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The objective of the study was to identify the impact of renewable energy on Saudi economy during 2000-2021. Analytical techniques were used to conduct this study. An analysis of the study used a set of variables, in which Renewable energy perceives as independent variable and the dependent variables are GDP per capita, net foreign direct investment, unemployment, fixed capital formation, and net foreign trade. The data of the study were analyzed using the E-views program. According to the study, renewable energy has an impact on certain economic variables and does not have an impact on others. A partial validity is found for the study's central hypothesis. According to our findings, renewable energy contributes significantly to net foreign direct investment, unemployment, and fixed capital formation, but not to GDP per capita, net foreign trade, or fixed capital formation.

This paper documents the findings of the financial performance ranking of Jordanian Islamic banks employing the similarity-to-ideal preference ranking technique Evaluation based on (TOPSIS) and Average Distance from Solution (EDAS) method. With the proposed approach we determine Criteria that affect the financial performance of the bank under consideration. The TOPSIS and EDAS methods are used to determine the significance weight of the criteria and the bank's performance score. The paper discovers that there are fluctuations in the EDAS and TOPSIS performance scores of analyzed Islamic banks in Jordan. This outcome demonstrates the performance instability of the considered banks. This consequence suggests that performance testing is meaningful in today's intensely competitive environment.

This research is concerned with the study and analysis of the economic impacts of renewable energy on the UAE's economy. We took annual time series data for variables that include renewable energy consumption, per capita GDP, capital creation, employment, trade, inflation rate, interest rate, fixed exchange rate, foreign direct investment, and trade openness for the time period from 2010 to 2020. Initially, we applied the autoregressive distributed lag (ARDL) model to assess the long-term relationship between the variables. We encountered a multi-linearity problem where we found the correlation between the independent variables. Therefore, we adopted the alternative approach of the Ordinary Least Squares (OLS) technique to measure the relationship between renewable energy and the UAE economy. We have made an equation from OLS, in which we take renewable energy and economic growth as the independent and dependent variables, respectively. The results confirmed that there is a statistically significant relationship between renewable energy and the economy in the United Arab Emirates.

Businesses and websites have rapidly increased their energy consumption, necessitating the development of data centres tailored to the cloud. Predicting when a system's resources will be needed means you can allocate them more efficiently and save money in the cloud. Predictive accuracy may be increased by classifying loads first. In this research, we offer a new method for predicting future demand for cloud-centric data centres. The Phase Space Reconstruction (PSR) and Extended Approximation-Group Method of Data Handling (EA-GMDH) methods are compared to the Bayesian model for predicting the mean load over a long-term time period. Multi-step ahead CPU load prediction using Support Vector Regression is very stable, i.e., its prediction error increases quite slowly as the predicted steps increase; this is in contrast to a neural network, which predicts the future load based on the past historical data and is distinguished by the presence of hidden layers

This study investigates the dynamic relationship between a set of banking sector development indicators and interest rate volatility for 12 emerging market countries during the period of 1980-2019. For this purpose, the bounds testing within autoregressive distributed lag (ARDL) methodology is employed. The empirical results reveal that the interest rate volatility has negative impacts on the majority of the banking sector development indicators which also play a significant role in dampening the banking sector development path in the long-run. These findings suggest that the banking sectors of emerging countries are vulnerable to interest rate risks. Thus, the results have important implications for policymakers to improve the banking system and to promote economic growth of emerging economies.

The design and optimization of a Rayleigh- Benard convection based polymerase chain reaction (PCR) microchip is presented. This PCR microchip simplifies the fabrication, implementation and control of microfluidic PCR while enhancing the detection of the bacterial pathogen Salmonella Enteritidis . The device’s dimensions were optimized using multiphysics simulation then it was fabricated using high precision milling of Poly(methyl methacrylate) (PMMA) substrate. The device incorporated a single heater at the bottom end of its cylindrical chamber and two sensors at both ends. The heater was controlled using a fuzzy proportional, integral, derivative (PID) controller to maintain denaturation and annealing temperatures necessary for efficient PCR to take place. Salmonella Enteritidis DNA was successfully detected using the device while reducing the required assay time by 66%.

