Education and lifelong learning are necessary components of daily city life for urban communities to encourage sustainable and positive communities. The study attempts to analyze the actual school distribution patterns and densities in Baghdad, the Iraqi capital. The significance of this study is that it is associated with one of the essential aspects of humanity: the improvement and affluence of schooling; it impacts school attendance limitations and educational evolution. The education process has been inextricably tied to students' timely and orderly entrance to their schools. Hence the decision maker and planner are concerned by this. The statistics examined elementary and high schools, and the investigated data are related to the Ministry of Education that was available in 2004. Regarding spatial analysis and results assessment, this work employs Microsoft Excel techniques, ArcGIS, C# simulator, and the buffer methodology. It has depicted the school densities and assessed the distances between them using spatial analytic techniques. The study reveals that the school distribution is uneven, and there is a disparity among neighbors in the spatial distribution. Also, the in-between distances of observed schools formulate four significant patterns. At the same time, this study offers important information about the spatial distribution of schools as a significant influencing indicator. In the future, it will be beneficial to investigate the student's geographical accessibility to their residence and the transportation to/from these schools.

Nowadays, the effectiveness of Flying Ad-hoc Networks (FANETs) has proven their importance in many fields, such as the military, healthcare, entertainment, etc. In such networks, realistic mobility modeling is pivotal for accurately simulating Unmanned Aerial Vehicle (UAV) behaviors and their interactions within the network. To achieve this realism and improve network performance metrics of the network, multiple Mobility Models (MMs) can be integrated, allowing UAVs to exhibit complex movement patterns that reflect real-world dynamics. This paper proposes a combination of Circular Mobility (CM) and Gauss-Markov (GM) models (CCGM) in a superposition mobility model to determine the final pattern of the model and how it affects the Quality of Services (QoS). So, the OMNeT++ was used as a simulation tool to achieve this purpose. The QoS used for analyzing the model are End-to-End Delay (E2ED), throughput, Packet Delivery Ratio (PDR), jitter, and packet loss. The simulation results were introduced and compared with respect to four scenarios. The first two scenarios implied the models independently, while the last two scenarios implied the proposed model (CCGM); each scenario was evaluated with Ad-hoc On Demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), and Greedy Perimeter Stateless Routing (GPSR) routing protocols. The simulation results demonstrate that the proposed model (CCGM) outperforms the GM and CM models in terms of E2ED, PDR, throughput, jitter, and packet loss. Consequently, the CCGM model exhibits an average E2ED of best results with a GPSR of 0.009 seconds in scenario 4, while it exhibits the best results in AODV with PDR of 90.82%, throughput of 669614, jitter of 0.021, and packet loss of 9.18% in scenario 4 as well. This indicates that the CCGM model could enhance the QoS and movement realism, making it useful in FANET applications

Malaria is a contagious disease that infects many people each year. It is diagnosed in the laboratory using an expert and thorough examination of healthy and si Malaria is a contagious disease that infects many people each year. It is diagnosed in the laboratory using an expert and thorough examination of healthy and si

In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of societies and their medical information, the identification of nodes, the method of communication with individuals and their spread, the analysis of their transmission through complex networks, and the detection of mathematical methods over the past century. Secondly, the types of epidemiological models and complex networks and the extent of their impact on humans are presented.

Accurate prediction of activity location is a crucial component in various mobility applications and is particularly vital for the creation of customized, environmentally friendly transport systems. Next-location prediction, which entails predicting a user's forthcoming place by analyzing their previous movement patterns, has substantial ramifications in diverse fields, such as urban planning, geo-marketing, disease transmission, wireless network performance, recommender systems, and numerous other sectors. Recently, researchers have proposed a variety of predictors, including cutting-edge ones that utilize advanced deep learning methods, to tackle this problem. This study introduces robust models for predicting a user's future location based on their previous location. It proposes a Recurrent Neural Networks (RNNs) prediction scheme and a Gated Recurrent Unit (GRU), which are well-suited for learning from sequential data. Additionally, the clustering technique Density-Based Clustering (DBSCAN) is implemented to extract the stay points. Furthermore, the suggested method is more accurate at predicting the future than the current method, showing improvements in loss mean square error of up to 0.0005 in the RNN model and 0.01 in the GUR model. So, the models that were used led to a decrease in loss MSE, which was shown in the real-world dataset (Geolife) in this paper. The results are also consistent with other similar works that look at the same issue, showing how well the models can predict mobility.