Hasil untuk "Technological innovations. Automation"

Chunling Chen, Ziyue Zheng, Tongyu Xu et al.

In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

Adel Said Elmaghraby, M. Losavio

The world is experiencing an evolution of Smart Cities. These emerge from innovations in information technology that, while they create new economic and social opportunities, pose challenges to our security and expectations of privacy. Humans are already interconnected via smart phones and gadgets. Smart energy meters, security devices and smart appliances are being used in many cities. Homes, cars, public venues and other social systems are now on their path to the full connectivity known as the “Internet of Things.” Standards are evolving for all of these potentially connected systems. They will lead to unprecedented improvements in the quality of life. To benefit from them, city infrastructures and services are changing with new interconnected systems for monitoring, control and automation. Intelligent transportation, public and private, will access a web of interconnected data from GPS location to weather and traffic updates. Integrated systems will aid public safety, emergency responders and in disaster recovery. We examine two important and entangled challenges: security and privacy. Security includes illegal access to information and attacks causing physical disruptions in service availability. As digital citizens are more and more instrumented with data available about their location and activities, privacy seems to disappear. Privacy protecting systems that gather data and trigger emergency response when needed are technological challenges that go hand-in-hand with the continuous security challenges. Their implementation is essential for a Smart City in which we would wish to live. We also present a model representing the interactions between person, servers and things. Those are the major element in the Smart City and their interactions are what we need to protect.

Shadrack Obeng, Toluwalase Vanessa Iyelolu, Adetola Adewale Akinsulire et al.

Financial Technology (FinTech) has emerged as a disruptive force in the banking sector, revolutionizing the way financial services are delivered and consumed. This review explores the transformative impact of FinTech on regulatory compliance within the banking industry. The integration of advanced technologies such as artificial intelligence, blockchain, and big data analytics has enabled financial institutions to enhance operational efficiency, improve customer experience, and expand market reach. However, these innovations have also posed unprecedented challenges to traditional regulatory frameworks designed to safeguard financial stability and consumer protection. This review examines how FinTech innovations have necessitated regulatory adaptation and evolution. It highlights the complexities introduced by novel financial products, digital payment systems, and decentralized finance (DeFi) platforms, which often operate beyond conventional regulatory boundaries. Regulatory compliance in areas such as anti-money laundering (AML), know your customer (KYC) requirements, and data privacy has become more intricate as FinTech solutions blur geographical and jurisdictional lines. Moreover, the strategies employed by regulatory bodies and financial institutions to address these challenges effectively. These include leveraging regulatory technology (RegTech) solutions for enhanced monitoring and compliance automation, fostering collaboration between regulators and industry stakeholders, and advocating for agile regulatory frameworks capable of accommodating rapid technological advancements. Looking ahead, the review anticipates ongoing shifts in regulatory paradigms to accommodate the transformative impact of FinTech. It emphasizes the importance of proactive regulatory approaches that balance innovation with risk management, ensuring the integrity and resilience of the banking sector amidst a rapidly evolving digital landscape. This provides a comprehensive overview of how FinTech is reshaping regulatory compliance in banking. It underscores the need for adaptive regulatory strategies and collaborative efforts to harness the full potential of FinTech while safeguarding financial stability and consumer trust.

Daohai Zhang, Yang Lu, Haoran Li

ABSTRACT As a key transmission component, the gear failure (such as broken teeth, wear, pitting, etc.) of the gearbox can easily lead to equipment shutdown, production interruption and even cause safety accidents, which is extremely harmful. The existing fault diagnosis methods have obvious shortcomings: the traditional BP neural network has weak global optimisation ability and slow convergence; the BP model optimised by traditional particle swarm optimisation (PSO) is limited in diagnostic accuracy because PSO is easy to fall into local optimum. In this paper, the data of four working conditions of gears are collected. After preprocessing, an improved PSO algorithm combining weight index change and particle disturbance strategy is proposed to optimise the BP neural network to construct the diagnosis model. Experiments show that the accuracy of this fault diagnosis model is 29% higher than that of the traditional BP model. It provides an efficient and reliable solution for mechanical fault diagnosis, which is of great significance for reducing losses and ensuring safety.