Mowaffaq A Nasif,1 Hiba H Falana,2 Heba KH Hamed,1 Qusai GH Yousef,3 Mohammad A Jaradat1 1Intensive Care Unit, Palestine Medical Complex, Ramallah, Palestine; 2Department of Pharmacy, Birzeit University, Ramallah, Palestine; 3Internal Medicine Department, Al-Quds University, Jerusalem, PalestineCorrespondence: Hiba H Falana, Department of Pharmacy, Birzeit University, PO Box 14, Birzeit, Ramallah, Palestine, Tel +970-595193486, Fax +970-2-2982017, Email hfalana@birzeit.eduAbstract: Carbamazepine intoxication is not uncommon and accounts for many cases of poisoning among anticonvulsive medications users. Since there is no specific antidote for carbamazepine overdose, management is limited to gastric decontamination and supportive therapy. With its high protein binding, the role of extracorporeal elimination in carbamazepine intoxication is still questionable. Here two cases of severe carbamazepine intoxication are presented; the cases were brought to the emergency department after the ingestion of 12,000 mg of controlled release carbamazepine for the first case, and unknown amounts of the same drug for the second case. Both cases were presented with altered mental status, convulsion, and high serum carbamazepine levels of more than 20 mcg/mL. They were intubated and treated with continuous venovenous hemofiltration, after which carbamazepine levels declined significantly along with subsequent clinical improvement and complete neurological recovery. Both cases were discharged home for further psychiatric care.Keywords: carbamazepine, poisoning, continuous renal replacement therapy

This paper presents a low-cost based approach for solving the navigation problem of wheeled mobile robots to perform required tasks within indoor and outdoor environments. The presented solution is based on probabilistic approaches for multiple sensor fusion utilizing low-cost visual/inertial sensors. For the outdoor environment, the Extended Kalman Filter (EKF) is used to estimate the robot position and orientation, the system consists of wheel encoders, a reduced inertial sensor system (RISS), and a Global Positioning System (GPS). For the indoor environment, where GPS signals are blocked, another EKF algorithm is proposed using low cost depth sensor (Microsoft Kinect stream). EKF indoor localization is based on landmarks extracted from the depth measurements. A hybrid low-cost reduced navigation system (HLRNS) for indoor and outdoor environments is proposed and validated in both simulation and experimental environments. Additionally, an input-output state feedback linearization (I-O SFL) technique is used to control the robot to track the desired trajectory in such an environment. According to the conducted validation simulation and experimental testing, the proposed HLRNS provides an acceptable performance to be deployed for real-time applications.

The main purpose of this research is to study and highlight that central bank of Jordan (CBJ) plays an important role in economic development. The objective of the financial organization shall be to keep up financial and money stability, to confirm the interchangeability of the Dinar, and to contribute in achieving the banking and money stability within the Kingdom likewise as promoting sustained economic process in accordance with the overall economic policies of the government. To achieve the above- mentioned objectives, CBJ assumes many tasks portrayed in drawing and implementing the financial policy within the Kingdom through an integrated system of monetary policy instruments, setting a evaluation policy of the Dinar compatible with the Jordanian economy, maintaining and managing the Kingdom’s reserves of gold and foreign currencies, regulation credit within the Jordanian economy so as to realize financial and money stability likewise as comprehensive economic process, and issue and regulation bank notes and coins. Subsequently, the central bank plays necessary role within the economic resource allocation of the country. The banking industry may be a major issue that affects the organization of social and economic life cycle within the economies of the planet. it is thought about as associate degree indicator of economic and social growing.. Also, developed financial set up ought to be characterized by the existence of a contemporary and complicated banking industry that contributes to achieving economic balance. It conjointly encourages domestic and foreign investment through the banking system’s ability to states. The aim of the banking industry is to draw in savings domestically and abroad, and direct those savings into productive investment. As a result, this contributes to the accomplishment of economic and social development method, and conjointly facilitates investment activity.

