Applied Sciences

The stability of roadway sidewalls is crucial to ensuring people’s safety and production efficiency in coal mining. This paper investigated the deformation and failure of deep roadway sidewalls, particularly the effects of height-width ratios and lateral pressure coefficients. Our research results indicate that brittle failure occurred in the diabase sidewall rock of the Datai coal mine, and a V-shaped pit was formed as a result of shear damage caused by high stress. When the height-width ratio of a roadway increases from 0.25 to 2.00, the tensile and shear plastic failure area of the sidewall increases, and vertical stress is transferred to a deep part of the roadway sidewall. There are two stress concentration zones and two stress peak points in the sidewall of a roadway. When the lateral pressure coefficient increases from 0.10 to 1.00, the tensile plastic zone of rock mass in the sidewall first decreases and gradually reaches stability. On the other hand, the shear failure area increases and then decreases. Similarly, the sidewall horizontal displacement decreases and then increases. Additionally, the vertical stress concentration position is located near the roadway sidewall. Full article

Increased performance with reduced overall cost, and precise design and optimization of launch systems are critical to affordability. In this respect, the use of hybrid motors has increased to ease handling based on motor selection. In the current study, the launch vehicle’s performance is enhanced by incorporating a hybrid rocket motor into the last stage and optimized using particle swarm optimization to develop a six-degrees-of-freedom tool. This modification aims to increase payload placement flexibility, facilitate handling, and reduce costs. Thanks to the interactive subsystems within this research, this innovative study more comprehensively considers the launch vehicle trajectory design problem, allowing the simultaneous consideration of the effect of launch vehicle geometry along with other parameters in the system. In this context, the proposed method is applied to the Minotaur-I launch vehicle, and contributions of the detailed design and optimization are presented. Optimization results show that the percentage differences between these models for the original vehicle were observed to be 11.55% in velocity and 8.02% in altitude. However, there were differences of 10.06% and 48.8%, 15.8% and 23.2%, and 19.5% and 78.9% in altitudes and velocities when the center of gravity and moment of inertia changes were neglected, and constant aerodynamic coefficients were assumed, respectively. In all these cases, it was observed that the flight path angle was not close to zero. Moreover, the same mission was achieved by the launch vehicle with the optimized hybrid last stage and the propulsion performance was increased by about 7.64% based on the specific impulse and total impulse-over-weight ratio. Full article

Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods. Full article

Background: One of the rarest complications of fixed orthodontic retention is inadvertent tooth movement of the teeth bonded to the retainer. A 25-year-old patient presented at the orthodontist as she was preoccupied about the position of the lower teeth. The aim of this case report was to present a follow-up of anterior teeth alignment after fixed retainer removal and to analyze the structure of the removed fixed orthodontic retainer in a patient suffering from a twist effect. Materials and Methods: The retainer that caused inadvertent movement has been removed, and subsequent teeth displacement was assessed with scan superimposition. The retainer structure and diameter were analyzed with a laser confocal microscope. Results: The superimposition showed significant improvements in the position of the teeth. The sole removal of the activated retainer resulted in a partial self-correction of the twist effect. Under microscopic observation, changes in the dimensions of the wire were found but were too small to cause significant changes in tooth position. Conclusions: A fixed retainer should always be removed when a twist effect is diagnosed. The wire sections covered with composite are less likely to untwist. In the presented case, the changes in the dimensions of the round retainer wire could not have led to changes in the teeth’s positions. Full article

High-speed rubber cup polishing can exacerbate tooth surface damage, especially when preexisting conditions such as early caries or cracks exist. This study aimed to quantify the extent of damage to sound teeth based on rotating rubber cup speed and assess the damage in relation to the tooth surface condition. Using a rubber cup, 36 sound teeth were polished at 100, 3000, and 10,000 rpm, and 24 teeth with early carious lesions and 24 cracked teeth were polished at 3000 and 10,000 rpm. Polishing was performed using a rubber cup and prophylaxis paste, applying an on–off method (3.0 N force for 3 s). Damage depth was quantified using a surface profilometer and examined using scanning electron microscopy. Polishing at 10,000 rpm caused significantly more damage to sound teeth than polishing at lower speeds (depth increase: 71.45 ± 15.12 µm at 100 rpm; 61.91 ± 17.82 µm at 3000 rpm; p < 0.001). Teeth with early carious lesions or cracks demonstrated more damage after polishing than sound teeth (p < 0.05). Therefore, the rotational speed of the rubber cup has a critical impact on the extent of enamel damage. Higher speeds can increase the damage depth in both sound and damaged tooth surfaces. Full article

