Appl. Sci., Volume 12, Issue 13 (July-1 2022) – 534 articles

Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach–dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (n = 134) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO₃ content and the distributional range of sediment grain sizes within a sample (here termed span) are the parameters with greatest statistical significance—and thus greatest potential interpretive value—with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach–dune environments using hyperspectral remote sensing techniques. Full article

The study’s aim was to synthesize new unsymmetrical sulfanyl zinc(II) porphyrazines and subject them to physicochemical and electrochemical characterization and also an initial acute toxicity assessment. The procedure was initiated from a commercially available dimercaptomaleonitrile disodium salt and o-phthalonitrile using Linstead’s macrocyclization reaction conditions, which led to magnesium(II) tribenzoporphyrazine with 4-(3,5-dibutoxycarbonylphenoxy)butylthio substituents. The obtained macrocycle was demetallated with trifluoroacetic acid and subsequently remetallated with zinc(II) acetate toward the zinc(II) porphyrazine derivative. The zinc(II) tribenzoporphyrazine with 4-(3,5-dibutoxycarbonylphenoxy)butylthio substituents was then subjected to the reduction reaction with LiAlH₄, yielding zinc(II) tribenzoporphyrazine with 4-[3,5-di(hydroxymethyl)phenoxy]butylthio substituents. The new zinc(II) tribenzoporphyrazines were characterized by UV-Vis spectroscopy, various NMR techniques (¹HNMR, ¹³CNMR, ¹H-¹H COSY, ¹H-¹³C HSQC, and ¹H-¹³C HMBC), and mass spectrometry. In the UV-Vis spectra, both macrocycles revealed characteristic Soret and Q-bands, whose positions were dependent on the solvent used for the measurements. Zinc(II) tribenzoporphyrazines were studied using electrochemical and photochemical methods, including the singlet oxygen generation assessment. Both zinc(II) porphyrazines revealed high singlet oxygen generation quantum yield values of up to 0.59 in DMSO, which indicates their potential photosensitizing potential for photodynamic therapy. In addition, new derivatives were subjected to a Microtox^® bioluminescence assay. Full article

SARS-CoV-2 is the causative agent of severe acute respiratory diseases. Its main transmission pathway is through large and small respiratory droplets, as well as a direct and indirect contact. In this paper, we present the results of the development and research of copper filters produced by powder technology. Four types of copper powders were tested. Technological parameters, a microstructure, an energy dispersive X-ray (EDX) analysis, and fractography of copper (Cu) filters are reported. The pressure losses in the P-Cu-AW315 filter showed a very favorable value for using the filter in half-face masks that meet the requirements of European norms (EN). An X-ray tomography measurement was carried out at the I12-JEEP beamline. A relative volume of grains and pores was estimated (on the basis of the segmentation results) to be approximately 50% to 50% of the investigated filter volume. Full article

Fabric quality plays a crucial role in modern textile industry processes. How to detect fabric defects quickly and effectively has become the main research goal of researchers. The You Only Look Once (YOLO) series of networks have maintained a dominant position in the field of target detection. However, detecting small-scale objects, such as tiny targets in fabric defects, is still a very challenging task for the YOLOv4 network. To address this challenge, this paper proposed an improved YOLOv4 target detection algorithm: using a combined data augmentation method to expand the dataset and improve the robustness of the algorithm, obtaining the anchors suitable for fabric defect detection by using the k-means algorithm to cluster the ground truth box of the dataset, adding a new prediction layer in yolo_head in order to have a better effect on tiny target detection, integrating a convolutional block attention module into the backbone feature extraction network, and innovatively replacing the CIOU loss function with the CEIOU loss function to achieve accurate classification and localization of defects. Experimental results show that compared with the original YOLOv4 algorithm, the detection accuracy of the improved YOLOv4 algorithm for tiny targets has been greatly increased, the AP value of tiny target detection has increased by 12%, and the overall mean average precision (mAP) has increased by 3%. The prediction results of the proposed algorithm can provide enterprises with more accurate defect positioning, reduce the defect rate of fabric products, and improve their economic effect. Full article

