Release and distribution of heavy metals through industrial wastewaters has adverse affects on the environment via contamination of surface- and ground-water resources. Biosorption of heavy metals from aqueous solutions has been proved to be very promising, offering significant advantages such as low cost, availability, profitability, ease of operation, and high efficiency, especially when dealing with low concentrations. Residual biomasses of industrial microorganisms including bacteria, algae, fungi, and yeast have been found to be capable of efficiently accumulating heavy metals as biosorbent. This paper presents and investigates major mechanisms of biosorption and most of the functional groups involved. The biosorption process includes the following mechanisms: transport across cell membrane, complexation, ion exchange, precipitation, and physical adsorption. In order to understand how metals bind to the biomass, it is essential to identify the functional groups responsible for metal binding. Most of these groups have been characterized on the cell walls. The biosorbent contains a variety of functional sites including carboxyl, imidazole, sulfydryl, amino, phosphate, sulfate, thioether, phenol, carbonyl, amide, and hydroxyl moieties that are responsible for metal adsorption. These could be helpful to improve biosorbents through modification of surface reactive sites via surface grafting and/or exchange of functional groups.

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids.

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State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols is developed to analyse the performance of the state estimator for the IEEE 5 Bus case study microgrid.

It is quite known that there are various methods for treatment of cancer. Although virus therapy has been proved to effective in the improvement of cancer, this method is still at its primary stage. Therefore, treatment methods such as chemotherapy and radiotherapy are still versatile. In these methods, drugs are prescribed. The most important question in the treatment of brain tumors is the rate of drug prescription for the patient so that it can help the patient recover and minimize damages to the healthy cells. A.El-Ghohary demonstrated that a mathematical model of brain tumor system can be seen in an optimal nonlinear control problem. In this paper, attempt is made to transform the nonlinear optimal control problem into an optimal control problem in the measure theory and to approximate a new problem with a linear programming problem and subsequently, to specify the drug dose for the patients with cancer. In addition, we deal with the examination of stability of system balance points. Using drug dose control stabilizes the unstable balance points of the tumor system. In the end, a comparison is made between the results obtained from the above mentioned method and the approximate solution proposed by Al-Gohary.