Search Results (544)

This paper investigates the cultural, spiritual, and ecological use and value of fish by-products in the material practices of Alaska Native (Indigenous Peoples are the descendants of the populations who inhabited a geographical region at the time of colonisation and who retain some or all of their own social, economic, cultural, and political institutions. In this paper, I use the terms “Indigenous” and “Native” interchangeably. In some countries, one of these terms may be favoured over the other.) and Greenlandic Inuit women. It aims to uncover how fish remnants—skins, bones, bladders, vertebrae, and otoliths—were transformed through tanning, dyeing, and sewing into garments, containers, tools, oils, glues, and adornments, reflecting sustainable systems of knowledge production rooted in Arctic Indigenous lifeways. Drawing on interdisciplinary methods combining Indigenist research, ethnographic records, and sustainability studies, the research contextualises these practices within broader environmental, spiritual, and social frameworks. The findings demonstrate that fish-based technologies were not merely utilitarian but also carried symbolic meanings, linking wearers to ancestral spirits, animal kin, and the marine environment. These traditions persisted even after European contact and the introduction of glass trade beads, reflecting continuity and cultural adaptability. The paper contributes to academic discourse on Indigenous innovation and environmental humanities by offering a culturally grounded model of zero-waste practice and reciprocal ecology. It argues that such ancestral technologies are directly relevant to contemporary sustainability debates in fashion and material design. By documenting these underexamined histories, the study provides valuable insight into Indigenous resilience and offers a critical framework for integrating Indigenous knowledge systems into current sustainability practices. Full article

This study proposes a method for estimating the kinetic coefficient of friction (COF) for asphalt pavements by improving and applying Persson’s friction theory. The method utilizes the power spectral density (PSD) of the top surface topography instead of the full PSD to better reflect the actual contact conditions. This approach avoids including deeper roughness components that do not contribute to real rubber–pavement contact due to surface skewness. The key aspect of the method is determining an appropriate cutting plane to isolate the top surface. Four cutting strategies were evaluated. Results show that the cutting plane defined at 0.5 times the root mean square (RMS) height exhibits the highest robustness across all pavement types, with the estimated COF closely matching the measured values for all four tested surfaces. This study presents an improved method for estimating the kinetic coefficient of friction (COF) of asphalt pavements by employing the power spectral density (PSD) of the top surface roughness, rather than the total surface profile. This refinement is based on Persson’s friction theory and aims to exclude the influence of deep surface irregularities that do not make actual contact with the rubber interface. The core of the method lies in defining an appropriate cutting plane to isolate the topographical features that contribute most to frictional interactions. Four cutting strategies were investigated. Among them, the cutting plane positioned at 0.5 times the root mean square (RMS) height demonstrated the best overall applicability. COF estimates derived from this method showed strong consistency with experimentally measured values across all four tested asphalt pavement surfaces, indicating its robustness and practical potential. Full article

Optimising the friction coefficient helps reduce friction losses and improve the efficiency of mechanical systems. The purpose of this study is to experimentally investigate the impact of roughness orientation on the friction coefficient in elastohydrodynamic (EHD) contact. Tests were carried out on a twin-disc machine. Three pairs of discs of identical material (nitrided steel) and geometry were tested: a smooth pair (the root mean square surface roughness Sq = 0.07 µm), a pair with transverse roughness and another with longitudinal roughness. The two rough pairs have similar roughness amplitudes (Sq = 0.5 µm). A comparison of the friction generated by these different pairs was carried out to highlight the effect of the roughness orientation under different operating conditions (oil injection temperature from 60 to 80 °C, Hertzian pressure from 1.2 to 1.5 GPa and mean rolling speed from 5 to 30 m/s). Throughout all the tests conducted in this study, longitudinal roughness resulted in higher friction than transverse, with an increase of up to 30%. Moreover, longitudinal roughness is more sensitive to variations in operating conditions. Finally, in all tests, the asperities of longitudinal roughness were found to influence the friction behaviour, unlike transverse roughness. Full article

