Interdisciplinary Applications Research Articles

The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling

This research examines the energy efficiency of city districts through the Malmquist–DEA model and investigates the spatial effects of the service trade and the digital economy on energy efficiency in urban green development. The study also delves into the specific context of the AI service trade to gain insights into and align with the emerging digital intelligence industry. The interplay of the service trade with the digital economy, alongside the AI service trade with innovation, significantly enhances urban energy efficiency and demonstrates positive externalities. Building on the empirical findings, this research employs cluster analysis to explore the green development of city districts and uses AI technology to program green communication and cooperation mechanisms across district clusters, employing quantum computation based on QUBO modeling. This study contributes to a deeper understanding of the cofunction of the service trade and the digital economy in terms of energy efficiency and aids in developing new quality productivities for green cities through quantum-based AI advancements. This research has clear implications for cutting-edge interdisciplinary applications of green AI technologies in social computing science.

Advancements in Computer Vision: A Comprehensive Survey of Image Processing and Interdisciplinary Applications

Computer vision and image processing are rapidly evolving fields with broad applications across numerous domains, including healthcare, autonomous driving, surveillance, and entertainment. These fields have transformed from simple data recording techniques into sophisticated systems that incorporate digital image processing, pattern recognition, machine learning, and computer graphics. This evolution has prompted interdisciplinary interest, pushed the technology’s boundaries and expanded its practical uses. This paper offers a comprehensive survey of recent advancements in computer vision, focusing on image processing and its applications across various fields. It delves into the theoretical foundations and technologies that make computer vision a valuable tool for interpreting images and videos, extracting relevant information, recognizing patterns, and understanding events. The ability of computer vision to analyze large datasets across multiple application domains makes it instrumental in tasks such as object identification, facial recognition, scene understanding, and even real-time action prediction. This versatility has established computer vision as a key driver of data-driven insights in both scientific and commercial sectors. The study categorizes computer vision into four main areas: image processing, object recognition, machine learning, and computer graphics. Each of these categories is essential to the functionality of modern computer vision systems. Image processing involves techniques for enhancing image quality and extracting important features. Object recognition and machine learning enable the identification of specific elements within images and allow systems to learn from large datasets, enhancing accuracy over time. Computer graphics, on the other hand, aid in visualizing and interpreting processed data. By offering insights into the latest techniques and evaluating their performance, this survey highlights the current state of computer vision while shedding light on future trends. Computer vision’s expanding utility across various fields underscores its critical role in driving interdisciplinary innovation and addressing complex challenges.

Advancements of Exploiting Convolutional Neural Networks for Solving Differential Equations

Solving Partial Differential Equations (PDEs) is essential across various fields, including physics, mathematics, and engineering. This study explores innovative methods for solving PDEs using Convolutional Neural Networks (CNNs). Traditionally, solving PDEs through numerical methods like finite difference or spectral approaches is computationally intensive, particularly when addressing high-dimensional problems and complex boundary conditions. CNNs, with their ability to handle spatial data efficiently, offer a promising alternative. This research evaluates different neural network architectures, including data-driven, physics-driven, and hybrid approaches, and their role in enhancing the accuracy and scalability of PDE solutions. Data-driven methods rely on extensive datasets for training neural networks, while Physics-Informed Neural Networks (PINNs) integrate the underlying physical laws into the solution process. Hybrid approaches combine both strategies, leveraging data and physical principles. The paper emphasizes CNNs potential to transform traditional PDE-solving methods, making them more efficient and adaptable to complex, real-world applications. Future research directions include optimizing these methods for greater scalability and interdisciplinary applications.

High-Sensitivity, High-Resolution Miniaturized Spectrometers for Ultraviolet to Near-Infrared Using Guided-Mode Resonance Filters

