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Climate change might have direct impacts on water quantity in Egypt and lead to indirect effects on Mediterranean saltwater intrusion to groundwater, which exposes agriculture to vulnerability. This study investigated impacts of climate change on agriculture, with particular regard to food security and socioeconomy, and quantified the effectiveness of cropping pattern adaptation measures by integrating three mathematical models. The BlueM model was used for hydrological simulations of Nasser Lake under flooding scenarios to predict the water supply from the High Aswan Dam. The water and salinity balance (WB-SAL) model was adopted to estimate the water salinity in the Nile Delta. The simulated results from the BlueM and WB-SAL models were integrated with the agricultural simulation model for Egypt (ASME) to project cropping patterns, food security, and socioeconomy throughout the country. The results showed that future climate change will directly affect the total crop area; crop areas for 13 crop types; the self-sufficiency of wheat, rice, cereal, and maize supplies; and socioeconomic indicators. The proposed cropping pattern adaptation measures focus on fixing the crop areas of rice and orchards and providing half of the population with lentils, maize, onion, vegetables, milk, and meat. The adaptation measures have the potential to promote food security without causing deterioration of the socioeconomic situation. However, water availability has much more significant effects on food security and socioeconomy than cropping pattern adaptation measures do. Accordingly, the country should rationalize water use efficiency and increase water supply.

Heat dissipation is one of the major problems in the construction of electronic devices. Reversible computing is one of the emerging computing paradigms to overcome heat dissipation problem. One of the nanoscale devices that has low power consumption and can be used to construct digital logic components is quantum-dot cellular automata (QCA). In this paper, the benefits of reversible computing of QCA will be used to propose QCA designs for two main reversible gates, the CNOT and the TOFFOLI gates. The proposed CNOT gate has been used to design QCA for double CNOT gate to show its reversibility, and QCA design for the SWAP circuit that contains 3 CNOT gates. The suggested TOFFOLI gate is utilized to construct QCA for double TOFFOLI gate and a reversible circuit which consists of several reversible gates. The suggested QCA designs for double CNOT, double TOFFOLI and reversible circuits such as SWAP circuit have been proposed by adding cells with certain clocks to the wires that connects the proposed gates. The proposed QCA designs show better cell count, area used in the construction, number of majority gate, wire crossover, and/or clock cycle delay when compared with relevant designs in literature.

Rehabilitation on a regular basis is essential to recover functional ability for patients with physical disabilities. However, performing correct movements without the therapist's guidance is considered to be a challenging problem. To this end, we propose an interactive system to support patients through rehabilitation, where a movement guidance agent is included. Our system recognizes and assists patients while engaging in rehabilitation activities. The proposed approach consists of three significant steps: (1) the estimation of patient movement through the camera; (2) the detection of patient movement; and (3) the detected movements, are then observed by an agent in order to instruct correct movement, if the movement was incorrectly performed. To this end, we propose an agent-based system that allows users to perform rehabilitation exercises anywhere with the proper guidance system. The proposed system is applied to track patient activities in real-time, with lightweight devices such as mobile phones.

In online discussion platforms, participants gather to discuss the effective approaches to solve the common issues they face. To facilitate these discussions proceeding smoothly and reaching consensus efficiently, human facilitators are introduced into these discussions. However, human facilitator-related problems such as human bias and scalability arise with the increasing sophistication of these online discussions. As a result, it becomes critical to find approaches that support facilitation in these online discussions. Towards this end, we propose a novel case-based reasoning framework to support online discussion facilitation. In the proposed framework (CBR), each discussion thread is styled using an issue-based information system (IBIS), where complex problems are modeled as argumentation processes among several stack holders. Upon identifying a new discussion case, facilitation suggestions are generated by retrieving the most similar facilitated case from the discussion case base. For the sake of experimental evaluation, we build an online facilitation dataset that contains the facilitation information from annotated real-world discussion data. The experimental results show the validity of the generated facilitation suggestions.

