Comparative analysis of firefly algorithm for solving optimization problems

Firefly algorithm was developed by Xin-She Yang [1] by taking inspiration from flash light signals which is the source of attraction among fireflies for potential mates. All the fireflies are unisexual and attract each other according to the intensities of their flash lights. Higher the flash light intensity, higher is the power of attraction and vice versa. For solving optimization problem, the brightness of flash is associated with the fitness function to be optimized. The light intensity I (r) of a firefly at distance r is given by equation (1

An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption

The Impact of Internationalization on Post-IPO Performance of Firms

This study examines the relationship between the degree of internationalization at the time of IPO (initial public offering) and post-IPO market performance of US and Canadian firms. This proposition derives its support from the synthesis of major theories of international business with signaling and information asymmetry theory. Theories of international business are developed from the perspectives of the behaviour and decision-making of managers of the firms, whereas signaling and information asymmetry theory are about communicating to external investors; and thereby incorporates the assessments of these investors. After the IPO, investors become substantial equity holders in the firms. Therefore, the integration of the two streams of theories will help us understand how investors evaluate the degree of internationalization at the time of IPO. The basic premise of this integration is that theories of internationalization have identified a number of benefits of international expansion of firms. In this research, I assume that these benefits of international expansion provide positive signals to potential investors. As signaling is related to information asymmetry, these positive signals reduce the information asymmetry of investors, inducing them to value firms with the most internationalization. Further, drawing support from international new venture theory and the resource-based view (RBV) of the firm, this study explores the premise that international new ventures (INVs) go public earlier than other traditional firms. Rapid growth through international expansion requires substantial financial resources. One way to raise capital for this rapid international expansion is to go public earlier. As the speed of internationalization and early IPO might send strong positive signals to external investors, INVs would go public earlier. Data on post-IPO returns, volatility of returns, underpricing, and other characteristics of the firm, including the scale and scope of international sales, was extracted from a number of secondary sources including different databases and company prospectuses. The sample is restricted to IPO firms in the manufacturing and service sectors, headquartered in the US and Canada, that issued initial public offerings from 2001 to 2011. Post-IPO performance was measured in three ways: compound holding period returns, relative volatility of returns, and underpricing. The three measures of post-IPO performance are used to capture different aspects of performance, including the value assessed by external investors and insiders, and risk diversification. Internationalization-performance relationships have been extensively evaluated using linear models. However, recent studies have found non-linear forms of the relationship. This study provides a theoretical rationale and evaluates the relationship between internationalization and post-IPO performance using both linear and non-linear models. Internationalization is a complex phenomenon and may not be appropriately evaluated using simple linear models. Therefore, this study assumes a U-shaped relationship between the degree of internationalization and compound holding period returns and an inverted U-shaped relationship between the degree of internationalization and relative volatility of returns and underpricing. Using least square regression, the results confirm the existence of non-linear relationships between internationalization and compound holding period returns and relative volatility of returns. I find support for the idea that higher geographic scope provides a positive signal to potential investors. My findings indicate that higher geographic scope at the time of an IPO not only results in higher post-IPO returns, but also reduces the relative volatility of returns and underpricing. In the case of underpricing, higher intensity also leads to lower underpricing. The present study identifies an optimal point beyond which internationalization has a positive impact on performance. The implication is that management could signal future performance through both higher geographic scope and higher intensity of internationalization. In a similar way, investors can make more informed decisions using these signals. In contrast, at lower levels, internationalization is not related to compound holding period returns and underpricing, but it is positively associated with relative volatility. This implies that investors perceive lower levels of internationalization as more risky compared to higher levels. The findings have implications for both investors and management. Investors can utilize the findings of higher geographic scope resulting in a desirable outcome of higher returns and lower risk to make decisions that are more informed. The results also provide a strong strategic message to management considering going public of the potential benefits of higher internationalization. Contributions to the literature include: synthesizing theories of internationalization with signaling and information asymmetry theories, testing the non-linearity of the internationalization-performance relationship in the IPO context, evaluating the risk diversification aspect of multinationality in the context of IPO, and addition to the limited research on the link between internationalization and post-IPO performance. As simple linear and curvilinear approaches may not reveal the complexity of internationalization-performance relationship, this study introduces a dummy category approach in order to examine the relationship from different angles such as the impact of high and low internationalization, separating the effects of domestic firms, and identification of a threshold

