Journal of Economy and Technology最新文献

Vehicle automation has been in the works for a long time now. Automatic brakes, cruise control, GPS satellite navigation, etc. are all common features seen in today's automobiles. Automation and artificial intelligence breakthroughs are likely to lead to an increase in the usage of automation technologies in cars. Because of this, mankind will be more reliant on computer-controlled equipment and car systems in our daily lives. All major corporations have begun investing in the development of self-driving cars because of the rapid advancement of advanced driver support technologies. However, the level of safety and trustworthiness is still questionable. Imagine what the assailants could do if they had access to a car. Control of braking, acceleration and even steering by an attacker can have disastrous results. Most of the assaults against autonomous vehicle software and hardware are covered in this study, along with their prospective consequences. This work explores an extended analysis of the security threat and cyber-attacks on different sensors and perception systems in autonomous vehicles. This work also showed machine learning-based possible defensive techniques to prevent the security threat. An overview of most of the conceivable assaults against autonomous vehicle software and hardware and their prospective consequences is presented in this study.

Multi-access Edge Computing (MEC, formerly, Mobile Edge Computing) offers users of Mobile Devices (MDs) such as smartphones advantages in computational task completion times by offloading to superior but geographically close computing resources. There is a need to make these services more sustainable (energy-efficient) as an initial step towards net zero emissions. We have modelled a zero-latency edge node-MD connection as a single base station linked to a devoted server with the MD transmitting and receiving data via a Wireless Local Area Network (WLAN) with, wherever possible, identifiable real-world power ratings. In this paper, we demonstrate that the total energy usage by the MD and edge node hardware can be markedly less than that of the MD alone in 3 G, 4 G and 5 G WLAN networks. The energy savings, computed as percentages of MD-alone energy usage, are independent of data file size, are greater with computationally more complex tasks, and have a higher MD CPU workload but moderate server CPU workloads. Energy savings are highly dependent on the precise configuration of the base station with server type (peak power rating, the number of CPU cores and the maximum number of jobs processed simultaneously). A 5 G base station has the highest power consumption, but this is offset by much faster WLAN speeds, which can result in energy savings in excess of 90% compared with MD computation alone. We discuss the implications of these results for reducing global electricity use and achieving carbon neutrality to contribute towards net zero emissions.

The whole purpose of the present research was to advance the comprehension of the mediating effect of emotional advertising on consumer decision-making style and purchase intention. A conceptual framework showing linkages among variables, based on past literature was made which is tested with Partial Least Squares (PLS) procedure on data acquired through a survey based on the structured questionnaire that yielded 525 respondents across India. For stimulation purposes, we selected the ten most watched advertisements on YouTube in India in a month. It was found that customer style inventory (CSI) influences purchase decisions. It was also found that customers’ responses towards emotional advertising and the attitude that develop through that are mediating consumer decision-making style and purchase intention. More research is required to find the degree of influence on each consumer's decision-making style through emotional advertising. It is also necessary to conduct research separately across different industries. The findings propose that the relationship between consumer style inventory (CSI) is seen to influence purchasing intention. Further, their response toward emotional advertising and the consequent attitude that develops mediate their consumer decision-making style and purchase intention (PI) can be influenced and mold the attitude of consumers by using emotions in advertising. Moreover, the effect of response towards emotional advertising is the same in both utilitarian and hedonic products which implies, that if a customer is going to respond positively towards emotional advertisement, he will have a positive attitude towards the brand and will show purchase intention. This research study is the first written endeavor to use PLS to test the linkages and mediation effect between CSI and PI.

Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. Smart contracts, powered by blockchain, can be developed by using the AI capabilities. These contracts can execute themselves based on predefined conditions, automating various processes and reducing the need for intermediaries. Blockchain provides a transparent and immutable ledger for supply chain transactions. AI can be integrated to analyze this data, providing insights into the entire supply chain. This helps in tracking and tracing products, ensuring authenticity, and optimizing the supply chain based on data-driven decisions. AI algorithms can be employed to analyze data from the manufacturing process recorded on the blockchain. This allows for predictive maintenance and quality control, helping to identify and address issues before they result in defects or downtime. AI and blockchain can work together to secure the massive amount of data generated by IoT devices in an Industry 4.0 setting. Blockchain ensures the integrity and immutability of IoT data, while AI can analyze this data for insights and optimization. AI and blockchain can facilitate the creation of decentralized energy grids where AI algorithms optimize energy distribution and blockchain ensures transparent and secure energy trading through smart contracts. AI and blockchain can support decentralized and collaborative manufacturing networks where different entities can securely and efficiently collaborate on production processes. Blockchain enables individuals or organizations to securely monetize their data. AI can help analyze and determine the value of data, ensuring fair compensation in tokenized ecosystems. In this paper, applications of AI-Powered Blockchain Technology in Industry 4.0 is reviewed and discussed and future research works are also suggested. As a result, efficiency, transparency, and security across various industrial processes can be enhanced by analyzing the recent achievements in AI-Powered Blockchain Technology in Industry 4.0.

