Using a teaching model framework, we systematically review empirical evidence on the impact of entrepreneurship education (EE) in higher education on a range of learning outcomes, analysing 159 published articles from 2004-2016. The teaching model framework allows us for the first time to start rigorously examining relationships between pedagogical methods and specific outcomes. Re-confirming past reviews and meta-analyses, we find that EE impact research still predominantly focuses on short-term and subjective outcome measures and tends to severely under-describe the actual pedagogies being tested. Moreover, we use our review to provide an up-to-date and empirically rooted call for less obvious, yet greatly promising, new or underemphasised directions for future research on the impact of university-based entrepreneurship education. This includes, for example, the use of novel impact indicators related to emotion and mindset, focus on the impact indicators related to the intention-to-behaviour transition, and explore the reasons for some of the contradictory findings in impact studies including person-, context- and pedagogical model-specific moderators.

The impact of entrepreneurship education in higher education: A systematic review and research agenda

In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously.Until recently, it was not possible to implement a load balancer with PCC in a merchant switching ASIC, because high-performance switching ASICs typically can not maintain per-connection states with PCC. Newer switching ASICs provide resources and primitives to enable PCC at a large scale. In this paper, we explore how to use switching ASICs to build much faster load balancers than have been built before. Our system, called SilkRoad, is defined in a 400 line P4 program and when compiled to a state-of-the-art switching ASIC, we show it can load-balance ten million connections simultaneously at line rate.

https://dl.acm.org/doi/pdf/10.1145/3098822.3098824

SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs

As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance and energy overhead. While offloading DNNs to the cloud for execution suffers unpredictable performance, due to the uncontrolled long wide-area network latency. To address these challenges, in this paper, we propose Edgent, a collaborative and on-demand DNN co-inference framework with device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that adaptively partitions DNN computation between device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. (2) DNN right-sizing that accelerates DNN inference through early-exit at a proper intermediate DNN layer to further reduce the computation latency. The prototype implementation and extensive evaluations based on Raspberry Pi demonstrate Edgent's effectiveness in enabling on-demand low-latency edge intelligence.

https://dl.acm.org/doi/pdf/10.1145/3229556.3229562

Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

The coronavirus disease (COVID-19) created enormous pressure across the globe due to an increasing number of COVID-19 infected cases. All the governments’ primary focus is to save humanity from this pandemic problem, and they have lockdown almost the entire nation to stop the spread of infection. This lockdown resulted in a considerable impact on the global as well as a local economy that will take a long time to perform with business as usual scenario. However, improvement in the air quality of the cities across the globe has emerged as a key benefit of this lockdown. Therefore, this study aims to assess the overall impact of social and travel lockdown in five megacities of India; Delhi, Mumbai, Chennai, Kolkata, and Bangalore. The study evaluated the spatiotemporal variations in five criteria pollutants over two time periods, i.e., March–April 2019 and March–April 2020 and 10th–20th March 2020 (before lockdown) and 25th March to 6th April 2020 (during lockdown). The results highlighted a statistically significant decline in all the pollutants in all the megacities except for ozone. It was observed that the concentration of PM2.5, PM10, NO2 and CO declined by ~41% (66–39 µg m–3), ~52% (153–73 µg m–3), ~51% (39–19 µg m–3) and ~28% (0.9–0.65 mg m–3) during the lockdown phase in comparison to the before lockdown in Delhi, respectively. Similar decline in pollutant concentration was observed in other megacities as well. Further, the study conducted an expert survey to identify the possible challenges India might face after lockdown is over. All the experts said that reviving the economy will be a big challenge for the government, and it may result in some tradeoff while managing the air quality in the near future due to scarcity of funds, etc.

/pdf/social-and-travel-lockdown-impact-considering-coronavirus-4am5vkm7y0.pdf

Social and travel lockdown impact considering coronavirus disease (Covid-19) on air quality in megacities of india: Present benefits, future challenges and way forward

Biomaterials with suitable surface modification strategies are contributing significantly to the rapid development of the field of bone tissue engineering. Despite these encouraging results, utilization of biomaterials is poorly translated to human clinical trials potentially due to lack of knowledge about the interaction between biomaterials and the body defense mechanism, the "immune system". The highly complex immune system involves the coordinated action of many immune cells that can produce various inflammatory and anti-inflammatory cytokines. Besides, bone fracture healing initiates with acute inflammation and may later transform to a regenerative or degenerative phase mainly due to the cross-talk between immune cells and other cells in the bone regeneration process. Among various immune cells, macrophages possess a significant role in the immune defense, where their polarization state plays a key role in the wound healing process. Growing evidence shows that the macrophage polarization state is highly sensitive to the biomaterial's physiochemical properties, and advances in biomaterial research now allow well controlled surface properties. This review provides an overview of biomaterial-mediated modulation of the immune response for regulating key bone regeneration events, such as osteogenesis, osteoclastogenesis, and inflammation, and it discusses how these strategies can be utilized for future bone tissue engineering applications.

윤재량 ( Jae Ryang Yoon )

Papers

2004아테네 올림픽대회출전 한국레슬링 대표선수의 무산소성 운동능력 및 등속성 근력