Search Results (3,139)

This paper explores how participatory science can enhance building services on a higher education campus. The use of participatory science aims to involve students, faculty members, and technical teams in improving the management of the campus through their participation in data collection and evaluation of the building services. It represents a valuable alternative for campuses needing more building monitoring. The paper also shows how the performance of participatory science could be improved by combining digital technologies such as Building Information Modeling (BIM) and artificial intelligence (AI). The framework is applied to the Faculty of Engineering at An-Najah National University to improve the building services of the campus. A combination of users’ feedback and AI-generated synthetic data is used to explore the performance of the proposed method. Results confirm the high potential of participatory science for improving the services and quality of life on higher education campuses. This is achieved through students’ active participation and involvement in data collection and reporting on their individual experiences. Full article

Improving one’s appearance is one of the main reasons to undergo an orthodontic therapy. While occlusion is important, not just for long-term stability, aesthetics is often considered a key factor in patient’s satisfaction. Following recent advances in artificial intelligence (AI), this study set out to investigate whether AI can help guide orthodontists in diagnosis and treatment planning. In this study, 25 male and 25 female faces were generated and consequently enhanced using FaceApp (ver. 11.10, FaceApp Technology Limited, Limassol, Cyprus), one of the many pictures transforming applications on the market. Both original and FaceApp-modified pictures were then assessed by 441 respondents regarding their attractiveness, and the pictures were further compared using a software for picture analyses. Statistical analysis was performed using Chi-square goodness of fit test R Studio Studio (ver. 4.1.1, R Core Team, Vienna, Austria) software and the level of statistical significance was set to 0.05. The interrater reliability was tested using Fleiss’ Kappa for m Raters. The results showed that in 49 out of 50 cases, the FaceApp-enhanced pictures were considered to be more attractive. Selected pictures were further analyzed using the graphical software GIMP. The most prominent changes were observed in lip fullness, eye size, and lower face height. The results suggest that AI-powered face enhancement could be a part of the diagnosis and treatment planning stages in orthodontics. These enhanced pictures could steer clinicians towards soft-tissue-oriented and personalized treatment planning, respecting patients’ wishes for improved face appearance. Full article

In this study, we introduce progress in intelligent control for reducing structural vibrations. The field of intelligent control for dampening structural vibrations is evolving rapidly, driven by advancements in materials science, AI, and actuator technology. These innovations have led to more efficient, reliable, and adaptable vibration-control systems with applications ranging from civil engineering to aerospace. The use of smart materials has opened new avenues for vibration control of piezoelectric materials. When mechanical stress is applied to these materials, an electric charge response is formed, allowing for precise control over the vibrations. Improved computational models and simulations play crucial roles in the design and testing of vibration-control systems. Finite element analysis helps in accurately predicting the behavior of structures under various loads, thereby aiding in the design of effective vibration-control systems. In our work, we use intelligent control theory to dampen structural vibrations in engineering structures. Full article

The aim of this study was to study the impact of artificial intelligence (AI) on enterprises in terms of strategy, technology, business operations, and organizational management. This study used grounded theory analysis to identify the influencing factors of AI technology application maturity in Chinese enterprises. Taking Chinese film and television enterprises as an example, this study constructed an AI technology application maturity evaluation index system for enterprises based on the analytic network process (ANP) and evaluated the application maturity of AI technology in enterprises in terms of enterprise strategy, technology, business operations, and organizational management. To comprehensively evaluate and empirically analyze the application maturity of enterprise AI technology, this study calculated the index weight based on the ANP, and combined it with the fuzzy comprehensive evaluation method to construct a comprehensive evaluation model. The research results showed that intelligence strategy was the element that was believed to be most affected by the maturity of enterprise AI technology. For technology, intelligence technology and equipment were the elements that were believed to be affected the most. For business operations, smart shooting was the element that was believed to be affected the most. With respect to organizational management, corporate culture was the element that was believed to be most affected. The results showed that the proposed methods for evaluating the application maturity of enterprise AI technology are scientific and effective. The results of this study provide a reference for promoting the application of AI, implementing the intelligence transformation, and enhancing the core competitiveness of enterprises. Full article

