Pakistan's leading stock exchange and COVID-19 nexus : evidence from quantile regression analysis

The contagious pandemic COVID-19 outbreak has disrupted numerous economic and business activities worldwide. This study focuses on COVID-19's effects on KSE-100 index (Karachi stock exchange), which is a part of a developing country. From 2 March 2020 to 9 November 2021, COVID-19 confirmed, recovered and deaths cases were taken as covariates for the COVID-19. To explore the conditional distributional impact of COVID-19 on KSE-100, we employ robust quantile regression analysis with detailed asymmetric evidence. The results show that the confirmed and recovered cases have a significant positive impact on KSE-100 whereas expired cases having a significant negative influence. These findings contradict previous studies in the world, which claimed that COVID-19 had a negative impact on developed stock markets while aligning with a vast literature of Pakistan stock exchange. It seems that as the result of timely policy implemented by the government of Pakistan. For investors, these findings are robust, which leads to providing practical policy to combat such circumstances in the future.peer-reviewe

Integration of the environmental management aspect in the optimization of the design and planning of energy systems

The increasing concerns regarding the environmental pollution derived from anthropogenic activities, such as the use of fossil fuels for power generation, has driven many interested parties to seek different alternatives, e.g. use of renewable energy sources, use of “cleaner” fuels and use of more effective technologies, in order to minimize and control the quantity of emissions that are produced during the life cycle of conventional energy sources. In addition to these alternatives, the use of an integrated procedure in which the environmental aspect will be taken into account during the design and planning of energy systems could provide a basis on which emissions reduction will be dealt with a life cycle approach. The work presented in this paper focuses on the examination of the possibilities of integrating the environmental aspects in the preliminary phase of the conventional design and planning of energy systems in conjunction with other parameters, such as financial cost, availability, capacity, location, etc. The integration of the environmental parameter to the design is carried out within a context where Eco-design concepts are applied. Due to the multi-parameter nature of the design procedure, the tools that are used are Life Cycle Analysis and Multi-criteria Analysis. The proposed optimization model examines and identifies optimum available options of the use of different energy sources and technologies for the production of electricity and/or heat by minimizing both the financial cost and the environmental impacts, with regard to a multiple objective optimization subject to a set of specific constraints. Implementation of the proposed model in the form of a case study for the island of Rhodes in Greece revealed that an optimized solution both cost and environmental-wise, would be an almost balanced participation of renewables and non-renewable energy sources in the energy mix

A Self-Organizing Multisensor Fusion Classification Algorithm

A self-organizing multisensor fusion algorithm to classify the inputs (data or images) into classes (targets, backgrounds) is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. The algorithm is a self-organizing algorithm, since it has the ability to form and adjust the number of clusters without being given the correct number of clusters. This algorithm implements a clustering algorithm that is very similar to the simple sequential leader clustering algorithm and the Carpenter/Grossberg net algorithm (CGNA). The algorithm differs from CGNA in that (1) the data inputs and data pointers may take on real values, (2) it features an adaptive mechanism for selecting the number of clusters, and (3) it features an adaptive threshold. The algorithm does not require the number of classes been known apriori. The problem of threshold selection is considered and the convergence of the algorithm is shown. An example is given to show the application of the algorithm for multisensor fusion for classifying targets and backgrounds, and the results of using this algorithm is compared to the results of using K-nearest neighbor algorithm

A fuzzy inference model for short-term load forecasting

This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes.Fuzzy sets Power generation Short-term load forecasting