Miniaturized spectrometers have significantly advanced real-time analytical capabilities in fields such as environmental monitoring, healthcare diagnostics, and industrial quality control by enabling precise on-site spectral analysis. However, achieving high sensitivity and spectral resolution within compact devices remains a significant challenge, particularly when detecting low-concentration analytes or subtle spectral variations critical for chemical and molecular analysis. This study introduces an innovative approach employing guided-mode resonance filters (GMRFs) to address these limitations. Functioning similarly to notch filters, GMRFs selectively block specific spectral bands while allowing others to pass, maximizing energy extraction from incident light and enhancing spectral encoding. Our design incorporates narrow band-stop filters, which are essential for accurate spectrum reconstruction, resulting in improved resolution and sensitivity. Our spectrometer delivers a spectral resolution of 0.8 nm over a range of 370–810 nm. It achieves sensitivity values that are more than ten times greater than those of conventional grating spectrometers during fluorescence spectroscopy of mouse jejunum. This enhanced sensitivity and resolution are particularly beneficial for chemical and biological applications, facilitating the detection of trace analytes in complex matrices. Furthermore, the spectrometer’s compatibility with complementary metal oxide semiconductor (CMOS) technology enables scalable and cost-effective production, fostering broader adoption in chemical analysis, materials science, and biomedical research. This study underscores the transformative potential of the GMRF-based spectrometer as an innovative tool for advancing chemical and interdisciplinary analytical applications.

Various CVD-grown ZnO nanostructures for nanodevices and interdisciplinary applications.

This work presents a simple chemical vapour deposition (CVD) method to grow ZnO nanostructures. By annealing Zn powder under atmospheric pressure conditions, we collected nanocrystals with various morphologies, including rods, pencils, sheets, combs, tetrapods, and multilegs. Raman scattering study reveals that the samples are monophasic with a hexagonal structure, and fall into the P63 mc space group. Depending on the morphology and crystal quality, their photoluminescence spectra have only a strong UV emission associated with the exciton radiative recombination, or both UV and defect-related visible emissions with their relative intensity ratio varying with the excitation power density. The obtained results prove that ZnO exhibits many novel nanostructures that can foster the development of next-generation optoelectronic nanodevices and new applications in biological and biomedical fields.

All-optical method for generating transient microstructured targets in laser ion acceleration

We demonstrate an all-optical method that online produces “microstructured” targets by manipulating a Laguerre-Gaussian (LG) prepulse. Besides the enhancement of the proton number, the cutoff energy is nearly doubled. Such transient “microstructured” targets are characterized using both offline and online approaches. Based on probe experiments and hydrodynamic simulations, we develop a simple model to describe the electron density distribution upon the arrival of the main pulse. Two-dimensional particle-in-cell simulations reveal that the annular plasma acts as an oscillating cavity for hot electrons and results in a cascaded acceleration effect. The combination of low-intensity LG prepulses and ultraintense Gaussian pulses opens another avenue for fine-tuning laser-plasma interactions spatiotemporally in interdisciplinary applications. Published by the American Physical Society 2024

Innovation and Development of Talent Management Model in Colleges and Universities Under the Background of Digitalization

The vigorous development of the digital economy has put forward new and higher requirements for talent training in colleges and universities. In order to cultivate outstanding talents in the new era with both interdisciplinary knowledge application ability and innovative thinking, colleges and universities must keep up with the pace of the development of the digital era, from macro to local, comprehensive innovation of talent management mode, and lead the development with innovation. Only in this way can they flexibly respond to various challenges and opportunities under the digital background, and help colleges and universities achieve the goal of long-term development. Based on the current situation of talent management in colleges and universities, this paper elaborates the feasible path of innovation and development of talent management mode in colleges and universities under the background of digitalization from the perspectives of talent training and talent service. At the same time, the exposed data security and privacy protection issues are also discussed in depth. To contribute to the realization of the goals of refinement, individuation, and intelligence of talent management in colleges and universities through the research and related exposition.

Context-Dependent Criteria for Dirichlet Process in Sequential Decision-Making Problems

In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing objectives efficiently. Building upon the foundations laid by existing research, this methodology is expanded to encompass experiments with multiple outcome categories using Dirichlet processes. The core of our approach is centered around weighted entropy measures, particularly in scenarios dictated by Dirichlet distributions, which have not been extensively explored previously. We innovatively adapt the technique initially applied to binary case to Dirichlet distributions/processes. The primary contribution of our work is the formulation of a sequential minimization strategy for the main term of an asymptotic expansion of differential entropy, which scales with sample size, for non-binary outcomes. This paper provides a theoretical grounding, extended empirical applications, and comprehensive proofs, setting a robust framework for further interdisciplinary applications of information-theoretic paradigms in sequential decision-making.