The rapid popularity of ChatGPT has brought generative AI into broad focus. The content generation model represented by AI-generated content (AIGC) has reshaped the advertising industry. This study explores the mechanisms by which the characteristics of AI-generated advertisements affect consumers’ willingness to accept these advertisements from the perspectives of perceived eeriness and perceived intelligence. It found that the verisimilitude and imagination of AI-generated advertisements negatively affect the degree of perceived eeriness by consumers, while synthesis positively affects it. Conversely, verisimilitude, vitality, and imagination positively affect the perceived intelligence, while synthesis negatively affects it. Meanwhile, consumers’ perceived eeriness negatively affects their acceptance of AI-generated advertisements, while perceived intelligence positively affects their willingness to accept AI-generated advertisements. This study helps explain consumers’ attitudes toward AI-generated advertisements and offers strategies for brands and advertisers for how to use AI technology more scientifically to optimize advertisements. Advertisers should cautiously assess the possible impact of AI-generated advertisements according to their characteristics, allowing generative AI to play a more valuable role in advertising. Full article

Prehospital electrocardiogram (PH-ECG) transmission is an important technology for reducing door-to-balloon time, but the decision to transmit often depends on the discretion of emergency medical technicians (EMTs). Additionally, studies based on real-world data remain insufficient. This study reports a machine learning-based method for classifying the severity of PH-ECG images and explores its feasibility. PH-ECG data were compiled from 120 patients between September 2017 and September 2020. The model we created from these data was the first classification model for PH-ECG images using data from a Japanese study population and showed a weighted F1-score of 0.85 and an Area Under the Curve (AUC) of 0.93. This result can be interpreted as having an excellent balance of sensitivity and specificity. The Cohen’s Kappa coefficient between AI’s inferences and the correct labels created by two cardiologists was 0.68 (p < 0.05), which is considered “substantial” according to the guidelines presented by Landis and Koch. In this study, although we were not able to remove noise caused by patient movement or electrode detachment, the results indicate that image-based abnormality detection from PH-ECGs is feasible and effective, particularly in regions like Japan where ECG data are often stored and transmitted as images. In addition, in our region, paramedics follow a multi-step process to decide whether to transmit an ECG, which takes time for the first screening. However, if the ECG is transmitted when either the paramedics or the deep learning model detects an abnormality, it is expected to reduce reading time and door-to-balloon time, as well as decrease false negatives. Full article

Purpose—The objective of this study was to conduct a detailed South African study that sought to explore and analyse the views of South African accountants regarding the factors that affect the adoption of AI in financial reporting. In other words, this study aimed to understand what accountants in South Africa think about the use of AI in their field, especially concerning its integration into financial reporting practices. Three main theories underpinned the study, namely, the diffusion of innovation, technology, organisation, and environment framework, and the institutional theory. In essence, the study sought to determine the perception of South Africa’s accountants on these factors. Design/methodology/approach—This study adopted the quantitative research method and descriptive design. In this regard, positivism as a philosophy was preferred. An online survey was developed to collect information from the participants. Participants were recruited based on their affiliation with the four IFAC-recognised accounting bodies in South Africa: SAICA, SAIPA, CIMA, and ACCA. Findings—Th study found that, overall, South African accountants believe that organisational, technological, and environmental factors play a role in adopting artificial intelligence in financial reporting. Originality/value: This study contributes by enriching the understanding of South African accountants’ perceptions of the adoption of artificial intelligence in financial reporting through the lenses of the selected theories. Full article

Artificial intelligence (AI) has emerged as an effective solution to alleviate excessive carbon emissions in sustainable building projects. Although there are numerous applications of AI, there is no state-of-the-art review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. Therefore, this review study aims to conduct a systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects, thereby expediting the realization of NZCEs in building projects. A mixed-method approach (i.e., systematic literature review and science mapping) consisting of four comprehensive stages was used to retrieve relevant published articles from the Scopus database. A total of 154 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. Six mainstream research topics were identified and discussed. These include (1) life cycle assessment and carbon footprint, (2) practical applications of AI technology, (3) multi-objective optimization, (4) energy management and energy efficiency, (5) carbon emissions from buildings, and (6) decision support systems and sustainability. In addition, this review suggests six research gaps and develops a framework depicting future research directions. The findings contribute to advancing AI applications in reducing carbon emissions in sustainable building projects and can help researchers and practitioners to realize its economic and environmental benefits. Full article

Artificial intelligence (AI) technologies have been increasingly developed and applied in the audiovisual sector. Over-the-top (OTT) services, directly distributed to viewers via the Internet, are associated with a shift towards automation through algorithmic mediation in audiovisual content led by digital platforms. However, scientific knowledge regarding algorithmic recommender systems and automation in OTT services is not yet systemized; researchers, practitioners, and the public thus lack full awareness about the still largely opaque phenomena. To address this gap, we conduct a systematic literature review in the communication domain (2010–2022) and answer four key research questions: What research objectives have been pursued? What concepts have been developed and/or applied? What methodologies have been privileged? Which OTT platforms have received the most research attention? Challenges and opportunities are highlighted, and an agenda for future research is advanced. Full article

