Search Results (1,041)

The Hybrid Contra Rotating Propeller is a developing propulsion system that combines a conventional single-shaft propeller with a POD propeller to achieve high energy-saving performance through a Contra Rotating Propeller. In this paper, a new towing tank test method for the Hybrid Contra Rotating Propeller was suggested. By conducting seven patterns of propeller open-water tests and measuring the individual propeller performance and the interaction between the propeller and the POD, the propeller’s mutual interaction can be obtained. Towing tank tests for a study ship were conducted, and the analyzed results are shown. There exists the effect of the wake of the propeller open boat at an unusual (reversed) test layout, which simulates the Hybrid Contra Rotating Propeller, and this effect must be removed for the accurate estimation of the ship’s performance. In conventional towing tank test methods, this effect on the front propeller was obtained and used to correct the performance of the total unit of the Hybrid Contra Rotating Propeller. The presented method allows for the correct removal of the open boat effect on the performance of each propeller and the propeller mutual interaction, resulting in more accurate power estimation. Furthermore, by using the individual performance of two propellers and interaction terms, the presented method enables us to conduct a power estimation at an arbitrary revolution rate of two propellers. Full article

In the author’s previous studies, new infinite numbers, their properties, and calculations were introduced. These infinite numbers quantify infinity and offer new possibilities for solving complicated problems in mathematics and applied sciences in which infinity appears. The current study presents additional properties and topics regarding infinite numbers, as well as a comparison between infinite numbers. In this way, complex problems with inequalities involving series of numbers, in addition to limits of functions of x $\in$ ℝ and improper integrals, can be addressed and solved easily. Furthermore, this study introduces rotational infinite numbers. These are not single numbers but sets of infinite numbers produced as the vectors of ordinary infinite numbers are rotated in the complex plane. Some properties of rotational infinite numbers and their calculations are presented. The rotational infinity unit, its inverse, and its opposite number, as well as the angular velocity of rotational infinite numbers, are defined and illustrated. Based on the above, the Riemann zeta function is equivalently written as the sum of three rotational infinite numbers, and it is further investigated and analyzed from another point of view. Furthermore, this study reveals and proves interesting formulas relating to the Riemann zeta function that can elegantly and simply calculate complicated ratios of infinite series of numbers. Finally, the above theoretical results were verified by a computational numerical simulation, which confirms the correctness of the analytical results. In summary, rotational infinite numbers can be used to easily analyze and solve problems that are difficult or impossible to solve using other methods. Full article

Although falls commonly occur in post-stroke patients during turning, the characteristics of trunk movement during turning in individuals at a high risk of falling remain unclear. The aim of this study was to clarify the characteristics of trunk translational and rotational movements during turning in post-stroke patients with a high risk of falling. Trunk acceleration and angular velocity were measured using the inertial measurement unit of an iPhone in the timed up and go test and compared among 13 post-stroke patients with a high risk of falling (age: 69.38 ± 12.44 years, Berg Balance Scale (BBS) < 45), 18 post-stroke patients with a low risk of falling (age: 71.22 ± 8.50 years, BBS $\ge$ 45), and 10 age-matched healthy controls (age: 65.90 ± 11.57). We examined the differences in trunk movement during turning between groups and the relationships between the BBS score and trunk movement. The high-risk group exhibited the longest completion time (χ² = 31.21, p < 0.001) and the lowest maximum of trunk angular velocity along the vertical axis among groups (χ² = 28.51, p < 0.001). Furthermore, the high-risk group showed a higher minimum (absolute value) of trunk angular velocity along the mediolateral axis compared to the low-risk group (χ² = 9.80, p = 0.007). The maximum trunk angular velocity along the vertical axis (r = 0.66, p < 0.001) and the minimum trunk angular velocity along the mediolateral axis (r = 0.51, p = 0.003) were significantly correlated with the BBS score. We found that post-stroke patients with a high risk of falling exhibited slower trunk rotation angular velocity and faster trunk flexion angular velocity during turning compared to low-risk groups. Our findings suggest that despite the decrease during turning speed due to poor balance control, post-stroke patients with a high risk of falling exhibit a greater disturbance in the sagittal plane. Full article

