Appl. Sci., Volume 13, Issue 22 (November-2 2023) – 444 articles

Internet of things (IoT)-based wireless communication technology has been applied for efficient work and safety in mines. However, underground mines are surrounded by walls and have numerous curves, which reduce communication stability. For smooth communication between devices, a line of sight (LOS) must be connected without obstacles. If optimal installation locations in a virtual space can be confirmed before installing the device in the field, trial and error can be avoided. In this study, a 3D model-based LOS analysis technology was developed using Python and a ray-casting algorithm. A place with numerous LOS connections has good communication with other places; consequently, it is a suitable location to install the device. To indicate the degree of communication smoothness, a smooth communication index was proposed. A preliminary experiment was conducted in an indoor space within the Samcheok Campus of the Kangwon National University, and a field experiment was conducted at the Samdo Mine in Dogye-eup, Samcheok-si, Gangwon-do. Based on these results, an effective wireless sensor network (WSN) was established by installing a ZigBee-based monitoring device. The results of this study can be further improved and used for constructing smooth WSNs in underground mines in the future. Full article

The vibration response of a partial composite beam bridge under the influence of moving vehicular loads was investigated. Due to the coupling effect between the vehicle and the bridge, the vibration information of the vehicle encompassed the vibration information of the bridge. Consequently, the dynamic response of the vehicle could be utilized to extract the dynamic information of the composite beam. A moving mass-spring-damping system and composite beam elements considering interfacial slips were used for the interaction vibration of a vehicle-composite bridge. A finite element program for the interaction vibration analysis of the vehicle-composite beam bridge was developed. The program was used to extract the vibration information of the composite beam bridge by analyzing the vehicle displacement, velocity, and acceleration in the interaction vibration of the beam and the vehicle. Taking the Hangzhou Jiubao Bridge as the engineering background, the influences of structural parameters such as shear stiffness of connections, prestress magnitude, as well as vehicle parameters, including vehicle stiffness, damping, and mass, on frequency identification were analyzed. Furthermore, the influences of road roughness, disturbance force generated by vehicle random vibrations, and interference signals generated by signal transmission on frequency identification of the bridge were investigated. Full article

Natural polyphenol-rich plant resources, such as agricultural waste, were proven to diminish insulin resistance and weight gain in rats on a high-fat diet. To test whether date seed polyphenol pills (DSPPs) might lower adipose tissue accumulation by precisely affecting adipocytes, we explored the impacts of DSPPs on cell proliferation, differentiation, and lipolysis in 3T3-L1 cells. We utilized tablets made commercially from date seed polyphenols that were mostly composed of epicatechin (45.9 g/kg). The total polyphenol and antioxidant capacities of the digested and non-digested DSPPs were also evaluated. DSPPs at doses of 25, 50, and 100 µg/mL hindered the proliferation of both pre-confluent preadipocytes and mature post-confluent adipocytes. DSPPs decreased the quantity of viable cells in completely developed adipocytes. Treatment with 100 µg/mL of DSPPs decreased the basal lipolysis of completely differentiated adipocytes but modestly boosted epinephrine-induced lipolysis. A significant transcription factor for the adipogenic gene, the peroxisome proliferator-activated receptor (PPAR), was repressed by DSPPs, which significantly decreased lipid buildup. The total polyphenol and antioxidant capacities were also increased after digestion with a good bubble Pearson correlation between both. DSPPs may have anti-obesity and anti-diabetic characteristics by inhibiting adipocyte development and basal lipolysis, which could be commercially industrialized. Full article

The research problem is oriented to shortening the development time of products for the automotive and engineering industry and to improving their output properties, such as weight reduction by implementation of advanced design methods (DfAMs). The intention of the study is to achieve positive properties in components and to shorten the development phase when applying DfAM methods, specifically the use of topological optimization (TO). In development of the design methodology using TO, the procedure and results were addressed and consulted with a specific manufacturer in the industry who provided the necessary materials for the research. The methodology was formed based on the partial results and their analysis for selecting the right solutions, such as the analysis of traditional procedures, strength checks, meshes, boundary conditions, etc. The procedure and design were focused and limited to additive manufacturing, specifically SLM. The results agreed with the research aim, and a significant reduction in times was achieved over traditional design methods. There was also a reduction in masses. The research concludes with an evaluation of the results together with those of the manufacturer, and a statement of the benefits, particularly for the scientific discipline and practice. It was concluded that, by implementing the given design methods, it is possible to significantly reduce the financial costs with proper application, simplify the operation of design software and create the possibility of use in training workplaces. With further research and extension of the applicability of the given methodology, substantial positive factors for development can be assumed. However, it should not be forgotten that the use of DfAM methods is greatly influenced by advances in additive manufacturing. Full article

