Search Results (6,264)

Global climate change mitigation has prompted the construction sector to pursue decarbonization strategies, with timber structures offering significant carbon reduction potential. Wood serves as a sustainable material that sequesters carbon during growth while reducing emissions across the entire construction supply chain. Robotic construction of timber structures is increasingly promoted as a low-carbon, intelligent alternative for small- and medium-scale projects, yet the energy consumption and environmental impacts of robotic automated assembly using self-tapping screws remain understudied. This study presents a construction-phase life-cycle assessment (LCA) of an innovative vertically mobile robotic construction system for automated timber structure. The system integrates a KUKA KR 6 R900 (KUKA Robotics Corporation, Augsburg, Germany) six-axis robot with an electrically actuated lifting platform and specialized end-effector, enabling fully autonomous assembly of a Layered Interlaced Timber Arch-Shell (LITAS) structure using Hinoki cypress timber and self-tapping screws. This research provides the first comprehensive LCA dataset for robotic screw-fastened timber construction and establishes a replicable framework for sustainable automated building practices, with methodology scalability enabling application to diverse timber construction scenarios and advancing intelligent and decarbonized transformation in the construction industry. Full article

Arsenic (As) mobility in aquifer systems is mainly governed by its adsorption and desorption behaviour at the sediment-water interface, directly influencing its environmental availability and risks to water quality. This study explores the adsorption-desorption behaviour of inorganic As species through batch experiments on environmental sediments collected from three representative depths, selected to reflect local contrasting geochemical, mineralogical, and granulometric characteristics of the Como basin aquifer (Northern Italy). This setting was selected as a case study owing to its notable gradient in As concentration in groundwater: the shallow aquifers host concentrations typically below 10 µg/L, while the deep aquifer reaches concentrations of about 250 µg/L. Statistical analyses (ANOVA and simple linear regression) identified Mn- and Al-(hydr)oxide content, grain size, and mineralogy as strong predictors of As(V) retention, whereas As(III) showed no significant correlation with individual sediment properties within the tested conditions. Shallow, Mn- and Al-rich sediments exhibited higher adsorption capacity and corresponded to lower dissolved As in groundwater, while deeper, finer-grained sediments with lower oxide content coincided with elevated groundwater As concentrations. Desorption experiments indicated that As(III) dominated the released fraction, reflecting its greater mobility under variable pH and redox aquifer conditions. These results provide mechanistic insight into sediment-water interactions controlling As distribution in multilayer aquifers, supporting improved risk assessment and management of As in complex groundwater systems. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article

Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age $70.06\pm 9.45$ years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE $=0.00$–$6.12$, $r=0.92$–$1.00$), followed closely by the Azure Kinect (MAE $=0.01$–$6.07$, $r=0.68$–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations. Full article

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

Background/Objectives: Hematopoietic stem cell (HSCs) mobilization from the bone marrow to the peripheral blood (PB) is a critical step in stem cell transplantation. Although some experimental studies have suggested that cholesterol levels may affect this process, the clinical relevance of lipid profiles in healthy donors remains unclear. This study aimed to investigate whether serum cholesterol parameters are associated with peripheral blood CD34+ HSC mobilization in healthy stem cell donors. Methods: A total of 251 healthy donors who underwent granulocyte colony-stimulating factor (G-CSF)-based mobilization were retrospectively analyzed. Peripheral blood CD34+ cell counts and yields (×106/kg) were recorded. Laboratory parameters, including total cholesterol, HDL-C, LDL-C, and triglyceride levels were evaluated. Correlations between mobilization outcomes and donor characteristics or laboratory findings were also assessed. Results: No significant association was found between serum lipid parameters (total cholesterol, LDL-C, HDL-C, triglycerides) and CD34+ cell mobilization or yield. However, white blood cell count, hemoglobin level, platelet count, absolute neutrophil count, and lymphocyte count showed significant positive associations with mobilization efficacy. In contrast, body mass index (BMI) was inversely correlated with CD34+ cell yield. Conclusions: Serum cholesterol levels do not appear to influence stem cell mobilization outcomes in healthy donors. Classical hematologic parameters remain reliable predictors of CD34+ cell yield. These findings suggest that cholesterol is not a suitable biomarker for predicting mobilization efficiency in this population group. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article

Combining public transportation (PT) with Bike-Sharing Systems (BSSs) offers a pathway toward the sustainable development of urban mobility. These systems can reduce fuel consumption, air pollution, and street congestion, especially during peak hours. Moreover, PT and BSS are frequently used by individuals without access to private vehicles, including low-income groups and students. Whereas increasing PT network infrastructure is constrained by issues such as high capital costs and limited street space (which inhibits mass transit options like BRT or trams), BSS can be used as an adaptable and affordable solution to fill these gaps. In particular, BSS can facilitate the “first-mile–last-mile” legs of PT journeys. However, many transit agencies still rely on traditional joint service planning and overlook BSS as a critical mode in integrated travel chains. This paper proposes that PT and BSS be considered as a unified network and introduces a framework to assess whether access to this integrated system is equitably distributed across urban areas. The framework estimates demand for travel using public mobility options and supply at the level of Traffic Analysis Zones (TAZs), treating PT and BSS as complementary modes. Spatial accessibility analysis is employed to examine connectivity using factors that affect access to both PT and BSS. The proposed approach is tested by taking Tehran as the focus of the case analysis. The results identify the most accessible areas and highlight those that require improved PT-BSS integration. These findings provide policy-relevant suggestions to promote equity and efficiency in urban transport planning. The outcomes reveal that central TAZs in Tehran receive the highest level of PT-BSS integration, while the western and southern TAZs are in urgent need of adjustment to ensure better distribution of integrated public transportation and bike-sharing services. Full article

