Appl. Sci., Volume 13, Issue 24 (December-2 2023) – 381 articles

Device-to-device (D2D) technology is a promising technique in terms of being capable of providing efficiency, decreased latency, improved data rate, and increased capacity to cellular networks. Allocating power to users in order to reduce energy consumption and maintain quality of service (QoS) remains a major challenge in D2D communications. In this paper, we aim to maximize the throughput of D2D users and cellular users subject to QoS requirements and signal-to-interference-plus-noise ratio (SINR). To this end, we propose a resource and power allocation approach called optimal power allocation and delay minimization based on the conflict graph (OP-DMCG) algorithm that considers optimal power allocation for D2D multi-users in the cellular uplink channels and minimization of the total network delay using conflict graphs. Based on the simulations presented in this paper, we show that the proposed OP-DMCG algorithm outperforms the greedy throughput maximization plus (GTM+), delay minimization conflict graph (DMCG), and power and delay optimization based uplink resource allocation (PDO-URA) algorithms in terms of both total network throughput and total D2D throughput. Full article

Insulators find extensive use across diverse facets of power systems, playing a pivotal role in ensuring the security and stability of electrical transmission. Detecting insulators is a fundamental measure to secure the safety and stability of power transmission, with precise insulator positioning being a prerequisite for successful detection. To overcome challenges such as intricate insulator backgrounds, small defect scales, and notable differences in target scales that reduce detection accuracy, we propose the AC-YOLO insulator multi-defect detection network based on adaptive attention fusion. To elaborate, we introduce an adaptive weight distribution multi-head self-attention module designed to concentrate on intricacies in the features, effectively discerning between insulators and various defects. Additionally, an adaptive memory fusion detection head is incorporated to amalgamate multi-scale target features, augmenting the network’s capability to extract insulator defect characteristics. Furthermore, a CBAM attention mechanism is integrated into the backbone network to enhance the detection performance for smaller target defects. Lastly, improvements to the loss function expedite model convergence. This study involved training and evaluation using publicly available datasets for insulator defects. The experimental results reveal that the AC-YOLO model achieves a notable 5.1% enhancement in detection accuracy compared to the baseline. This approach significantly boosts detection precision, diminishes false positive rates, and fulfills real-time insulator localization requirements in power system inspections. Full article

One approach employed in brain–computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve SSVEP detection in BCIs. Implementing CNNs for this task does not require specialized knowledge. The subsequent layers of the CNN extract valuable features and perform classification. Nevertheless, a significant number of training examples are typically required, which can pose challenges in the practical application of BCI. This article examines the possibility of using a CNN in combination with data augmentation to address the issue of a limited training dataset. The data augmentation method that we applied is based on the spectral analysis of the electroencephalographic signals (EEG). Initially, we constructed the spectral representation of the EEG signals. Subsequently, we generated new signals by applying random amplitude and phase variations, along with the addition of noise characterized by specific parameters. The method was tested on a set of real EEG signals containing SSVEPs, which were recorded during stimulation by light-emitting diodes (LEDs) at frequencies of 5, 6, 7, and 8 Hz. We compared the classification accuracy and information transfer rate (ITR) across various machine learning approaches using both real training data and data generated with our augmentation method. Our proposed augmentation method combined with a convolutional neural network achieved a high classification accuracy of 0.72. In contrast, the linear discriminant analysis (LDA) method resulted in an accuracy of 0.59, while the canonical correlation analysis (CCA) method yielded 0.57. Additionally, the proposed approach facilitates the training of CNNs to perform more effectively in the presence of various EEG artifacts. Full article

Many applications suitable for multicast transmission, such as video conferencing and live e-commerce, demand high Quality of Service (QoS) and require data delivery to be completed within specified delay constraints. Some methods have been proposed for constructing delay-constrained multicast routing based on network state. However, obtaining precise network latency can be challenging, resulting in inaccuracies in delay-constrained routing calculations and, ultimately, the inability to meet application requirements. Additionally, many methods engage in an indiscriminate exploration of potential paths in the network, causing significant message processing overhead. This paper proposes an Information-Centric Networking (ICN)-based approach for delay-constrained multicast routing. Our method dynamically constructs multicast paths from tree nodes to receivers based on real-time path status detection during the join message propagation phase. Additionally, we present a method for acquiring neighborhood state information to facilitate real-time routing decisions. To curtail indiscriminate path exploration, our approach uses the ICN Name Resolution System (NRS) to obtain and select potential optimal tree nodes. For this purpose, we design a multicast service registration and resolution mechanism using the ICN Name Resolution System (NRS). Simulation results indicate that our approach exhibits a higher success ratio and concurrently incurs lower message processing overhead than some other methods, particularly in situations with stringent delay constraints. Full article

