The recent era of technology, Industry 4.0 finds its way to the field of manufacturing based on advanced technology such as artificial intelligence, internet of things, augmented reality, robotics, and more. These technologies can truly lead a massive transformation in production and manufacturing processes. This study aims to understand the extent to which undergraduate students in the field computer science and information technology in Iraq are aware of the techniques and applications of Industry 4.0. For this purpose, we have reviewed the literature as well as have collected opinions of students using a paper-based survey at the college of computer science and IT, University of Anbar. This can help in determining the level of students’ awareness of Industry 4.0 and to identify limitations, if any, when it comes to their educational journey. Data has been analyzed and results shown no differences between males and females when it comes to Industry 4.0 applications understanding. Beside this, students from different departments shown somewhat different sense regarding their knowledge of Industry 4.0 techniques and applications, which may be a big indication of the urgent need of curriculum revision and update.

The Internet of Things refers to a vast and interconnected distributed systems in which components have a high degree of heterogeneity in terms of software, hardware, and connectivity, while they deliver different services. The quality of service for users accessing the Internet of Things supported by cloud computing is increasing exponentially. However, the deployment of Internet of Things applications in a cloud environment can be very dynamic, resulting in service demands and high resource requirements. The task of effectively managing and optimising resource allocation while taking into account time-sensitive requests is a significant and complex problem that has implications on service quality. The proposed solution aims to improve task scheduling in a hybrid cloud environment for Internet of Things applications. This improvement involves implementing load-balancing techniques to achieve cost reduction, energy consumption, and enhanced overall execution time. The designed solution encompasses two main phases. In the first phase, clustering is performed on computing hosts. In the subsequent phase, user requests are assigned to a suitable cluster, utilising the enhanced Firefly algorithm. The simulation results demonstrated the efficiency of the proposed solution in terms of energy consumption and execution time compared to a benchmark algorithm.

RSM modelling has been applied in this study to understand the effects of inter-electrodes on the performance of the electrochemical reactors in the removal of pollutants. RSM has been selected because it has the ability to predict the effects of more than one parameter on the targeted variable. Thus, the RSM has been used in this article to model the effects of inter-electrodes spaces (IES) (4 to 10 mm) and treatment time (TT) (5 – 55 min) on the ability of the electrocoagulation (EC) cells to remove phosphate from water. The results showed the best removal of phosphate was 92.5% at I-ES of 4 mm and TT of 50 min. High agreement was noticed between experimental and predicted removals (R2= 0.984).

Intracranial pressure (ICP) could be an indicator of a neurological disorder known as hydrocephalus, which is currently managed by shunting procedure. This paper investigates the current advances of shunting valves and provides an overview of ICP readings interpretation from a medical point of view with reference to Alder Hey hospital in Liverpool, UK. Moreover, this paper helps to express ICP readings using advanced data science approach and prepares for implementing intelligent approaches as an alternative pathway to improve the use of ICP within the current medical system. It is assumed that this paper would help specialists and non-specialists in an informative way to comprehend ICP readings. It also allows combining ICP reading with other parameters to derive a proper action with respect to patients with hydrocephalus.

Data science is the process of liberating meaning from raw data using scientific methods and algorithms, and is becoming much more commonly used in healthcare with the emergence of personalised healthcare. Alzheimer's disease (AD) is a neurodegenerative disease that has no proven curative treatment, however a new treatment protocol, ReCODE, has been proposed to slow and reverse the progression of the disease. In this paper, an overview of AD is provided, followed by a description of the ReCODE protocol, including the new proposed methods and data to be used in prediction diagnosis and treatment. The ways in which data science can help with prediction and diagnosis are then reviewed, along with the data science techniques that can help with each treatment in the protocol. It is concluded that current data science techniques are useful in aiding the successful treatment of AD patients with he ReCODE protocol, and though there is much promise to the use of data science techniques to predict and diagnose AD, no such technique yet exists that can process all the necessary data. Future research should be conducted to develop such a data science technique. Further research should also be conducted to improve current data science techniques used to support the treatment of AD.

