Architecture Integration of 5G Networks and Time-Sensitive Networking with Edge Computing for Smart Manufacturing

Digitalization and networking are taking on an increasingly important role in manufacturing. Fifth Generation mobile networks (5G) allow us to wirelessly connect multiple assets in factories with guaranteed quality of service (QoS). A 5G non-public network (5G-NPN) realizes a dedicated network with secure communication within the factory. Time-sensitive networking (TSN) provides deterministic connectivity and reliability in local networks. Edge computing moves computing power near factory locations, reducing the latency of edge applications. Making production processes more flexible, more robust, and resilient induces a great challenge for integrating these technologies. This paper presents the benefits of the joint use of 5G-NPN, TSN, and edge computing in manufacturing. To that end, first, the characteristics of the technologies are analyzed. Then, the integration of different 5G-NPN deployment options with edge (and cloud) computing is presented to provide end-to-end services. For enhanced reliability, ways of interworking between TSN and edge computing domains are proposed. Afterward, as an example realization of edge computing, the investigation on the capabilities of the Kubernetes container orchestration platform is presented together with the gap analysis for smart manufacturing requirements. Finally, the different integration options, interworking models, and Kubernetes-based edge computing are evaluated to assist smart factories to use these new technologies in combination in the future.

METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss.

Toward a Theory of Family Social Capital in Wealthy Transgenerational Enterprise Families

Over generations, decaying family social capital is a primary cause of enterprise wealth loss in enterprise families that share ownership of multiple entities and multiple assets. We answer recent calls to uncover the origins of family social capital by studying seven old, wealthy, and large transgenerational enterprise families. We complement existing research on wealth creation through entrepreneurship by offering a deeper understanding of how family social capital is enhanced in transgenerational enterprise families. Our inductive qualitative study finds that enterprise family social capital appears to be enhanced by family governance, family learning, family identity, and physical grounding.

Stock market trend prediction using regression errors

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

Stock market trend prediction using regression errors

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

Protecting Source Location Privacy in IoT Enabled Wireless Sensor Networks: the Case of Multiple Assets

Till date source location privacy preserving techniques have aimed at fake backed or fake source approaches. Also, the work is concentrated to a single source scenarios. In this work, aim to explore the random-walk approach to mitigate passive eavesdropping attacker who backtracks to the source of information. Random-walk based solutions have proven to be effective for energy constrained WSNs. However, there is very little work that has worked for the case of multiple asset (sources) scenario till date. To understand the effect of the random-walk based solutions on level of location privacy in WSN intended IoT systems, for the multiple asset scenario, we developed two solutions. Through simulations we show the performance of the proposed two solutions by comparing them with existing random-walk based solutions. Our findings suggest that mere presence of multiple sources in the network alone does not provide location privacy, as one is intended to expect. It rather, need careful planning and designing of routing protocols to provide better privacy in presence of multiple-assets.<div>The work also presents future research direction for prospective researcher.</div>

Protecting Source Location Privacy in IoT Enabled Wireless Sensor Networks: the Case of Multiple Assets

Till date source location privacy preserving techniques have aimed at fake backed or fake source approaches. Also, the work is concentrated to a single source scenarios. In this work, aim to explore the random-walk approach to mitigate passive eavesdropping attacker who backtracks to the source of information. Random-walk based solutions have proven to be effective for energy constrained WSNs. However, there is very little work that has worked for the case of multiple asset (sources) scenario till date. To understand the effect of the random-walk based solutions on level of location privacy in WSN intended IoT systems, for the multiple asset scenario, we developed two solutions. Through simulations we show the performance of the proposed two solutions by comparing them with existing random-walk based solutions. Our findings suggest that mere presence of multiple sources in the network alone does not provide location privacy, as one is intended to expect. It rather, need careful planning and designing of routing protocols to provide better privacy in presence of multiple-assets.<div>The work also presents future research direction for prospective researcher.</div>

The sum of two independent polynomially-modified hyperbolic secant random variables with application in computational finance

In this paper, by reshaping the hyperbolic secant distribution using Hermite polynomial, we devise a polynomially-modified hyperbolic secant distribution which is more flexible than secant distribution to capture the skewness, heavy-tailedness and kurtosis of data. As a portfolio possibly consists of multiple assets, the distribution of the sum of independent polynomially-modified hyperbolic secant random variables is derived. In exceptional cases, we evaluate risk measures such as value at risk and expected shortfall (ES) for the sum of two independent polynomially-modified hyperbolic secant random variables. Finally, using real datasets from four international computers stocks, such as Adobe Systems, Microsoft, Nvidia and Symantec Corporations, the effectiveness of the proposed model is shown by the goodness of Gram–Charlier-like expansion of hyperbolic secant law, for performance of value at risk and ES estimation, both in and out of the sample period.