Polymer-modified cement mortar has been increasingly used as a runway/road pavement repair material due to its improved bending strength, bonding strength, and wear resistance. The flexural strength of polyurethane–cement mortar (PUCM) is critical in achieving a desirable maintenance effect. This study aims to evaluate and optimize the flexural strength of PUCM involving nano silica (NS) using a central composite design/response surface methodology (CCD/RSM) to design and establish statistical models. The PU binder and NS were utilized as input parameters to evaluate the responses, such as compressive and flexural strength. Moreover, machine learning (ML) algorithms including artificial neural networks (ANN) and Gaussian regression process (GPR) were used. The PUCM mixtures were prepared by adding a PU binder at 0%, 10%, 15%, and 25% by weight of cement. At the same time, NS was incorporated into the mortar mixes at 0 to 3% (interval of 1%) by cement weight. The results showed that the simultaneous effect of PU binder at the optimal content and NS improved the performance of PUCM. Adding NS to the mortar mixture mitigated some of the strength lost due to the PU binder, which remarkably reduces the strength properties at a high content. The optimized PUCM can be obtained by partly adding 3.5% PU binder and 2.93% NS particles by the weight of cement. The performance of the machine learning algorithms was tested using performance indicators such as the determination of coefficient (R²), mean absolute error (MAE), mean-square error (MSE), and root-mean-square error (RMSE). The GPR algorithm outperformed the ANN with higher R² and lower MAE values in the training and testing phases. The GPR can predict flexural strength with 90% accuracy, while ANN can predict it with 75% accuracy. Full article

Background: Studying the effects of traffic vibration on adjacent structures has produced fruitful results, but there is a lack of systematic research on the protection, assessment, and ambient vibration effects on cultural relics, and the majority of the studies focus on above-ground buildings, with less research conducted on underground cultural relic sites. Objective: In order to investigate the effects of road-traffic-induced vibration on nearby underground sites, the distance between them was precisely determined. Methodology/approach: The site of Chengshang Village in Jurong City, Nanjing, China, was chosen as the research object, and the vibration of the underground site caused by traffic volume was measured on-site. Based on statistical analysis of experimental data, the vibration velocity was deduced as a function of the vehicle’s speed and the vibration source’s distance. Results: The excellent frequency band for traffic load vibration is between 0 and 40 Hz, and the attenuation speed of high-frequency vibration is faster than that of low-frequency vibration; the vibration speed is positively correlated with the speed of the vehicle, and the distance from the vibration source is exponentially attenuated; and under the condition of the determined limit value of the load and the vibration speed, the safety distance increases. Conclusions: This research utilizes the collected data to describe the relationship between the vibration velocity and the distance from the vibration source. Additionally, it estimates the appropriate distance at which cultural relics should be placed from the road to ensure their safety. The study’s findings may serve as a valuable point of reference for traffic planning and the preservation of underground cultural monuments. Full article

Over the last decade, event prediction has drawn attention from both academic and industry communities, resulting in a substantial volume of scientific papers published in a wide range of journals by scholars from different countries and disciplines. However, thus far, a comprehensive and systematic survey of recent literature has been lacking to quantitatively capture the research progress as well as emerging trends in the event prediction field. Aiming at addressing this gap, we employed CiteSpace software to analyze and visualize data retrieved from the Web of Science (WoS) database, including authors, documents, research institutions, and keywords, based on which the author co-citation network, document co-citation network, collaborative institution network, and keyword co-occurrence network were constructed. Through analyzing the aforementioned networks, we identified areas of active research, influential literature, collaborations at the national level, interdisciplinary patterns, and emerging trends by identifying the central nodes and the nodes with strong citation bursts. It reveals that sensor data has been widely used for predicting weather events and meteorological events (e.g., monitoring sea surface temperature and weather sensor data for predicting El Nino). The real-time and multivariable monitoring features of sensor data enable it to be a reliable source for predicting multiple types of events. Our work offers not only a comprehensive survey of the existing studies but also insights into the development trends within the event prediction field. These findings will assist researchers in conducting further research in this area and draw a large readership among academia and industrial communities who are engaged in event prediction research. Full article

