Hasil untuk "Technological innovations. Automation"

Wolfgang Kuhle

We study the conditions under which technological advances, in combination with a lognormal wage distribution, incentivize agents into an inefficient educational arms race. Our model emphasizes that lognormal wage distributions imply that agents' wages increase exponentially in the level of their skill as well as in the level of technology. In turn, this exponential relation between skills, technology, and wages pressures agents into an exhausting race for the tails of the economy's skill distribution. Moreover, technological advances and overinvestment in education increase GDP and inequality, while welfare may decline. In an alternative interpretation, our model studies firms that invest in artificial intelligence of their chatbots and AI agents. For a wide range of specifications, firms, just like humans, have an incentive to choose corner solutions where investment is limited only by borrowing constraints.

Igor Kaganovich, Michael Tendler

Partially ionized plasma physics has attracted a lot of attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of the manufacturing for computer components. The partially ionized plasma is characterized by a significant presence of neutral particles in contrast to fully ionized plasma. The theoretical analysis is based upon solutions of the kinetic Boltzmann equation yielding the non-Maxwellian electron energy distribution function (EEDF) thereby emphasizing the difference with a fully ionized plasma. The impact of the effect on discharges in inert and molecular gases is described in detail yielding the complex nonlinear phenomena in plasma self-organization. A few examples of such phenomena are given including the non-monotonic EEDFs in the discharge afterglow in mixture of argon with the molecular gas NF3; the explosive generation of cold electron populations in capacitive discharges, hysteresis of EEDF in inductively coupled plasmas. Recently, highly advanced computer codes were developed in order to address the outstanding problems of plasma technology. These developments are briefly described in general terms.

Jasmine Chiat Ling Ong, Yilin Ning, Mingxuan Liu et al.

The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. GenAI and LLMs offer broad applications, from automating clinical workflows to personalizing diagnostics. However, the non-deterministic outputs, broad functionalities and complex integration of GenAI and LLMs challenge existing medical device regulatory frameworks, including the total product life cycle (TPLC) approach. Here we discuss the constraints of the TPLC approach to GenAI and LLM-based medical device regulation, and advocate for global collaboration in regulatory science research. This serves as the foundation for developing innovative approaches including adaptive policies and regulatory sandboxes, to test and refine governance in real-world settings. International harmonization, as seen with the International Medical Device Regulators Forum, is essential to manage implications of LLM on global health, including risks of widening health inequities driven by inherent model biases. By engaging multidisciplinary expertise, prioritizing iterative, data-driven approaches, and focusing on the needs of diverse populations, global regulatory science research enables the responsible and equitable advancement of LLM innovations in healthcare.

Jun Cui

This study examines the impact of Digital-GenAI-Enhanced Human-Computer Interaction (HCI) in DevOps on sustainable innovation performance among Chinese A-share internet technology firms. Using panel data from 2018-2024, we analyze 5,560 firm-year observations from CNRDS and CSMAR databases. Our empirical framework reveals significant positive associations between AI-enhanced HCI implementation and sustainable innovation outcomes. Results demonstrate that firms adopting advanced HCI technologies achieve 23.7% higher innovation efficiency. The study contributes to understanding digital transformation's role in sustainable business practices. We identify three key mechanisms: operational efficiency enhancement, knowledge integration facilitation, and stakeholder engagement improvement. Findings provide practical implications for technology adoption strategies in emerging markets

Gregor Zens, Jakub Bijak

Motivated by the challenge of analyzing the dynamics of weekly sea border crossings in the Mediterranean (2015-2025) and the English Channel (2018-2025), we develop a Bayesian dynamic framework for modeling heteroskedastic count time series. Building on theoretical considerations and empirical stylized facts, our approach utilizes a Poisson random walk model that allows for heavy-tailed innovations or stochastic volatility dynamics, while incorporating an explicit mechanism to separate structural from sampling zeros. Posterior inference is carried out via a straightforward Markov chain Monte Carlo algorithm. Applying this methodology to Mediterranean and English Channel data, we compare alternative model specifications through a comprehensive out-of-sample forecasting exercise. Using log predictive scores and empirical coverage at predictive quantiles to evaluate each model, we find strong evidence for stochastic volatility in migration innovations. These models deliver the strongest out-of-sample forecasts with empirical coverage close to nominal levels up to the 99th percentile. Our framework can be used to develop risk indicators with direct policy implications for improving governance and preparedness for migration surges. More broadly, the methodology extends to other zero-inflated non-stationary count time series applications, including epidemiological surveillance and public safety incident monitoring.