Adaptive smart antenna is a combination of antenna arrays and beamforming algorithm. One of the important adaptive beamforming algorithms is Minimum Variance Distortionless Response algorithm (MVDR), which has optimal weight and very good output Signal-to-Interference Noise Ratio (SINR). MVDR is very sensitive to the steering vector errors that may occur due to manufacturing or calibration errors at antennas of the array. Signal suppress (selfnull) phenomena appeared due to this error, where mismatch between presume and actual signals Direction of Arrival (DOA) occurred. This error also may be occurred during the work time of smart antenna and after the final calibration test due the hard weather changes. According to this weather change, the shape of array changes and leads to error in the antenna interdistance. This leads to major degradation in the MVDR beamformer performance. In this paper, a novel diagnoses algorithm of smart antenna self-calibration is proposed. This algorithm aimed to overcome the calibration/manufacturing or shape distortion errors that may essentially exist or may occur by weather changes effect. According to this algorithm the reference element is re-localized to be in the middle of the Uniform Linear Array and the rest antenna elements distributed identically at the two sides of this reference. This configuration generates two identical sub-arrays at the two sides of the reference. Due to identical performance of the two subarrays, the inter-distance change between array's antenna elements easily distinguish. Simulation results showed that the proposed algorithm has an effective response against steering vector errors since the desired signal is served by the main radiation beam while the interference is blocked by null.

Brain hemorrhage is one of the conditions that could affect people for several reasons such as high blood pressure, drug abuse, aneurysm, and trauma. Neurologists ordinarily use Magnetic Resonance Imaging (MRI) scan to examine patients for brain hemorrhage. In this study, we have developed an intelligent automatic model to identify MRIs of patients with hemorrhage from intact ones using an ensemble learning approach. Moreover, our proposed model can annotate the affected area of the brain in an axial view of MRI, which helps trainee doctors to improve their reasoning and decision making. In our experimental settings, we have applied a segmentation-based feature texture analysis to prepare MRIs for classification using an adaptive boosting algorithm. Our proposed method has achieved a classification accuracy of 89.2%, with 100% sensitivity in detecting the affected area of the brain.

Hydrocephalus is currently managed using traditional mechanical shunts. A smart patient monitoring and shunting system is needed for both patient follow-up and drainage of the cerebrospinal fluid. eHealth is a current and necessary trend for better management of chronic-diseases such as hydrocephalus. This paper demonstrates the analysis of questionnaire data to test the user's acceptance of healthcare technology. The paper also presents a concept for a smart shunting system in-terms of the hardware required for such system to function. The valve mechanism is put under focus as it is the most crucial component of this system.

The process of cement manufacturing produces a huge amount of carbon dioxide (CO2). The utilization of alternative waste materials from various industrial processes as a partial substitution to cement is encouraged due to environmental and specific technical requirements. This strategy will have the potential to reduce cost of cement, conserve energy, and reduce waste volumes. Therefore, the aim of this research is to investigate effect of the replacement of cement with modified fly ash (MFA) and ground granulated blast furnace slag (GGBS) to reach 80% total replacement on mechanical and durability performance of cement mortar. Normal consistency, the initial and final setting times, compressive strength and electrical resistivity of all the ternary mixtures were determined and compared with the control binder. Compressive strength and electrical resistivity were tested at various curing ages of 3, 7, 14, and 28 days. Test results revealed that the normal consistency of the ternary mixtures increased with increasing the GGBS and MFA content, while the initial and final setting time decreased compared to that of control mixture. The results also showed that the compressive strength of all the ternary blends mortars were lower at early and later ages in comparison with control mortar. The reductions in the compressive strengths of the ternary mixtures T40, T60 and T80 compared to the control mixture were approximately 16%, 29% and 37%, respectively at 28 days. The surface electrical resistivity of ternary blends mixtures was higher than the control mixture at all curing ages. The use of GGBS and MFA in the production of cement mortar and concrete can significantly help in reducing the CO2 emissions of the cement industry and reduce the overall cost of cement.

