Appl. Sci., Volume 14, Issue 10 (May-2 2024) – 384 articles

The main objective of this study was (1) to analyze the patterns of acceleration (Ac) and deceleration (Dec) during football matches in elite youth football, both within and between different segments of the match; and (2) to investigate the impact of ball possession and various playing positions on these acceleration and deceleration patterns. To provide a broader explanatory context, the influence of tactical space management was assessed in terms of depth and width. A descriptive comparative design was used, and data were collected during two friendly matches. Player and ball tracking data were collected using a local positioning system. In the attack phase, differences were obtained in the average Ac (first half: 0.42 ± 0.06 m·s⁻², second half: 0.38 ± 0.07 m·s⁻²; p = 0.021, d = 0.50) and average Dec (first half: −0.44 ± 0.09 m·s⁻², second half: −0.36 ± 0.08 m·s⁻²; p = 0.001, d = 0.84). Wingers in the attack phase obtained higher values in maximum Ac (1.65 ± 0.65 m·s⁻²; p = 0.007, η² = 0.03), and in the total number of both Ac (68.7 ± 45.22; p = 0.001, η² = 0.10) and Dec (70.6 ± 45.70; p = 0.001, η² = 0.10). In the defense phase, full-backs obtained higher values in average Ac (0.53 ± 0.17 m·s⁻²; p = 0.001, η² = 0.07) and average Dec (−0.49 ± 0.18 m·s⁻²; p = 0.001, η² = 0.05) and wingers in the total number of Ac (43.9 ± 27.30; p = 0.001, η² = 0.11) and Dec (43.8 ± 28.60; p = 0.001, η² = 0.10). In young football players, Ac and Dec do not follow a decreasing end throughout the match, and their behavior is uneven depending on ball possession and the position assigned to the player, with the highest demands on Ac/Dec in winger and full-back positions. Full article

This study proposes a three-dimensional upper bound solution for estimating the soil thrust of tracked vehicles on saturated clay slopes. The present study considered block, triangular wedge, and trapezoidal wedge failure modes to formulate an upper bound solution for each. The analytical solution for soil thrust was determined as the minimum upper bound solution among those for each failure mode. This analytical solution was validated through numerical simulations that modeled track-ground interactions. Parametric studies, based on the upper bound solution, assessed the impact of track system shape, vehicle weight, undrained shear strength, and ground slope on soil thrust. The analytical solutions and parametric studies provide a rapid method for assessing vehicle operability on clay slopes and offer references for designing tracked vehicles suitable for site conditions. Full article

This study presents the effects of piston bowl size on the characteristics of a four-stroke single-cylinder diesel engine, which is considered in relation to changes in factors such as fuel injection pressure and turbocharger pressure. The study was carried out by 3D modeling using AVL Fire with an omega combustion chamber size and dimensions determined by the ratio between the diameter and depth of the piston bowl, which varies from 3.4 to 10.0. Additionally, the turbocharger pressure varies from 0.15 to 0.45 MPa at an engine speed of 1400 rpm and fuel injection pressure up to 300 MPa. The results show that the engine reaches the best values of indicated power, fuel efficiency, and a substantial decrease in emissions of nitrogen oxides at a turbocharger pressure from 0.25 to 0.35 MPa and with a ratio of the diameter to the depth from 7.8 to 10. However, the injection angle changes slightly, and the penetration depth and the tip velocity decrease with increasing boost pressure. While the piston bowl parameters only impact significantly on the tip velocity, the penetration and the spray angle are almost unchanged. In addition, the variation in the diameter of the combustion chamber has an influence on the fluctuation of the spray tip velocity and penetration. Full article