Volodymyr Rashkivskyi, Mykola Prystailo, Bohdan Fedyshyn et al.

The purpose of the proposed article is the development and construction of a mechanized mobile platform for serving people, which is caused by the need to increase the safety of the operation of such technical means, in particular, in the case of the need for mass customer service. The methodology is based on search, research and creative approaches. The methods of development analysis, patent search, synthesis of technical solutions, simulation modelling was used. Scientific novelty. The study of the features of various approaches to the creation of effective mechanized moving platforms, the analysis of solutions and the dynamics of patenting made it possible to substantiate the directions of development of technical solutions and the prospects of developments. The authors proposed constructive solutions for mobile platforms, developed approaches to the technical implementation of increasing the safety of their operation, proposed energy-saving approaches aimed at reducing the energy consumption of mechanized means, which is especially relevant in the mass implementation of platforms for serving people. Research results. The article solves important safety issues of human service, in particular in the entertainment industry through the development of structural parts, drives and rules for the operation of mechanized moving platforms. Synthesized constructive solutions obtained in the course of patent research, analysis of modern technical solutions, rational technical design, and expert evaluation are presented. It was determined that the safety of the operation of mechanized moving platforms, which are intended for the transport of people in the field of tourism, depends on effective approaches to the design and components of the technical system in the form of a moving platform, its structural components, elements of its mechanism and the drive system as a whole, which with the optimization of technical indicators the stability of the overall system, the smoothness of movement and braking of the platform, the optimization of the materiality of the structure in total allow to have a qualitative effect on improving the safety of human operation.

Bettina Maisch, Steven Gedeon, Barbara Wolf et al.

In the scientific spirit of CERN, this Special Issue focuses on humanity's biggest experiment: Life Design. It is about transformations and innovations related to the most profound questions of what it means to design your life in your personal and professional context for sustainable flourishing, meaning, happiness, and well-being on individual and societal levels.

Joshua Auld, Jamie Cook, Krishna Murthy Gurumurthy et al.

Rapid technological progress and innovation in the areas of vehicle connectivity, automation and electrification, new modes of shared and alternative mobility, and advanced transportation system demand and supply management strategies, have motivated numerous questions and studies regarding the potential impact on key performance and equity metrics. Several of these areas of development may or may not have a synergistic outcome on the overall benefits such as reduction in congestion and travel times. In this study, the use of an end-to-end modeling workflow centered around an activity-based agent-based travel demand forecasting tool called POLARIS is explored to provide insights on the effects of several different technology deployments and operational policies in combination for the Chicago region. The objective of the research was to explore the direct impacts and observe any interactions between the various policy and technology scenarios to help better characterize and evaluate their potential future benefits. We analyze system outcome metrics on mobility, energy and emissions, equity and environmental justice and overall efficiency for a scenario design of experiments that looks at combinations of supply interventions (congestion pricing, transit expansion, tnc policy, off-hours freight policy, connected signal optimization) for different potential demand scenarios defined by e-commerce and on-demand delivery engagement, and market penetration of electric vehicles. We found different combinations of strategies that can reduce overall travel times up to 7% and increase system efficiency up to 53% depending on how various metrics are prioritized. The results demonstrate the importance of considering various interventions jointly.

Rohit Singh, Aryan Kaushik, Wonjae Shin et al.

Over the past few decades, wireless communication has witnessed remarkable growth, experiencing several transformative changes. This article aims to provide a comprehensive overview of wireless communication technologies, from the foundations to the recent wireless advances. Specifically, we take a neutral look at the state-of-the-art technologies for 5G and the ongoing evolutions towards 6G, reviewing the recommendations of the International Mobile Communication vision for 2030 (IMT-2030). We first highlight specific features of IMT 2030, including three IMT-2020 extensions (URLLC+, eMBB+, and mMTC+) and three new innovations (Ubiquitous connectivity and integrating the new capabilities of sensing & AI with communication functionality). Then, we delve into three major challenges in implementing 6G, along with global standardization efforts. Besides, a proof of concept is provided by demonstrating terahertz (THz) signal transmission using Orbital Angular Momentum (OAM) multiplexing, which is one of the potential candidates for 6G and beyond. To inspire further potential research, we conclude by identifying research opportunities and future visions on IMT-2030 recommendations.

