Data Analysis Technology Research Articles

Technology and Institutions: What can research on Artificial Intelligence (AI) technology and institutions learn from each other?

Artificial Intelligence (AI) is not contained within the walls of technological organizations; over the decades, it has significantly impacted other industries due to the exponential practical implications of the technology and major break throughs as a result of AI implementation. Many authors have conducted relevant research about AI and its assistance in industries like banking and finance, education, manufacturing, healthcare, and others to find direct correlation between application of AI technology in data analysis, decision-making, end user impact and satisfaction, time, and cost savings. This study aims to perform in-depth analysis of various previous studies, and to conduct market research to comprehend the meeting point of Artificial general intelligence and Institutions and what they have to offer to each other; a detailed analysis of the way various strategies applicable through machine learning, deep learning techniques, voice and face recognition applications, expert systems assisting in comprehending customer behavior and patterns, and to identify potential demand, automation of key functions of inter-organization departments through algorithms, and building blocks that form institutions can be adopted by AI for future models and prospectives, and also to understand any gaps in the practical implications of AL that institutions can possibly incorporate in the future; thereby increasing efficiency and effectiveness of both AI and Institutions.

Fiber-Based Noncontact Sensor with Stretchability for Underwater Wearable Sensing and VR Applications.

The rapid development of artificial intelligent wearable devices has led to an increasing need for seamless information exchange between humans, machines, and virtual spaces, often relying on touch sensors as the primary interaction medium. Additionally, the demand for underwater detection technologies is on the rise owing to the prevalent wet and submerged environment. Here, a fiber-based capacitive sensor with superior stretchability and hydrophobicity is proposed, designed to cater to noncontact and underwater applications. The sensor is constructed using bacterial cellulose (BC)@BC/carbon nanotubes (CNTs) (BBT) helical fiber as the matrix and methyltrimethoxysilane (MTMS) as the hydrophobic modified agent, forming a hydrophobic silylated BC@BC/CNT (SBBT) helical fiber by the chemical vapor deposition (CVD) technique. These fibers exhibit an impressive contact angle of 132.8°. The SBBT helicalfiber-based capacitive sensor presents capabilities for both noncontact and underwater sensing, which exhibits a significant capacitance change of -0.27 (at a distance of 0.5 cm). We have achieved interactive control between real space and virtual space through intelligent data analysis technology with minimal interference from the presence of water. This work has laid a solid foundation of noncontact sensing with attributes such as degradability, stretchability, and hydrophobicity. Moreover, it offers promising solutions for barrier-free communication in virtual reality (VR) and underwater applications, providing avenues for smart human-machine interfaces for submerged use.

Retracted: Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology

Retracted: Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology

ЗАСТОСУВАННЯ МЕТОДУ РОЮ ЧАСТОК В ОПТИМІЗАЦІЙНИХ ЗАДАЧАХ ЕНЕРГЕТИКИ

The article considers the application of the particle swarm method in energy domain. The problem of effective load distribution of energy-generating capacities under the conditions of minimum fuel consumption is one of those that arises most often. The economic expediency of the operation of one or another power plant at the appropriate capacity determines the distribution of generating capacities in the power system. It is no secret that power units of thermal power plants, which were built in different time periods, differ in their cost characteristics. This makes it necessary to find optimal configurations of the power system, in which the relevant energy objects are involved. Particle Swarm Optimization (PSO) is a computational optimization method inspired by the social behavior of birds in a flock or fish in a shoal. This method was first proposed by Kennedy and Eberhart in 1995. In PSO, a population of possible solutions, called particles, moves through the search space according to a set of mathematical rules. The motion of each particle affects its own bestknown position and the global best-known position of the entire population. The basic idea is that each particle adjusts its position based on its own experience and that of the entire swarm. This correction takes place with the help of two main components: 1. Cognitive component (personal best result): The particle remembers the best solution it found before. 2. Social component (global best result): A particle also takes into account the best solution found by any other particle in the swarm. These components are used to update the particle's velocity and position iteratively, with the goal of converging to an optimal solution. PSO is widely used in various optimization problems, including engineering design, robotics, finance, and data analysis. It is known for its simplicity, ease of implementation, and ability to solve non-linear, non-convex optimization problems. However, like any optimization algorithm, its performance can be sensitive to the parameters and the nature of the problem to be solved. The article solves a typical problem of distributing the total load between two thermal power plants under the conditions of minimum fuel consumption. The obtained values of the solutions confirm commonly known the statements about the achievement of adequate indicators in the range from 10 to 30 particles, in our case - 20.Analyzing the obtained results, one can see that the objective function changes almost linearly from the very beginning until the 30th iteration, after which the improvement in the result is almost imperceptible. The main reason is that at this moment the result of the algorithm is as close as possible to the reference value, namely 250. That is, in fact, it can be considered that the solution comes at the 31st iteration. Carrying out a comparison with the solution of such a problem using the genetic algorithm from the previous work, it can be seen that when solving such a problem, the algorithms demonstrate similar performance with comparable accuracy of the result. From the above studies, it can be concluded that evolutionary algorithms can be used to solve similar energy problems.