A theoretical model, based on the classical Pennes’ bioheat theory, incorporating various boundary conditions, was established and compared to analyze the influence of the latent heat of vaporization via simulation. The aim was to elucidate the extent of its influence. The thermal damage rate, governed by the vaporization heat of biological tissue, is introduced as a key factor. Functional relationships between temperature and incident laser power, spatial position, and time are derived from the classical Pennes’ bioheat equation. According to the theoretical model, numerical simulations and experimental validations are conducted using Comsol Multiphysics 6.0, considering the tissue latent heat of vaporization. The model incorporating the latent heat of vaporization proved more suitable for analyzing the interactions between laser and biological tissue, evident from the degree of fit between simulated and experimental data. The minimum deviations between theoretical and experimental observations were determined to be 2.43% and 5.11% in temperature and thermal damage, respectively. Furthermore, this model can be extended to facilitate the theoretical analysis of the impact of vaporization heat from different primary tissue components on laser-tissue interaction. Full article

Pathologies in concrete structures can be visually evidenced on the concrete surface, such as by fissures or cracks, fragmentation of part of the concrete, concrete efflorescence, corrosion stains on the concrete surface, or exposed steel bars, the latter two occurring in reinforced concrete. Therefore, these pathologies can be analyzed via the images of concrete structures. This article proposes a methodology for visually inspecting concrete structures using deep neural networks. This method makes it possible to speed up the detection task and increase its effectiveness by saving time in preparing the identifications to be analyzed and eliminating or reducing errors, such as those resulting from human errors caused by the execution of tedious, repetitive analysis tasks. The methodology was tested to analyze its accuracy. The neural network architecture used for detection was YOLO, versions 4 and 8, which was tested to analyze the gain with migration to a more recent version. The dataset for classification was Ozgnel, which was trained with YOLO version 8, and the detection dataset was CODEBRIM. The use of a dedicated classification dataset allows for a better-trained network for this function and results in the elimination of false positives in the detection stage. The classification achieved 99.65% accuracy. Full article

This study presents the utilization of the microwave infrared thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. At first, a set of numerical models was created, comprising the broadband pyramidal horn antenna and the analyzed specimen. The utilization of the system operating at a power of 1000 W in a continuous mode, operating at frequency of 2.45 GHz, was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer and, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and the CFRP within the adhesive layer. In the modeling stage, the study investigated three separate scenarios—a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte—to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from numerical modeling. In the experiments, the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These conclusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP. Full article

Propaganda in the digital era is often associated with online news. In this study, we focused on the use of large language models and their detection of propaganda techniques in the electronic press to investigate whether it is a noteworthy replacement for human annotators. We prepared prompts for generative pre-trained transformer models to find spans in news articles where propaganda techniques appear and name them. Our study was divided into three experiments on different datasets—two based on an annotated SemEval2020 Task 11 corpora and one on an unannotated subset of the Polish Online News Corpus, which we claim to be an even bigger challenge as an example of an under-resourced language. Reproduction of the results of the first experiment resulted in a higher recall of 64.53% than the original run, and the highest precision of 81.82% was achieved for gpt-4-1106-preview CoT. None of our attempts outperformed the baseline F1 score. One of the attempts with gpt-4-0125-preview on original SemEval2020 Task 11 achieved an almost 20% F1 score, but it was below the baseline, which oscillated around 50%. Part of our work that was dedicated to Polish articles showed that gpt-4-0125-preview had a 74% accuracy in the binary detection of propaganda techniques and 69% in propaganda technique classification. The results for SemEval2020 show that the outputs of generative models tend to be unpredictable and are hardly reproducible for propaganda detection. For the time being, these are unreliable methods for this task, but we believe they can help to generate more training data. Full article

