Field Of Technology Research Articles

Entrepreneurial Mindset: Cultivating Innovation for Success in the Digital Age

The development of an entrepreneurial mindset is becoming more widely acknowledged as essential for promoting innovation and attaining success in today’s quickly changing digital environment. An overview of the vital role that an entrepreneurial mindset plays in fostering innovation in the digital age is given in this abstract. It looks at how people and organizations can adopt entrepreneurial values to take advantage of new opportunities, manage uncertainty, and adjust to technology improvements. Developing an entrepreneurial mindset is crucial for success in the fast-paced digital world as it promotes creativity. This study explores the importance of fostering an entrepreneurial mindset in the modern day as a means of promoting innovation. It looks at how people and organizations may use entrepreneurial ideas to overcome uncertainty, take advantage of opportunities, and adjust to the rapid growth of technology. This study explores the characteristics of an entrepreneurial mindset by utilizing interdisciplinary viewpoints from the fields of technology, psychology, and entrepreneurship. It also covers methods for encouraging an entrepreneurial attitude in a variety of settings, such as business, community, and education. This seeks to shed light on how an entrepreneurial attitude is essential for fostering innovation and offer strategies for individuals and companies to prosper in the rapidly changing digital environment.

Combinatorial innovation and lifecycle analysis of low-carbon energy technologies in China

Measuring the lifecycle of low-carbon energy technologies is critical to better understanding the innovation pattern. However, previous studies on lifecycle either focus on technical details or just provide a general overview, due to the lack of connection with innovation theories. This article attempts to fill this gap by analyzing the lifecycle from a combinatorial innovation perspective, based on patent data of ten low-carbon energy technologies in China from 1999 to 2018. The problem of estimating lifecycle stages can be transformed into analyzing the rise of fall of knowledge combinations. By building the IPC co-occurrence matrix, this paper demonstrates the lifecycle evolution of technologies graphically constructs an efficient quantitative index to divide lifecycle stages. The mathematical measurement can effectively reflect the evolutionary pattern of technologies. Additionally, this article relates the macro evolution of lifecycle to the micro dynamic mechanism of technology paradigms. The sign of technology maturity is that new inventions tend to follow the patterns established by prior ones. Following this logic, this paper identifies different trends of paradigms in each technology field and analyze their transition. Furthermore, catching-up literature shows that drastic transformation of technology paradigms may open “windows of opportunity” for laggard regions. From the results of this paper, it is clear to see that latecomers can catch up with pioneers especially when there is a radical change in paradigms. Therefore, it is important for policy makers to capture such opportunities during the technology lifecycle and coordinate regional innovation resources.

The Practice ofHeutagogy, Peeragogy andCybergogy Approach among Vocational College Instructors

This study investigates the implementation levels of Heutagogy, Peeragogy, and Cybergogy approaches among instructors at vocational colleges in Malaysia. Employing a quantitative survey research design, data were collected from a sample of 241instructors across 15 vocational colleges, representing various Technology fields such as Automotive, Electrical, Electronic, Construction, Welding, Industrial Machining, Refrigeration & Air Conditioning, and Computer Systems & Network Technology. The sample was selected using a cluster stratified convenientsampling method.Demographic information, including gender, age, race, institutional zone, vocational college, program, experience, qualification, certificate, and class, was gathered. A structured questionnaire comprising 53 items served as the primary data collection instrument. Data analysis was conducted using SPSS software to derive frequency values, percentages, mean scores, and standard deviations. The Kruskal-Wallis and Mann-Whitney U tests were employed to identify differences in the implementation of Heutagogy, Peeragogy, and Cybergogy approaches among the instructors.The findings indicate a high level of implementation for Heutagogy and Peeragogy approaches, while the Cybergogy approach is practiced at a moderate level. Significant differences were observed in the implementation of these approaches based on the instructors’ fields and teaching experience. To enhance the practice of Cybergogy, it is recommended that vocational college administrations provide training workshops or courses to improve the teachingskills of their academic stafffor 21stcentury learning environment.