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

Development path of rural financial services for rural revitalization based on big data analysis

Abstract This paper is based on big data analysis technology to process the data related to rural construction and analyze the rural financial service development measurement after constructing the rural financial service indicator system combined with the principal component analysis method. The entropy value method is utilized to measure the constructed rural development index system and assess the development level of rural financial services for rural revitalization. On this basis, the benchmark regression model of rural financial services and rural revitalization is constructed, descriptive statistics are carried out, and the development effect of rural financial services on rural revitalization is analyzed after passing the smoothness test to explore its development path. The results show that the development measure of rural financial services from 1978 to 2021 rises from less than 0.5 to more than 0.7, and the development index of rural revitalization also rises by about 0.1, and the levels of both rural financial services and rural revitalization are rising. The mean value of the rural revitalization index is 0.282, the minimum value of the rural revitalization index is 0.143, and the maximum value is 0.494, and all the variables of rural financial services on rural revitalization are significant at the 1% level, with a significant impact. Based on this study, rural financial reform is synergistically promoted in three major aspects, namely, coordinating all kinds of financial organizations, innovating financial support for agricultural products and strengthening financial supervision and prevention.

Retracted: Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Retracted: Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

The influence of e-commerce algorithms on Consumers’ behaviour

With the development of the Internet and data analysis technology, the e-commerce plat-form continues to optimise the e-commerce algorithm system, improve consumers' online shopping experience and willingness, reduce the time cost of consumers' retrieval of commodity information, achieve accurate marketing, and increase sales. Therefore, it is of great significance to understand the functions and characteristics of the algorithm and how does it affects consumers' shopping decisions. Based on collating relevant literature on consumer decision-making, online shopping behaviour, and algorithm recommendation system, a process model of an algorithm system affecting shopping decisions is constructed. Conduct a questionnaire survey to analyse the impact of the algorithm system on shopping decisions through a series of data. The following conclusions are drawn: The influence of the algorithm system is inevitable and strong, but it still needs to be improved, such as improving the accuracy of product recommendations. In addition, the development of an algorithm recommendation system is optimistic.

Review of Models for Estimating and Predicting the Amount of Energy Produced by Solar Energy Systems

Energy production based on renewable sources is a fundamental aspect of society’s sustainable development. The involvement of renewable energy sources in the implementation of modern energy systems can significantly reduce the amount of harmful emissions into the atmosphere and provide greater flexibility of energy infrastructure. The first step in determining the feasibility of involving a particular energy source in the overall energy system of the region is a preliminary assessment of the energy potential to determine the possible percentage of substitution of traditional energy. To solve this problem, it is necessary to use the models of energy supply, which are currently presented in a wide variety. In this regard, this paper proposes to consider various models for estimating the solar energy potential, which can be divided into empirical models and models based on the application of modern intelligent data analysis technologies. Such models are based on many different climatic and geographical indicators, such as: longitude of sunshine, ambient temperature, serial number of the day of the current year, amount of precipitation, average and maximum values of wind speed and so on. The paper analyzed the existing models for estimating the amount of energy, which can be used in the system designed to determine the most optimal configuration of the energy system based on the use of various conversion technologies most relevant to the case under study, and also serve as the basis for creating digital twins designed to model and optimize the operation of the projected energy complex