Symbolic interval data analysis (SIDA) has been successfully applied in a wide range of fields, including finance, engineering, and environmental science, making it a valuable tool for many researchers for the incorporation of uncertainty and imprecision in data, which are often present in real-world scenarios. This paper proposed the interval improved fuzzy partitions fuzzy C-means (IIFPFCM) clustering algorithm from the viewpoint of fast convergence that independently combined with Euclidean distance and city block distance. The two proposed methods both had a faster convergence speed than the traditional interval fuzzy c-means (IFCM) clustering method in SIDA. Moreover, there was a problem regarding large and small group division for symbolic interval data. The proposed methods also had better performance results than the traditional interval fuzzy c-means clustering method in this problem. In addition, the traditional IFCM clustering method will be affected by outliers. This paper also proposed the IIFPFCM algorithm to deal with outliers from the perspective of interval distance measurement. From experimental comparative analysis, the proposed IIFPFCM clustering algorithm with the city block distance measure was found to be suitable for dealing with SIDA with outliers. Finally, nine symbolic interval datasets were assessed in the experimental results. The statistical results of convergence and efficiency on performance revealed that the proposed algorithm has better results. Full article

The infection countermeasure, in which the main idea is to prevent adversaries from exploiting faulty ciphertexts to break the key by spreading the induced fault, is a very effective countermeasure against fault attacks. However, most existing infection countermeasures struggle to defend against double-fault attacks effectively due to the single-fault assumption. By analyzing the principle of infection mechanism and adding different random Boolean masks in the two encryption paths, this paper proposes a measure called a random mask infection countermeasure to defend against double-fault attacks. In addition, the multiplication mask is used to randomize the fault diffusion to further resist single-byte fault attacks. The experimental results indicate that the random mask infection countermeasure proposed can perform fault diffusion effectively when the cryptographic circuit suffers double-fault attacks, and the fault diffusion shows randomness, and can effectively defend against these fault attacks. Full article

Existing transmission lines and pipelines are frequently crossed and erected in parallel, meaning that if lightning strikes a wire and causes insulator flashovers, the resulting lightning current will spread through the grounding of the tower where the flashover insulator is located. This dispersion of current can lead to overvoltage effects on nearby pipelines. This study performs simulation calculations to analyze the overvoltage experienced by pipelines due to the dispersion of grounding current from the tower. Furthermore, this paper proposes a method for protecting the pipeline from such an overvoltage. Firstly, the lightning transient calculation model of a transmission line tower is constructed using the electromagnetic transient software ATP-EMTP 5.5. The model calculates the effects of lightning peak currents and soil resistivity on the distribution characteristics of lightning current in the tower, specifically in the area where the flashover insulator is located. Subsequently, a calculation model of the tower grounding grid–natural gas pipeline is developed, taking into account the distribution characteristics of lightning current in the tower. This model analyzes the impact of lightning peak currents, soil resistivity, and pipeline spacing on pipeline overvoltage. Finally, the effectiveness of the solid-state decoupler in mitigating lightning overvoltage in the pipeline is verified. The results demonstrate a positive correlation between the lightning current entering the tower grounding grid through the flashover insulator and the lightning current distribution characteristics. The solid-state decoupling device proves to be effective in reducing the voltage of the pipeline insulation layer, and the simulation results provide the optimal laying length of the bare copper wire. Full article

