Appl. Sci., Volume 14, Issue 11 (June-1 2024) – 489 articles

This study investigated the effect of digital therapeutics on ROM, flexibility, dynamic balance, satisfaction, and adherence. A sample of 34 volunteers was randomly assigned into a Digital Therapeutic Group (DTG) (n = 17) and a Non-Digital Therapeutic Group (NDG) (n = 17). The groups performed four calf muscle stretches and two hamstring stretches for 4 weeks. Flexibility and balance were evaluated to assess the effectiveness of the exercise program. A survey was conducted to assess subjects’ satisfaction, and exercise performance record papers and video records were assessed for exercise adherence. The paired t-test was used to compare the two populations before and after the program. The independent t-test was used to compare the change scores between groups. In the pre- and post-comparison within each group, the DTG group showed significant differences in all items except the dorsiflexion range of motion (ROM) (p < 0.05). The left straight leg raise (SLR) was significantly different (p < 0.05), and there was a significant difference in exercise satisfaction and participation between the two groups (p < 0.05). DTG showed improved flexibility, dynamic balance, and higher exercise satisfaction and adherence than NDG. The application, providing lower extremity stretching, can improve dorsiflexion ROM, flexibility of the lower extremity, dynamic balance, exercise satisfaction, and exercise adherence. Full article

This study investigated the impact of tobacco exposure, specifically through heating and burning, on periodontopathogens in the subgingival microbiome among clinically healthy individuals and those diagnosed with periodontitis. The sample comprised 66 subjects (26–56 years, median 38 yrs; 64% females) classified as non-smokers, classic cigarette smokers, and tobacco heating system (THS) smokers (each N = 22). Full-mouth periodontal examination was performed, and 330 paper-point samples from periodontal pockets were collected. Next-generation sequencing of 16S rRNA genes was conducted to identify the composition of subgingival microbiome. Periodontitis prevalence among the groups was ranked as THS (41%) < non-smokers (44%) < cigarette smokers (68%), without statistically significant differences between the groups. The number of perio-pathogenic species was higher in subjects with periodontitis compared to those without (median 7 vs. 6 species; p = 0.005) but without significant differences between exposure groups: non-smokers (6) = smokers (6) < THS (6.5). When combining exposure and periodontal status, each smoker group had more perio-pathogenic species than non-smokers: non-smokers without periodontitis (5) < smokers without periodontitis (5.5) < THS without periodontitis (6); non-smokers with periodontitis (6.5) < THS with periodontitis (7) = smokers with periodontitis (7). Multiple linear regression indicated periodontitis as the sole predictor of perio-pathogenic species quantity, irrespective of the type of tobacco consumption, sex, age, or oral hygiene (R² = 0.163; p = 0.005). In conclusion, the quantity of perio-pathogenic species in the subgingival microbiome was more influenced by periodontitis than by exposure to tobacco smoke, regardless of whether it was heated or burned. Full article

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development. Full article

An experiment was conducted on a prestressed concrete (PC) composite girder bridge with steel truss webs to investigate its flexural performance. The mechanical characteristics and failure modes of a PC composite girder bridge with steel truss webs was clarified. Finite element (FE) analysis was carried out, and the influence of the girder height-to-span ratio and eccentric loading effect on the flexural performance of a composite beam bridge with a steel truss web was discussed. The method for calculating the cracking bending moment, the bending moment at the rebar yield stage, and the ultimate bending moment of a PC composite girder with steel truss webs was proposed. Key findings include that, in both the elastic and cracking elastic stages, the strain of the bottom and top conforms to the plane-section assumption. Throughout the loading process, there was no occurrence of joint failure or local buckling failure in the steel truss webs; the composite girder ultimately fails due to excessive deformation, indicating that the overall mechanical performance of the composite beam is good. The deflection and stress in the mid-span section decrease with an increasing height-to-span ratio, and there are significant impacts of eccentric loading on deflection and stress. Compared with the results of the FE analysis and test, the calculation methods of the cracking moment, reinforcement yield moment, and ultimate moment of PC composite girders with steel truss webs presented in this paper have a high accuracy. Full article