One of the largest oil fields in the GCC was developed in the 1960's. The field was initially produced under natural depletion supplemented by gas injection. The high offtake rates led to a rapid displacement of the gas/oil contact; thus, the field has now been suffering from early gas/water breakthrough and uneven fluid influx along with the horizontal wells. The reservoir has been on production for more than 50 years. Water/gas breakthrough from fractures being the major challenge which negatively affects wells oil production rates. Applying technology which can manage water/gas breakthrough in a cost-effective manner whilst allowing increased oil production was a key goal from operators in this field. Passive Inflow Control Devices (ICD) were introduced to the global oil and gas market in mid/late-1990's, and the first generation of Autonomous ICD (AICD) that can help reduce more unwanted gas or water was first installed in 2007. ICD's successfully demonstrated that they could delay the gas and/or water breakthrough within horizontal wells, but they could not choke gas when the coning/gas-breakthrough occurred and along with limited abilities to stop unwanted water production. To help solve this problem, the Autonomous Inflow Control Devices (AICD-RCP) with a movable disc was introduced to the market and demonstrated reduction of gas production by 20-30% with similar gains in oil production[1]. In this paper, the newest generation of Autonomous Inflow Control Valve (AICV) technology is presented. The AICV technology has a movable piston that can close and reduce the unwanted gas and water production by up to 95%[2]. The application of AICVs discussed herein were deployed within several wells which had extremely high Gas Oil Ratio (GOR) and low oil production. The novel AICV technology can differentiate between fluid types based on viscosity and density. When undesired fluid (gas and/or water) starts to be produced, the AICV chokes the valve flow area gradually until completely shutting off, all without well intervention[3]. Well production performances are documenting the benefits of installing AICV completions. The results demonstrate the AICVs closing the zones with high gas production and favoring oil-rich zones. Majority of evaluated wells demonstrated clearly that the extremely high GOR was reduced; some wells have returned to solution GOR for more than two years, and at the same time, the daily oil production is increased.

With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when processed by a centralized cloud server. Motivated by this privacy concern, a new machine learning paradigm has emerged, namely Federated Learning (FL). Specifically, FL allows for each client to train a learning model locally and performs global model aggregation at the centralized cloud server in order to avoid the direct data leakage from clients. However, despite this efficient distributed training technique, an individual&rsquo, s private information can still be compromised. To this end, in this paper, we investigate the privacy and security threats that can harm the whole execution process of FL. Additionally, we provide practical solutions to overcome those attacks and protect the individual&rsquo, s privacy. We also present experimental results in order to highlight the discussed issues and possible solutions. We expect that this work will open exciting perspectives for future research in FL.

Bridges are one of the important structures across the Nile River; these bridges may be subjected to collapse if there is no monitoring or safety precaution for local scour during high floods. This research was commenced in order to evaluate the scour around bridge piers. 1-D and 2-D mathematical models were applied to Aswan and El Minia bridges, in the 1st and 4th reaches, respectively. The two bridges are considered two major bridges so their impacts should be studied. Total Scour (local, contraction and long-term degradation) was evaluated and the expected morphological changes were estimated. The local scour and contraction scour at Aswan Bridge is higher than El-Minia Bridge due to the big dimensions of piers. 2D model gives accurate results especially in the non-uniform cross sections. It was found that the smallest local scour was obtained in case of sharp nosed piers at normal flow to the piers. Keywords: Local scour, Bridge piers, HEC-RAS, CCHE-2D, Aswan bridge, El-Menia bridge

This research was commenced to enhance flood management via improving the operation rules of multi-purpose reservoirs (i.e. Aswan High Dam Reservoir “AHDR”). Literature in the field of reservoir operation rules was reviewed. Available data, about Aswan High Dam (AHD) together with its reservoir releases, were assembled and analyzed. Accessible numerical models were investigated to select the applicable model in terms of the assembled available data. Tenth scenarios (the current scenario and 9 proposed scenarios) for AHDR operation rules were designed and simulated. These scenarios include climate change, static operation (fixed level at beginning of water year from the 1st of August) and dynamic operation. Results were obtained and compared to the existing operation rules. The comparison considered the storage, water release, hydropower generation and total losses. In addition, the comparison considered the dam safety. The comparison indicated that the dynamic operation rules would achieve satisfying results; this scenario will provided an increase in the yearly AHDR storage from 3% to 4% during 9 years. The total increase in the storage was 22.46 BCM during 9 years without any reduction in the release downstream AHD and the hydropower generation. This increase in storage can be used for Toshka project. Keywords: Aswan High Dam Reservoir, BlueM model, Operation rules

Similar to distributed generation inverters, active load has to use a dedicated synchronization unit such as a phase-locked loop (PLL) to get the reference voltage of the microgrid. To avoid PLL drawbacks, a new method is proposed to track the microgrid reference voltage for active load synchronization. An autonomous microgrid is modeled and designed. Additionally, the impact of active loads on the dynamic stability of the autonomous microgrid is investigated. An optimal design is performed to obtain the optimal controller parameters. Based on the time-domain simulation, the control design problem is optimally solved by minimizing a weighted objective function to limit the error in the dc voltage and the measured active power. Different step change disturbances are applied to examine and verify the effectiveness of the new synchronization method and the proposed controllers. Finally, a comparison between the proposed synchronization scheme and the traditional PLL is provided to prove the superiority of the proposed technique. It is concluded that the proposed synchronization method has a potential to enhance the microgrid stability efficiently. With the proposed method, PLL disadvantages such as design complexity, bad accuracy, nonlinearity, tuning difficulties, slow response, and negative impact on the control performance can be avoided.

