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Search Results (657)

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Keywords = big data platform

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13 pages, 2474 KiB  
Article
Business Case for a Regional AI-Based Marketplace for Renewable Energies
by Jonas Holzinger, Anna Nagl, Karlheinz Bozem, Carsten Lecon, Andreas Ensinger, Jannik Roessler and Christina Neufeld
Sustainability 2025, 17(4), 1739; https://doi.org/10.3390/su17041739 - 19 Feb 2025
Abstract
The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven [...] Read more.
The global energy sector is rapidly changing due to decentralization, renewable energy integration, and digitalization, challenging traditional energy business models. This paper explores a startup concept for an AI-assisted regional marketplace for renewable energy, specifically suited for small- and medium-sized enterprises (SMEs). Driven by advancements in artificial intelligence (AI), big data, and Internet of Things (IoT) technology, this marketplace enables efficient energy trading through real-time supply–demand matching with dynamic pricing. Decentralized energy systems, such as solar and wind power, offer benefits like enhanced energy security but also present challenges in balancing supply and demand due to volatility. This research develops and validates an AI-based pricing model to optimize regional energy consumption and incentivize efficient usage to support grid stability. Through a SWOT analysis, this study highlights the strengths, weaknesses, opportunities, and threats of such a platform. Findings indicate that, with scalability, the AI-driven marketplace could significantly support the energy transition by increasing renewable energy use and therefore reducing carbon emissions. This paper presents a viable, scalable solution for SMEs aiming to participate in a resilient, sustainable, and localized energy market. Full article
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20 pages, 7145 KiB  
Article
AERQ—A Web-Based Decision Support Tool for Air Quality Assessment
by Pierluigi Cau, Davide Muroni, Guido Satta, Carlo Milesi and Carlino Casari
Appl. Sci. 2025, 15(4), 2045; https://doi.org/10.3390/app15042045 - 15 Feb 2025
Abstract
Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor [...] Read more.
Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO2, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance. Full article
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28 pages, 1820 KiB  
Article
Synergistic Evolution in the Digital Transformation of the Whole Rural E-Commerce Industry Chain: A Game Analysis Using Prospect Theory
by Yanling Wang and Junqian Xu
Systems 2025, 13(2), 117; https://doi.org/10.3390/systems13020117 - 12 Feb 2025
Abstract
In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in [...] Read more.
In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in the digital transformation of platform enterprises and participating enterprises across the whole rural e-commerce industry chain. To achieve this, this paper combines prospect theory and evolutionary game theory, introduces the value function and decision weight of prospect theory, and constructs a two-party game model between platform enterprises and participating enterprises. Based on the demonstration of the impact of individual changes in major objective factors, such as the cooperative innovation benefit coefficient, as well as major behavioral characteristic factors, such as decision-makers’ risk attitude coefficients, on enterprises’ strategic choices, we further reveal the influence of the interaction of key factors on the evolutionary results through case simulations. The findings indicate that when the behavior characteristics of the players are introduced, the threshold interval of the cost–benefit ratio of the two sides to reach the optimal state of decision-making is obviously reduced. Under moderate risk attitudes and degrees of loss sensitivity, enhancing the resource absorption capacity of enterprises in the chain and reducing the potential risk loss of platform enterprises to alleviate the influence of subjective behavior characteristics on cooperation willingness are effective measures. Improving innovation ability is the key factor in alleviating the negative impact of uncertainty on the decision-making of both parties. This paper is one of the few studies to integrate prospect theory with evolutionary game analysis in examining the collaborative behaviors between platform enterprises and participating enterprises. Effective strategies are proposed to promote enterprises achieving synergy. Full article
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12 pages, 4475 KiB  
Article
Integrated Photonic Processor Implementing Digital Image Convolution
by Chensheng Wang, Wenhao Wu, Zhenhua Wang, Zhijie Zhang, Wei Xiong and Leimin Deng
Electronics 2025, 14(4), 709; https://doi.org/10.3390/electronics14040709 - 12 Feb 2025
Abstract
Upon the advent of the big data era, information processing hardware platforms have undergone explosive development, facilitating unprecedented computational capabilities while significantly reducing energy consumption. However, conventional electronic computing hardware, despite significant upgrades in architecture optimization and chip scaling, still faces fundamental limitations [...] Read more.