Rethinking the trade-off relationship between innovation quantity and quality: the evidence from public energy R&D programs in South Korea

This study examines the relationship between quantitative and qualitative aspect of innovation in South Korea’s public energy research and development programs. Through an analysis of 1170 projects, the study confirms a complementary association between the quantity and quality of patents. The findings underscore the importance of a strategic approach to managing innovation, challenging the notion of a trade-off and advocating balance. It also discusses how this relationship varies across technological fields. This emphasizes the need to consider moderating variables in future research on this domain. This study thus not only contributes theoretically by expanding the discourse on innovation dynamics but also offers practical insights for policymakers and industry practitioners.

Exploring the influence of sintering temperature on the phase composition, mechanical strength, and dielectric constant of porous ca-stabilized zirconium dioxide ceramics

ZrO2 ceramics have wide applications as structural materials in industrial, scientific, and technological fields. Despite the large number of works devoted to the influence of synthesis parameters on the formation of different phases of ZrO2 and the properties of the obtained ceramics, the effect of sintering temperature on the characteristics of the porous ZrO2 ceramics has not been addressed sufficiently. This study focuses on examining how sintering temperature affects the phase composition, microstructure, dielectric properties, and mechanical properties of porous stabilized zirconium dioxide ceramics. Phase transformations during sintering at various temperatures were identified using powder X-ray diffraction and Raman spectroscopy. The results indicate that fully stabilized cubic ZrO2 with satisfactory mechanical properties (HV0.5 = 580) and a porosity of 21.5% was achieved at temperatures of 1400 and 1500 ℃. At lower sintering temperatures, the obtained samples exhibited multiphase composition and high porosity. The reasons for the formation of the CaZrO3 phase at sintering temperatures of 1000–1200 ℃ were established using scanning electron microscopy, energy dispersive analysis, and thermodynamic considerations of the solid-phase reactions. AC dielectric measurements showed that the synthesized samples have high-Q characteristics; and that the dielectric constant can be controlled by changing the sintering temperature.

Hidden data recovery using reservoir computing: Adaptive network model and experimental brain signals.

The problem of hidden data recovery is crucial in various scientific and technological fields, particularly in neurophysiology, where experimental data can often be incomplete or corrupted. We investigate the application of reservoir computing (RC) to recover hidden data from both model Kuramoto network system and real neurophysiological signals (EEG). Using an adaptive network of Kuramoto phase oscillators, we generated and analyzed macroscopic signals to understand the efficiency of RC in hidden signal recovery compared to linear regression (LR). Our findings indicate that RC significantly outperforms LR, especially in scenarios with reduced signal information. Furthermore, when applied to real EEG data, RC achieved more accurate signal reconstruction than traditional spline interpolation methods. These results underscore RC's potential for enhancing data recovery in neurophysiological studies, offering a robust solution to improve data integrity and reliability, which is essential for accurate scientific analysis and interpretation.

The influence of longitudinal duct profiling on unsteady gas dynamics and the heat transfer of pulsating gas flows in the outlet system of reciprocating-engine

Heat machines based on reciprocating-engines remain in demand in various fields of engineering and technology. Therefore, further research is needed to improve the efficiency, reliability, and environmental friendliness of engines. The thermomechanical improvement of non-stationary processes in outlet systems is an appropriate way to improve engine performance. The purpose of this research is to obtain and analyse the gas-dynamic and heat transfer characteristics of pulsating gas flows in an outlet system with ducts of various designs, using a laboratory model to simulate the outlet process in an engine. Thermal anemometry is used to receive data on the instant velocity values and local heat transfer coefficient of unsteady flows in ducts. The article examines two designs of outlet ducts, namely cylindrical (basic) and conical (with a taper of 0.0225) ducts. Spectral analysis of velocity, pressure and heat transfer coefficient pulsations, assessment of turbulence intensity, and calculation of flow characteristics of pulsating flows were performed to obtain detailed information on gas dynamics in the outlet system. The use of a conical duct (in comparison with a cylindrical one) leads to a slight increase in the turbulence intensity by up to 12 %, a decrease in the heat transfer coefficient by 15–20 %, and a change in volumetric gas flow within ± 7.5 %. Thus, the use of a conical duct will lead to the stabilisation of the flow in the outlet system, improved cleaning of the cylinder from outlet gases, a reduction in thermal stress, and a slight growth in the specific power of engines.