The current water shortage in Egypt is 13.5 Billion cubic meter per year (BCM/yr) and is expected to continuously increase. Currently, this water shortage is compensated by drainage reuse which consequently deteriorates the water quality. Therefore, this research was commenced with the objective of assessing different scenarios for 2025 using the Water Evaluation and Planning (WEAP) model and by implementing different water sufficiency measures. Field data were assembled and analyzed, and different planning alternatives were proposed and tested in order to design three future scenarios. The findings indicated that water shortage in 2025 would be 26 BCM/yr in case of continuation of current policies. Planning alternatives were proposed to the irrigation canals, land irrigation timing, aquatic weeds in waterways and sugarcane areas in old agricultural lands. Other measures were suggested to pumping rates of deep groundwater, sprinkler and drip irrigation systems in new agricultural lands. Further measures were also suggested to automatic daily surveying for distribution leak and managing the pressure effectively in the domestic and industrial water distribution systems. Finally, extra measures for water supply were proposed including raising the permitted withdrawal limit from deep groundwater and the Nubian aquifer and developing the desalination resource. The proposed planning alternatives would completely eliminate the water shortage in 2025.

The widespread use of web services in forming complex online applications requires service composition to cope with highly dynamic and heterogeneous environments. Traditional centralized service composition techniques are not sufficient to address the needs of applications in decentralized environments. In this paper, a stigmergic-based approach is proposed to model the decentralized service interactions and handle service composition in highly dynamic open environments. In the proposed approach, web services and resources are modeled as multiple agents. Stigmergic-based self-organization mechanisms among agents are deployed to facilitate adapting service composition. In addition, to overcome the limitations of traditional $QoS$ -based approaches, trust measurements are deployed as a criterion for service selection. To improve the performance of the proposed stigmergic-based approach under dynamic scale-free environments, we investigate the hybridization with local search operators to consolidate adaptation, and diversity schemes are introduced to facilitate continual service adaptation. Extensive experiments show the efficiency of the proposed approach in dealing with incomplete information and dynamic factors in composing and adapting web services in open environments. The experiment results also show that the proposed approach achieves a better performance than other traditional approaches.

There are many economic losses due to the crowded and poorly managed traffic conditions in urban areas. In this regard, reinforcement learning (RL) has emerged as a promising approach for optimizing urban traffic control (UTC) based on the real-time traffic conditions. One of the RL-based algorithms is Distributed W-Learning (DWL) in which each agent has multiple policies and then this agent learns to optimize the traffic light control (green, red and so on) based on these policies. In this context, there are situations in which it is only necessary for the control agent to achieve its own policy, and other situations where the priority must be given to the performance of other control agents. As a result, DWL is able to learn how to optimize the entire UTC. In addition, DWL has a mediator that informs the control agents the traffic volume of each road, and these agents learn based on that information. However, the limitation of DWL is that it is based on Q-learning, therefore, it is difficult to optimize multiple policies when the control area expands. In addition, because the action values are represented by tables, these tables grow exponentially when applied to large-scale action spaces. Likewise, the ability to adapt to new environments becomes poor. Therefore, in this paper, we propose an approach to address this problem. The proposed approach uses Deep Q Network (DQN) algorithm in order to optimize the traffic conditions more flexibly. The proposed approach utilizes deep learning for value function approximation. Therefore, the proposed approach has the advantage of being highly extensible and capable of predicting an action in large-scale action spaces even in unknown environments. The experimental results show the proposed approach is capable of optimizing multiple policies of the UTC, efficiently.