As the amount of malware has grown rapidly in recent years, it has become the most dominant attack method in network security. Learning execution behavior, especially Application Programming Interface (API) call sequences, has been shown to be effective for malware detection. However, it is troublesome in practice to adequate mining of API call features. Among the current research methods, most of them only analyze single features or inadequately analyze the features, ignoring the analysis of structural and semantic features, which results in information loss and thus affects the accuracy. In order to deal with the problems mentioned above, we propose a novel method of malware detection based on semantic information of behavioral features. First, we preprocess the sequence of API function calls to reduce redundant information. Then, we obtain a vectorized representation of the API call sequence by word embedding model, and encode the API call name by analyzing it to characterize the API name’s semantic structure information and statistical information. Finally, a malware detector consisting of CNN and bidirectional GRU, which can better understand the local and global features between API calls, is used for detection. We evaluate the proposed model in a publicly available dataset provided by a third party. The experimental results show that the proposed method outperforms the baseline method. With this combined neural network architecture, our proposed model attains detection accuracy of 0.9828 and an F1-Score of 0.9827. Full article

Transverse cracking is thought of as the typical distress of asphalt pavements. A faster detection technique can provide pavement performance information for maintenance administrations. This paper proposes a novel vehicle-vibration-based method for transverse cracking detection. A theoretical model of a vehicle-cracked pavement vibration system was constructed using the d’Alembert principle. A testing system installed with a vibration sensor was put in and applied to a testing road. Then, parameter optimization of the Short-time Fourier transform (STFT) was conducted. Transverse cracking and normal sections were processed by the optimized STFT algorithm, generating two ideal indicators. The maximum power spectral density and the relative power spectral density, which were extracted from 3D time–frequency maps, performed well. It was found that the power spectral density caused by transverse cracks was above 100 dB/Hz. The power spectral density at normal sections was below 80 dB/Hz. The distribution of the power spectral density for the cracked sections is more discrete than for normal sections. The classification model based on the above two indicators had an accuracy, true positive rate, and false positive rate of 94.96%, 92.86%, and 4.80%, respectively. The proposed vehicle-vibration-based method is capable of accurately detecting pavement transverse cracking. Full article

Dextran, a microbial metabolite of diverse molecular configurations, can be biosynthesized employing selected strains of characterized species of bacteria. Dextran molecules are secreted as an extracellular polysaccharide in the culture medium of the bacterial fermentation system. This microbially produced polymer of glucose possesses multi-faceted characteristics such as its solubility in different solvents and formation of dextran solutions of needed viscosity. Several preparations can be formulated for the desired thermal and rheological properties. Due to such multifunctional characteristics, dextran with different structural specifications is a desired polysaccharide for clinical, pharmaceutical, and food industry commercial applications. Dextran and its derivative products with various molecular weights, in a range of high and low, have established their uses in drug delivery and in analytical devices using columns packed with polysaccharide gel. Therefore, being a neutral raw material, the resourcefulness of dextran preparations of different molecular weights and linkages in their polymer configuration is important. For this purpose, several studies have been performed to produce this commercially important polysaccharide under optimized bacterial cultivation processes. This article aims to overview recently published research reports on some significant applications of dextran in the pharmaceutical and food industries. Studies conducted under optimized conditions in fermentation processes for the biosynthesis of dextran of diverse molecular configurations, which are responsible for its multifunctional properties, have been summarized. Concise information has been presented in three separate tables for each group of specific bacterial species employed to obtain this extracellular microbial polysaccharide. Full article

The flexible bearing is the kernel component of the cam wave generator. In precision transmission applications such as miniature robot joints, flexible bearings are required to have a smaller mass, lower moment of inertia, and larger inner diameter. Based on the theory of conventional rolling bearing, and by considering the working characteristics of flexible bearing for harmonic drive, this paper studies the influence law of structural parameters on the fatigue life of the flexible bearing; achieves a technical approach for expanding the bore size of the standard flexible bearing; and deduces the theoretical formulas applicable to the design of the flexible bearing. The new formula is utilized to improve the design parameters of the flexible bearing. The novel design with smaller diameter and more numbers of rolling elements can meet the above performance requirements compared to the conventional flexible bearing. Under the same conditions of radial deformation and materials used, finite element analysis results show that the maximum equivalent stress in the improved flexible bearing is smaller and that the novel design can meet the strength requirements of the harmonic drive. Full article