Harnessing biological insights: Integrating proton regulation, pH buffer and zincophility for highly stable Zn anode

Zinc anodes are severely threatened by hydrogen evolution, dendrite growth and by-products. Inspired by the mechanisms of L-carnosine (L-car) in maintaining intracellular pH stability and fostering mucosal repair in biological systems, this study innovatively proposed a comprehensive strategy integrating proton regulation, pH buffer and zincophility by introducing L-car as an electrolyte additive to achieve remarkable stability of zinc anode. Experimental validations and theoretical calculations demonstrate that the abundant N and O sites in L-car can form robust hydrogen bonds with protons, effectively impeding interfacial proton transport and substantially mitigating HER. Moreover, dual pH buffering sites of L-car facilitate dynamic modulation of proton concentration, stabilizing the pH of electrolyte, and suppressing Zn corrosion/passivation/by-product. Concurrently, the robust chelation between L-car and Zn2+ orchestrates uniform zinc ion deposition. This synergistic trifecta of L-car endows Zn ion batteries with an extraordinary cycling stability, extending to an ultralong duration of 5500 h. Furthermore, the Zn//MnO2 full battery, demonstrates remarkable stability, retaining a specific capacity of 106.1 mAh/g with 79.2 % retention after 1000 cycles at 3 A/g. This study pioneers the interdisciplinary application of L-car in electrolyte modification, presenting a novel paradigm for stabilizing zinc anodes.

Exploration and Practice of Mechanical Engineering Talent Cultivation Model in the Context of Multidisciplinary Integration

In the context of rapid global technological development, the cultivation of mechanical engineering talents faces severe challenges. Traditional cultivation models, with relatively single-discipline content, are inadequate in meeting the modern industrial demands for interdisciplinary skills and innovative capabilities. To address this challenge, this study, based on the concept of multidisciplinary integration, deeply analyzes the limitations of the existing mechanical engineering talent cultivation model and proposes innovative strategies for curriculum systems, teaching content, teaching methods, and evaluation systems. Furthermore, it explores interdisciplinary integration and application in practical teaching. The results of the study show that multidisciplinary integration can significantly enhance the overall quality and innovative capabilities of mechanical engineering students, providing strong support for cultivating mechanical engineering talents in the new era.

The Stanislavsky System of Actor Training and Piano Performance: The Need for Interdisciplinary Applications

ABSTRACT This article presents the need to address common performance challenges faced by university-level piano performance majors by adapting techniques from the acting System of Konstantin Stanislavsky, a Russian actor, director, writer, and founder of the Moscow Art Theatre (MXAT) who lived from 1863 to 1938. The common performance challenges addressed in this study are performance anxiety, physical tension in performance, interpretation challenges, and stage presence – the “visual rhetoric” of piano playing. Because the impact of Stanislavsky’s methods on piano pedagogy and performance is not widely understood, this study also gives background and context for the influence of Stanislavsky on a generation of Russian-born and other influential pianists and teachers who became part of the American system of piano pedagogy in the first half of the twentieth century. The article provides support and context for i) the four performance challenges addressed in the study from writings in the field of piano performance and pedagogy, and ii) provides an overview of the Stanislavsky’s acting System, outlining what Stanislavsky described as a lifelong process of learning and implementing his theory of performance. Finally, this article explores iii) the relationship between acting (particularly the methods of Stanislavsky) and piano performance through statements and writings by pianists and pedagogues including Theodore Leschetizky, Heinrich Neuhaus, George Kochevitsky, Rosina Lhévinne, Sviatoslav Richter, and Arthur Rubinstein.

Revolutionizing Fitness on the Go! A Case Study of MAXPRO Fitness

The case opens by focusing on the fitness industry, particularly the increased demand for home-based fitness solutions in the context of COVID-19. MAXPRO is presented as an innovative concept that blends entrepreneurship and creativity through the story of entrepreneur Nizar Akil and the challenges he faced in bringing MAXPRO to life. From another perspective, the case illustrates MAXPRO as a solution that adapts to the evolving needs of the health and fitness industry. The discussions cover the concept, design thinking toolkit and production of MAXPRO, showcasing the interdisciplinary application of design engineering, finance, risk management, leadership and decision-making by the entrepreneur. This case allows students to understand the characteristics of a successful entrepreneur, recognize the entrepreneurial process for a start-up and apply design thinking principles to the case. Additionally, it enables learners to recommend growth strategies for new start-ups while considering the implications of their suggestions.

