Abstract

Abstract
This package provides all published resources used and produced in the context of the research study leading to the article "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs", presented in ICSA 2022's technical track. It includes materials used to conduct the study as well as aggregated and anonymized data produced in its context. Making this package available intends to foster transparency and to support researchers attempting to replicate the study. The package complies with the Research Object Reviewed (ROR) and Open Research Object (ORO) badges, awarded by the Artifact Evaluation Track at ICSA 2022, and is available under Creative Commons Attribution 4.0 International. The package is openly available in Zenodo [1] and the article is available in ICSA 2022's proceedings [2] and as a pre-print [3].

Abstract
With the rising uncontrolled growth of energy demand, smart multi-energy systems (MESs) have emerged as a promising energy-efficient concept that provides the opportunity of supplying multiple energy services (electricity, heating, and cooling) to end-users simultaneously. To this end, a smart multi-energy system consisting of renewable energy sources (RESs), combined heat and power units (CHPs), heat pumps (HPs), community energy storage (CES), electric vehicles (EVs) and multi-energy demands are considered in this study, with the objective of maximizing the total system efficiency and minimizing the total operating costs, while meeting the required demands. The optimal operation of the smart MES is also evaluated by considering demand response (DR) based on the time-of-use (TOU) tariff for electricity, and the associated constraints throughout the whole operational horizons with the interconnected relationship between heating, cooling and power are taken into account. © 2022 IEEE.

Abstract
This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.

Abstract
The IEEE Power Electronics Society (PELS) Students and Young Professionals (S&YP) committee is a sub-group within PELS membership committee. The PELS S&YP helps students, and recent graduates transition to be young professionals within the larger framework of the IEEE community, and more specifically within PELS. © 2014 IEEE.

Abstract
Osteoarthritis (OA) is a progressive degenerative disease that causes severe pain and functional limitation, especially during locomotion. It is the most common arthritis type that damages the surface of articular cartilage until the underlying bone. In the past decade, the scientific community has made a considerable effort to improve or discover therapeutical products used as a form of conservative treatment capable of restoring the damaged articular tissue, avoiding, as far as possible, the use of surgical practices. The most common and direct nonoperative application available for OA treatment is the viscosupplementation (VS) procedure that demonstrates a safe, effective method and is less painful for the patients. The most recent works dealing with the design, development, and validation of viscosupplement products in preclinical and clinical trials for OA treatment are overviewed herein. In general, despite the development of new products, hyaluronic acid continues to be among the most reported intra-articular viscosupplement products used in clinical trials, typically used as an isolated product or conjugated with other biologicals or drugs, such as platelet-rich plasma and corticosteroids (CS). However, this issue is still demanding innovation. Approaches comprising new biomaterials as VS products, with intrinsic bioactivity, economical, and environmental friendliness, are required.