This work investigates the stability of a linear oscillator with a periodically varying stiffness composed of constant and linearly time-dependent segments. By combining Floquet theory with an analytical formulation in terms of Airy functions, the monodromy matrix is obtained in closed form and the characteristic multipliers that determine the stability regime are calculated. Unlike the classical literature on Hill or Mathieu systems, where the stiffness profile is assumed to switch instantaneously or vary sinusoidally, the present model explicitly incorporates finite transition times through linear ramps. This allows us to quantify how the duration of these transitions affects the onset of parametric resonance. The resulting stability map reveals alternating bands of stable and unstable regions reminiscent of Arnold tongues, and shows that the proportion of the cycle spent in the linear-ramp stage plays a decisive role in either promoting or suppressing instability. Overall, the study provides a compact analytical and numerical framework for assessing stability in periodically driven parametric systems of practical relevance in physics and engineering.

The stock market, a cornerstone of the global financial system, is characterized by its dynamic and volatile nature, which makes accurate price-trend prediction challenging. However, traditional statistical models often fail to capture this complexity. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have transformed stock market forecasting by using diverse datasets and algorithms. This review examines recent studies on AI methodologies for stock market price trend prediction models by analyzing architectures, datasets, performance metrics, and limitations, with a focus on hybrid models, sentiment analysis, and dataset diversity. Hybrid approaches, including the Multi-Model Generative Adversarial Network Hybrid Prediction Algorithm (MMGAN-HPA), K-means long short-term memory (LSTM), and LSTM autoregressive output (LSTM-ARO), improve predictive accuracy by combining statistical methods with deep learning. Sentiment analysis models such as Stock Senti WordNet (SSWN) and Hybrid Quantum Neural Network (HQNN) integrate social media sentiment to capture market dynamics. Real-time frameworks that use stream processing show promise for high-frequency trading applications. This review addresses key challenges including data noise, nonstationarity, overfitting risks, and black-box model interpretability. Solutions include GAN-based synthetic data generation, transformer-based architectures such as SpectralGPT, and optimization techniques for computational efficiency. This review provides a taxonomy of AI-based approaches, while identifying gaps for future research. These findings highlight the potential of AI in financial forecasting while emphasizing the need for interdisciplinary collaboration to address its limitations in data quality, methodology, interpretability, and ethics.

Rolling bearings are key components of rotating machinery such as electric motors, and their health status directly affects the reliability and safety of the equipment. In order to improve the fault classification accuracy of electric motor rolling bearing, this paper proposes a diagnostic method based on CMFSE-SVM. Firstly, the composite multi-scale fuzzy slope entropy (CMFSE) method proposed in this paper is used to extract the characteristics of the vibration signal of the motor rolling bearings. Finally, the obtained feature vectors are sent to the support vector machine (SVM) for fault classification. This paper verifies the classification accuracy of the method proposed in this paper on two publicly available datasets of electric motor rolling bearing faults. The experimental results show that the method proposed in this paper achieves average classification accuracies of 100 % and 99.6 % respectively on all working conditions corresponding to these two datasets. And the classification accuracies were 2.4 % and 2.8 % higher respectively than those of the compared methods.

Trapezoidal and circular channels are commonly employed as conveyances of open channel flow in irrigation canals, for storm and wastewater drainage and at times for transporting raw water. Open channel flow problems in such cross sections typically involve determination of alternate depths, critical depths and specific energy that require solving equations for which explicit solutions are difficult to obtain. This paper demonstrates the development of a solution for such problems through the use of single universal dimensionless specific energy-depth curves for each of the trapezoidal and circular shaped channels. The dimensionless curves are demonstrated to be simple to develop and use and can be adapted for wide range of user environments. Simulation of the graphical solution over broader range of flows and dimensionless depths of flow shows that the solution is reasonably accurate with percentage error of estimation of depth varying between 0 and 4 % with mean percentage error of 1.1 % for trapezoidal shaped channels and 1.2 % for circular shaped channels. Further application examples have been provided that show that the proposed graphical procedure can achieve results that are within close range of the precision of results obtained using numerical methods.

This research utilized a multilayer perceptron model based on an artificial neural network (ANN) to improve concrete blocks by partially replacing stone dust and cement with coconut shell and coconut shell ash. From 35 experimental data points, 68.6 % were used for training and 31.4 % for testing. Chemical analyses showed that coconut shell ash, rich in silicon and other oxides, enhanced pozzolanic reactions as a cement substitute. Results indicated that higher proportions of coconut shell ash and shells reduced density, making the mixture lighter but potentially affecting structural integrity. However, increased replacement levels improved workability, shown by higher slump values. The curing period significantly impacted strength, with longer times leading to increased strength due to continued hydration. An optimal compressive strength of 14.17 N/mm2 was achieved with a 5 % replacement after 70 days. The ANN model demonstrated a sum square error of 0.198 and a high correlation coefficient of 0.983 when predicting strength. Optimal conditions for achieving a compressive strength of 33.4 N/mm2 were identified, underscoring the cost-effectiveness of using coconut byproducts instead of traditional materials.