Research on the potential accessibility of medical services has made great progress, but there is a large gap between the analysis results and the actual feelings of residents. With the refinement of urban management, the need for actual accessibility calculations reflecting the current status of medical service levels is becoming stronger. In modern society, as people work and live at an increasingly fast pace, people increasingly focus on time saving. However, in addition to travel time and distance, personal perceptions of medical facilities and access habits also influence residents’ choice of specific hospitals for medical treatment. With the combined effect of these factors, the actual status of accessibility of medical facility services is formed. In order to improve estimates of the actual accessibility and narrow the gap with residents’ subjective perceptions, this study leverages realistic data, such as real-time navigation prediction data that approximates residents’ actual travel time to hospitals and information on residents’ subjective behaviors in choosing specific hospitals for medical treatment. Finally, a new approach is proposed to further improve the existing Gaussian two-step floating catchment area (Ga2SFCA) method by fully respecting the important effects of distance cost and time cost, and combining them by using a weighted mean. Full article

Hydrothermally obtained α-MnO₂ nanowire characterizations confirm the tetragonal crystalline structure that is several micrometers long and 20–30 nm in diameter with narrow distributions in their dimensions. The absorption calculated from diffuse reflectance of α-MnO₂ occurred in the visible region ranging from 400 to 550 nm. The calculated band gap with Quantum Espresso using HSE approximation is ~2.4 eV for the ferromagnetic case, with a slightly larger gap of 2.7 eV for the antiferromagnetic case, which is blue-shifted as compared to the experimental. The current work also illustrates the transformations that occur in the material under heat treatment during TGA analysis, with the underlying mechanism. Electrochemical studies on graphite supports modified with α-MnO₂ compositions revealed the modified electrode with the highest electric double-layer capacitance of 3.444 mF cm⁻². The degradation rate of an organic dye—rhodamine B (RhB)—over the compound in an acidic medium was used to examine the catalytic and photocatalytic activities of α-MnO₂. The peak shape changes in the time-dependent visible spectra of RhB during the photocatalytic reaction were more complex and progressive. In two hours, RhB degradation reached 97% under sun irradiation and 74% in the dark. Full article

This study employed a comprehensive systematic review of the literature (SRL) process with the Content Analysis Toolkits for Academic Research (CATAR) for conducting a bibliometric analysis of the 2814 general SDG-related papers and 92 review papers selected from the Web of Science database from 2013 to 2022. The overview analysis found that the US and UK took the lead in publication and citation. The WHO and several universities were identified as the most prominent institutes around the globe. The field distribution of the most cited papers revealed the existence of a “strong sustainability” paradigm and the importance of science and technology. A landscape of 1123 papers was included in eight clusters according to the bibliographic coupling algorithms in the Multi-stage Document Clustering (MSDC) process. These clusters were then categorized into three groups, “synergies and trade-offs”, “networking”, and “systems analysis”, demonstrated in the theme maps. As for the 92 SDG-related review papers, most were shaped based on literature analysis without specified countries. Moreover, SDG 3 was identified as that exclusively studied in most papers. The information presented is expected to help research scholars, public sectors, and practitioners monitor, gather, check, analyze, and use the growing volume of SDG-related academic articles. Full article

For the requirements of rigorous CO₂ and emissions regulations, steam assist technology is an effective method for thermal efficiency enhancement. However, few studies apply steam assist technology in modern internal combustion engines. Stimulated by its application prospects, the present study proposes a thermodynamic analysis on the in-cylinder steam assist technology. An ideal engine thermodynamic model combined with a heat exchanger model is established. Some critical parameters, such as steam injection temperature, injection pressure and intake pressure, are calculated under different steam injection masses. The thermal efficiency boundaries are also analyzed at different compression ratios to investigate the maximum potential thermal efficiency of the technology. The analysis shows that the in-cylinder steam-assisted cycle has the potential to increase engine efficiency considerably. Both steam injection temperature and injection mass improve thermal efficiency. Considering the energy trade-off relationship between steam and exhaust gas, the maximum gain in thermal efficiency achieved with the cycle is 14.5% at a compression ratio of 10. The optimum thermal efficiency can be increased from 54.0% to 59.71% by increasing the compression ratio from 10 to 16. The mechanism lies in the specific heat ratio enhancement from a thermodynamic perspective, which improves the thermal-heat conversion efficiency. The results provide considerable guidance for the future experimental and numerical studies of in-cylinder steam assist technology into modern engines. Full article