Open spaces and emergent dwellings. Innovative solutions to sustainable renovation of student housing.

Abstract The paper aims to report the research results that deal with the theme of educational open spaces such as schoolyards or student housing, framing it in an environmental dimension capable of enhancing the existing identity and landscape quality elements, at the same time responding to climatic vulnerabilities, but also to new needs of users, with adequate performance responses. The proposed research is part of a scientific collaboration agreement developed aim of supporting it in the field of environmental issues and innovative technologies for student housing with Agenzia del Demanio at the end to write guidelines for spatial functional, technological and environmental aspects of its sustainable design of heritage to be converted into student housing. University student living is an evolving form of housing, subject to significant investment and design experimentation. In addition to the issue of housing, the open space related to student housing even in the face of emergent dwelling becomes a continuation of housing and therefore cannot be underestimated. The proposed methodology from best practice highlights a new demand framework useful for the development of the guidelines of which an excerpt concerning open space is proposed here.

Low-temperature/room-temperature gas sensing application for NO2 gas by SnO2 and Ni doped SnO2 nanostructures synthesized by sol-gel method

Gas sensors are the attentive parts for the day-to-day applications in advanced manufacturing, chemical industries and advanced applications need the better performing materials. SnO2 (wide band gap-3.27eV, n-type semiconductor) and Ni-doped SnO2 are the suitable materials for gas sensing in the fields of forward-looking technology. Pure and Ni-doped SnO2 particles were synthesized by sol-gel method and started with 0.2 mol of SnCl2, added with 2 ml of acetic acid, and pH controlled by the addition of 8 mol NaOH, till pH reaches desired value and Ni is doped. Finally washed, dried, and calcined at 400° C. The prepared pure and Ni(0.02 and 0.04 mol)-doped SnO2 nanostructures were characterized by XRD, EDAX, DSC, and SEM techniques. Average crystallite size is calculated by XRD and varied from 8 to 5 nm. Morphology shows difference, while the Ni concentrations changed. Particle size is estimated by transmission electron microscope (TEM), found to be 8–12 nm and well correlated with XRD analysis. The Energy gap is calculated with the UV–Vis spectrometer, and values are 3.21, 3.60, and 3.56 eV. Finally, the application part was carried out for NO2 gas sensing at a low level of 7–19 parts per million (PPM) at 40 °C, response and recovery times are 6 and 9 min respectively for all PPMs. Sensitivity of the samples of pure and Ni doped samples have 99 to 99.9 and 80 %. Pure and 0.02 mol Ni doped samples have the good achievement for sensitivity and 0.04 mol Ni doping has increase in conductance.

Copper-Based Para-Phthalic Acid Metal-Organic Framework: An Efficient Anodic material for Super-capattery Hybrid Systems

The battery-supercapacitor hybrid, an asymmetric energy storage system, has garnered considerable attention due to its outstanding energy storage capabilities. Anodic materials have become a central focus, driven by the quest for enhanced energy density, power density, cycle life, and overall performance. In this study, we have synthesized copper-based para-phthalic acid (PTA) metal-organic frameworks (MOFs) via a facile hydrothermal method to be employed as the positive electrode material in a battery-supercapacitor hybrid matrix. The initial testing indicates the battery-type nature of the prepared electrode along with maximum specific capacity of 260.5 C g-1 at 0.7 A g-1. Subjection of Cu-PTA MOF electrode to real device configuration (Super-capattery device) results in considerable specific capacity of 152.2 C g-1 along with specific energy and power of 35.9 Wh kg-1 and 5950 W kg-1, respectively at 0.3 A g-1. The device also demonstrated a stability potential of 92.15% when tested for 5000 continuous charge-discharge cycles. The present study presents Cu-PTA MOF an exciting addition for advancing current state of research in the field of energy storage technology.

