition temperature (T_g) often reported from the tan d peak at a single excitation frequency. However, such an approach neglects the inherently kinetic nature of the glass transition and may obscure thermally induced changes in relaxation response. Multi-frequency DMTA was employed to investigate the evolution of glass transition response of a wet-layup unidirectional carbon/epoxy composite subjected to thermal aging at temperatures ranging from 66 °C to 260 °C for periods up to 72 h, using unexposed (23 °C) results as an ambient baseline reference. Tests were conducted using a single cantilever mode in both longitudinal and transverse configurations over a range of excitation frequencies from 0.3 to 30 Hz. Results demonstrate that thermal exposure affects not only the absolute value of the glass transition temperature, but also its frequency sensitivity and directional dependence. A frequency sensitivity parameter and a directional amplification factor are introduced to quantify frequency–direction coupling. While post-cure-dominated aging regimes exhibit relatively stable coupling behavior, degradation-dominated conditions at elevated temperatures and longer periods of thermal exposure lead to pronounced increases in transverse frequency sensitivity, which reflects early evolution of matrix- and interphase-level deterioration. These findings highlight the value of multi-frequency DMTA with tests in both primary directions for the mechanistic assessment of effects of thermo-oxidative response in polymer matrix composites. Full article

Accurate short-term traffic flow prediction is vital for effective signal control and sustainable urban mobility. Deep learning models, such as the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, have demonstrated strong capability in modelling temporal traffic dynamics. However, the influence of their architectural and hyperparameter configurations remains underexplored. This study proposes a systematic methodology to assess the impact of hyperparameter optimization on GRU and LSTM models for predicting traffic flow at a signalized intersection. The methodology is evaluated using minute-level traffic data from the Globe Roundabout in Jeddah, Saudi Arabia. Bayesian optimization is applied to identify the best-performing hyperparameters. The results show that the optimized GRU model achieves a Root Mean Square Error (RMSE) of 0.0953, representing a 90.2% improvement compared to the baseline GRU (RMSE ≈ 0.969). Likewise, the optimized LSTM model attains an RMSE of 0.0960, corresponding to an 85.2% improvement relative to its baseline (RMSE ≈ 0.648). Similar gains are observed for the Mean Absolute Error. Visual analysis further shows that optimized models reduce smoothing bias, enhance the tracking of transient fluctuations, and produce stable, low-variance residuals. The findings demonstrate that hyperparameter optimization substantially improves predictive accuracy while preserving computational efficiency, enabling lightweight recurrent architectures to perform at a level comparable to more complex models. Full article

The recent revision of the European Energy Performance of Buildings Directive (EPBD) introduces mandatory whole-life global warming potential (GWP) reporting, creating practical challenges for building life-cycle assessment due to incomplete life-cycle phase coverage in conventional Environmental Product Declarations (EPDs). This study develops and validates an integrated BIM–LCA framework for structured whole-building GWP evaluation through harmonized life-cycle module alignment and cross-tool comparison, with emphasis on the early design stages. The workflow combines rapid BIM-based screening with detailed external LCA validation, establishing a tiered assessment strategy that enables iterative material optimization within the BIM environment prior to expert review. The methodology is applied to two residential construction systems (masonry and timber), and three functional units are evaluated: total whole-building GWP, area-normalized GWP, and material-level contributions. Five comparative scenarios are analyzed, including reference, nationally representative, optimized low-carbon, and European benchmark configurations. The results show progressive GWP reductions ranging from 5% to 30% across scenarios. Although substantial absolute deviations are observed between BIM-integrated and professional LCA tools, scenario-level rankings remain fully consistent across all functional units, confirming the robustness of the screening approach for comparative decision-making. Cross-tool validation focuses on an aligned embodied-carbon scope (A1–A3 plus selected end-of-life modules) to ensure screening robustness, while full whole-life LC-GWP (including B-modules and services) is positioned as the regulatory context for subsequent expert-stage assessment. The framework provides an efficient and transferable decision-support methodology that supports early-stage carbon optimization while preserving methodological transparency for regulatory reporting. Full article

Continuous non-invasive blood pressure monitoring holds significant promise for cardiovascular disease management, yet cuff-based methods remain limited by their intermittent nature. Machine learning approaches leveraging photoplethysmography (PPG) and electrocardiography (ECG) signals present compelling alternatives, though questions persist about which signal type contributes more predictive value. This study compares traditional machine learning models, ensemble methods, and deep learning architectures for estimating systolic blood pressure from physiological waveforms. We extracted 55 features from PPG and ECG recordings of 100 subjects in the MIMIC-III Waveform Database, yielding 3000 segments with invasive arterial blood pressure as ground truth. Data splitting was performed at the subject level (70/15/15 train/validation/test) to prevent data leakage. Evaluation included regression metrics, British Hypertension Society grading, SHAP-based explainability, and ablation studies. Among all models, LightGBM achieved the best performance with mean absolute error of 15.97 mmHg, placing it at BHS Grade D. While SHAP analysis showed ECG features contributing 54.7% of importance versus 45.3% for PPG, our ablation study revealed that PPG-only models achieved comparable performance (MAE 15.97 vs. 16.23 mmHg), with the difference not statistically significant (p = 0.226). These results suggest that PPG-only wearable devices are viable for blood pressure estimation, as adding ECG features provides no statistically significant improvement. However, all configurations achieved only BHS Grade D, indicating that personalized calibration may be necessary for clinical acceptability. Full article

This article presents the findings of experimental research conducted to assess the stability of the force mode of the UR5e cobot from Universal Robots in the low-force range, from 1 N to 10 N. The set values of the robot’s forces and the physically measured values were verified by an OptoForce Hex six-axis Force/Torque sensor attached to the robot’s wrist, additionally coupled with an end-effector specially designed for research purposes. The results were recorded using proprietary software developed in the LabVIEW environment and a configured test lab station with a UR5e cobot. Three experimental tests were performed, in which the parameters of the effective force were measured while varying (1) the position of the task in the workspace of the robot, (2) the position and the level of force, and (3) the controller parameters of the force mode. The results of the experiments were compiled and presented in tables containing descriptions of, among other parameters, the following: the mean forces and their standard deviation; the mean maximum forces and its standard deviation; the mean root mean square error and its standard deviation; the mean absolute error and its standard deviation; the mean rate of force and its standard deviation; and the mean overshoot and its standard deviation. The findings of Experiment 1 demonstrated that when a setpoint of 10 N was employed, the UR5e cobot yielded an actual mean force ranging from 8.95 N to 13.26 N within the workspace plane. Experiment 2 showed that the average deviation from the set value within the 1–10 N range was approximately 0.38 N, with a maximum deviation of 0.61 N occurring at the limits of the working space. Experiment 3 showed that for the force range of 1–4 N, the best controller settings are Gain = 0.5 and Damping = 0.7; for the force range of 5–7 N: Gain = 1.0 and Damping = 0.6; and for the force range of 8–10 N: Gain = 2.0 and Damping = 0.8. Polynomial regression models were developed for each positioning scenario that can be used when making decisions regarding practical applications of the low-force mode. Full article