The electric solar wind sail (E-sail) is a propellantless propulsion system concept based on the use of a system of very long and thin conducting tethers, which create an artificial electric field that is able to deflect the solar-wind-charged particles in order to generate a net propulsive acceleration outside the planetary magnetospheres. The radial rig of conducting tethers is deployed and stretched by rotating the spacecraft about an axis perpendicular to the nominal plane of the sail. This rapid rotation complicates the thrust vectoring of the E-sail-based spacecraft, which is achieved by changing the orientation of the sail nominal plane with respect to an orbital reference frame. For this reason, some interesting steering techniques have recently been proposed which are based, for example, on maintaining the inertial direction of the spacecraft spin axis or on limiting the excursion of the so-called pitch angle, which is defined as the angle formed by the unit vector perpendicular to the sail nominal plane with the (radial) direction of propagation of the solar wind. In this paper, a different control strategy based on maintaining the pitch angle value constant during a typical interplanetary flight is investigated. In this highly constrained configuration, the spacecraft spin axis can rotate freely around the radial direction, performing a sort of conical motion around the Sun-vehicle line. Considering an interplanetary Earth–Venus or Earth–Mars mission scenario, the flight performance is here compared with a typical unconstrained optimal transfer, aiming to quantify the flight time variation due to the pitch angle value constraint. In this regard, simulation results indicate that the proposed control law provides a rather limited (percentage) performance variation in the case where the reference propulsive acceleration of the E-sail-based spacecraft is compatible with a medium- or low-performance propellantless propulsion system. Full article

The two-spotted spider mite (Tetranychus urticae) is a highly destructive and economically significant pest in agricultural, horticultural, and ornamental agroecosystems worldwide, including hop (Humulus lupulus) and mint (Mentha spp.) fields in the Pacific Northwest (PNW) region of the United States. Repeated acaricide applications and rotations have led to widespread resistance, resulting in control failures. In this study, we investigated the mechanisms of resistance to four different acaricides (bifenthrin, bifenazate, etoxazole, and abamectin) across 23 field-collected TSSM populations by integrating diagnostic bioassays, genetic screening for resistance-associated mutations, structural modeling, and molecular docking. Several kdr mutations and mutation combinations were detected in TuVGSC across all tested populations. The G132A in Tucytb was identified in 68.75% of hop and 40% of mint TSSM populations, while the I1017F in TuCHS 1 was found in 94% of hop and 100% of mint populations. Structural analysis revealed key interactions between acaricides and target proteins in both wild-type and mutant variants, providing novel insights into the functional impacts of these mutations. Our findings enhance the understanding of TSSM adaptation to acaricides among different crops, supporting the development of more effective resistance management strategies to mitigate economic losses in hops, mint, and other crop production. Full article

In the current energy landscape, efficiency is a critical topic. Therefore, even in the case of geared transmissions, it is essential to predict and calculate power losses as accurately as possible from the design phase. There are mainly three categories of losses in a gear unit: friction—the power losses due to the contact between teeth in rotation on the one hand and the seals with the spindles on the other hand; churning—the power losses generated by the air–lubricant mixture compression around teeth roots during rotation; and windage—the power losses due to the teeth aerodynamic trail in the air–lubricant mixture. While the first two categories of losses are intensively studied in the literature, the papers focusing on windage power losses are less representative. An estimation of windage power losses can be performed by numerical simulation, and the accuracy of the results depends on the mesh density and the available computing power. The present study discusses the influence of meshing on the windage torque of an orthogonal face gear immersed in air and compares numerical results generated by SolidWorks 2025 Flow Simulation software with experimental data measured on a test rig. Full article

As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. The lightweight convolutional attention is adaptively captured by the dynamic convolutional kernel generation strategy to adaptively capture local features in the time domain, and combined with grouped convolution to enhance the computational efficiency further; in addition, parameterized revised linear units are introduced to retain fault-sensitive negative information, which enhances the model’s ability to detect weak faults. The experimental findings demonstrate that the proposed model achieves an accuracy greater than 98.9%, highlighting its exceptional diagnostic accuracy and robustness. Moreover, compared to other fault diagnosis methods, the model exhibits superior performance under complex working conditions. Full article