Application Research of Image-based Methods in Aby Walburg's Emotive Formula

This paper centers around Aby Walburg's theory of 'motive Formula' and applies the image-based research method known as the 'Good Neighbor Principle' to analyze two case studies, aiming to validate the interdisciplinary applicability of the 'Good Neighbor Principle.' Firstly, the method is employed to analyze and interpret Plate 30 from Walburg's work, 'The Goddess of Memory Atlas,' to validate the theories related to Walburg's image-based emotive formula. Subsequently, the same research method is applied to analyze a case study in design, revealing that the design genes of Apple products originated from the German classical functionalism of Braun. This further confirms the effectiveness of the 'Good Neighbor Principle' and demonstrates how Walburg's concepts of memory and emotion are given new interpretations within the current social context. Drawing conclusions from these two case studies, it is evident that the power of memory and emotion has long been present, not only in artistic works but also in various other fields and different eras. Walburg's image-based re-search method, the 'Good Neighbor Principle,' can be applied across multiple disciplines and establish energetic connections among research sub-jects in different fields.

Interdisciplinary perspective on architectural programming: current status and future directions

PurposeArchitectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.Design/methodology/approachThis study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.FindingsThe study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.Originality/valueBy combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

Innovating Thought: Theorizing Metacognition

The present article explores how the concept of metacognition has evolved, from its philosophical origins to current interdisciplinary applications. The paper transversally traces, how metacognitive theories have been integrated by various fields. First of all, is emphasized the progressive character of the development of the metacognitive theory; second, the features that establish it as an independent theory are highlighted. Finally, the article explores current and future concerns, trends and directions in research. Thus, the following discourse exploratively investigates how metacognition with its ancient history on ‘’thinking about thinking’’, have been innovated by various current scientific fields.

Green Preparation of S, N Co-Doped Low-Dimensional C Nanoribbon/C Dot Composites and Their Optoelectronic Response Properties in the Visible and NIR Regions.

The green production of nanocomposites holds great potential for the development of new materials. Graphene is an important class of carbon-based materials. Despite its high carrier mobility, it has low light absorption and is a zero-bandgap material. In order to tune the bandgap and improve the light absorption, S, N co-doped low-dimensional C/C nanocomposites with polymer and graphene oxide nanoribbons (the graphene oxide nanoribbons were prepared by open zipping of carbon nanotubes in a previous study) were synthesized by one-pot carbonization through dimensional-interface and phase-interface tailoring of nanocomposites in this paper. The resulting C/C nanocomposites were coated on untreated A4 printing paper and the optoelectronic properties were investigated. The results showed that the S, N co-doped C/C nanoribbon/carbon dot hybrid exhibited enhanced photocurrent signals of the typical 650, 808, 980, and 1064 nm light sources and rapid interfacial charge transfer compared to the N-doped counterpart. These results can be attributed to the introduction of lone electron pairs of S, N elements, resulting in more transition energy and the defect passivation of carbon materials. In addition, the nanocomposite also exhibited some electrical switching response to the applied strain. The photophysical and doping mechanisms are discussed. This study provides a facile and green chemical approach to prepare hybrid materials with external stimuli response and multifunctionality. It provides some valuable information for the design of C/C functional nanocomposites through dimensional-interface and phase-interface tailoring and the interdisciplinary applications.