Epidemiological studies have evidenced that natural dietary products can prevent or manage gastric cancer. Neptunia oleracea, an aquatic vegetable and edible plant, has been reported to have anti-cancer properties. In this study, N. oleracea extract’s suppression of gastric cancer cells was investigated on an in vitro experimental model. We found that ethyl acetate (EtOAc) extract inhibited cell proliferation at IC₅₀ value of 172 µg/mL. Moreover, the treatment of EtOAc extract at a concentration of 50 µg/mL for 24 h caused suppression of cancer cell migration. Notably, a real-time PCR assay revealed that EtOAc extract induced the process of apoptosis via upregulating the mRNA expression level of caspase-8, Bax, caspase-9, and caspase-3 in cancer cells. In conclusion, N. oleracea had potential anti-cancer activity against gastric cancer cells, suggesting its role in the prevention and management of gastric cancer. Full article

In the face of emerging technological achievements, cyber security remains a significant issue. Despite the new possibilities that arise with such development, these do not come without a drawback. Attackers make use of the new possibilities to take advantage of possible security defects in new systems. Advanced-persistent-threat (APT) attacks represent sophisticated attacks that are executed in multiple steps. In particular, network systems represent a common target for APT attacks where known or yet undiscovered vulnerabilities are exploited. For this reason, intrusion detection systems (IDS) are applied to identify malicious behavioural patterns in existing network datasets. In recent times, machine-learning (ML) algorithms are used to distinguish between benign and anomalous activity in such datasets. The application of such methods, especially autoencoders, has received attention for achieving good detection results for APT attacks. This paper builds on this fact and applies several autoencoder-based methods for the detection of such attack patterns in two datasets created by combining two publicly available benchmark datasets. In addition to that, statistical analysis is used to determine features to supplement the anomaly detection process. An anomaly detector is implemented and evaluated on a combination of both datasets, including two experiment instances–APT-attack detection in an independent test dataset and in a zero-day-attack test dataset. The conducted experiments provide promising results on the plausibility of features and the performance of applied algorithms. Finally, a discussion is provided with suggestions of improvements in the anomaly detector. Full article

In this paper, the main aim of the study was the investigation of the possibilities of power inductor design, reflecting the performance of the component itself, as well as the operational efficiency of the power factor correction (PFC) converter. PFC inductors represent a key component of the converter, while within the design of any magnetic component, several design rules must be considered to provide proper operational performance. Here we discuss skin-effect, while the proximity effect and formation of fringing flux pose a more serious problem in terms of mitigating their negative impact. Therefore, in this study, the space is devoted exclusively to the analysis of the impact of the fringing flux of the PFC inductor and subsequently to the possibilities of its suppression. The resulting optimizations are reflected in the investigation of the operational efficiency of the PFC converter. Full article

Cold plasma spray, a powder deposition method by means of an atmospheric pressure plasma jet is a promising coating technology for use on temperature sensitive surfaces. For further improvement of this coating process, a deeper understanding of its thermokinetic properties is required. By means of optical emission spectroscopy, the plasma effluent of an atmospheric pressure nitrogen arc jet is characterized by different distances from the nozzle and different gas flow rates of 35 ${\mathrm{L}\min }^{-1}$ and 45 ${\mathrm{L}\min }^{-1}$. A Boltzmann plot of ${\mathrm{N}}_2^{+}$(B-X) was used to determine rotational temperatures, which were found to be around 4000 K at the nozzle exit. Excitation temperatures, analyzed using atomic nitrogen lines, were around 6000 K for all distances. Stark broadening of the H${}_{\alpha }$-line was too weak for determination of electron density for both gas flow rates. Overall no influence on gas flow rate was found. Full article