Industry 4.0 is a term frequently used to describe the new upcoming industry era. Higher education institutions aim to prepare students to fulfill the future industry needs. Advancement of the digital technology has paved the way for evolution of education and technology. Evolution of education has proven its conservative nature and a high level of resistance to changes and transformation. The gap between the industry's needs and competencies offered generally by education is revealing the increasing need to find new educational models to face the future. The aim of this study was to identify the main issues faced by both universities and students in preparing future workforce. From December 2018 to April 2019, a regional qualitative study was undertaken in Liverpool, United Kingdom (UK). Interviews were conducted with faculty members and undergraduate students, and the results were analyzed using the open coding method. Four main issues had been identified which are the characteristics of future workforce, students' readiness to work, expectations on different roles played at the tertiary education level and awareness of the latest trends. In conclusion, this technology era requires the employers, academic practitioners and students to work together in order to face the upcoming challenges and fast changing technologies. We suggest that an interactive system should be provided as a platform for these three different parties to play their roles.

Chronic Kidney Disease is a serious lifelong condition that induced by either kidney pathology or reduced kidney functions. Early prediction and proper treatments can possibly stop, or slow the progression of this chronic disease to end-stage, where dialysis or kidney transplantation is the only way to save patient's life. In this study, we examine the ability of several machine-learning methods for early prediction of Chronic Kidney Disease. This matter has been studied widely; however, we are supporting our methodology by the use of predictive analytics, in which we examine the relationship in between data parameters as well as with the target class attribute. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models. This study starts with 24 parameters in addition to the class attribute, and ends up by 30 % of them as ideal sub set to predict Chronic Kidney Disease. A total of 4 machine learning based classifiers have been evaluated within a supervised learning setting, achieving highest performance outcomes of AUC 0.995, sensitivity 0.9897, and specificity 1. The experimental procedure concludes that advances in machine learning, with assist of predictive analytics, represent a promising setting by which to recognize intelligent solutions, which in turn prove the ability of predication in the kidney disease domain and beyond.

The initial monitoring of patients with headache is an essential part of ongoing patient safety. Usually, patients are asked to fill in traditional paper-based diaries or outcome measures (e.g., HIT-6 and MIDAS) on a regular basis to measure the impact of headache on a patient's life. However, within publicly funded health care systems such as the UK's National Health Service (NHS), long term monitoring in neurology clinics appears not to be possible for all patients with chronic headache due to the continued decline in funding over the past decade. Nowadays, there is scope to improve patient monitoring and safety in the headache clinic by employing mobile health (mhealth) technologies. The M-health application represents an intelligent solution and holds potential to allow specialists to monitor a larger number of patients than would be possible within the current service model. Mobile applications could replace traditional paper-based diaries and outcome measures and provide several advantages including improved monitoring of historical responses to therapies, improved recording of side effects and can be adapted to improve communication between patients and clinicians. We therefore developed a mobile application-based system to allow remote monitoring of patients with chronic headache.

The rate at which people miss hospital appointments has decreased but remains a big concern for health care professionals as well as funding agencies. This research paper used an open data obtained from the NHS database to determine the factors that may lead to missed appointments and create a model that can be used to predict the likelihood of a patient missing an appointment. Logistic regression models and bivariate analysis were used to determine whether there was a meaningful relationship/association between "did not attend" and forgetfulness, gender, apathy, and transportation. An extensive literature review was conducted to narrow down the reasons that might lead to missed appointments. In conclusion, the research showed there was a significant difference between gender, type of clinic and apathy in organizations.

The contextual compendium analysis presented in this paper focuses on the Industry 4.0 and healthcare services innovation that relate to it. The appraisal discerns the specific components of Industry 4.0 and their related innovations or contribution in the healthcare industry. The first component, Cyber-physical systems, has led to Medical Cyber-physical systems applied in different circumstance to improve the efficiency of service provision. The second component, Internet of Things, has brought with it expanded networks, biosensors, smart pharmaceuticals, and other artificial organs. The final component has inspired the integrated of Natural Language Processing model as a calm-system operating in the background to complete a host of the process that improves diagnoses among other service provision and assistance functions. Additionally, the paper discusses Cognitive Computing, mHealth, and eHealth as emerging medical fields that can benefit from Industry 4.0.