Joy Y. Zhang

This paper examines the critical role of epistemic injustice in Responsible Research and Innovation (RRI), particularly through the lens of reproductive medicine scandals in the Global South. It unpacks the perception of the Global South as a hotbed for scandalous ‘firsts’. That is, there seems to be a tendency for these regions to chase major scientific breakthroughs and global recognition, often at the cost of transgressing scientific and/or ethical norms. By analysing two kinds of scientific scandals – described as ‘science out of place’ and ‘science out of pace’ – this paper highlights both the challenges and opportunities for RRI to ‘de-link’ from hegemonic perceptions of legitimacy in science and to promote productive epistemic socialisation. This is essential to develop our ethical sensibility in the fast evolving and inherently contentious field of biotechnology.

Roberto Cruz Romero

Innovation research has grown steadily over the years, with different foci and methodological approaches. The abundance of literature on the topic makes clear that innovative processes are at the centre of many narratives, in academia, the public sector in general and in industry. This contribution scopes the literature and traces some key considerations regarding a determining factor: openness. The paper explores the literature in order to narrow down the characteristics of so-called ‘open innovation’. An emphasis is placed on the main channels that determine collaboration practices, particularly between academia and the private sector, namely university-industry linkages. It focuses on open transfers of knowledge and open science research practices. The overarching discussion develops key questions underlining the relevance of open innovation for science, industry and the consolidation of narratives promoting access and collaboration. The paper concludes by offering some insights into trends and challenges from a research perspective as well as from the view of innovation dynamics.

Gavin Wang, Lynn Wu

Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1,800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes.

Richard Guest, Eva Lievens, Martin Sas et al.

Automated facial age assessment systems operate in either estimation mode - predicting age based on facial traits, or verification mode - confirming a claimed age. These systems support access control to age-restricted goods, services, and content, and can be used in areas like e-commerce, social media, forensics, and refugee support. They may also personalise services in healthcare, finance, and advertising. While improving technological accuracy is essential, deployment must consider legal, ethical, sociological, alongside technological factors. This white paper reviews the current challenges in deploying such systems, outlines the relevant legal and regulatory landscape, and explores future research for fair, robust, and ethical age estimation technologies.

Pavel Malinovskiy

Dhaka, Bangladesh, one of the world's most densely populated cities, faces severe challenges in maintaining reliable, high-speed internet connectivity. This paper presents an innovative framework that addresses poor mobile data connections through the integration of advanced WebRTC technology with adaptive streaming and server-side recording solutions. Focusing on the unique network conditions in Dhaka in 2025, our approach combines dynamic transcoding, real-time error correction, and optimized interface selection to enhance connectivity. We analyze empirical data on connection speeds, mobile tower density, district-level population statistics, and social media usage. Extensive mathematical formulations, including novel models for bitrate estimation, round-trip time optimization, and reliability analysis, are provided alongside detailed diagrams and multiple examples of code in both Python and C++. Experimental results demonstrate significant improvements in throughput, latency reduction, and overall service quality, offering a scalable blueprint for next-generation communication systems in hyper-dense urban environments.

Rodrigo Barra Novoa

The study explores the evolution of Chile's industrial policy from 1990 to 2022 through the lens of state capacity, innovation and endogenous development. In a global context where governments are reasserting their role as active agents of innovation, Chile presents a paradox. It is a stable and open economy that has expanded investment in science and technology but still struggles to transform this effort into sustainable capabilities. Drawing on the works of Mazzucato, Aghion, Howitt, Mokyr, Samuelson and Sampedro, the study integrates evolutionary economics, public policy and humanist ethics. Using a longitudinal case study approach and official data, it finds that Chile has improved its innovation institutions but continues to experience weak coordination, regional inequality and a fragile culture of knowledge. The research concludes that achieving inclusive innovation requires adaptive governance and an ethical vision of innovation as a public good.

