Efficient Creation Research Articles

An automatic hybrid mesh generation method for combustion fluid dynamics simulations

PurposeAutomatic mesh generation is still a challenge problem for combustion fluid dynamics simulations because of the high-quality requirement and complexity of geometries. This paper aims to find an efficient automatic analysis model creation and mesh generation method to save the time for pre-processing in numerical simulations.Design/methodology/approachBased on the previous work, we explore effective model processing and mesh generation methods from practical engineering applications. Considering the automation and high quality requirement, we construct an automatic mesh generation procedure for combustion fluid dynamics problems.FindingsThe numerical results show that the procedure we proposed in this paper can automatically generate high quality mesh for combustion fluid dynamics simulations. The strategy of fluid model construction is time-saving and with high-precision. The mesh generation method in our procedure is automatic and efficient.Practical implicationsThe procedure proposed in this paper is applicable to the practical engineering application model, such as aircraft simulation, aeroengine simulation and so on. The procedure has been integrated into an numerical simulation software.Originality/valueThe method proposed in this paper has very practical application value. It can be used in the practical application and saves a lot of manual processing time for numerical stimulation specialists.

Diverse Attitudes Toward AI-Produced Music: A Case Study between Instrument Learners and Non-Instrument Learners in Yangtze River Delta Region

Recently, with the gradual popularization of AI technology in music, AI music generation technology, as a tool, has triggered thoughts about the transformation of traditional music composition while enhancing the innovation and efficiency of music creation. This study aims to explore the different attitudes toward AI-generated music among instrumental and non-instrumental learners in the Yangtze River Delta region of China, so as to increase the knowledge of AI music composition technology. This study uses a questionnaire method to collect quantitative data, with semi-structured interviews as a qualitative supplement. It is found that most people are aware of AI music technology and express acceptance. The style, uniqueness, and quality of AI-generated works, as well as the convergence of the technology on traditional platforms, and copyright issues, are topics of concern. The differential impact of whether or not one has learned a musical instrument on attitudes towards AI music centers on personal preference. The low price of the service was a significant influencing factor for those who embraced the improved AI technology. In addition, instrumental learners placed more emphasis on the professionalism, artistry, and emotionality of AI music, while non-instrumental learners placed more emphasis on the technological convenience and affordability of AI music. This study helps to demonstrate the influence of instrumental learning experiences on people’s attitudes towards AI music generation, and can provide inspiration for further exploration of music education research in cross-cultural contexts. Meanwhile, the emotional relationship and cognitive responses between humans and AI music can be revealed more.

Agency conflicts, product market competition and investment efficiency: new evidence from emerging market

PurposeThis study examines the influence of product market competition on investment inefficiency of Indian firms in context of agency problems. Additionally, this study also investigates whether intense competition is a substitute for or complementary to corporate governance in reducing investment inefficiency of firms.Design/methodology/approachUtilizing the residuals extracted from Biddle et al. (2009) investment model, investment inefficiency, overinvestment and underinvestment are measured for 506 non-financial Indian listed firms with 6,998 firm-year observations from 2009 to 2022. Product market competition is measured using various proxies such as the Herfindahl–Hirschman index, top-four firm concentration ratio, total number of firms in industry, industry market size, weighted average of entry costs and research and development (R&D) to sales ratio. Firms' internal governance is measured using a newly corporate governance index developed with 65 new governance stipulations. Several pooled ordinary least squares (OLS) panel regressions were estimated involving investment inefficiency of firms, product market competition, governance index and firm-specific variables. Endogeneity issues were addressed through two-stage least squares. Robustness checks were also conducted using a two-step system generalized method of moments (GMM).FindingsThe main finding of the study indicates that heightened product market competition reduces investment inefficiency, overinvestment and underinvestment among the selected Indian firms suggesting that firms facing intense competition are less prone to invest below or above optimal levels. This is primarily because in highly competitive industries, managers face greater liquidation risks, prompting more efficient capital investment decisions. The results also indicate that robust corporate governance significantly mitigates investment inefficiencies in non-competitive industries compared to competitive ones suggesting heightened competition reduces managerial slack, resulting in diminished benefits from good governance in competitive settings. Overall results indicate a substitution effect between corporate governance and competition in reducing investment inefficiency, with robustness across various empirical specifications, industry classifications and alternative competition and inefficiency measures.Practical implicationsThe evidence from this work emphasizes the pivotal roles of market competitiveness and corporate governance in shaping investment efficiency. Regulators must closely monitor monopolistic behaviors to safeguard stakeholder interests, enhance investment efficiency and foster value creation. Recognizing the positive impact of market competition, firms should prioritize initiatives to promote industrial openness and intensify competition while strengthening market mechanisms. Policymakers should consider implementing competition-centric governance policies, such as deregulation and antitrust laws, to stimulate market competition. These measures can mitigate governance-related costs and promote a competitive marketplace.Originality/valueThis study provides fresh evidence concerning the effects of product market competition on investment efficiency of Indian firms under new governance norms, an unexplored area in India as most of the existing work has primarily examined the association between competition and investment levels. To best of our knowledge, this is one of the earliest studies demonstrating the substitution effects of competitiveness and governance systems on lowering investment inefficiency suggesting a significant influence of corporate governance in non-competitive industries. Finally, this study contributes to the field of methodology by developing a new firm-specific governance index based on 65 governance indicators.

