Healthy diets can slow disease progression, but their effectiveness may decrease. Patients often give up these diets due to limited food choices, unappetizing meals, and reduced physical activity from cutting calories. To address this, we developed an intelligent nutritional balance system to prevent cardio-diabetic diseases. This system creates diets that optimize cholesterol and glycemic control through the following steps: (a) Characterizing Moroccan foods based on 19 nutrients and their glycemic load, (b) Classifying foods using a Gaussian mixture model, (c) Modeling the optimal diet with a fuzzy mathematical model using recommendations from the WHO, USDA, and FAO, (d) Solving the model with a genetic algorithm, (e) Translating portions and food groups to meet constraints, and (f) Resolving the final model using the backtracking method. We implemented this strategy based on the main foods consumed in Morocco, considering different levels of belief (0.25, 0.5, 0.75) regarding the glycemic load of these foods. The results show that the custom artificial diets align with WHO, USDA, FAO, and DGA recommendations. The menus are flexible, allowing for substituting expensive or rare foods with more affordable and readily available alternatives without compromising the quality of the diets.

Cloud computing has transformed how companies utilize and manage resources, but effectively balancing loads presents a challenge. Traditional load balancing techniques have resulted in efficiency loss and increased latency. With artificial intelligence (AI) and machine learning, predictive load balancing has emerged as a viable solution, providing real-time resource distribution and performance improvement. In this study, a systematic literature review of AI-based techniques for load balancing is conducted. Findings highlight AI capabilities in terms of increased scalability, reduced energy consumption, and increased service availability. Computational complexity and training overhead present ongoing challenges, necessitating continued investigation into hybrid AI models to enhance adaptability and efficiency.

This article studies salary prediction under distributional drift using explainable boosting models and hybrid forecasting. We integrate unseen-aware feature engineering, robust objectives, SHAP-based interpretability, drift detection, and time-series forecasting (Prophet/SARIMAX) on multi-year data (2020–2024), and report a comprehensive evaluation aligned with typical MMC guidelines. Modern salary data are heterogeneous, heavy-tailed, and non-stationary. Therefore we combine robust tree-based learners with drift monitoring and explainable forecasting to prioritize stable absolute error, transparency, and maintainability over raw variance capture. Our best integrated pipeline reaches R2=0.31 on a 2024 hold-out while keeping MAE/RMSE stable across folds, and uncovers year-to-year drift that necessitates periodic retraining monthly and quarterly forecasts indicate a sustained upward trend with seasonality, where SARIMAX captures short-term fluctuations and Prophet yields interpretable trend decompositions.

Commuting graph for a finite group G, denoted by ΓG, with its set of vertices G∖Z(G), where Z(G) is the center of G, is a graph with vp,vq∈G∖Z(G), vp≠vq, are adjacent whenever vpvq=vqvp. In recent years, there has been significant research into the energy of graphs, particularly focusing on matrices associated with the degree of vertices. Therefore, motivated by that, our study elaborates on the energy of ΓG for dihedral groups of order 2n, D2n, concerning some graph matrices related to the degree of elements of D2n∖Z(D2n) and examine the correlation between those energies. The matrices involved are known as geometric-arithmetic, symmetric division deg, degree exponent, inverse sum indeg and Sombor matrices. Based on these five matrices, it is found that the lowest graph energy is the geometric-arithmetic energy of ΓG whilst the highest is the degree exponent energy. Furthermore, the geometric-arithmetic, symmetric division deg, and degree exponent energies are always positive even integers. In contrast, the inverse sum indeg energy is a positive integer that can be either even or odd. Meanwhile, the Sombor energy is never an odd integer.

Sports line marking is essential in Malaysian schools, with transfer wheel markers being the most commonly used machines. However, these machines have issues such as clogging from loose grass and soil, unstable tyres, and poor handling. This study aims to improve the design using the Quality Function Deployment (QFD) model integrated with the Fuzzy Analytic Network Process (FANP) method. A questionnaire based on user interviews identified the user needs, which were quantified and processed to address uncertainties in evaluating customer and engineering requirements (CRs and ERs). From the integration, weight ranked highest in importance (0.378), followed by tyre friction (0.193), handle height (0.156), and oil consumption (0.092). This shows that the designers should prioritize machine weight, tyre quality, handle height, and marking oil efficiency. Further research could incorporate multi-objective optimization to balance cost, sustainability, and manufacturability, demonstrating the QFD-FANP framework's adaptability to other industries.