Информационные системы учета и анализа данных жилищно-коммунального хозяйства многоквартирных домов

The article discusses the development of information systems of housing and communal services of the Russian Federation from the point of view of resource consumption data accounting and analysis. The object of the study is "smart" apartment buildings. The subject of the research is information platforms, information systems and individual software developments for monitoring resource consumption in apartment buildings. It is noted that the digitalization of housing and communal services is just beginning and mainly refers to new construction. There is a potential for using digital services to manage a "smart" apartment building, including for the organization of telemetry. Three levels of development of digital services for resource management are identified. The list of the main digital platforms supporting the telemetry function in apartment buildings is presented. The possibilities of integration of various automated information systems are discussed. It is noted that the analytical processing of housing and communal services information is focused on the traditional tasks of management companies, such as the formation of statistics and reporting on resource consumption. It is proposed to pay attention to machine learning methods and intelligent big data analysis technologies.

Retracted: Problems and Countermeasures of China’s International Trade in Agricultural Products under the Belt and Road Strategy Based on Big Data Analysis Technology in the Internet of Things Era

Retracted: Problems and Countermeasures of China’s International Trade in Agricultural Products under the Belt and Road Strategy Based on Big Data Analysis Technology in the Internet of Things Era

Older Adults' Perceptions About Using Intelligent Toilet Seats Beyond Traditional Care: Web-Based Interview Survey.

In contemporary society, age tech (age technology) represents a significant advancement in health care aimed at enhancing patient engagement, ensuring sustained independence, and promoting quality of life for older people. One innovative form of age tech is the intelligent toilet seat, which is designed to collect, analyze, and provide insights based on toileting logs and excreta data. Understanding how older people perceive and interact with such technology can offer invaluable insights to researchers, technology developers, and vendors. This study examined older adults' perspectives regarding the use of intelligent toilet seats. Through a qualitative methodology, this research aims to unearth the nuances of older people's opinions, shedding light on their preferences, concerns, and potential barriers to adoption. Data were collected using a web-based interview survey distributed on Amazon Mechanical Turk. The analyzed data set comprised 174 US-based individuals aged ≥65 years who voluntarily participated in this study. The qualitative data were carefully analyzed using NVivo (Lumivero) based on detailed content analysis, ensuring that emerging themes were coded and classified based on the conceptual similarities in the respondents' narratives. The analysis revealed 5 dominant themes encompassing the opinions of aging adults. The perceived benefits and advantages of using the intelligent toilet seat were grouped into 3 primary themes: health-related benefits including the potential for early disease detection, continuous health monitoring, and seamless connection to health care insights. Technology-related advantages include the noninvasive nature of smart toilet seats and leveraging unique and innovative data collection and analysis technology. Use-related benefits include ease of use, potential for multiple users, and cost reduction owing to the reduced need for frequent clinical visits. Conversely, the concerns and perceived risks were classified into 2 significant themes: psychological concerns, which included concerns about embarrassment and aging-related stereotypes, and the potential emotional impact of constant health monitoring. Technical performance risks include concerns centered on privacy and security, device reliability, data accuracy, potential malfunctions, and the implications of false positives or negatives. The decision of older adults to incorporate intelligent toilet seats into their daily lives depends on myriad factors. Although the potential health and technological benefits are evident, valid concerns that need to be addressed remain. To foster widespread adoption, it is imperative to enhance the advantages while simultaneously addressing and mitigating the identified risks. This balanced approach will pave the way for a more holistic integration of smart health care devices into the routines of the older population, ensuring that they reap the full benefits of age tech advancements.