Yifan Zhou, Yibo Wang, Chao Shang

Many real-world datasets are time series that are sequentially collected and contain rich temporal information. Thus, a common interest in practice is to capture dynamics of time series and predict their future evolutions. To this end, the recurrent neural network (RNN) has been a prevalent and effective machine learning option, which admits a nonlinear state-space model representation. Motivated by the resemblance between RNN and Kalman filter (KF) for linear state-space models, we propose in this paper Innovation-driven RNN (IRNN), a novel RNN architecture tailored to time-series data modeling and prediction tasks. By adapting the concept of "innovation" from KF to RNN, past prediction errors are adopted as additional input signals to update hidden states of RNN and boost prediction performance. Since innovation data depend on network parameters, existing training algorithms for RNN do not apply to IRNN straightforwardly. Thus, a tailored training algorithm dubbed input updating-based back-propagation through time (IU-BPTT) is further proposed, which alternates between updating innovations and optimizing network parameters via gradient descent. Experiments on real-world benchmark datasets show that the integration of innovations into various forms of RNN leads to remarkably improved prediction accuracy of IRNN without increasing the training cost substantially.

Lang Tong, Xinyi Wang

This paper introduces an Artificial Intelligence (AI) foundation model for time series in engineering applications, where causal operations are required for real-time monitoring and control. Since engineering time series are governed by physical, rather than linguistic, laws, large-language-model-based AI foundation models may be ineffective or inefficient. Building on the classical innovations representation theory of Wiener, Kallianpur, and Rosenblatt, we propose Time Series GPT (TS-GPT) -- an innovations-representation-based Generative Pre-trained Transformer for engineering monitoring and control. As an example of foundation model adaptation, we consider Probabilistic Generative Forecasting, which produces future time series samples from conditional probability distributions given past realizations. We demonstrate the effectiveness of TS-GPT in forecasting real-time locational marginal prices using historical data from U.S. independent system operators.

A. Sharabati, Heba Ziad Awawdeh, Samer Sabra et al.

The research aims to investigate the potential impact of Artificial Intelligence (AI) on the digital supply chain in light of extant literature on the Decision-Oriented Information (DOI) theory and the Technology-Oriented Enterprise (TOE) framework. The research further attempts to unpack the strategic implications of AI integration in supply chain management, and its association with operational excellence and business model innovation. The study is exploratory and employs a mixed-methods approach. We develop propositions that examine the decision-making processes within AI-enhanced supply chains based on an analysis of concepts central to the DOI theory. We also employ the TOE framework to develop further propositions regarding the technological infrastructure required for AI implementation. Empirical case studies encompassing AI applications in different industries (e.g. manufacturing, healthcare, and pharmaceuticals) are presented to gain a broad perspective of the impact of AI on the digital supply chain. AI technologies inherently make supply chains more agile, transparent, and responsive. Machine Learning algorithms allow for more accurate forecasting and demand management under conditions of supply chain risk and volatility. Robotics and automation, allow for greater flexibility and efficiency in executing operations and logistics. Additionally, the successful implementation of AI is heavily contingent on the organization’s current level of technological infrastructure and its alignment with its current and future business objectives. Furthermore, the DOI theory and TOE framework may serve as a blueprint for how one could evaluate AI implementation beyond the scope of supply chain management.

Nazia Shehzad, Bharti Ramtiyal, Fauzia Jabeen et al.