Upon the advent of the big data era, information processing hardware platforms have undergone explosive development, facilitating unprecedented computational capabilities while significantly reducing energy consumption. However, conventional electronic computing hardware, despite significant upgrades in architecture optimization and chip scaling, still faces fundamental limitations in speed and energy efficiency due to Joule heating, electromagnetic crosstalk, and capacitance. A new type of information processing hardware is urgently needed for emerging data-intensive applications such as face identification, target tracking, and autonomous driving. Recently, integrated photonics computing architecture, which possesses remarkable compactness, wide bandwidth, low latency, and inherent parallelism, has harvested great attention due to its enormous potential to accelerate parallel data processing, such as digital image convolution. In this study, an integrated photonic processor based on a Mach-Zehnder interferometer (MZI) network is proposed and demonstrated. The processor, being scalable and compatible with complementary metal oxide semiconductors, facilitates mass production and seamless integration with other silicon-based optoelectronic devices. An experimental verification for digital image convolution is also performed, and the result deviations between our processor and a commercial 64-bit computer are less than 2.3%. Full article
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22 pages, 7406 KiB  
Article
Analog Frontend for Big Data Compression and Instantaneous Failure Prediction in Power Management Systems
by Erez Sarig, Michael Evzelman and Mor Mordechai Peretz
Electronics 2025, 14(3), 641; https://doi.org/10.3390/electronics14030641 - 6 Feb 2025
Abstract
An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires [...] Read more.
An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection from many wide-bandwidth signals within a system, as low-bandwidth information such as DC output voltage is of limited value for decision-making and failure prediction. Analog compression, data profiling, and anomaly detection methods enabled by the unique analog frontend are presented. The system significantly reduces the demand for high computational power, fast communication, and large storage space required for the task. A real-time compression ratio exceeding 100:1 was achieved by the experimental analog frontend, digitizing the analog signal at a rate of 135 MS/s with a 10-bit resolution. The motivation, existing solutions, performance metrics, and advantages of the analog frontend are demonstrated, along with the details of the circuit operation principle. The process of data collection, its intelligent processing using the analog frontend, and anomaly detection are simulated to validate the theoretical hypotheses. For experimental validation, a laboratory setup that includes a dedicated analog frontend prototype and step-down DC-DC converter was built and evaluated to demonstrate the robust performance in sampling and monitoring wide-bandwidth signals and smart data processing using analog frontend for quick decision-making. Full article
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14 pages, 268 KiB  
Article
Artificial Intelligence-Enhanced Interview Success: Leveraging Eye-Tracking and Cognitive Measures to Support Self-Regulation in College Students with Attention-Deficit/Hyperactivity Disorder
by Tahnee L. Wilder and Nicole E. Stratchan
Educ. Sci. 2025, 15(2), 165; https://doi.org/10.3390/educsci15020165 - 31 Jan 2025
Abstract
This study investigates how cognitive and self-regulation factors impact online interview performance among college students with ADHD. With unemployment rates for individuals with disabilities significantly higher than the general population, understanding the unique challenges posed by AI-driven virtual interviews is critical. Forty-six students [...] Read more.
