Hasil untuk "Technological innovations. Automation"

Jun Cui

This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative artificial intelligence technology adoption. Using a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023, we employ multiple econometric techniques to examine this relationship. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism. Specifically, digital innovation positively influences GAI technology adoption, which subsequently improves ESG performance. Furthermore, our heterogeneity analysis indicates that this relationship varies across firm size, industry type, and ownership structure. Finally, our results remain robust after addressing potential endogeneity concerns through instrumental variable estimation, propensity score matching, and differenc in differences approaches. This research contributes to the growing literature on technologydriven sustainability transformations and offers practical implications for corporate strategy and policy development in promoting sustainable business practices through technological advancement.

Hongjian Zhou, Pingchuan Ma, Jiaqi Gu

Photonic Integrated Circuits (PICs) offer tremendous advantages in bandwidth, parallelism, and energy efficiency, making them essential for emerging applications in artificial intelligence (AI), high-performance computing (HPC), sensing, and communications. However, the design of modern PICs, which now integrate hundreds to thousands of components, remains largely manual, resulting in inefficiency, poor scalability, and susceptibility to errors. To address these challenges, we propose PoLaRIS, a comprehensive Intelligent Electronic-Photonic Design Automation (EPDA) framework that spans both device-level synthesis and system-level physical layout. PoLaRIS combines a robust, fabrication-aware inverse design engine with a routing-informed placement and curvy-aware detailed router, enabling the automated generation of design rule violation (DRV)-free and performance-optimized layouts. By unifying physics-driven optimization with machine learning and domain-specific algorithms, PoLaRIS significantly accelerates PIC development, lowers design barriers, and lays the groundwork for scalable photonic system design automation.

SK. Golam Saroar, Waseefa Ahmed, Elmira Onagh et al.

GitHub, a central hub for collaborative software development, has revolutionized the open-source software (OSS) ecosystem through its GitHub Marketplace, a platform launched in 2017 to host automation tools aimed at enhancing the efficiency and scalability of software projects. As the adoption of automation in OSS production grows, understanding the trends, characteristics, and underlying dynamics of this marketplace has become vital. Furthermore, despite the rich repository of academic research on software automation, a disconnect persists between academia and industry practices. This study seeks to bridge this gap by providing a systematic analysis of the GitHub Marketplace, comparing trends observed in industry tools with advancements reported in academic literature, and identifying areas where academia can contribute to practical innovation.

Pablo Dorta-González, Alejandro Rodríguez-Caro, María Isabel Dorta-González

This study explores the connection between patent citations and scientific publications across six fields: Biochemistry, Genetics, Pharmacology, Engineering, Mathematics, and Physics. Analysing 117,590 papers from 2014 to 2023, the research emphasises how publication year, open access (OA) status, and discipline influence patent citations. Openly accessible papers, particularly those in hybrid OA journals or green OA repositories, are significantly more likely to be cited in patents, seven times more than those mentioned in blogs, and over twice as likely compared to older publications. However, papers with policy-related references are less frequently cited, indicating that patents may prioritise commercially viable innovations over those addressing societal challenges. Disciplinary differences reveal distinct innovation patterns across sectors. While academic visibility via blogs or platforms like Mendeley increases within scholarly circles, these have limited impact on patent citations. The study also finds that increased funding, possibly tied to applied research trends and fully open access journals, negatively affects patent citations. Social media presence and the number of authors have minimal impact. These findings highlight the complex factors shaping the integration of scientific research into technological innovations.

Manar Ashkanani, Alanoud AlAjmi, Aeshah Alhayyan et al.

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

Richard Rigó, Adriana Grenčíková, Mantas Švažas et al.