This study aims to integrate data-driven methodologies with user perception to establish a robust design paradigm. The study consists of five steps: (1) theoretical research—a review of the subject background and applications of Kansei engineering and gray relational analysis (GRA); (2) algorithmic framework research—the discussion delves into the intricate realm of Kansei engineering theory, accompanied by a thorough elucidation of the gray relational analysis (GRA) algorithmic framework, a crucial component in constructing a fuzzy logic model for product image modeling; (3) Kansei data collection—18 groups of perceptual words and six classic samples are selected, and the electric recliner chair samples are scored by the Kansei words; (4) Kansei data analysis—morphological analysis categorizes the electric recliner chair into four variables. followed by the ranking and key consideration areas of each area; (5) GRA fuzzy logic model verification—the GRA fuzzy logic model performs simple–complex (S-C) imagery output on 3D models of three modeling instances. By calculating the RMSE value of the seat image modeling design GRA fuzzy logic model, it is proven that the seat image modeling design GRA fuzzy logic model performs well in predicting S-C imagery. The subsequent experimental study results also show that the GRA fuzzy logic model consistently produces lower root mean square error (RMSE) values. These results indicate the efficacy of the GRA fuzzy logic approach in forecasting the visual representation of the electric recliner chair shape’s 3D model design. In summary, this research underscores the practical utility of the GRA model, harmoniously merged with perceptual engineering, in the realm of image recognition for product design. This synergy could fuel the extensive exploration of product design, examining perceptual engineering nuances in product modeling design. Full article

ZrO₂-based nanocomposites are highly versatile materials with huge potential for photocatalysis. In this study, ZrO₂-CdWO₄ nanocomposites (NC) were prepared via the green route using aqueous Brassica rapa leaf extract, and its photocatalytic water-splitting application was evaluated. Brassica rapa leaf extract acts as a reducing agent and abundant phytochemicals are adsorbed onto the nanoparticle surfaces, improving the properties of ZrO₂-CdWO₄ nanocomposites. As-prepared samples were characterized by using various spectroscopic and microscopic techniques. The energy of the direct band gap (E_g) of ZrO₂-CdWO₄ was determined as 2.66 eV. FTIR analysis revealed the various functional groups present in the prepared material. XRD analysis showed that the average crystallite size of ZrO₂ and CdWO₄ in ZrO₂-CdWO₄ was approximately 8 nm and 26 nm, respectively. SEM and TEM images suggested ZrO₂ deposition over CdWO₄ nanorods, which increases the roughness of the surface. The prepared sample was also suggested to be porous. BET surface area, pore volume, and half pore width of ZrO₂-CdWO₄ were estimated to be 19.6 m²/g. 0.0254 cc/g, and 9.457 Å, respectively. PL analysis suggested the conjugation between the ZrO₂ and CdWO₄ by lowering the PL graph on ZrO₂ deposition over CdWO₄. The valence and conduction band edge positions were also determined for ZrO₂-CdWO₄. These band positions suggested the formation of a type I heterojunction between ZrO₂ and CdWO₄. ZrO₂-CdWO₄ was used as a photocatalyst for hydrogen production via water splitting. Water-splitting results confirmed the ability of the ZrO₂-CdWO₄ system for enhanced hydrogen production. The effect of various parameters such as photocatalyst amount, reaction time, temperature, water pH, and concentration of sacrificial agent was also optimized. The results suggested that 250 mg of ZrO₂-CdWO₄ could produce 1574 µmol/g after 5 h at 27 °C, pH 7, using 30 vol. % of methanol. ZrO₂-CdWO₄ was reused for up to seven cycles with a high hydrogen production efficiency. This may prove to be useful research on the use of heterojunction materials for photocatalytic hydrogen production. Full article

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature $T_{f_{out}}$ and humidity ${RH}_{out}$ at the end of the water trickling roadway. The importance scores (ISs) indicate that $T_{f_{out}}$ is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While ${RH}_{out}$ is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control. Full article