This study investigates how cognitive and self-regulation factors impact online interview performance among college students with ADHD. With unemployment rates for individuals with disabilities significantly higher than the general population, understanding the unique challenges posed by AI-driven virtual interviews is critical. Forty-six students with ADHD completed a structured interview simulation using the Big Interview platform, coupled with eye-tracking data and cognitive assessments. Results reveal that higher-performing participants (Gold tier) demonstrated a balanced focus on content comprehension and interviewer engagement, while lower-performing participants (Bronze tier) spent significantly more time on content fixation. Logistic regression indicated that cognitive flexibility, as measured by NIH Dimensional Card Sorting, predicts interview success, emphasizing the importance of task-switching skills in virtual environments. These findings suggest the need for targeted interventions, such as executive function training, to prepare neurodivergent individuals for the demands of AI-driven hiring practices. The study highlights the potential of psychophysiological metrics in understanding and enhancing interview performance, advocating for inclusive, evidence-based strategies that align with Diversity, Equity, Inclusion, and Belonging (DEIB) principles. This research provides actionable insights for educators, employers, and technology developers aiming to create accessible and equitable virtual interview platforms. Full article
(This article belongs to the Special Issue Application of AI Technologies in STEM Education)
26 pages, 15073 KiB  
Article
Attitude Mining Toward Generative Artificial Intelligence in Education: The Challenges and Responses for Sustainable Development in Education
by Yating Wen, Xiaodong Zhao, Xingguo Li and Yuqi Zang
Sustainability 2025, 17(3), 1127; https://doi.org/10.3390/su17031127 - 30 Jan 2025
Abstract
Generative artificial intelligence (GenAI) technologies based on big language models are becoming a transformative power that reshapes the future shape of education. Although the impact of GenAI on education is a key issue, there is little exploration of the challenges and response strategies [...] Read more.
Generative artificial intelligence (GenAI) technologies based on big language models are becoming a transformative power that reshapes the future shape of education. Although the impact of GenAI on education is a key issue, there is little exploration of the challenges and response strategies of GenAI on the sustainability of education from a public perspective. This data mining study selected ChatGPT as a representative tool for GenAI. Five topics and 14 modular semantic communities of public attitudes towards using ChatGPT in education were identified through Latent Dirichlet Allocation (LDA) topic modeling and the semantic network community discovery process on 40,179 user comments collected from social media platforms. The results indicate public ambivalence about whether GenAI technology is empowering or disruptive to education. On the one hand, the public recognizes the potential of GenAI in education, including intelligent tutoring, role-playing, personalized services, content creation, and language learning, where effective communication and interaction can stimulate users’ creativity. On the other hand, the public is worried about the impact of users’ technological dependence on the development of innovative capabilities, the erosion of traditional knowledge production by AI-generated content (AIGC), the undermining of educational equity by potential cheating, and the substitution of students by the passing or good performance of GenAI on skills tests. In addition, some irresponsible and unethical usage behaviors were identified, including the direct use of AIGC and using GenAI tool to pass similarity checks. This study provides a practical basis for educational institutions to re-examine the teaching and learning approaches, assessment strategies, and talent development goals and to formulate policies on the use of AI to promote the vision of AI for sustainable development in education. Full article
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19 pages, 7037 KiB  
Article
An Artificial Intelligence Home Monitoring System That Uses CNN and LSTM and Is Based on the Android Studio Development Platform
by Guo-Ming Sung, Sachin D. Kohale, Te-Hui Chiang and Yu-Jie Chong
Appl. Sci. 2025, 15(3), 1207; https://doi.org/10.3390/app15031207 - 24 Jan 2025
Viewed by 326
Abstract
This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud [...] Read more.
