Purpose: (i) Analyze and test CEO Narcissism Against Corporate Value and Earnings Management; (ii) Analyzing and testing the effect of CEO Narcissism Intervening on Company Value and Earnings Management in Manufacturing Companies in the Industrial Sector Listed on the Indonesia Stock Exchange for period 2016-2020.

Methodology: This study uses descriptive analysis, a type of quantitative research, which, when viewed from the data analysis method. The data analysis technique used in the study used linear regression. Then for the company's value using the Tobin's Q ratio and to assess the level of earnings management using the Modified Jones Model.

Findings: CEO narcissism has a positive effect on firm value, which means that every increase in CEO narcissism will increase firm value. CEO narcissism has a positive and significant effect on earnings management which this result explains that every increase in CEO narration will increase earnings management. CEO narcissism has a positive and significant effect on earnings management which this result explains that every increase in CEO narration will increase earnings management.

Originality/Value: This research is to have a view terms of the Effect of CEO Narcissism on Company Value and Earnings Management as an Intervening Variable in Manufacturing Companies in the Industrial Sector Listed on the Indonesia Stock Exchange for the 2016-2020 period in terms of contributions in the field of education and research results by the hypothesis, therefore That's the formulation for further research to consider the sector and additional variables.

Purpose: This study was conducted to examine the effect of CEO characteristics and firm reputation on firm performance after conducting mergers and acquisition. The object of this research is companies that carry out mergers and acquisitions listed on the Indonesia Stock Exchange (IDX) from 2014-2018. Design/methodology/approach: In selecting the sample, this research used a purposive sampling method. This study uses the SmartPLS program to analyze the data. Company performance is measured by Buy and Hold Abnormal Return. This study uses acquisition experience, previous acquisitions with positive performance, average acquisitions, acquisition success rate, experience in acquiring the same industry, and political connections to measure CEO characteristics. The Firm Reputation is measured by price earning ratio. Findings: The results of this research indicate that choosing a CEO who has high experience, knowledge, and capability will increase the firm's reputation and performance. Practical implications: The findings in this study will greatly help management to maximize the firm's performance when conducting mergers and acquisitions and management can also minimize failures by choosing a CEO who has the capability and is more experienced. Originality/value: The novelty of this research is implementing the theory of hubris and RBV by adding firm reputation as a mediating variable that strengthens the relationship between CEO characteristics and CEO political connections on firm performance after mergers and acquisitions.

Handwriting reflects a person's true nature, phobias, emotional outbursts, honesty, defenses and many more characteristics. Analysis of handwriting, also known as graphology, is a science that uses the strokes and patterns disclosed by handwriting to identify, evaluate, and analyze personality. It is the study of the patterns and physical characteristics of handwriting to identify the author, indicate the author's psychological state while writing, or analyze personality traits. Traditionally, professionals also called graphologists predict the behavior of the writer by analyzing their handwriting, but this procedure is tedious and expensive. Therefore, this paper focuses on developing an application for personality identification that can predict behavioral characteristics directly using a computer without any human involvement. Most of the existing applications use English as the primary language to identify the personality trait of the writer however, our approach uses Devanagari scripts for prediction, thereby eliminating the language barrier. Our proposed method uses a machine learning approach to predict personality by analyzing Devanagari samples using Artificial Neural Network. We have created our own Devanagari word dataset. There are almost 4000 images which belong to 5 classes namely Introvert, Extrovert, Optimistic, Pessimistic and Stable mind-set. The testing accuracy achieved by the proposed method is 94.75%.

The aim of this investigation is to check the impact of digitalization and trade openness on economic growth for top ten richest Asian countries. Static Gravity Model and Generalized Method of Moments Model were estimated. We found that digitalization and trade openness have a significant positive effect on economic growth. These results prove that trade openness and digitalization is a source of economic growth for richest Asian countries. Due to the magnitude of the positive externalities attached to the trade openness and digitalisation, in terms of technology transfer bias, financial capacities, economic policies, human expertise, plenty of natural resources, large markets size, and spillover effect added to the domestic capacities and the national investment, the pace of the phenomenal economic performance of the Asian economies is very well marked.

In this paper, we use the ARDL method to find the Impact of institutional quality on economic growth in Tanzania from 1990 to 2021. The ARDL technique frees variables from residual correlation as all variables are assumed to be endogenous. They distinguish between dependent and explanatory variables in any long-run relationship, identify the co-integrating vectors with multiple co-integrating vectors, and derive the Error Correction Model (ECM) or Error Correction Model (ECM) Vector Error Correction Model (VECM) by integrating short-run adjustments with long-run equilibrium without losing extended-run information. Our results show all adjustment terms in the respective models that have a long-run relationship have correct (negative) signs and are more than one, implying there is convergence in the long run; that is, the models returned to their long-run equilibrium; the rate (or speed) at which this happened ranged between $15 \%$ to $106.6 \%$ annually. Institutional quality has a significant affirmative (0.047) causal long-run effect on economic growth.