Underground coal mining will inevitably cause serious ground deformation, and therefore, preventive mining-induced deformation analysis (MIDA) is of great importance in assisting mining planning and decision-making. Current web-based Geographic Information System (webGIS)-based applications usually use 2D GIS data and lack a holistic framework. This study presents a multi-scalar oblique photogrammetry-supported unified 3D webGIS framework for MIDA applications to fill this gap. The developed web platform uses multiple open-source JavaScript libraries, and the prototype system provides user-friendly interfaces for GIS data collecting and corresponding database establishment, geo-visualization and query, dynamic prediction, and spatial overlapping analysis within the same framework. The proposed framework was tested and evaluated in the Qianyingzi mining area in eastern China. The results demonstrated that multi-scalar oblique photogrammetry balances data quality and acquisition efficiency and provides a good source of GIS datasets, and the web-based platform has a good absolute and relative spatial accuracy verified by two types of validation data. Practical application results proved the feasibility and reliability of the system. The developed web-based MIDA prototype system attains an appealing performance and can be easily extended to similar geoscience applications. Full article

In constructing hydraulic tunnels, construction disturbances and complex geological conditions can induce variations in the surrounding rock parameters. To navigate the complex non-linear interplay between rock material parameters and tunnel displacement during construction, this study proposes a hybrid learning model. It employs particle swarm optimization (PSO) to refine the hyperparameters of the eXtreme Gradient Boosting (XGBoost) technique. Sensitivity analysis and inversion of rock parameters is performed by using orthogonal design and the Sobol method to analyze the sensitivity of environmental and rock material factors. The findings indicate that the tunnel depth, elastic modulus, and Poisson ratio are particularly sensitive parameters. Mechanical parameters of the rock mass, identified through sensitivity analysis, are the focal point of this research and are integrated into a three-dimensional computational model. The resulting tunnel displacement calculations serve as datasets for the inversion of the actual engineering project’s surrounding rock mechanical parameters. These inverted parameters were fed into the FLAC3D software (version 7.0), yielding results that align closely with field measurements, which affirms the PSO-XGBoost model’s validity and precision. The insights garnered from this research offer a substantial reference for determining rock mass parameters in tunnel engineering amidst complex conditions. Full article

During the milling of thin-walled blades, the removal of material exhibits strong time-varying dynamics, leading to chatter and a decrease in surface quality. To address the issue of milling vibrations in the machining of complex thin-walled blades used in aerospace applications, this work proposes a process optimization approach involving non-uniform allowances. The objective is to enhance of he stiffness of the thin-walled parts during the milling process by establishing a non-uniform allowance distribution for the finishing process of thin-walled blades. By applying the theory of sensitive process stiffness and conducting finite element simulations, two processing strategies, namely uniform allowances and non-uniform allowances, are evaluated through cutting experiments. The experimental results demonstrate that the non-uniform allowance processing strategy leads to a more evenly distributed acceleration spectrum and a 50% reduction in amplitude. Moreover, the surface exhibits no discernible vibration pattern, resulting in a 35% decrease in roughness. The non-uniform allowance-processing strategy proves to be effective in significantly improving the rigidity of the thin-walled blade processing system, thereby enhancing the stability of the cutting process. These findings hold significant relevance in guiding the machining of typical complex thin-walled aerospace components. Full article

The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit. Full article

Milling machines have made denture fabrication possible with high accuracy in a short time. However, the relationship between the milling conditions, accuracy, and milling duration has not been clarified. This study aimed to clarify the effects of milling conditions on surface roughness and milling duration. The specimen was designed using CAD software and milled using PMMA disks. In milling, the parameters of finishing the specimen surface were adjusted. Three different spindle speeds and four different feed rates were set. Twelve combinations of each parameter were used for milling, and the surface roughness and milling duration were measured. Results showed that the surface roughness significantly increased with the feed rate on the slopes of the specimen. The surface roughness differed with the spindle speed on the left and right slopes. The spindle speed and feed rate did not affect the surface roughness on the flat surface. The milling duration was not affected by the spindle speed but decreased as the feed rate increased. In conclusion, by increasing both the spindle speed and feed rate, the milling duration could be shortened while maintaining a constant surface quality. The optimum milling conditions were a spindle speed of 40,000 rpm and feed rate of 3500 mm/min. Full article

