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Considerable advances in the capabilities of modern mobile devices have enabled their use as powerful educational tools. Today, mobile learning games are widely used as creative platforms for teaching and learning, offering enjoyable and ubiquitous educational content. This study describes the design and evaluation of a mobile Arabic orthography game aimed at improving Arabic orthographical skills among young learners. In particular, 52 female fourth-grade students participated in this study to answer the following research questions: what impact does the use of mobile Arabic orthography games have on students' performance, and how do students perceive this learning approach? A mixed-method research design was adopted to answer these research questions, including pre-and post-tests, interviews, and classroom observations. Analysis of the data revealed that, although there were a few challenges involved in using mobile games as a learning tool, significant improvements were found in students' performance and engagement, and positive attitudes were developed towards using the mobile game. In addition, there was an overall increase in students' motivation and interaction. The pedagogical implications of these findings can be linked to the gamification of the teaching and learning environments. Teachers are encouraged to consider integrating mobile educational games into their instructional approaches, as they can serve as great incentives to learning (especially for young students).

Due to the importance of safety in the process of structural designing of the modern buildings' ceiling, this study gravitates on a novel topic of analyzing thermoelastic response of the concrete ceiling reinforced with nanoparticles of graphene-platelets (GPL). To this end, the mentioned ceiling is considered as a fully-clamped rectangular plate exposed to simultaneous impact of thermal and mechanical loads. The exact form of the elasticity theory in conjunction with the differential quadrature method is employed for acquiring the behavior of the system. Then, an artificial intelligence-based method is used to accelerate the computational process. Efficiency and accuracy of the employed solution are examined and verified by means of a comparative study. The results of this article would be broadly employed in the structural design of the modern constructions in the near future. [ABSTRACT FROM AUTHOR]

The present study is done to perform the optimal design of the structural components of the buildings against the unwanted wind load exerted on their outer face. To this end, the case study of the research is the outer wall made of a one-floor building modeled as a rectangular plate with only one free edge and three clamped ones. It is assumed that the wall is a sandwich plate whose core is made of auxetic material and dace-sheets are reinforced with nanoparticles of graphene platelets (GPL). Differential equations governing the system's motion are obtained within the background of the plate's shear-deformation theories. The stability analysis of the sandwich wall is performed based on the application of artificial intelligence (AI) methods optimized with an innovative optimization approach to gain a high level of accuracy. To determine the stability information of the system at the train points, the differential quadrature approach (DQA) is applied as the solver of differential equations of motion. The accuracy of the methods used in this paper is examined and verified by comparing the results with those acquired in the articles published previously. The results obtained in this study provide very useful information about the stability response of lightweight building components through AI-based solutions. [ABSTRACT FROM AUTHOR]

The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated their capability to precisely detect anomalies. This study designs and enhances a novel anomaly-based intrusion detection system (AIDS) for IoT networks. Firstly, a Sparse Autoencoder (SAE) is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error. Secondly, the Convolutional Neural Network (CNN) technique is employed to create a binary classification approach. The proposed SAE-CNN approach is validated using the Bot-IoT dataset. The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%, precision of 99.9%, recall of 100%, F1 of 99.9%, False Positive Rate (FPR) of 0.0003, and True Positive Rate (TPR) of 0.9992. In addition, alternative metrics, such as training and testing durations, indicated that SAE-CNN performs better. [ABSTRACT FROM AUTHOR]

This study follows a novel path toward alarming the dangerous conditions of the buildings by means of piezoelectricity. piezoelectric materials have the ability to translate electrical voltage and displacement to each other interchangeably. This feature is employed in this research to define a critical threshold for the displacements. The case study of this research is a graphene-platelets reinforced functionally-graded piezoelectric (GPLR-FGP) rectangular wall with one free edged at its top. The voltage would be gradually increased until the natural frequency approaches to zero. This circumstance puts the wall in the direct danger of failing its stability. Accordingly, displacements of the wall at this condition can be considered as the critical deflections of the system. The wall's governing equations are established in the background of higher-order shear deformation theory (HOSDT). Differential quadrature approach (DQA) is employed to obtain the natural frequency of the wall. Deep-learning is utilized to accelerate the computation process by means of training a deep network of neurons through the instances determined by the explained numerical approach. Efficiency of the innovative implemented solution in this research is verified through a comparative analysis with the results of the published articles. This research can be considered as the main stone of developing innovative solutions for alarming safety issues in the smart cities of near future. [ABSTRACT FROM AUTHOR]

Digitization has completely changed the landscape of supply chain management, which enables businesses to streamline their processes and attain higher levels of profitability and sustainability. This study investigates the relationships between digitalization and supply chain elements, particularly integration, communication, operation, and distribution, and their effects on corporate profitability and sustainability. The research is based on an empirical investigation conducted through a questionnaire survey of agri-food industries in Pakistan. PLS-SEM was used for the analysis of data. The results show a positive relationship between digitalization and supply chain integration, processes, operation, and distribution. Moreover, a positive and significant relationship exists between digitalized supply chain integration, processes, operation, and distribution with business profitability and sustainability. The research concludes that the synergistic effect of digital advancements leads to increased business profitability and sustainability. Business organizations may put themselves at the forefront of supply chain excellence by adopting digitalization, benefiting from effective integration, communication, operations, and distribution with increased profitability and sustainability. The findings have a lot of practical and theoretical implications for the excellence of supply chain management and help attain several sustainable development goals, e.g., SDG-8, SDG-9, SDG-11, and SDG-12. [ABSTRACT FROM AUTHOR]