This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud data storage and computation, and end device control. A mobile application was developed using MongoDB software, which is a file-oriented NoSQL database management system developed using C++. This system represents a new database for processing big sensor data. The k-nearest neighbor (KNN) algorithm was used to impute missing data. Node-RED development software was used within the server as a data-receiving, storage, and computing environment that is convenient to manage and maintain. Data on indoor temperature, humidity, and carbon dioxide concentrations are transmitted to a mobile phone application through the MQTT communication protocol for real-time display and monitoring. The system can control a fan or warning light through the mobile application to maintain ambient temperature inside the house and to warn users of emergencies. A long short-term memory (LSTM) model and a convolutional neural network (CNN) model were used to predict indoor temperature, humidity, and carbon dioxide concentrations. Average relative errors in the predicted values of humidity and carbon dioxide concentration were approximately 0.0415% and 0.134%, respectively, for data storage using the KNN algorithm. For indoor temperature prediction, the LSTM model had a mean absolute percentage error of 0.180% and a root-mean-squared error of 0.042 °C. The CNN–LSTM model had a mean absolute percentage error of 1.370% and a root-mean-squared error of 0.117 °C. Full article
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34 pages, 4610 KiB  
Article
Digital Solutions in Tourism as a Way to Boost Sustainable Development: Evidence from a Transition Economy
by Anna Polukhina, Marina Sheresheva, Dmitry Napolskikh and Vladimir Lezhnin
Sustainability 2025, 17(3), 877; https://doi.org/10.3390/su17030877 - 22 Jan 2025
Viewed by 539
Abstract
This paper examines the role of digital economy tools, including big data, mobile applications, e-commerce, and sharing economy platforms, in the sustainable development of the tourism sector. The focus is on studying how the digital economy tools can contribute to more efficient and [...] Read more.
This paper examines the role of digital economy tools, including big data, mobile applications, e-commerce, and sharing economy platforms, in the sustainable development of the tourism sector. The focus is on studying how the digital economy tools can contribute to more efficient and sustainable tourism services, to service quality improvement, to reducing the negative environmental impact, and thus increase the availability of tourism resources in local destinations. Using the example of the successful use of digital technologies in Russian regions, this paper discusses the introduction of online platforms for booking services, the use of mobile applications for navigation and obtaining information about tourist sites, as well as the use of digital tools for predicting consumer preferences. A systematic approach to the analysis of tourism services digitalization, based on a set of technical and functional–digital indicators, allowed us to evaluate the impact of the digitalization level on the local destination’s sustainable development in transition economy conditions. The proposed methodology for assessing and applying tourism services digitalization tools in Russian regions takes into account the transition economy specifics and aims to promote more sustainable practices. This study will add to the existing literature by defining both technical and functional criteria for the implementation of digital technologies as tools for the creation of new business models in tourism, and the development of a tourism services digitalization model, based on the assessment of the regional digitalization level, to ensure the movement towards achieving sustainable development goals in local destinations. Full article
(This article belongs to the Special Issue Digital Economy and Sustainable Development)
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25 pages, 528 KiB  
Article
Trends in InsurTech Development in Korea: A News Media Analysis of Key Technologies, Players, and Solutions
by Yongsu Lee and Hyosook Yim
Adm. Sci. 2025, 15(1), 25; https://doi.org/10.3390/admsci15010025 - 14 Jan 2025
Viewed by 672
Abstract
This study aims to understand how InsurTech has developed in Korea. To achieve this, we collected InsurTech-related news articles published in the Korean media over the past eight years. Using a relatedness analysis based on the TopicRank algorithm, a text-mining technique, we extracted [...] Read more.
This study aims to understand how InsurTech has developed in Korea. To achieve this, we collected InsurTech-related news articles published in the Korean media over the past eight years. Using a relatedness analysis based on the TopicRank algorithm, a text-mining technique, we extracted the top keywords associated with InsurTech by year. The extracted keywords were analyzed and discussed in terms of development trends: which technologies gained prominence over time, who the key players were, and what solutions were introduced. The analysis revealed several key trends in InsurTech’s development in Korea. First, regarding changes in InsurTech technology, blockchain and the Internet of Things initially garnered significant attention, but artificial intelligence and big data later emerged as more critical technologies. Second, in terms of market players, government agencies and research institutes initially created forums for discussion, such as seminars to draw social attention to InsurTech. Over time, innovative startups entered the market, general agencies specializing in insurance brokerage gained prominence in the online marketplace, and the entry of Big Tech platforms further diversified the market. Finally, in terms of InsurTech-related insurance solutions, early attention was focused on developing new products. However, the trend gradually shifted toward improving the accessibility and convenience of existing insurance services. Additionally, asset management and payment settlement services—linked to financial services beyond traditional insurance—emerged, along with new concepts such as healthcare, which reshaped the approach to insurance services. This study contributes to understanding how InsurTech has evolved by identifying key trends in emerging technologies, leading market players, and innovations in the insurance value chain. The Korean case provides insights that may help explore similar patterns in other countries. Full article
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26 pages, 17512 KiB  
Article
Evaluation of the Suitability of Urban Underground Space Development Based on Multi-Criteria Decision-Making and Geographic Information Systems
by Peixing Zhang, Tianlu Jin, Meng Wang, Na Zhou and Xueting Jia
Appl. Sci. 2025, 15(2), 543; https://doi.org/10.3390/app15020543 - 8 Jan 2025
Viewed by 420
Abstract
The rational development of urban underground space resources (UUSRs) is especially crucial for alleviating “urban diseases”, and it is of great significance for exploring the appropriateness of urban underground space (UUS) development under multiple constraints for the rational use of UUSRs. This research [...] Read more.