A Study on the Role of Technology in Creation of Banking Assets

The banking sector has undergone significant transformation with the advent of technology, changing how assets are created, managed, and utilized. This research paper explores the evolving role of technology in creating banking assets. It examines the impact of various technological innovations, such as digital banking, fintech solutions, artificial intelligence, blockchain, and big data analytics, on asset creation within the banking industry. The paper also discussed the challenges and opportunities these technologies present and provides insights into how banks can leverage them to enhance asset creation efficiency and profitability

Reshaping the Sound Landscape: The Application and Development of AI in the Sound Creation of Chinese Podcasts in the Era of Intelligent Media

With the rapid development of intelligent media technology, AI has greatly enriched the creation means and expression forms of Chinese podcasts and reshaped the diversity and personalization of speech synthesis, audio editing and content generation, through the diversity and personalization of sound landscape. Through the specific application cases of AI in the creation of Chinese podcast voice, this paper analyzes its positive effects in improving the efficiency of creation, optimizing user experience and promoting the development of the industry, and looks forward to the future development trend and challenges, aiming to provide valuable reference and inspiration for the development of the field of Chinese podcast

Preliminary Studies to Bridge the Gap: Leveraging Informal Software Architecture Artifacts for Structured Model Creation

This study addresses the prevalent gap between structured models and informal architectural methodologies in software engineering. Recognizing the potential of informal architecture artifacts in analytical processes, we introduce a methodology that efficiently transforms these informal components into structured models. This method facilitates understanding and utilizing informal diagrams and enhances analytical capabilities through graph analysis techniques. By leveraging user-friendly tools such as Draw.io, the methodology democratizes the modeling process, making sophisticated architectural analyses accessible to a broader spectrum of professionals without requiring deep expertise in formal methods. The innovative aspects of this methodology lie in its ability to streamline the transformation process, significantly improving both the efficiency and effectiveness of model creation and analysis. These enhancements are demonstrated through a practical application involving a sample architecture diagram, where the resulting model is thoroughly analyzed using advanced graph analysis tools. This approach bridges the theoretical and practical divides in software architecture.

Long‐term Motion In‐betweening via Keyframe Prediction

AbstractMotion in‐betweening has emerged as a promising approach to enhance the efficiency of motion creation due to its flexibility and time performance. However, previous in‐betweening methods are limited to generating short transitions due to growing pose ambiguity when the number of missing frames increases. This length‐related constraint makes the optimization hard and it further causes another constraint on the target pose, limiting the degrees of freedom for artists to use. In this paper, we introduce a keyframe‐driven approach that effectively solves the pose ambiguity problem, allowing robust in‐betweening performance on various lengths of missing frames. To incorporate keyframe‐driven motion synthesis, we introduce a keyframe score that measures the likelihood of a frame being used as a keyframe as well as an adaptive keyframe selection method that maintains appropriate temporal distances between resulting keyframes. Additionally, we employ phase manifolds to further resolve the pose ambiguity and incorporate trajectory conditions to guide the approximate movement of the character. Comprehensive evaluations, encompassing both quantitative and qualitative analyses, were conducted to compare our method with state‐of‐the‐art in‐betweening approaches across various transition lengths. The code for the paper is available at https://github.com/seokhyeonhong/long-mib

Automated orofacial virtual patient creation: A proof of concept

ObjectivesTo (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice. Materials and methodsA dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale. ResultsThe accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool. ConclusionThe AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication. Clinical significanceUsing AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

Annotated Dataset for Training Cloud Segmentation Neural Networks Using High-Resolution Satellite Remote Sensing Imagery

