The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions. Full article

Automatic music genre classification is a prevailing pattern recognition task, and many algorithms have been proposed for accurate classification. Considering that the genre of music is a very broad concept, even music within the same genre can have significant differences. The current methods have not paid attention to the characteristics of large intra-class differences. This paper presents a novel approach to address this issue, using a locally activated gated neural network (LGNet). By incorporating multiple locally activated multi-layer perceptrons and a gated routing network, LGNet adaptively employs different network layers as multi-learners to learn from music signals with diverse characteristics. Our experimental results demonstrate that LGNet significantly outperforms the existing methods for music genre classification, achieving a superior performance on the filtered GTZAN dataset. Full article

Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance. Full article

Laser-induced graphene (LIG) has garnered tremendous attention in the past decade as a flexible, scalable, and patternable alternative for fabricating electronic sensors. Superhydrophobic and superhydrophilic variants of LIG have been demonstrated by previous studies. However, stability analysis of the superhydrophobic surface property has not been explored. In this study, we use an infrared nanosecond laser to fabricate reduced graphene oxide (rGO)-based strain sensor on a carbon fiber reinforced polymer (CFRP) composite substrate. The fabricated sensor is characterized to determine its gauge factor using a three-point bend test demonstrating a gauge factor of 40. The fabricated LIG exhibits excellent superhydrophobic properties with a high contact angle (>160°). Both superhydrophobicity and piezoresistivity of the LIG under water immersion are studied for 25 h, demonstrating high stability. The obtained results could be of interest to several sectors, especially for maritime and high humidity applications. Full article

Africa, particularly sub-Sharan Africa (SSA), faces major challenges in respect to chronic kidney disease (CKD). There is a rising prevalence due to the combined effects of hypertension, diabetes, and human immunodeficiency virus (HIV) (and the interaction between them) and the effect of apolipoprotein L1 (APOL1) variants on the susceptibility to CKD. Epidemiological data on the prevalence of CKD are of low-to-medium quality, and reliable data are urgently needed for health planning. Furthermore, there are important deficiencies in creatinine-based equations in underestimating the prevalence of CKD in Africa, and evidence suggests that cystatin C based equations are more reliable. There is a changing spectrum of HIV related CKD with the greater availability of antiretroviral treatment. Major clinical trials using SGLT2 inhibitors have signalled a major advance in the treatment of CKD, especially in relation to type 2 diabetes, but the affordability, availability, and relevance to the African population is not established. The importance of the effects of hypertension in pregnancy and pregnancy related acute kidney injury on CKD and the newer concept of CKD of unknown cause (CKDu) are highlighted. Hypertension remains a dominant cause of CKD in Africa, and newer information suggests that the most appropriate treatment to control blood pressure and thus prevent CKD is the combination of either amlodipine plus a thiazide diuretic or angiotensin converting enzyme (ACE) inhibitor. Full article

The effect of the extraction conditions on the DPPH radical scavenging activity and isolation of bioactive compounds from the maca (Lepidium meyenii) root was investigated. Different extraction techniques (maceration, maceration with shaking, ultrasound-assisted extraction, and reflux extraction) were compared. Moreover, the effect of the extraction time and two various solvents (water and ethanol) was studied. The antioxidant activity of extracts was determined by the spectrophotometric method with the DPPH radical, while total phenolic content (TPC) was analyzed by the Folin–Ciocalteu method. Using gas chromatography with a mass selective detector (GC-MS), some characteristics of maca bioactive compounds were identified in the extracts: benzylalkamides (macamides), sterols, nitriles, fatty acids, and their derivatives. The influence of various factors on the extraction process of health-promoting antioxidant compounds from maca root was discussed. It was found that water was a more effective solvent than ethanol for obtaining extracts characterized by high radical scavenging activity and phenolics content. Nevertheless, some ethanol-extractable valuable compounds specific for maca, e.g., macamides or fatty acids derivatives, were not present in water extracts. In developing nutritional and therapeutic formulations based on maca extracts, it is important to take into account that the bioactivity of maca extracts varies depending on the solvent used. Full article