In the exploitation of ion-adsorption rare earth ores, the environmental effects of leaching agents are key constraints for green mining. Understanding the release behavior of typical heavy metals from soils under leaching conditions is of great significance. Laboratory column leaching experiments were conducted to systematically investigate the effects of three leaching agents—(NH₄)₂SO₄, Al₂(SO₄)₃, and MgSO₄—as well as varying concentrations of Al₂(SO₄)₃ on the release and speciation transformation of heavy metal Pb in mining-affected soils. The results revealed a three-stage pattern in Pb release—characterized by slow release, a sharp increase, and eventual stabilization—with environmental risks predominantly concentrated in the middle to late stages of leaching. Under 3% (NH₄)₂SO₄ and 3% Al₂(SO₄)₃ leaching conditions, Pb concentrations in soil increased significantly, with a higher proportion of labile fractions, indicating pronounced activation and risk accumulation. Due to its relatively weak ion-exchange capacity, MgSO₄ exhibited a lower and more gradual Pb release profile, posing substantially lower pollution risks compared to (NH₄)₂SO₄ and Al₂(SO₄)₃. Pb release under varying Al₂(SO₄)₃ concentrations showed a nonlinear response. At 3% Al₂(SO₄)₃, both the proportion of bioavailable Pb and the Risk Assessment Code (RAC) peaked, while the residual fraction declined sharply, suggesting a threshold effect in risk induction. All three leaching agents promoted the transformation of Pb in soil from stable to more labile forms, including acid-soluble, reducible, and oxidizable fractions, thereby increasing the overall proportion of active Pb (F1 + F2 + F3). A combined analysis of RAC values and the proportion of active Pb provides a comprehensive framework for assessing Pb mobility and ecological risk under different leaching conditions. These findings offer a theoretical basis for the prevention and control of heavy metal risks in the green mining of ion-adsorption rare earth ores. Full article

Background/Objectives: this study describes the development of a novel three-dimensional electrogoniometer for the quantitative assessment of knee mobility and stability during gait. The primary objective is to determine whether real-time measurements obtained during dynamic activity provide more clinically relevant information than traditional static assessments. Methods: the device employs angular position encoders to capture knee joint kinematics—specifically flexion, extension, rotation, and tibial translation—during locomotion. Data are transmitted in real time to an Android-based application, enabling immediate graphical visualization. A descriptive observational study was conducted involving healthy participants and individuals with anterior cruciate ligament (ACL) injuries to evaluate the device’s performance. Results: results showed that the electrogoniometer captured knee flexion-extension with a range of up to 90°, compared to 45° typically recorded using conventional systems. The device also demonstrated enhanced sensitivity in detecting variations in tibial translation during gait cycles. Conclusions: this electrogoniometer provides a practical tool for clinical assessment of knee function, enabling real-time monitoring of joint behavior during gait. By capturing functional mobility and stability more accurately than static methods, it may enhance diagnostic precision and support more effective rehabilitation planning in orthopedic settings. Full article

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

With the advancement of mobile communications, technologies based on high-element-count antenna systems—such as massive Multiple Input Multiple Output (massive MIMO)—are playing an increasingly important role in enhancing network capacity. However, they introduce new challenges in the measurement and evaluation of electromagnetic field (EMF) exposure. This study presents a detailed, laboratory-based methodology for assessing EMF exposure in cellular systems using Single Input Single Output (SISO) and MIMO technologies. To address the limitations of traditional exposure assessment techniques—particularly under the conditions introduced by 5G and active antenna systems—a shielded test environment with directional antennas was developed and applied across lowband and midband frequency ranges (700–2100 MHz). Downlink electromagnetic power density was measured under standardized modulation, coding, and bandwidth settings for both SISO and MIMO configurations. The results show that MIMO technology does not lead to a significant increase in EMF exposure compared to SISO, with average differences remaining below 1 dB. Moreover, in lower-frequency bands, massive MIMO systems can ensure the required user capacity at significantly lower transmission power, resulting in more than 15 dB reductions in EMF exposure. These findings confirm the potential of massive MIMO to enhance network performance while reducing the level of electromagnetic exposure. Full article

The mobile tunnel scanning system (MTSS) enables efficient tunnel inspection; however, motion blur (MB) generated at high travel speeds remains a major factor undermining the reliability of deep-learning-based crack detection. This study focuses on investigating how horizontally oriented MB in MTSS imagery affects the crack-detection performance of convolutional neural networks (CNNs) and proposes a data-centric quality-assurance framework that leverages no-reference image quality assessment (NR-IQA) to optimize model performance. By intentionally applying MB to both public and real-world MTSS datasets, we analyzed performance changes in ResNet-, VGG-, and AlexNet-based models and established the correlations between four NR-IQA metrics (BRISQUE, NIQE, PIQE, and CPBD) and performance (F1 score). As the MB intensity increased, the F1 score of ResNet34 dropped from 89.43% to 4.45%, confirming the decisive influence of image quality. PIQE and CPBD exhibited strong correlations with F1 (−0.87 and +0.82, respectively), emerging as the most suitable indicators for horizontal MB. Using thresholds of PIQE ≤ 20 and CPBD ≥ 0.8 to filter low-quality images improved the AlexNet F1 score by 1.46%, validating the effectiveness of the proposed methodology. The proposed framework objectively assesses MTSS data quality and optimizes deep learning performance, enhancing the reliability of intelligent infrastructure maintenance systems. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article