The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‘ boost economic efficiency‘ increase the user’s satisfaction by choosing between electricity sellers and buyers‘ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‘ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‘ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.
Keywords: Energy management | Smart homes | Smart microgrid | Energy storage system | Wind turbine

Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging

Policymakers in developing countries increasingly see science, technology, and innovation (STI) as an avenue for meeting sustainable development goals (SDGs), with STI-based startups as a key part of these efforts. Market failures call for government interventions in supporting STI for SDGs and publicly-funded incubators can potentially fulfil this role. Using the specific case of India, we examine how publicly-funded incubators could contribute to strengthening STI-based entrepreneurship. Indias STI policy and its links to societal goals span multiple decades—but since 2015 these goals became formally organized around the SDGs. We examine why STI-based incubators were created under different policy priorities before 2015, the role of public agencies in implementing these policies, and how some incubators were particularly effective in addressing the societal challenges that can now be mapped to SDGs. We find that effective incubation for supporting STI-based entrepreneurship to meet societal goals extended beyond traditional incubation activities. For STI-based incubators to be effective, policymakers must strengthen the ‘incubation system’. This involves incorporating targeted SDGs in specific incubator goals, promoting coordination between existing incubator programs, developing a performance monitoring system, and finally, extending extensive capacity building at multiple levels including for incubator managers and for broader STI in the country.
Keywords: Science technology and innovation | developing countries | entrepreneurship | incubators | public policy | sustainable development goals | science policy

This paper defines and analyses incubators that mainly support start-ups with a significant social impact. In 2016, a survey was conducted on the 162 incubators active in Italy, and a total of 88 responses were received. An analysis of the literature and of this dataset led to the identification of three types of incubators: Business, Mixed, and Social. Thirty of the respondents sent information on their tenants. Thanks to the data regarding 247 tenants, it was possible to analyze the impact of the three different types of incubators (Business, Mixed, and Social) on the tenants’ growth through OLS regression analyses. A Social Incubator is here defined as an incubator that supports more than 50% of start-ups that aim to introduce a positive social impact. The study shows that Social Incubators perceive social impact measurement and training/consulting on business ethics and CSR as being more important services than other incubator types. The regression analyses explain that Social Incubators are as efficient as other incubators, in terms of tenants’ economic growth, notwithstanding the focus of Social Incubators on start-ups that do not pursue only economic objectives. Finally, this study indicates that policy- makers can foster Social Incubators to support social entrepreneurship.
Keywords: Incubators | Accelerators | Social start-up | Social entrepreneurship | Social innovation | Entrepreneurship