At present, intelligent buildings have formed a relatively mature and complete industrial chain and industrial scale in China, but there are still some technical and application problems to be solved urgently, mainly including the lack of linkage between different demand-side energy demand scenarios, the inability to guarantee information security, and serious building energy consumption. In view of the above problems, scholars at home and abroad have launched relevant research, but they have not comprehensively considered the relevance of the above problems. Therefore, this article sorts out the research status of source-load joint forecast method of intelligent building clusters, and analyzes the related development trends, including three major directions: source-load joint forecast method of intelligent building clusters, key technologies of energy supply and demand data security of intelligent building clusters, and distributed energy transaction strategy of intelligent building clusters. Through combing and analysis, this article has formed a number of valuable research directions, which can provide directional reference and knowledge for the accurate response of electric-thermal load and energy transaction strategy of intelligent building clusters and P2P method theory in other scenarios.

The study assessed the hedge or safe-haven property of five cryptocurrencies for stocks of three COVID- 19 worst-hit African countries. We address two main concerns bordering on the predictive capacity of African stocks for cryptocurrency returns and the safe-haven property that cryptocurrencies could offer to African stocks. A distributed lag model, with explicitly incorporated salient statistical features, was adopted based on its efficient management of parameter proliferation and estimation biases. We ascertained the model’s in-sample predictability and evaluate its out-of-sample forecasts performance in comparison with the historical average model, using Clark and West statistics. While African stocks significantly predicted cryptocurrency returns, the cryptocurrency-stocks nexus revealed the diversifier and safe-haven property of cryptocurrencies for African stocks in periods of normalcy and crisis/pandemic, respectively. Our predictive model outperformed the historical average model in the out-of- sample. Our results may be sensitive to cryptocurrency-stocks nexus and sample periods but not the out-of-sample forecast horizons.

Market changes last years have led to an additional understanding of people importance as the main resources of companies. Truck drivers are one of the occupations with the greatest shortage. More attention is being paid to ways of retaining employees. One of the most important measures is bonus or reward. There is a lack of models in the literature and it is exactly the main motive of this research. Proposed models create a basis for future theoretical research, but also for practical applications. The main assumption is that models must provide a fair way to earn bonuses in a "healthy environment". Two models are proposed. The first model for distribution company with a heterogeneous fleet of vehicles with less capacity. The second model refers to homogenous heavy truck fleet. In the first case, several criteria are used: distance (kilometers) driven, number of tours/rides, number of unloading stops and number of pallets. The second model is based on fuel consumption, distance driven, vehicle maintenance, driver experience (years in the company) and overall dispatcher score. The results show the convenience of applying the proposed models. Certain differences were also identified in the observed models. It can be concluded that there is no universal model for performance appraisal and bonus calculation. Ideas for overcoming and improving models are also proposed. Described models in original or adapted form can be applied to evaluate the performance of drivers in a wide variety of transport systems.

Road capacity utilization is causally connected with an appropriate level of efficiency and an optimal level of traffic safety. Therefore, in this paper, it is considered the issue of maximum utilization of road capacity through the maximization of the input parameter AADT (Annual Average Daily Traffic), and the minimization of output parameters related to the categories of traffic accidents. It was defined six main road sections, which were evaluated based on seven techno-operational criteria using an integrated Multi-criteria decision-making (MCDM) model. The data refer to buses as a vehicle category. The Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA) method was chosen to determine the weights of criteria, while the road sections were ranked using the Evaluation based on distance from average solution (EDAS). In addition, in one of the stages of applying the model when it comes to AADT, the Bonferroni operator (BFO) is used. The results show that the highest level of safety refers to a main road section with the following characteristics: average AADT, minimal deviation from the speed limit, an ascent of 7% and the lowest number of traffic accidents by all categories. In the paper, it was performed a multi-phase sensitivity analysis in order to identify possible differences in results when determining new circumstances.