This paper presents an ultra-wideband, low insertion loss, and high accuracy 6-bit digital step attenuator (DSA). To improve the accuracy of amplitude and phase shift of the attenuator, two innovative compensation structures are proposed in this paper: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) intended for large attenuation bits. Additionally, we propose insertion loss reduction techniques (ILRTs) to reduce insertion loss. The fabricated 6-bit DSA core area is only 0.51 mm², and it exhibits an attenuation range of 31.5 dB in 0.5 dB steps. Measurements reveal that the root-mean-square (RMS) attenuation and phase errors for the 64 attenuation states are within 0.18 dB and 7°, respectively. The insertion loss is better than 2.54 dB; the return loss is better than −17 dB; and the input 1 dB compression point (IP1 dB) is 29 dBm at IF 12 GHz. To the best of our knowledge, this chip presents the highest attenuation accuracy, the lowest insertion loss, the best IP1dB, and a good matching performance in the range of 2–22 GHz using the 0.25 μm GaAs p-HEMT process. Full article

This study introduces an innovative two-range, 12-stage Marx pulse generator employing thyristor switches designed specifically for the electroporation of biological cells. The generator consists of two module capacitors of different capacitances (1 μF and 0.25 μF), which enable the generation of electrical pulses with different durations and amplitudes of up to 25 kV. Safety aspects, including overcurrent and overvoltage protection mechanisms, are implemented in both the software and the hardware. In the experimental section, the tests of the Marx generator with resistive load are described in detail, and the results for the voltage fluctuations, pulse duration, and output characteristics of the generator are presented. The advantages of the design, including the high output voltage, the wide range of repetition rates, and the flexibility of the pulse parameters, are emphasized. Additionally, the research showcases the utilization of the devised generator for industrial purposes. Hence, an investigation into the efficiency of protein extraction from microalgae (Chlorella vulgaris) and the impacts of pulsed electric fields (PEFs) on the structural characteristics of casein micelles (CSMs) was chosen as an illustrative example. The obtained results provide valuable insights into the application of PEF in food processing and biotechnology and underline the potential of the developed generator for sustainable and environmentally friendly practices. Full article

This article presents the operation of an automatic pine sawn timber inspection system, which was developed at the Woodinspector company and is offered commercially. The vision inspection system is used to detect various wood defects, including knots, blue stain, and mechanical damage caused by worms. A blue stain is a defect that is difficult to detect based on the color of the wood, because it can be easily confused with wood defects or dirt that do not impair its strength properties. In particular, the issues of detecting blue stain in wood, the use of artificial neural networks, and improving the operation of the system in production conditions are discussed in this article. While training the network, 400 boards, 4 m long, and their cross-sections of 100 × 25 [mm] were used and photographed using special scanners with laser illuminators from four sides. The test stages were carried out during an 8-hour workday at a sawmill (8224 m of material was scanned) on material with an average of 10% blue stain (every 10th board has more than 30% of its length stained blue). The final learning error was assessed based on defective boards detected by humans after the automatic selection stage. The system error for 5387 boards, 550 m long, which had blue staining that was not detected by the scanner (clean) was 0.4% (25 pieces from 5387), and 0.1 % in the case of 3412 boards, 610 mm long, on which there were no blue stains, but were wrongly classified (blue stain). For 6491 finger-joint boards (180–400 mm), 48 pieces were classified as class 1 (clean), but had a blue stain (48/6491 = 0.7%), and 28 pieces did not have a blue stain, but were classified as class 2 (28/3561 = 0.7%). Full article

The pneumatic centralized fertilizer discharge system is an important part of pneumatic fertilization machinery and mainly includes a fertilizer discharge device, an air–fertilizer mixing device and a pneumatic distribution device. In this paper, the mechanical structure, key parameters and research methods of pneumatic centralized discharge devices, air–fertilizer mixing devices and pneumatic distribution devices at home and abroad are briefly analyzed. The advantages and disadvantages of these existing devices are summarized, the existing problems are discussed and improvement methods are put forward. In this paper, the structural adaptability, uniformity and stability of the fertilizer discharge of different types of fertilizer discharge devices, such as external groove wheel types, spiral types and centrifugal types, are comprehensively analyzed. The working principle of air–fertilizer mixing devices using the Venturi effect to achieve the uniform mixing of fertilizer and airflow is expounded. The effects of air–fertilizer mixing devices with different structural forms and parameters on mixing performance and the motion characteristics of air–solid two-phase flows are analyzed. The influence of the internal structure, key parameters and distribution mode of pneumatic distribution devices on the uniformity and accuracy of fertilizer distribution are analyzed. This paper focuses on how to improve the uniformity, stability and consistency of discharge across rows provided by the pneumatic centralized fertilizer discharge system. The research status and progress made regarding the core components of the pneumatic centralized fertilizer discharge system at home and abroad are summarized. Based on different research results, the key factors and methods for improving the uniformity of fertilizer discharge are discussed. Full article

