Concrete is a building material that is most widely used because of its excellent mechanical performance and durability. Compressive strength is an essential property of concrete, which changes with time under various factors. In this paper, the time variation law of the compressive strength of concrete was reviewed from three aspects: single, multiple and material internal factors. The mathematical models of compressive strength relative to time under single factors such as carbonization, freeze–thaw cycle, temperature effect and sulfate attack were summarized. Based on the statistical analysis of laboratory experimental data and field test data, the time variation laws of concrete under the coupling action of two or more factors were analyzed. The results show that the strength loss of concrete under the coupling effect of multiple factors is more serious than under the effect of a single factor. In addition, the time variation models of compressive strength in existing buildings were discussed, and it was observed that there are obvious differences between these models. After analysis, it is known that the different data sources and normalization methods are the primary causes of differences. Finally, the influences of concrete internal factors on compressive strength were outlined. The main conclusions of the time variation law of compressive strength were summarized, and further research directions were also proposed. Full article

The rapid evolution of network infrastructure through the softwarization of network elements has led to an exponential increase in the attack surface, thereby increasing the complexity of threat protection. In light of this pressing concern, European Telecommunications Standards Institute (ETSI) TeraFlowSDN (TFS), an open-source microservice-based cloud-native Software-Defined Networking (SDN) controller, integrates robust Machine-Learning components to safeguard its network and infrastructure against potential malicious actors. This work presents a comprehensive study of the integration of these Machine-Learning components in a distributed scenario to provide secure end-to-end protection against cyber threats occurring at the packet level of the telecom operator’s Virtual Private Network (VPN) services configured with that feature. To illustrate the effectiveness of this integration, a real-world emerging attack vector (the cryptomining malware attack) is used as a demonstration. Furthermore, to address the pressing challenge of energy consumption in the telecom industry, we harness the full potential of state-of-the-art Green Artificial Intelligence techniques to optimize the size and complexity of Machine-Learning models in order to reduce their energy usage while maintaining their ability to accurately detect potential cyber threats. Additionally, to enhance the integrity and security of TeraFlowSDN’s cybersecurity components, Machine-Learning models are safeguarded from sophisticated adversarial attacks that attempt to deceive them by subtly perturbing input data. To accomplish this goal, Machine-Learning models are retrained with high-quality adversarial examples generated using a Generative Adversarial Network. Full article

Solar systems connected to the grid are crucial in addressing the global energy crisis and meeting rising energy demand. The efficiency of these systems is totally impacted by varying weather conditions such as changes in irradiance and temperature throughout the day. Additionally, partial shading (PS) adds to the complexity of the nonlinear characteristics of photovoltaic (PV) systems, leading to significant power loss. To address this issue, maximum power point tracking (MPPT) algorithms have become an essential component in PV systems to ensure optimal power extraction. This paper introduces a new MPPT control technique based on a novel reptile search optimization algorithm (RSA). The effectiveness of the proposed RSA method is evaluated under different weather conditions with varying irradiance and partial shading. The results of the RSA algorithm are compared to other existing bio-inspired algorithms and show superior performance with an average efficiency of 99.91%, faster dynamic response of 50 ms, and less than 20 watts of oscillation. The RSA-MPPT based technique provides higher efficiency, faster settling time, and minimal oscillation around the maximum power point (MPP), making it a reliable solution for effective solar power harvesting. 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

Phlorotannins are phenolic compounds existing in large amounts in Phaeophyta, with this amount differing according to the season and collection area. There are many pharmacological properties of phlorotannins, such as being antioxidant, antidiabetic, and anti-cancer. In this work, phlorotannins from the Phaeophyta Sargassum linifolium were extracted, characterized, and identified, for use as an antioxidant and an anti-diabetic in a streptozotocin-induced diabetes rat model. Phlorotanins were characterized using ultraviolet (UV) and Fourier transform infrared (FT-IR) analysis, dimethoxy benzaldehyde assay (DMBA), and Folin–Ciocalteu assays. Groups of rats were tested as follows: normal control (negative control) (G1), normal rats treated with 60 mg/kg body weight of phlorotannins (G2), positive control diabetic rats injected with one dose of streptozotocin (G3), and a diabetic group treated with phlorotannins at 60 mg kg⁻¹ body weight (G4). The biochemical parameters were determined after 4 weeks of treatment. The results demonstrated that the extracted compound was a phlorotannin, which had antioxidant properties. An in vivo study confirmed that the glucose and insulin levels in G4 were relatively similar to those in the normal control G1. The glucosidase, alpha-amylase, glutathione, and catalase levels were 0.11 ± 0.097, 420.5 ± 13, 11.27 ± 3.3, and 8.01 ± 1.31 µmol/min/g in G1, and 0.04 ± 0.016, 184.75 ± 55.24, 12.78 ± 2.1, and 11.28 ± 1.74 µmol/min/g) in G4, respectively. There were no side effects in the kidney function of both G2 and G4, and the levels of cholesterol and triglyceride were also normal. The results demonstrated that phlorotannins have antioxidant properties in vivo and that the diabetic rats had an activated AMPK expression. According to the histological analysis, phlorotannins improved the islet size and reversed necrotic and fibrotic alterations in the pancreas. The results of the present study suggest the use of phlorotannins derived from Sargassum linifolium as an antioxidant and anti-diabetic for an in vivo study. They could be used in developing medicinal preparations for treating diabetes and its related symptoms. Full article