Design and Application of Parameter Self-tuning Regulator for DC Motor based on Neural Network

This study introduces a cutting-edge approach to regulating DC motors, featuring a unique combination of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. This innovative system capitalizes on the adaptive learning capabilities of ANNs to dynamically fine-tune the control parameters of DC motors. This adaptability ensures optimal motor performance across diverse operational conditions, addressing the challenges posed by fluctuating loads and varying speed requirements. The integration of LSTM networks into this framework adds a layer of predictive functionality, allowing the system to anticipate future motor states. Such foresight enables the regulator to make proactive adjustments, significantly enhancing its responsiveness to changes in operational demands. The dual application of ANN’s adaptive control mechanisms and LSTM’s predictive capabilities is particularly effective in overcoming the non-linearity and variability that are typical challenges in DC motor control. This synergy ensures that the motor operates efficiently, stably, and with a quick response time, even under varying and unpredictable conditions. The practical application of this advanced regulator in real-world scenarios has shown marked improvements in motor performance. These enhancements are evident in the increased efficiency, stability, and responsiveness of the motors, making them more suitable for a wide range of industrial applications. This study marks a notable progression in the field of DC motor control technology. By integrating advanced machine learning techniques, it offers a solution that is not only more efficient and reliable but also adaptable to the evolving demands of industrial environments. The innovative combination of ANN and LSTM networks in this regulator design paves the way for smarter, more responsive, and efficient motor control systems, potentially transforming how motors are managed in various industrial applications.

A novel compact xerographic system for 3D printing of fluoropolymer powders onto metal surfaces

Abstract This study introduces a compact xerographic 3D printing system that utilizes precise layer-by-layer dry powder transfer techniques, facilitating the fabrication of 3D objects directly on metal substrates. By leveraging electrostatic force to coat dry fluoroethylene vinyl ether powder onto metallic surfaces, our innovative method significantly broadens the spectrum of printable materials. Through the optimization of electrostatic potentials and powder transfer efficiency, the system successfully demonstrates the ability to produce intricate 3D structures with heights ranging from millimeters to centimeters. This novel approach not only showcases the potential for creating flexible electronic materials with complex 3D geometries directly on the metal substrate but also opens new avenues for diverse material applications within the field of advanced xerographic 3D printing technology.

Enhanced continuous lithium extraction using self-designed porous nanosorbent fibers in a fixed-bed system: Optimization and mechanistic insights

This study introduced a novel lithium/aluminum layered double hydroxide (Li/Al-LDH) nanosorbent fiber (Li-PNF) engineered for continuous lithium extraction from brine using a fixed-bed system. These fibers featured a three-dimensional interconnected mesh structure and were distinctively encapsulated with needle-like Li/Al-LDH, exhibiting a layer-by-layer configuration. Compared to conventional Li/Al-LDH, Li-PNFs demonstrated abundant mesoporous structure and larger average pore size, facilitating the mass transfer and molecular diffusion of Li+ ions. Li-PNFs possessed a static lithium adsorption capacity of approximately 13.0 mg/g, with notable selectivity against Na+ and K+. Additionally, the Li+ binding energy across three distinct sites within the atomic structure of Li-PNFs proved advantageous, particularly at the Triangle-site-Al-overlapping-O3, which reached −5.72 eV. Fixed-bed adsorption experiments confirmed that lower feed flow rates, higher initial lithium concentrations, and increased bed heights significantly enhanced the Li+ capture efficiency, achieving up to 23.83 % in a single fixed-bed experiment. Based on the prediction of adsorption penetration curves using four empirical models, it was found that the Clark and Thomas models could accurately predict the behavior of Li+ penetration through the bed. Fixed-bed desorption results indicated that optimal lithium recovery was achieved at lower feed rates, reduced lithium concentrations in the desorption solution, and elevated temperatures. Finally, the excellent cyclic adsorption/desorption performance and low preparation cost of Li-PNF further highlighted its potential for real industrial applications, offering a significant advancement in the field of lithium extraction technology.

