Advances In Information Technology Research Articles

Minimum Dataset of Lung Cancer Patients: An Initial Step Towards Developing a Web-Based Personal Health Record

Background: Personal Health Records (PHR), which utilize advanced health information technology tools, play a crucial role in patient self-management and improving the control of chronic diseases such as lung cancer. To optimize the design of these systems, it is essential to determine the necessary data elements. Objectives: This study aims to identify the minimum dataset required for designing a web-based PHR for lung cancer patients. Methods: This descriptive, cross-sectional research was conducted in 2023. Initially, a lung cancer dataset was extracted through text analysis. In the next phase, a proposed minimum lung cancer dataset was formulated into a questionnaire containing 18 data groups, including 126 data elements. The dataset underwent expert validation in two phases using the Delphi technique. Data analysis was performed using SPSS version 26, with descriptive statistics employed. Results: The minimum web-based PHR dataset for lung cancer patients, consisting of 18 data groups (112 data elements), includes demographics, insurance information, emergency contact details, patient symptoms, tumor-related data, physician details, treatment information, patient-reported measurements, personal medical history, history of procedures and surgeries, visits, allergies, family history, medication information, test results, imaging data, dietary information, and educational materials. Conclusions: Based on the study findings, it is recommended that lung cancer data management encompass not only routine information but also additional dimensions such as allergies, tumor-related information, and dietary details. Collecting comprehensive and complete data can significantly enhance the treatment process and post-treatment follow-up.

Implementation of Business Intelligence for Analysis Data of Drug Sales Using Exploratory Data Analysis (EDA) and Visualization Data Using Looker Studio

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments.

Navigating the Social Shift: A Preliminary Study of Consumer Behavior on Malaysia's Social Commerce Platforms

The proliferation of online businesses coupled with the advancement of information and communication technologies (ICT) has led to the emergence of social commerce. The integration of social media platforms into e-commerce offers businesses a convenient avenue for engaging with consumers and a novel method for capturing additional sales volume. This seamless integration of social media platforms into current e-commerce practices has significantly increased its success. Research on the factors that affect purchase intentions in social commerce is still sparse, especially in the context of Malaysian consumers. In general, the causal model of purchase intentions on social commerce in this study aims to investigate the impact of the constructs of visibility and metavoicing on purchase intentions among Malaysian millennial consumers. Henceforth, this initial study specifically aims to evaluate the reliability and validity of the IT affordance constructs of visibility and metavoicing through rigorous assessments. At the items-construct level, the internal consistency assessment was conducted using McDonald’s Omega, which is known for its robustness in measuring the reliability of multi-item constructs. At the construct level, discriminant validity and the strength of linear relationships among variables were assessed using Pearson correlation analysis. The pilot dataset gathered was analyzed with SPSS 29, and the results have validated that the predictive model achieved high levels of reliability and validity at both the item and construct levels. Furthermore, the analysis revealed that the independent variables, metavoicing and visibility, have a significant association with purchase intentions, underscoring their importance in understanding consumer behavior.

Understanding Maritime Subjects in Vocational High Schools through Online Learning

This study explores the effectiveness of online learning for maritime subjects in Indonesian vocational high schools, focusing on how the rapid advancement of information technology has reshaped education in this unique field. Using a quantitative approach with 275 respondents, the research analyzes key factors that influence students' understanding of maritime subjects, such as digital literacy, motivation, and their perceptions of the effectiveness of online learning. The findings indicate moderate levels of both digital literacy and motivation for online learning, with flexibility being the most appreciated benefit. However, challenges related to social isolation were also noted. A predictive model developed during the research accounts for 43% of the variance in comprehension of the subject matter, identifying digital literacy and perceived online learning effectiveness as the strongest predictors. Additionally, the grade level of students significantly influenced their comprehension scores. This study emphasizes the need to integrate digital skills training and to employ engaging instructional strategies to enhance learning outcomes. Future research could investigate the use of immersive technologies, such as virtual reality or simulations, to better support practical skills development in maritime education, ensuring that students are well-prepared for real-world challenges in the maritime industry

Product recommendation for online sales systems based on transaction sessions

The advancement of information technology has influenced and accelerated the growth of many economic fields and industries, including e-commerce. This is one area of information technology that has rapidly developed and gained widespread popularity due to the significant benefits it offers to the community. Today, the need for online shopping is increasing because of its convenience and timesaving, especially for busy people. Historical session data plays an important role in helping sales systems recommend orders to customers that suit their personal preferences. This study proposes models for recommending products on online sales systems based on transaction sessions using memory-based collaborative filtering methods, including user-based and item-based, and model-based collaborative filtering methods including SVD and KNN. The experimental results show that the SVD model has a better rating prediction performance than other techniques. Therefore, it is probably proposed for product recommendations on online sales systems.

