The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R²) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves. Full article

One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes. Full article

The quadriceps femoris muscle (QF) is of clinical importance since it has been correlated with pathologies at knee level, such as anterior cruciate ligament (ACL) injury, pain processes and complex clinical conditions. Among the variables that have been related to these clinical conditions are anthropometric measurements, architecture and muscular behavior of the QF. The aim of this study was to determine the relationship between the rectus femoris (RF) and vastus intermedius (VIM) muscles’ behavior measured by rehabilitative ultrasound imaging (RUSI) M-mode under maximal voluntary isometric contraction (MVIC) and anthropometric measurements. This was a cross-sectional, observational study. Sixty-two asymptomatic volunteers were included (20.42 ± 4.97 years, most women 59.7%). RUSI measurements were muscle contraction/rest thickness and contraction/relaxation velocity. Anthropometric measurements were, lower limb length, RF length, QF tendon length, distance between spines, proximal, middle and distal thigh perimeter. Statistically significant correlations (p < 0.05) were found between VIM thickness at rest and contraction with thigh perimetry, RF length and dominant lower limb length. For the RF, a correlation was found between the thickness at rest and the length of this muscle (p = 0.003). There is a correlation between anthropometric variables and muscular behavior measured by RUSI M-mode. Full article

This research article examines the impact of economic, health, environmental, and social-economic factors on diverse forms of pro-environmental consumption: energy conservation, water conservation, and recycling. Primary data concerning these variables were collected from 430 individuals using a structured questionnaire following the cluster sampling methodology. Results indicate that one unit increase in environmental, economic, and health concerns improve pro-environment behavior by 52, 64, and 25 units, respectively. In contrast, a 1 unit increase in income deteriorates pro-environment behavior by 0.01 units. Education, age, gender, and owning a home have an insignificant impact on pro-environmental habits. The model explains a 52% variation in pro-environmental habits. The study recommends that effective electronic and social media campaigns increase environmental, economic, and health concerns and improve green behavior. More courses on environmental sustainability in schools and universities can effectively increase ecological knowledge and concerns. Full article

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area. Full article

Recent developments in engineered timber products, and their availability, durability, and renewability, have led to taller and more flexible buildings. However, these buildings may experience excessive vibrations, resulting in safety and serviceability issues due to wind or earthquake loads. This paper presents a dynamic analysis of a 42-story-tall hybrid-timber building, along with a comparative study of the performance of three damping devices: (i) pendulum pounding tuned mass damper (PTMD), (ii) tuned mass damper inerter (TMDI), and (iii) tuned mass damper (TMD). First, we evaluate the vibration reduction capability of the TMD and the TMDI under filtered white noise and variable frequency sinusoidal excitations. Then, we propose a robust pendulum PTMD designed using the Hertz contact law to minimize the responses under seismic excitations. For a fair comparison, the mass of the TMD, TMDI, and pendulum PTMD is kept the same. The results show that the pendulum PTMD has higher performance and can reduce the peak accelerations under earthquake loads when both TMD and TMDI fail to achieve this requirement. The superior performance of the proposed device in reducing peak accelerations relates to the reduction in damage to structural and nonstructural components under seismic loads. Nevertheless, coupling the inerter and TMD to form a TMDI may shift the optimum frequency and damping ratios, leading to reduced performance. Compared to TMD and TMDI, the proposed pendulum PTMD is more robust, with higher performance in reducing the base shear (55.7%), base moment (41%), and inter-story drift ratio (40%). The dominant capabilities of this novel device in a timber-hybrid building under different excitations reveal benefits that can shape the future of the physical infrastructure and contribute to climate change adaptation and mitigation for improved disaster resilience and circular economy policies. Full article

Natural organic matter, including humic substances (HS), comprises complex secondary structures with no defined covalent chemical bonds and stabilized by inter- and intra-molecular interactions, such as hydrogen bonding, Van der Waal’s forces, and pi-pi interactions. The latest view describes HS aggregates as a hydrogel-like structure comprised by a hydrophobic core of aromatic residues surrounded by polar and amphiphilic molecules akin a self-assembled soft material. A different view is based on the classification of this material as either mass or surface fractals. The former is intended as made by the clustering of macromolecules generating dendritic networks, while the latter have been modelled in terms of a solvent-impenetrable core surrounded by a layer of lyophilic material. This study reviews the evolution of the increasingly refined models that appeared in the literature, all capable to describing the physicochemical properties of HS. All the models are critically examined and revisited in terms of their ability to provide key information on the structural organization of HS. Understanding how the molecular association pathway influences aggregation of HS also provides a key acknowledgment of their role in the environment. Full article

Mobile applications and portable assessments make remote self-assessment of the countermovement jump (CMJ) test possible. This study aimed to investigate the concurrent validity and test–retest reliability of three portable measurement systems for CMJ. Thirty physically active college students visited the laboratory twice, with two days in between, and performed three jumps each day. All jumps were recorded by My Jump 2, HomeCourt, and the Takei Vertical Jump Meter (TVJM) simultaneously. Results indicated significant differences among the three systems (p < 0.01). HomeCourt tended to present the highest jump height mean value (46.10 ± 7.57 cm) compared with TVJM (42.02 ± 8.11 cm) and My Jump 2 (40.85 ± 7.86 cm). High concurrent validities among assessments were found (r = 0.85–0.93). Good to excellent reliability of jump assessments was demonstrated (ICC_3,1 = 0.80–0.96). Reliable coefficients of variation were shown in all measurements (2.58–5.92%). Significant differences were revealed among the three apparatuses while they demonstrated high intra-device test–retest reliability. TVJM was the most reliable, and average jump heights were recommended for analysis. Full article