An atmospheric pressure microwave plasma source operating on water vapor has many potential applications. To avoid the corrosion of metal electrodes in a traditional water vapor microwave plasma system, we propose a simple water vapor electrodeless microwave plasma device. By introducing a ceramic tube, the device can work directly with liquid water without complex evaporation equipment. This study examined the relationship between microwave power and water vapor torch plasma duration. When the microwave power is greater than 800 W, the plasma torch can be excited permanently and stably without the loss of ceramic. The excitation of the oxygen atom, hydroxyl radical, and hydrogen atom was found using optical spectroscopy, confirming the water vapor’s decomposition. In addition, it was also found that the crystallinity of the ceramic was improved after microwave discharge. This work enriches the microwave plasma techniques for water vapor for various applications, such as electric propulsion, hydrogen production, and surface treatment. Full article

In this study, the orthogonal mechanical properties of additive manufacturing technology were explored. Firstly, six test pieces of different stacking methods were printed with a 3D printer, based on fused deposition modeling. The resonance frequency was measured by a laser Doppler vibrometer as the test piece was struck by a steel ball, which was used to calculate the orthotropic material constants. The accuracy of these orthotropic material constants was then verified using finite element software through a comparison of the experimental results from multiple natural modes. Thus, a set of methods for the measurement and simulation verification of orthotropic material constants were established. Only three specific test specimens are needed to determine the orthotropic material constants using the vibrating sensor technique, instead of a universal testing machine. We also analyzed the influence of different printing parameters, including raster angle and layer height, on the material constants of the test pieces. The results indicate that a raster angle of 0° leads to the highest Young’s modulus, a raster angle of 45° leads to the highest shear modulus G, and a layer height of 0.15 mm leads to the highest material strength. In various stack conditions, the mechanical properties of fuse deposition additive manufacturing can be measured by inversely calculating frequency domain transformation. Full article

Attempts to discover knowledge through data are gradually becoming diversified to understand complex aspects of social phenomena. Graph data analysis, which models and analyzes complex data as graphs, draws much attention as it combines the latest machine learning techniques. In this paper, we propose a new framework called link pruning for detecting clusters in complex networks, which leverages the cohesiveness of local structures by removing unimportant connections. Link pruning is a flexible framework that reduces the clustering problem in a highly mixed community structure to a simpler problem with a lowly mixed community structure. We analyze which similarities and curvatures defined on the pairs of nodes, which we call the link attributes, allow links inside and outside the community to have a different range of values. Using the link attributes, we design and analyze an algorithm that eliminates links with low attribute values to find a better community structure on the transformed graph with low mixing. Through extensive experiments, we have shown that clustering algorithms with link pruning achieve higher quality than existing algorithms in both synthetic and real-world social networks. Full article

An optimization model for the optical imaging system was established in this paper. It combined the modern design of experiments (DOE) method known as Latin hypercube sampling (LHS), Kriging surrogate model training, and the multi-objective optimization algorithm NSGA-III into the optimization of a triplet optical system. Compared with the methods that rely mainly on optical system simulation, this surrogate model-based multi-objective optimization method can achieve a high-accuracy result with significantly improved optimization efficiency. Using this model, case studies were carried out for two-objective optimizations of a Cooke triplet optical system. The results showed that the weighted geometric spot diagram and the maximum field curvature were reduced 5.32% and 11.59%, respectively, in the first case. In the second case, where the initial parameters were already optimized by Code-V, this model further reduced the weighted geometric spot diagram and the maximum field curvature by another 3.53% and 4.33%, respectively. The imaging quality in both cases was considerably improved compared with the initial design, indicating that the model is suitable for the optimal design of an optical system. Full article

