Univariate Financial Time Series Prediction using Clonal Selection Algorithm

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem

An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart data concept to analyze the intrusion alerts. In the edge layer, we deploy our detectors in edge devices. Smart data is a very promising approach for enabling lightweight and efficient intrusion detection, providing a path for detection of silent attacks such as botnet attacks in IoT-based systems. </p

Understanding circular business models: drivers, obstacles and conditions towards a successful transition

Abstract A circular economy is an alternative to a traditional linear economy (make, use, dispose) in which resources are kept in use for as long as possible, value creation is maximized in the use phase and products and materials are recovered at the end of each service life. The thesis explores this concept by taking a business model perspective. The theoretical part of the thesis clarifies the phenomenon of circular economy. It summarizes the development of the concept from an historical perspective and clarifies its position with regards to existing contemporary concepts (biomimicry, industrial ecology, cradle to cradle, blue economy, performance economy). By taking a business model perspective on the concept, the thesis attempts to offers a first typology of circular business models. Through the field work, the thesis extends knowledge on the understanding of circular business models at practical level. It highlights the differences between the theoretical underpinnings of the concept (its principles) and its implementation, showing that there is a gap between the concept and the way companies implement it. The findings allow the author to discuss how circular business models are classified and shows that many hybrid circular approaches can emerge. The analysis of the common features of the cases allow the author to draw a first set of normative requirements that define how a circular business model is organized. The cross analysis of the cases supports the development of a framework highlighting the current drivers at internal and external level pushing company to operate within circular economy principles, addressing a set of conditions allowing for the successful implementation of circular business models, while acknowledging a number of recurrent challenges preventing from a full implementation of the concept. At the core of the framework is set of key steps explaining how the transition occurs

Fundamentals of Strategy—The Legacy of Henry Eccles

Each generation must educate leaders for modern high command, creating an intellectual organization for the study of strategy and an environment conducive to original, valid, and valuable strategic insights. There are few better starting points than the pioneering contributions of the U.S. Naval War College in the age of Admiral Henry E. Eccles

A nonequilibrium thermodynamics perspective on nature-inspired chemical engineering processes

Nature-inspired chemical engineering (NICE) is promising many benefits in terms of energy consumption, resilience and efficiencyetc.but it struggles to emerge as a leading discipline, chiefly because of the misconception that mimicking Nature is sufficient. It is not, since goals and constrained context are different. Hence, revealing context and understanding the mechanisms of nature-inspiration should be encouraged. In this contribution we revisit the classification of three published mechanisms underlying nature-inspired engineering, namely hierarchical transport network, force balancing and dynamic self-organization, by setting them in a broader framework supported by nonequilibrium thermodynamics, the constructal law and nonlinear control concepts. While the three mechanisms mapping is not complete, the NET and CL joint framework opens also new perspectives. This novel perspective goes over classical chemical engineering where equilibrium based assumptions or linear transport phenomena and control are the ruling mechanisms in process unit design and operation. At small-scale level, NICE processes should sometimes consider advanced thermodynamic concepts to account for fluctuations and boundary effects on local properties. At the process unit level, one should exploit out-of-equilibrium situations with thermodynamic coupling under various dynamical states, be it a stationary state or a self-organized state. Then, nonlinear phenomena, possibly provoked by operating larger driving force to achieve greater dissipative flows, might occur, controllable by using nonlinear control theory. At the plant level, the virtual factory approach relying on servitization and modular equipment proposes a framework for knowledge and information management that could lead to resilient and agile chemical plants, especially biorefineries