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This book provides a survey of technologies available to tackle the problems associated with climate change in the energy, water and food security nexus with a special focus on the Middle East. It is divided into three main sections. The energy Section consists of six chapters, the water section of seven chapters and finally the food security section has six chapters. The individual chapters are authored by experts and provide discussions and in-depth views on the current status of each topic.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model’s efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

It is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff. We used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC). We enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions. Higher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy. •The 6-point cutoff on the NIHSS is overly sensitive in predicting large ischemic infarctions.•Assessment of large infarctions with 8, 9, and 10 points provides better differentiation of true negative cases of small infarcts.•Higher thresholds demonstrated improved area under the receiver operating characteristic curve (AUROC) values.•The corresponding AUROC values for the 6, 8, 9, and 10 points were 0.80, 0.86, 0.86, and 0.89 respectively.•Logistic regression analysis demonstrated 14-point as definitive cutoff for identifying large infarctions on the NIHSS.•Large infarctions affect left hemisphere more frequently than the right side.

After the end of IPv4 addresses, the Internet is moving towards IPv6 address architecture quickly with the support of virtualization techniques worldwide. IPv4 and IPv6 protocols will co-exist long during the changeover process. Some attacks, such as MITM attacks, do not discriminate by appearance and affect IPv4 and IPv6 address architectures. In an MITM attack, the attacker secretly captures the data, masquerades as the original sender, and sends it toward the receiver. The receiver replies to the attacker because the receiver does not authenticate the source. Therefore, the authentication between two parties is compromised due to an MITM attack. The existing authentication schemes adopt complicated mathematical procedures. Therefore, the existing schemes increase computation and communication costs. This paper proposes a lightweight and authentic end-to-end communication model to detect MITM attacks using a pre-shared symmetric key. In addition, we implement and analyze the performance of our proposed security model on Linux-based virtual machines connected to large-scale hybrid IPv4-IPv6 virtual networks. Moreover, security analyses prove the effectiveness of our proposed model. Finally, we compare the performance of our proposed security model with existing models in terms of computation cost and communication overhead.

Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.

Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

This study aims to establish a multicenter database to evaluate Parkinson's disease in the MENASA region in the context of expert care. The CGD-PD consortium includes 20 institutes from 9 MENASA countries. The database collects comprehensive data from PD patients. Initial data from participating sites showed significant heterogeneity in patient demographics, clinical characteristics, and healthcare management within the MENASA area. Descriptive analyses will include patient demographics and treatment methods, while multilevel regression models will explore correlations across care levels, environmental factors, and health outcomes. The results are anticipated to reveal region-specific patterns and gaps in the management of Parkinson's disease. The CGD-PD database will be instrumental in addressing the gap in PD research in the MENASA region, ultimately improving the quality of life for PD patients.

Background: Consciousness has remained a difficult problem for the scientists to explore its relationship to the brain activity. This is the first paper that presents the significance of focal areas of the cerebral cortex for consciousness. Objectives: To determine if consciousness is produced by the activity of the whole brain or one of its focal areas. Methods: We have performed a prospective cross-sectional study in eighty patients of acute ischemic stroke. The neurovascular territory of the middle cerebral artery (MCA) was sectioned into four similar areas. The association of any of these focal areas to consciousness was observed after their dysfunction with ischemic strokes. Results: Of the eighty patients, 57.5 % were males and 42.5 % were females. Mean age was 63 years ± 7 SD. The righthanded patients were 90 % (72) of the whole sample. Focal areas of the right MCA were generally less prone to consciousness disorder. Average statistics of the focal infarctions of the right MCA showed no tendency for consciousness disorder on the Glasgow coma scale (GCS) [Mean GCS of all focal areas; 14.5, SD; 0.71, 95 % CI; 14.27 to 14.72, P= 0.0000004]. Altered consciousness with focal infarctions of the territory of left MCA was also less likely [Mean GCS of all focal areas; 14.2, SD; 1.01, 95 % CI; 13.88 to 14.51, P= 0.0004]. Conclusion: Consciousness is not determined by the activity of a focal area of the cerebral cortex. Perhaps, we get our consciousness from the activity of “Neuronal Network of Coordination”.

INTRODUCTION:Although there are high success rates with biliary endoprostheses, there is also accumulating evidence of short and long term complications including: sepsis, migration, perforation, fistula formation and bowel obstruction. This case report presents successful nonsurgical intervention of a rare but grave complication following biliary stent placement.CASE DESCRIPTION/METHODS:A 78 year old male with choledocholithiasis underwent an ERCP with incomplete stone removal, necessitating a 10Fr x 5 cm double-pigtail stent. Repeat ERCP at 6 weeks and an abdominal x-ray suggested distal migration of the stent. Despite multiple laxatives, a followup CT scan confirmed stent retention localized to the IC valve. Although he remained asymptomatic, colonoscopy was pursued and unexpectedly revealed one end of the stent penetrating terminal ileum mucosa, with the other end transecting the serosal surface and perforating through the cecum. The stent was removed with a snare and hemostatic clips were placed to close the defect. At 6 months, he continues to do well without any complications.DISCUSSION:Stent migration occurs in up to 10% of cases, with less than 1% risk of perforation. Short stents tend to travel proximally while longer stents tend to travel distally causing abdominal pain or bowel obstruction. Stent length (>7 cm), benign strictures, sphincter of Oddi dysfunction, post-sphincterotomy and papillary stenosis have all been associated with higher rates of distal migration. Most stents will pass uneventfully into excreta. Rarely, bowel perforation can occur. The duodenum is the most reported site of perforation due to its fixed setting. However, there have been close to a dozen cases of colonic perforation. Both stent design and patient’s anatomy can be obvious risk factors. Straight plastic stents have been associated with higher risk as compared to metal or pigtail stents. Anatomic aberrancies such as abdominal hernias, extensive adhesions and colonic diverticulae are associated with higher risk. Our case highlights bizarre stent migration/perforation with a theoretically lower risk stent, in an asymptomatic patient without known risk factors. Despite this precarious scenario, endoscopic intervention without surgery was sufficient. The collection of case reports in the literature, including our own, suggests the need for guidelines outlining appropriate measures (e.g. followup intervals for repeat imaging) and guidelines outlining management of biliary endoprostheses complications.

In software, code is the only part that remains up to date, which shows how important code is. Code readability is the capability of the code that makes it readable and understandable for professionals. The readability of code has been a great concern for programmers and other technical people in development team because it can have a great influence on software maintenance. A lot of research has been done to measure the influence of program constructs on the code readability but none has placed the highly influential constructs together to predict the readability of a code snippet. In this article, we propose a novel framework using statistical modeling that extracts important features from the code that can help in estimating its readability. Besides that using multiple correlation analysis, our proposed approach can measure dependencies among di erent program constructs. In addition, a multiple regression equation is proposed to predict the code readability. We have automated the proposals in a tool that can do the aforementioned estimations on the input code. Using those tools we have conducted various experiments. The results show that the calculated estimations match with the original values that show the effectiveness of our proposed work. Finally, the results of the experiments are analyzed through statistical analysis in SPSS tool to show their significance.