This study scrutinizes the existing literature regarding the use of augmented reality and gamification in education to establish its theoretical basis. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was conducted. To provide complete and valid information, all types of related studies for all educational stages and subjects throughout the years were investigated. In total, 670 articles from 5 databases (Scopus, Web of Science, Google Scholar, IEEE, and ERIC) were examined. Based on the results, using augmented reality and gamification in education can yield several benefits for students, assist educators, improve the educational process, and facilitate the transition toward technology-enhanced learning when used in a student-centered manner, following proper educational approaches and strategies and taking students’ knowledge, interests, unique characteristics, and personality traits into consideration. Students demonstrated positive behavioral, attitudinal, and psychological changes and increased engagement, motivation, active participation, knowledge acquisition, focus, curiosity, interest, enjoyment, academic performance, and learning outcomes. Teachers also assessed them positively. Virtual rewards were crucial for improving learning motivation. The need to develop appropriate validation tools, design techniques, and theories was apparent. Finally, their potential to create collaborative and personalized learning experiences and to promote and enhance students’ cognitive and social–emotional development was evident. Full article

In this study, to improve the bond performance of reinforcing bars fixed inside concrete, a pullout test using ultra high-performance concrete (UHPC) and structural steel fibers was conducted and a model that could predict the performance was also presented. After creating a UHPC layer on the rebar surface, the specimens were prepared along with three types of structural fibers. The structural fibers with different shapes were mixed up to 0.2%, 0,4%, 0.6%, 0.8%, 1% and 2% to analyze their effects on the bond failure at the interface. As a result of the experiment, the pullout resistance ability of the specimen thinly coated with UHPC maintained high residual stress due to the steep section reaching the maximum load, increased the maximum pullout load, and delayed the bond failure during the extraction process. As a result of the cross-sectional examination of the specimen, the coating of UHPC was strongly attached to the rebar surface and the bond surface was broken through sliding at the interface (UHPC–ordinary Portland concrete (OPC)). It was found that the increase in the structural fiber significantly improved the pulling-out resistance at the interface. The proposed model based on the existing Cosenz–Manfredi–Realfonzo (CMR) and Bertero–Popov–Eligehausen (BPE) prediction models was found to be in good agreement with the experimental results. Full article

X-ray diffraction can non-destructively reveal microstructure information, including stress distribution on thermal coatings of aeroengine blades. In order to accurately pinpoint the detection position and precisely set the measurement geometry, a 3D camera is adopted to obtain the point cloud data on the blade surface and perform on-site modeling. Due to hardware limitations, the resolution of raw point clouds is insufficient. The point cloud needs to be upsampled. However, the current upsampling algorithm is greatly affected by noise and it is easy to generate too many outliers, which affects the quality of the generated point cloud. In this paper, a generative adversarial point cloud upsampling model is designed, which achieves better noise immunity by introducing dense graph convolution blocks in the discriminator. Additionally, filters are used to further process the noisy data before using the deep learning model. An evaluation of the network and a demonstration of the experiment show the effectivity of the new algorithm. Full article

Foot sole temperature, besides its importance in thermal comfort, can be considered an important factor in identifying tissue injuries due to heavy activities or diseases. Hyperthermia, which is a raise in the foot temperature, increases the risk of diabetic ulcers considerably. In this study, a model is proposed to predict the foot sole temperature with acceptable accuracy. This model for the first time considers both the thermal and mechanical properties of the shoe sole, the intensity of the activity, the ambient condition, and sweating, which are involved in the thermal interaction between the sole of the foot and footwear. Furthermore, the proposed model provides the opportunity to estimate the contributions of different parameters in foot thermal regulation by describing the interaction of activity, duration, and intensity as well as sweating in influencing the foot sole temperature. In doing so it takes into account the relative importance of heat capacitance and the thermal conductivity. The results of this study revealed that sweating is not as effective in cooling the ball area of the foot while it is the principal contributor to thermal regulation in the arch area. The model also showed the importance of trapped air in keeping the foot warm, especially in cold conditions. Based on the simulation results, in selecting the shoe sole, and in addition to the conductivity, the thermal capacity of the sole of the shoe needs to be considered. The developed analytical model allowed the investigation of the contribution of all the involved parameters in foot thermal regulation and has shown that a different foot temperature can be achieved when the amount of material versus air is changed in the insole design. This can have practical implications in the insole design for a variety of conditions such as hypo and hyper-thermia in physical activities in sports and exercise settings. Full article

