Currently there is not a single trusted infrastructure used for the exchange and storage of medical data along the healthcare value chain and, thus, there is no platform used for monitoring patients’ traceability within the entire healthcare chain. This situation leads to difficult communication and increased procedural costs, and thus it limits healthcare players from developing a better understanding and know-how of patients’ traceability that could further boost innovation and development of the best-fitted health services. PatientDataChain blockchain-based technology is a novel approach, based on a decentralized healthcare infrastructure that incorporates a trust layer in the healthcare value chain. Our aim was to provide an integrated vision based on interoperability principles, that relies on the usage of specific sensors from various wearable devices, allowing us to collect specific data from patients’ medical records. Interconnecting different healthcare providers, the collected data is integrated into a unitary personal health records (PHR) system, where the patient is the owner of his/her data. The decentralized nature of PatientDataChain, based on blockchain technology, leveraged the proper context to create a novel and improved data-sharing and exchange system, which is secure, flexible, and reliable. This approach brings increased benefits to data confidentiality and privacy, while providing secure access to patient medical records. This paper presents the design, implementation, and experimental validation of our proposed system, called PatientDataChain. The original contributions of our paper include the definition of the concept of unifying the entire healthcare value chain, the design of the architectural model of the system, the development of the system components, as well as the validation through a proof of concept (PoC) conducted with a medical clinic from Bucharest, using a dataset of 100 patients and over 1000 transactions. The proof of concept demonstrated the feasibility of the model in integrating the personal health records from heterogeneous sources (healthcare systems and sensors) in a unified, decentralized PHR system, with enhanced data exchange among healthcare players.

One of the most recent concepts in the framework of today's distributed systems is Cloud Computing. A very difficult problem that needs to be addressed is the management of the Cloud. When designing a Cloud scheduling strategy, the design trade-offs of the Cloud architecture should be evaluated. The easiest way to evaluate this infrastructure is to use a simulation tool (in this case CloudSim simulation toolkit). This article examines three different algorithms that consider the scheduling of tasks in Cloud, which are described as Directed Acyclic Graphs (DAGs) (interdependent tasks). The results of the scheduling algorithms are valuable because they are helpful for the design of the Cloud Computing scheduling algorithms and for the design of a Service Level Agreement (SLA) contract whose members are the Cloud Service Provider (CSP) and the Cloud Service Customers (CSCs). This paper shows that the selection of the scheduling algorithms is useful in the design of the SLA contract.

All activities of a company involve risk. In order to achieve its objectives, an organization must identify, analyze, evaluate and then treat all significant risks. According to ISO international standards in the risk field, risk management can be applied to an entire organization, at its many areas and levels, at any time, as well as to specific functions, projects and activities. An effective risk management helps top management of an organization to make optimum decisions and to prevent losses. This paper proposes a new sustainable model for risk management—RiMM. The Sustainable Risk Management model is based on the Monte Carlo method (adapted for risk management process) that is known in the literature but not or very rare applied for this issue of controlling the risks in an organization. There are proposed aspects regarding the design of the model (in five detailed steps—every step with sub steps), a software implementation and an example of a case study that emphasizes the way the model can be used (also to demonstrate its efficiency) for managing risks in an organization. At the end, on conclusions section, the most important points and the contributions of the paper are clearly presented.

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