Evaluating students’ academic performance is crucial for assessing the quality of education and educational strategies. However, it can be challenging to predict and evaluate academic performance under uncertain and imprecise conditions. To address this issue, many research works have employed fuzzy concepts to analyze, predict, and make decisions about students’ academic performance. This paper investigates the use of fuzzy concepts in research related to evaluating, analyzing, predicting, or making decisions about student academic performance. The paper proposes a fuzzy model, called FPM (Fuzzy Propositional Model), for reasoning and predicting students’ academic performance. FPM aims to address the limitations of previous studies by incorporating propositional logic with fuzzy sets concept, which allows for the representation of uncertainty and imprecision in the data. FPM integrates and transforms if-then rules into weighted fuzzy production rules to predict and evaluate academic performance. This paper tests and evaluates the FPM in two scenarios. In the first scenario, the model predicts and examines the impact of absenteeism on academic performance where there is no clear relation between the two parts of the dataset. In the second scenario, the model predicts the final exam results using the lab exam results, where the data are more related. The FPM provides good results in both scenarios, demonstrating its effectiveness in predicting and evaluating students’ academic performance. A comparison study of the FPM’s results with a linear regression model and previous work showed that the FPM performs better in predicting academic performance and provides more insights into the underlying factors affecting it. Therefore, the FPM could be useful in educational institutions to predict and evaluate students’ academic performance, identify underlying factors affecting it, and improve educational strategies. Full article

This article presents the design of a drive system for robots and manipulators, which is based on the finite element method of an induction motor. The design process involves constructing a computer-aided design (CAD) model of the induction motor, which enables the generation of design documentation and control programs for computer numerical control (CNC) tools for manufacturing motor parts or conducting further research. A CAD model is developed for performing a finite element analysis of the motor in the SolidWorks software based on the popular AIR63V2 motor. The design of the motor’s housing, rotor, and stator is developed. Additionally, the electrical parameters of the motor are calculated using Ansys Electronic Suite—Maxwell RMxprt, utilizing the classical analytical theory of electrical machines and the equivalent magnetic circuit method. This takes into account such effects as the non-linearity of electrical steel, the non-sinusoidality of the magnetic flux in the gap, and the displacement of electric current in massive conductors. A complete model of an induction motor for research has been created, enabling the study of dependencies of speed and electromagnetic torque of an induction motor. The natural frequency of the rotor is calculated, which ranges from 922 Hz to 1015 Hz. The obtained values of calculations of natural oscillations of the CAD model of the motor can be used for motor diagnostics. Furthermore, the created project in the Ansys software can be utilized to design an induction motor with its own characteristics, optimized for specific tasks. Full article

As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification. Full article

Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries. 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

The evolution of fiber optic technology in the past few decades has led to significant advancements in various fields, including high-speed and long-distance communication, big data transport, optical imaging, and sensing. However, relatively few studies have examined the use of fiber optic sensors (FOSs) as point and distributed sensors in geophysics. Distributed Acoustic Sensing (DAS) is a widely used method for subsurface imaging and monitoring in wells, specifically in Vertical Seismic Profiling (VSP) surveys. This method allows for detailed analysis of subsurface structures and properties of reservoirs. Four different strategies for deploying FOS cables in DAS VSP are evaluated and compared: cementing behind casing, cable behind inflatable liner, strapping to production tubing, and wireline deployment. Cementing the fiber behind casing is considered the most effective method for coupling with the formation. However, the other methods also have their own advantages and limitations. The fiber cable behind inflatable liner, for example, allows for accessibility to the fiber without affecting the acoustic signal, while strapping the fiber to production tubing can still record DAS signals; tubing noise and signal attenuation from the annular fluid, however, can make it difficult to differentiate from the seismic signal. Nonetheless, this method has the benefit of being simpler to deploy and replace in case of failure. Wireline deployment can pick up some acoustic signals in regions where the cable touches the well wall, but in vertical sections where the cable is not in contact with the wall, the signal is attenuated. Results from pilot tests in a field in Canada are discussed and evaluated, and suggestions for improving the VSP signal are provided. Full article

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has an imbalanced nature. Moreover, we designed a deep learning model that combines a convolution-based feature extractor and deep neural network blocks, the LSTM block, and the transformer encoder block, followed by a position-wise feedforward layer. We found that combining the input sequence with the convolution-learned features of different kernels tends to increase the performance of the fall-detection model. Last, we analyzed that the sensor signals collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately detect falls, especially differentiating falls from near-falls. Furthermore, we also used data from sixteen different body parts and compared them to determine the better sensor position for fall-detection methods. We found that the shank is the optimal position for placing our sensors, with an F1 score of 0.97, and this could help other researchers collect high-quality fall datasets. 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

In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, $F1$ score, and confusion matrix in both cases, demonstrating its strong robustness and stability. Full article

In the present paper, an investigation on the natural and anthropic radioactivity and heavy metals content in a basalt aggregate for concrete from Sicily, Southern Italy, was performed as a case study. In particular, the evaluation of the specific activity of radium-226, thorium-232, potassium-40 and caesium-137 radionuclides was performed by using High-Purity Germanium (HPGe) γ-ray spectrometry, together with the estimation of several indexes developed to evaluate the radiological risk for the population related to radiation exposure, i.e., the alpha index (I_α), the radium equivalent activity (Ra_eq), the absorbed γ-dose rate (D) and the annual effective dose equivalent outdoor (AEDE_out) and indoor (AEDE_in). Moreover, measurements of the average heavy metals (arsenic, cadmium, copper, mercury, nickel, lead, antimony, thallium and zinc) concentrations in the analyzed sample were performed by using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Furthermore, with the aim to investigate any possible chemical pollution, the Enrichment Factor (EF), Geo-accumulation Index (I_geo), Contamination Factor (CF) and Pollution Load Index (PLI) were assessed. Finally, the identification of the source of the aforementioned radioisotopes of natural origin was carried out by X-ray diffraction (XRD), thus identifying the major mineralogical phases present in the investigated basalt aggregate for concrete. Full article