The presented study examines labour market changes caused by generational shifts, specifically focusing on employees' value orientation toward work and changes in loyalty. Due to generational transitions, the labour market is experiencing shifts leading to workforce shortages. Understanding generational value differences is crucial for attracting and retaining the necessary workforce. The study aims to analyze survey data to determine generational attitudes toward employer loyalty and identify differences in perspectives on work activity across generational cohorts. The survey involved 405 respondents active in the Slovak labour market. The representation of generational cohorts in the sample aligns proportionally with their approximate labour market presence. The research focuses on testing several hypotheses aimed at identifying intergenerational differences in: (1) Willingness to work and remain in the labour market, (2) Loyalty to employers, (3) Perceptions of job stability. The primary method used to examine intergenerational differences and test hypotheses is the Kruskal-Wallis test. Results show a clear intergenerational decline in loyalty, with each successive generation spending shorter periods with a single employer. Additionally, the hypotheses reveal generational differences in: (1) Current willingness to change employers, (2) Perceptions of staying with one employer long-term, (3) Attitudes toward early retirement, (4) Views on work itself. These findings support long-term trends indicating that younger generations prioritize flexibility and work-life balance over job stability. As the results highlight emerging generational differences, it is crucial to adapt job designs to the specific values each generation brings.

Nina Jackson, Kari-Pekka Heikkinen, Harri Haapasalo

Developing creativity in higher education can be challenging because of the differences in students’ personality- and cognitive traits, creative competence and learning preferences. This pilot study presents a novel prototype of a pedagogical model that integrates principles of design thinking and personalised learning to cultivate creative confidence within a pedagogical setting in higher education. The model is based on a multi-level theoretical framework comprising pedagogy of creativity from micro- and macro level consisting of literature on creativity, creative confidence, how creativity can be practiced, creative diversity, personalised learning as well as design thinking and its educational applications. A quasi-experiment of the pedagogical intervention explores how the students experienced and perceived the outcomes of each step of the design thinking process when applied to their personal creative development. It also investigates what ways the empirical data collected from the intervention align with the core constructs of the proposed model for personalised creativity development. The data is analysed through directed content analysis. The results suggest that design thinking offers a structured approach for supporting cognitive processes of creativity as well as interaction with the surrounding system to increase creative confidence. The main contribution of the experiment is the new hypotheses for further research around the topical issues of design thinking’s pedagogical applications and personalised creativity development.

Conrado Boeira, Antor Hasan, Khaleda Papry et al.

The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are examples of applications that leverage 5G networks' support for extremely low latency, high bandwidth, and increased throughput. However, the complex architecture of 5G hinders innovation due to the lack of accessibility to testbeds or realistic simulators with adequate 5G functionalities. Also, configuring and managing simulators are complex and time consuming. Finally, the lack of adequate representative data hinders the data-driven designs in 5G campaigns. Thus, we calibrated a system-level open-source simulator, Simu5G, following 3GPP guidelines to enable faster innovation in the 5G domain. Furthermore, we developed an API for automatic simulator configuration without knowing the underlying architectural details. Finally, we demonstrate the usage of the calibrated and automated simulator by developing an ML-based anomaly detection in a 5G Radio Access Network (RAN).

Ramakant Kumar

Agriculture is a vital sector that significantly contributes to the economy and food security, particularly in regions like Varanasi, India. This paper explores various types of agriculture practiced in the area, including subsistence, commercial, intensive, extensive, industrial, organic, agroforestry, aquaculture, and urban agriculture. Each type presents unique challenges and opportunities, necessitating innovative approaches to enhance productivity and sustainability. To address these challenges, the integration of advanced technologies such as sensors and communication protocols is essential. Sensors can provide real-time data on soil health, moisture levels, and crop conditions, enabling farmers to make informed decisions. Communication technologies facilitate the seamless transfer of this data, allowing for timely interventions and optimized resource management. Moreover, programming techniques play a crucial role in developing applications that process and analyze agricultural data. By leveraging machine learning algorithms, farmers can gain insights into crop performance, predict yields, and implement precision agriculture practices. This paper highlights the significance of combining traditional agricultural practices with modern technologies to create a resilient agricultural ecosystem. The findings underscore the potential of integrating sensors, communication technologies, and programming in transforming agricultural practices in Varanasi. By fostering a data-driven approach, this research aims to contribute to sustainable farming, enhance food security, and improve the livelihoods of farmers in the region.

Oluchukwu Obinna Ogbuagu, Akachukwu Obianuju Mbata, O. D. Balogun et al.