The processing of fruit generates large amounts of different by-products, such as pomace. The extrusion process gives an opportunity for their utilization as a good source of pro-health components. Therefore, this research focused on the utilization of fruit pomaces (cherries, blackcurrants, and chokeberries) as a value-added component of extruded corn snacks. The effect of the level of pomace addition on the content of bioactive polyphenols and nutritional value in cornmeal-based extrudates, as well as antioxidant capacity, was investigated. Additionally, the influence of fruit pomace on the quality of extruded gluten-free snacks was also investigated. It was found that pomace can be a good pro-health addition to corn snacks due to the enrichment of bioactive compounds and dietary fiber in this product. Especially valuable proved to be chokeberry pomace added at a 20% level. Such additions to snacks caused an increase in the content of total phenolic compounds, phenolic acids, flavonoids, flavonols, anthocyanins, and antioxidant activity, respectively, by about 10 times, 2 times, 5 times, 2 times, 10 times, and 5 times, as compared to control snacks. It was observed that the addition of chokeberry pomace did not worsen the physical properties (WBC, hardness, and expansion ratio) of the resulting snacks, which affect the quality of the obtained product. Therefore, such snacks could be recommended for commercial production in order to increase the availability of gluten-free products for people with celiac disease. Full article

In this study, the chemical stabilization of moderately to highly plastic expansive soil using calcium chloride with added cement is introduced as an effective alternative to the conventional approaches using a single additive such as lime, cement, or a by-product of industrial processes. Using only calcium chloride may lead to its leaching or dissolution over time, leaving a collapsing skeleton with weak bonds. The chemical effect produced by additives is dependent on the constituents of the stabilized soil and the curing period considered. Herein, calcium chloride concentrations of 2%, 4%, and 8% with the addition of 2% cement by dry weight of the soil were considered. The main objective of this study is to investigate the addition of a low amount of cement as a binder to improve the strength and durability of a chemically treated expansive soil. The engineering properties were investigated at 3 curing times: 3 days, 7 days, and 28 days. A laboratory investigation was carried out to investigate the effect of the addition of calcium chloride with cement on the swell potential, swell pressure, compression index, suction, and unconfined compressive strength. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) testing was conducted. The X-ray diffraction patterns were recorded to observe the mineralogy of the material. The results confirmed that calcium chloride with cement is very effective for stabilizing the expansive soil. A reduction in the swell potential by 8% and 25% and a reduction in swelling pressure by 28% and 37.4% were observed for 4% and 8% calcium chloride with cement addition. The compression index decreased with the increase in the calcium chloride content. Full article

The performance of male soccer players (MSPs) depends on multiple factors, such as body composition. It is understandable to think that, due to the physical demands and specific functions during play, body composition may vary depending on the playing position. The aim of this systematic review and meta-analysis was to describe the anthropometric, BC, and somatotype characteristics of professional MSPs and to compare the reported values according to playing position. We systematically searched Embase, PubMed, SPORTDiscus, and Web of Science following the PRISMA statement. Random-effects meta-analysis, a pooled summary of means, and 95% CI (method or equation) were calculated. Random models were used with the Restricted Maximum Likelihood (REML) method. Twenty-six articles were included in the systematic review and the meta-analysis. After comparing the groups according to the playing position (goalkeeper, defender, midfielder, and forward), significant differences were found in age, height, weight, the sum of skinfolds, kilograms of muscle mass, and kilograms of fat-free mass (p = 0.001; p < 0.0001). No significant differences were observed in fat mass, percentage of fat-free mass, percentage of muscle mass, bone mass, and somatotype. Despite the limitations, this study provides useful information to help medical–technical staff to properly assess the BC of professional MSPs, providing reference values for the different positions. Full article

