Development Of Groundwater Quality Management Models Using Artificial Intelligence (Ai) And Statistical Approaches – Case Study – Khanyounis Governorate – Gaza Strip – Palestine

Groundwater (GW) is the unique water source for more than one third of the world's populations. GW quality is under serious threat due to the recent rapid urbanization and industrialization. GW contamination is influenced by various interrelated variables, leading to high complexity in the GW quality modelling process. Statistical and artificial intelligence (AI) techniques have recently become common GW modelling tools due to their high performance. In this research, hybrid systems composed of two AI techniques namely artificial neural networks (ANNs) and support vector machine (SVM) in addition to various multivariate statistical techniques, were utilized to simulate the concentrations of two GW quality parameters particularly nitrate (NO3-) and chloride (Cl-) in complex aquifers. The models were trained using limited and irregular monitoring data from 22 municipal wells from 1998 to 2010 in Gaza Coastal Aquifer (GCA) which is a complex and highly heterogeneous aquifer. Results of the statistical analyses deepened the understanding of the GCA influencing variables and GW quality trends. Both ANNs and SVM techniques showed very satisfactory simulation performance with comparable results. The correlation coefficient (r) and mean average percentage error (MAPE) for NO3- simulation model were 0.996 and 7% respectively. Meanwhile r and MAPE for Cl- simulation model were 0.998 and 3.7% respectively. The results demonstrated also the merit of performing clustering of input data into consistent clusters prior to separate application of AI techniques for each cluster. Given their high performance and simplicity, the developed models were effectively utilized as GW quality management decision support tools by assessing the effects of various management scenarios on NO3- and Cl- concentration in GCA for 2020 and 2030. Evaluation of GW quality management scenarios indicated that NO3- and Cl- concentrations in the study area municipal wells would noticeably increase if the situation remained without any immediate intervention. On the other hand, GW quality levels in most study area wells would be highly improved if a combination of management scenarios was adopted

Iraq today: The failure of re-shaping a state on sectarian and quota lines.

By Professor Saad N Jawad Senior Research Fellow at the Middle East Centre, LSE & Dr Sawsan I al-Assaf Board Member of the Peace Building-Academy for the Middle East, Spain & Beirut

LearnSDN: optimizing routing over multimedia-based 5G-SDN using machine learning

With the advent of 5G networks and beyond, there is an increasing demand to leverage Machine Learning (ML) capabilities and develop new and innovative solutions that could achieve efficient use of network resources and improve users' Quality of Experience (QoE). One of the key enabling technologies for 5G networks is Software Defined Networking (SDN) as it enables fine-grained monitoring and control of the network. Given the variety of dynamic networking conditions within 5G-SDN environments and the diversity of routing algorithms, an intelligent control of these strategies should exist to maximize the Quality of Service (QoS) provisioning of multimedia traffic with more stringent requirements without penalizing the performance of the background traffic. This paper proposes LearnSDN, an innovative ML-based solution that enables QoS provisioning over multimedia-based 5G-SDN environments. LearnSDN uses ML to learn the most convenient routing algorithm to be employed on the background traffic based on the dynamic network conditions in order to cater for the QoS requirements of the multimedia traffic. The performance of the proposed LearnSDN solution is evaluated under a realistic emulation-based SDN environment. The results indicate that LearnSDN outperforms other state-of-the-art solutions in terms of QoS provisioning, PSNR and MOS

Performance evaluation of routing strategies over multimedia-based SDNs under realistic environments

Most of the existing performance evaluation studies of various routing algorithms are done under limited experimental setups leading to an incomplete picture of the routing algorithm performance under dynamic network conditions. This paper presents a study that compares state-of-the-art routing algorithms over realistic multimedia-based Software Defined Networks (SDNs) with dynamic network conditions and various topology. Routing algorithms remain a key element of the networking landscape as they determine the path the data packets follow. The next-generation networking paradigm offers wide advantages over traditional networks through simplifying the management layer, especially with the adoption of SDN. However, Quality of Service (QoS) provisioning still remains a challenge that needs to be investigated especially for multimedia-based SDNs. This study investigates the impact of state-of-the-art centralized routing algorithms (e.g. MHA, WSP, SWP, MIRA) on multimedia QoS traffic u nder a realistic environment in terms of PSNR, Throughput, Packet Loss, Delay and QoS rejection

REDO: a reinforcement learning-based dynamic routing algorithm selection method for SDN

The current increase in the Internet traffic along with the global crisis have accelerated the roll-out of the next generation 5G network and key enabling technologies. In this context, addressing the end-to-end Quality of Service (QoS) provisioning in order to guarantee a sustainable service delivery to the end-users became of paramount importance. Some of the enabling technologies that could play a key role in this regard are Software Defined Network (SDN) and Machine Learning (ML). This paper proposes REDO, a Reinforcement lEarning-based Dynamic rOuting algorithm selection method that decides on the conventional routing algorithm to be applied on the traffic flows within a SDN environment. REDO will dynamically select the most appropriate routing algorithm from a set of centralized routing algorithms (MHA, WSP, SWP, MIRA) that maximizes the reward function from the network. The proposed REDO solution is implemented and evaluated using an experimental setup based on Mininet, Floodlight controller and Open vSwitch switches. The results show that REDO outperforms other state-of-the-art solutions