Since various opinions are openly discussed on the Web and there is a growing need to facilitate such discussions, discussion-support systems have been attracting attention. In this regard, human facilitator plays an important role for leading constructive discussions on the Web. However, human facilitation for large-scale Web discussions is limited in terms of the available resources. For proper facilitation, it is necessary to understand the content of a discussion to effectively lead the discussion and build consensus. Towards this end, we propose an automated facilitator for supporting large-scale online discussions. Specifically, the proposed automated facilitator structures online discussions using the issue-based information system in order to make these discussions easy to understand for both humans and intelligent agents. In addition, the proposed automated facilitator employs several strategies that encourage participants to conduct discussions appropriately. The experimental results demonstrate the efficiency of the proposed automated facilitator in promoting the progress of large-scale online discussions, and thus enabling open and constructive discussions to be conducted.

This paper goes through the process of designing an electromagnetic Micropower Generator (MPG) to be used for human activity tracking. Finite Element Method (FEM) was employed to simulate the MPG and calculate the power output under different excitations. The MPG was fabricated, tested, and the results compared to the implemented FEM simulations. Good agreement is observed between the two sets of results. The MPG generated 7.7 mW of power under a load of $220\ {\Omega}$ at a natural frequency of 8.9 Hz and acceleration level of 0.24g. The natural frequency is further reduced to 7.2 Hz by removing the top fixed magnet. This achieved higher levels of power output reaching 12 mW at resonance.

Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. In this regard, the Automated Negotiating Agents Competition(ANAC) is being held annually since 2010. The ANAC is an international competition that promotes researchers to design intelligent agents that are able to operate effectively in several kinds of scenarios. In this competition, researchers analyze the negotiating agents from several perspectives including utility, social welfare, distance to Nash solution, distance to Pareto efficiency and so on. Most of these analyses are based on the negotiation results. In fact, the efficiency of the negotiation process greatly affects the negotiation results. In our previous work, we introduced a metric that is able to evaluate the efficiency of the negotiation process. In this paper, we propose a novel meta-strategy that utilizes this metric in order to obtain higher negotiating efficiency.

Online discussion systems have recently attracted great attention as an enabling approach of realizing collective intelligence. During online discussions, human facilitators are introduced in order to help these discussions to proceed more efficiently and productively. However, there are a number of challenges such as human bias and time restriction that need to be solved in the human facilitator-based online discussion systems. As a result, automated facilitation becomes necessary in order to overcome these shortcomings. This paper proposes a novel approach for automated facilitation that utilizes case based reasoning (CBR) in order to imitate the human facilitator thinking style. The proposed approach works in issue based information system (IBIS) discussion style where complex problems are designed as a conversation amongst several stockholders. These stockholders, in turn, bring their expertise in order to resolve the discussion point. Experimental results show the ability of the proposed approach to improve the performance of online discussion systems, and to guide the online discussion towards consensus and towards gathering wisdom efficiently.

Controller gains and power-sharing parameters are the main parameters affect the dynamic performance of the microgrid. Considering an active load to the autonomous microgrid, the stability problem will be more involved. In this paper, the active load effect on microgrid dynamic stability is explored. An autonomous microgrid including three inverter-based distributed generations (DGs) with an active load is modeled and the associated controllers are designed. Controller gains of the inverters and active load as well as Phase Locked Loop (PLL) parameters are optimally tuned to guarantee overall system stability. A weighted objective function is proposed to minimize the error in both measured active power and DC voltage based on time-domain simulations. Different AC and DC disturbances are applied to verify and assess the effectiveness of the proposed control strategy. The results demonstrate the potential of the proposed controller to enhance the microgrid stability and to provide efficient damping characteristics. Additionally, the proposed controller is compared with the literature to demonstrate its superiority. Finally, the microgrid considered has been established and implemented on real time digital simulator (RTDS). The experimental results validate the simulation results and approve the effectiveness of the proposed controllers to enrich the stability of the considered microgrid.

In central receiver solar power plant, heliostat tracking accuracy affects the optical efficiency and the amount of energy received at the target. In the present article, an analysis of the effect of using different solar tracking time-steps on the energy consumption and power losses of a central receiver system. The drive system energy consumption is calculated by using a Solidworks model of the heliostat mechanical drive system. This Solidworks model is interfaced to a Labview model containing the model of the electrical drive and control systems. The power collected at the receiver is calculated using solar radiation mathematical model and Monte Carlo ray tracing software.

As service-oriented environments become widespread, there exists a pressing need for service compositions to cope with the high scalability, complexity, heterogeneity and dynamicity features inherent in these environments. In this context, reinforcement learning has emerged as a powerful tool that empowers adaptive service composition in open and dynamic environments. However, most of the existing implementations of reinforcement learning algorithms for service compositions are inefficient and fail to handle large-scale service environments. Towards this end, this paper proposes a novel approach for adaptive service composition in dynamic and large-scale environments. The proposed approach employs deep reinforcement learning in order to address large-scale service environments with large number of service providers. Experimental results show the ability and efficiency of the proposed approach to provide successful service compositions in dynamic and large-scale service environments.