Honey is well known for its antioxidant and antimicrobial properties, which significantly contribute to its high demand among consumers. While there is plenty of information available about the antioxidant potential of honey, there is still a lack of research specifically focused on monofloral honeys, as most studies have been based on market samples. To address this issue, in the present study we analyzed the total phenolic content and antioxidant activity of nine monofloral honey types produced in Greece: fir, chestnut, citrus, erica, cotton, Jerusalem thorn, pine, oak and thyme, in comparison with manuka honey. The samples were collected from beekeepers applying the appropriate beekeeping practices. In total, ninety-six representative monofloral honey samples meeting the microscopic, physicochemical, and sensory characteristics were analyzed. Oak honey stood out as the darkest type (L* = 33.67) with the highest total phenolic content (203.75 mg GAE/100 g) and antioxidant activity (106.2 mg AAE/100 g). Chestnut honey closely followed, having also the highest electrical conductivity (1.679 mS/cm). Although manuka honey had a high total phenolic content, its total antioxidant activity was found to be medium-low compared to fir, pine, and erica honeys. Citrus honey, being the lightest in color (L* = 37.2), exhibited the lowest total antioxidant activity (6.36 mg AAE/100 g). Statistical analysis revealed significant positive correlation between total antioxidant activity and electrical conductivity (r_a-e = 0.587, p_a-e = 0.000), and negative correlation between total antioxidant activity and L* parameter (r_a-L = −0.424, p_a-L = 0.000). Similar correlations were also observed regarding total phenolic content (r_p-e = 0.457, p_p-e = 0.000, r_p-L = −0.455, p_p-L = 0.000). In conclusion, oak and chestnut honeys seem to have a high antioxidant potential, that should be further explored, to highlight their value and help promote them worldwide. Full article

The American cranberry is a perennial North American fruit plant that is grown successfully on commercial plantations in Poland. The purpose of this study was to recognize filamentous fungi that colonize roots, leaves, and fruits without visible disease symptoms. Pure fungal cultures were isolated from disinfected plant fragments in agar media and identified by sequencing common taxonomic DNA markers such as the ITS region, the TEF-1α, or RPB2 genes. Of the 141 isolates studied, 59% were identified as closely related to soil saprotrophs. They were classified primarily as showing the greatest similarity to type strains of Trichoderma amoenum, Trichoderma dorothopsis, Paraphaeosphaeria sporulosa, and Penicillium murcianum. Additionally, isolates that are most similar to strains of Penicillium crustosum, Aspergillus flavus, and Aspergillus versicolor that produced mycotoxins were detected. The fungi identified as closest to Alternaria geophila, Alternaria senecionicola, Paraphoma radicina, Pestalotiopsis unicolor, Pestalotiopsis scoparia, and Neopestalotiopsis spp., whose hosts are plants other than American cranberry, represented 33.81% of the isolates tested. Only 7.2% of the isolates corresponded to the species of Physalospora vaccinia, Diaporthe vaccinii, and Diaporthe eres, known cranberry pathogens. The results of this study can be used to identify latent plant infection and potential disease risks. Full article

Broiler chicken meat is the preferred meat among the human population. Broiler meat contains high-quality protein and a low-fat content, alongside a desirable fatty acid profile. A frequent problem in human nutrition is an insufficient PUFA intake in the diet. One possible strategy to increase the dietary intake of polyunsaturated fatty acids (PUFA) in humans is to produce, and thereby enrich, broiler chicken meat with sufficient amounts of essential PUFA. A method to increase the proportion of essential fatty acids in chicken meat is by changing the fatty acid composition of the feed. Feed production via solid-state fermentation using lower filamentous fungi can be used to prepare valuable feed from cereal by-products enriched with important PUFA and pigments and can thus be included as a suitable feed ingredient in the diet of chickens. From previously published studies, it can be concluded that the application of 3–10% of the prepared fermented products to the diet of broiler chickens increased the proportion of essential fatty acids in the fat of the chicken meat and had a beneficial effect on the growth parameters of chickens. Full article

Aero-space aluminum alloys, as vital materials in aerospace engineering, find extensive application in various aerospace components. However, prolonged usage often leads to the emergence of fatigue natural cracks, posing significant safety risks. Therefore, research on accurate quantitative detection techniques for the cracks in aerospace-aluminum alloys is of vital importance. Firstly, based on the three-points bending experimental model, this paper prepared the fatigue natural crack specimen, and the depth of the natural crack is calibrated. Then, given the complexity of geometric characteristics inherent in natural cracks, the pulsed eddy current signal under the different natural crack depth is acquired and analyzed using an experimental study. Finally, to better exhibit the non-linearity between PEC signal and crack depth, a GA-based BPNN algorithm is proposed. The Latin Hypercube method is considered to optimize the population distribution in the genetic algorithm. The results indicate that the characterization accuracy reaches 2.19% for the natural crack. Full article