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

This paper presents a custom designed in-pipe inspection robot that is developed for a pipe of diameter 0.203 m, commonly found in the oil and gas industry. Several pressure sensors are incorporated on board the robot that are used for detecting leaks. The robot has a propeller arrangement that not only drives the robot forward but also helps simulate a flow in an empty pipe, and thus aids the detection of leaks. Furthermore, the leak detection system is augmented by a neural network-based verification framework that improves the robustness of leak detection by allowing the operator to check their identification of a leak by passing it through a neural network-based system. This paper presents the details of the construction of the actual robot and presents experimental data, which show successful neural-networks-based detection of leaks in various scenarios.

This paper discusses the design and validation processes involved in the development of a lightweight aerial manipulator. The structure consists of 3D printed links with a counterweight on one end and a gripper on the other, its task is a pick and place operation. The manipulator is inspired from pantograph mechanism, which acts as a center of gravity (CoG) compensation active mechanism. This mechanism is intended to be attached beneath a drone or quadrotor to solve the CoG shifting problem during manipulator operation. An experimental test bench is developed to simulate real flying operations. The mechanism base tilting pitch angle during movement is obtained and compared both with and without the using of proposed aerial mechanism to validate the performance and stability of the quadrotor while used for pick and place applications.

Achieving precise state estimation is needed for the unmanned aerial vehicle to perform a successful flight with a high degree of stability. Nonetheless, obtaining accurate state estimation is considered challenging due to the inaccuracies associated with the measurements of the onboard commercial-off-the-shelf inertial measurement unit. The immense vibration of the vehicle's rotors makes these measurements suffer from issues like large drifts, biases, and immense unpredictable noise sequences. These issues cannot be significantly tackled using classical estimators, and an accurate sensor fusion technique needs to be developed. In this paper, a deep learning (DL) framework is developed to enhance the performance of the state estimator. A deep neural network (DNN) is trained using a deep-learning-based technique to identify the associated measurement noise models and filter them out. The dropout technique is adopted for training DNN to avoid overfitting and reduce the complexity of nets computations. Compared to the classical estimation results, the proposed DL technique demonstrates capabilities in identifying the measurement's noise characteristics. As an example, an enhancement in estimating the attitude states at near hover is proven using this approach. Furthermore, an actual hover flight was performed to validate the proposed estimation enhancement method.

A simulation study for path planning of mobile robots in a 2D space using the potential field method is presented. The mathematical analysis represents the attractive force between the robot and the target, as well as the repulsive forces between robot and obstacles. The forces are decomposed to their resultant x-y components in 2D for simulation implementation purposes. These two forces control the motion of the mobile robot to safely avoid any obstacle within the robot environment and track the dynamic target. The simulations for many cases were carried out using the resultant forces. The integration between the robot’s equations of motion for constant acceleration, Newton’s second law, and the potential field forces are illustrated for simulation implementation purposes.