During high-current vacuum arcing, asymmetric arcing with off-center plasma columns may occur due to stochastic discharge initiation and mechanical motion, receiving less research attention than symmetric arcing. The objective of this paper is to numerically analyze the influence law of asymmetric arc ignition on arc parameters. For 60 mm diameter contacts, three arc conditions of symmetric arcing, 33% arc offset, and 67% arc offset were modeled. The results show that the arc offset causes asymmetry in the arc’s distribution. For 33% offset, the pressure and number density on the side away from the root of the arc is about 50% of root values, while these parameters fall below 20% for the 67% offset. Simultaneously, arc offset elevates peak parameter values: under 33% offset, maxima for ion pressure, ion density, ion temperature, electron temperature, and current density rise 12%, 11%, 6%, 6%, and 14% versus symmetric arcing; during 67% offset, these escalate significantly to 67%, 61%, 12%, 18%, and 47%. This study contributes to providing reference for the analysis of vacuum interruption processes under asymmetric arcing conditions. Full article

Microelectromechanical system (MEMS) devices are specialized electronic devices that integrate the benefits of both mechanical and electrical structures. However, the contact behavior between the interfaces of these structures can significantly impact the performance of MEMS devices, particularly when the surface roughness approaches the characteristic size of the devices. In such cases, the contact between the interfaces is not a perfect face-to-face interaction but occurs through point-to-point contact. As a result, the contact area changes with varying contact pressures and surface roughness, influencing the thermal and electrical performance. By integrating the CMY model with finite element simulations, we systematically explored the thermal conductance regulation mechanism of MEMS contact switches. We analyzed the effects of the contact pressure, micro-hardness, surface roughness, and other parameters on thermal conductance, providing essential theoretical support for enhancing reliability and optimizing thermal management in MEMS contact switches. We examined the thermal contact, gap, and joint conductance of an MEMS switch under different contact pressures, micro-hardness values, and surface roughness levels using the CMY model. Our findings show that both the thermal contact and gap conductance increase with higher contact pressure. For a fixed contact pressure, the thermal contact conductance decreases with rising micro-hardness and root mean square (RMS) surface roughness but increases with a higher mean asperity slope. Notably, the thermal gap conductance is considerably lower than the thermal contact conductance. Full article

Continuous monitoring of respiratory patterns is essential for disease diagnosis and daily health care. Contact medical devices enable reliable respiratory monitoring, but can cause discomfort and are limited in some settings. Radar offers a noncontact respiration measurement method for continuous, real-time, high-precision monitoring. However, it is difficult for a single radar to characterize the coordination of chest and abdominal movements during measured breathing. Moreover, motion interference during prolonged measurements can seriously affect accuracy. This study proposes a dual radar system with customized narrow-beam antennas and signals to measure the chest and abdomen separately, and an adaptive dynamic time warping (DTW) algorithm is used to effectively suppress motion interference. The system is capable of reconstructing respiratory waveforms of the chest and abdomen, and robustly extracting various respiratory parameters via motion interference. Experiments on 35 healthy subjects, 2 patients with chronic obstructive pulmonary disease (COPD), and 1 patient with heart failure showed a high correlation between radar and respiratory belt signals, with correlation coefficients of 0.92 for both the chest and abdomen, a root mean square error of 0.80 bpm for the respiratory rate, and a mean absolute error of 3.4° for the thoracoabdominal phase angle. This system provides a noncontact method for prolonged respiratory monitoring, measurement of chest and abdominal asynchrony and apnea detection, showing promise for applications in respiratory disorder detection and home monitoring. Full article