The Current Situation and Optimization Path of Enterprise Supply Chain Management Construction in the Context of Digitalization

Abstract: The advancement of information technology has resulted in a rapid rise of a new phase of technical and industrial revolution, propelling the global shift from an industrial economy to a digital economy at an accelerated rate. Digital transformation has become an irreversible and pervasive tendency in contemporary society. Despite the significant growth of China's economy, the industrial supply chain is not operating at its highest efficiency. To achieve sustainable cost reduction and efficiency, enterprises need to promote digital transformation and supply chain upgrading. By doing so, enterprises can optimize resource allocation among different links in the supply chain and enhance the quality of their development. This study conducted an investigation of the value benefits, problems, and difficulties of enterprise supply chain digital transformation, using Jingdong as an example. The following article aims to provide a credible reference for organizations by discussing the successful paths and measures of digitizing the supply chain, using Jingdong's experience and activities in supply chain digital transformation as a reference.

Epidemiological analysis of 2106 geriatric trauma patients in a level I trauma center in Lanzhou City, Gansu Province, China.

As the global population ages, geriatric trauma gains attention. Limited studies on geriatric trauma in developing countries necessitate comprehensive investigation. This study aims to delineate the epidemiological characteristics of geriatric trauma, providing insights for prevention and enhanced elderly trauma care. Clinical data of 2106 geriatric trauma patients (age ≥ 60) treated from November 1, 2020, to October 31, 2023, at the trauma center of the First Hospital of Lanzhou University were retrospectively analyzed. Descriptive statistics covered temporal and monthly distribution, trauma sites, causes, and length of stay. Inclusive were 2106 geriatric patients (941 males, 1165 females; male-to-female ratio 1:1.24). Trauma incidence was higher in summer and autumn (May-October) than winter (November-December). Peak incidents occurred between 14:00-16:00, with concentrations at 12:00-16:00 and 20:00-22:00. Falls (59.35%), road traffic accidents (16.62%), other traumas (14.10%), cutting/stabbing (4.56%), violence (3.42%), mechanical injuries (1.61%), winter sports injuries (0.14%), burns (0.14%), and animal bites (0.06%) were major causes. Extremity and skin injuries prevailed, with significant site distribution differences (P < .001). Average length of stay at the trauma center was 78.35 ± 48.32 minutes. The gender distribution, timing, causes, and sites of trauma in the elderly differ significantly from other age groups. Unlike the higher occurrence of trauma in young men compared to women, among the elderly, it is more prevalent in women than men, mainly attributed to falls. It is crucial to implement specific measures to prevent trauma in the elderly, and the advancement of information technology in the trauma care system could additionally improve the quality of care.

Research on Innovation of Food Logistics Supply Chain Management

In the context of globalization and constantly changing consumer demand, the management of food logistics supply chains has become increasingly complex and important. This paper mainly studies the problems and innovative methods in food logistics supply chain management, aiming to improve the efficiency and adaptability of the supply chain by optimizing management strategies. This paper adopts literature analysis and case analysis methods to analyze in detail the problems of information asymmetry, cost control, transportation efficiency, and inventory management in the food logistics supply chain. It proposes innovative supplier management models, digital management, modern supply chain management concepts, inter chain cooperation, and risk management solutions. Research has found that adopting information-sharing mechanisms and advanced information technology, as well as strengthening collaboration among various links in the supply chain, can effectively improve management efficiency and reduce operating costs. This study provides some useful references for food logistics enterprises in practical operation, helping them improve service quality and reduce operating costs in a fiercely competitive market environment, enhance market competitiveness, ensure food safety and freshness, and meet the growing needs of consumers.

