Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data

In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.

Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

Self regulated Learning with Open and Distance Learning for Foundation of Applied Mathematics Course

Foundation of Applied Mathematics is one of the mathematics servicing courses for Diploma of Applied Science programme in Universiti Teknologi MARA (UiTM), Malaysia. This course was previously taught in a physical classroom using direct confrontation. A few negative factors were found to contribute to the course's failure rate: i) inadequate exercises, ii) poor study habit and iii) reluctance to consult the lecturer oropting to study with peers only. Due to the ongoing COVID-19outbreak, the students are forced to do independent learning at home. Ascribable to this issue, the lecturers were inspired to investigate their students' motivation and participation in this course, which associate to their self-regulated learning (SRL). This paperpresents the implementation of open and distance learning (ODL) in teaching the subject of Foundation ofApplied Mathematics. The ODL was intended to promote SRL for student, thereby increasing their motivationand participation in learning activities. At present, no literature has been found study on SRL in current ODLmethod, mainly for this course. Online questionnaires were collected at the end of March - July 2020 academic semester to investigate students' perceptions on ODL and its effects to Self-Regulated Learning (SRL). Descriptive analysis was used to analyze the data. The findings indicated that the level of agreement for ODL has positive contributions to the students' SRL.

System Dynamic and Simulation of Business Model Innovation in Digital Companies: An Open Innovation Approach

Digital companies must improve their business models evolutionarily and innovatively. Therefore, IT investment, especially for revitalizing digital capabilities for operational changes, is important. Most companies are looking to source innovation from outside organizations. Partnership assessment is a crucial problem since it is not easy to integrate internal and external capabilities. The study aims to define business model innovation based on system dynamics with partnership scenarios. Open innovation is needed to evolve to meet market expansion. Partnership and IP strategies are discussed. System Dynamics modeling is utilized to map a system structure to capture its behavior and the relationships between elements, creating a simulation over time. The study develops a BMI to show how OI variables significantly contribute to the engine of growth for digital companies. The simulation reveals that OI has a significant effect on the company’s performance, indicated by significantly growing revenue after two years. At the early stage, patenting IP is not practical since the companies involved are unclear about the detailed IP. The IP success or the partner failure rate does not affect revenue significantly. After two years, companies sharing IP find the best way to benefit from it and lead to sustainable growth.

The Green Competitiveness of Enterprises: Justifying the Quality Criteria of Digital Marketing Communication Channels

The omnichannel approach to forming marketing strategies for the development of the green competitiveness of enterprises is seen as a process for the inseparable interaction of marketing-mix elements that are aimed at promoting green competitiveness. This approach integrates traditional and digital marketing communication channels and provides consideration for stakeholder interests. The effectiveness of applying the omnichannel approach to the formation of marketing strategies to develop the green competitiveness of enterprises depends on a set of marketing communication channels, which, in various combinations, can increase or decrease the level of companies’ green competitiveness. For that purpose, this paper proposes a scientific approach to identifying the quality parameters of marketing communication channels, which involves testing the hypothesis that statistically significant relationships exist between their quality parameters and the levels of green competitiveness. The objects analyzed in the paper comprise large Ukrainian production companies that are part of the agro-industrial, mechanical engineering, and food industries, and that work in both the local and international markets. According to the results of the calculations, four relevant parameters were identified for determining the quality of the marketing communication channels: the speed of loading pages, the failure rate, image, and remarketing activities.