As a green, safe, and efficient method of coal development, underground coal gasification (UCG) technology has gradually moved from the experimental stage to the industrial production stage. This technology plays one of the key roles in the sustainable development of resources and energy. However, underground mining will inevitably lead to strata movement and surface subsidence, which will have certain impacts on the surface environment and buildings. Currently, limited research results on strata movement and surface subsidence under high-temperature environments hardly support the further development of the UCG technology. Hence, this study aims at the key problems of UCG strata movement and surface subsidence prediction. The study established a numerical model to analyze the effects of thermal stress and coal–rock burnt on strata movement and surface subsidence. Results show that coal–rock burnt caused by high temperature has greatly changed the characteristics of UCG strata movement and surface subsidence and is the main controlling factor for aggravating the strata movement and surface subsidence of UCG. The coordinated deformation calculation method of the UCG cavity roof-coal pillar-floor is formed. Moreover, the cooperative subsidence space is regarded as the mining space. A prediction model of surface subsidence based on continuous-discrete medium theory is also established using the probability integral method. The reliability of the predicted model is proved by comparing the measured value with the predicted value. Full article

Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel antimicrobial peptides, there is a growing interest in the development of computational prediction approaches, in parallel with the studies performing wet-lab experiments. The computational approaches aim to understand what controls antimicrobial activity from the perspective of machine learning, and to uncover the biological properties that define antimicrobial activity. Throughout this study, we aim to develop a novel prediction approach that can identify peptides with high antimicrobial activity against selected target bacteria. Along this line, we propose a novel method called AMP-GSM (antimicrobial peptide-grouping–scoring–modeling). AMP-GSM includes three main components: grouping, scoring, and modeling. The grouping component creates sub-datasets via placing the physicochemical, linguistic, sequence, and structure-based features into different groups. The scoring component gives a score for each group according to their ability to distinguish whether it is an antimicrobial peptide or not. As the final part of our method, the model built using the top-ranked groups is evaluated (modeling component). The method was tested for three AMP prediction datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. When we used 10 features (which are members of the physicochemical group), we obtained the highest area under curve (AUC) value for both the Gram-negative (99%) and Gram-positive (98%) datasets. AMP-GSM investigates the most significant feature groups that improve AMP prediction. A number of physico-chemical features from the AMP-GSM’s final selection demonstrate how important these variables are in terms of defining peptide characteristics and how they should be taken into account when creating models to predict peptide activity. Full article

The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys; M_age = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use. Full article

License plate identification remains a crucial problem in computer vision, particularly in complex environments where license plates may be confused with road signs, billboards, and other objects. This paper proposes a solution by modifying the standard car–license plate–letter detection approach into a preliminary license plate detection–precise license plate detection of the four corners where the numbers are located–license plate correction–letter identification. This way, the first algorithm identifies all potential license plates and passes them as input parameters to the next algorithm for more precise detection. The main difference between this approach and other algorithms is that it uses a relatively small image compared to the whole vehicle. Thus, a small but robust network is used to find the four corners and perform a perspective transformation. This simplifies the letter recognition task for the next algorithm, as no additional transformations are required. This solution could be useful for research focusing on this specific task. It allows to apply another compact but robust neural network, increasing the overall speed of the system. Publicly available datasets were used for training and validation. The CenterNet object detection algorithm was used as a basis with a modified Hourglass-type network. The size of the network was decreased by 40% and the average accuracy was 96.19%. Speed significantly increased, reaching 2.71 ms and 405 FPS on average. Full article

In a virtual learning environment, it is important to be able to correctly assess students to help them receive the best possible education. This can have a big impact on the way disabled students learn and their goals for gaining a high level of qualification. This paper investigated different fuzzy logic-based techniques for student academic evaluations in a 3D virtual learning environment (VLE). Some of the techniques were found to be especially helpful for disabled students, and the paper also described the development and design of evaluation systems that take this into account. The study used fuzzy logic to study how well disabled students are doing in their classes over a whole year. This fuzzy logic was developed using MATLAB software, which uses features extracted from student evaluations. Disabled students’ characteristics (such as experience and understanding, problem-solving skills, etc.) have been measured and combined with a 3D virtual learning environment built using open-source software, Moodle, and Sloodle. This way, disabled students can interact with their courses inside a 3D VLE using Sloodle. According to the findings, which were based on 20 disabled students, fuzzy logic technology used in 3D Virtual Learning Environments (VLEs) produces different results than traditional assessments. The difference between the two is about 3.9 points on average. Full article

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities. Full article