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Despite the knowledge that quality early childhood development programs, including those that target parental knowledge and behaviors, are essential for ameliorating the negative effects of early-life adversity, robust analyses of their implementation and impact in highly vulnerable settings are scarce. To address this knowledge gap, we conducted a pilot wait-list randomized controlled trial (RCT) to assess the impact and the process of implementing and evaluating the Mother-Child Education Program (MOCEP) among refugee families and one low-income community in Beirut, Lebanon. This paper focuses on the analysis of MOCEP's implementation (i.e., key enablers of and barriers to the application and evaluation of the program). Our analysis suggests that, despite multiple challenges, implementation and robust evaluations of early childhood parenting programs in fragile contexts are feasible and urgently needed. This study illustrates how implementation evaluations are a key component of RCTs and crucial to identifying strategies to optimize program uptake and maximize impact.

This study aims to determine the ruling on advance payment in Arab laws. Determine the contracts it enters into by tracing the texts of the statutes under study. And analyzing them to understand their provisions, reviewing the most important jurisprudential positions. As well, analyzing rulings, and judicial jurisprudence. The study reached several results, including the discrepancy in legal positions in determining the purpose of the advance payment. Some laws considered it to be an option of cancellation, while others considered its purpose to be an irrevocable option. Additionally, there is a discrepancy in identifying the party with the right to cancel the contract if an advance payment implies cancelation. The study recommended adopting the position of the Kuwaiti and Saudi laws in regulating the advance payment provisions, especially the coolingoff period option and the methods to determine this period. [ABSTRACT FROM AUTHOR]

Objective: This paper used three feature selection methods on a Jordanian automobile drivers' dataset to identify the most significant features for stress prediction algorithm performance. The dataset contains "stress" and "no-stress" classes with 30 features, categorised into physiological and contextual subsets. Methods: Eighteen classifiers from six prediction algorithm categories were evaluated: Rule-based, Tree-based, Ensemble-based, Function-based, Naïve Bayes-based and Lazy-based. Three Feature Subset Selection (FSS) methods were used: Gain Ratio, Chi-square and feature separation. Eight evaluation measures included F 1, Accuracy, Specificity, Sensitivity, Kappa Statistics, Mean Absolute Error (MAE), Area Under Curve (AUC) and Precision Recall Curve Area (PRCA). Results: Among the classifiers, Lazy-based LocalKNN performed significantly well in F 1, Accuracy, Kappa and MAE. Naïve Bayes-based Bayesian Network excelled in other measures. The original dataset with all features yielded the best overall performance, followed by the physiological-only subset. Gain Ratio and Chi-square FSS methods also showed promising results, though not significant. Conclusion: Four physiological (EMG, EMG Amplitude, Heart rate, Respiration Amplitude) and seven contextual (time range of driving, gender, age, driving skills, general accidents, last year's accidents, stress frequency) features contributed to the best prediction outcomes. The study highlights the importance of proper feature selection and identifies optimal algorithms for specific measures. [ABSTRACT FROM AUTHOR]

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as collective outcomes. The new model, which is referred to as CBL, provides educators with an alternative solution to overcome many of student's deficiencies associated with traditional learning practices; such as lack of motivation, lack of self esteem, insufficient practical and real-life experience, and inadequate team work practices. The new CBL model makes a clear distinction between PBL and competitions and CBL. It avoids the disadvantages of competitions, while at the same time gaining from the many benefits of PBL. Identification features of CBL, components of CBL, as well as advantages are presented. An open source Learning Management System (LMS), namely, Moodle is used for the implementation of a networked environment to support CBL.

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques. [ABSTRACT FROM AUTHOR]

Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and rational resource allocation is to attain the quality of service. It also includes identifying critical parameters to develop a resource allocation mechanism. A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment. The three main parameters of the proposed framework are data accessibility, optimization, and collaboration. Using an optimization technique, these three segments are further divided into subsets for resource allocation and long-term service quality. The CloudSim simulator has been used to validate the suggested framework. Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS. The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined. [ABSTRACT FROM AUTHOR]