Quality assurance (QA) in pharmaceutical manufacturing is essential for ensuring drug safety, efficacy, and consistency. As global pharmaceutical markets expand and regulations become increasingly complex, there is a pressing need for a holistic approach to integrate regulations, supply chain resilience, and technological innovations to maintain high-quality standards. This paper explores the critical role of regulatory frameworks, such as the FDA, EMA, and GMP guidelines, in ensuring compliance across diverse markets, while addressing the challenges associated with evolving laws and documentation requirements. It delves into the vulnerabilities within the pharmaceutical supply chain, including risks related to raw material sourcing, counterfeit drugs, and cold chain logistics, and highlights strategies such as traceability, serialization, and real-time monitoring to mitigate these risks. The paper also examines how AI, automation, blockchain, and continuous manufacturing revolutionize the quality assurance landscape by improving process control, transparency, and predictive capabilities. Ultimately, this paper recommends harmonizing regulatory frameworks with emerging technologies, ensuring compliance through AI-driven systems, and fostering the next generation of automation in pharmaceutical manufacturing to ensure product integrity and safety in a rapidly evolving global market.

Simon Thomine, Hichem Snoussi, Mahmoud Soua

Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific training and therefore a generalization for anomaly detection. The obvious use case is the fabric anomaly detection, where we have to deal with a really wide range of colors and types of textile and a stoppage of the production line for training could not be considered. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection. Combining the ability to generalize and the learning process offer a fast and precise anomaly detection and segmentation. The main contributions of this paper are the following: A domain-generalization texture anomaly detection method achieving the state-of-the-art performances, a fast specific training on good samples extracted by the proposed method, a self-evaluation method based on custom defect creation and an automatic detection of already seen fabric to prevent re-training.

Chenyu Zhang, Zhaozheng Yin, Ruwen Qin

Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection. This paper proposes an Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automatic structural condition assessment in visual bridge inspection. AECIF-Net can simultaneously parse structural elements and segment surface defects on the elements in inspection images. It integrates two task-specific relearning subnets to extract task-specific features from an overall feature embedding. A co-interactive feature fusion module further captures the spatial correlation and facilitates information sharing between tasks. Experimental results demonstrate that the proposed AECIF-Net outperforms the current state-of-the-art approaches, achieving promising performance with 92.11% mIoU for element segmentation and 87.16% mIoU for corrosion segmentation on the test set of the new benchmark dataset Steel Bridge Condition Inspection Visual (SBCIV). An ablation study verifies the merits of the designs for AECIF-Net, and a case study demonstrates its capability to automate structural condition assessment.

Christopher Leet, Chanwook Oh, Michele Lora et al.

We address the warehouse servicing problem (WSP) in automated warehouses, which use teams of mobile agents to bring products from shelves to packing stations. Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe. The WSP consists of four subproblems, concerning what tasks to perform (task formulation), who will perform them (task allocation), and when (scheduling) and how (path planning) to perform them. These subproblems are NP-hard individually and become more challenging in combination. The difficulty of the WSP is compounded by the scale of automated warehouses, which frequently use teams of hundreds of agents. In this paper, we present a methodology that can solve the WSP at such scales. We introduce a novel, contract-based design framework which decomposes an automated warehouse into traffic system components. By assigning each of these components a contract describing the traffic flows it can support, we can synthesize a traffic flow satisfying a given WSP instance. Component-wise search-based path planning is then used to transform this traffic flow into a plan for discrete agents in a modular way. Evaluation shows that this methodology can solve WSP instances on real automated warehouses.

Jacopo Staccioli, M. Virgillito

This paper, relying on a still relatively unexplored long-term dataset on U.S. patenting activity, provides empirical evidence on the history of labor-saving innovations back to early nineteenth century. The identification of mechanization/automation heuristics, retrieved via textual content analysis on current robotic technologies by Montobbio et al. (Robots and the origin of their labour-saving impact, LEM Working Paper Series 2020/03), allows to focus on a limited set of CPC codes where mechanization and automation technologies are more prevalent. We track their time evolution, clustering, eventual emergence of wavy behavior, and their comovements with long-term GDP growth. Our results challenge both the general-purpose technology approach and the strict 50-year Kondratiev cycle, while they provide evidence of the emergence of erratic constellations of heterogeneous technological artefacts, in line with the development-block approach enabled by autocatalytic systems.