Higher-Order Null Models as a Lens for Social Systems

Despite the widespread adoption of higher-order mathematical structures such as hypergraphs, methodological tools for their analysis lag behind those for traditional graphs. This work addresses a critical gap in this context by proposing two microcanonical random null models for directed hypergraphs: the directed hypergraph degree model () and the directed hypergraph JOINT model (). These models preserve essential structural properties of directed hypergraphs such as node in- and out-degree sequences and hyperedge head- and tail-size sequences, or their joint tensor. We also describe two efficient Markov chain Monte Carlo algorithms, - and -, to sample random hypergraphs from these ensembles. To showcase the interdisciplinary applicability of the proposed null models, we present three distinct use cases in sociology, epidemiology, and economics. First, we reveal the oscillatory behavior of increased homophily in opposition parties in the U.S. Congress over a 40-year span, emphasizing the role of higher-order structures in quantifying political group homophily. Second, we investigate a nonlinear contagion in contact hypernetworks, demonstrating that disparities between simulations and theoretical predictions can be explained by considering higher-order joint degree distributions. Last, we examine the economic complexity of countries in the global trade network, showing that local network properties preserved by explain the main structural economic complexity indexes. This work advances the development of null models for directed hypergraphs, addressing the intricate challenges posed by their complex entity relations, and providing a versatile suite of tools for researchers across various domains. Published by the American Physical Society 2024

How does organizational support for creativity affect research and development (R&D) employees’ innovative behavior?

Purpose: This study aimed to investigate the relationships among organizational support for creativity, employees’ creative self-efficacy, job satisfaction, and employees’ innovative behavior in the Chinese pharmaceutical manufacturing industry.Design/methodology/approach: A quota sample (n = 385) and a quantitative research methodology were employed in this study.Data from R&D staff at Chinese pharmaceutical manufacturing companies was gathered using an online survey. The study examined the validity and reliability of the measuring tools as well as the variables’ correlation analysis. Using structural equation modeling (SEM), hypotheses were investigated. The specific indirect impacts were quantified through the use of bootstrapping.Findings:The investigation indicates that organizational support is positively related to employees’ innovative behavior. Employee inventive behavior and organizational support for creativity are positively impacted by the twin mediation roles that creative self-efficacy and work satisfaction levels play. Job satisfaction was found to have a greater impact on inventive behavior among employees compared to creative self-efficacy in terms of size. Research, practical, and social implications: In addition to fostering the interdisciplinary application of psychology and organizational behavior, this study creates a dual-mediation model that bridges the gap in the mechanisms of individual cognitive and attitudinal roles between organizational support for creativity and employee innovative behavior. Furthermore, this research advances management strategies and fosters innovation in the pharmaceutical manufacturing sector.Originality/value: From the perspective of individual perceptions and attitudes, this study examined the mechanism of action between employees’ innovative behaviors and the organizational support for creativity among employees. This investigation offers a fresh viewpoint on the factors influencing employees’ innovative behaviors. The research enhances our comprehension of the correlation between employee job contentment, their belief in their creative abilities, and their capacity for innovative performance. The outcomes of the study can offer valuable perspectives for executives in the business realm.

A size-dependent nonlinear isogeometric approach of bidirectional functionally graded porous plates

Recent progress in materials science and fabrication technologies, such as additive manufacturing (AM) or 3D printing, has facilitated the design and production of intricate multi-directional functionally graded (FG) materials which are playing a pivotal role in interdisciplinary engineering applications. Developing mathematical modeling for such nanostructures, however, remains challenging. The primary objective of this research, therefore, is to present a mathematical model for studying the static bending characteristics considering the geometric nonlinearity of bidirectional FG nanoplates with internal pores. The mathematical formulations are established by utilizing the first-order shear deformation theory and the von-Kármán assumption within the framework of an isogeometric approach based on NURBS basis functions. To account for the influence of both strain gradient and nonlocal elasticity, we adopt the nonlocal strain gradient theory including two independent material length scale parameters to predict the geometric nonlinearity performance of structures at small-scale levels. The mechanical properties of multi-directional FG material models are assumed to be continuously graded in two directions based on power law, while internal pores can be dispersed into two typical distribution patterns. Several numerical investigations are performed to evaluate the substantial impact of certain parameters on the nonlinear deflections of bidirectional FG nanoplates with pores under various conditions. Compared to existing studies, the key contribution is to present a robust numerical model aimed at elucidating both the stiffness-softening and stiffness-hardening mechanisms of multi-directional FG nanostructures under static bending conditions with geometric nonlinearity. These findings enhance our insights into nonlinear bending performance and contribute valuable design guidelines for future small-scale engineering structures. These innovative designs have become popular and are now crucial in state-of-the-art engineering applications, such as aerospace, nuclear systems, or thermal resistance devices.

Generative artificial intelligence: a systematic review and applications

AbstractIn recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.