Pakistan is a developing country with more than a quarter of its population living under the poverty line. Data relating to the socioeconomic conditions of the population is scarce and sporadic. Government, NGOs, and other interna- tional organizations perform door-to-door surveys to collect data, but these can be expensive and time-consuming to conduct. Currently, statistics about poverty in Pakistan exist only as estimates from field surveys, which are often unofficial and limited. This lack of reliable information leads to ineffective policy decisions. This is one of the biggest challenges in fighting issues such as poverty. Reliable information is thus needed for the unified and concerted direction of development activities. This thesis aims to build a computer vision system that can automat- ically extract information about poverty from publicly available high-resolution satellite imagery. The proposed system output is heat maps indicating poverty and development levels. These heat maps can be overlaid on digital maps for easy visualization. We propose to use transfer learning techniques to extract indica- tors such as geographical and man-made structural features from high-resolution satellite imagery. The trained model learns to filter and distinguish between vari- ous terrains and man-made features, such as highways, buildings, and farmlands. We show that these learned traits are quite useful for mapping socioeconomic variables and even come close to matching the prediction data with field survey data. For a developing country like Pakistan, this thesis is useful because the approach uses publicly available data and is a scalable and inexpensive alterna- tive to traditional surveys. The results from our thesis can aid policymakers and NGOs in distributing their funds to areas that are most deserving and enacting and assessing policies more effectively.

Knowledge-driven economy tends to enjoy tremendous technological transformation due to rapid advances in new technologies, which have given rise to a disruptive technological revolution known as the “Industry 4.0 or Fourth Industrial Revolution (4IR)” with profound impacts on firms. The adoption of Internet-based technological innovations (IBTI) indicates a critical paradigm shift that transforms the status quo by which firms’ relationships are built across borders, compete in markets, interact with their customers and ensures the flow of economic benefits. However, there is limited study known in the literature to have assessed the adoption dynamics and phenomena of IBTI in industrial clusters, and also the evidence on the impact of the technological innovations on the performance of industrial clusters in Nigeria are scarce. The results show that despite the low level of investment and expenditure on Internet-based systems and infrastructure in the Nigerian fast-moving consumer goods clusters, the adoption of IBTI have beneficial effects on firms’ capabilities and growth, customer relationships, product/service features and firm’s reputation. Holistically, the study reveals that the level of the adoption of IBTI is significantly low as the fast-moving consumer goods firms still currently utilize a combination of IBTI with internally owned IT systems in operations. This is an indication that the fast-moving consumer goods industry is still a late majority adopter of the disruptive technologies. This poses a threat to the industry, especially in this era of 4IR. Thus, the Nigerian industrial sector needs to be proactive and keep up with the rising use of disruptive and digital technologies, so as to maintain their competitive advantage.

Despite the deepening trend of neo-protectionism and the exacerbation of disproportions in national economies, trade integration remains a relevant form of collaboration among countries worldwide and a driver of their innovation and technological development. The article provides comparative analysis of a number of methods for economic-statistical and management analysis that can be used to assess the impact of the economic asymmetry of partner countries on the effectiveness of their trade and economic integration, and shows that they have both certain potential for solving the task and a number of limitations. It is substantiated that the co-integration test is a promising instrument for testing the hypothesis about the expediency of trade integration of economically asymmetric partners. The presence of co-integration between bilateral trade indicators of asymmetric countries is to mean the synchronization of their dynamics and relative changes, despite differences in absolute values. Furthermore, confirmed cointegration of bilateral trade flows between countries can be considered as an indicator of adherence to market economy principles and the conditions of free trade, as well as an indicator of the level of integration of countries into the system of international economic relations. Prospects for further research consist in testing for the presence of indicators on bilateral trade between asymmetric partners co-integration on the example of particular pairs of countries that are parties to regional trade agreements being characterized by significant differences through the pace of economic development.

With the rise of the digital economy and the depression in the travel industry due to the Covid-19 pandemic, the feasibility and profitability of innovative products on digital platforms, such as probabilistic selling, have garnered significant public attention. This paper utilizes a Salop circular city model featuring a single digital platform, two sellers, and a continuum of consumers to investigate the impact of probabilistic selling on the profits of the digital platform and sellers. We demonstrate that when the consumption utility is relatively low and product differentiation is at a moderate level, the market is in partial coverage. Offering appropriate probabilistic selling products in such cases expands the market, benefiting the digital platform, sellers and consumers simultaneously. Our findings contribute to the existing research on probabilistic selling techniques, digital business models, and the travel industry (e.g., hotel, car rental, airline).

Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more. Customer lifetime value (CLV) is the total monetary value of transactions or purchases made by a customer with the business over an intended period of time and is used as a means to estimate future customer interactions. CLV finds application in a number of distinct business domains, such as banking, insurance, online entertainment, gaming, and e-commerce. The existing distribution-based and basic (recency, frequency, and monetary)-based models face limitations in terms of handling a wide variety of input features. Moreover, the more advanced deep learning approaches could be superfluous and add an undesirable element of complexity in certain application areas. We, therefore, propose a system that is able to qualify as both effective and comprehensive, yet simple and interpretable. With that in mind, we develop a meta-learning-based stacked regression model that combines the predictions from bagging and boosting models that are found to perform well individually. Empirical tests have been carried out on an openly available online retail dataset to evaluate various models and show the efficacy of the proposed approach.

The proliferation of plug-in electric vehicles (PEVs) advocates a distributed paradigm for coordinating PEV charging. Unlike existing primal-dual decomposition or consensus methods, this paper proposes a cutting-plane-based distributed algorithm considering a peer-to-peer (P2P) communication network, which enables asynchronous coordination among PEVs. First, an equivalent surrogate model is established by exploiting the duality of the original optimization problem, which masks the private information of individual users by equivalent transformation. Then, a cutting-plane based algorithm is derived to solve the surrogate problem in a distributed manner with intrinsic superiority to cope with various asynchrony. Critical implementation issues, including distributed initialization, distributed cutting-plane generation, and localized stopping criteria, are discussed in detail. Numerical tests on IEEE 37- and 123-node feeders with actual data illustrate that the proposed method is resilient to several asynchronies and admits the plug-and-play operation mode. The proposed methodology is expected to provide an alternative path toward the robust protocol for PEV charging.