DRASTIC indexing and integrated electrical conductivity (IEC) modeling are approaches for assessing aquifer vulnerability to surface pollution. DRASTIC indexing is more common, but IEC modeling is faster and more cost-effective because it requires less data and fewer processing steps. This study aimed to compare DRASTIC indexing with IEC modeling to determine whether the latter is sufficient on its own. Both approaches are utilized to determine zones vulnerable to groundwater pollution in the Nile Delta. Hence, assessing the nature and degree of risk are important for realizing effective measures toward damage minimization. For DRASTIC indexing, hydrogeological factors such as depth to aquifer, recharge rate, aquifer media, soil permeability, topography, impact of the vadose zone, and hydraulic conductivity were combined in a geographical information system environment for assessing the aquifer vulnerability. For IEC modeling, DC resistivity data were collected from 36 surface sounding points to cover the entire area and used to estimate the IEC index. Additionally, the vulnerable zones identified by both approaches were tested using a local-scale resistivity survey in the form of 1D and 2D resistivity imaging to determine the permeable pathways in the vadose zone. A correlation of 0.82 was obtained between the DRASTIC indexing and IEC modeling results. For additional benefit, the obtained DRASTIC and IEC models were used together to develop a vulnerability map. This map showed a very high vulnerability zone, a high-vulnerability zone, and moderate- and low-vulnerability zones constituting 19.89, 41, 27, and 12%, respectively, of the study area. Identifying where groundwater is more vulnerable to pollution enables more effective protection and management of groundwater resources in vulnerable areas.

Research into the security of the Internet of Things (IoT) needs to utilize particular algorithms that offer ultra-low power consumption and a long lifespan, along with other parameters such as strong immunity against attacks, lower chip area and acceptable throughput. This paper offers an ASIC-oriented comparative review of the most popular and powerful cryptographic algorithms worldwide, namely AES, 3DES, Twofish and RSA. The ASIC implementations of those algorithms are analyzed by studying statistical data extracted from the ASIC layouts of the algorithms and comparing them to determine the most suited algorithms for IoT hardware-security applications. The AES algorithm is found to be the most suitable algorithm for IoT applications.

An efficient power management control for microgrids with energy storage is presented in this paper. The proposed control scheme increases the reliability and resiliency of the microgrid based on three distributed energy resources (DERs), namely Photovoltaic (PV), battery, and diesel generator with local active loads. Coordination among the DERs with energy storage is essential for microgrid management. The system model and the control strategy were developed in Real Time Digital Simulator (RTDS). Decoupled d-q current control strategy is proposed and implemented for voltage source converters (VSCs) used to interface the PV and battery sources to the AC grid. A dc-dc buck converter with a maximum power point tracking function is implemented to maximize the intermittent energy generation from the PV array. A controller is proposed and employed for both grid connected and island modes of operation. In grid connected mode, the system frequency and voltage are regulated by the grid. During a fault in island mode, the diesel generator controls the system frequency and voltage in isochronous mode. Results based on the real time digital simulator are provided to verify the superiority and effectiveness of the proposed control scheme.

The voltage regulator is designed to automatically maintain a constant voltage level in the power system. It may be used to regulate one or more AC or DC voltages in power systems. Voltage regulator may be designed as a simple “feed-forward” or may include “negative feedback” control loops. Depending on the design, it may use an electromechanical mechanism, or electronic components. The role of an AVR is to keep constant the output voltage of the generator in a specified range. The PID controller can used to provide the control requirements.The chapter discusses the methods to get the best possible tuning controller parameters for an automatic voltage regulator (AVR) system of a synchronous generator. It was necessary to use PID controller to increase the stability margin and to improve performance of the system. Some modern techniques were defined. These techniques as Particle Swarm Optimization (PSO), also it illustrates the use of a Adaptive Weight Particle Swarm Optimization (AWPSO), Adaptive Acceleration Coefficients based PSO, (AACPSO), Adaptive Acceleration Coefficients based PSO (AACPSO). Furthermore, it introduces a new modification for AACPSO technique, Modified Adaptive Acceleration Coefficients based PSO (MAAPSO) is the new technique which will be discussed inside the chapter, A comparison between the results of all methods used will be given in this chapter. Simulation for comparison between the proposed methods will be displayed. The obtained results are promising.