Compared to spiral bevel gear drives with localized conjugation, line contact spiral bevel gears possess a significantly larger meshing area, theoretically achieving full tooth surface contact and substantially enhancing load capacity. To accurately support the root strength calculation and parameter design of line contact spiral bevel gear drives, this paper presents a theoretical analysis and experimental study of the root bending stress of gear pairs. First, based on the analysis of the meshing characteristics of line contact spiral bevel gear pairs, the load distribution along the contact lines is investigated. Using the slicing method, the load distribution characteristics along the contact line are obtained, and the load sharing among multiple tooth pairs during meshing is further studied. Then, by applying a cantilever beam bending stress model, the root bending stress on such a gear drive is calculated. A root bending moment distribution model is proposed based on the characteristics of the line load distribution previously obtained, from which a formula for calculating root bending stress is derived. Finally, static-condition experiments are conducted to test the root bending stress. The accuracy of the proposed calculation method is verified through experimental testing and finite element analysis. The results of this study provide a foundation for designing lightweight and high-power-density spiral bevel gear drives. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s² and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies. Full article

Terpenes represent a structurally diverse class of natural compounds with increasing scientific interest due to their potential anti-inflammatory properties. This study investigates the in silico binding behavior of six plant-derived terpenes—α-pinene, β-pinene, menthol, camphor, limonene, and linalool—against four key enzymes in the arachidonic acid (AA) metabolic pathway: cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), 5-lipoxygenase (5-LOX), and phospholipase A2 (PLA₂). AA serves as a reference for binding energy comparison. Blind rigid-body molecular docking is performed using AutoDock 4.2 and the Lamarckian Genetic Algorithm, with 100 runs per ligand–enzyme pair and the energy-based selection of optimal poses. The analysis includes binding energy (ΔG), inhibition constants (Ki), root-mean-square deviation (RMSD), and residue-level interactions. Several terpenes exhibit favorable binding energies and inhibition constants across the evaluated enzymes. For COX-1 and COX-2, menthol and camphor show low Ki values, indicating stable binding. Menthol and limonene also show the strongest affinities for PLA₂, exceeding AA. The focus is on compounds with potential to modulate arachidonic acid metabolism. In this context, β-pinene engages the catalytic site of PLA₂, linalool forms multiple contacts within key regions of 5-LOX, and menthol, α-pinene, and β-pinene align with functionally important regions in both COX isoforms. These targeted interactions suggest that the highlighted compounds may selectively interfere with enzymatic activity in inflammation-related pathways. By modulating key steps in AA metabolism, these terpenes may influence the biosynthesis of pro-inflammatory mediators, offering a promising avenue for the development of safer, plant-derived anti-inflammatory agents. The findings lay the groundwork for further experimental validation and the structure-based optimization of terpene-derived modulators. Full article

Existing activity measurement methods, such as gas analyzers, activity trackers, and camera-based systems, have limitations in accuracy, convenience, and privacy. To address these issues, this study proposes an improved activity estimation algorithm using a 60 GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional methods that rely solely on distance variations, the proposed method incorporates both distance and velocity information, enhancing measurement accuracy. The algorithm quantifies activity levels using Shannon entropy to reflect the spatial–temporal variation in range signatures. The proposed method was validated through experiments comparing estimated activity levels with motion sensor-based ground truth data. The results demonstrate that the proposed approach significantly improves accuracy, achieving a lower Root Mean Square Error (RMSE) and higher correlation with ground truth values than conventional methods. This study highlights the potential of FMCW radar for non-contact, unrestricted activity monitoring and suggests future research directions using multi-channel radar systems for enhanced motion analysis. Full article

Legged robots face inherent challenges in energy efficiency and stability at high speeds due to the repetitive acceleration–deceleration cycles of swing-based locomotion. To address these limitations, this paper presents a motion strategy that uses rolling gait instead of swing gait to improve the energy efficiency and stability. First, a wheel-legged quadruped robot, R-Taichi, is developed, which is capable of switching to legged, wheeled, and RHex mobile modes. Second, the mechanical structure of the transformable two-degree-of-freedom leg is introduced, and the kinematics is analyzed. Finally, experiments are conducted to generate wheeled, legged, and RHex motion in both swing and rolling gaits, and the energy efficiency is further compared. The experimental results show that the rolling motion can ensure stable ground contact and mitigate cyclic collisions, reducing specific resistance by up to 30% compared with conventional swing gaits, achieving a top speed of 0.7 m/s with enhanced stability (root mean square error (RMSE) reduction of 22% over RHex mode). Furthermore, R-Taichi exhibits robust multi-terrain adaptability, successfully traversing gravel, grass, and obstacles up to 150 mm in height. Full article