The integration of satellite data with deep learning has revolutionized various tasks in remote sensing, including classification, object detection, and semantic segmentation. Cloud segmentation in high-resolution satellite imagery is a critical application within this domain, yet progress in developing advanced algorithms for this task has been hindered by the scarcity of specialized datasets and annotation tools. This study addresses this challenge by introducing CloudLabel, a semi-automatic annotation technique leveraging region growing and morphological algorithms including flood fill, connected components, and guided filter. CloudLabel v1.0 streamlines the annotation process for high-resolution satellite images, thereby addressing the limitations of existing annotation platforms which are not specifically adapted to cloud segmentation, and enabling the efficient creation of high-quality cloud segmentation datasets. Notably, we have curated the Annotated Dataset for Training Cloud Segmentation (ADTCS) comprising 32,065 images (512 × 512) for cloud segmentation based on CloudLabel. The ADTCS dataset facilitates algorithmic advancement in cloud segmentation, characterized by uniform cloud coverage distribution and high image entropy (mainly 5–7). These features enable deep learning models to capture comprehensive cloud characteristics, enhancing recognition accuracy and reducing ground object misclassification. This contribution significantly advances remote sensing applications and cloud segmentation algorithms.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

Revolutionizing Knowledge Synthesis: Introducing AI-Enhanced Collaborative Theory (AIECT)

In an era of unprecedented data development, standard literature review approaches can result in information overload and missed chances for theoretical innovation. This paper introduces AI-Enhanced Collaborative Theory (AIECT), a paradigm for dynamic collaboration between artificial intelligence (AI) systems and human researchers to improve knowledge synthesis and theoretical progress. AIECT focuses on the intersection of human intuition and AI capabilities, including essential components like dynamic co-creation, iterative feedback loops, contextual intelligence, and ethical frameworks. Empirical experiments show that AIECT can significantly increase the efficiency and depth of literature reviews and theory creation, showing its potential to revolutionize research processes and address information overload concerns. While limitations such as sample size and the need for additional validation are acknowledged, the findings indicate that AIECT represents a transformative approach to academic inquiry, encouraging innovative, ethical, and nuanced research techniques. By safely integrating AI, AIECT sets the way for advances in knowledge synthesis and provides a systematic foundation for future scholarly endeavors in various domains.

Impact of ChatGPT on case creation efficiency and learning quality in case-based learning for undergraduate nursing students

Impact of ChatGPT on case creation efficiency and learning quality in case-based learning for undergraduate nursing students

Review of AI-Driven Solutions in Business Value and Operational Efficiency

Introduction. Artificial intelligence (AI) refers to a wide spectrum of breakthroughs that offer multiple advantages to companies in terms of increased sales. Considering the abundance of data and the significant increase in computational resources, organisations have rapidly turned to artificial intelligence (AI) to create financial benefits. Nevertheless, businesses continue to discover it is challenging to implement and employ AI in their everyday activities. Therefore, a comprehensive understanding is required due to the absence of an integrated comprehension of how artificial intelligence creates business value and what kind of corporate worth is anticipated. Aim and tasks. The study aims to review value-generating methods and explain how enterprises might use AI technology in their business activities. To accomplish its main aims, this study offers a thorough literature review. The working hypothesis claims that the use of AI can increase business value. Results. This study examines the research capabilities of AI, its use in the corporate environment, and its initial and secondary impacts. The impact of AI includes process efficiency, generating insights hidden in huge amounts of data, and transforming business processes in terms of procedural actions, operational efficiency, financial efficiency, market efficiency, and sustainability in terms of company profile. In addition to the favourable impacts, several recent cases have shown that unwanted and undesired consequences may develop in the absence of effective management procedures. These effects hurt the reputation of the businesses concerned and, in certain cases, resulted in huge fines and financial losses. Such findings increase the responsibility of AI enterprises to incorporate solutions that reduce the bias in data and algorithms at every stage of implementation. Conclusions. The role of artificial intelligence in the corporate environment in value creation and operational efficiency is extending. AI technologies can be used by companies to increase automation of corporate processes without direct interaction with customers, including applications that mean the use of AI in customer-facing services and products. Learning about the means by which AI might be employed will assist businesses in generating rational choices regarding the strength of implementing technologies in the supply chain. Assessing the possible implications of AI acceptance of artificial intelligence may enable firms to plan more successfully on a technology’s launch.

Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction.

Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.