To enhance the continuity of character in the preservation of architectural heritage, this approach focuses on the horizontal self-similarity characteristics of architectural texture. A method using K-means and the Bhattacharyya approach for color selection in architectural repairs is proposed. It quantifies the visual coherence between the repaired structure and the original structure. Analyzing 12 images (A–L), with the original façade (image 0) as a reference, demonstrates that repairs using color-matched materials significantly improve visual coherence. Image A, created using the Visual Enhancement Through Adaptive Repair (VETAR) method, achieves the highest visual alignment with the original image. VETAR, grounded in Gestalt psychology, moves away from traditional materials to concentrate on visual consistency. Its successful implementation in the restoration of Mengzhong Fort illustrates a complex approach to material use in heritage conservation. After comparison, this method is deemed superior to traditional techniques, integrating modern interventions with historical aesthetics. The study suggests that VETAR may offer a referential method for architectural conservation, potentially facilitating a balanced integration of historical and contemporary elements in architectural renovation. Full article

Structural, conformational and spectroscopic investigations of the L-dopa molecule were made at the b3lyp/6-311++g** level using the Gaussian 09 software. IR, Raman and UV-vis spectra were measured and analyzed in light of the computed spectral quantities. Total energy vs. dihedral angle scans yielded 108 pairs of stable conformers of L-dopa. All the conformers had energies above 500 K relative to the lowest-energy conformer C-I. The observed spectra could be explained in terms of the computed spectra of the lowest-energy dimer of the C-I monomer. MEP and HOMO-LUMO analysis were carried out, and barrier heights and bioactivity scores were determined. The positive bioactive scores represent its higher medicinal and pharmaceutical applications. The present investigation suggests that the molecule has three active sites with moderate bioactivity. Full article

As the stress of the frame, especially the bottom side rail supports and bottom inclined supports, of a traditional LNG tank container could be significantly greater than its allowable stress, and the container cannot meet the strength requirement of the specification when it is impacted by a transport vehicle during railway transportation, three improved frame structures were suggested, which removed or changed the side rails or bottom inclined supports; the stress and deformation of these improved frames and the tank container were analyzed using the finite element method under the impact test. The results show that all three improved frames can meet the strength requirement, i.e., the maximum Mises stress is less than the allowable stress and the deformation requirement of the diagonal length difference is less than the allowable value, meaning that the tank containers with improved frames can pass the impact test. Moreover, for the FRP support rings and impact side heads, although the maximum values are different, they are still less than the respective allowable stresses. In addition, the maximum value of the middle cross section of the outer vessel in the direction of gravity does not increase with the change in the frame, and the deformation of the outer vessel remains within the elastic range. Therefore, the improvements of the frames have little effect on the stress and deformation of the other components of the tank container, in particular, the inner vessel and outer vessel. Compared to the frame of the traditional tank container, removing the side rails partially or completely can reduce the weight of the frame by 17.99% and 38.34%, respectively, greatly reducing manufacturing and transportation costs. It can also reduce the maximum Mises stress by 38.89% and 39.24% and the maximum diagonal difference by 57.95% and 61.16%. Full article

In this paper, a novel global time-varying path planning (GTVP) method is proposed. In the method, real-time paths can be generated based on tunable Bezier curves, which can realize obstacle avoidance of manipulators. First, finite feature points are extracted to represent the obstacle information according to the shape information and position information of the obstacle. Then, the feature points of the obstacle are converted into the feature points of the curve, according to the scale coefficient and the center point of amplification. Furthermore, a Bezier curve representing the motion path at this moment is generated to realize real-time adjustment of the path. In addition, the 5-degree Bezier curve planning method consider the start direction and the end direction is used in the path planning to avoid the situation of abrupt change with oscillation of the trajectory. Finally, the GTVP method is applied to multi-obstacle environment to realize global time-varying dynamic path planning. Through theoretical derivation and simulation, it can be proved that the path planned by the GTVP method can meet the performance requirements of global regulation, real-time change and multi-obstacle avoidance simultaneously. Full article