PurposeThis research looks into the revolutionary potential of Industry 5.0, healthcare, sustainability and the metaverse, with a focus on the transformation of healthcare firms through cutting-edge technologies such as artificial intelligence (AI) and Internet of Things (IoT). The study emphasizes the significance of sustainability, human-machine collaboration and Industry 5.0 in the development of a technologically advanced, inclusive and immersive healthcare system.Design/methodology/approachThe study surveyed 354 medical professionals and used structural equation modeling (SEM) to investigate healthcare sustainability, Industry 5.0 and the metaverse, emphasizing the integration of modern technology while maintaining ethical issues.FindingsThe findings highlight Industry 5.0’s and the metaverse’s transformational potential in healthcare firms. The study finds that human centricity (HC) has only a minor direct impact on healthcare sustainability, whereas intelligent automation (IA) and innovation (INN) play important roles that are regulated by external factors.Practical implicationsUtilizing IA inside healthcare organizations can result in significant industrial advancements. However, these organizations must recognize the importance of moderating factors and attempt to find a balance between INN and thesev restraints.Originality/valueThis study makes a substantial contribution to the field by investigating the potential of Industry 5.0, healthcare, sustainability and the metaverse. It discusses how these advances can transform healthcare firms, with an emphasis on patient-centered treatment, environmental sustainability and data ethics. The study emphasizes the importance of having a thorough awareness of these trends and their implications for healthcare practices.

Benjamin T. Ostrander, Daniel Massillon, Leo Meller et al.

Eyitayo Raji, Tochukwu Ignatius Ijomah, Osemeike Gloria Eyieyien

This paper explores the intricate dynamics of product strategy development and financial modeling within the burgeoning fields of AI and agritech start-ups. It begins by delineating the stages of product development—from idea generation and market research to launch and scaling—emphasizing customer-centricity, innovation, and collaborative partnerships as pivotal drivers of success. Financial modeling techniques, ranging from basic revenue and cost structures to advanced scenario analysis and risk mitigation, are examined for their role in guiding strategic decision-making and ensuring financial sustainability. In the AI sector, rapid advancements in machine learning and data analytics are reshaping industries through intelligent automation and predictive insights. Agritech, meanwhile, leverages technology to optimize agricultural processes, enhance productivity, and promote sustainable practices amid global challenges. Both sectors share synergies in integrating AI technologies to innovate product offerings and enhance financial performance, albeit facing distinct challenges such as regulatory compliance and market adoption. Practical examples illustrate how AI and agritech start-ups apply these insights to refine product strategies and financial models, enhancing market competitiveness and scalability. The implications for practice underscore the importance of adapting to market dynamics, leveraging technological innovations, and fostering strategic collaborations to drive growth and innovation. Keywords:  AI, Agritech, Product Strategy Development, Financial Modeling, Start-Ups

Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Gil-Ozoudeh Ifechukwu et al.

The integration of Building Information Modeling (BIM) and Artificial Intelligence (AI) is revolutionizing infrastructure development, offering innovative solutions for design, construction, and operational challenges. This study explores the synergy between BIM and AI, examining their conceptual framework, impact on sustainability and decision-making, and the challenges associated with their integration. Through a comprehensive review of recent literature and industry practices, the study elucidates the transformative potential of these technologies in modern infrastructure projects. The findings reveal that BIM and AI enhance project efficiency, resource management, and stakeholder collaboration by leveraging AI’s predictive analytics and automation alongside BIM’s digital modeling capabilities. These technologies enable precise resource allocation, dynamic risk mitigation, and sustainable energy solutions, aligning infrastructure projects with global environmental goals. However, challenges such as data interoperability, high implementation costs, and ethical concerns remain significant barriers to their widespread adoption. The study underscores the need for industry-wide data standards, advanced cybersecurity frameworks, and targeted workforce upskilling to address these challenges. Future trends identified include the integration of generative AI, blockchain technology, and the Internet of Things (IoT) into BIM systems, promising to further enhance the scope and functionality of these technologies. These advancements hold the potential to redefine infrastructure practices, driving innovation and sustainability. In conclusion, BIM and AI represent a paradigm shift in infrastructure development, fostering efficiency, sustainability, and resilience. It is recommended that stakeholders invest in research, adopt ethical AI practices, and build collaborative ecosystems to fully realize the benefits of these technologies. By addressing identified challenges and embracing emerging trends, BIM and AI can become foundational tools in achieving sustainable and technologically advanced infrastructure solutions.

