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Technology has become a fundamental means to encourage reliable and more effective assessments. Rapid technological developments have led to the widespread use of digital platforms and devices in all aspects of life. Educational institutions worldwide had to take advantage of this technological leap during pandemics such as COVID-19, which changed the shape of higher education and prompted the entire globe to adopt online learning as a new form of teaching. E-learning has ushered in a revolution in the educational process. E-assessment has become a significant tool of e-learning in many parts of the world, and has served as an alternative to evaluate students' performance. E-assessment has many advantages such as being reliable, flexible, and accessible through many devices. However, it is unfamiliar to both teachers and students and vulnerable to piracy, cheating, and impersonation. Thus, this study aims to investigate the challenges of e-assessment faced by teachers and students during e-learning at Aligarh Muslim University (AMU), India. The theory of planned behavior (TPB) was applied to test participants' attitudes toward implementing e-assessment during online learning. The study used a quantitative method, and an online questionnaire was delivered to 120 participants. The survey addressed three domains: (1) technological and technical challenges, (2) teachers' challenges, and (3) students' challenges. The findings of the study showed that both teachers and students were unfamiliar with this type of assessment as they used it for the first time.

This article examines the mechanisms of communication between the poet and the audience, focusing on how poetic imagery conveys sensory experiences. It explores techniques that enhance emotional connections and the harmony between the poet’s objectives and the audience's perceptions. Using analytical and psychological methods, the study highlights the psychological, sound, and semantic aspects that influence the effectiveness of poetry. The findings show that poetic composition impacts emotional engagement, evoking both explicit and implicit meanings, creating a shared experience of pleasure and aesthetic harmony, while emphasizing the need for coherence and depth in poetic elements.

Abstract Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

For the first time, this study conducts a computational analysis by employing density functional theory (DFT) to investigate the effects of silicon doping as substitutional defects on the structural, electronic, and magnetic characteristics of the L10-FeNi alloy. The aim of this study was to explore the potential applications of Si-doped FeNi compounds as alternatives to rare-earth permanent magnets. For this, we have performed full potential calculations of L10-FeNi with substitutional Si-doping within a generalized gradient approximation. Two types of substitutional Si-doping (ONi/OFe) in the Ni/Fe site of the parent alloy have been investigated. The computed formation energy (Ef) indicates that the incorporation of silicon defects increases the structural stability of tetragonally distorted L10-FeNi. Moreover, our findings demonstrate that the FeNi:Si(ONi) in the L10-structure has a stable saturation magnetization (Ms), whereas the FeNi:Si (OFe) has a small reduction in Ms. Therefore, Si-substituted FeNi alloys can be tuned to become a good candidate for permanents magnets.

The hydrothermal synthesis of Ba1−xCoxTiO3 (BCT) ceramic nanocrystals across varied substitution fractions (x = 0, …, 1) is the subject of this study. Hydrothermal synthesis is well known for producing high-purity and well-crystallized nanocrystals. A thorough examination is conducted to examine the effects on the structural and electrical properties of the resultant BCT nanocrystals by altering the cobalt substitution fraction. X-ray diffraction (XRD) is used to analyze the structure, while complex impedance spectroscopy (CIS) is used to analyze the electrical properties. As the cobalt content rises, XRD examination reveals a smooth transition from the ferroelectric BaTiO3 phase to the ferromagnetic CoTiO3 phase, offering extensive insights into the phase composition and crystallographic alterations. This phase shift is important because it creates new opportunities to adjust the properties of the material for particular uses. The electrical activity of BCT nanocrystals is clarified further by CIS measurements. A distribution of relaxation times, frequently linked to complex microstructures or heterogeneous materials, is suggested by the detected non-Debye relaxation. A thermally activated conduction process, in which higher temperatures promote the passage of charge carriers, is suggested by the temperature-dependent increase in conductivity. This behavior is strongly dependent on the cobalt content, suggesting that cobalt enhances electrical conductivity and crystallinity through a catalytic effect. A frequency-dependent dielectric constant that rises with temperature and cobalt content is shown by investigating the dielectric characteristics of BCT nanocrystals. Improved polarization mechanisms inside the material are suggested by this increase in dielectric constant, which may be the result of cobalt ion presence. With a thorough grasp of the dielectric behavior, the examination of the loss angle further validates the non-Debye relaxation process.