NeRF has garnered extensive attention from researchers due to its impressive performance in three-dimensional scene reconstruction and realistic rendering. It is perceived as a potential pivotal technology for scene reconstruction in fields such as virtual reality and augmented reality. However, most NeRF-related research and applications heavily rely on precise pose data. The challenge of effectively reconstructing scenes in situations with inaccurate or missing pose data remains pressing. To address this issue, we examine the relationship between different resolution encodings and pose estimation and introduce BiResNeRF, a scene reconstruction method based on both low and high-resolution hash encoding modules, accompanied by a two-stage training strategy. The training strategy includes setting different learning rates and sampling strategies for different stages, designing stage transition signals, and implementing a smooth warm-up learning rate scheduling strategy after the phase transition. The experimental results indicate that our method not only ensures high synthesis quality but also reduces training time. Compared to other algorithms that jointly optimize pose, our training process is sped up by at least 1.3×. In conclusion, our approach efficiently reconstructs scenes under inaccurate poses and offers fresh perspectives and methodologies for pose optimization research in NeRF. Full article

Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features. Full article

In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence. Full article

The purpose of this paper is to analyze the seismic response effect caused by the mass eccentricity of individual equipment when conducting base isolation for the improvement of the seismic performance of a nuclear power plant. Recent research has interpreted and confirmed through analysis and testing that base isolation for safety-related equipment in nuclear power plants is an efficient alternative to designing for excessive seismic loads. Depending on the equipment, unavoidable mass eccentricity can occur, which necessitates verification of the response impact caused by eccentricity. In this paper, we analyze the seismic response impact of equipment with mass eccentricity using small base isolators. To do so, sensitivity analysis of the seismic response due to mass eccentricity is conducted for a base-isolated concentrated mass model. Furthermore, three efficient mass eccentricity models suitable for testing are designed and manufactured. Simulation analyses using the finite element method (FEM) models are performed, followed by three-axis shake table tests to validate the seismic response impact of mass eccentricity. In conclusion, it is confirmed that applying small base isolators to equipment with mass eccentricity can affect seismic response impact to some extent when compared for a beyond-design-basis earthquake (BDBE). Full article

Containers enable high performance and easy deployment due to their lightweight architecture, thus facilitating resource utilization in edge computing nodes. Secure container runtimes have attracted significant attention because of the necessity for overcoming the security vulnerabilities of containers. As the runtimes adopt an additional layer such as virtual machines and user-space kernels to enforce isolation, the container performance can be degraded. Even though previous studies presented experimental results on performance evaluations of secure container runtimes, they lack a detailed analysis of the root causes that affect the performance of the runtimes. This paper explores the architecture of three secure container runtimes in detail: Kata containers, gVisor, and Firecracker. We focus on file I/O, which is one of the key aspects of container performance. In addition, we present the results of the user- and kernel-level profiling and reveal the major factors that impact the file I/O performance of the runtimes. As a result, we observe three key findings: (1) Firecracker shows the highest file I/O performance as it allows for utilizing the page cache inside VMs, and (2) Kata containers offer the lowest file I/O performance by consuming the largest amount of CPU resources. Also, we observe that gVisor scales well as the block size increases because the file I/O requests are mainly handled by the host OS similar to native applications. Full article

This paper offers a study regarding regression and correlation analysis and intercomparison of stress concentration factors obtained from FEM analysis with factors imported from external sources. The procedure for obtaining the stress concentration factors is implemented and demonstrated on the shape configuration of an axially symmetric structural element with offset, tension loading. It is a typical representation of stress concentrators of the shape-discontinuity-dimension-load configuration applied in structural elements mainly from the engineering and construction fields. The data thus obtained are then subjected to regression and simple correlation analysis. Three regression models based on 2nd- and 3rd-degree polynomials and power function are applied. These results are further subjected to a detailed procedure of comparison with the values of the stress concentration factors obtained from two other independent sources. Finally, a detailed analysis of the possible reasons for the registered value deviations is performed. Full article