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages. Full article

Weight loss solutions are on the rise, and there is growing interest in natural alternatives to the pharmaceuticals in the market, which are not devoid of relevant side effects. To this end, the objective of the current study was to assess the effects of a botanical-based blend comprised of lemon verbena and hibiscus extracts on weight management, body fat, appetite, lipid, and glucose profiles in an overweight population for 90 days. The volunteers presented significant weight loss in absence of diet (approximately 3 kg, −3.4% total body weight), which coincided with decreased body fat (−1.7%, equivalent to an almost 6% decrease compared to initial value). Body fat was assessed by anthropometry, electrical bioimpedance and DEXA scans. Cholesterol levels were also significantly decreased (approximately 7% decrease). Satiety assessment revealed that the product contributed to increased feeling of fullness and satisfaction after a meal compared to the placebo group, which could contribute to the weight loss observed. Blood glucose and blood pressure levels remained unchanged, which was expected as the volunteers were normoglycemic and their blood pressure was in the healthy range. In conclusion, daily supplementation of a botanical-based blend contributes to weight loss in overweight individuals in absence of diet, while not affecting other parameters that are in the healthy range. This product could be a candidate alternative or add-on to other weight loss products as part of a healthy lifestyle. Full article

This study layed the groundwork for the creation of haploid and triploid celestial goldfish, presenting essential data derived from preliminary investigations. The research delved into three main areas: the short-term preservation of celestial goldfish gametes, the interaction between oocytes and foreign sperm, and the effects of temperature on fertilized eggs concerning hatching rates and late-stage ocular development. Initially, the study explored the optimal semen dilution ratio for celestial goldfish under microscopic examination. And the hybridization of the largemouth bass and celestial goldfish was investigated: largemouth bass sperm was crossbred with celestial goldfish eggs, and it was found that their sperm could not stimulate the development of celestial eye eggs. At last, celestial goldfish fertilized eggs were stimulated at 4 °C and −20 °C, respectively, to observe their impact on the hatching rate and later celestial eye rate. The results revealed no significant differences in hatching rate and celestial eye rate between the cold stimulus groups and the control group, but numerically, the 4 °C cold stimulation reduced the celestial eye rate of celestial goldfish fertilized eggs. The research provided fundamental data for artificial breeding and hybridization experiments in celestial goldfish. Full article

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative condition. It causes cognitive impairment and memory loss in individuals. Healthcare professionals face challenges in detecting AD in its initial stages. In this study, the author proposed a novel integrated approach, combining LeViT, EfficientNet B7, and Dartbooster XGBoost (DXB) models to detect AD using magnetic resonance imaging (MRI). The proposed model leverages the strength of improved LeViT and EfficientNet B7 models in extracting high-level features capturing complex patterns associated with AD. A feature fusion technique was employed to select crucial features. The author fine-tuned the DXB using the Bayesian optimization hyperband (BOHB) algorithm to predict AD using the extracted features. Two public datasets were used in this study. The proposed model was trained using the Open Access Series of Imaging Studies (OASIS) Alzheimer’s dataset containing 86,390 MRI images. The Alzheimer’s dataset was used to evaluate the generalization capability of the proposed model. The proposed model obtained an average generalization accuracy of 99.8% with limited computational power. The findings highlighted the exceptional performance of the proposed model in predicting the multiple types of AD. The recommended integrated feature extraction approach has supported the proposed model to outperform the state-of-the-art AD detection models. The proposed model can assist healthcare professionals in offering customized treatment for individuals with AD. The effectiveness of the proposed model can be improved by generalizing it to diverse datasets. Full article

The hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of these veins. This study was aimed at understanding flow disturbances caused by such stenoses. The computational fluid dynamics software Flowsquare+ was used. We constructed 3-dimensional models of the venous outflow, comprising two alternative routes: the tube representing the internal jugular vein and an irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently shaped and sized short strictures representing nozzle-like strictures were built in. At the end of this tube, differently shaped membranes representing the jugular valve were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by the nozzle-like strictures in the upper part of the internal jugular vein that were small, long, or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance. Full article

Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset. Full article

Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer support. Building upon the S3PRL-VC toolkit and on pre-trained speech and speaker representation models, this paper introduces a feature disentanglement approach to improve the de-identification performance of the state-of-the-art anonymization approaches based on voice conversion. The proposed approach achieves state-of-the-art speaker de-identification and causes minimal impact on the intelligibility of the signal after conversion. Full article

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios. Full article

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings. Full article