Cross-disciplinary Technology Research Articles

Analysis of Factors Influencing Wait Times at Disneyland Resort

This paper aims to enhance the accuracy of wait time predictions and optimize park management at Disneyland, a popular leisure destination facing long wait times for attractions. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. The study employs advanced data analysis and machine learning algorithms to develop high-precision wait time prediction models. Utilizing big data techniques, the research integrates multi-dimensional data, including weather, visitor flow, and historical wait times, to optimize resource allocation and improve operational efficiency. optimize resource allocation and improve operational efficiency and visitor satisfaction. The study further explores the application of these models in other domains such as healthcare, healthcare, and healthcare services. The study further explores the application of these models in other domains such as healthcare and transportation, demonstrating the potential for cross-disciplinary technology transfer to enhance overall service quality and efficiency. The findings underscore the significant impact of big data behavior analysis in theme park management, contributing to the findings underscore the significant impact of big data behavior analysis in theme park management, contributing to increased visitor satisfaction, enhanced competitiveness, and long-term sustainability of the park.

Security and Privacy in Fog/Cloud-based IoT Systems for AI and Robotics

Integration of Internet of Things (IoT) systems based on the fog or the cloud with Artificial Intelligence (AI) and Robotics has prepared the way for breakthrough advancements in a variety of different fields of business. However, these cross-disciplinary technologies present significant difficulties in terms of maintaining confidentiality and safeguarding data. This article digs into the issues of establishing robust security and protecting user privacy in IoT systems that are based in the fog or the cloud and are utilized for AI and robotics applications. This study gives insights into the possible hazards encountered by such interconnected systems by conducting an in-depth review of existing security threats, vulnerabilities, and privacy concerns. In addition, the study investigates cutting-edge security mechanisms, encryption approaches, access control strategies, and privacy-preserving solutions that can be utilized to safeguard data, communications, and user identities. The results of this study highlight the demand for comprehensive security and privacy solutions to support the mainstream deployment of Fog/Cloud-based Internet of Things systems in the field of artificial intelligence and robotics.

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges.

A Cross-Disciplinary Outlook of Directions and Challenges in Industrial Electronics

How to build a sustainable society in view of industrial electronics has been discussed from energy, information and communication technologies, cyber-physical systems (CPSs), and other viewpoints. This paper presents a cross-disciplinary view that integrates the fields of human factors, professional education, electronic systems on chip, resilience and security for industrial applications, technology ethics and society, and standards. After explaining the efforts and challenges in these fields, this paper shows a methodology for cross-disciplinary technology that integrates the technical committees in Cluster 4, Industrial Electronics Society. A project, which was launched in March 2020, implements a ‘Proof of Concept’ trial of the methodology.

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.

The Importance of Cross-disciplinary Technology Creativity in the Field of Healthcare

"Technology - Born from Human Nature" is a familiar Taiwan advertising slogan. Healthcare is a profession that is intimately entwined with human nature. In order to keep up with today's fast-changing healthcare environment and the broadly diverse needs of different healthcare settings, how to best use technology to creatively and efficiently develop patient-friendly care services and interdisciplinary teaching and learning strategies is a topic that is now receiving significant and growing attention in this field. The increasing utilization of big data worldwide is allowing us to better understand public needs and to more-accurately predict changing trends. Related technologies apply knowledge from the field of AI learning, as this process requires deep understanding of how target populations think and reason. This technology has the capacity to significantly improve the accuracy of healthcare diagnoses and to better anticipate patient safety risks in terms of prescription medicine use, the reporting of critical test results, and other factors, thus generating savings in terms of time and costs (Cheong, 2018). Furthermore, healthcare education is increasingly emphasizing the use of simulated environments in order to improve scenario-based learning and teaching efficacy. Virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications help educators overcome the limitations of traditional models of education (Hsieh & Lin, 2017). Educators are now able to incorporate simulations of situations that would otherwise be difficult or impossible to experience otherwise, such as disaster scenes, in-flight medical emergencies, surgeries, emergency rescue scenarios, and gender-care scenarios, into integrated innovative-technology education strategies. Moreover, related technology applications are assisting healthcare professionals to improve their familiarity with clinical techniques, outreach and communication skills, and training. Moreover, whether applied in healthcare education and training, clinical caregiving, emergency medical units, or chronic care settings, these simulation-related, cross-disciplinary technology applications have much to contribute.

MANUFACTURING INDUSTRY TECHNOLOGIZATION WITHIN PRIORITIES OF THE SCIENTIFIC AND TECHNICAL COMPLEX DEVELOPMENT IN RUSSIAN FEDERATION

The development of the global economic system leads to the fact that modern production is becoming more technological and capital intensive, requires highly skilled labor and cannot provide work places for a large number of low-skilled workers. In addition, each high-tech work place creates several additional work places in other industries. Many technologies that were first used in industrial production were later used in other industries, increasing their efficiency and competitiveness and thus ensuring the overall economic development. Scientific research and development for the industry are becoming more transdisciplinary; hybrid research areas as well as cross-disciplinary technologies appear. The main problem is that the Russian industrial complex is not ready for unconditional acceptance of these global technological challenges for the most part organizationally, not economically. We need a simple and well-defined organizational and economic mechanism of technologizing of domestic production. Despite the current economic difficulties, there are enough sources of such technological transition funding. They are: entry into the large-scale investment and infrastructure projects on a national scale; participation in projects of field development of the leading oil and gas companies; the programmes of innovative development and R&D programmes of state companies, industrial state programmes; subsidies and grants of development institutions; private funds of enterprises, etc.