The phytochemical characterization of Castanea spp. fruits is very important for the development of effective strategies for the biodiversity conservation and traceability of chestnuts, as the chestnut is one of the most important Italian and European nut and forest species. In this study, several cultivars of C. sativa (sweet chestnuts and “marrone-type”), C. crenata, and hybrids of C. sativa × C. crenata were characterized by spectrophotometric (Folin–Ciocalteu assay for the total polyphenolic content and ferric reducing antioxidant power test for the antioxidant capacity) and chromatographic (high-performance liquid chromatography coupled to a diode array UV-Vis detector) protocols to define their phytochemical composition and nutraceutical properties. The phytochemical results were then used to build a multivariate statistical model (by principal component analysis) and obtain an effective and rapid tool to discriminate unknown cultivars (i.e., no information about their origin) belonging to different species. The multivariate approach showed that the genotype was a significantly discriminating variable (p < 0.05) for the phytochemical composition. Polyphenols (in particular, phenolic acids and tannins) have been identified as the main bioactive classes with the highest discriminating power among the different genotypes. The total polyphenol content (TPC) and antioxidant capacity (AOC) showed a rich presence of bioactive compounds (74.09 ± 15.10 mg_GAE 100 g⁻¹ DW and 11.05 ± 1.35 mmol Fe²⁺ kg⁻¹ DW, respectively), underlining the potential health benefits and functional traits of chestnuts. The principal component analysis applied to phytochemical variables has proved to be an excellent and effective tool for genotype differentiation to be used as a preliminary method for identifying the species of Castanea spp. fruits with an unknown origin. The present study showed that a multivariate approach, based on phytochemical data and preliminary to genetic analysis, may represent a rapid, effective, and low-cost tool for the traceability and quality evaluation of chestnuts from different species and hybrids with no information on their origin. Full article

An investigation into tectonics and erosion reveals that they play an important role in causing uplifting, valley incision, and soil erosion. The analysis of drainage basins at different scales is irreplaceable in the development of sustainable plans, particularly in arid regions. Morphotectonics and morphometric characterization analyses are very effective methods for defining the evolution of different landforms, current-day tectonic activity, and hydrological and morphological signatures of basins under investigation. The reorganization of critical drainage basins and sub-basin risk priority ranking are essential for effective and accurate sustainable plans for drainage basin management and water resources. In this study, the coupling of geospatial techniques and statistical strategies was used to examine the tectonic activity and priorities in terms of soil erosion for 15 sub-basins of Wadi Al-Lith along the Red Sea coast of Saudi Arabia. Two effective models, namely, the relative tectonic activity model and the weighted sum analysis model, were applied for examining each geomorphological and hydrological characteristic based on an analysis of the morphotectonics and morphometric parameters. Regarding the relative tectonic activity model, the 15 sub-basins were classified into three classes of tectonic activity: low, moderate, and high. Sub-basins 5, 6, 13, and 15 were considered to be in class 1 (high relative tectonic activity). On the other hand, the weighted sum analysis model assigned the sub-basins into three different ranks: low-, moderate-, and high-soil-erosion priorities. The current study’s results suggest that sub-basins 5, 6, 10, 13, and 15 were recorded within the high-soil-erosion zone and highly relative tectonic activity, covering approximately 53.52% of the total sub-basin areas. The relative tectonic activity and weighted sum analysis models proved their validity in the risk studies, which will be very useful for decision makers in various fields, including natural resources and agriculture. Full article

The article presents a study on the influence of aerodynamic model fidelity on dynamic characteristics. The Simulation and Dynamic Stability Analysis (SDSA) package was used to calculate the dynamic characteristics, using both eigenvalues (linearized model) and a time history approach (nonlinear model). The tests were carried out for a rocket aircraft designed in a tailless configuration with a leading edge extension and rotating side plates. Due to these features, the rocket plane can be classified as an unconventional configuration, which requires special attention. Aerodynamic characteristics of the rocket plane were measured in a subsonic wind tunnel and calculated using Euler model equations-based software (MGAERO) and low-order potential-flow code (PANUKL). The paper presents the results of dynamic analysis in the form of standard modes of motion characteristics. A comparison of dynamic characteristics calculated using a set of aerodynamic data with different fidelity is shown and discussed. Both longitudinal and lateral cases were included. The presented results show that the potential methods, considered old-fashioned and despite many simplifications, are still an attractive tool and can be used to analyze even complex, unconventional configurations. Full article

