Deep learning-based landslide tsunami run-up prediction from synthetic gage data

The present study proposes a deep learning model based on Long-Short Term Memory (LSTM) that uses gage measurements for prediction of landslide-driven maximum tsunami run-up. In an attempt to overcome the limitation of insufficient real-world data in the field, our methodology refers to analytical models to create a comprehensive dataset employing a time series recorded from an offshore gage as input and its corresponding maximum run-up at the shoreline as output, for different landslide scenarios with pre-determined parameters. The LSTM-based model is then trained using this dataset in order to predict the maximum run-up. The results, with mean values of 0.211 m, 0.149 m, 1.745% and 0.9988 for RMSE, MAE, MAPE and R2, respectively, indicate that our model is both accurate and precise. As the data-driven models such as the one proposed here are often utilized to identify relationships that may not be immediately apparent from the physical models alone, our interdisciplinary approach has the potential to foster the development of innovative solutions and methodologies for addressing complex natural hazards by enhancing early warning systems, preparedness and response to tsunamis

A novel repair method for the lifespan and performance improvement of a shell-and-tube heat exchanger: A thermo-mechanical approach

Heat exchangers play a critical role in the functioning of many engineering systems. Shell-and-tube heat exchangers (STHEs) are more traditional and widely used devices due to their efficiency, versatility, and ability to handle a range of flow conditions and fluid types. STHEs experience a number of problems over time, including corrosion, mechanical wear, or leaking, and thus need repairs to keep operating. This study has introduced a novel repair approach for extending the lifespan of damaged STHE tubes by fitting new tubes. An original thermo-mechanical model, including the analyses of the STHE, thermal contact resistance between the fitted tubes, and mechanical design of the built structures, is proposed for the problem solution, and all governing equations are simultaneously solved in Engineering Equation Solver (EES). All submodels are validated with analytical or experimental data, and good agreements are obtained. The most significant design parameters and their effects on the thermal and mechanical performances of an STHE are parametrically investigated. Results reveal that increasing the contact surface slope over 10 degrees but lowering the effective surface roughness below 3 mu m provides an advantage for keeping the heat load of the STHE high. Among the interference fits, the locational interference fit is the most advantageous in terms of thermal and mechanical performances relative to other fit conditions. Both increasing operating pressure and tube diameter are two key pillars that can allow for a safety factor > 1.5. Fitting tube materials are parametrically independent and applicable to any STHE tube diameter as the yield strength > 300 MPa. Even if all tubes are press-fitted, the maximum heat load drop in the current repair method corresponds to 4.23 % which is lower than the tolerable value i.e., <10 % of the initially planned heat load

Analytical study on mild steel corrosion inhibition in acidic environment: DFT modeling and RSM optimization

This study investigates the corrosion inhibition potential of various heterocyclic compounds, including 1,3-Thiazole-4-carbothioamide, 4-aminopyrazolo[3,4-d]pyrimidine, pyrimidine-2-thiocarboxamide, 1,2,4-oxadiazole-3carbothioamide, 1H-imidazole-4-carbothioamide, 2-methyl-1,3-thiazole-4-carbothioamide, 4-aminothieno[2,3d]pyrimidine-2-thiol, and 2-isopropyl-4-methyl-1,3-thiazole-5-carboxylic acid, selected for their structural characteristics that make them effective in fuel applications. The presence of functional groups such as thiol, amide, carboxylic acid, imidazole, and thiazole in these compounds enhances their ability to adsorb onto metal surfaces, forming protective layers that significantly inhibit corrosion. These compounds were chosen not only for their strong interaction with metal substrates but also for their stability and durability under various environmental conditions, which are important for fuel systems. Density Functional Theory (DFT) calculations were performed to give structural insights, which are essential for understanding the corrosion inhibition mechanism of the examined compounds. The inhibition performance of these molecules were investigated in 0.5 M HCl via electrochemical impedance spectroscopy technique for mild steel (MS) containing various inhibitor concentrations (1;3 and 5 mM) and exposure times (1; 24 and 48 h). Particularly, the higher inhibition efficiency of compounds; 2-methyl-1,3-thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol from their structural and electronic properties. The variable inhibition efficiency observed among different compounds investigates the importance of methods Response Surface Methodology (RSM) for systematically analyzing concentration, time, and molecular structure interactions. The experimental results indicated that 2-methyl-1,3thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol exhibited significantly higher inhibition efficiency at a concentration of 5 mM and an exposure duration of 48 h, with inhibition efficiencies of 98.96 % and 98.66 % respectively