This paper proposes a novel hybrid framework, Q-IG, to solve the permutation flow shop scheduling problem with sequence-dependent setup times (PFSP-SDST). Recent advancements in learning-based methods demonstrate significant potential in addressing flow shop scheduling, yet they often struggle with the enormous solution space and the design of effective reward functions. To overcome these challenges, Q-IG integrates the iterated greedy metaheuristic (IG) with Q-learning. It begins by applying the Nawaz–Enscore–Ham (NEH) heuristic to generate high-quality initial solutions. During each IG iteration, the schedules are partially destroyed and reconstructed, and Q-learning is used to guide the selection of neighborhood moves that are expected to minimize makespan. We evaluated both standalone IG and the Q-IG hybrid in the Taillard benchmark suite, measuring performance through a relative percentage deviation from the best-known makespan. The results demonstrate that Q-IG outperforms its competitors, confirming its effectiveness for PFSP-SDST and highlighting the promise of incorporating Q-learning into traditional metaheuristic approaches.

Network topology can be used to simplify the complexity of the datasets. We are exploring its function in performing survival analysis to identify the most important factors that contributed to the survival time from diagnosis to death. This technique has the potential to illustrate easily some types of complex interactions in dataset. Then, based on those interactions, the most important factors in survival analysis are identified. However, network topology based on classical estimator is extremely sensitive to outlying observations, i.e., conclusions drawn from contaminated network topology could be misleading. Hence, in this paper, the classical estimator, i.e., classical correlation matrix of network topology is substituted with robust estimator, i.e., robust correlation matrix which is developed based on Index Set Equality (ISE). Then, the interpretation of that robust network topology is delivered by using centrality measure, i.e., degree centrality. A case study of the survival time for cervical cancer patients is presented and discussed. Robust network topology revealed that the most important factors that influence the survival of cervical cancer patients is stage at diagnosis (STG). The higher stage of cervical cancer led to shorter survival time of cervical cancer patients. Consequently, early diagnosis is very important. Early diagnosis of cancer allows early intervention to try to slow or prevent cancer development and lethality, hence, the survival improves.

This study presents new numerical findings that analysis Bonded Materials (BMs) subject to various mechanical loadings that weakened by a curvilinear crack. This crack is located in the upper part of BMs and exposed to the shear, normal, tearing, and mixed loads. This issue arises from a previous study that focused solely on shear stress as a single type of mechanical loading. By utilizing the Modified Complex Potentials (MCPs) function, along with smoothness constraints for the resultant force and displacement function and the crack opening displacement (COD) component as unknowns, the problem is formulated into hypersingular integral equations (HSIEs). The material strength associated with the curvilinear crack is then evaluated numerically by solving the HSIEs using the curve length coordinate method and Gauss quadrature procedures to determine the nondimensional stress intensity factors (NSIFs). Graphical representations vividly illustrate the significant impacts of mechanical loadings, elastic constant ratios, and geometrical parameters on the NSIFs at the crack tips. The results show that elastic constant ratios, crack geometries, and mechanical loadings collectively influence a material's strength. Notably, mixed loads and certain elastic ratios increase stress concentration near the crack tip, while sharper crack curvatures further amplify stress, underscoring the importance of these factors in evaluating crack vulnerability and material strength under complex loading conditions.

The evaluation of scientific publications is a cornerstone of scholarly research, providing essential insights into the impact, significance, and intellectual contributions of research outputs. Traditional bibliometric indicators, including Impact Factor (IF), h-index, and citation counts, have historically been the dominant measures to assess research quality. However, with the rapid evolution of Artificial Intelligence (AI) and its increasing integration into various scientific disciplines, these conventional evaluation methodologies are being reevaluated due to their inherent limitations and lack of adaptability to modern research landscapes. This study conducts an in-depth analysis of both citation-based and content-driven assessment methodologies, critically assessing their efficacy in contemporary academic evaluation. The study highlights the fundamental constraints of traditional bibliometric metrics, particularly their inability to fully encapsulate the qualitative dimensions of scientific contributions. In response, we explore state-of-the-art AI-driven approaches, employing advanced Natural Language Processing (NLP) and machine learning (ML) techniques to refine and enhance the precision, contextual understanding, and multidimensional assessment of research quality. Building on these advancements, we propose a novel AI-based assessment metric aimed at addressing the deficiencies of conventional approaches, offering a more comprehensive and nuanced evaluation framework. Empirical case studies are used to validate the practical implementation of this metric, allowing for direct performance comparisons with established bibliometric techniques. In addition, we examine the broader implications of AI-driven assessment methodologies, discussing ethical considerations, algorithmic biases, and the challenges associated with their large-scale adoption. The study concludes by advocating for the integration of AI-enhanced assessment tools in academic publishing and research evaluation, providing strategic recommendations for researchers, journal editors, funding agencies, and policymakers to promote a more rigorous, equitable, and transparent scientific assessment ecosystem.

In this paper, we establish the existence of a class of discrete nonlinear systems involving the anisotropic p⃗(⋅)-Laplacian operator using an optimization based approach. We then simulate the solutions by implementing a deep learning model. The numerical results demonstrate that the proposed method is stable and robust compared to conventional approaches such as the Newton–Krylov method.