Applied Sciences

Satellite communications face difficulties such as intensified environmental attenuation, dynamic time-varying links, and diverse business scenarios, which usually require channel coding schemes with high coding gain and high throughput. Low-density parity-check (LDPC) codes are dominant in satellite communication coding schemes due to their excellent performance in approaching the Shannon limit and the characteristics of parallel computing. The traditional weighted-Algorithm B decoding algorithm ignores the channel received information and involves frequent multiplication operations and iteration, which introduces the channel received information for hard-decision and constellation mapping processing. Meanwhile, we design the correlated reliability between the extrinsic information and the mapping processing information to improve the correctness of decoding. The multiplication operation in the iterative process can be replaced by the simple sum of the Hamming distance coefficient, the correlated reliability between the extrinsic information and the mapping processing information, and the extrinsic information frequency, thereby reducing the complexity and storage load of the system. The simulation results show that the presented MRAI-LDPC algorithm can obtain about 0.4 dB performance gain, and the average number of iterations is reduced by 68% under a low SNR. The algorithm can achieve better error-correcting performance and higher throughput, providing strong support for reliable transmission of satellite communications. Full article

The accidental spillage of flammable liquids on in-service ships and offshore installations may lead to pool fires, which are likely to spread over a particularly large area in large compartments under ship motion, resulting in extensive damage. However, the effect of the spreading extent of liquid fuel due to inclined ship motion on pool fire consequences has not been considered in the existing literature. Thus, in this study, fuel discharge experiments were conducted to investigate the spreading behaviour under different substrate inclination angles and discharge rates. The experimental results were analysed to derive closed-form expressions to predict the spreading extent of liquid fuel in large compartments. Additionally, the effects of surface inclination on fire consequences were investigated using the Fire Dynamics Simulator in terms of the heat release rate. The findings can provide guidance for effective fire safety design and establishing a realistic fire modelling methodology for ships and offshore installations. Full article

Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, datasets, and benchmarks specifically designed to tackle mathematical problems, reporting impressive achievements in disparate fields such as automated theorem proving, numerical integration, and the discovery of new conjectures or matrix multiplication algorithms. However, despite this notable success it is still unclear whether deep learning models possess an elementary understanding of quantities and numbers. This survey critically examines the recent literature, concluding that even state-of-the-art architectures and large language models often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge. Full article

In this study, the adsorption of naproxen sodium, ibuprofen sodium, and diclofenac sodium on activated carbon is investigated. Comprehensive studies of adsorption equilibrium and kinetics were performed using UV-Vis spectrophotometry. Thermal analysis and zeta potential measurements were also performed for pure activated carbon and hybrid materials (activated carbon–drug) obtained after adsorption of naproxen sodium, ibuprofen sodium, and diclofenac sodium. The largest amount and rate of adsorption was demonstrated for naproxen sodium. A significant impact of temperature on the adsorption of the tested salts of non-steroidal anti-inflammatory drugs was also indicated. Faster kinetics and larger amounts of adsorption were recorded at higher temperatures. Thermodynamic parameters were also determined, based on which it was indicated that adsorption in the tested experimental systems is an endothermic, spontaneous, and thermodynamically privileged process of a physical nature. The generalized Langmuir isotherm was used to study the equilibrium data. The adsorption rate data were analyzed using numerous adsorption kinetics equations, including FOE, SOE, MOE, f-FOE-, f-SOE, f-MOE, and m-exp. Full article

Automated Machine Learning (Auto-ML) has primarily been used to optimize network hyperparameters or post-processing parameters, while the most critical component for training a high-quality model, the dataset, is usually left untouched. In this paper, we introduce a novel approach that applies Auto-ML methods to the process of generating synthetic datasets for training machine learning models. Our approach addresses the problem that generating synthetic datasets requires a complex data generator, and that developing and tuning a data generator for a specific scenario is a time-consuming and expensive task. Being able to reuse this data generator for multiple purposes would greatly reduce the effort and cost, once the process of tuning it to the specific domains of each task is automated. To demonstrate the potential of this idea, we have implemented a point cloud generator for simple scenes. The scenes from this generator can be used to train a neural network to semantically segment cars from the background. The simple composition of the scene allows us to reuse the generator for several different semantic segmentation tasks. The models trained on the datasets with the optimized domain parameters easily outperform a model without such optimizations, while the optimization effort is minimal due to our Auto-ML approach. Although the development of such complex data generators requires considerable effort, we believe that using Auto-ML for dataset creation has the potential to speed up the development of machine learning applications in domains where high-quality labeled data is difficult to obtain. Full article