A multi-dimensional repair approach is the most effective policy for restoring repairable systems. This research study analyzes a complex repairable system that consists of four subsystems with different configurations. The system consists of four subsystems: Subsystem 1 contains a single unit, Subsystem 2 includes n identical units operating under a k-out-of-n: G policy, Subsystem 3 features two identical load balancers responsible for distributing the load (with at least one required for operation), and Subsystem 4 consists of four identical units functioning under a 3-out-of-4: G policy. It is assumed that all subsystems have constant failure rates that are distributed exponentially. General repair, which is utilized while the system continues to function in accordance with the established operating policy, and copula-based repair, which is employed when the system stops completely, are the two types of repair techniques that are put into practice. A supplementary variable approach is incorporated to analyses system performance. Various reliability measures are computed using Maple 18, software, and future behaviour of system have been predicted in long run operation. By means of illustrations in the tables and graph it clearly shown how the copula repair is beneficial over ordinary repair.

The natural vibration frequency analysis of beams is vital for their design against resonance failures because such failures occur when the excitation load frequencies of vibration coincide with such natural frequencies. This work presents a single variable shear deformable beam equation formulated using Shimpi’s displacement field assumptions. This results in a quadratic shear stress profile over the depth and a satisfaction of the transverse shear stress-free boundary conditions. The governing equation is obtained using a first principles consideration and equilibrium method as a partial differential equation (PDE) which is non-homogenous for forced vibrations and homogeneous for free vibrations. The study then used the Stodola-Vianello iteration method to solve the resulting homogeneous PDE for simply supported boundary conditions and harmonic response. The problem reduced to an iterative problem of algebra involving the computation of an (n+1)th vibratory modal shape function from an nth shape function that satisfies the boundary conditions. This work used a sinusoidal shape function which is exact for the simply supported boundary condition investigated. The use of boundary conditions solved the integration constants involved. Application of the convergence rule led to the eigenequation from which the eigenvalues were found. The eigenvalues were presented for the first four modes of vibration and for a rectangular beam. It was found that for l/h varying from 5 to 100, the natural vibration frequencies were identical with the ωn values obtained using Navier method for other thick beam vibration problems. It was also found that ωnwas close to the exact values for all vibration modes and for all values of l/h between 5 and 100. For all vibration modes and all considered l/h values negligible differences, were observed between the ωn obtained using SVIM and the exact values obtained by previous researchers.

Serang Regency has a sub-tropical climate and is classified as lowland, generally consisting of rice fields, fields, and gardens. These three lands have the potential to flood during the rainy season, which has implications for a decrease in agricultural production. Flood forecasts are carried out on agricultural land to avoid decreased production. The data used included rainfall, harvest area, and agricultural production. Estimates of farmland flooding and agricultural production changes are overlayed with Geographic Information Systems (GIS) and production percentages. The results of rainfall analysis show that the monthly average rainfall in Serang Regency is categorized as low to high. Meanwhile, heavy rain has the potential to occur in January and February. After being coated with GIS, it is spatially predicted that six sub-districts with an altitude below 10 meters above sea level have the potential to flood. Of the six sub-districts, 2,902.80 ha of agricultural land is estimated to have the potential to be flooded. Flooding in the area has implications for decreasing the production of seasonal crops, vegetables and fruits. Food crop production decreased by 0.42 %, while seasonal vegetables and fruits decreased by 48.04 %. There are five types of food crops and 12 types of vegetables and fruits that have the potential to be developed in Serang Regency. The food crop with the most significant production is rice, while the seasonal production of vegetables and fruits is cucumbers. In the event of a flood, rice production in Serang Regency is predicted to decrease by 1.23 % and cucumber production is expected to decrease by 0.48 %. It is necessary to adopt a planting pattern with a climate approach to minimize the decline in production.

According to the wheel/rail actual dimensions, the modeling process of a 3-D full-size wheel/rail sliding contact finite element model is introduced in detail. During modeling process, the partitioning strategy method and MPC method are adopted. The temperature characteristics of the contact region during sliding contact are researched. The research results show the contact patch shape is close to an ellipse. The stress in the contact area is very concentrated, and the maximum von Mises stress appears in the subsurface at a distance of 2 mm from the contact interface. During the sliding contact, the maximum temperature appears at the contact center. The temperature on wheel contact surface ascends continuously and is significantly greater than the rail surface temperature. High temperatures of contact region are mainly distributed in the contact surface and subsurface, and the influence depth of temperature does not exceed 3 mm.

Explainable Artificial Intelligence (XAI) emerged when researchers attempted to identify methods that would interpret the models that are used to perform classification and predictions, in order to avoid having a black box just informing about the result. Methods of XAI are crucial to determine details of the model feature contribution towards the result. One of these methods is attributed to cooperative game theory and especially Shapley values. With this method the features are considered as players and the marginal contribution of the features are employed. In this paper, we take onboard the Potential Game paradigm to show the interconnection between them and the Shapley values. We show that the Shapley values are interlinked with the potential function. Moreover, we setup a game with the marginal contribution of the players as their utility functions and we prove that the game is a potential game. Finally, we show that the price of stability of this game is 1. We utilise the Simulated Annealing (SA) method to find the optimal solution.