Siyu Chen, Qing Guo

Employing a comprehensive survey of micro and small enterprises (MSEs) and the Digital Financial Inclusion Index in China, this study investigates the influence of fintech on MSE innovation empirically. Our findings indicate that fintech advancement substantially enhances the likelihood of MSEs engaging in innovative endeavors and boosts both the investment and outcomes of their innovation processes. The underlying mechanisms are attributed to fintech's role in fostering long-term strategic incentives and investment in human capital. This includes the use of promotions and stock options as rewards, rather than traditional perks like gifts or trips, the attraction of a greater number of university graduates, and the increase in both training expenses and the remuneration of technical staff. Our heterogeneity analysis reveals that fintech exerts a more pronounced effect on MSEs situated in economically developed areas, those that are five years old or younger, and businesses with limited assets and workforce. Additionally, we uncover that fintech stimulates the innovation of MSEs' independent research and development (R\&D) efforts. This paper contributes to the understanding of the nuanced ways in which fintech impacts MSE innovation and offers policy insights aimed at unleashing the full potential of MSEs' innovative capabilities.

Zhiyu Quan, Changyue Hu, Panyi Dong et al.

Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This paper presents a groundbreaking effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies' risk management. Our study further utilizes various machine learning techniques to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house. The quantification process provides a deeper understanding of the value of the InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic-industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.

Amnon Bleich, Antje Linnemann, Bjoern H. Diem et al.

Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HCPs) as training data. These reports, while often inconsistent, provide a valuable foundation for automated learning. We introduce an encoder-decoder-based method that uses these reports to train models to generate detailed descriptions of ECG episodes. This represents a significant advancement in ECG analysis automation, with potential applications in zero-shot classification and automated clinical decision support. The model is tested on various datasets, including both 1- and 12-lead ECGs. It significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.53% compared to 24.51% achieved by the reference model. Furthermore, several key design choices are discussed, providing a comprehensive overview of current challenges and innovations in this domain. The source codes for this research are publicly available in our Git repository https://git.zib.de/ableich/ecg-comment-generation-public

Ray Wai Man Kong, Theodore Ho Tin Kong, Miao Yi et al.

Automated machinery design for garment manufacturing is essential for improving productivity, consistency, and quality. This paper focuses on the development of new pulling gear for automated pant bottom hem sewing machines. Traditionally, these machines require manual intervention to guide the bottom hem sewing process, which often leads to inconsistent stitch quality and alignment. While twin-needle sewing machines can create twin lines for the bottom hem, they typically lack sufficient pulling force to adequately handle the fabric of the pants' bottom hem. The innovative design of the pulling gear aims to address this issue by providing the necessary pulling force for the bottom hem of eyelet pants. The research and design discussed in this article seek to solve technical challenges, eliminate the need for skilled manual operators, and enhance overall productivity. This improvement ensures smooth and precise feeding of fabric pieces in the automated twin needle sewing machine, ultimately improving the consistency and quality of the stitching. By integrating this innovation, garment manufacturers can boost productivity, reduce reliance on manual skilful labour, and optimize the output of the production process, thereby reaping the benefits of automation in the garment manufacturing industry.

Eleni Oikonomaki, Dimitris Belivanis

In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of efforts of cities. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovation with various innovation scoreboards. The current paper aims at increasing the understanding of the existing indicators and complementing the various innovation assessment toolkits, using large datasets from non-traditional sources. The success of both top down implemented innovation districts and community-level innovation ecosystems is complex and has not been well examined. Yet, limited data shed light on the association between indicators and innovation performance at the neighborhood level. For this purpose, the city of Boston has been selected as a case study to reveal the importance of its neighborhood's different characteristics in achieving high innovation performance. The study uses a large geographically distributed dataset across Boston's 35 zip code areas, which contains various business, entrepreneurial-specific, socio-economic data and other types of data that can reveal contextual urban dimensions. Furthermore, in order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations. The outcomes of this analysis aim to introduce a 'Neighborhood Innovation Index' that will generate new planning models for higher innovation performance, which can be easily applied in other cases. By publishing this large-scale dataset of urban informatics, the goal is to contribute to the innovation discourse and enable a new theoretical framework that identifies the linkages among cities' socio-economic characteristics and innovation performance.