Comprehensive analysis of arc methods of 3D printing of metal products: assessment of the efficiency and prospects of using TIG as a heat source

In recent years, additive technologies have become increasingly important for the production of parts with complex geometries, enabling the rapid and efficient creation of objects with different shapes and configurations in industrial sectors such as medicine, aerospace and construction. The focus of the study was the analysis of arc processes for additive manufacturing, particularly through non-consumable electrode welding in an inert gas environment (TIG) and its applications as a heat source. Modern methods of 3D printing metal products, such as SLM (Selective Laser Melting), EBM (Electron Beam Melting), and LMD (Laser Metal Deposition), allow the production of parts with good quality indicators: accuracy, surface roughness, mechanical properties, and others. However, these methods are expensive due to the technological complexity of the equipment, and a weakness of these methods is their low productivity compared to arc methods. The results presented in this article show that the productivity of arc methods in additive manufacturing is several times higher than that of SLM, EBM, and LMD, and arc methods are more cost-effective due to lower equipment costs and reduced energy consumption. The article presents the schemes of arc methods of additive manufacturing. One of the promising directions in the development of hybrid technology, namely the use of the TIG heat source for sintering metal powders, offers an effective way to reduce the cost of additive manufacturing by replacing the laser as a heat source, while allowing the continued use of various types of metal powders, reinforcing materials, and metal-ceramic blends.

Development of a GIS-module for analog search using an analytical no-code platform

The article is devoted to the development of GIS-applications for searching analogs of oil and gas objects. Depending on the tasks to be solved and data sources used in oil and gas industry different methods of analogs search are applied. Program products developed on the basis of private methods can be used only to a limited extent, which raises the question about the expediency of resources spent on their development. For efficient creation of programs for analogues search we propose a two-stage scheme including the stage of development of a formalized search model based on analytical no-code platform and the stage of traditional final product development based on the formalized model. It is shown that analytical no-code platforms are an effective and accessible for non-programming specialists means of creating formalized models as a basis for software product development. The expediency of the development of applications for the search of analogs on the basis of geoinformation systems is substantiated. An example of implementing two-stage scheme in the development of module for searching analogs based on free geoinformation system QGIS with the use of Russian analytical no-code-platform Loginom is presented.

Entrepreneurship Education in International Context: Skills, Attitudes and Competencies

Entrepreneurship plays an important role in the economic development of the nations by driving innnovation, economic efficiency, and job creation. On the other hand, entrepreneurship education and international education seem to be an important antecedent for entrepreneurship intentions. In this context, universities and campuses have an important role in fostering entrepreneurial activities, namely extra curricular and curricular programs that empower young and nascent entrepreneurs to develop skills, attitudes and competencies that can later be applied to business creation. The main objective of this study is to present the extra curricular program, EXIT program, and confirm its impact on entrepreneurial skills, attitudes and competencies acquired or developed by students. This study employs a mixed research approach that includes both qualitative and quantitative methods. The results reveals that EXIT program effectively contributes to the development of various attributes associated with student resilience. It notably boosts critical thinking, creativity, initiative, problem-solving, proactivity, responsibility, cooperation, social responsibility, and a balanced mix of ambition, optimism, and realism. EXIT’s impact on student skills development underscores its substantial positive influence, especially in domains like teamwork and communication. Feedback from the focus group discussions attested to the overall positive reception of EXIT by participants. The program demonstrated its effectiveness in improving their entrepreneurial skills, facilitating multicultural experiences, and encouraging collaborative efforts.

FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention

AbstractDiffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend identity among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300$$\times $$ × –2500$$\times $$ × speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available here (https://github.com/mit-han-lab/fastcomposer).

Generative AI and the Future of Service Design: The potential to transform the service design practice in dramatic and efficient ways

AI is dramatically increasing service designers’ capacity to deliver value. A McKinsey report (June 2023) values the potential productivity improvement of AI in customer operations at over US00 billion annually, and the potential for marketing productivity improvement at over US50 billion annually, thanks to efficient content creation, enhanced use of data and individual personalisation.1 To learn how AI is transforming service design, we conducted secondary research and interviews with a 40-year veteran of the tech industry, the head of design quality and collaboration for a global healthcare brand and the head of operating system customer experience for a leading luxury car brand.

Illustrating the benefits of efficient creation and adaption of behavior models in intelligent Digital Twins over the machine life cycle

The concept of the Digital Twin, which in the context of this paper is the virtual representation of a production system or its components, can be used as a "digital playground" to master the increasing complexity of these assets. One of the central subcomponents of the Digital Twin are behavior models that can enable benefits over the entire machine life cycle. However, the creation, adaption and use of behavior models throughout the machine life cycle is very time-consuming, which is why approaches to improve the cost-benefit ratio are needed. Furthermore, there is a lack of specific use cases that illustrate the application and added benefit of behavior models over the machine life cycle, which is why the universal application of behavior models in industry is still lacking compared to research. This paper first presents the fundamentals, challenges and related work on Digital Twins and behavior models in the context of the machine life cycle. Then, concepts for low-effort creation and automatic adaption of Digital Twins are presented, with a focus on behavior models. Finally, the aforementioned gap between research and industry is addressed by demonstrating various realized use cases over the machine life cycle, in which the advantages as well as the application of behavior models in the different life cycle phases are shown.