Lebanese International University

This paper develops a record-based framework for detecting structural changes in climate time series, with a focus on daily air temperature. The approach combines three elements: a linear drift model (LDM) to represent progressive warming with approximately stationary daily variability; the concept of δ records, requiring each new record to exceed the previous maximum record level within a fixed threshold, and filtering out records that have become inflated in the presence of trend; a CUSUM-like statistic, calibrated on a Brownian empirical model, for testing changes in the probability of occurrence of records. Theoretically, the main properties of classical records are recalled, applied to the LDM framework, and a numerical integral for the likelihood of δ -record events is derived. Empirically, we analyzed a series of seasonally adjusted daily temperatures over the period 2000–2025. Removing the annual cycle, the data are well described by a linear warming of about 0.45 ^∘C per decade, with a residual daily variability of about 2 ^∘C . The observed numbers of both classical and δ records (with δ equal to one residual standard deviation) are consistent with Monte Carlo performance within the adjusted LDM framework, and the CUSUM bridge statistics remain below the Kolmogorov critical values, indicating that there is no statistically significant breakpoint in record-making. Overall, the results indicate a regime of gradual warming with no detectable structural break in the behavior of extremes and illustrate how record-based utilities may be used for monitoring the non-stationarity of weather extremes.

Various enhancement technologies have been proposed recently to enhance the productivity and efficiency of solar sills. Among these, water surface disturbance, where the surface of saltwater is disturbed using external means, has emerged as one of the most significant, efficient, and cost-effective methods. This approach aims to disrupt the surface tension of the salt water, facilitate the separation of salt ions from water molecules, and reduce the thickness of the water surface, thereby accelerating and increasing the evaporation rates and overall yield. This review work presents a detailed evaluation of recent studies that investigate various water surface disturbance techniques to achieve the highest performance for solar stills. A comprehensive assessment and comparison of their economic visibility, operating principles, classification, performance parameters, and practical applicability have also been conducted. Furthermore, the latest advancements, limitations, and future research directions are highlighted. Providing an evaluative review of these effective techniques represents a very important and new step towards charting the future direction for increasing the performance of solar distillers and overcoming its specific challenges. Among the reviewed technologies, magnetic field methods enhanced water production by 19.6-218 %, while ultrasonic vaporizers achieved increases between 9.3 % and 415 %. Rotary systems showed greater improvements in productivity, increasing by 200-350 % with rotating drums, by 124-660.5 % with rotating discs, and by 51-300 % with rotating wick belts. The maximum production rate among these is obtained in the case of a rotary disc method. Overall, considering both performance improvement and production cost, water surface perturbation is one of the most promising methods to improve the efficiency of solar stills.

Childhood abuse is still a prevalent problem in modern societies. Adolescents with a history of harm become increasingly susceptible to being bullied. One of the overlooked physical and mental damage seems centered around altered perception of one’s body such as muscle dysmorphia. This study assesses the correlation between abuse, bullying victimization and dysmorphia, taking into consideration the co-moderating role of mindfulness and social support among Lebanese adolescents. A convenient sample of 403 participants aged 15 to 18 was included in the study. Adolescents filled demographic questions as well as standardized questionnaires for childhood abuse (Child Abuse Self-Report Scale), bullying victimization (The Illinois Bully scale), muscle dysmorphia (Muscle Dysmorphic Disorder Inventory), social support (Multidimensional Scale of Perceived Social Support) and mindfulness (The Freiburg Mindfulness Inventory). Our results show a significant correlation between history of abuse and bullying in adolescents and pathological concerns over their body. We further illustrate that factors such as mindfulness and a network of support, although negatively correlating with the psychopathological concerns, do not mediate the relationship between abuse, bullying and dysmorphia. Inasmuch as participants with higher levels of mindfulness and social support report less victimization and dysmorphia, taken alone, they are not enough to curb the burden of childhood-induced distress in teens. Addressing this remains a major challenge to avoid long term worsening of physical and mental health during the sensitive developmental stages at school.

Reinforcement learning (RL) techniques have increasingly been integrated into fault diagnosis (FD) and fault-tolerant control (FTC) systems due to their robust feature representation capabilities and adaptability. To facilitate related research, this paper presents a synthesis of recent progress in reinforcement learning methodologies for FD and FTC systems. Initially, it outlines key concepts and formulations of RL systems. Subsequently, it reviews the commonly employed RL architectures, with particular emphasis on model-free, model-based and deep learning methods. Finally, it delves into the challenges faced in current applications of RL-based FD and FTC, covering aspects like imbalanced data, safety and robustness, interpretability and explainability, along with potential solutions. This paper strives to offer a comprehensive roadmap for furthering RL-based FD and FTC research within the community.

The integration of artificial intelligence (AI) in education is prompting a reevaluation of personalized learning terminology and its impact on teaching practices and learner engagement. Personalized learning (PL) involves various instructional strategies tailored to individual student needs and interests, utilizing data and technology to boost engagement and success. The evolving landscape requires a clear understanding of how AI can support personalized learning, distinguishing it from traditional methods. The variability in PL terminology reflects diverse interpretations of AI technologies in education, necessitating a common framework to clarify definitions and practices. This document presents an overview of the latest research literature on personalized learning, highlighting how technology is transforming the framework and effectiveness of individualized learning experiences. By analyzing reputable articles from 6 databases, the review seeks to provide insights into how AI can redefine personalized learning, enabling more precise definitions. The findings emphasize the use of PL terms in technological contexts and call for a unified term to enhance clarity and effectiveness in educational technology practices. Ultimately, the review aims to inform educators and policymakers about precise terms defining personalized learning in the AI context.