Saurabh Pujar, Luca Buratti, Xiaojie Guo et al.

The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages, such as the ones used for IT Automation, have received far less attention, despite involving many active developers and being an essential component of modern cloud platforms. This work focuses on the generation of Ansible-YAML, a widely used markup language for IT Automation. We present Ansible Wisdom, a natural-language to Ansible-YAML code generation tool, aimed at improving IT automation productivity. Ansible Wisdom is a transformer-based model, extended by training with a new dataset containing Ansible-YAML. We also develop two novel performance metrics for YAML and Ansible to capture the specific characteristics of this domain. Results show that Ansible Wisdom can accurately generate Ansible script from natural language prompts with performance comparable or better than existing state of the art code generation models. In few-shot settings we asses the impact of training with Ansible, YAML data and compare with different baselines including Codex-Davinci-002. We also show that after finetuning, our Ansible specific model (BLEU: 66.67) can outperform a much larger Codex-Davinci-002 (BLEU: 50.4) model, which was evaluated in few shot settings.

Piotr Gago, Anna Voitenkova, Daniel Jabłonski et al.

RPA (Robotic Process Automation) helps automate repetitive tasks performed by users, often across different software solutions. Regardless of the RPA tool chosen, the key problem in automation is analyzing the steps of these tasks. This is usually done by an analyst with the possible participation of the person responsible for the given activity. However, currently there exists no one-size-fits-all description language, which would allow to record, process, and easily automate steps of specific tasks. Every RPA solution uses a different notation, which is not easily human-readable, editable, and which cannot be applied to a different automation platform. Therefore, in this paper, we propose a new eXtensible Robotic Language (XRL) that can be understood by both programmers and non-programmers to automate repetitive business processes.

Mirza M. Shah

The present author’s general correlation for condensation in mini and macro channels which has been verified with an extreme range of data was further evaluated at quality x close to 1. Large deviations were found at quality x ≥ 0.99. The correlation was modified to improve the accuracy in this range of quality. The improved correlation has a MAD (mean absolute deviation) of 22.1% in this range of quality compared to 95% in the published correlation. This improvement is important for the calculation of heat transfer in the condensation of superheated vapor as it requires the value of the heat transfer coefficient at quality x = 1. The new correlation is presented together with a comparison of data. Various aspects of the correlation are discussed. Results of the comparison of all data with the new correlation as well as other correlations are given.

Steven Umbrello, Steffen Steinert, Tristan Emile de Wildt

Georgian Simion, Adrian Filipescu, Dan Ionescu et al.

The main contribution of this paper is the modeling and control for a complex autonomous system (CAS). It is equipped with a visual sensor to operate precision positioning in a technology executed on a laboratory mechatronics line. The technology allows the retrieval of workpieces which do not completely pass the quality test. Another objective of this paper is the implementation of an assisting technology for a laboratory processing/reprocessing mechatronics line (P/RML) containing four workstations, assisted by the following components: a complex autonomous system that consists of an autonomous robotic system (ARS), a wheeled mobile robot (WMR) PeopleBot, a robotic manipulator (RM) Cyton 1500 with seven degrees of freedom (7 DOF), and a mobile visual servoing system (MVS) with a Logitech camera as visual sensor used in the process of picking, transporting and placing the workpieces. The purpose of the MVS is to increase the precision of the RM by utilizing the look and move principle, since the initial and final positions of the CAS can slightly deviate from their trajectory, thus increasing the possibility of errors to appear during the process of catching and releasing the pieces. If the processed piece did not pass the quality test and has been rendered as defective, it is retrieved from the last station of the P/RML and transported to the first station for reprocessing. The control of the WMR is done using the trajectory-tracking sliding-mode control (TTSMC). The RM control is based on inverse kinematics model, and the MVS control is implemented with the image moments method.

Philip Wane