Hazard and risk assessment strategies for nanoparticle exposures: how far have we come in the past 10 years?

Nanotechnology is an emerging, cross-disciplinary technology designed to create and synthesize new materials at the nanoscale (generally defined as a particle size range of ≤10 -9 meters) to generate innovative or altered material properties. The particle properties can be modified to promote different and more flexible applications, resulting in consumer benefits, particularly in medical, cosmetic, and industrial applications. As this applied science matures and flourishes, concerns have arisen regarding potential health effects of exposures to untested materials, as many newly developed products have not been adequately evaluated. Indeed, it is necessary to ensure that societal and commercial advantages are not outweighed by potential human health or environmental disadvantages. Therefore, a variety of international planning activities or research efforts have been proposed or implemented, particularly in the European Union and United States, with the expectation that significant advances will be made in understanding potential hazards related to exposures in the occupational and/or consumer environments. One of the first conclusions reached regarding hazardous effects of nanoparticles stemmed from the findings of early pulmonary toxicology studies, suggesting that lung exposures to ultrafine particles were more toxic than those to larger, fine-sized particles of similar chemistry. This review documents some of the conceptual planning efforts, implementation strategies/activities, and research accomplishments over the past 10 years or so. It also highlights (in this author’s opinion) some shortcomings in the research efforts and accomplishments over the same duration. In general, much progress has been made in developing and implementing environmental, health, and safety research-based protocols for addressing nanosafety issues. However, challenges remain in adequately investigating health effects given 1) many different nanomaterial types, 2) various potential routes of exposure, 3) nanomaterial characterization issues, 4) limitations in research methodologies, such as time-course and dose-response issues, and 5) inadequate in vitro methodologies for in vivo standardized, guideline toxicity testing.

Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country

It is critical for “catching-up” countries to narrow innovation gaps with developed countries by developing emerging industries. This research introduces a data-mining based method to systematically assess the national innovation gap that is specifically for emerging industries. The method examines the five key attributes of emerging industries, including the ownership of platform technologies, globalization intention, international knowledge position, university-industry linkage, and cross-disciplinary technology development. In particular, this method combines data-mining with experts' knowledge to build patent-training examples, and then uses a support vector machine-based classifier to single out all high-quality patents for each innovation attribute. Based on the selected high-quality patents, the authors utilize a factorial design analysis to systematically evaluate the innovation gap between countries. This method can significantly reduce measurement bias of traditional single patent indicators. In addition, it also can robustly adjust measuring weights in response to the specifics of each innovation attribute, while traditional multi-attribute evaluation methods cannot. As a result, this research empirically shows that China' industrial robot sector has apparent innovation gaps compared to developed economies, specifically in university-industry linkage, cross-disciplinary competence, and globalization intention, and this calls for the attention of policy makers and industrial experts.

Defining Communication at the Bottom

Nanoscale communication is expected to offer unprecedented benefits. However, lack of a precise definition and general framework for nanoscale communication has resulted in limited impact and dissipated effort. The IEEE P1906.1/Draft 1.0 Recommended Practice for Nanoscale and Molecular Communication Framework provides the precise, common definition of nanoscale communication and a standard, general framework. The definition of nanoscale communication must carefully depict the field so that it captures the unique aspects of small-scale physics with respect to communication. Both the definition and framework must be broad enough to cover the scope of cross-disciplinary technologies that may be utilized while simultaneously be precise enough to allow for interoperable and reusable components.The implication for the field is significant. The IEEE P1906.1/ Draft 1.0 Recommended Practice for Nanoscale and Molecular Communication Framework will enable diverse disciplines to have a common language and reference for making lasting contributions. A lasting impact will be possible because others can now build upon these results through rational design and synthesis.

STEAM-Powered Computing Education: Using E-Textiles to Integrate the Arts and STEM

Incorporating novel, cross-disciplinary technologies such as e-textiles in computing education can broaden participation, particularly by women, and improve learning outcomes.

Rheology: A cross-disciplinary technology evolving to take on new challenges (22nd Nordic Rheology Conference 2013)

Rheology: A cross-disciplinary technology evolving to take on new challenges (22nd Nordic Rheology Conference 2013)

Future technology analysis for biosecurity and emerging infectious diseases in Asia-Pacific

This paper presents a future-oriented technology analysis (FTA) project on the control and management of emerging infectious diseases (EID) in the Asia-Pacific Economic Cooperation region. It combines several FTA methods. Technologies that are potentially effective in biosecurity and in combating EID were first identified by bibliometric analysis, online survey and scenario planning. Roadmaps of these technologies were then built. Workshops to conduct the foresight process were held in the region. Four scenarios and six technology roadmaps for the region were developed. The results show that many future technologies will converge to become most effective in dealing with biosecurity and EID. This paper deals with cross-disciplinary technologies in the context of a cross-boundary regional setting, which has important methodological implications. Copyright , Beech Tree Publishing.