In response to increasing global temperatures and energy demands, optimizing buildings’ energy efficiency, particularly in hot climates, is an urgent challenge. While current research often relies on conventional energy estimation methods, there has been a decrease in the efforts dedicated to leveraging AI-based methodologies as technology advances. This implies a dearth of multiparameter examinations in AI-driven extreme case studies. For this reason, this study aimed to enhance the energy performance of residential buildings in the hot climates of Dubai and Riyadh by integrating Building Information Modeling (BIM) and Machine Learning (ML). Detailed BIM models of a typical residential villa in these regions were created using Revit, incorporating conventional, modern, and green building envelopes (BEs). These models served as the basis for energy simulations conducted with Green Building Studio (GBS) and Insight, focusing on crucial building features such as floor area, external and internal walls, windows, flooring, roofing, building orientation, infiltration, daylighting, and more. To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. GBM consistently outperformed the others, demonstrating superior prediction accuracy with an R² of 0.989. This indicates that the model explains 99% of the variance in EUI, highlighting its effectiveness in capturing the relationships between building features and energy consumption. Feature importance analysis (FIA) revealed that roofs (29% in Dubai scenarios (DS) and 40% in Riyadh scenarios (RS)), external walls (19% in DS and 29% in RS), and windows (15% in DS and 9% in RS) have the most impact on energy consumption. Additionally, the study explored the potential for energy optimization, such as cavity green walls and green roofs in RS and double brick walls with VIP insulation and green roofs in DS. The findings of the paper should be interpreted in light of certain limitations but they underscore the effectiveness of combining BIM and ML for sustainable building design, offering actionable insights for enhancing energy efficiency in hot climates. Full article

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females. Full article

The “technological dividends” brought by AI development provide a new model for the country to achieve green governance, enhance enterprises’ ability to manage pollutant emissions during production and operations, and create a new driving force for improving environmental quality. In this regard, this paper systematically examines the impact of AI on environmental quality in China by employing provincial panel data spanning from 2000 to 2020. Focusing on energy conservation, carbon reduction, and emissions mitigation, the analysis is conducted through the application of a two-way fixed-effects model and mediation effects model to explore both the effects and the mechanisms of AI’s influence on environmental quality. The findings indicate that the development and implementation of AI contribute positively to China’s efforts in energy conservation, carbon reduction, and emissions mitigation, ultimately leading to an enhancement in environmental quality. This conclusion remains valid after multiple robustness checks. Mechanism tests reveal that the optimization of regional energy structures, advancements in green technological innovation, and upgrades in industrial structures serve as crucial pathways through which AI facilitates energy conservation, carbon reduction, and emissions mitigation. Heterogeneity analysis uncovers a notable “path dependence” effect in China’s AI development; regions characterized by higher material capital investment, more advanced technological market development, and greater levels of marketization experience a relatively more pronounced impact of AI on the enhancement of environmental quality. This study offers direct references and practical insights for countries globally to foster AI development, enhance environmental quality, and advance high-quality economic growth amid the ongoing wave of digital and intelligent transformation. Full article

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate. Full article

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies. Full article

Polymer dielectric materials have recently attracted attention for their versatile applications in emerging electronic devices such as memory, field-effect transistors (FETs), and triboelectric nanogenerators (TENGs). This review highlights the advances in polymer dielectric materials and their integration into these devices, emphasizing their unique electrical, mechanical, and thermal properties that enable high performance and flexibility. By exploring their roles in self-sustaining technologies (e.g., artificial intelligence (AI) and Internet of Everything (IoE)), this review emphasizes the importance of polymer dielectric materials in enabling low-power, flexible, and sustainable electronic devices. The discussion covers design strategies to improve the dielectric constant, charge trapping, and overall device stability. Specific challenges, such as optimizing electrical properties, ensuring process scalability, and enhancing environmental stability, are also addressed. In addition, the review explores the synergistic integration of memory devices, FETs, and TENGs, focusing on their potential in flexible and wearable electronics, self-powered systems, and sustainable technologies. This review provides a comprehensive overview of the current state and prospects of polymer dielectric-based devices in advanced electronic applications by examining recent research breakthroughs and identifying future opportunities. Full article