Modified CMIS Factors Predicting Chinese Women's Mental Health Information Seeking in Douyin

The popularity of Douyin (known as TikTok overseas) could provide additional mental health information services to Chinese women. However, the factors that influence women to seek mental health information services in Douyin are still understudied. The study applied the Comprehensive Model of Information Seeking (CMIS) to Chinese women's mental health information seeking behaviors on Douyin to examine this. The study used a questionnaire (N=505), and the data analysis was conducted using partial least squares structural equation modeling (PLS-SEM). SmartPLS 4.0 was the primary data analysis technology. Results showed that direct experience, beliefs, salience, and characteristics of mental health information could significantly affect the utility of mental health information on Douyin. In addition, utility, social support, and characteristics directly affect Chinese women's mental health information seeking. Moreover, utility partially mediates the relationship between direct experience, beliefs, salience, characteristics, and mental health information seeking. Rather than targeting specific diseases or pan-health information, this study demonstrates the possibility of applying CMIS to mental health information seeking. This insight could contribute to Douyin and government health interventions to better meet the mental health information needs of Chinese women.

An Analysis of the Applicability of Pricing Models in the Chinese Stock Market

The stock market in China has an important position in the world, but it has not been able to find a pricing model suitable for the market. This paper reviews the history and development of various pricing models, analyzes relevant research on their application in the Chinese stock market, and discusses the applicability of various pricing models. It is found that the applicability of the capital asset pricing model (CAPM) in China is low; the applicability of the Fama-French three-factor model and the Fama-French five-factor model is better than CAPM, but there is significant controversy over the advantages and disadvantages of the two. It is also found that the applicability of the five-factor model may have regional differences in different markets, and there may be differences in redundant factors in different markets. It is proposed that future research should obtain more accurate market data, explore new influencing factors, and build a pricing model more suitable for the stock market in China by combining the characteristics based on more advanced data analysis technology.

Data Analytics: A Catalyst for Innovation and Decision-Making across Industries

Abstract: Data analytics has developed as an essential tool in the modern era of digital technology, where it plays a crucial role in driving innovation and allowing informed decision-making across a variety of different industries. This research study investigates the enormous impact that data analytics has had on companies and organizations, shedding light on its function as a driving force behind innovation and decision-making. This article investigates how data analytics techniques and technologies enable enterprises to extract important insights from huge datasets, hence stimulating innovation and optimizing decisionmaking processes. This is accomplished through an exhaustive study of current literature, case studies, and real-world examples. Ultimately, the focus of the research is on how these techniques and technologies empower industries. Businesses have the opportunity to gain a competitive advantage, improve their operational efficiency, and more effectively adapt to the everchanging demands of the market when they make use of sophisticated data analytics technologies.

Stock Returns Analysis with Dividend Policy as an Intervening Variable in IDX30 Index Companies on the Indonesia Stock Exchange

Stock Return is the profit the investor obtains for investing in a company. Shareholders or investors in their investments can get a return offered by a stock in the form of capital gains (the difference between the selling value and the purchase value) and dividends. This study aimed to determine whether the influence of the Leverage Ratio and Market Ratio can contribute to Stock Returns with Dividend Policy as an intervening variable in IDX30 Index Companies listed on the Indonesia Stock Exchange. The population in this study were all IDX30 Index companies listed on the Indonesia Stock Exchange. The sample selection in this study used Probability Sampling, namely the purposive sampling method. The samples in the study amounted to 15 companies based on specific criteria for 2019-2022. The type of data in this study uses quantitative data. Data analysis technology in this study uses path analysis. The results showed that Leverage can contribute to increasing Stock Return, Market Ratio can contribute to increasing Stock Return, Leverage can contribute to increasing Stock Return through Dividend Policy, and Market Ratio can contribute to increasing Stock Return through Dividend Policy.