Innovation-Performance relationship: the moderating role of the Degree of Internationalization

Moderator variables are typically introduced when there is an unexpectedly weak or inconsistent relationship between a predictor and a criterion variable (Baron and Kenny, 1986). Holak, Parry and Song (1991) and Zhang, Li, Hitt, and Cui (2007) found an inconsistent relationship between R&D spending (a measure of innovation) and firm performance and so concluded that this relationship should be studied under different contextual factors. One such factor is the Degree of Internationalization (DOI) of a firm. Therefore, this paper evaluates the innovation-performance link in the presence of a moderator - the Degree of Internationalization (DOI). It proposes that DOI moderates the innovation-performance relationship. In addition, this research tests the hypothesis that DOI can affect either the form or the strength of the innovation-performance relationship. Only one previous study has evaluated the moderating effect of DOI on innovation-performance relationship, but this paper did not investigate the influence on the form of the relationship. The findings of this study are based on time series cross-sectional data of 102 large U.S. manufacturing firms from seven different industries. Data for each firm was obtained for eight years (2000-2007) from the Compustat database. Hypotheses were tested using the TSCSREG procedure with Fuller-Battese method implemented in SAS. The identification and the differentiation of the moderation effect into form and strength were carried out by using the typology from the work of Sharma, Durand and Gur-Arie (1981). The results show that DOI moderates the innovation-performance relationship positively and significantly. In addition, DOI affects the form (direct) and is a quasi moderator of the innovation-performance relationship. In terms of theory, there are two implications. First, that DOI is an important contingency factor when examining the innovation-performance relationship. Predicting the innovation-performance relationship without including DOI may lead to misleading conclusions. Second, when evaluating the relationship between R&D and firm performance, identifying whether DOI moderates the form or the strength of the relationship is needed in order to use a proper analytical technique. In terms of practice, the results sensitize managers to the need to focus not only on innovation activities, but also on their internationalization in order to appropriate the full benefits of their innovations

Hybrid of firefly algorithm and pattern search for solving optimization problems

Firefly algorithm (FA) is a newly introduced meta-heuristic, nature-inspired, stochastic algorithm for solving various types of optimization problems. FA takes inspiration from natural phenomenon of light emission by fireflies and is one of the robust and easily implementable algorithms. The standard FA consists of three stages namely initialization, firefly position changing stage and termination stage. A major drawback associated with standard FA in its termination stage is its failure in getting the most optimal value due to the fact that after a fixed number of iterations, no significant improvement can be observed in the solution quality. In this paper, this issue is resolved by introducing pattern search (PS) at the termination stage of standard FA when there is no further improvement in the solution quality. The proposed approach consists of three stages. In the first stage, the parameters of standard FA are initialized. In the firefly changing position stage, the randomization factor is used to update the solution in each iteration of operational stages. In the final stage, the optimized values obtained from the FA during its maximum number of iteration are given as inputs to the pattern search algorithm. The pattern search is an optimization algorithm that further optimizes the values obtained in the maximum iterations of standard FA. The proposed technique has been named as FA-PS in which PS has been used to introduce enhancement in the solution quality of standard FA. The developed approach has been applied to various types of maximization and minimization functions and the performance has been compared with standard FA and genetic algorithm in terms of getting the most optimal values for the functions being considered. A significant improvement has been observed in the solution quality of FA

A deep contractive autoencoder for solving multiclass classification problems

Contractive auto encoder (CAE) is on of the most robust variant of standard Auto Encoder (AE). The major drawback associated with the conventional CAE is its higher reconstruction error during encoding and decoding process of input features to the network. This drawback in the operational procedure of CAE leads to its incapability of going into finer details present in the input features by missing the information worth consideration. Resultantly, the features extracted by CAE lack the true representation of all the input features and the classifier fails in solving classification problems efficiently. In this work, an improved variant of CAE is proposed based on layered architecture following feed forward mechanism named as deep CAE. In the proposed architecture, the normal CAEs are arranged in layers and inside each layer, the process of encoding and decoding take place. The features obtained from the previous CAE are given as inputs to the next CAE. Each CAE in all layers are responsible for reducing the reconstruction error thus resulting in obtaining the informative features. The feature set obtained from the last CAE is given as input to the softmax classifier for classification. The performance and efficiency of the proposed model has been tested on five MNIST variant-datasets. The results have been compared with standard SAE, DAE, RBM, SCAE, ScatNet and PCANet in term of training error, testing error and execution time. The results revealed that the proposed model outperform the aforementioned models