BlenderBot 2.0 represents a significant advancement in open-domain chatbots by incorporating real-time information and retaining user information across multiple sessions through an internet search module. Despite its innovations, there are still areas for improvement. This paper examines BlenderBot 2.0’s limitations and errors from three perspectives: model, data, and user interaction. From the data perspective, we highlight the challenges associated with the crowdsourcing process, including unclear guidelines for workers, insufficient measures for filtering hate speech, and the lack of a robust process for verifying the accuracy of internet-sourced information. From the user perspective, we identify nine types of limitations and conduct a thorough investigation into their causes. For each perspective, we propose practical methods for improvement and discuss potential directions for future research. Additionally, we extend our analysis to include perspectives in the era of large language models (LLMs), further broadening our understanding of the challenges and opportunities present in current AI technologies. This multifaceted analysis not only sheds light on BlenderBot 2.0’s current limitations but also charts a path forward for the development of more sophisticated and reliable open-domain chatbots within the broader context of LLM advancements. Full article

Abstract: In this paper, the nonlinear dynamic system of a brushless DC motor with voltage disturbance is studied analytically via a generalized harmonic balance method. A truncated Fourier series with time-varying coefficients is utilized to represent the analytical variations of nonlinear currents and voltages within this dynamic system. Bifurcations of periodic currents and voltages are obtained, and their stability is discussed through eigenvalue analysis. The frequency–amplitude characteristics of periodic currents and voltages exhibit complexity in the frequency domain. Comparative illustrations are provided to contrast the analytical solutions with numerical outcomes for periodic currents and voltages. These analytical findings can be effectively employed for controlling the brushless DC motors experiencing voltage disturbances. Full article

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field. Full article

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rainwater. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan Island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima in 2019. The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increase approximately 17%–18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions. Full article

With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed. Full article

Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios. Full article

Table tennis is a sport that is enjoyed by many, including those with physical and intellectual disabilities. This scoping review summarised the current test protocols for assessing table tennis technical proficiency in individuals with disabilities. Relevant articles were searched through four databases (Scopus, PubMed, SPORTDiscus, and Web of Science) covering three key aspects: disability, table tennis, and technical proficiency. The search resulted in 14 studies included for data extraction, covering physical impairments, intellectual disability, and development coordination disorder. Almost all studies (93%) were conducted on well-trained para-table tennis athletes competing in high-level competitions. There exist protocols to assess service accuracy and stroke accuracy, hand–eye coordination, quality of specific skills and ball control, functional reach, and trunk rotation. The forehand topspin and backhand topspin drives were tested the most. Table tennis robots and video cameras are the common equipment used. Moving forward, future research should develop technical proficiency tests for players across all competency levels. The skill assessment criteria and scoring methods should be standardised and clearly explained. The validity and reliability of tests should be established. Lastly, there is great potential in using artificial intelligence to enhance the assessment of table tennis proficiency in individuals with disabilities. Full article

This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human–machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades. Full article

A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor FCDC is firstly derived by analyzing the converter operation state under various modes. On this basis, the control relationship between inductor current and flying capacitor (FC) voltage is redefined and an MPC power balance strategy based on the inductor current with single-step optimization is proposed. The proposed MPC strategy not only achieves decoupled power balancing control but also solves multi-objective dynamic optimization control of the inductor current and FC voltage, greatly reducing the computation load. A detailed theoretical analysis of the proposed strategy is presented and the balancing performance is effectively verified through the experiments. Full article

The study aims to investigate the impact of uniform, grooving and non-uniform corrosion degradation on the hot-spot stresses of a T-shaped tubular joint using the finite element method. The through-thickness linearization method is employed to estimate the hot-spot stresses, allowing a more reasonable consideration of the effect of grooving corrosion and non-unform corrosion. The grooving corrosion degradation is modelled assuming that the corrosion rate of the weld metal is 1.4 times that of the base metal. The non-uniform corrosion is modelled by moving the nodes around the weld by a random distance along the direction perpendicular to the surface. The random distances are generated based on the surface roughness parameter Ra. The results indicate that the stress concentration factor (SCF) increases with the uniform corrosion depth. The grooving corroded tubular joint results in a higher SCF than those of the corresponding uniformly corroded tubular joint. The non-uniform corrosion can lead to SCF deviations from the SCF of the uniformly corroded tubular joint. The SCF deviation at the critical region follows the normal distribution, and its standard deviation increases with Ra. Full article