Osman Şahin, D. Karayel

This systematic review examines the transformative potential of Generative Artificial Intelligence (GenAI) across diverse sectors, including information technology, education, manufacturing, creative industries, healthcare, transportation, management, marketing, finance, energy, law, media, agriculture, and e-commerce. By analyzing its applications, the study highlights how GenAI enhances efficiency, fosters innovation, and addresses sector-specific challenges. Key benefits include the automation of complex processes, optimization of resource use, and acceleration of decision-making. However, delayed adoption risks such as workforce displacement and ethical dilemmas are also discussed. The review identifies critical barriers like data privacy concerns, algorithmic bias, and regulatory challenges. Practical strategies for successful GenAI integration are explored, emphasizing infrastructure readiness, workforce upskilling, and ethical governance. This includes leveraging generative models such as Generative Adversarial Networks (GANs), Transformer-based models, Variational Autoencoders (VAEs), and diffusion models to adapt to industry-specific demands. Furthermore, the study underscores the necessity of balancing technological advancements with responsible AI deployment to minimize risks and maximize societal benefits. By synthesizing existing research, this review provides actionable insights for stakeholders aiming to leverage GenAI's transformative capabilities responsibly. It emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets. As the study concludes, it advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology and calls for adaptive policies to align innovation with ethical principles and societal values.

P. Shimpi, Bhargav Balinge, Tejas Golait et al.

This paper presents Job Crafter, a novel initiative designed to disrupt the current placement paradigm within educational institutions. The project aims to address the pervasive issues of complexity, inefficiency, and outdated methodologies that plague traditional placement processes. Job Crafter seeks to achieve this by establishing a user-centric, streamlined, and technologically advanced platform. The core functionality of Job Crafter revolves around the automation of critical placement-related tasks. This is accomplished by leveraging the MERN stack (MongoDB, Express.js, React, Node.js), a robust technology suite that facilitates the development of a seamless web-based platform. Agile development methodologies are employed to ensure iterative development and rapid adaptation to user needs. Additionally, the platform incorporates robust data collection practices, fostering the generation of valuable insights for informed decision-making. The anticipated outcomes of Job Crafter are multifaceted. The platform has the potential to significantly reduce the time and effort expended on placement processes for both institutions and students. Furthermore, by optimizing student-opportunity matching through intelligent algorithms, Job Crafter fosters an environment that enhances placement success rates. Ultimately, Job Crafter aspires to serve as a cornerstone for the evolution of education management systems, emphasizing innovation and a user-centric design philosophy.

Givi Bedianashvili, Murman Tsartsidze, Nino Mikeladze et al.

In the modern globalized world, human capital and economic growth roles have become increasingly vital, demanding a closer examination of their dynamics. It is crucial to grasp the unique nuances of globalization to comprehend how it shapes global socio-economic systems and impacts individual nations. This understanding also highlights the evolving challenges and opportunities for human capital in driving economic progress. Moreover, the distinct circumstances small countries face amidst globalization must be noted. Studies have confirmed that these nations operate under unique mechanisms that influence their socioeconomic development. This study focuses on a macro systemic analysis of the interplay between human capital and economic growth, explicitly offering tailored recommendations for Georgia. Research leveraged econometric methods and harnessed the power of artificial intelligence, specifically employing machine learning models. The analysis shows that the null hypothesis about no cointegration was rejected for the GDP per capita and spending on healthcare with the P-value of 0.0354, while for the GDP per capita (or GDP growth) and spending on environment had the P-value of 0.0074 (0.0052) in Georgia. This reflects the impact of spending on human capital on the economy (in Georgia and Ireland) and vice versa (in Georgia, Latvia and Lithuania). Research findings show that future-oriented skills training for entrepreneurs and specialists is essential in a modern, globally competitive environment, focusing on adaptability and global best practices.