The primary emphasis of the present study is to introduce some novel characterizations of the interval-valued $ (\mathcal{I}.\mathcal{V}) $ right symmetric quantum derivative and antiderivative operators relying on generalized Hukuhara difference. To continue the study, we start with the concept of symmetric differentiability in the interval-valued sense and explore some important properties. Furthermore, through the utilization of the $ (\mathcal{I}.\mathcal{V}) $ symmetric derivative operator, we develop the right-sided $ (\mathcal{I}.\mathcal{V}) $ integral operator and explore its key properties. Also, we establish various $ (\mathcal{I}.\mathcal{V}) $ trapezium-like inequalities by considering the newly proposed operators and support line. Later on, we deliver another proof of the trapezium inequality through an analytical approach. Also, we present the numerical and visual analysis for the verification of our results.

Abstract This study explores the extension of Milne-type inequalities to the realm of Katugampola fractional integrals, aiming to broaden the analytical tools available in fractional calculus. By introducing a novel integral identity, we establish a series of Milne-type inequalities for functions possessing extended s-convex first-order derivatives. Subsequently, we present an illustrative example complete with graphical representations to validate our theoretical findings. The paper concludes with practical applications of these inequalities, demonstrating their potential impact across various fields of mathematical and applied sciences.

This note focuses on establishing a fractional version akin to the Schweitzer inequality, specifically tailored to accommodate the left-sided Riemann-Liouville fractional integral operator. The Schweitzer inequality is a fundamental mathematical expression, and extending it to the fractional realm holds significance in advancing our understanding and applications of fractional calculus.

This paper introduces a novel parametrized integral identity that forms the basis for deriving a comprehensive class of generalized fractional integral inequalities. Building on recent advancements in fractional calculus, particularly in conformable fractional integrals, our approach offers a unified framework for various known inequalities. The novelty of this work lies in its ability to generate new and more general inequalities, including Hermite–Hadamard-, Maclaurin-, and corrected Maclaurin-type inequalities, by selecting specific parameter values. These results extend the scope of fractional integral inequalities and provide new insights into their structure. To demonstrate the practical applicability and accuracy of the theoretical findings, we present a detailed numerical example along with graphical representations.

Résumé : L’étude présentée dans cet article se propose d’évaluer la charge socio-lexico-sémantique d’une désignation purement algérienne, à savoir « el kahwi », qui a suscité toute une polémique sur les réseaux sociaux à la suite de la publication de la vidéo « Pour la première fois un documentaire sur les ‘’kahwis’’ en Algérie » par le comédien Anes Tina. Cette polémique est motivée essentiellement par le fait que ladite désignation ainsi que son utilisation dans la vidéo auraient un effet de sens racisant. Notre objectif est alors de savoir, dans une perspective critique, si cette désignation, qui étymologiquement désigne une couleur, connait en effet un glissement sémantique dangereux. Les résultats de notre évaluation montrent, d’un côté, que cette désignation est discriminatoire et, de l’autre côté, que cette vidéo participe à l’alimentation d’un mépris social et d’un régionalisme caché qui pourraient se transformer en un phénomène d’expulsion. Mots-clés : désignation el kahwi ; glissement lexico-sémantique racisant ; Anes Tina ; plans filmiques ; remarques critiques

