Hasil untuk "Industrial relations"

P. Florence, F. J. Roethlisberger, W. J. Dickson

P. Goldman, Andrew G. Walder

C. Sabel

Xiangyu Guo, Shiru Lin, Jinxing Gu et al.

On-site production of hydrogen peroxide (H2O2) using electrochemical methods could be more efficient than the current industrial process. However, due to the existence of scaling relations for the ...

Bernard Raffournier

D. Ones, S. Dilchert

Rupert Brown, S. Condor, Audrey Mathews et al.

Weidong Liu, M. Dunford

P. Boxall, J. Purcell, P. Wright

A. Clark

M. Khurana, Zhenyuan Yin, Praveen Linga

Nigar Hashimzade, Haoran Sun

Industrial policy has returned to the centre of economic governance, particularly in the high-tech sectors where positive network externalities in demand make market dominance self-reinforcing. This paper studies the welfare effects of an industrial policy targeting a sector with network externalities in a two-country model with strategic trade and R&D investment. We show how the welfare consequences of this policy are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods. When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than it creates. Under sufficiently strong externalities and weak substitutability or complementarity of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of the mutual business-enhancement effect. The case is stronger for the product innovation than for the process innovation, as the former directly affects the demand and triggers a stronger network effects than the latter which operates indirectly through the supply. Thus, the network externalities create an opportunity for a win-win industrial policies, but its realisation depends on the market structure and the nature of innovation.

Mingchun Sun, Rongqiang Zhao, Zhennan Huang et al.

In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in augmentation, nor is there an established metric to evaluate the accuracy of OSS or its deviation from the ground truth. To address these issues, we propose an information-theoretic optimal sample size estimation (IT-OSE) to provide reliable OSS estimation for industrial data augmentation. An interval coverage and deviation (ICD) score is proposed to evaluate the estimated OSS intuitively. The relationship between OSS and dominant factors is theoretically analyzed and formulated, thereby enhancing the interpretability. Experiments show that, compared to empirical estimation, the IT-OSE increases accuracy in classification tasks across baseline models by an average of 4.38%, and reduces MAPE in regression tasks across baseline models by an average of 18.80%. The improvements in downstream model performance are more stable. ICDdev in the ICD score is also reduced by an average of 49.30%. The determinism of OSS is enhanced. Compared to exhaustive search, the IT-OSE achieves the same OSS while reducing computational and data costs by an average of 83.97% and 93.46%. Furthermore, practicality experiments demonstrate that the IT-OSE exhibits generality across representative sensor-based industrial scenarios.

Nathan Weis

Abstract: Recent regulation theory inspired studies have observed a re-orientation in the German governments’ industrial strategy toward a more activist state despite opposition of German industry. By highlighting a critical tension between politics and industry, this article evaluates the role of the state in establishing industrial strategic guidelines and the ways in which governments try to engage companies in a context of geo-economically motivated statecraft. The ongoing electromobility challenges in the German automotive industry underscore the fundamental contradictions inherent in the government-driven sectoral transformation, which attempts to replace an existing, successful business model with an alternative that has yet to prove viable. How can we explain the strategic positioning of the most powerful industry in Germany with regard to the politically mandated phasing out of its core business and the establishment of electric vehicle production, which is currently still not profitable? Furthermore, what contradictory trajectories characterize this transition? The analysis focuses on state-business relations during this transition, examining the interplay between political regulation and corporate strategies. This dynamic does not occur in isolation; rather, it is heavily influenced by specifically developed national modes of growth and companies’ productive models that emerge and constantly evolve within the context of global competitive conditions. In the case of the export-oriented German automotive industry, particular attention must be paid to the markets of China and North America. To adequately understand these complexities, a regulation theory framework will be employed.

John Calvert, Vivian Price, Christopher Winch et al.

How far can climate literacy be embedded into the construction vocational education and training (VET) systems? This is investigated in North America (US and Canada) and six European countries (Belgium, Denmark, Germany, Ireland, Sweden and the UK). Climate literacy entails understanding the impact of climate change on society, the sector and its different occupations. Achieving zero carbon building (ZCB) is shown to involve respecting construction workers’ embodied knowledge and experience, and empowering them to make informed decisions regarding their actions through climate-literate VET programmes, thus enhancing labour agency. Drawing on research for Canada’s Building Trade Unions’ programme Building It Green, involving interviews and analysis of case study VET systems in eight countries, this article defines climate literacy and adapts a transparency tool to map and evaluate the range of knowledge, know-how and competences required for climate-literate construction workers. The findings reveal wide variations between case studies in coordinated and liberal market economies in the incorporation of climate literacy and obstructions to this, reflecting conflicts between approaches minimising workers’ discretion and those facilitating building workers acquiring abilities and agency to make informed decisions. Understanding the value of achieving socially beneficial climate outcomes can add meaning and purpose to construction work. POLICY RELEVANCE ZCB requires a climate-literate construction workforce, and a VET system capable of providing the necessary knowledge, skills and competences. Worker agency is important for delivering effective ZCB outcomes. To achieve this, policymakers need a clear definition of a climate-literate construction worker and an understanding of how this applies to construction VET systems. A transparency tool is created that can be used to describe various attributes for inclusion into construction qualifications to achieve climate literacy and identify conditions under which VET programmes can promote this. In assessing the strengths and limitations of VET systems in Europe (Belgium, Denmark, Germany, Ireland, Sweden and the UK), Canada and the US, policy guidance is provided for governments, unions and employers to achieve a climate-literate workforce.

George Tsebelis

Marcos Lima Romero, Ricardo Suyama

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.

Jiaqi Li, Xizhong Guo, Yang Zhao et al.

Rapid industrial digitalization has created intricate cybersecurity demands that necessitate effective validation methods. While cyber ranges and simulation platforms are widely deployed, they frequently face limitations in scenario diversity and creation efficiency. In this paper, we present SpiderSim, a theoretical cybersecurity simulation platform enabling rapid and lightweight scenario generation for industrial digitalization security research. At its core, our platform introduces three key innovations: a structured framework for unified scenario modeling, a multi-agent collaboration mechanism for automated generation, and modular atomic security capabilities for flexible scenario composition. Extensive implementation trials across multiple industrial digitalization contexts, including marine ranch monitoring systems, validate our platform's capacity for broad scenario coverage with efficient generation processes. Built on solid theoretical foundations and released as open-source software, SpiderSim facilitates broader research and development in automated security testing for industrial digitalization.

Juha-Matti Runtti, Usman Virk, Pekka Kyosti et al.

6G radio access architecture is envisioned to contain a network of short-range in-X subnetworks with enhanced capabilities to provide efficient and reliable wireless connectivity. Short-range communications in industrial environments are actively researched at the so-called mid-bands or FR3, e.g., in the EU SNS JU 6G-SHINE project. In this paper, we analyze omni-directional radio channel measurements at 10--12 GHz frequency band to estimate large-scale channel characteristics including power-delay profile, delay spread, K-factor, and pathloss for 254 radio links measured in the Industrial Production Lab at Aalborg University, Denmark. Moreover, we perform a comparison of estimated parameters with those of the 3GPP Indoor Factory channel model.

Aaditya Baranwal, Abdul Mueez, Jason Voelker et al.

Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app