In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial–Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial–temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial–Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial–temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model’s performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data. Full article

Gold nanoparticles (AuNPs) radiolabeled with therapeutic and diagnostic radioisotopes have been broadly studied as a promising platform for early diagnosis and treatment of many diseases including cancer. Our main goal for this study was the comparison of the biodistribution profiles of four different concentrations of gold nanoconjugates radiolabeled with Technetium-99m (^99mTc). More specifically, AuNPs with an average diameter of 2 nm were functionalized with a tridentate thiol ligand. Four different concentrations were radiolabeled with ^99mTc-tricarbonyls with high radiolabeling yields (>85%) and were further purified, leading to radiochemical purity of >95%. In vitro stability of the radiolabeled nanoconstructs was examined in cysteine and histidine solutions as well as in human serum, exhibiting robust radiolabeling up to 24 h post-preparation. Moreover, in vitro cytotoxicity studies were carried out in 4T1 murine mammary cancer cells. In vivo tracking of the radiolabeled nanoconjugates at both concentrations was examined in normal mice in order to examine the effect of AuNPs’ concentration on their in vivo kinetics. Our work demonstrates that varying concentrations of radiolabeled AuNPs lead to notably different biodistribution profiles. Full article

To explore the causes of mine tremors in coal mines with sandstone roofs, a three-point bending loading experiment was designed for composite sandstone layers, and the fracture and interlayer shear slip characteristics of the composite sandstone layers were studied using optical measurement and acoustic emission techniques. The results show that the bending of the rock layers led to interlayer sliding deformation, while the fracturing greatly promoted interlayer sliding. The maximum interlayer slip accelerations during bending deformation and fracturing were 0.6 mm/s² and 3.8 mm/s², respectively. During the fracturing of the rock layers, the proportion of acoustic emission shear fracture events increased with the continuous occurrence of long-lasting and high-amplitude acoustic emission events. The mechanism of mine tremors in thick sandstone roofs is as follows: the increase in the area of the goaf causes rock bending deformation and fracturing, accompanied by interlayer shear slip, fracturing of the sandstone layer, and friction dislocation at the cementation surface of the adjacent sandstone layers, which jointly cause vibration of the roof. Full article

With the widespread adoption of smart farms and continuous advancements in IoT (Internet of Things) technology, acquiring diverse additional data has become increasingly convenient. Consequently, studies relevant to deep learning models that leverage multimodal data for crop disease diagnosis and associated data augmentation methods are significantly growing. We propose a comprehensive deep learning model that predicts crop type, detects disease presence, and assesses disease severity at the same time. We utilize multimodal data comprising crop images and environmental variables such as temperature, humidity, and dew points. We confirmed that the results of diagnosing crop diseases using multimodal data improved 2.58%p performance compared to using crop images only. We also propose a multimodal-based mixup augmentation method capable of utilizing both image and environmental data. In this study, multimodal data refer to data from multiple sources, and multimodal mixup is a data augmentation technique that combines multimodal data for training. This expands the conventional mixup technique that was originally applied solely to image data. Our multimodal mixup augmentation method showcases a performance improvement of 1.33%p compared to the original mixup method. Full article