Amusan Lekan, Aigbavboa Clinton, J. Owolabi

Construction 4.0 (C4.0) has tremendously impacted construction activities worldwide in recent times. This effect was made possible on account of innovations brought about by Industry 4.0 (I4.0). Industry 4.0 has the potential to create Construction 4.0 through the integration of the design, construction and maintenance of infrastructure through useful component integration for industrial and technological development. Therefore, this study aimed to present a pathway for achieving sustainable innovations and inclusive technological and infrastructural developments. The following parameters were reviewed in this study as part of the goals and objectives set in the survey: identifying the adaptable areas of Construction 4.0 in design, planning, construction and maintenance as part of infrastructural innovation in order to study the industrial application drivers of I4.0 and C4.0 hindrances in achieving C4.0; achieving the automation dream through C4.0, benchmarking the social and economic implications of C4.0 and identifying the issues and challenges in achieving sustainable innovation through infrastructural development and documenting the disruptive tools of C4.0 in achieving a sustainable design through technological development and examining the critical factors influencing the effective adaptation of C4.0 in achieving growth. The authors utilised 200 construction firms for this study using the Cochran and Slovin’s formulas. In addition, the sample size of 150 respondents that constituted the study were construction professionals. The respondents used the simple percentage, relative index, Spearman’s rank, Mann–Whitney U test, Kendall’s Tau test, Student’s t-test, ANOVA and chi-square tools in the data processing. The study found out, among other things, the following as part of the parameters earlier proposed: the introduction of a circular economy by adopting intelligent innovation, engaging new tools, technological innovation diffusion and the vertical and horizontal integration of versatile tools like I4.0 and C4.0 for inclusive technological development. This study recommended the objective and effective adaptation of I4.0 tools to enhance C4.0 for technical development, circular economic integration and a framework for sustainable innovation and a system for the inclusive monitoring of innovations in the design and planning of construction maintenance.

Ziteng Fan, Jing Ning, A. He

Workplace automation fueled by technological innovations has been generating social policy implications. Defying the prevalent argument that automation risk triggers employment insecurity and prompts individuals to favour redistribution, this study doesn’t find empirical evidence in the Chinese context. Analysing national survey data, this study reveals a very strong association between automation risk and popular preference for government responsibility in old-age support. Further analysis suggests that more generous local welfare systems generate a reinforcing effect between automation risk and individuals’ support for government involvement in old-age support. In a welfare system in which major redistributive policies are not employment-dependent, automation risk may not necessarily trigger stronger preferences for short-term immediate protection through redistributive programmes, but may stimulate individuals to project their need for social protection towards middle- or longer-term and employment-related policies. The generosity of subnational welfare systems moderates the formation of individuals’ social policy preferences through policy feedback.

B. Ottoni, P. Oliveira, Lucas Estrela et al.

Abstract Technological innovations are enabling machines to further replace human labor. In this context, we estimate - based on the Frey and Osborne (2017) study, which uses data from the United States of America (USA) - how many Brazilian jobs may be eliminated in one or two decades due to currently existing technologies. We add to earlier research, that included the Brazilian case, as we consider the entire employment structure - including both formal and informal sectors - in order to estimate the proportion of jobs in the country that may be substituted by machines. Our results indicate that 58.1% of Brazilian jobs may disappear over the next 10 to 20 years due to automation. Moreover, we observe that jobs in the informal sector face higher probabilities of elimination by automation when compared to the formal sector.

Sri Ants

The technological innovation system is a concept developed within the scientific field of innovation studies, which serves to explain the nature and rate of technological change. A Technological Innovation System can be defined as ‘a dynamic network of agents interacting in a specific economic/industrial area under a particular institutional infrastructure and involved in the generation, diffusion, and utilization of technology’.