Aim/purpose The present investigation was undertaken to find the existing position including the policies, practices and the issues and concerns of librarians in collecting, processing and archiving the research data in public university libraries in Jordan. Background Research is the bastion of scientific growth and development of a society and the data is its chief ingredient which communicates the results and inferences of the research. The research process is directly interrelated with the data life cycle and the two cannot be segregated. Keeping the importance of the curation and management of the research data and the trivial policy interests of the funding agencies and the governments, the current study was undertaken to explore the Research Data Management (RDM) status and practices in Jordan. Methodology The study used the online questionnaire to get the opinions and the prevailing state of affairs from the librarians/heads of the libraries in the public universities. Contribution The study is an eye opener for the higher education commission in Jordan to design the policies and suitable guidelines for streamlining the RDM system in the country especially the Public and Private Universities. It can be pursued for the exploration of RDM practices and policies in similar other Middle East countries for determining the issues and challenges in implementing the RDM policies in their research institutes. Findings It revealed that the Jordan lags RDM polices at all fronts of government, institutional as well as that of the funding agencies which hinders all the progress and growth of RDM in university libraries. However, keeping all the obstacles and the challenges aside, the Jordanian Public University Libraries (JPUL) are providing RDM services, although on a limited scale, at different levels of curating only some valuable research data, preserving and providing access to the preferred users through online platforms substituting the data repositories. There is a lack of national and international RDM policies and ineffective implementation. Coupled with this, is the wider gap between the required skills and the available skills among the library staff which can be filled by conducting suitable training programs and workshops on continuous basis to impart the skills required to manage RDM practices in libraries. Further improvements in providing the effective RDM services is the need among the JPUL and similar may be the case in other middle east countries including the development of infrastructure, policy framing, designing of data repository, specialized courses on data archiving, data cataloguing, data sharing, etc. while protecting the intellectual property rights. Future research The similar studies can be explored to find the existing policies and the loopholes in implementation of national and international policies of data management in different research institutes. The more important area would be to pursue the role of funding agencies and the framing of suitable policies regarding the data curation and management of their funded projects. Research implications The distinction of the present research study can be considered by the fact that there is a lack of sufficient studies in Jordanian universities which can present a comprehensive proclamation on the processes and policies adopted by the libraries for research data collection, archiving and its accessibility. The study can help initiating the new policies regarding the data management tools and techniques in Jordan as we witness in some of the well developed countries of the world and even there are many courses running on research data management and its implementation in universities. Relevance of the present study The results of the present study can be used by other libraries especially in designing and developing the strategies of research data management which can help improving the data re-use and sharing in Jordan universities for secondary analysis. It can also help in refocusing the data and its immense value among research community. Besides, it can also help the research funding agencies and the government of the country to make policy decisions regarding the submission of reports along with the data so as to save lot of human efforts and resources for collecting and collating the necessary data for conducting researchers in future. The study will be helpful in reducing the financial burden of research projects of different funding agencies which otherwise are spending hugely on data collection, surveys, questionnaire designing, pilot studies, interviews, etc.

Autoregressive neural network fusion architecture is presented for low-cost global positioning system (GPS) and inertial measurement unit (IMU) measurements integration. The proposed intelligent fusion architecture is a non-linear method that takes into account the variable delay between GPS measurement epochs. This delay is due to possible operation of the GPS/IMU integrated system in urban canyon environments. To verify the performance of the proposed method, a simulation environment is constructed. In the simulation environment, the vehicle’s truth model is known and GPS/IMU measurements are simulated with a number of GPS measurements outages. The performance of the proposed fusion architecture is evaluated against the truth state of the vehicle. Subsequently, the proposed method is used in an experimental setup to estimate the state of a vehicle that is driven through a number of chosen paths. The performance of the fusion architecture is compared against a commercial off-the shelf solution.

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

Indoor Localization has been a challenging task in robotics for years, due to the obstruction of the GPS signals. Although multiple techniques have been proposed, determining the best among them is still highly dependent on applications and no best solution is agreed on. One technique, researchers and developers are adopting –especially in the robotics field-, is the visual/inertial sensor fusion. However, the high number of these systems and their diversity, makes selecting the adequate one for an application a challenging task. This paper aims at presenting an exhaustive list indoor positioning techniques with more emphasis on visual/depth sensors, along with some recommendations for selected applications.

In this project, a 3D-printed, cost-effective caged drone was designed, manufactured and programmed such that it can move through industrial-sized HVAC ducts by rolling and hovering motion, and can switch between remote-control and autonomous navigation to detect thermal leaks and blockages. The 3D-printed cylindrical cage enables the rolling motion of the drone through straight ducts simply by pitching the drone forward, thus it provides an energy-efficient alternative to continuous hovering, prolongs operation time and protects the drone and the operators. The drone is equipped with both IR and ultrasound proximity sensors for navigation and crack detection, an RGB camera to record a video feed of the drone's expedition through the duct, a temperature sensor and thermal camera to detect thermal leaks.