Polyethylene terephthalate (PET) is a widely used plastic that accounts for almost 18 % of global polymer production and is non-biodegradable, making it a major cause of environmental pollution. Innovative solutions are needed to address PET waste management and reduce its impact. This study aims to investigate the impact of adding PET, on the engineering characteristics of a Stone Mastic Asphalt (SMA-20) mixture. Laboratory tests were conducted to assess the volumetric and mechanical properties of asphalt mixes containing different proportions of PET as fine and filler (ranging from 0 % to 30 %) by volume of aggregates. The results show that the PET incorporation into the asphalt mixture has significantly improved the performance. The addition of PET (fine and filler) to asphalt mixtures significantly increases their resilient modulus (MR). The mixtures containing 10 % PET as fine and filler are 20 % and 22 % higher in MR than the control mixture. Moreover, the moisture assessment shows that the PET mixture has a higher tensile strength ratio (TSR) value of 85.9 % and 83.90 % for Fine-10 and Filler-20, thus meeting the AASHTO requirement of ≥80 %. The utilization of PET as aggregate replacement significantly improves the permanent deformation characteristics of asphalt mixtures by decreasing permanent strain. The Fine-20 and Filler-20 revealed promising results by reducing the strain to 0.92 % and 0.81 %. Also, the enhancement in the creep stiffness modulus with 216 and 244 MPa. Furthermore, the addition of PET up to a concentration of 20 % had a beneficial effect on the fatigue response of mixtures. For instance, the failure cycles of the Filler-20 and Fine-10 are 23,231 and 17,521 cycles at a load of 2250 N, whereas the control mixture only endures 8581 failure cycles. In conclusion, PET addition significantly improves SMA properties, promoting waste material utilization in the pavement industry.

In the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges. More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. These models may be used to estimate the compressive strength of masonry or repair mortars, probable damage scenarios in buildings, concrete models, beams, and columns for determining the mechanical characteristics of materials, damage detection in civil structures, and so on. This comprehensive review aims to clarify the array of ML-based methods employed in civil engineering, specifically focusing on their efficacy in strengthening energy efficiency and cost-effectiveness. In combination with ML, the review explores corresponding soft computing methodologies such as fuzzy logic (FL) and design of experiments (DOE). A variety of case examples that highlight the versatility of these approaches, particularly in applications linked to structural reinforcement, enhance the story. The review navigates difficulties associated with the integration of soft computing in civil engineering and expands its scope to include emerging research directions. This synthesis of advanced artificial intelligence (AI) serves as a guide, providing new researchers with knowledge about a developing field. These methods could revolutionize the current situation by providing creative answers to complex problems that arise in civil structural applications.

Background and purpose: Cardiovascular diseases are the most important diseases of the elderly, and recently the effect of triglyceride glucose (TYG) index as one of the indicators of insulin resistance in type 2 diabetes and coronary artery disease has attracted the attention of researchers. Previous research has shown a link between insulin resistance and coronary artery calcification. Since triglyceride glucose index (TyG) is an index that is easily available, this study aimed to investigate the relationship between triglyceride index and fasting blood sugar and coronary heart disease and diabetes in the elderly of Amirkola. Materials and methods: This cross-sectional study was conducted on the elderly participating in the second phase of the Amirkola Health and Aging Cohort Study (AHAP). The elderly were divided into two groups of 200 people, the first group was diabetics with cardiovascular disease and the second group was diabetic elderly without cardiovascular disease. Patient information including age, sex, body mass index, diabetes, coronary artery disease, history of dyslipidemia, medications, triglycerides, fasting blood sugar, smoking habits, systolic and diastolic blood pressure, cholesterol, creatinine, physical activity, and HbA1c were collected in a checklist. The TYG index was calculated based on the LN formula [fasting triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Ultimately, SPSS version 25 software was utilized to describe the data using mean, standard deviation, and percentage. The qualitative variables were compared with the coronary artery disease using the chi-square test, while the quantitative variables were compared using the independent t-test in univariate analysis. Furthermore, the process of adjusting for confounding variables was carried out by using multivariate logistic regression. Results: In this study, 400 diabetic elderly participants were examined based on the relationship between triglyceride glucose index and coronary artery disease. The average age of the elderly with coronary artery disease was 70.46±6.10 and the elderly without coronary artery disease was 70.50±6.49 years. Triglyceride glucose index in diabetic elderly with and without coronary artery disease was 9.3±0.68 and 9.30±0.65, respectively, which was not statistically significant (P=0.92). Mean creatinine (P=0.004), number of comorbidities (P

Abstract This research aims to scrutinize specific parametrized integral inequalities linked to 1, 2, 3, and 4-point Newton-Cotes rules applicable to local fractional differentiable generalized ( s , P ) $(s,P)$ -convex functions. To accomplish this objective, we introduce a novel integral identity and deduce multiple integral inequalities tailored to mappings within the aforementioned function class. Furthermore, we present an illustrative example featuring graphical representations and potential practical applications.

Abstract The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.