The utilization of hydrogen (H₂) as a renewable and clean energy carrier, free from the reliance on fossil fuels, represents a significant technological challenge. The use of renewable energy sources for hydrogen production, such as photocatalytic hydrogen generation from water under solar radiation, has garnered significant interest. Indeed, the storage of hydrogen presents another hurdle to the ongoing advancement of hydrogen energy. Concerning solid-state hydrogen storage, magnesium hydride (MgH₂) has emerged as a promising option due to its high capacity, excellent reversibility, and cost-effectiveness. Nevertheless, its storage performance needs improvement to make it suitable for practical applications. Titanium dioxide (TiO₂) has distinguished itself as the most extensively researched photocatalyst owing to its high photo-activity, good chemical and thermal stability, low toxicity, and affordability. This review highlights the application of TiO₂ for hydrogen production under visible and solar light, with a particular focus both on its modification without the use of noble metals and its utilization as a catalyst to enhance the hydrogen storage performance of MgH₂. Full article

Software-Defined Networking (SDN) enhances network control but faces Distributed Denial of Service (DDoS) attacks due to centralized control and flow-table constraints in network devices. To overcome this limitation, we introduce a multi-path routing algorithm for SDN called Trust-Based Proximal Policy Optimization (TBPPO). TBPPO incorporates a Kullback–Leibler divergence (KL divergence) trust value and a node diversity mechanism as the security assessment criterion, aiming to mitigate issues such as network fluctuations, low robustness, and congestion, with a particular emphasis on countering DDoS attacks. To avoid routing loops, differently from conventional ‘Next Hop’ routing decision methodology, we implemented an enhanced Depth-First Search (DFS) approach involving the pre-computation of path sets, from which we select the best path. To optimize the routing efficiency, we introduced an improved Proximal Policy Optimization (PPO) algorithm based on deep reinforcement learning. This enhanced PPO algorithm focuses on optimizing multi-path routing, considering security, network delay, and variations in multi-path delays. The TBPPO outperforms traditional methods in the Germany-50 evaluation, reducing average delay by 20%, cutting delay variation by 50%, and leading in trust value by 0.5, improving security and routing efficiency in SDN. TBPPO provides a practical and effective solution to enhance SDN security and routing efficiency. Full article

In this work, the tight focusing of vector beams with azimuthal polarization and beams with a V-line of polarization singularity (sector azimuthal polarization) was simulated numerically using the Richards–Wolf formulas. It was demonstrated that in a tight focus for these beams, there is no longitudinal component of the electric field. Previously, a similar effect was demonstrated for azimuthally polarized light only. The longitudinal component of the spin angular momentum for these beams was calculated, and the possibility of creating sector azimuthally polarized beams (beams with V-line singularities) using vector waveplates was demonstrated. Full article

In recent decades, cellular metallic materials have increasingly been used for control of reverberation and cutback. These materials offer a unique combination of expanded pores, high specific surfaces, improved structural performance, low weight, corrosion resistance at high temperatures, and a fixed/rigid pore network (i.e., at the boundaries, porosity does not change). This study examines the ability of sphere-packing models combined with numerical modelling and simulations to predict the acoustic properties of bimodal and modulated bottleneck-shaped macroporous structures that can realistically be achieved through liquid melts infiltration casting technique. The simulations show that porosity, openings, pore sizes and permeability of the material have significant effects on acoustics, and the predictions are consistent with experimental data substantiated in the literature. The modelling suggests that the creation of bimodal structures increases the capacity of the interstitial pores and pore contacts. The result is improved sound absorption properties and spectra, characterised by a pore volume fraction of 0.73 and a mean pore size to mean pore opening ratio of 4.8 for the 50% volume bimodal structure created at a 10 µm capillary radius. The importance of how pore structure-related parameters and existing fluid flow regimes can modulate the sound absorption performance of macroporous structures was revealed by numerical simulations of the sound absorption spectra for dual-porosity and dilated macroporous structures working from high-resolution tomography datasets. Sound absorption properties were optimised for structures having pore volume fractions between 0.68 and 0.76, maintaining the mean pore size to mean pore opening ratios between 4.0 and 6.0. Using this approach, enhanced and self-supporting macroporous structures may be designed and fabricated for efficient sound absorption in specific applications. Full article