ADVANCING INFORMATION TECHNOLOGY WITH IMMUNOLOGICAL COMPUTING – SOFT COMPUTING TECHNIQUES FOR ADAPTIVE AND ROBUST SYSTEMS

The field of Information Technology (IT) is evolving rapidly, and with this growth comes the need for systems that are both adaptive and robust. Biological systems, especially the human immune system, demonstrate remarkable adaptability and resilience, inspiring the development of Immunological Computing (IC). This paper explores the application of immunological principles in Soft Computing techniques to create systems capable of responding to dynamic environments. Current IT systems often face challenges such as handling unpredictable changes, scalability, and security threats. Traditional computing approaches struggle to address these issues efficiently due to their rigid structures and limited adaptability. Immunological Computing, inspired by the immune system’s ability to learn, remember, and adapt, offers a promising solution. The proposed method integrates immune system mechanisms like clonal selection, immune memory, and self/non-self-recognition into computational models. These models are coupled with soft computing techniques such as fuzzy logic, genetic algorithms, and neural networks, enhancing the system’s ability to adapt to changing environments and uncertainties. In simulated tests, this approach demonstrated a significant improvement in robustness and adaptability compared to traditional IT systems. For instance, in a cybersecurity application, the immunological-based system detected and neutralized 94.6% of threats, a notable improvement over the 82.3% detected by conventional systems. Similarly, in a resource optimization scenario, the system adapted to dynamic workloads with an efficiency increase of 15% compared to static systems.

Systemic Perspective on Companies and the Digital Transformation: A Prototypical Methodological Approach as a Solution to Complex Business Problems

Due to changing market conditions, customer needs and environmental influences, organisations are facing increased pressure to transform in today’s world. The consequences of the COVID-19 pandemic have made many organisations aware of the importance of digital transformation in order to remain successful even in times of disruptive change. However, the pandemic has also shown how connected and complex the world is. The aim of this work is to answer the question of what a possible systematic method for dealing with complex business problems might look like. Dealing with complexity is a critical factor for any organisation and determines how resilient it can position itself in the face of dynamic environmental influences. To answer the research question, a prototype was first developed based on Niklas Luhmann’s systems theory, from which a systematic method was derived. The prototypes were then further developed and validated in qualitative expert interviews. A systems theory approach was chosen because it claims to construct a holistic picture of the company as a social system and thus to capture the interactions of all system components. Qualitative interviews based on a semi-standardised approach were used to test the prototypes for comprehensibility, applicability, and validity. German experts in the fields of system technology, change management and digital transformation were interviewed.

Digital Protection and Inheritance Path of Intangible Cultural Heritage based on Image Processing Algorithm

This research paper introduces a novel approach in the realm of digital preservation and inheritance of Intangible Cultural Heritage (ICH) through a Customized 3D Convolutional Neural Network (CNN). The core of this study lies in the development of an advanced image processing algorithm tailored to accurately recognize, categorize, and archive diverse forms of ICH, which include traditional performances, ceremonies, oral traditions, and crafts. Utilizing a volumetric 3D CNN, this paper demonstrates how complex ICH elements can be effectively captured and analyzed, overcoming the limitations of traditional 2D image processing methods. The network is trained on a comprehensive dataset of ICH imagery, ensuring sensitivity to the subtle nuances and dynamic nature of these cultural expressions. This paper highlights the algorithm’s capability in not only safeguarding the visual aspects of ICH but also in providing an interactive, digital medium for education and cultural dissemination. The proposed method shows significant promise in aiding the efforts of cultural preservationists and educators, offering a technologically advanced pathway for the protection and inheritance of the world’s rich, yet vulnerable, cultural heritage. This study sets a precedent in the interdisciplinary field of cultural heritage conservation, digital technology, and artificial intelligence, providing a scalable and effective solution for global ICH preservation initiatives.

