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Likelihood Based Inference In Cointegrated Vector Autoregressive Models

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable progress in the development of time series forecasting methods that exploit many predictors, and this chapter surveys these methods. The first group of methods considered is forecast combination (forecast pooling), in which a single forecast is produced from a panel of many forecasts. The second group of methods is based on dynamic factor models, in which the comovements among a large number of economic variables are treated as arising from a small number of unobserved sources, or factors. In a dynamic factor model, estimates of the factors (which become increasingly precise as the number of series increases) can be used to forecast individual economic variables. The third group of methods is Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model. The chapter also discusses empirical Bayes methods, in which the hyperparameters of the priors are estimated. An empirical illustration applies these different methods to the problem of forecasting the growth rate of the U.S. index of industrial production with 130 predictor variables.

Forecasting with Many Predictors

The revision of the Rating Outlook to Stable from Negative reflects Fitch’s belief that the city may be required by the EPA to move to secondary treatment at its two largest treatment plants at some time in the future. However, the timing and costs associated with this upgrade could be long term, near or beyond the final year of the current large capital plan (2019). Furthermore, negotiations and the final agreement regarding the timing of such regulatory upgrades may not occur quickly. The Stable Rating Outlook reflects Fitch’s expectation that the third rate package will be adopted and become effective in fiscal 2012 as anticipated and that a change in political administrations or New Issues Wastewater System Revenue Bonds (First Bond Resolution), Senior Series 2009A, 2009B, and 2009C Wastewater System Revenue Bonds (Second Bond Resolution), Junior Series 2009A

“公共财政”和“Public Finance”辨析

Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Although crucial to software quality and widely deployed by programmers and testers, software testing still remains an art, due to limited understanding of the principles of software. The difficulty in software testing stems from the complexity of software: we can not completely test a program with moderate complexity. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality.

Software testing

This paper investigates the reliability of a standby system incorporating waiting time to repair. The considered system consists of two units, namely, the main unit and the standby unit. Whenever the main unit fails, the whole load is transferred to the standby unit instantaneously by a switching-over device. As regards to the repairing of the main unit, it has to wait for repair whenever it fails due to unavailability of repair facility. When both the main and standby units fail, then the system goes to the complete failure mode. The system may also fail due to incorrect start of the system, which can occur due to an untrained and inexperience operator. The repair of the main and standby units follows general distribution, whereas repair due to human error is obtained with the help of Gumbel-Hougaard family copula. The system is analyzed by supplementary variable technique and Laplace transformation. Various reliability measures like availability, mean time to failure, and profit function have been evaluated for the considered system. A numerical example with a way to illustrate the utility of the model has also been presented.

Stochastic Analysis of a Standby System With Waiting Repair Strategy

In the modern scenario, reliability has becomes the most challenging and demanding theory. The theory and the methods of reliability analysis have been developed significantly during the last 40 years and have also been acknowledged in a number of publications. So, a reliability engineer is aware about the importance of each reliability measure of the system and its fields. In this research work, a survey of reliability approaches in various fields of engineering and physical sciences is carried out. In this survey, the author tried to provide the major areas i.e. past, current and future trends of reliability methods and applications for the readers.

On system reliability approaches: a brief survey

This paper discusses about the availability of a complex system, which consists of two independent repairable subsystems A and B in (1-out-of-2: F) and (1-out-of-n: F) arrangement respectively. Subsystem A has two identical units arranged in parallel redundancy (1-out-of-2: G), subsystem B has n unit in series (1-out-of-n: F) with two types of failure, viz., partial and catastrophic. Except at two transitions where two types of repair namely exponential and general possible all other transitions have single possibility between any two states. The failure and repair time for both subsystems follow exponential and general distributions respectively. The model is analyzed under "preemptive-resume repair discipline" where A is considered to be in priority while B is non-priority. By employing supplementary variable technique, Laplace transformation and Gumbel-Hougaard family copula various transition state probabilities, availability, Mean Time to Failure (MTTF) and cost analysis (expected profit) are obtained along with steady-state behaviour of the system. Inversions have also been carried out so as to obtain time dependent probabilities, which determine availability of the system at any instant. At last some special cases of the system have been taken.

Availability and cost analysis of a parallel redundant complex system with two types of failure under preemptive-resume repair discipline using gumbel-hougaard family copula in repair

For the past two decades, nature-inspired optimization algorithms have gained enormous popularity among the researchers. On the other hand, complex system reliability optimization problems, which are nonlinear programming problems in nature, are proved to be non-deterministic polynomial-time hard (NP-hard) from a computational point of view. In this work, few complex reliability optimization problems are solved by using a very recent nature-inspired metaheuristic called gray wolf optimizer (GWO) algorithm. GWO mimics the chasing, hunting, and the hierarchal behavior of gray wolves. The results obtained by GWO are compared with those of some recent and popular metaheuristic such as the cuckoo search algorithm, particle swarm optimization, ant colony optimization, and simulated annealing. This comparative study shows that the results obtained by GWO are either superior or competitive to the results that have been obtained by these well-known metaheuristic mentioned earlier. Copyright © 2016 John Wiley & Sons, Ltd.

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

The concept of the circular economy (CE) has gained importance worldwide recently since it offers a wider perspective in terms of promoting sustainable production and consumption with limited resources. However, few studies have investigated the barriers to CE in circular food supply chains. Accordingly, this paper presents a systematic literature review of 136 papers from 2010 to 2020 from WOS and Scopus databases regarding these barriers to understand CE implementation in food supply chains. The barriers are classified under seven categories: “cultural”, “business and business finance”, “regulatory and governmental”, “technological”, “managerial”, “supply-chain management”, “knowledge and skills”. The findings show the need to identify barriers preventing the transition to CE. The findings also indicate that these challenges to CE can be overcome through Industry 4.0, which includes a variety of technologies, such as the Internet of Things (IoT), cloud technologies, machine learning, and blockchain. Specifically, machine learning can offer support by making workflows more efficient through the forecasting and analytical capabilities of food supply chains. Blockchain and big data analytics can provide the necessary support to establish legal systems and improve environmental regulations since transparency is a crucial issue for taxation and incentives systems. Thus, CE can be promoted via adequate laws, policies, and innovative technologies.

https://www.mdpi.com/2071-1050/13/12/6812/pdf

Mangey Ram

Papers

Stochastic Analysis of a Standby System With Waiting Repair Strategy

On system reliability approaches: a brief survey

Availability and cost analysis of a parallel redundant complex system with two types of failure under preemptive-resume repair discipline using gumbel-hougaard family copula in repair

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

Analyzing Barriers of Circular Food Supply Chains and Proposing Industry 4.0 Solutions