Modern induction cookers have started to demand challenging features such as slim design, high power ratings, high performance, and silence. All those requirements are directly related to the power semiconductors used in power converters. Si (silicon)-based power semiconductors are not capable of answering those demands because of strict operating conditions, such as high ambient temperatures. Therefore, WBG (Wide Band Gap) power semiconductors have been getting attention. In this study, WBG power semiconductors will be compared with Si-based IGBT (Insulated Gate Bipolar Transistor) under different operating conditions. The best option to use WBG power semiconductors in modern induction cookers will be analyzed. The performance of a series-resonant half-bridge converter was evaluated under various operating conditions. Measurements were obtained from the real operating conditions of induction hobs. The switching frequency is changed from 20 kHz to 100 kHz, while the power rating is increased to 3.7 kW. In addition to traditional 4-zone induction cooktops, this discussion also provides a comprehensive analysis of high-segment, fully flexible induction cooktops. While the IGBT-based design exhibits 25.79 W power loss per device, the WBG device exhibits 6.87 W in the maximum power condition of conventional induction cooker operation. When it comes to high-frequency operation, the WBG power device exhibits 10.05 W at 95 kHz. Total power loss is still well below that of the IGBT-based conventional design. Appropriate usage of WBG power semiconductors in modern induction cookers can exploit many more benefits than Si-based designs. Full article

Steam ejectors could improve the energy efficiency of ships by efficiently utilizing low-grade waste heat from ships for seawater desalination or cooling. The internal flow characteristics of steam ejectors can be deeply analyzed through numerical simulation, which is of great significance for improving their performance. Due to the influence of the nonequilibrium phase change, the results of the wet steam model and the ideal gas model are significantly different. In this paper, the flow field characteristics of the wet steam model and the ideal gas model under different primary flow pressures (P_m) are compared and analyzed. The results show that the structures of the shock wave train for the wet steam model and the ideal gas model are different under different P_m. When the first shock wave of the shock wave train changes from a compression shock wave to an expansion shock wave, the P_m for the ideal gas model is 75,000 Pa and that for the wet steam model is 55,000 Pa. The phase change reduces the energy loss of the shock wave. With the increase in the P_m, the variation in the length of the shock wave train for the wet steam model decreases by 61%, the variation of the primary temperature at the nozzle exit increases by 60% and the variation in the choke temperature decreases by 50% compared with the ideal gas model. The investigation in this paper provides guidance for the design theory of a ship waste heat steam ejector. Full article

This manuscript investigates the bias introduced by scaling aftershock ground motions when evaluating the performance of structures subjected to earthquake sequences. The study focuses on different hysteretic behaviors exhibited by structures and selects eight intensity measures as scale indicators. A benchmark database comprising 274 recorded mainshock–aftershock ground motions is utilized for analysis. The findings reveal that scaling aftershock records using intensity measures such as SI (seismic intensity), PGV (peak ground velocity), I_C (Arias intensity), and S_a (spectral acceleration) relative to mainshock records effectively controls the mean bias within 30% throughout the entire period range, given a maximum scale factor of 10.0. However, it is observed that the additional damage in systems exhibiting un-degrading hysteretic behavior is more significantly affected by aftershock ground motion scaling compared to systems with degrading hysteretic behavior. Furthermore, scaling aftershock ground motions upwards using relative S_a tends to overestimate the additional damage incurred by structures. These results emphasize the importance of considering the specific hysteretic behavior of structures when applying aftershock ground motion scaling, as well as selecting appropriate intensity measures for accurate evaluation of structural performance. Full article