Recently, Asymmetric pixel–shuffle downsampling and Blind–Spot Network (AP-BSN) has made some progress in unsupervised image denoising. However, the method tends to damage the texture and edge information of the image when using pixel-shuffle downsampling (PD) to destroy pixel-related large-scale noise. To tackle this issue, we suggest a denoising method for mural images based on Cross Attention and Blind–Spot Network (CA-BSN). First, the input image is downsampled using PD, and after passing through a masked convolution module (MCM), the features are extracted respectively; then, a cross attention network (CAN) is constructed to fuse the extracted feature; finally, a feed-forward network (FFN) is introduced to strengthen the correlation between the feature, and the denoised processed image is output. The experimental results indicate that our proposed CA-BSN algorithm achieves a PSNR growth of 0.95 dB and 0.15 dB on the SIDD and DND datasets, respectively, compared to the AP-BSN algorithm. Furthermore, our method demonstrates a SSIM growth of 0.7% and 0.2% on the SIDD and DND datasets, respectively. The experiments show that our algorithm preserves the texture and edge details of the mural images better than AP-BSN, while also ensuring the denoising effect. Full article

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. The training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combined with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise suppression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. The results have been assessed with objective (mathematical metrics) and subjective (listening to a set of audios by humans) methods. Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work should involve refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical applications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality. Full article

Continuous large amounts of precipitation can lead to a rapid increase in the water content of concrete in village building foundations, which can adversely affect the mechanical properties, such as the compressive strength of concrete. There are few experimental studies on the compressive stochastic mechanical properties of concrete in the wet state after considering different soaking times (different water contents and saturations), but there is no corresponding stochastic damage principal structure model. In this study, the mechanical properties of concrete under different immersion times were tested to obtain the mechanical properties of the concrete degradation law, and the random damage intrinsic model of wet concrete was established. The results of this paper were compared with the classical test results from the literature to verify the validity of the model. The results show that the proposed stochastic damage model is able to consider both the effects of the saturation and the damage behavior of wet concrete under compression, which is beneficial to the structural design and maintenance protection of village buildings in areas with abundant precipitation. Full article

Friction losses and scuffing failures are interesting research topics for worm gears. One of the factors leading to scuffing is the heat generated in the contact of gear teeth. The contact geometry of worm gears is complex, leading to high friction between contact surfaces. High friction between contact surfaces during operation generates heat friction that causes the occurrence of scuffing, which in turn determines the scuffing load capacity. To analyse the thermal characteristics of a worm-gear pair and the thermal behaviour of contact teeth, a direct-coupled thermal–structural 3D finite element model was applied. The heat flux due to friction-generated heat was determined on the gear tooth to investigate thermal characteristics and predict transient temperature fields. This study permits an in-depth understanding of the temperature fields and the friction heat generation process. Also, better control of the contact pattern between worm-gear teeth would decrease friction heat and increase scuffing load capacity. This paper investigates the transient thermal behaviour among different pinion machine setting parameters that can result in an optimal tooth-contact pattern that produces a lower temperature field, thus achieving higher transmission efficiency. Full article

The 100CrMo7, commonly employed for bearings in rotating machinery, relies on precise heat treatment parameters to ensure an optimal microstructure and, in turn, mechanical properties. Typically, an austenitizing treatment, followed by rapid cooling in a salt bath for martempering or austempering, is crucial in achieving the desired microstructure and hardness. The present industrial-scale study involved a comparative analysis between martempering and austempering routes regarding the hardness and microstructure evolution of EN 100CrMo7 large-size rings. The investigation delves into the effects of austempering temperatures, holding times, and austenitizing temperature. Furthermore, the role of tempering in reducing the amount of retained austenite was also considered. The results indicate that martempering yielded the highest hardness values while austempering exhibited a decrease in hardness at the center of the rings, though a lower amount of retained austenite (in the range of 3.0–4.9 vol.%) was detected in comparison with martempering. In addition, a 850 °C austenitizing temperature reduced the hardness by 16% in the center of the rings and promoted a high content of upper bainite, thus suggesting its inefficacy for the involved large-size rings. In contrast, a 880 °C austenitizing temperature maintained consistently high HRC values across the ring’s height. Lastly, the analysis highlighted that the cooling rate played a more crucial role than the austempering holding time. Such industrial-scale investigations performed on real components improve the knowledge and control of heat treatment parameters in comparison with the nominal guidelines provided by steel suppliers. These outcomes offer insights for optimizing industrial heat treatment parameters, with practical implications for enhancing steel bearings’ microstructural and mechanical performance and lifespan. Full article

