Governments rely on collected data to analyze their citizens’ health requirements and needs. Such data is usually of scattered manner gathered by several agencies and entities that fall under different business models and goals. Hence, the scattered nature of this data creates a burden on governments in precisely planning adequate health bills that guarantee the wellbeing of their citizens. Furthermore, the issue of synchronizing and having a real-time access to such data exacerbates the problem even more. Therefore, a system that guarantees the integrity, accessibility, correctness and security of medical data in a highly synchronized environment is crucial for governments to accurately project their future needs. In this paper, we present Smart-Health (SHealth), a blockchain-based health management system. In its crux, SHealth is a private multi-layered blockchain integrated with a multi-tiered addressing scheme that defines the privileges and permissions of entities in the system. Blockchain-based systems assure security, reliability, availability, resistance against tampering and malicious attacks, seamless integration, and easy data management. SHealth proposes a complete autonomy to its users. Through a user-friendly graphical interface it capitalizes on smart contracts to initiate various requests and inquiries pertaining to patients such as appointments, medical tests, medications, medical procedures, or history. SHealth is simple, robust, efficient, secured, and completely automated. All the stakeholders in the system can access the health related data stored in a distributed database without compromising its authenticity. SHealth covers all the possible scenarios in health systems from which some of them are explained in this work.

This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs.

Non-Orthogonal Multiple Access (NOMA) has become one of the potential candidate for the upcoming 5G wireless networks due to low latency, higher spectral efficiency, system throughput, and massive connectivity. The uplink NOMA can employ Orthogonal Frequency Division Multiplexing (OFDM) modulation for the construction of its basic primary signal waveform. The major weakness of uplink NOMA employing OFDM modulation is high Peak-to-Average Power Ratio (PAPR), which makes this scheme inefficient. Therefore, in this paper the Discrete-Sine Transform (DST) matrix precoding based uplink NOMA scheme has been proposed to reduce the higher PAPR. Computer simulations indicate that the DST matrix precoding based uplink NOMA scheme has lower PAPR as compared to the Walsh-Hadamard Transform (WHT) matrix precoding based uplink NOMA scheme and non-precoded uplink NOMA scheme available in the literature.

High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.