A Virtual Assembly Technology for Virtual–Real Fusion Interaction of Ship Structure Based on Three-Level Collision Detection

With the rapid advancement of new-generation information technology, the virtual–real fusion interaction has increasingly become a crucial technique for structural analysis to determine the strength envelope of hulls. A high-precision assembly of the experimental devices in a virtual environment is vital. A virtual assembly method for a structural virtual–real fusion test based on the oriented bounding box (OBB) algorithm, the Devillers and Guigue algorithm, and differential triangle facets algorithm are proposed in this paper. The experiment of the connector in a typical offshore floating platform is performed as a case, which indicates that the virtual assembly method proposed in this paper enables the assessment of assembly virtually prior to the actual experiment, and the assembly accuracy can reach 0.01 mm. The digitalization and virtual–real fusion interaction for the mechanical experiment of hulls are advanced to ensure efficiency, safety, and economy.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Human risk recognition and prediction in manned submersible diving tasks driven by deep learning models

The complexity of human cognition is increased by the multiple and interactive information clusters brought about by the application of advanced intelligent information technologies. Especially in systems operating in extreme regions far from society, human errors are more pronounced than ever before. Prolonged social isolation, extreme weightless or overweight environments, stressful atmospheres, and lack of situational awareness are all added potential elements contributing to human risk. Although the development of human reliability analysis methods and their variants continues to mature, accurately predicting the potential risk of dynamic human behavior from sparse and discrete events remains a great challenge. We focus on deep learning computational architectures that are similar to the cognitive processes and mechanisms of the brain, and build neural networks that match the perceptual activation and memory cycling of the cognitive features of the brain. This study focuses on investigating the ability of the joint SNN-ITLSTM network to predict human error behavior and the clusters of performance shaping factors that effectively characterize the far-social nature. Combining the bionic properties of SNN and the temporal update mechanism of LSTM in the form of hierarchical events constitutes a computationally efficient network architecture. Our results show that the joint model proposed in this study has the performance to strengthen temporal influences and characterize cognitive features of the brain.

Research on the Influence of Digital Economy on the Income Gap between Urban and Rural Areas in China

In recent years, with the rapid advancement of information technology and the widespread proliferation of the Internet, digital technology has been reshaping the value chains of traditional industries and giving rise to entirely new business models and industrial forms. Technologies such as the Internet, big data, and artificial intelligence are being applied across various sectors, while new formats like e-commerce, the sharing economy, and online education are flourishing. These developments have driven new consumption patterns and employment methods. The rapid rise of the digital economy not only boosts production efficiency and economic growth but also influences the income distribution between urban and rural areas to some extent. This paper begins by examining the current state and characteristics of the digital economy and then analyzes its impact on the urban-rural income gap. Additionally, it offers countermeasures and recommendations for narrowing this gap, providing theoretical support for promoting common prosperity.

DESAFIOS E OPORTUNIDADES DA COMUNICAÇÃO NA SOCIEDADE DIGITAL

Communication in a digital society presents challenges and opportunities that transform the way we connect and share information. With the advancement of information technologies, communication becomes more agile, accessible and democratic. However, this transformation also brings challenges such as the spread of fake news, user privacy and excessive dependence on social networks. New digital platforms offer unique opportunities for business, education and social relationships, enabling global reach. However, the speed of information often compromises the depth of dialogues and the discernment between what is true or manipulation. The exponential growth of online content requires reflection on how to use digital tools ethically and effectively. Furthermore, digital communication creates new forms of interaction, which challenge traditional models and require new communication skills. Digital education and media literacy emerge as solutions to prepare individuals to deal critically with this new environment. Therefore, the balance between the opportunities for innovation and the challenges of maintaining authentic and secure communication is essential for the future of communication in the digital society.

The Influence of Information Technology Investment on the Organizational Performance: The Moderating Role of External Environment

In the information era, enterprises can enhance organizational performance through advanced information technology. Based on the resource-based view, this paper examines the mechanism by which information technology (IT) investment influences organizational performance, with the external environment as a moderating variable. Using 265 valid samples collected from agricultural enterprises in Shandong province, this study conducts an empirical test through partial least squares structural equation modeling. The results demonstrate that IT investment has a significant positive effect on organizational performance, and the external environment significantly moderates the relationship between IT investment and organizational performance. This research contributes to a deeper understanding of the relationship between IT investment and organizational performance, enriches resource-based theory, expands the research framework on IT investment, and offers guidance for enterprises seeking sustainable growth.