Valves are prone to wear under harsh environments, such as high temperatures and reciprocating impacts, which has become one of the most severe factors reducing the service life of engines. As a lightweight ceramic, CrN is considered an excellent protective material with high-temperature strength and resistance to wear. In this study, a CrN coating was applied onto the valve cone surface via double-layer glow plasma surface metallurgy technology. The formation process, microstructure, phase composition, hardness, and adhesion strength were analyzed in detail. Impact wear tests were conducted on the valve using a bench test device. The SEM and EDS results showed that the CrN coating evolved from an island-like form to a dense, cell-shaped surface structure. The thickness of the coating was approximately 46 μm and could be divided into a deposition layer and a diffusion layer, from the outer to the inner sections. The presence of element gradients within the diffusion layer proved that the coating and substrate were metallurgically bonded. The adhesion strength of the CrN coating measured via scratch method was as high as 72 N. The average Vickers hardness of the valve cone surface increased from 377.1 HV_0.5 to 903.1 HV_0.5 following the plasma alloying treatment. After 2 million impacts at 12,000 N and 650 °C, adhesive wear emerged as the primary wear mode of the CrN coating, with an average wear depth of 42.93 μm and a wear amount of 23.49 mg. Meanwhile, the valve substrate exhibited a mixed wear mode of adhesive wear and abrasive wear, with an average wear depth of 118.23 μm and a wear amount of 92.66 mg, being 63.7% and 74.6% higher than those of the coating. Thus, the CrN coating showed excellent impact wear resistance, which contributed to the enhancement of the service life of the valve in harsh environments. Full article

Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system. Full article

Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the You Only Look Once version 8 (YOLOv8) model exhibits limitations in accuracy when confronted with complex backgrounds and densely clustered targets. To address these challenges, this study proposes an optimized CCS-YOLOv8 (Comprehensive Contextual Sensing YOLOv8) model. First, we curated a comprehensive livestock detection dataset encompassing the Qinghai region. Second, the YOLOv8n model underwent three key enhancements: (1) incorporating a Convolutional Block Attention Module (CBAM) to accentuate salient image information, thereby boosting feature representational power; (2) integrating a Content-Aware ReAssembly of FEatures (CARAFE) operator to mitigate irrelevant interference, improving the integrity and accuracy of feature extraction; and (3) introducing a dedicated small object detection layer to capture finer livestock details, enhancing the recognition of smaller targets. Experimental results on our dataset demonstrate the CCS-YOLOv8 model’s superior performance, achieving 84.1% precision, 82.2% recall, 84.4% [email protected], 60.3% [email protected], 53.6% [email protected]:0.95, and 83.1% F1-score. These metrics reflect substantial improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline model. Compared to mainstream object detection models, CCS-YOLOv8 strikes an optimal balance between accuracy and real-time processing capability. Its robustness is further validated on the VisDrone2019 dataset. The CCS-YOLOv8 model enables rapid and accurate identification of livestock age groups and species, effectively overcoming the challenges posed by complex grassland backgrounds and densely clustered targets. It offers a novel strategy for precise livestock population management and overgrazing prevention, aligning seamlessly with the demands of modern precision livestock farming. Moreover, it promotes local environmental conservation and fosters sustainable development within the livestock industry. Full article

This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response. Full article

This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RM ANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (F = 0.96, p = 0.38), VO (F = 0.23, p = 0.87), and SL (F = 1.07, p = 0.35), and a small (η² = 0.004) yet statistically significant difference in LSS (F = 5.52, p = 0.03) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running. Full article

Addressing the issue of increased shaft-system vibration in high-altitude mixed-flow hydropower generating units due to sand wear on turbine blades, a three-dimensional model of a specific mixed-flow water turbine was constructed. CFD numerical simulations were employed to analyze the fluid exciting force acting on the turbine runner under varying degrees of blade wear. An approximate analytical model was then established for the variation of fluid exciting force in the turbine runner system using the Fourier harmonic analysis method. A multi-degree-of-freedom mathematical model of flexural and inclined coupling vibration of a hydropower unit’s shafting, considering blade wear, was constructed. The nonlinear dynamic model was numerically calculated by the Runge–Kutta method. The vibration responses of the shafting of hydropower units under different wear degrees were obtained by means of a time-domain diagram, frequency-domain diagram, axis-locus diagram, phase-locus diagram, and Poincare mapping. Based on the formula for calculating the wear amount of the blade material, the runner amplitude degradation trajectory model was established, and the pseudo-failure time of turbine blades was determined according to the allowable value of amplitude. Full article