Trajectory patterns of technology fusion: Trend analysis and taxonomical grouping in nanobiotechnology

The potential of technology fusion has been advanced as a promising breakthrough function to create hybrid technologies. Despite its importance, however, the evolutionary path of technology fusion is yet unexplored. In this paper, by employing the case of nanobiotechnology, we attempt to deepen understanding of the development trajectories of technology fusion in three important aspects. The first aspect is the development of an index that measures the degree of fusion of cross-disciplinary technology at the meso level. The second aspect is to classify the trajectory patterns of technology fusion in terms of fusion degree. We analyze fusion mechanism by utilizing citation network analysis. The third aspect is to visualize the relationship between patents and their backward and forward patent citations, at the patent class level, with their direction on a citation map. This facilitates understanding of the overview as well as fusion patterns. The changes in fusion patterns are analyzed using time series comparisons. An empirical analysis in the nanobiotechnology field shows no positive relationship between the inflow and outflow degree of fusion. We also observe changes in the trajectory patterns of fusion over time. Analysis demonstrates that each fusion pattern has evolved in such a way that technologies focus more on their niche technologies, and that those technologies which cannot incorporate the technology fusion have been eliminated during the development process.

A cross-disciplinary technology transfer for search-based evolutionary computing: from engineering design to software engineering design

Although object-oriented conceptual software design is difficult to learn and perform, computational tool support for the conceptual software designer is limited. In conceptual engineering design, however, computational tools exploiting interactive evolutionary computation (EC) have shown significant utility. This article investigates the cross-disciplinary technology transfer of search-based EC from engineering design to software engineering design in an attempt to provide support for the conceptual software designer. Firstly, genetic operators inspired by genetic algorithms (GAs) and evolutionary programming are evaluated for their effectiveness against a conceptual software design representation using structural cohesion as an objective fitness function. Building on this evaluation, a multi-objective GA inspired by a non-dominated Pareto sorting approach is investigated for an industrial-scale conceptual design problem. Results obtained reveal a mass of interesting and useful conceptual software design solution variants of equivalent optimality—a typical characteristic of successful multi-objective evolutionary search techniques employed in conceptual engineering design. The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.

Cross-Disciplinary Technology Transfer in Trans-World War II Japan: The Japanese High-Speed Bullet Train as a Case-Study

This empirical case study examines a successful cross-disciplinary technology transfer in a non-Western country. The author argues that the invisible hand of contextual variables defined the contours of the cross-disciplinary application of engineering knowledge. More specifically, the migration of engineers, shaped largely by sociopolitical and cultural factors, constituted the pivotal part of the technology transfer. The central focus is the international and domestic context in which wartime aeronautical engineers developed the car truck of the Japanese high-speed bullet train after World War II. First, the paper analyzes the domestic and institutional context of postwar Japan—namely the Japan National Railways and Railway Technical Research Institute—to illustrate mainly sociopolitical factors that promoted the migration of the wartime engineers and cross-disciplinary technology transfer. As a case study, Dr. Tadashi Matsudaira's laboratory amply demonstrates the successful outcome of the process. The second half of this paper contextualizes the aircraft-to-bullet-train transition in a larger international framework. A series of German-Japanese comparisons highlights the sociopolitical cultures at the national level that contained accumulated wealth of wartime knowledge inside Japan. This case study is among the first to examine the cross-disciplinary transfer of military technology in an international framework.

Notes from the Field

We need more such articles as Cross-Disciplinary Technology Transfer in Trans-World War II Japan: The Japanese High-Speed Bullet Train as a Case Study. This well-researched paper is just a sample of what is surely a rich legacy of technology transfer episodes experienced by the Japanese, only a few of which are known in any detail to Western historians. (During the last third of the 20th century, such transfer went both to and from Japan.)

NEWS AND COMMENT ON REVENT DEVELOPMENTS FROM AROUND THE WORLD : World summit on information society set to take place under ITU’s leadership

It is critical for “catching-up” countries to narrow innovation gaps with developed countries by developing emerging industries. This research introduces a data-mining based method to systematically assess the national innovation gap that is specifically for emerging industries. The method examines the five key attributes of emerging industries, including the ownership of platform technologies, globalization intention, international knowledge position, university-industry linkage, and cross-disciplinary technology development. In particular, this method combines data-mining with experts' knowledge to build patent-training examples, and then uses a support vector machine-based classifier to single out all high-quality patents for each innovation attribute. Based on the selected high-quality patents, the authors utilize a factorial design analysis to systematically evaluate the innovation gap between countries. This method can significantly reduce measurement bias of traditional single patent indicators. In addition, it also can robustly adjust measuring weights in response to the specifics of each innovation attribute, while traditional multi-attribute evaluation methods cannot. As a result, this research empirically shows that China' industrial robot sector has apparent innovation gaps compared to developed economies, specifically in university-industry linkage, cross-disciplinary competence, and globalization intention, and this calls for the attention of policy makers and industrial experts.