In order to improve the accuracy and reliability of an existing rutting performance prediction model, based on the long-term observation data of the RIOHTrack’s full-scale pavement structure, the rutting performance prediction model in China’s Specifications for Design of Highway Asphalt Pavement was evaluated, and the model correction method was proposed, which improves the model’s reliability and makes it more suitable for rutting estimation in the region. The research found that the rutting model in China’s Specifications for Design of Highway Asphalt Pavement has significant structural dependence. The model with the highest prediction accuracy and the smallest error is the semi-rigid base asphalt pavement structure with an asphalt concrete layer thickness of 12 cm; the prediction accuracy of other structures is not high. In order to improve the accuracy and reliability of the rutting prediction model, a new model is established by introducing local correction coefficients into the existing model. After local correction, the accuracy of the rutting prediction models for all structures has been greatly improved, and the determination coefficient R² is greater than 0.87. Since the basic data has already reflected the characteristics of different pavement structures and materials, as well as the impact of local climate environment and traffic load conditions, the new model is more suitable for rutting prediction of various pavement structures in the region where the RIOHTrack is located. Full article

Strain rates have been included in multiplicative hybrid modelling of the long-term spatial distribution of earthquakes in New Zealand (NZ) since 2017. Previous modelling has shown a strain rate model to be the most informative input to explain earthquake locations over a fitting period from 1987 to 2006 and a testing period from 2012 to 2015. In the present study, three different shear strain rate models have been included separately as covariates in NZ multiplicative hybrid models, along with other covariates based on known fault locations, their associated slip rates, and proximity to the plate interface. Although the strain rate models differ in their details, there are similarities in their contributions to the performance of hybrid models in terms of information gain per earthquake (IGPE). The inclusion of each strain rate model improves the performance of hybrid models during the previously adopted fitting and testing periods. However, the hybrid models, including strain rates, perform poorly in a reverse testing period from 1951 to 1986. Molchan error diagrams show that the correlations of the strain rate models with earthquake locations are lower over the reverse testing period than from 1987 onwards. Smoothed scatter plots of the strain rate covariates associated with target earthquakes versus time confirm the relatively low correlations before 1987. Moreover, these analyses show that other covariates of the multiplicative models, such as proximity to the plate interface and proximity to mapped faults, were better correlated with earthquake locations prior to 1987. These results suggest that strain rate models based on only a few decades of available geodetic data from a limited network of GNSS stations may not be good indicators of where earthquakes occur over a long time frame. Full article

In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%. Full article

The incidence of humerus greater tuberosity (GT) fractures is about 20% in patients with proximal humerus fractures. This study aimed to investigate the biomechanical performances of the humerus GT fracture stabilized by a locking plate with rotator cuff function for shoulder rehabilitation activities. A three-dimensional finite element model of the GT-fracture-treated humerus with a single traction force condition was analyzed for abduction, flexion, and horizontal flexion activities and validated by the biomechanical tests. The results showed that the stiffness calculated by the numerical models was closely related to that obtained by the mechanical tests with a correlation coefficient of 0.88. Under realistic rotator cuff muscle loading, the shoulder joint had a larger displacement at the fracture site (1.163 mm), as well as higher bone stress (60.6 MPa), higher plate stress (29.1 MPa), and higher mean screw stress (37.3 MPa) in horizontal flexion rehabilitation activity when compared to that abduction and flexion activities. The horizontal flexion may not be suggested in the early stage of shoulder joint rehabilitation activities. Numerical simulation techniques and experimental designs mimicked clinical treatment plans. These methodologies could be used to evaluate new implant designs and fixation strategies for the shoulder joint. Full article

In recent years, the culture and fishing of precious marine product are heavily dependent on manual work, which is labor-intensive, high-cost and time-consuming. To address this issue, an underwater robot can be used to monitor the size of the marine products and fish the mature ones automatically. Automatic detection of marine products from underwater images is one of the most important steps in developing an underwater robot perceiving method. In the traditional detection model, the CNN based backbone suffers from the limited receptive field and hinders the modeling of long-range dependencies, due to the small kernel size. In this paper, a novel detection model FFT_YOLOX based on a modified YOLOX is proposed. Firstly, a unique FFT_Filter is presented, which is a computational efficient and conceptually simple architecture to capture global information of images. Then, a novel FFT_YOLOX model is introduced with fewer model parameters and FLOPs by replacing the standard 3 × 3 kernel in the original backbone of the YOLOX model with a FFT_Filter, for an underwater object detection vision task. Extensive experimental results demonstrate the effectiveness and generalization of the visual representation of our proposed FFT_YOLOX model. Full article