Graph neural networks (GNNs) are crucial tools for processing non-Euclidean data. However, due to scalability issues caused by the dependency and topology of graph data, deploying GNNs in practical applications is challenging. Some methods aim to address this issue by transferring GNN knowledge to MLPs through knowledge distillation. However, distilled MLPs cannot directly capture graph structure information and rely only on node features, resulting in poor performance and sensitivity to noise. To solve this problem, we propose a lightweight optimization method for GNNs that combines graph contrastive learning and variable-temperature knowledge distillation. First, we use graph contrastive learning to capture graph structural representations, enriching the input information for the MLP. Then, we transfer GNN knowledge to the MLP using variable temperature knowledge distillation. Additionally, we enhance both node content and structural features before inputting them into the MLP, thus improving its performance and stability. Extensive experiments on seven datasets show that the proposed KDGCL model outperforms baseline models in both transductive and inductive settings; in particular, the KDGCL model achieves an average improvement of 1.63% in transductive settings and 0.8% in inductive settings when compared to baseline models. Furthermore, KDGCL maintains parameter efficiency and inference speed, making it competitive in terms of performance. Full article

The artificially submerged cavitation water jet is effectively utilized by ejecting a high-pressure water stream into a low-pressure water stream through concentric nozzles and utilizing the cavitation phenomenon generated by the shear layer formed between the two streams. In this study, we investigated the cavitation characteristics of artificially submerged cavitation water jets by combining numerical simulations and erosion experiments. The results indicate that an appropriate standoff distance can generate more cavitation clouds on the workpiece surface, and the erosion characteristics of the artificially submerged cavitation water jet are most pronounced at a dimensionless standoff distance of S_D = 30. The shear effect formed between the two jets plays a crucial role in generating initial cavitation bubbles within the flow field of the artificially submerged cavitation water jet. Moreover, increasing the convergent angle between the two jets can significantly enhance the cavitation effect between them and lead to a more substantial cavitation effect. Simultaneously, increasing the pressure of the high-pressure inner nozzle also contributes to enhancing the cavitation effect of the artificially submerged cavitation water jet. Full article

Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering means, and the forensic detection of speech resampling is of great significance. For speech resampling detection, most of the previous works used traditional methods of feature extraction and classification to distinguish original speech from forged speech. In view of the powerful ability of deep learning to extract features, this paper converts the speech signal into a spectrogram with time-frequency characteristics, and uses the feature pyramid network (FPN) with the Squeeze and Excitation (SE) attention mechanism to learn speech resampling features. The proposed method combines the low-level location information and the high-level semantic information, which dramatically improves the detection performance of speech resampling. Experiments were carried out on a resampling corpus made on the basis of the TIMIT dataset. The results indicate that the proposed method significantly improved the detection accuracy of various resampled speech. For the tampered speech with a resampling factor of 0.9, the detection accuracy is increased by nearly 20%. In addition, the robustness test demonstrates that the proposed model has strong resistance to MP3 compression, and the overall performance is better than the existing methods. Full article

Six different commercial powders, finer than 45 μm, were used for examining the effects of particle characteristics on mean particle size and specific surface area. The measurements were carried out using the most commonly used air permeability- and laser light diffraction (scattering) techniques. As the air permeability method has been used as a benchmark for decades in the powder metallurgy (P/M) industry, the physical phenomena that govern the passage of gas through the powder bed under laminar flow conditions were also presented. The experimental data indicate that both methods give similar results for spherical powders. The advantage of laser light systems over gas permeameters is the ability to provide additional information on particle size distribution. Irregularly shaped powders should be analyzed by both techniques, relying on gas permeametry for surface area measurements and on laser light diffraction for the estimation of mean particle size and size distribution. Application of scanning electron microscopy as a complementary technique was found very helpful in the interpretation of data through visualization of individual particles. Full article

As the world’s population grows and resource consumption rises, there will be an increase in household waste and, consequently, the fly ash and slag from after the incineration of this waste need to be properly recycled or used as secondary raw materials in other industries. The authors of this work propose an innovative technology that has not yet been studied much in the literature; the obtained slag and fly ash are processed by plasma technology, and from those, fiber is formed. As shown by SEM studies, the raw material (before plasma–chemical treatment) consists of irregularly shaped granules or rod-shaped microforms. EDS studies show that both the original material and the products obtained after plasma–chemical processing are composed of various metal elements, but they make up a significantly small part, as the predominant elements are C, O, Cl, Si, and Ca. BET studies show that when the fiber is obtained with a plasma torch power of 55.5; 55.6 and 74 kW, respectively, the specific surface area is 11.9; 8.5 and 3 m²/g. It is worth noting that the raw material obtained after plasma–chemical processing is sufficiently clean and harmless to the environment, so it can be used as a secondary raw material, e.g., for the production of thermal insulation, in the production of catalysts, as a component in the production of concrete, or in other industries. Full article