In this study, the technology for producing ridge waveguides with a minimal roughness of the sidewalls and material surface in a near-waveguide region was developed with the purpose of fabricating miniature photonic integrated circuits on a LiNbO₃ substrate. Plasma etching processes were used for the ridge waveguide fabrication on different material substrates. The specifications of the equipment and plasma source, method of mask fabrication and substrate material determined the process conditions for producing ridge waveguides with minimal sidewall roughness. In this work, for the ridge waveguide fabrication, the processes of reactive ion etching of LiNbO₃ with a chromium mask were carried out in a mixture of SF₆/Ar with an ICP/TCP plasma source. The process of plasma etching the LiNbO₃ with the ICP/TCP plasma source is not well studied, especially for integrated photonics purposes. As a result of our experimental work, the narrow ranges of technological parameters suitable for producing ridge waveguides on LiNbO₃ with smooth sidewalls, a slope angle of 60°–75° and a minimal quantity of observed defects in the near-waveguide region were identified. A model explaining the kinetics of the etching process of LiNbO₃ in SF₆/Ar plasma as a physical–chemical process was proposed. Full article

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses. Full article

Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent−player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent−player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent−player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent−players for soccer simulators on mobile platforms. Full article

We present high-temperature thermodynamic properties for uranium in its γ phase (γ-U) from first-principles, relativistic, and anharmonic theory. The results are compared to CALPHAD modeling. The ab initio electronic structure is obtained from density-functional theory (DFT) that includes spin–orbit coupling and an added self-consistent orbital-polarization (OP) mechanism for more accurate treatment of magnetism. The first-principles method is coupled to a lattice dynamics scheme that is used to model anharmonic lattice vibrations, namely, Self-Consistent Ab Initio Lattice Dynamics (SCAILD). The methodology can be summarized in the acronym DFT + OP + SCAILD. Upon thermal expansion, γ-U develops non-negligible magnetic moments that are included for the first time in thermodynamic theory. The all-electron DFT approach is shown to model γ-U better than the commonly used pseudopotential method. In addition to CALPHAD, DFT + OP + SCAILD thermodynamic properties are compared with other ab initio and semiempirical modeling and experiments. Our first-principles approach produces Gibbs free energy that is essentially identical to CALPHAD. The DFT + OP + SCAILD heat capacity is close to CALPHAD and most experimental data and is predicted to have a significant thermal dependence due to the electronic contribution. Full article

Natural language inference (NLI) is one of the most important natural language understanding (NLU) tasks. NLI expresses the ability to infer information during spoken or written communication. The NLI task concerns the determination of the entailment relation of a pair of sentences, called the premise and hypothesis. If the premise entails the hypothesis, the pair is labeled as an “entailment”. If the hypothesis contradicts the premise, the pair is labeled a “contradiction”, and if there is not enough information to infer a relationship, the pair is labeled as “neutral”. In this paper, we present experimentation results of using modern deep learning (DL) models, such as the pre-trained transformer BERT, as well as additional models that relay on LSTM networks, for the NLI task. We compare five DL models (and variations of them) on eight widely used NLI datasets. We trained and fine-tuned the hyperparameters for each model to achieve the best performance for each dataset, where we achieved some state-of-the-art results. Next, we examined the inference ability of the models on the BreakingNLI dataset, which evaluates the model’s ability to recognize lexical inferences. Finally, we tested the generalization power of our models across all the NLI datasets. The results of the study are quite interesting. In the first part of our experimentation, the results indicate the performance advantage of the pre-trained transformers BERT, RoBERTa, and ALBERT over other deep learning models. This became more evident when they were tested on the BreakingNLI dataset. We also see a pattern of improved performance when the larger models are used. However, ALBERT, given that it has 18 times fewer parameters, achieved quite remarkable performance. Full article

Coastal regions in Southwest Europe have experienced major interventions and transformations of the territory with unprecedented urban development, primarily related to growing tourism activity. The coast is the place where marine and terrestrial processes converge, making it highly vulnerable to the effects of climate change. However, the lack of information on the frequency of these extreme weather events and their impacts on the coast hampers an accurate analysis of the consequences of global change. This paper provides a detailed analysis of the extreme weather events (EWE) that have affected the Atlantic and Mediterranean coasts of Southwest Europe during the period from 1 January 2009 to 28 February 2020, as well as a quantification of their impacts: fatalities, injuries and economic damage. Official sources from France, Portugal and Spain were consulted, along with technical reports, scientific articles, etc., to generate a unified database. A total of 95 significant extreme events have caused 168 fatalities, 137 injuries and almost €4000 M in direct economic losses. Cyclone Xynthia (February 2010) on the French Atlantic coast stands out, having caused 47 fatalities, 79 injuries and substantial economic losses valued at €3000 M. The study shows a slight upward trend in the number of events recorded, especially during the last three years of the analysis, as well as in human losses and damages. The results reveal a higher exposure of the Mediterranean coast of Southwest Europe when compared to the Atlantic, especially the Spanish Mediterranean coast, with 61% of the fatalities recorded there during the study period. This is primarily due to a model of exponential tourism growth on the Mediterranean coast, with an enormous urban and infrastructure development during the last decades. Traditionally, the Mediterranean coast is less prepared to reduce the effects of marine storms, extreme events that are becoming more frequent and virulent in the context of climate and global change. This work highlights the need to create a continuous monitoring system–at the European level–of the impacts of extreme weather events on the coast, where 40% of the European population is concentrated. This observatory should serve as a source of information for risk mitigation policies (predictive, preventive and corrective), as well as for emergency management during disasters. Full article

Parkinson’s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of Parkinson’s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing—a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms—extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)—were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, $F_1$ score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying Parkinson’s disease using RNA-seq data. Full article