This work aimed to evaluate the potential of three fungi strains, Lentinula edodes, Pleurotus eryngii, and Pleurotus citrinopileatus, to degrade lignin and enhance the nutritive value of grape stalks (GS). The GS was inoculated with the fungi and incubated under solid-state fermentation at 28 °C and 85% relative humidity for 7, 14, 21, 28, 35, and 42 days, in an incubation chamber. The influence of the treatments was evaluated by analyzing the potential modifications in the chemical composition, in vitro organic matter digestibility (IVOMD) and enzymatic kinetics. An increase (p < 0.001) in the crude protein content was observed in the GS treated with L. edodes and P. citrinopileatus at 42 days of incubation (50 and 75%, respectively). The treatment performed with L. edodes decreased (p < 0.001) lignin content by 52%, and led to higher (p < 0.001) IVOMD values at 42 days of incubation. By contrast, P. eryngii did not affect lignin content and IVOMD. A higher activity of all enzymes was also detected for the treatment with L. edodes. Results indicated that L. edodes has a great potential to enhance the nutritive value of GS as an animal feed, due to its lignin degradation selectivity. Full article

The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure as powerful tools for transforming multidimensional data into visual information according to similarity features. Integrating this class of methods into a framework devoted to data sciences can contribute to generating more expressive means of visual analytics. Although the Principal Component Analysis (PCA) is a well-known method in this context, it is not the only one, and, sometimes, its abilities and limitations are not adequately discussed or taken into consideration by users. Therefore, knowing in-depth multidimensional projection techniques, their strengths, and the possible distortions they can create is of significant importance for researchers developing knowledge-discovery systems. This research presents a comprehensive overview of current state-of-the-art multidimensional projection techniques and shows example codes in Python and R languages, all available on the internet. The survey segment discusses the different types of techniques applied to multidimensional projection tasks from their background, application processes, capabilities, and limitations, opening the internal processes of the methods and demystifying their concepts. We also illustrate two problems, from a genetic experiment (supervised) and text mining (non-supervised), presenting solutions through multidimensional projection application. Finally, we brought elements that reverberate the competitiveness of multidimensional projection techniques towards high-dimension data visualization, commonly needed in data sciences solutions. Full article

In recent years, a new tomographic inversion method based on the Maximum Likelihood (ML) approach has been adapted to JET bolometry. Apart from its accuracy and reliability, the key advantage is its ability to provide reliable estimates of the uncertainties in the reconstructions. The original algorithm was implemented and validated using the MATLAB software tool. This work presents the accelerated version of the algorithm implemented using a compatible ITER fast controller platform with the Ubuntu 18.04 or the ITER Codac Core System distributions (6.1.2). The algorithm has been implemented in C++ using the open-source libraries: ArrayFire, ALGLIB, and MATIO. These libraries simplify the management of specific hardware accelerators such as GPUs and increase performance. The speed-up factor obtained is approximately 10 times. The work presents the methodology followed, the results obtained, and the advantages and drawbacks of implementation. Full article

With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels, it is challenging to achieve satisfactory performance for HSI classification. In order to overcome these challenges, this paper proposes a feature extraction method called spatial perception correntropy matrix (SPCM), which makes use of spatial and spectral correlation simultaneously to improve the classification accuracy and robustness. Specifically, the dimension reduction is carried out firstly. Then, the spatial perception method is designed to select the local neighbour pixels. Thus, local spectral-spatial correlation is characterized by the correntropy matrix constructed using the selected neighbourhoods. Finally, SPCM representations are fed into the support vector machine for classification. The extensive experiments carried out on three widely used data sets have revealed that the proposed SPCM performs better than several state-of-the-art methods, especially when the training set is small. Full article