Preface

The 2024 International Conference on Computational Intelligence and Communication System (CCICS2024), held in Wuhan, China from June 26-28, 2024, is a testament to the ever-evolving nature of fields of computational intelligence and communication technology, has attracted a diverse and brilliant array of researchers, engineers, and scholars from around the globe. CCICS2024 attracted more than 80 participants from around the world, which is a vibrant and engaging event, with a diverse program that included an opening ceremony, keynote speeches, oral presentations, poster sessions, Q&A discussions, and an ending ceremony. The keynote speakers, renowned experts worldwide in their respective fields, provided insightful overviews of the latest developments and trends in domains related to computational intelligence and communication technology. The oral presentations and poster sessions were equally enriching. Researchers and practitioners shared their latest findings, discussed challenges and opportunities, and explored potential collaborations. The lively discussions and interactions among the participants created a vibrant intellectual atmosphere that fostered innovation and knowledge sharing. The submissions received for this conference are vivid evidence of the creativity and innovation that flourishes in related areas. The manuscripts cover a wide range of topics, including computing for emerging technology, signal processing, sensor and detection technology, futuristic communication and network technologies, etc. The diversity and depth of these contributions reflect the vibrant and interdisciplinary nature of relevant industries. All papers submitted to the conference have been reviewed using a double-blind peer-review process. Each paper was tested for similarity with already published content using the Turnitin platform. All sources of similarities were checked and only original content was accepted. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the above-mentioned topics. Building on the success of this event, we hope CCICS 2024 will be one of the most spectacular events in those areas. We would like to thank the reviewers and the members of the international scientific committees. We also extend our sincere thanks to Prof. Joarder Kamruzzaman, Prof. Yangjun Chen, Prof. Oscar Eduardo Ruiz Salguero, Prof. Jie Yang, Prof. JingTao Yao and Dr. Iacovos Ioannou for their wonderful keynote talks. We are grateful to everyone who has contributed to the realization of CCICS 2024. We also look forward to the next conferences in this series, which will continue to build upon the foundation laid by this one. Organizing Committee of CCICS 2024 Prof. Yuriy S. Shmaliy (Conference Chair) IEEE Fellow, AAIA Fellow, University of Guanajuato, Mexico List of Conference Committee is available in this Pdf.

How does Building Information Modeling influence decision-making process in the project design? Input, decision and output analysis.

Decision-making is critical throughout the entire project cycle, particularly during the project design stage, where the detailed concept is developed based on stakeholders’ requirements and project constraints. To improve project design, various digital technologies are employed to provide stakeholders with comprehensive data for informed decision-making. The paper aims to understand how BIM influence the decision-making process – input, decision and output - during the project design. More specifically, we aim to answer the following question: which decision-making challenges could restrict BIM benefits during the project design stage? Utilizing two embedded case studies and a focus group, we explore the perceived benefits and challenges of BIM in decision-making among project actors. This research contributes to the field of digital technologies in project management by highlighting specific benefits and challenges, such as decision validation and the transformation of decision makers’ roles. Our findings illustrate the interconnected nature of these benefits and challenges through the Input-Process-Output model. We specifically emphasize that the advantages of BIM in the decision-making process can be significantly affected if project organizations do not adapt the roles and competencies of decision makers to effectively utilize BIM. The use of BIM therefore brings novel decision-making challenges, which are presented in the discussion.

Prussian Blue-Carbonized wood photothermal composites for efficient solar evaporation and seawater desalination

Freshwater scarcity, exacerbated by environmental pollution, poses a significant global challenge. This study addresses these challenges by developing a Prussian blue/carbonized wood (PB/CW) photothermal composite for solar desalination. The PB/CW composite exhibits enhanced solar absorption and photothermal conversion. The integration of Prussian blue (PB) on the honeycomb structure of wood facilitates efficient water transport and minimizes heat loss, underscoring the composite’s potential in practical solar desalination applications. Under natural sunlight, the PB/CW system achieves an evaporation rate of 4.39 kg m-2h−1, demonstrating its high efficiency in converting solar energy into water evaporation. This work offers a novel and effective approach to designing low-cost, high-efficiency solar evaporators, advancing the field of sustainable desalination technology.

Information Standards and Guidelines on AI: Ethical Concerns, Trustworthiness, Quality Assessment, and Human Oversight

ABSTRACTThis panel, sponsored by the ASIS&T Standards Committee, has two purposes. First, the panel gives a brief report on the most recent activities of the national and international standards in the field of information science and technologies in which the ASIS&T Standards Committee and members have been involved. More importantly, the panel will focus on several recent significant standardization efforts that address the ethical concerns, trustworthiness, quality assessment, and human oversight of artificial intelligence (AI) systems. A final open discussion on these issues will involve the audience, to highlight the benefits, challenges, and standards needed while addressing the important role of information science within our evolving society.