The rational development of urban underground space resources (UUSRs) is especially crucial for alleviating “urban diseases”, and it is of great significance for exploring the appropriateness of urban underground space (UUS) development under multiple constraints for the rational use of UUSRs. This research selects the UUS in Nantong City, Jiangsu Province, as the research object, and establishes an evaluation index system for the suitability of UUS development under the perspective of sustainable development, including terrain and geomorphology, engineering geological environment, hydrogeological environment, sensitive geological factors, the regional development level, and the distribution of ecological reserve, as well as other multi-source heterogeneous data. On this basis, the relationship between the appropriateness of underground space development and the utilization and various factors was studied. We constructed a comprehensive evaluation model for the suitability of UUS using the Analytic Hierarchy Process (AHP) and the multi-objective linear weighting method. The results of the study show that ecological protection constraints and geological hazards have a greater impact on the evaluation of suitability. The suitable and secondarily suitable areas for the development of the underground space in Nantong City account for 14.74% and 30.66% of the total area, respectively. These areas are mainly distributed in Rugao City and Chongchuan District. The less suitable and unsuitable areas account for 37.17% and 17.44%, with a significant concentration in near-sea areas. Full article
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29 pages, 5761 KiB  
Review
Bridging the Maturity Gaps in Industrial Data Science: Navigating Challenges in IoT-Driven Manufacturing
by Amruta Awasthi, Lenka Krpalkova and Joseph Walsh
Technologies 2025, 13(1), 22; https://doi.org/10.3390/technologies13010022 - 6 Jan 2025
Viewed by 818
Abstract
This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, [...] Read more.
This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. It underlines that data appropriate for its intended application is considered quality data. The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. Manufacturing industries leveraging their data thoughtfully can optimise efficiency and facilitate informed and productive decision-making by establishing a robust technical infrastructure and developing intuitive platforms and solutions. This study highlights the significance of IDS in revolutionising manufacturing sectors within the framework of Industry 4.0 and the Industrial Internet of Things (IIoT), demonstrating that big data can substantially improve efficiency, reduce costs, and guide strategic decision-making. The gaps or maturity levels among industries show a substantial discrepancy in this analysis, which is classified into three types: Industry 4.0 maturity levels, data maturity or readiness condition index, and industrial data science and analytics maturity. The emphasis is given to the pressing need for resilient data science frameworks enabling organisations to evaluate their digital readiness and execute their data-driven plans efficiently and effortlessly. Simultaneously, future work will focus on pragmatic applications to enhance industrial competitiveness within the heavy machinery sector. Full article
(This article belongs to the Section Manufacturing Technology)
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35 pages, 6160 KiB  
Review
State of Health Estimation and Battery Management: A Review of Health Indicators, Models and Machine Learning
by Mei Li, Wenting Xu, Shiwen Zhang, Lina Liu, Arif Hussain, Enlai Hu, Jing Zhang, Zhiyu Mao and Zhongwei Chen
Materials 2025, 18(1), 145; https://doi.org/10.3390/ma18010145 - 2 Jan 2025
Viewed by 531
Abstract
Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current [...] Read more.