In order to study the influence law of gas desorption accumulation and emission characteristics under hydraulic fracturing, this experiment uses coal-rock adsorption–desorption test equipment to carry out isothermal desorption tests of water-bearing coal under various stress paths. The experimental object is anthracite from Four Seasons Chun coal mine in Guizhou Province. In this experiment, the influence law of the desorption emission characteristics of coal under different stresses is analyzed. Research shows that the stress directly affects the gas desorption of coal and plays a decisive role in the gas desorption and emission characteristics of water-bearing coal in the stress-affected zone. Under equivalent gas adsorption of water-bearing coal, the total accumulated gas desorption displayed by coal increases with the increase in stress under certain conditions and the increase rate slows down with the time; coal samples differing in moisture content are subjected to various stress paths, leading to the difference in the total gas desorption. The total accumulated gas desorption displayed by coal with higher moisture content is generally smaller than that with lower moisture content. Through field observation, a zone with high accumulated gas desorption is formed in the stress-affected zone beyond the radius of effective fracture influence, generating an imbalance of gas desorption and emission. The study results are of theoretical and practical engineering significance for the prevention and control of stress-induced disasters and gas disasters in deep coal seams. Full article

For high ground-stress soft-rock tunnels surrounding rock with large deformation, rapid deformation rate, a long creep time, and a high likelihood of to causing initial and secondary lining damage, the yielding and relief-pressure support technology of a lining filled with a granular material is proposed. A layer of granular material is placed at the reserved deformation layer of the tunnel to provide the surrounding rock with a certain amount of deformation space. Confined compression tests were undertaken to study the laws of compressive strain, load reduction law, and horizontal force variation of different granular materials under different rock stresses. The research showed that the compressibility and load reduction performance of 8 mm soil was optimal. Its maximum compressive strain reached 47.6%, and the total load reduction rate reached 71%. The yielding- and relief-pressure effects of the granular sand-filled lining support were analyzed from the angles of deflection, pressure, and energy. The results show that the highest reduction rate of deflection was 36.7%, and the greatest load reduction rate of pressure was 78%. The grainy filling material can remove part of the load imposed by the surrounding rock on the support structure of the secondary lining through yielding pressure and relief pressure, which dramatically reduces the damage to the secondary lining from the surrounding rock. The research results have specific reference significance for designing and constructing tunnel support structures. Full article

Squeeze film dampers are often used to suppress vibration in turbine engines and play an important role in rotor systems. In this paper, the nonlinear dynamic characteristics of an industrial turbine engine fitted with squeeze film dampers are investigated with the static eccentricity of the SFDs. A recently developed time domain technique that combines the finite element method and the fixed interface modal synthesis method is applied to predict the nonlinear unbalance response of the industrial turbine engine under different unbalanced and static eccentricity configurations. By comparing the results obtained using SFDs with and without static eccentricity, it can be concluded that increasing the static eccentricity of the SFDs promotes non-periodic motion, while an increase in the unbalance level promotes the jump phenomenon. The efficiency of the rotor system would improve with an appropriate amount of unbalance applied to compressor IV, resulting in a reduction in the vibration level. If static sprung eccentricity occurs, the center of the journal orbit would be offset from the SFD center, rendering it inefficient or even leading to rub impact. Therefore, it is crucial to control the static eccentricity of the SFDs for optimal performance. The time domain technique is verified by the experimental results reported in the literature. Full article

Nowadays, rapid development has been achieved with respect to the electric wheel loader (EWL). The operational efficiency of EWLs is affected by many factors; especially, shovel force is a very important factor. For large-tonnage EWLs, when employing empirical, formula-based methods to predict shovel force, the generated errors are significant, with errors frequently reaching levels of up to 30%. To solve this problem, a method, based on the discrete element method (DEM), to predict shovel force is put forward in this paper. The material parameters are calibrated by a backpropagation (BP) neural network learning algorithm (NNLA). The material model is inputted into multi-body-dynamics software. A simulation model to accurately predict the shovel force is created. The error between the test results and the simulation results is 7.8%, demonstrating a high level of consistency. To validate the reliability of this method, the 35-ton EWL is taken as an example for research, and the straight-line driving test and the power-matching test are conducted. While ensuring the operational efficiency of the EWLs, the power loss is also a crucial consideration. The drastic changes in shovel force often result in front-tire slippage of the EWLs. To minimize wheel slippage during the shoveling section, the matching of the electric motor was optimized. In summary, material parameters were calibrated using a combined method of BP NNLA to predicate shovel force of a large-tonnage EWL. Additionally, the power matching of the EWL has been optimized to accord with the shoveling section of the device. Full article

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects. Full article