Artificial-Intelligence-Enhanced Study on the Optimization of the Responsibility and Compensation Mechanism for Provincial Cultivated Land Retention from a Fairness Perspective

Research Objectives: Considering the complex challenges arising from urbanization, population growth, and the consequential strain on China’s cultivated land resources, we integrate insights from advanced artificial intelligence (AI) technology. The primary objective is to quantitatively assess the cultivated land-holding responsibility of local governments in China from a fairness perspective. Our aim is to integrate the cultivated land responsibility ownership amount into the compensation mechanism for cultivated land protection, thereby promoting fairness, cultivated land conservation, and sustainable agricultural development across Chinese provinces while ensuring efficient agricultural product distribution. Research Methodology: Beyond a traditional literature review and model analysis, we incorporated AI technology for data analysis and model optimization. Findings: ① The findings demonstrate a surplus in both food security and ecological security within cultivated land. ② In-depth analysis via AI revealed that the provinces with the largest surplus include Heilongjiang, Jilin, and Hubei. ③ With the precision provided by AI, we delineated the minimum value of the cultivated land responsibility ownership amount for each province. Eleven provinces met this criterion, primarily located in the northeast and central regions and including Yunnan in the west. ④ From a fairness perspective, coupled with AI insights, we established a compensation mechanism for cultivated land protection that is better suited to the Chinese context. Based on this mechanism, the national average compensation standard was calculated to be 26,900 CNY per hectare. The highest standards were observed in Shanghai, Beijing, and Jiangsu, while the lowest were in Shanxi, Heilongjiang, and Gansu.

Digitized Terms: The Regulation of Standard Contract Terms in the Digital Age

Abstract Digital transformation is also transforming standard contract terms. With the emergence of online platforms, supported by algorithmic data analysis and self-enforcing technologies, platform terms are becoming increasingly common. The article argues that this type of terms differs from traditional standard terms in a number of ways, and it considers whether the existing unfair contract terms regime is still appropriate. The regulatory tools once developed for standard terms in bilateral agreements – transparency requirements, fairness review, and restrictions on contracting around – seem inadequate to meet the new challenges of digital transformation. In order to preserve – or rather recreate – the architecture of choice for private contracts, the paper argues for a new regulatory strategy: In order to strike the right balance between protecting private autonomy and avoiding significant imbalances, the regulatory objective should be to ensure the impartiality of platforms by focusing on the structural conditions of their rule-making.

Development of Big Data Analytics Technology on Sea Freight Operational Performance Using the Agile Model

The development of industry 4.0 is still difficult for a country to produce all goods according to the required needs. Big data analytics technology has the potential to revolutionize how logistics companies, including freight forwarders, can facilitate real-time monitoring in the supply chain by increasing flexibility. Management information systems produce a number of data from different sources and in different formats according to business needs. The development of big data analytics uses a dynamic system development method or model (DSDM), which is a dynamic system development model that uses a rapid application development (RAD) approach. Software development with an emphasis on collaboration, customer engagement, and continuous delivery of working software. This model is very suitable for the development of big data analytics technology that can provide solutions that often involve complex and rapidly changing requirements with large amounts of data and the business need to provide feedback from stakeholders in real-time. It is hoped that the final results of this research will provide insight into sea freight operations that are more informative and efficient for the logistics industry in Indonesia.

Optimising Organisational Performance Through Human Resource Management Strategy and Technology Integration to Enhance Innovation

In the evolving era of digitalization and globalization, organizations face increasingly complex and dynamic challenges. Rapidly advancing information technology and intense global market competition necessitate organizations to evaluate and enhance their performance to remain relevant and competitive. This research aims to investi-gate best practices in HRM and technology integration to promote innovation, with a focus on its positive impact on organizational performance. The approach entails conducting an in-depth examination of literature through qualitative analysis, aiming to attain a comprehensive comprehension of the topic spanning the years 2001 to 2023. The study's findings indicate that human resource management (HRM) strategies focusing on employee recruitment, training, and development, along with effective performance management, form the basis for a competent team. A positive work environment with constructive feedback enhances productivity. Technology integration, through information and communication technology (ICT) and technology based HRM systems, improves operational efficiency. The use of technology for decision-making, administration, and employee data analysis accelerates business processes. Technology integration also stimulates innovation through collaboration and artificial intelligence, aiding organizations in identifying new opportunities in the digital era.