MicroRNAs: Synthesis, mechanism, function, and recent clinical trials

AbstractMicroRNAs (miRNAs) are a class of small, endogenous RNAs of 21–25 nucleotides (nts) in length. They play an important regulatory role in animals and plants by targeting specific mRNAs for degradation or translation repression. Recent scientific advances have revealed the synthesis pathways and the regulatory mechanisms of miRNAs in animals and plants. miRNA-based regulation is implicated in disease etiology and has been studied for treatment. Furthermore, several preclinical and clinical trials have been initiated for miRNA-based therapeutics. In this review, the existing knowledge about miRNAs synthesis, mechanisms for regulation of the genome, and their widespread functions in animals and plants is summarized. The current status of preclinical and clinical trials regarding miRNA therapeutics is also reviewed. The recent findings in miRNA studies, summarized in this review, may add new dimensions to small RNA biology and miRNA therapeutics

An Alternate Account on the Ethical Implications of Autonomous Vehicles

Given the widespread popularity of Autonomous Vehicles (AVs), researchers have been exploring the ethical implications of AVs. Researchers believe that empirical experiments can provide insights into human characterization of ethically sound machine behavior. Previous research indicates that humans generally endorse utilitarian AVs, however, this paper explores an alternative account on the discourse of ethical decision-making in AVs. We refrain from favoring consequentialism or non-consequential ethical theories, and argue that human moral decision-making is pragmatic, or in other words, ethically and rationally bounded. We hold the perspective that our moral preferences shift based on various externalities and biases. To further this concept, we conduct two Amazon Mechanical Turk studies to investigate factors, such as, the \u27degree of harm\u27, and \u27level of affection\u27, which influence people\u27s moral decision-making. Our experimental findings seem to suggest that human moral judgements cannot be wholly deontological or utilitarian. We discovered that as the degree of harm decreased, people became less utilitarian (more deontological), and as the level of affection increased, people became less utilitarian (more deontological). These findings offer evidence on the ethical variations in human decision-making processes and refutes the view that aim to advocate application of a specific moral framework based on empirical evidence. The findings also offer useful insights for policymakers to explore the overall public perception on the ethical implications of AV

A glance at research-driven university\u27s technology transfer office in the UAE

© 2018 Inderscience Enterprises Ltd. Technology transfer offices (TTOs) in research-driven universities serve as an intermediary between suppliers of innovations and those who can potentially commercialise them. In the United Arab Emirates (UAE), TTOs are expected to take an important role in the evolution of successful spin-off companies from innovation to production to sales to sustainable profit. TTOs aim to help businesses to innovate and prosper leading to improving local and national economic prosperity. TTOs often support spin-off companies becoming a learning organisation and easing into an articulated management of activities complementary to the research and development activities that create the innovation and drive the transition from innovation to product lines. This paper aims to investigate the current situation of research-driven university\u27s TTO in UAE using a case study of Etisalat BT Innovation Center at Khalifa University and Masdar Institute for university policy implications. The findings suggest that TTOs assist university researchers in many ways

An enhanced approach of artificial bee colony for energy management in energy efficient residential building

The residential sector consumes the largest amount of total energy produced by diferent energy production resources. Therefore, an efective energy management and control system is required for residential buildings to utilize the energy resources in an efcient manner. The primary objective of energy management and control system is to improve the energy efciency and achieve the occupant’s preferred indoor environmental comfort. Many approaches have been proposed in the literature to efectively manage the energy systems in residential buildings. In this paper, a new approach of Artifcial Bee Colony with Knowledge Base (ABC-KB) is proposed for the management of power and occupant’s preferred environment inside the residential building. The complete energy efcient and user-friendly model has diferent components in which ABC-KB was used for the optimization purpose, whereas the status of diferent actuators was controlled using fuzzy controllers. The experimental results show that the developed system signifcantly enhances the energy efciency and occupant’s comfort inside the residential building as compared to some previously proposed approaches. The model has shown efciency in achieving high comfort index along with the minimized energy consumption