A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction

The current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of T & uuml;rkiye. The performance and error metrics, including determination coefficient (R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2$$\end{document} ), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2$$\end{document} of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2$$\end{document} of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction

An efficient control scheme for operational performance enhancement of vehicular fuel cell integrated power system

Recent advancements in fuel cell (FC) technology have positioned it as a promising alternative energy source across various stationary, mobile, and transportation applications. As fuel cell vehicles (FCVs) become increasingly prominent in the transportation sector, they also offer the potential to function as supplementary stationary energy providers when parked, thereby contributing to the grid. In this study, a control method to enhance the energy transfer capability of an FCV-integrated grid system is proposed. To manage energy transfer between grid and FCVs, classical strategies using fixed parameter controllers suffer from performance degradation due to the nonlinear and external parameter-dependent nature of the FC stacks. The proposed adaptive fractional-order proportional-integral strategy bears the advantage of self-tuning parameters feature for designing the control parameters of power conditioning unit. Using fractional-order control endows the system with memory and heredity, enhancing its ability to handle nonlinearity and uncertainty. Through case studies, it is demonstrated that the proposed strategy reduces vehicular FC output power variations by over 60 % while enhancing transient response by 50 % compared to classical control methods. Thus, the limitations such as low control flexibility, slow transient response, and high undershoot/overshoot rates addressed by the classical controllers are countered thanks to the developed strategy. © 2024 Elsevier B.V

K-Salp Swarm Anomaly Detection (K-SAD): A novel clustering and threshold-based approach for cybersecurity applications

Anomaly detection is a critical task in various domains, particularly in cybersecurity, where ensuring data integrity and security is paramount. In this study, we propose a novel approach to anomaly detection utilizing both the K-medoid and Salp Swarm Algorithms. Our methodology involves clustering the data using K-medoid and determining thresholds with an improved Salp Swarm Algorithm, enabling the identification of outliers within datasets. We conducted experiments on real-world datasets to evaluate the effectiveness of our approach. Significantly, proposed method surpassed alternative methods in performance across 5 of the 10 datasets, thereby showcasing its superior efficacy. For example, It demonstrated superior performance compared to alternative methods, achieving an AUC value of 0.8651 on the Thyroid dataset. Additionally, our approach yielded outcomes falling within the average spectrum across 3 datasets. These observations underscore the effectiveness of our proposed method in factifying anomaly detection methods and factifying cybersecurity protocols

Analytical study on mild steel corrosion inhibition in acidic environment: DFT modeling and RSM optimization

This study investigates the corrosion inhibition potential of various heterocyclic compounds, including 1,3-Thiazole-4-carbothioamide, 4-aminopyrazolo[3,4-d]pyrimidine, pyrimidine-2-thiocarboxamide, 1,2,4-oxadiazole-3carbothioamide, 1H-imidazole-4-carbothioamide, 2-methyl-1,3-thiazole-4-carbothioamide, 4-aminothieno[2,3d]pyrimidine-2-thiol, and 2-isopropyl-4-methyl-1,3-thiazole-5-carboxylic acid, selected for their structural characteristics that make them effective in fuel applications. The presence of functional groups such as thiol, amide, carboxylic acid, imidazole, and thiazole in these compounds enhances their ability to adsorb onto metal surfaces, forming protective layers that significantly inhibit corrosion. These compounds were chosen not only for their strong interaction with metal substrates but also for their stability and durability under various environmental conditions, which are important for fuel systems. Density Functional Theory (DFT) calculations were performed to give structural insights, which are essential for understanding the corrosion inhibition mechanism of the examined compounds. The inhibition performance of these molecules were investigated in 0.5 M HCl via electrochemical impedance spectroscopy technique for mild steel (MS) containing various inhibitor concentrations (1;3 and 5 mM) and exposure times (1; 24 and 48 h). Particularly, the higher inhibition efficiency of compounds; 2-methyl-1,3-thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol from their structural and electronic properties. The variable inhibition efficiency observed among different compounds investigates the importance of methods Response Surface Methodology (RSM) for systematically analyzing concentration, time, and molecular structure interactions. The experimental results indicated that 2-methyl-1,3thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol exhibited significantly higher inhibition efficiency at a concentration of 5 mM and an exposure duration of 48 h, with inhibition efficiencies of 98.96 % and 98.66 % respectively

