درخت حوزه‌های تخصصی

The study has two main objectives: firstly, to examine the opportunity of the Momentum and Contrarian investment strategy for three different monetary systems to trade currencies in Forex markets using Symlet wavelet decomposition approach. Secondly, to examine the co-movements between the three monetary systems using wavelet coherence analysis. The findings indicate that an investor with momentum strategy can consider investing in Bitcoin and Gold market, while the contrarian investment strategy is more advisable for the fiat money market during crisis period. Furthermore, the wavelet coherence analysis indicates that Bitcoin currency is the most leading monetary system during the Covid-19 pandemic crisis, followed by gold. However, US dollar mostly leads Bitcoin during non-crisis periods, while Gold is found to lead the US dollar throughout the sample period of the study. This suggests that the cryptocurrency system or gold standard should be considered as the alternative monetary system for better economic stability specially during the crisis period. Moreover, Bitcoin and gold had an anti-phase correlation before the Covid-19 pandemic crisis, which implies better benefits of hedging in the non-crisis period, while during a crisis they are moving together across different horizons. In contrast, Bitcoin and Fiat Money are strongly correlated during non-crisis periods, while during covide-19 pandemic crisis the correlation is statistically insignificant. Overall, the outcomes offer significant guidance for policymakers in understanding which monetary system leads to better economic stability during the crisis period and provides many implications for market players such hedging and Diversification investments strategy in forex markets.

The global spread of internet technology and the associated advancements are making it easier for women entrepreneurs to manage the work-family boundary. However, there is a need for more research on digital entrepreneurship (DE), especially on how different degrees of DE influence the success of work-family boundary management (WFBM). This study explores the effect of extreme, moderate, and mild pursuit of DE on women’s abilities to manage the boundary between work and family. This study uses a quantitative research method and collected data from 312 women entrepreneurs. The results show that DE enables women entrepreneurs to manage the work-family boundary. We found that with extreme DE, women are more likely to experience high levels of cross-role interruption behaviours and perceived boundary control, while with moderate DE, women experience high levels of identity centrality of work and family roles. Therefore, this study contributes to the literature on women’s DE by investigating different degrees of DE and its effects on work-family boundary management. The study also contributes to the literature on WFBM through examining the dynamics of DE in enabling women entrepreneurs to manage work-family boundaries to different extents. Therefore, this study captures the interplay between DE and managing work-family boundaries, which facilitates our understanding of women entrepreneurship and the role DE has in enabling the agentic potential of entrepreneurial actors.

This paper sought to examine the impact of perceived Social Media Marketing Activities (SMMAs) on customer purchase intention via brand awareness in an online context. An online questionnaire was used to collect data from 188 samples. The data were analyzed using the structural equation modeling approach, and the research hypotheses were examined using SEM. The study measured SMMAs through personalization, customer community, and live video. The results revealed that SMMAs were insignificant towards brand awareness and purchase intention. The result also stated that brand awareness does not mediate the relationship between SMMA and purchase intention. However, brand awareness was found to affect purchase intention positively. The current study introduces the stimulus–organism–response model as a theoretical support to examine SMMAs of e-commerce to customers' purchase intention via brand awareness.

Scientific sources demonstrate different attitudes of researchers to cryptocurrencies because they treat them as a category of currency, virtual money, commodity, etc. Accordingly, the relation to the valuation and risk of cryptocurrency as an investment object is different. The purpose of the article is to identify cryptocurrency value formation factors and determine the risks of investing in cryptocurrency. Cryptocurrency is simultaneously considered a currency, an asset with uncertain income, and a specific product, the price of which is determined by the energy costs for mining new cryptocurrency blocks. Thus, the paper examines the risks of investing in cryptocurrency from several positions. First, the study identifies the factors of formation of the value and risk of cryptocurrency as ordinary money based on comparing cryptocurrency with traditional money. Unlike traditional money, cryptocurrency is not tied to the economic performance of a particular country; also, central banks do not control or regulate their mining. Instead, the cryptocurrency emissions depend on the computational capacity of the equipment used for their mining. As a financial asset, cryptocurrency can be a “financial bubble” because their value increasing often exceeds the cost of mining. On the other hand, given the emergence of cryptocurrency as a phenomenon of the information economy, the paper analyses the impact of specific technical features (cryptographic hashing algorithm, the complexity of creating new blocks, the technology of verification of mining operations, etc.) on the risk of investing in cryptocurrency assets.

The economic events of recent years caused by the effects of the pandemic have contributed to rising farm costs. Determining the unit cost of current biological assets of crop production and detailing these costs by major categories is one way to optimize them and increase the efficiency of the main activities of the producer. It requires formation and implementation effective cost control mechanisms in crop production.The purpose of this study is development of scientifically sound proposals and recommendations for controlling the cost of current biological assets of crop production, identifying priority cost items at each stage of cost formation. The main standards for accounting for current biological assets of crop production in Ukraine are IAS 41 "Agriculture" and NP (S) FA 30 "Biological assets". Control is regulated by the company's accounting policy. It has established that it is possible to determine the actual cost of agricultural products only at the end of the year. Currently, in Ukraine there is no legal act that would give the right to reduce the time of determining the actual cost of agricultural products. To determine the financial result, there is a method of sequential closing of accounts of different types of agricultural production. In order to improve the system of control over the value of current biological assets of crop production in agricultural enterprises, it is proposed to strengthen internal control by means of information systems and technologies. Control of costs that form the cost of crop production should be carried out both at the planning stage and during the execution of works and at the stage of their completion.

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance. This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i.e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system

Information Management and Analysis (IMA) implies the single data and knowledge base of the business organization whose algorithm and software complex is designed to solve functional problems of operating and dispatch management, marketing, financial arrangements, accounting, strategic planning and organizing e-document flow between the sources and users of information. The “information – communication - control” vector is directed at the achievement of corporate objectives. Assessment of IMA implementation efficiency is complicated by the fact that implementation of managerial (informational) solutions is possible indirectly, with the flows of material, financial and informational resources; the IMA algorithms are valid for a certain range of situations and modes, whose frequency and duration of action are not known in advance. The study is aimed at formalizing compatibility conditions for a set of economic and technical, actual, and probabilistic indicators applied for justifying the IMA implementation efficiency for industrial entrepreneurship. The IMA effectiveness is expected to be calculated as a total of network (communication) effects per each block of IMA algorithms, factored the presentation of information operability; to calculate cost-effectiveness (recoupment of investment) it is supposed to modernize the structural capital evaluation procedure according to A.Puliс. The article contains an introduction, concept of IMA, application of system efficiency theory to IMA, building the structure of IMA efficiency indicators, and conclusion. The main method of study: system approach.

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.