Lithium-ion batteries are a key technology for addressing energy shortages and environmental pollution. Assessing their health is crucial for extending battery life. When estimating health status, it is often necessary to select a representative characteristic quantity known as a health indicator. Most current research focuses on health indicators associated with decreased capacity and increased internal resistance. However, due to the complex degradation mechanisms of lithium-ion batteries, the relationship between these mechanisms and health indicators has not been fully explored. This paper reviews a large number of literature sources. We discuss the application scenarios of different health factors, providing a reference for selecting appropriate health factors for state estimation. Additionally, the paper offers a brief overview of the models and machine learning algorithms used for health state estimation. We also delve into the application of health indicators in the health status assessment of battery management systems and emphasize the importance of integrating health factors with big data platforms for battery status analysis. Furthermore, the paper outlines the prospects for future development in this field. Full article
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13 pages, 2280 KiB  
Article
Measuring Destination Image Using AI and Big Data: Kastoria’s Image on TripAdvisor
by Anastasia Yannacopoulou and Konstantinos Kallinikos
Societies 2025, 15(1), 5; https://doi.org/10.3390/soc15010005 - 28 Dec 2024
Viewed by 1061
Abstract
In recent years, the growing number of Online Travel Review (OTR) platforms and advances in social media and search engine technologies have led to a new way of accessing information for tourists, placing projected Tourist Destination Image (TDI) and electronic Word of Mouth [...] Read more.
In recent years, the growing number of Online Travel Review (OTR) platforms and advances in social media and search engine technologies have led to a new way of accessing information for tourists, placing projected Tourist Destination Image (TDI) and electronic Word of Mouth (eWoM) at the heart of travel decision-making. This research introduces a big data-driven approach to analyzing and measuring the perceived and conveyed TDI in OTRs concerning the reflected perceptive, spatial, and affective dimensions of search results. To test this approach, a massive metadata analysis of search engine was conducted on approximately 2700 reviews from TripAdvisor users for the category “Attractions” of the city of Kastoria, Greece. Using artificial intelligence, an analysis of the photos accompanying user comments on TripAdvisor was performed. Based on the results, we created five themes for the image narratives, depending on the focus of interest (monument, activity, self, other person, and unknown) in which the content was categorized. The results obtained allow us to extract information that can be used in business intelligence applications. Full article
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15 pages, 792 KiB  
Article
Can Big Data Comprehensive Pilot Zone Promote Low-Carbon Urban Development? Evidence from China
by Shenhua Liu and Deheng Xiao
Sustainability 2025, 17(1), 97; https://doi.org/10.3390/su17010097 - 26 Dec 2024
Viewed by 708
Abstract
Big data, artificial intelligence, and other cutting-edge technologies are combined in a novel way by big data comprehensive pilot zones (BDCPZs) to provide cities with more comprehensive and precise evaluation and management services. However, it is still unclear how this platform will affect [...] Read more.
Big data, artificial intelligence, and other cutting-edge technologies are combined in a novel way by big data comprehensive pilot zones (BDCPZs) to provide cities with more comprehensive and precise evaluation and management services. However, it is still unclear how this platform will affect cities, especially with regard to carbon emissions. A sample of Chinese prefecture-level cities is used in this study. It examines the impact of BDCPZ buildings on carbon emissions in urban settings using a double-difference model. According to our data, even under rigorous testing, the use of BDCPZ substantially reduces carbon emissions. According to our analysis of the mechanism, the BDCPZ lowers carbon emissions by raising environmental awareness among the general population and strengthening urban green innovation capacities. The effect of BDCPZ in reducing urban carbon emissions is more pronounced in cities that are not dependent on natural resources, and are located in the eastern and western regions, and have greater levels of human capital, according to an examination of heterogeneity. Drawing from the aforementioned findings, this essay makes specific policy recommendations to support the development of low-carbon development in urban areas. Full article
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