We propose a novel inverse analysis method that utilizes shockwaves to detect the operational condition of tested rock. To achieve this back analysis, an in-depth investigation of the dynamic properties of granite specimens was conducted. The dynamic properties of the granite specimens were investigated using a triaxial cyclic loading machine, under different confining pressures, loading frequencies, stress amplitudes, and numbers of cycles, and a dynamic response model was constructed from the test data. The results show that the dynamic elastic modulus increased with the increase in confining pressure, while its damping ratio decreased. The dynamic elastic modulus and damping ratio increased with the increase in loading frequency. As the dynamic stress amplitude increased, the dynamic elastic modulus of the granite increased, but the dynamic damping ratio decreased. As the number of cycles increased, the dynamic elastic modulus and dynamic damping ratio of the granite decreased and gradually stabilized. The modified Duncan–Chang model was used to construct the dynamic response model of the specimens. It is worth saying that the correlation coefficient of the model is low at a loading frequency of 20 Hz. This indicates that the frequency has a greater effect on the dynamic response of the specimen compared with the confining pressure. The conclusions obtained from these tests can be used to study more comprehensively the interaction and causal relationship between different factors, and to prepare for the next steps of tunnel rock stress-state prediction. Full article

Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learning-based method for image defocus deblurring was proposed, which can be applied to medical images, traffic monitoring, and other fields. The developed approach is equipped with wavelet transform, an iterative filter adaptive module, and graph neural network and was specifically designed for handling defocused blur. Our network exhibits excellent properties in preserving the original information during the restoration of clear images, thereby enhancing its capability to spatially address varying blurriness and improving the quality of deblurring. From the acquired experimental results, the superiority of the introduced method in the context of image defocus deblurring compared to the majority of the existing algorithms was clearly demonstrated. Full article

Basketball players should train at intensities similar to those recorded in competition, but are the intensities really similar? This study aimed to quantify and compare the internal and external intensities assimilated by professional basketball players, both in training and in competition, according to context and the specific player position. Players from the same team in the Spanish ACB competition were monitored for three weeks. The sample recorded intensities in 5 vs. 5 game situations in both training (n = 221) and competition (n = 32). The intensities, as dependent variables, were classified into kinematic external workload demands (distances, high-intensity displacements, accelerations, decelerations, the acceleration:deceleration ratio, jumps, and landings), neuromuscular external workload demands (impacts and player load), and internal workload demands (heart rate). They were measured using inertial measurement devices and pulsometers. The playing positions, as independent variables, were grouped into guard, forward, and center. According to the context, the results reported a significant mismatch of all training intensities, except jumps, with respect to competition; these intensities were lower in training. According to the playing position, inside players recorded more jumps and landings per minute than point guards and outside players in training. In turn, inside players recorded a higher average heart rate per minute than outside players in this same context. There were no significant differences in intensity according to the playing position in the competition. Considering the context–position interaction, no differences were observed in the intensities. Adjusting and optimizing training intensities to those recorded in competition is necessary. Full article

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods. Full article

Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission tomography (PET) and computed tomography (CT) images. However, the existing DL-based LC detection models demand substantial computational resources. Healthcare centers face challenges in handling the complexities in the model implementation. Therefore, the author aimed to build a DL-based LC detection model using PET/CT images. Effective image preprocessing and augmentation techniques were followed to overcome the noises and artifacts. A convolutional neural network (CNN) model was constructed using the DenseNet-121 model for feature extraction. The author applied deep autoencoders to minimize the feature dimensionality. The MobileNet V3-Small model was used to identify the types of LC using the features. The author applied quantization-aware training and early stopping strategies to improve the proposed LC detection accuracy with less computational power. In addition, the Adam optimization (AO) algorithm was used to fine-tune the hyper-parameters in order to reduce the training time for detecting the LC type. The Lung-PET-CT-Dx dataset was used for performance evaluation. The experimental outcome highlighted that the proposed model obtained an accuracy of 98.6 and a Cohen’s Kappa value of 95.8 with fewer parameters. The proposed model can be implemented in real-time to support radiologists and physicians in detecting LC in the earlier stages. In the future, liquid neural networks and ensemble learning techniques will be used to enhance the performance of the proposed LC detection model. Full article