Sediment erosion of hydraulic turbines has gradually become a key factor affecting their long-term stable operation. There are many different factors that can cause erosion in the Francis hydraulic turbine; among them, the vortex occurs in the turbine, which is also a negative factor for the unit. In this paper, the purpose is to study the complex erosion characteristics and specific influencing factor mechanism. The method is based on numerical simulation, combined with the verification data on site. Research results show that the differences in flow patterns among various components correspond to the erosion distribution of the unit at the same location, indicating that local flow patterns affect the unit’s erosion. The highest total erosion rate is on the surface of the runner at 1.08 × 10⁻³ kg·s⁻¹·m⁻². The erosion rate on the guide vane wall is second highest, also at 9.8 × 10⁻⁴ kg·s⁻¹·m⁻². The total erosion rate in the clearance is lower than that on the guide vane wall at 7.03 × 10⁻⁴ kg·s⁻¹·m⁻². The lowest total erosion rate is found in the draft tube at 2.57 × 10⁻⁴ kg·s⁻¹·m⁻². The effect of local vortices not only exacerbate the microscopic damage on the blade surface but also leads to a more obvious nonuniform erosion distribution, especially at the clearance, where erosion is more severe. The vortex in the guide vane passage alters the particle trajectory at the guide vane outlet, increasing the erosion in the guide vane clearance. Similarly, the vortex in the draft tube increases particle rotation, enhancing erosion on the draft tube wall. Research indicates that eliminating vortices is beneficial for reducing sediment erosion within the unit. The research results provide a theoretical basis for the optimization design of Francis hydraulic turbine. Full article

In alpine skiing, accurate and real-time estimation of body pose and inclinations due to turning is critical as it demonstrates the skier’s turning behavior and abilities. Although inertial measurement units (IMUs) ease measuring kinematics in extreme conditions and provide such indications of skiers’ behavior, they often suffer from sensor placement and orientation variability. This study explains the impact of sensor placement and orientation on the captured signals and proposes a preprocessing algorithm that can rotate raw signals from various locations and orientations similar to those near the Center of Mass (CoM). The preprocessing algorithm involves a sensor fusion approach using a quaternion-based complementary filter (CF) to rotate raw signals and extract turning motions via the global wavelet spectrum. Our experiment, validated on data collected from 14 sensors including two smartphones placed on different body parts during skiing sessions, demonstrates that the preprocessing algorithm can effectively reconstruct side motions, represent skiing turns, and detect turns independent of sensor placement and orientation. In field experiments with six skiers, the suggested preprocessing algorithm consistently detected skiing turns with an overall RMSE of 0.77 and MAE of 0.50 on all of the sensors relative to a reference sensor. Full article

This paper proposes a two-dimensional (2D) generalized equivalent magnetic network (GEMN) model suitable for surface-mounted permanent magnet electric machines (SPEMs). The model divides the SPEM into eight types of regions: stator yoke, stator tooth body, stator tooth tips, stator slot body, stator slot openings, air gap, rotor permanent magnets, and rotor yoke. Each region is subdivided radially and tangentially into multiple 2D magnetic network units containing radial and tangential magnetic circuit parameters, forming a regular magnetic network covering all regions of the SPEM. The topology of this magnetic network remains unchanged during rotor rotation and can accommodate various surface-mounted permanent magnet structures including Halbach arrays, which enhances the generality of the model significantly. The proposed model can be used to calculate the 2D magnetic flux density distribution, winding electromotive force, electromagnetic torque, stator iron loss, and permanent magnet demagnetization in the influence of magnetic saturation, stator slotting, and current harmonic. Comparative analysis with the accurate subdomain method (ASDM) and finite element method (FEM) demonstrates that the GEMN model achieves a good balance between computational speed and accuracy, making it particularly suitable for efficient electromagnetic performance evaluation of SPEMs. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article