Education With the inclusion and integration of internet and digital learning Education 2. 0 brought tools in the different context of education. The use of social networking concepts such as chat rooms and the ever-growing student data have placed education on the brink of becoming one of the craters and users of Big Data. As such, this paper explores educational data mining techniques alongside some of the emerging learning analytics with the objective of gaining insight into some of the common learning behaviors among students. The task at hand embraces predictive analytics and it employs decision trees, neural networks, and Naïve Bayes algorithms to classify and cluster student learning patterns that can explain academic performance. Predictive analytics has emerged as one of the tools furthering adaptive learning among other lifechanging novelties. Nonetheless, integration of big data in academia is in its infancy although the western hemisphere is making progress towards the integration. Such progress will increase the relevance of data mining in education and this paper envisages to be among the first ones to address the applicability of machine learning in improving education. Hence, the objective of this paper is to develop predictive models based on the decision tree, neural network, and Naïve Bayes algorithms.

This article considers how teachers and university administrators can use a significant amount of data stored in the information systems of institutions. Intelligent analysis of these learning processes is of great use in the higher education system. The use of learning analytics (LA) by a large number of higher educational institutions shows the interest and participation of universities in this matter. Learning analytics can tell a lot about the progress of students and the environment in which learning takes place. Intellectualization of educational analytics will help provide predictive models that can serve as a basis for quality assurance and quality improvement. This article gives an idea of the current level of LA development at the international level. The article also draws conclusions about the problems and limitations associated with learning analytics (LA). The existing experience has been studied, and the conclusion have been made about the existing limitations that prevent the wider use of LA.

Nowadays, the internet is considered as one of the key building blocks of modern communities and a primary function to every aspect of our daily life activities. The internet is widespread and progress in the realm of the information and communication technologies demonstrated great improvements that can be utilized by government entities that strive towards achieving sustainable excellence and utmost performance. In the meantime, government organizations should maintain the delivery of high quality of services and continuously monitor and measure their performance based on appropriate approaches namely, organizing the map of corporate strategy, set of organizational strategic objectives and formulating key performance indicators. The quality levels of organizations operations can be witnessed and improved by identifying an effective planning procedure which indicate areas of enhancements and effective process for decision making to respond to dynamical changes. This paper aims to develop an interactive system to enhance strategic planning processes and quality of aviation operations using balanced scorecard. The proposed system will be integrated with the balanced scorecard approach and shall be evaluated for its effectiveness and usefulness in the aviation operations.

Abnormal joint moments during gait are validated predictors of knee pain in osteoarthritis. Calculation of moments necessitates measurement of forces and moment arms about joints during walking. Dynamically changing moment arms can be calculated from motion trackers either optically or with wireless inertia sensing units, but the measurement of forces is more problematic. Either the patient has to walk over a force platform or a force sensing device has to be built into the sole of the shoes. One possible means of registering abnormal joint moments without the restrictions due to force measurements is to predict moments from the movement of body segments using advanced machine learning techniques. To test the viability of this approach, we aimed to predict the frontal plane internal knee abduction moment form 3D Euler angles of the ankle, knee, hip and pelvis during a single gait cycle of 31 patients with alkaptonuria. Four machine-learning algorithms were used in our experiment to predict moments namely: Decision Tree, Random Forest, Linear Regression and Multilayer Perceptron neural network. Based on performance measures of prediction (R2, root mean squared error and area under the recall curve), the random forest algorithm performed best but this was also the slowest by a factor of 10. Considering both performance and speed, the Multilayer Perceptron neural network method was superior with R2, root mean square of error, area under the recall curve and required training time of 0.8616, 0.0743, 0.874 and 730 ms, respectively.