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications. Full article

This study aims to address challenges in media monitoring by enhancing closed-set topic classification in multilingual contexts (where both training and testing occur in several languages) and crosslingual contexts (where training is in English and testing spans all languages). To achieve this goal, we utilized a dataset from the European Media Monitoring webpage, which includes approximately 15,000 article titles across 18 topics in 58 different languages spanning a period of nine months from May 2022 to March 2023. Our research conducted comprehensive comparative analyses of nine approaches, encompassing a spectrum of embedding techniques (word, sentence, and contextual representations) and classifiers (trainable/fine-tunable, memory-based, and generative). Our findings reveal that the LaBSE+FFNN approach achieved the best performance, reaching macro-averaged F1-scores of 0.944 ± 0.015 and 0.946 ± 0.019 in both multilingual and crosslingual scenarios. LaBSE+FFNN’s similar performance in multilingual and crosslingual scenarios eliminates the need for machine translation into English. We also tackled the open-set topic classification problem by training a binary classifier capable of distinguishing between known and new topics with the average loss of ∼0.0017 ± 0.0002. Various feature types were investigated, reaffirming the robustness of LaBSE vectorization. The experiments demonstrate that, depending on the topic, new topics can be identified with accuracies above ∼0.796 and of ∼0.9 on average. Both closed-set and open-set topic classification modules, along with additional mechanisms for clustering new topics to organize and label them, are integrated into our media monitoring system, which is now used by our real client. Full article

For some chalcogenide glasses, the temperature dependence of the activation energy E(T) of viscous flow in the glass transition region was calculated using the Williams–Landel–Ferry (WLF) equation. A method for determining the activation energy of viscous flow as a function of temperature is proposed using the Taylor expansion of the function E(T) using the example of chalcogenide glasses As-Se, Ge-Se, Sb-Ge-Se, P-Se, and AsSe-TlSe. The calculation results showed that the temperature dependence of the activation energy for the Ge-Se, As-Se, P-Se, AsSe-TlSe, and AsSe systems is satisfactorily described by a polynomial of the second degree, and for Sb-Ge-Se glass by a polynomial of the third degree. The purpose of this work is to compare the values of the coefficients obtained from the Taylor series expansion of E(T) with the characteristics of the E(T) versus (T − T_g) curves obtained directly from the experimental temperature dependence of viscosity. The nature of the dependence E(T) is briefly discussed. Full article

Today, Permanent Magnet Synchronous Motors (PMSMs) are a dominant choice in industry applications. During operation, different possible faults in the system can occur, so early and automated fault detection and severity estimation are required to ensure smooth operation and optimal maintenance planning. In this direction, outlier detection methods are employed in this paper. The motor’s current signals are used to extract useful indicators of the fault, along with d-q transform. Statistical indicators in both time and frequency domains are selected to describe fault-related patterns. Based on the extracted features, three outlier detection methods are investigated: the Isolation Forest, the One Class Support Vector Machine, and the Robust Covariance Ellipse. Each method is investigated through different model parameters to evaluate fault detection and severity estimation capabilities. Finally, an ensemble approach is proposed based on decisions and outlier score ensemble. The proposed methodology is verified through different operating conditions in a PMSM test bench. Full article

Short-term (with a lead time of about one week) earthquake (EQ) prediction is one of the most challenging subjects in geoscience and applied science; however, it is highly required by society because it is of essential importance in mitigating the human and economic losses associated with EQs. Electromagnetic precursors have recently been agreed to be the most powerful candidate for short-term prediction, because a lot of evidence has been accumulated on the presence of electromagnetic precursors (not only from the lithosphere, but also from the atmosphere and ionosphere) prior to EQs during the last three decades. On the other hand, unusual animal behavior associated with EQs, which is the main topic of this review, has been investigated as a macroscopic phenomenon for many years, with a much longer history than the study of seismo-electromagnetics. So, in this paper, we first summarize the previous research work on this general unusual animal behavior with reference to its relationship with EQs, and then we pay the greatest attention to our own previous work on dairy cows’ milk yield changes. We recommend this unusual animal behavior as an additional potential tool for short-term EQ prediction, which may be a supplement to the above seismo-electromagnetic effects. Finally, we will present our latest case study (as an example) on unusual changes of cows’ milk yields for a particular recent Tokyo EQ on 7 October 2021, and further propose that electromagnetic effects might be a possible sensory mechanism of unusual animal behavior, suggesting a close link between electromagnetic effects and unusual animal behavior. Full article