A wave glider is an ocean observation platform that utilizes wave energy to drive and solar energy to power. Its metal structure will generate related electromagnetic fields due to corrosion and underwater motion. In the detection of weak electromagnetic field signals underwater, its own electromagnetic field characteristics will have an impact on signal detection. To study the applicability of electric field sensors and magnetic field sensors on wave glider platforms, the structural characteristics of the wave glider were analyzed, and the installation positions of electric field sensors and magnetic field sensors were designed based on the different motion states of the water surface mother body and underwater towing body. The measured electromagnetic field data of the wave glider platform were measured, and the measured data were analyzed. It was determined that the interference electric field energy under typical working conditions of the wave glider was mainly concentrated within 1 Hz, which decreased with increasing frequency, and the magnitude was mV/m. The magnitude of the interference magnetic field is several tens of nT, indicating that the electromagnetic field interference is significant during the working state of the wave glider. Installing an electric field sensor directly at the bottom of the wave glider will cause significant noise interference, while installing the magnetic field sensor directly at the bottom of the tractor will affect the servo and the shaking-induced magnetic field. Moreover, wave gliders should not use electric field signals below 1 Hz as signal sources, but they can utilize axial frequency electromagnetic fields to detect weak electromagnetic signals underwater. Full article

Contemporary murals and street art play a critical role in urban culture, serving as platforms for social activism and reflecting the vibrancy of city life. This study within the SuperStaAr project framework examines the challenge of graffiti removal while safeguarding the original synthetic paint layers. Through a detailed investigation using Q-Switch and Long Q-Switch lasers (Nd:YAG), we evaluate the effectiveness and safety of laser cleaning techniques on both unaged and artificially aged mural mock-ups. The initial findings highlight the Q-Switch and Long Q-Switch lasers as promising for removing graffiti without compromising the paint integrity. Our assessment criteria—encompassing residue presence, surface roughness, color changes, cleaning effectiveness, and pigment pickup—were validated through empirical evaluation and supported by colorimetric, micro–ATR–FTIR, and Py–GC/MS analyses. Notably, the incorporation of a passive sampling system for Py–GC/MS analysis facilitates a deeper understanding of the ablated materials without direct sampling from the artwork. This research contributes a foundational framework for the evaluation of laser cleaning in mural conservation, emphasizing the importance of tailored strategies to enhance the sustainability of urban art conservation efforts. Full article

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model’s coefficients of determination (R²s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake. Full article

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. Full article

Approved medical face masks have been shown to prevent the spread of respiratory droplets associated with coronavirus transmission in specific settings. The primary goal of this study was to develop a new strategy to assess the filtering and transmissibility properties of medical- and non-medical-grade face masks. In this study, we designed and assessed the filtering efficiency of particles through six different masks with a diverse set of fabrics, textures (woven and non-woven), fiber diameters, and porosity. The filtering and transmissibility properties of face mask layers individually and in combination have been assessed using mathematical analyses and new experimental data. The latter provided velocity profiles and filtration efficiencies for which the data were shown to be predictable. The filtration efficacy and pressure drop across each fabric have been tested using an aerosol particle spray and scanning electron microscopy. To assess clinical significance, the temperature and humidity of the masks were tested on a group of healthy volunteers spanning various age ranges (9–79 years old), utilizing an embedded temperature sensor disc. Also, a mask filter model was developed using fluid dynamic simulations (Solidworks Flow) to evaluate the aerodynamic dispersion of respiratory droplets. Overall, the FFP2 and FFP3 masks demonstrated the highest filtration efficiencies, each exceeding 90%, a feature of multi-layered masks that is consistent with simulations demonstrating higher filtering efficiencies for small particles (<5 µm). The velocity and temperature simulations of all six masks revealed a low air velocity (~1 m/s) inside the mask and a temperature variation of approximately 3 °C during the breathing cycle. Full article