An innovative reinforcement learning-based framework for quality of service provisioning over multimedia-based SDN environments

Within the current global context, the coronavirus pandemic has led to an unprecedented surge in the Internet traffic, with most of the traffic represented by video. The improved wired and guided network infrastructure along with the emerging 5G networks enables the provisioning of increased bandwidth support while the virtualization introduced by the integration of Software Defined Networks (SDN) enables traffic management and remote orchestration of networking devices. However, the popularity and variety of multimediarich applications along with the increased number of users has led to an ever increasing pressure that these multimedia-rich content applications are placing on the underlying networks. Consequently, a simple increase in the system capacity will not be enough and an intelligent traffic management solution is required to enable the Quality of Service (QoS) provisioning. In this context, this paper proposes a Reinforcement Learning (RL)-based framework within a multimedia-based SDN environment, that decides on the most suitable routing algorithm to be applied on the QoS-based traffic flows to improve QoS provisioning. The proposed RL-based solution was implemented and evaluated using an experimental setup under a realistic SDN environment and compared against other state-of-the-art solutions from the literature in terms of throughput, packet loss, latency, peak signal-to-noise ratio (PSNR) and mean opinion score (MOS). The proposed RL-based framework finds the best trade-off between QoS vs. Quality of User Experience (QoE) when compared to other state-of-the-art approaches

Study Some Mechanical Properties of Binary Polymer Blends Fabricated by Friction Stir Processing

تعتبر طريقة الاحتكاك والخلط تقنية واعدة لتحسين الخواص الميكانيكية لسطح الخلائط البوليميرية مع الاحتفاظ بخواص باقي الجسم، في هذا العمل جرت محاولة لإضافة نسب مختلفة من البولي بروبلين، ستيرين أكريلونيتريل والبولي فينيل كلوريد كمادة ثانية إلى البولي اثيلين عالي الكثافة (كمادة اساس). تم أجراء الاختبارات الميكانيكية (الصلادة والشد) لتقييم أداء عملية الاحتكاك والخلط لتحضير الخلائط البوليميرية الثنائية: (البولي اثيلين عالي الكثافة: بولي فينيل كلوريد)، (البولي اثيلين عالي الكثافة: ستيرين أكريلونيتريل) و (البولي اثيلين عالي الكثافة: بولي بروبلين) على عمق 3 مم من سطح (البولي اثيلين عالي الكثافة). أظهرت نتائج الاختبارات أن أفضل قيم للصلادة ومقاومة الشد تم الحصول عليها عند إضافة نسبة (15٪) من (البولي فينيل كلوريد) إلى المادة الاساس (البولي اثيلين عالي الكثافة).  في ضوء ما سبق، يمكن الاستنتاج أن تقنية الاحتكاك والخلط، يمكن أن تستخدم لإصلاح التشققات والعيوب التي تتكون على سطح المواد البوليميرية.Friction stir processing (FSP) is a promising technique to improve the mechanical properties of the polymer blends surface with retainment of bulk properties, in this work an attempt was done to add different ratios of polypropylene (PP), styrene acrylonitrile (SAN) and polyvinyl chloride (PVC) as a second material to the matrix plate high density polyethylene (HDPE). Mechanical properties were estimated for hardness and tensile tests were carried out to assess the performance of friction stir processed to prepared the binary polymers blends (HDPE: PP), (HDPE: SAN) and (HDPE: PVC) at depth 3 mm from the surface of (HDPE) plate. The results of the tests showed that the best values of the tensile strength, young modulus and hardness, it was obtained when adding the ratio of (15%) of the (PVC) to matrix (HDPE). The friction stir processing technique was successfully used to improve the mechanical properties of polymer surface. In view of the foregoing, it can be concluded that the friction stir processing technique, can be utilizing to repair the cracks and imperfections that are formed in polymeric materials

Adsorption of iron ions from palm oil mill effluent using novel adsorbent of alginate–mangrove composite beads coated by chitosan

This study was about the investigation of the removal of iron ions from Palm Oil Mill Effluent (POME) by using novel adsorbent which is Alginate–Mangrove Composite Beads Coated by Chitosan (AMCBCC). The adsorbent was characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) with Energy Dispersive X-ray Spectroscopy (EDX) to prove the successful coating by Chitosan and also to provide an evidence of iron ions were adsorbed on the surface of the beads. Batch studies were conducted by using different parameters, such as pH, dosage, contact time, and initial concentration. It was found that at pH value of 3, 300 g/L of AMCBCC concentration, and a contact time of 72 hours the maximum removal of iron ions was 92.7%. The isotherm equilibrium data were followed Freundlich isotherm model and the adsorption kinetic data were well fitted by the pseudo second order