In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the development and rapid progression of transformer-based models becomes imperative in order to gain an understanding of the rise of this key architecture. This paper presents a survey of key works related to the early development and implementation of transformer models in various domains such as generative deep learning and as backbones of large language models. Previous works are classified based on their historical approaches, followed by key works in the domain of text-based applications, image-based applications, and miscellaneous applications. A quantitative and qualitative analysis of the various approaches is presented. Additionally, recent directions of transformer-related research such as those in the biomedical and timeseries domains are discussed. Finally, future research opportunities, especially regarding the multi-modality and optimization of the transformer training process, are identified. Full article

In this study, black chokeberry (Aronia melanocarpa) juice pomace was used to enrich the extrudates from rice in order to create a functional food. A response surface methodology was applied to optimize the physical (expansion ratio, bulk density, moisture content, hardness, pellet durability index, and color) and functional (water solubility index, water absorption index) characteristics of the extrudates. A laboratory single-screw extruder was used to produce the extrudates and a full factorial experimental design was applied (N = 3²) to present the effect of the amount of chokeberry pomace (10 and 20%), the feed moisture content (14 and 20%) and the working screw speed (180 min⁻¹ and 220 min⁻¹) of the extruder on the physical and functional characteristics. The results showed that the three factors influenced all studied characteristics. An exception with statistically insignificant effect was the amount of chokeberry pomace on the expansion ratio, pellet durability index, water adsorption index, lightness, redness, and yellowness, the feed moisture content on the water solubility, water adsorption index, redness and yellowness, and the working screw speed on the bulk density and hardness. Full article

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability. Full article

Axial force is one of the important factors affecting the life and reliability of centrifugal pumps. Based on the SST turbulence model, the unsteady internal flow field of an aero fuel centrifugal pump under various working conditions was analyzed by using the finite volume method and the axial force of the impeller component was predicted. The position servo force measuring system was used to measure the axial force of the fuel centrifugal pump and the theoretical formula of axial force was modified according to the numerical results and experimental values. The study shows that the pressure distribution of the front and rear pump chambers presented uneven circumferential distribution under the influence of dynamic and static interference through numerical simulation. The simulated head number is basically consistent with the test result and the maximum error of the axial force value obtained by the numerical calculation and the experimental value was 9.7% under different speeds, which verified the accuracy of the numerical simulation. Furthermore, the modified formula can accurately calculate the axial force of the fuel centrifugal pump with an error of less than 9.88%. The results of the study provide a theoretical basis for the calculation and balance of axial force in an aero aero fuel centrifugal pump. Full article

The purpose of this study is to determine the material properties of CFRP composites in the form of a fabric for the construction of racing car bodywork. This work focused on the determination of the strength and tribological properties as well as investigating the effects of the operating environment on the developed material. Three material variants, differing in the number of layers used to produce the reinforcement, were used in this study. The tests were carried out on two-/three-/four-layer sheets produced by infusion. Due to the later use of the tested composites for the sheathing of a racing car, the results obtained were analysed in terms of the most favourable strength properties while keeping the weight as low as possible. In this study, the hardness, impact strength, and tensile and bending stresses of the developed composites were examined. In addition to the strength properties, the density, the effects of immersion in water, and the composite’s resistance to staining and friction in the presence of aggressive media were also checked. The structure and the breakthroughs resulting from the strength tests were observed using a stereoscopic microscope. The material’s resistance to sunlight and UVB was also tested. Full article