Risk monitoring and risk prediction are of great significance to improve the safety of urban rail operation. Existing studies often analyze the topological characteristics of accident networks from the perspective of network theory, in order to point out the role of specific influencing factors in urban rail accidents. This article proposes a risk analysis method of urban rail operation accidents, which takes risk factors, risk points and risk events as nodes to form a network, and combines the interaction between risk points to evaluate the safety of the whole system. The existing system safety analysis methods all build models based on the accidents that have occurred. Based on the analysis of the existing urban rail transit infrastructure and operating environment, this article puts forward the risk factors and risk points that may cause risk events, and combines the mechanical connection, electrical connection and signal connection among risk points to deeply explore the interaction between risks so as to find the key risk points that cause accidents and evaluate the safety of the whole system. The results show that the proposed risk analysis method can provide effective theoretical support for risk monitoring.

Artificial intelligence, in a larger sense, refers to computers that have human intelligence-specific capabilities such as obtaining information, perceiving, seeing, thinking, and making decisions. At first glance, artificial intelligence, often known as "Artificial Intelligence" (AI) in the literature, causes everyone to associate something distinct. According to researches, the concept of artificial intelligence evokes an electro-mechanical robot replacing human beings, but everyone involved in this field is aware that there is a definite difference between human beings and machines. The aim of this article is to show the importance of using AI in today’s HR practices. In this context, one of the qualitative research designs, phenomenological research, was deemed 1appropriate for the thesis study. Because phenomenology establishes a framework for exploring subjects that aren't utterly unfamiliar but whose meaning isn't quite clear.AI-based HR apps have the ability to boost employee productivity while also assisting HR personnel in becoming educated advisers who can boost employee performance. AI-enabled HR solutions are capable of evaluating, predicting, diagnosing, and locating more powerful and capable employees.

Lack of access to finance constitutes a major setback to the development of the micro-, small and medium-sized enterprise (MSME) sector in the countries of the Association of Southeast Asian Nations (ASEAN). MSMEs are confronted with stringent funding constraints in traditional lending and capital markets, in particular at the early stages of their activity. Demand and supply of capital to MSMEs thus entails more complex issues compared to the larger firms. This paper presents a number of policy actions that have the potential to mitigate the financing challenges faced by MSMEs in ASEAN at the start-up stage by enhancing the potential of alternative funding sources such as business angel investment, crowdfunding, venture capital investment and SME stock markets.

In This research article represents the study of optical, and electrical properties of Methylammonium lead (MAPbBr_3-nI_n; n=0, 1, 2 and 3) (CH₃NH₃PbI₃, CH₃NH₃PbI₂Br, CH₃NH₃PbIBr₂, and CH₃NH₃PbBr₃) based Perovskite solar cell. An FTO/TiO₂/ MAPbBr_3-nI_n/Spiro-OMeTAD/Al based structure with TiO₂ as electron transport layer and Spiro-OMeTAD hole transport layer has been used for this study. The opto-electrical properties such as resonance time period, indirect and direct band gap have been studied. The results shows that the resonance time period, indirect band gap, and direct band gap for each of the Perovskite layer CH₃NH₃PbI₃ is 9.09 µs, 1.4 eV and 2.6 eV, for CH₃NH₃PbI₂Br is 6.25 µs, 1.5 eV and 2.7 eV, for CH₃NH₃PbIBr₂ is 6.25 µs, 1.7 eV, and 2.8 eV and for CH₃NH₃PbBr₃ is 5.55 µs, 2.1 eV and 2.9 eV respectively.

This paper is the second part of the attempt to examine the governance, efficiency, and development of the Agricultural Knowledge and Innovation System (AKIS) in Bulgaria. In the years of EU membership, the expenditures for AR&D significantly decreased absolutely and relatively as a share in the total expenditures for R&D, which indicates diminishing importance and deteriorating financial, personnel, and material potential of the agrarian knowledge and innovation sector.

The research continues the first part already published in the previous issue with the third point of view related to the governance of agrarian research in Bulgaria is unpacked; forth, the state of the system of education and training of agricultural producers in the country is analysed.

The progress of a society depends on and can only be sustained through permanent education, which implies the acquisition of new knowledge, and in this way human capital can be exploited to the maximum. Through education, any individual acquires the ability to develop his potential, and in order to do this, he must undertake a process of education through which he accumulates knowledge and skills, harmoniously develops his personality, as well as shapes his character. Through education and culture, creativity, adaptability, but also other necessary qualities are developed, and as the finality of education, insertion and social integration are of particular importance for any graduate, contributing to his fulfilment from several points of view, material, spiritual, moral.

The article analyses the participation in education of the Romanian population, the evolution of the teaching staff and the general expenses for education in the period 2010-2021.

The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation. Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created. One real-world medical dataset, namely Parkinson disease data set is used to test the proposed approaches. When compared to older existing operators, the new classifiers have better classification accuracy.