Dental caries can cause premature loss of deciduous teeth, affecting children’s growth and development. Endodontic treatment using polymer posts is an effective solution. This study explores biodegradable root canal posts made from Polylactic Acid (PLA), Polycaprolactone (PCL), and amorphous calcium phosphate (ACP), aiming to enhance mechanical properties, minimize polymer degradation acidity, and prevent inflammation. A root canal post with a spherical head and serrated structure was designed and produced via micromolding and optimized using the Taguchi experimental method. The melt temperature, injection speed, and holding speed were analyzed for their influence on shrinkage, revealing an optimal rate of 2.575%, representing the sum of axial and radial shrinkage. The melt temperature had the highest impact (55.932%), followed by holding speed (33.575%), with there being minimal effect from injection speed. The composite exhibited a flexural strength of 21.936 MPa, a modulus of 2.083 GPa, and a hydrophilic contact angle of 73.73 degrees. Cell survival tests confirmed biocompatibility, with a survival rate exceeding 70% and no toxicity. These findings highlight the potential of PLA/PCL/ACP composites, combined with injection molding, for developing biodegradable root canal posts in primary teeth. Full article

Root lesion nematodes (RLNs) are major plant parasites causing significant global yield losses in a wide range of crops. Current management strategies largely depend on synthetic nematicides, which raise environmental and human health concerns due to their broad-spectrum toxicity and persistence in the ecosystem. Volatile allelochemicals offer a promising, environmentally safer alternative due to their biodegradability and lower toxicity to mammals. In this study, we assessed the nematicidal activity of five allelochemical volatiles—dimethyl sulphide (DMS), dimethyl disulphide (DMDS), trans-cinnamaldehyde (TCA), trans-2-decenal (T2D), and trans-2-undecenal (T2U)—against Pratylenchus penetrans, using direct-contact bioassays, in comparison with the conventional nematicide oxamyl. Additionally, we assessed their environmental behaviour and toxicity profiles through in silico modelling. At 1 mg/mL, TCA, T2D, and T2U exhibited strong activity against P. penetrans, outperforming oxamyl by up to 1.6-fold, while DMS and DMDS showed reduced activity. The environmental risk assessment revealed that these compounds have a lower predicted persistence and bioaccumulation compared with oxamyl or fluopyram, a new generation nematicide. Though these findings boost the potential of these compounds as sustainable alternatives for RLN management, field validation and testing with non-target organisms remain necessary for the development of biopesticides. Nevertheless, this study emphasizes the need for an integrated risk-based assessment in the selection of nematicidal agents, warranting efficacy as well as environmental safety. Full article

Wheel-legged robots combine the advantages of both wheeled robots and traditional quadruped robots, enhancing terrain adaptability but posing higher demands on the perception of foot–ground contact forces. However, existing approaches still suffer from limited accuracy in estimating contact positions and three-dimensional contact forces when dealing with flexible tire–ground interactions. To address this challenge, this study proposes a foot–ground contact state detection technique and optimization method based on multi-sensor fusion and intelligent modeling for wheel-legged robots. First, finite element analysis (FEA) is used to simulate strain distribution under various contact conditions. Combined with global sensitivity analysis (GSA), the optimal placement of PVDF sensors is determined and experimentally validated. Subsequently, under dynamic gait conditions, data collected from the PVDF sensor array are used to predict three-dimensional contact forces through Gaussian process regression (GPR) and artificial neural network (ANN) models. A custom experimental platform is developed to replicate variable gait frequencies and collect dynamic contact data for validation. The results demonstrate that both GPR and ANN models achieve high accuracy in predicting dynamic 3D contact forces, with normalized root mean square error (NRMSE) as low as 8.04%. The models exhibit reliable repeatability and generalization to novel inputs, providing robust technical support for stable contact perception and motion decision-making in complex environments. Full article