Breast cancer is the most common disease in women, with 287,800 new cases and 43,200 deaths in 2022 across United States. Early mammographic picture analysis and processing reduce mortality and enable efficient treatment. Several deep-learning-based mammography classification methods have been developed. Due to low-contrast images and irrelevant information in publicly available breast cancer datasets, existing models generally perform poorly. Pre-trained convolutional neural network models trained on generic datasets tend to extract irrelevant features when applied to domain-specific classification tasks, highlighting the need for a feature selection mechanism to transform high-dimensional data into a more discriminative feature space. This work introduces an innovative and effective multi-step pathway to overcome these restrictions. In preprocessing, mammographic pictures are haze-reduced using adaptive transformation, normalized using a cropping algorithm, and balanced using rotation, flipping, and noise addition. A 32-layer convolutional neural model inspired by YOLO, U-Net, and ResNet is intended to extract highly discriminative features for breast cancer classification. A modified Grey Wolf Optimization algorithm with three significant adjustments improves feature selection and redundancy removal over the previous approach. The robustness and efficacy of the proposed model in the classification of breast cancer were validated by its consistently high performance across multiple benchmark mammogram datasets. The model’s constant and better performance proves its robust generalization, giving it a powerful solution for binary and multiclass breast cancer classification. Full article

(1→3)-α-d-Glucan is an important component of the cell wall of most fungi. The polymer has many applications, including as a therapeutic agent in the prevention or treatment of various diseases, as well as a heavy metal sorbent and a component of new materials used in the plastics industry. The presence of (1→3)-α-d-glucan (water-insoluble, alkali-soluble polysaccharide) in the cell wall of Pleurotus djamor (pink oyster mushroom) was confirmed using specific fluorophore-labeled antibodies. Therefore, the water-insoluble fraction (WI-ASF) of P. djamor B123 fruiting bodies was isolated by alkaline extraction and used for further analyses. The structural features of the WI-ASF were determined by composition analysis, linkage analysis, Fourier transform infrared and Raman spectroscopy, ¹H and ¹³C nuclear magnetic resonance spectroscopy, scanning electron microscopy, as well as viscosity, specific rotation, and gel permeation chromatography. These studies revealed the presence of glucose units linked by α-glycosidic bonds and scanty amounts of mannose and xylose. Furthermore, methylation analysis of WI-ASF demonstrated that the (1→3)-linked glucopyranose (Glcp) is the primary moiety (86.4%) of the polymer, while the 3,4- and 3,6-substituted hexoses are the branching residues of the glucan. The results of chemical and spectroscopic investigations indicated that the analyzed WI-ASF is a (1→3)-linked α-d-glucan type with a molecular weight of 552 kDa. Full article

Over the past four decades, cheerleading evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling—encompassing team synchronicity, ground interactions, choreography, and artistic expression—makes objective assessment challenging. Artificial intelligence (AI) revolutionised various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Our results indicate that certain machine learning models can effectively identify different tumbling elements with high accuracy despite inter-individual variability and data noise. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports in order to provide objective metrics that complement traditional judging methods. Full article

Permanent magnet thrust bearings have garnered significant attention due to their high rotational speeds, low noise levels, and excellent vibration-damping performance. However, existing designs of these bearings often suffer from low load-carrying capacity and are tailored to specific machines, which limits their broader applicability. To address these limitations, this paper proposes a novel modular multi-unit cell structure for permanent magnet thrust bearings. The load-carrying performance of this design is validated through theoretical analysis, simulation, and experimentation. The inspiration for this design comes from bionics and honeycomb structures, emphasizing modularization and the combination of multiple unit cells. The unit cell consists of four permanent magnets, and multiple unit cells can be connected to form a structure that replaces the traditional design of directly embedding a permanent magnet ring into the bearing structure. Moreover, the designed unit cell structure can expand in both axial and radial directions, allowing for the creation of various nested or cross structures tailored to specific usage requirements. With this modular approach, the theoretical model of the bearing structure can be extended from the traditional single-layer cross-nested structure to an arbitrary number of nested cross-nested configurations using the equivalent magnetic circuit method. The bearing’s performance is validated through finite element simulations and experimental testing. The results demonstrate that the bearing with a four-layer cross-nested structure achieves a maximum load capacity of 48.45 kN, with a deviation of 7.3% from the theoretical value and 4% from the simulation results. By leveraging the generalization of the unit cell, the maximum axial load capacity across various configurations ranges from 6.78 kN to 288.9 kN, significantly enhancing the bearing’s adaptability to diverse operational scenarios. Full article