This review emphasizes the recent ongoing shift in the wood coating industry towards bio-based resources and circular economy principles, promoting eco-friendly alternatives. In addressing wood’s vulnerabilities, this study investigates the use of natural compounds and biopolymers to enhance wood coatings. These materials contribute to protective matrices that safeguard wood surfaces against diverse challenges. Essential oils, vegetable oils, and bio-based polymers are explored for their potential in crafting eco-friendly and durable coating matrices. Furthermore, this review covers efforts to counter weathering and biological decay through the application of various natural compounds and extracts. It evaluates the effectiveness of different bio-based alternatives to traditional chemical preservatives and highlights promising candidates. This review also delves into the incorporation of sustainable pigments and dyes into wood coatings to enhance both protective and aesthetic qualities. Innovative pigments are able to provide visually appealing solutions in line with sustainability principles. As the wood coating industry embraces bio-based resources and the circular economy, researchers are actively developing protective solutions that encompass the coating matrix, preservatives, bio-based fillers, and natural-pigment dyes. This review showcases the continuous efforts of academia and industry to enhance wood coatings’ effectiveness, durability, and sustainability, while maintaining their aesthetic appeal. Full article

The Jabal Al Bayda, located in the Hijaz terrain of northwest Saudi Arabia, comprises magmatic rocks that represent the ending phase in the Precambrian development of the Arabian Shield. Two granitic suites have been studied petrologically and geochemically, the monzogranite and alkali granite suites, to gain knowledge about their origin and geotectonic implications. The geochemical characteristics of the monzogranites align with their formation in a subduction-related environment. These rocks have a composition that is rich in strontium and barium, and low in rubidium, and displays a high-K calc-alkaline to shoshonitic nature. In contrast, alkali granites typically have lower concentrations of Sr and Ba, and higher rubidium contents. The differences in geochemical composition between monzogranites and alkali granites found in Jabal Al Bayda indicate differences in their origin and geotectonic environment. The evolution of granitoid magmatism in the Jabal Al Bayda area is linked to the Hijaz orogenic cycle, during which northwest-dipping subduction led to the formation of the Midyan, Hijaz, and Jeddah arc assemblage, followed by the collision and accretion of these arcs along the Yanbu and Bir Umq sutures. Due to crustal thickening during the subduction-related stage, the deeper parts of the overlying metagraywackes and metatonalites contribute melt to the early crustal magma, which eventually solidifies to form monzogranites. Later on, during the post-orogenic stage, anatexis of metapelites can occur, leading to the generation of magmas that give rise to alkali granites. Full article

Intelligent transfemoral prostheses, which have recently been studied, are equipped with a microcontroller, providing appropriate motion functions for their walking environments. Thus, studies have been conducted to estimate user intentions in locomotion movements by applying biomechanical sensors inside the socket. Among them, a pressure sensor is used to determine the intentions of locomotion movements through changes in the internal pressure of the prosthetic socket. However, existing studies have a problem in that the reproducibility of pressure change data is degraded due to the non-detection and saturation of the pressure measurement value. Accordingly, this study proposes a fabrication method for a wide and flexible pressure sensor that can solve this problem and a method for the identification of user intentions in locomotion movements using it. The proposed system was fabricated with Velostat film, which has a smaller noise impact and can be fabricated in various sizes and shapes. The fabricated sensor was attached to four points inside the socket, confirming the possibility of detecting the intention of six movements according to the multi-critical detection method. The proposed pressure-sensor-based intention detection system can be applied individually by prosthetic users through simple tasks. Moreover, it will be universally applicable for commercialization. Full article

Traveltime picking accuracy is frequently affected by incoherent or random data noise. Within this context, we put forth a new denoising method called iterative interferometric denoising filtering. This method leverages the pseudo-Wigner distribution function to capture the offset and time-symmetric patterns of source wavelets convolved in seismic signals. Incoherent or random noises without this characteristic are eliminated via this approach. The processed data have waveform information distortion and more frequency components. However, the traveltime information can be considered correct, and the improved signal-to-noise ratio makes traveltime picking much more convenient. Our method’s practical applications in a synthetic and in two field datasets show that this technology can increase the signal-to-noise ratio, and the picked traveltime information can be used in traveltime tomography. These two field datasets were collected near the Aqaba Gulf and the Qademah fault, located in King Abdullah Economic City. Full article