Optimizingthe cache hit rate in a multi-access edge computing (MEC) system is essential in increasing the utility of a system. A pivotal challenge within this context lies in predicting the popularity of a service. However, accurately predicting popular services for each MEC server (MECS) is hindered by the dynamic nature of user preferences in both time and space, coupled with the necessity for real-time adaptability. In this paper, we address this challenge by employing the Convolutional Long Short-Term Memory (ConvLSTM) model, which can capture both temporal and spatial correlations inherent in service request patterns. Our proposed methodology leverages ConvLSTM for service popularity prediction by modeling the distribution of service popularity in a MEC system as a heatmap image. Additionally, we propose a procedure that predicts service popularity in each MECS through a sequence of heatmap images. Through simulation studies using real-world datasets, we compare the performance of our method with that of the LSTM-based method. In the LSTM-based method, each MECS predicts the service popularity independently. On the contrary, our method takes a holistic approach by considering spatio-temporal correlations among MECSs during prediction. As a result, our method increases the average cache hit rate by more than $6.97\%$ compared to the LSTM-based method. From an implementation standpoint, our method requires only one ConvLSTM model while the LSTM-based method requires at least one LSTM model for each MECS. Thus, compared to the LSTM-based method, our method reduces the deep learning model parameters by 32.15%. Full article

The ground surface of green parks in arid and semi-arid areas may not be comfortable at specific times during the day and night due to the sun and the rate at which the subsoil gains or loses heat. Knowledge of the subsurface soil’s thermal properties can provide designers with convenient and comfortable settings. Design focus is generally directed toward stability, density, and hydraulic conductivity. An assessment of the thermal properties of clay–sand mixtures of 10%, 20%, and 30% clay content is conducted. The proposed clay–sand layers are subjected to three different thermal gradients of 30, 20, and 10 degrees of magnitude. The profile of temperature changes was monitored using 5TE sensors and data loggers. The mixtures were also subjected to cooling at room temperature. The results indicate that the clay type and the clay content govern the response of subsurface clay–sand liners to temperature gain and loss. Two field sections with clay–sand layers of 15% and 20% clay were examined for temperature changes over an extended period. In winter, green areas rich in clays were found to keep heat for several hours and provide relatively warm evenings. In summer, the mixture retains a cool temperature for some time during the day. Full article

To identify the magnitude and direction of in situ stress in deeply buried tunnels, an inversion method for the stress field was proposed based on a finite number of measurement points of surface strain. Firstly, elastic strain data of finite points on the surface of tunnel surrounding rock were acquired using the borehole stress relief method at the engineering site. Secondly, a finite element model of the tunnel surrounding rock with plastic damage was established, and the parameters of the finite element model were substituted using the SIGINI subroutine. Then, an improved Surrogate Model Accelerated Random Search (SMARS) was developed using genetic algorithm programming on the MATLAB™ platform to invert and attain the globally optimal boundary conditions. Finally, the obtained optimal boundary conditions were applied to the numerical model to calculate the stress distribution in the engineering site. The reliability of this method was validated through a three-dimensional example. The method has been successfully applied to the stress-field analysis of deep tunnels in Macheng Iron Mine, Hebei Province, China. The research results show that this method is a low-cost, reliable approach for stress-field inversion in the rock around a tunnel. Full article

In the mine hoisting system, rigid guide failures and the influence of internal and external airflow intensify vessel transverse vibration, heightening demands on operational safety and equipment reliability. This paper focuses on integrating magnetorheological dampers and disc springs as the roller cage shoe buffer for vibration control, resulting in an innovative buffer device. The structure and magnetic circuit were meticulously designed. Using Maxwell simulation, we analyzed the impact of magnetic circuit parameters—specifically the damping gap and core radius—on the magnetorheological damper. We optimized these parameters through orthogonal testing to enhance damping and vibration reduction. This led to a notable 58% increase in the damper output force. A virtual prototype of the lifting system under actual working conditions was established. A simulation analysis verified the vibration-damping performance of the optimized roller cage shoe. The results indicate that the new roller cage shoes effectively inhibit transverse vibration, surpassing traditional roller cage shoe performance. This is scientifically and practically significant for ensuring safe cage shoe lifting system operation. This paper can provide a crucial theoretical basis for the design of roller cage shoes in ultra-deep mine lifting systems. Full article