Research on the Role of E-commerce Vocational Skills Competitions in Cultivating Students' Innovation and Entrepreneurship Abilities

In the context of globalization and rapid advancements in information technology, e-commerce has become a significant force driving economic development. To enhance students' practical abilities and qualities for innovation and entrepreneurship, e-commerce vocational skills competitions have emerged, gradually becoming an important means of cultivating students' comprehensive capabilities. This paper explores the definitions and types of e-commerce vocational skills competitions and analyzes their specific role in fostering students' innovative and entrepreneurial abilities. The focus lies on how competitions impact the cultivation of innovative thinking, improvement of technical application skills, and enhancement of entrepreneurial awareness and management capabilities. The research results indicate that e-commerce vocational skills competitions not only effectively improve students' innovative abilities but also significantly enhance their entrepreneurial awareness and practical skills, providing essential pathways for universities to nurture competitive, innovative talents.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

Virtual Computer Laboratory Learning System Integrated With Social Media And Video Conference

The advancement of information and communication technology has driven innovation in various aspects of education, including computer laboratory learning. Traditional computer laboratories often face challenges in terms of time, cost, and accessibility. To address these challenges, this study developed a virtual computer laboratory learning system integrated with social media and video conferencing. This system is designed to provide flexible access, allowing students to learn and practice anytime and anywhere. The integration of social media aims to enhance interaction and collaboration between students and instructors, while video conferencing enables real-time learning sessions with direct interaction. The research methods used include system design, software development, and system testing. The results showed that the virtual computer laboratory learning system integrated with social media and video conferencing can facilitate learning and is expected to be an innovative solution in information technology education.

Towards full autonomous driving: challenges and frontiers

With the rapid advancement of information technology and intelligent systems, autonomous driving has garnered significant attention and research in recent years. Key technologies, such as Simultaneous Localization and Mapping (SLAM), Perception and Localization, and Scene Segmentation, have proven to be essential in this field. These technologies not only evolve independently, each with its own research focus and application paths, but also complement and rely on one another in various complex autonomous driving scenarios. This paper provides a comprehensive review of the development and current state of these technologies, along with a forecast of their future trends.

Enhancing University English Teaching through Mobile Interactive Applications from an Educational Technology Perspective

In the context of globalization and rapid advancements in information technology (IT), the application of educational technology in higher education has become increasingly prevalent. Particularly in university English teaching, mobile interactive applications have emerged as a significant teaching model. These applications transcend the constraints of time and space, providing students with a flexible learning environment that promotes interaction between teachers and students, thereby enhancing learning outcomes. However, existing research has primarily focused on the technological development and effectiveness evaluation of mobile interactive applications, neglecting the impact of student interaction and trust on learning outcomes. Additionally, there is a lack of dynamism and personalization in the recommendation of educational resources and study partners. This study constructs global and local dynamic trust models for mobile interactions, calculates trustworthiness based on mobile interactive behaviors, and integrates mobile interaction trust relationships and behaviors with a dynamic English recommendation algorithm, thereby providing a new theoretical framework and a practical approach for university English teaching, aiming to enhance the teaching effectiveness and improve students’ learning experiences.

Analysis of the Selection of the Best Household Ceramics Using the Complex Proportional Assessment (COPRAS) Method

Rapid advances in communication and information technology due to globalization have had a significant impact on a number of industries, including the industrial sector. The industry is taking great advantage of the capabilities of this technology to search, store, distribute and present information. The ceramic sector in Indonesia looks increasingly promising every year. One type of building material that functions to cover the floor and beautify its appearance is ceramic. When choosing ceramics, consumers become confused because of the availability of various brands (vendors) with different themes and quality. When deciding on product quality, a decision support system can be implemented to offer a structured evaluation that assists stakeholders in the business and consumers in assessing high-quality ceramic options. DSS The complex proportional assessment method, or COPRAS, is used in system design. In improving the accuracy and efficiency of decision making, the COPRAS approach can evaluate several options and estimate them based on their utility level when attribute values ??are expressed in intervals. Based on the findings of this research, the application of the COPRAS method in the decision-making process to determine the best household ceramics can be used in selecting the best household ceramics by collecting data on ceramic criteria and the alternative used is the type of ceramic. The weights obtained for each criterion are then normalized which are then used to determine the Ui for each alternative, so that based on the results of this research the best household ceramics are obtained, namely Redhorse type ceramics with a Ui value of 100%, Fortuna type with a Ui value of 99.27%. , Prato type with a Ui value of 98.82%, Crystal type with a Ui value of 98.71%, Mulia type with a Ui value of 88.50%, Vancouver type with a Ui value of 88.24%, Murano type with a Ui value of 84.97% and the Virginia type with a Ui value of 79.77%.