ANFIS-SA-based design of a hybrid reconfigurable antenna for L-Band, C-band, 5G and ISM band applications

This study presents a novel hybrid reconfigurable antenna design optimized using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with a Simulated Annealing (SA) algorithm for L-band, C-band, 5G, and ISM applications. The antenna is fabricated on an FR-4 substrate with dimensions of 17 x 28 x 1.6 mm3, and two PIN diodes are employed to achieve frequency and radiation pattern reconfigurability. In the ON-ON state, the antenna operates in dual bands, covering 1.33-1.38 GHz (L-band) and 3.57-3.95 GHz (C-band). For the OFF-ON state, it operates from 3.56 to 3.95 GHz (C-band, 5G). In the ON-OFF state, it covers 1.50-1.54 GHz (L-band) and 5.66-5.90 GHz (ISM band), while in the OFF-OFF state, it operates from 5.49 to 5.82 GHz (ISM band). The antenna exhibits common bands at 3.8 GHz (C-band) and 5.8 GHz (ISM) across different states, facilitating pattern reconfigurability. ANFIS-SA is applied to optimize the switch locations, significantly improving resonance frequency and S11 performance. The antenna supports beam steering between 0 degrees and 180 degrees, enhancing adaptive coverage for modern applications such as Wi-Fi, Vehicle-to-Vehicle (V2 V), and Vehicle-to-Infrastructure (V2I) communication. This study addresses a critical gap by combining hybrid optimization techniques to improve frequency agility and radiation pattern control for next-generation wireless systems

A cross-cultural latent profile analysis of university students' cognitive test anxiety and related cognitive-motivational factors

The successful treatment of test anxiety treatment requires an understanding of the unique barriers and challenges faced by test-anxious students. Therefore, the current study utilized a combination of person-centered and qualitative methods to investigate the existence of unique subpopulations or subtypes of test-anxious students within Turkish and United States student samples. University students (N = 422) completed measures of cognitive test anxiety, self-efficacy, academic buoyancy, failure appraisal, academic self-handicapping, and goal commitment. Participants also completed open-ended questions assessing facilitators and inhibitors of academic success. The results of a multigroup latent profile analysis identified four learner subtypes in both the Turkish and United States samples. However, our analyses revealed structural differences in latent profiles identified in two cultural contexts. Furthermore, the qualitative results emphasized the importance of self-regulated learning, buoyancy, goal commitment, and self-actualization to academic success. Our discussion emphasizes the importance of considering learners' unique characteristics when designing educational supports. Students from both samples were found to be differentiated into unique subgroups, capturing a broad variation in test anxiety severity from low to moderate to high. One key takeaway for practitioners was the finding that students who question their ability to implement effective self-regulated learning strategies are prone to elevated test anxiety. Students' responses revealed noteworthy parallels with the quantitative results. Specifically, students characterized by elevated self-efficacy, effective regulation of their learning strategies, and unwavering commitment to academic goals were more likely to persist despite encountering academic setbacks. One notable pattern that was identified is the relatively consistent relationship between academic buoyancy and test anxiety observed across profiles within both cultural contexts

HBDFA: An intelligent nature-inspired computing with high-dimensional data analytics

The rapid development of data science has led to the emergence of high-dimensional datasets in machine learning. The curse of dimensionality is a significant problem caused by high-dimensional data with a small sample size. This paper proposes a novel hybrid binary dragonfly algorithm (HBDFA) in which a distance-based similarity evaluation algorithm is embedded before the dragonfly algorithm (DA) searching behavior to select the most discriminating features. The two-step feature selection mechanism of HBDFA enables the method to explore the feature space reduced by the distance-based similarity evaluation algorithm. The model was evaluated on two datasets. The first dataset contained 200 reports from 4 evenly distributed categories of Daily Mail Online: COVID-19, economy, science, and sports. The second dataset was the publicly available Spam dataset. The proposed model is compared with binary versions of four popular metaheuristic algorithms. The model achieved an accuracy rate of 96.75% by reducing 66.5% of the top 100 features determined on the first dataset. Results on the Spam dataset reveal that HBDFA gives the best classification results with over 95% accuracy. The experimental results show the superiority of HBDFA in searching high-dimensional data, improving classification results, and reducing the number of selected features