E-commerce is referred as any transaction in which the sale and purchase of goods or services takes place via the Internet and leads to the import or export of goods or services. Supply always needs demand. We need marketing to expand the demand in order to sell more services or products. Organizations need to have a good understanding of their customers and their desires in order to become succeed in business, and to get this understanding, they must use tools and techniques to measure the customer's interest. With using of data mining techniques and with the discovery of hidden and valuable knowledge of data, organizations don’t miss the opportunity to sell more and provide better customer satisfaction. The customer segmentation is one of the methods of customer recognition. This method is used when we look for groups of similar data. Segmenting is one of the most important topics in reaching modern marketing and managing successful customer relationship management. The purpose of this paper is to design an electronic marketing model using the k-mean algorithm. First, customer`s data is collected and after preparing and pre-processing data, using the k-mean algorithm, segmentation customers and future marketing strategies and recommendations are discussed and eventually using the theory of the possibility, the possibility and requirement for the proposal to be considered, and each of the recommendations or strategies are given numbers with the name of the possibility and necessity of the system output, and a more favorable proposal is obtained.

The invention of chemically flexible solar cells, known as dye-sensitive solar cells (DSSC), has led to cheaper, more ecologically friendly, yet inefficient solar cells. The poor link between the semiconductor and the substrate, which impacts the DSSC electrons' mobility, is the root reason of the low efficiency. TiCl₄ pre-coatings have been used in many studies on semiconductor engineering to boost electron mobility. In order to lower the internal resistance in the DSSC, it is known that using TiCl₄ pre-coating affects the mechanical strength between the semiconductor and the substrate. TiCl₄ pre-coating can be done by immersing FTO glass, where semiconductors have deposited, in the TiCl4 solution. This study examines how the TiCl₄ pre-coating time in the production of TiO₂ semiconductors affects DSSC performance. To reveal the effects on alterations in the semiconductor morphology of TiO₂, immersion times in the TiCl4 treatment were set to 10, 20, 30, 40, 50, and 60mins. The results show that TiO₂ nanoparticles with a 60min TiCl₄ treatment had better connectivity between individual particles than those with shorter treatments. The performance metrics like open circuit photovoltage (Voc), short-circuit photocurrent density (Jsc), and fill factor (FF), and efficiency (η) were 0.569 V, 7,616 mA/cm2, 43.3%, and 2.208%, respectively.

A proposal is made in the sanitary hot water system in a hotel installation consisting in the change of the black steel pipe system by high density polypropylene pipes in the primary circuit of the system. A field of vacuum tube solar collectors is sized to work in replacement of the heat recovery system of the water chiller. An economic and environmental analysis of the proposal is made. With the installation of the solar collectors, the hotel will deduct 27,545 liters (15,425 kg) of liquid gas propane (LPG) from its annual consumption, equivalent to 51,728 USD, avoiding the emission of 104,583 kg of CO₂eq into the environment. The simple recovery time of the investment will be 5.88 years. The results obtained demonstrate the feasibility of using solar thermal energy in the heating of sanitary water due to the decrease in the consumption of liquefied petroleum gas and, therefore, the environmental damage is reduced when greenhouse gases are no longer emitted.

Environmental issues have remained one of the most challenging social-economic impacts on the world and most countries. Tackling these challenges has remained an underlying issue as a concise approach, method, and policy are yet to be globally made available. Machine learning (ML) with support from IoTs, big data, NLP, and cloud computing is radicalizing the development of a modern-day economy via human support systems. With technical devices, systems, and processes intricately oriented to human understanding. Little environmental needs have been developed to give humans a comfortable place. Even though sensors capture and satisfy human needs, global ecosystem barriers have weighed beyond. Following changes in the world today, automated restrictions and barriers have been seen limiting humans from enjoying opportunities offered by IoTs, big data, NLP, and cloud computing due to environmental impact. Machine learning with capabilities to help humans become more informed is insignificantly exploited on the environmental needs. To suggest an integrated system, method, and areas that IoTs, big data, NLP, and cloud computing should focus on to fight negative environmental impact as a major step to fight climate change. In the study, two research questions and a hypothesis were used. Daily data on emission accusations was collected and used to respond to research questions and hypotheses. In 30 minutes per day and within a month, 412 diesel cars emitted 54,384 g CO₂/km, 636 petrol cars emitted 76,320 g CO₂/km, and 157 LPG cars emitted 9,577 g CO₂/km. Predictions and forecasts were determined based on the data collected. Data accusations reveal they worsen the future impact as both hypotheses and research questions positively support findings that integration of sensors with machine learning can predict future climate situations. Improved gardens are needed, limit artificial items and diesel cars, and improved afforestation is needed in this city.