Proteins can adsorb on the surface of materials, such as soft contact lenses (SCLs), and can affect the hydrophobicity, roughness, and surface properties of the contact lenses (CLs), which, in turn, can influence the friction between the lenses and the ocular surface. Excessive friction between contact lenses and the ocular surface can lead to discomfort for the wearer and may cause irritation or inflammation of the cornea, better known as corneal ulcers (keratitis). Bovine Serum Albumin (BSA) is often used as a standard protein in biocompatibility testing of materials, including contact lenses. One standard commercial contact lens was tested under lubricated conditions to access the coefficient of friction (CoF). The contact was lubricated with a tear-like fluid (TLF) solution containing six different concentrations of BSA. In all cases, good linearity of the results of the friction force was verified, suggesting that the first friction law can be applied to determine the value of the coefficient of friction. It was found that friction increases with the increase in protein concentration. Full article

The purpose of this study was to obtain a biodegradable film based on polyvinyl alcohol with reduced water vapor permeability. The hydrophobic character of the films was achieved by incorporating beeswax, vegetable bio-surfactant, citric acid as a cross-linking agent, and glycerol to provide elasticity, along with the application of thermal treatment. Water vapor permeability was determined gravimetrically. The results indicated that all films produced had lower water vapor permeability compared to unmodified or untreated polyvinyl alcohol films. The barrier to water vapor varied directly with the mass of beeswax used, and the homogeneous dispersion of beeswax in the polyvinyl alcohol matrix was essential for achieving an efficient hydrophobic film. The best performing-material exhibited a water vapor permeability 5.15 times lower than that of the neat polyvinyl alcohol and 15 times higher than that of polyethylene. Considering the fact that the water vapor barrier property of neat polyvinyl alcohol was 78 times lower than that of polyethylene, the combination of beeswax, citric acid, and vegetable bio-surfactant—along with thermal treatment—can be a viable solution to reduce the hygroscopicity of polyvinyl alcohol-based films. Full article

The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD combination based on lateral distance measurements and lane constraints. Firstly, a lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurements, odometer, and lane markings. Experimental results demonstrate that, for a 4.6 km tunnel, the average absolute error in the lateral positioning is 0.294 m, and the longitudinal positioning error is no more than 0.6 m, which can effectively meet practical operational requirements. Full article

Ultrasound is a highly adaptable medical imaging modality that offers several applications and a wide range of uses, both for diagnostic and therapeutic purposes. The principles of sound wave propagation and reflection enable ultrasound imaging to function as a highly secure modality. This technique facilitates the production of real-time visual representations, thereby assisting in the evaluation of various medical conditions such as cardiac, gynecologic, and abdominal diseases, among others. The ultrasound modality encompasses a diverse range of modes and mechanisms that serve to enhance the methodology of pathology and physiology assessment. Doppler imaging and US elastography, in particular, are two such techniques that contribute to this expansion. Elastography-based imaging methods have attracted significant interest in recent years for the non-invasive evaluation of tissue mechanical characteristics. These techniques utilize the changes in soft tissue elasticity in various diseases to generate both qualitative and quantitative data for diagnostic purposes. Specialized imaging techniques collect data by identifying tissue stiffness under mechanical forces such as compression or shear waves. However, in this review paper, we provide a comprehensive examination of the fundamental concepts, underlying physics, and limitations associated with ultrasound elastography. Additionally, we present a concise overview of its present-day clinical utilization and ongoing advancements across many clinical domains. Full article

As the construction industry is facing the challenge of meeting the ever-increasing demand for environmentally friendly and durable concrete, the role of viscosity-modifying admixtures (VMAs) has become increasingly essential to improve the rheological properties, stability, and mechanical properties of concrete. Additionally, natural polymers are ever evolving, offering multiple opportunities for innovative applications and sustainable solutions. This comprehensive review delves into the historical context and classifications of VMAs, accentuating their impact in enhancing the rheological properties, stability, and mechanical properties of concrete. Emphasis is placed on the environmental impact of synthetic VMAs, promoting the exploration of sustainable alternatives derived from plant-based biopolymers. Indeed, biopolymers, such as cellulose, starch, alginate, pectin, and carrageenan are considered in this paper, focusing on understanding their efficacy in improving concrete properties while enhancing the environmental sustainability within the concrete. Full article