Roadbed construction typically employs layered and staged filling, characterized by a periodic feature of ‘layered filling—filling interval’. The load and settlement histories established during staged construction offer crucial insights into long-term deformation under filling loads. However, models often rely solely on post-construction settlement data, neglecting the rich filling data. To accurately predict composite foundation ground (CFG) settlement, an LSTM–Transformer deep learning model is used. Five factors from the ‘fill height–time–foundation settlement’ curve are extracted as input variables. The first-layer LSTM model’s gate units capture long-term dependencies, while the second-layer Transformer model’s self-attention mechanism focuses on key features, efficiently and accurately predicting ground settlement. The model is trained and analyzed based on the newly constructed Changsha–Zhuzhou–Xiangtan intercity railway section CSLLXZQ-1, which has a CFG pile composite foundation. The research shows that the proposed LSTM–Transformer model for the settlement prediction of composite foundations has an average absolute error, mean absolute percentage error, and root mean square error of 0.224, 0.563%, and 0.274, respectively. Compared to SVM, LSTM, and Transformer neural network models, it demonstrates higher prediction accuracy, indicating better reliability and practicality. This can provide a new approach and method for the settlement prediction of newly constructed CFG composite foundations. Full article

Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP₅₀, and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations. Full article

The demand for object pose estimation is steadily increasing, and deep learning has propelled the advancement of this field. However, the majority of research endeavors face challenges in their applicability to industrial production. This is primarily due to the high cost of annotating 3D data, which places higher demands on the generalization capabilities of neural network models. Additionally, existing methods struggle to handle the abundance of textureless objects commonly found in industrial settings. Finally, there is a strong demand for real-time processing capabilities in industrial production processes. Therefore, in this study, we introduced a dual-channel Siamese framework to address these challenges in industrial applications. The architecture employs a Siamese structure for template matching, enabling it to learn the matching capability between the templates constructed from high-fidelity simulated data and real-world scenes. This capacity satisfies the requirements for generalization to unseen objects. Building upon this, we utilized two feature extraction channels to separately process RGB and depth information, addressing the limited feature issue associated with textureless objects. Through our experiments, we demonstrated that this architecture effectively estimates the three-dimensional pose of objects, achieving a 6.0% to 10.9% improvement compared to the state-of-the-art methods, while exhibiting robust generalization and real-time processing capabilities. Full article

Aspect-level sentiment analysis is a task of identifying and understanding the sentiment polarity of specific aspects of a sentence. In recent years, significant progress has been made in aspect-level sentiment analysis models based on graph convolutional neural networks. However, existing models still have some shortcomings, such as aspect-level sentiment analysis models based on graph convolutional networks not making full use of the information of specific aspects in a sentence and ignoring the enhancement of the model by external general knowledge of sentiment. In order to solve these problems, this paper proposes a sentiment analysis model based on the Syntax-Aware and Graph Convolutional Network (SAGCN). The model first integrates aspect-specific features into contextual information, and second incorporates external sentiment knowledge to enhance the model’s ability to perceive sentiment information. Finally, a multi-head self-attention mechanism and Point-wise Convolutional Transformer (PCT) are applied to capture the semantic information of the sentence. The semantic and syntactic information of the sentences are considered together. Experimental results on three benchmark datasets show that the SAGCN model is able to achieve superior performance compared to the benchmark methods. Full article

In the present study, an immersion experiment was carried out to examine how N80 steel corrodes when exposed to formation water containing dissolved CO₂ and supercritical CO₂ (Sc-CO₂) along with water vapor. We employed electrochemical and surface analysis methods to examine the influence of various factors, including the temperature and duration of immersion, on the extent of corrosion. The results show that the corrosion patterns of N80 steel in a supercritical CO₂ environment and CO₂-saturated formation water differed significantly. The presence of similar corrosion features was suggested by the constant structure of the corrosion products identified in the formation water. However, the morphology of the corrosion product was complex in the supercritical CO₂ environment, exhibiting features of pitting and localized corrosion. Furthermore, a non-linear trend in the corrosion rate was observed between 40 °C and 120 °C. Specifically, the rate of corrosion declined from 40 °C to 80 °C, but it then resumed its growth from 80 °C to 120 °C. These findings suggest that very high temperatures could lead to the destruction of corrosion products and subsequently enhance the corrosion process. Full article