Hasil untuk "Industrial relations"

J. Johanson, L. Mattsson

Allan Flanders, J. Dunlop

R. Hassink, A. Isaksen, Michaela Trippl

ABSTRACT Path creation is a key concept in economic geography. So far, particularly scholars within evolutionary economic geography have pioneered research on this topic. This paper critically discusses their work and proposes a broader understanding of how new economic activities emerge in regions, which is referred to here as ‘new regional industrial path development’. The paper develops a future research agenda, which stresses the need to develop a multi-actor and multi-scalar approach, to integrate the future into analyses of path development, and to offer a broader view on inter-path relations.

C. Crouch

J. Kelly

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

Kiran Abraham‐Aggarwal, Xiaoxuan Chen, Daniel J. Spertus et al.

Abstract Objective To evaluate the perceptions of American Rhinologic Society (ARS) members on the compensation models of academic rhinologists and their impact on clinical practice, teaching, and academic responsibilities. Study Design Survey study. Setting Academic rhinologists across the United States who are members of the ARS. Methods A twenty‐six‐question survey was distributed to 295 ARS members. The survey collected demographic information such as years of experience, geographic location, practice setting, and consultation volume. It also explored various compensation models and their impact on compensation, patient volume, case types, and the ability to support teaching and academic responsibilities. Results Out of 295 surveyed ARS members, 107 responded (36%), and 80 academic rhinologists were included in the final sample. Respondents varied in experience and geographic distribution. Most respondents were salaried (69%), while 63% were under relative value units (RVU)‐based models, and 25% were under collections‐based models. Additionally, 66% reported poor or no support for research and educational activities. Compensation models were found to influence patient volume (28%), procedure choices (14%), and academic duties, with 55% of respondents indicating reduced engagement with students. Conclusion Although a plurality of respondents (39%) believed that salaried models are most conducive to balancing academic and clinical responsibilities, survey findings highlight a dissonance. Respondents under collections‐based models were more likely to feel adequately supported (64.71%) compared to those under salaried or RVU‐based models. This suggests that although many perceive salaried models as ideal for balance, collections‐based models may better address financial and structural needs, emphasizing the importance of developing flexible, tailored compensation structures that align with individual and institutional goals while fostering academic productivity.

Nuo Chen, Ping Du, Tao Liu et al.

Industrial explosion accidents often cause severe casualties, property damage, and environmental impacts, posing major challenges to process safety and emergency management. This study constructs an industrial-explosion eventic graph grounded in a domain-specific ontology and implements a Retrieval-augmented Generation (RAG) Q&A system powered by large language models (LLM) to support emergency decision-making. We designed an accident-emergency ontology that systematically captured accident characteristics and response workflows. A zero-shot information-extraction framework automatically identifies events from historical reports, and template-based matching extracts inter-event relations. Uncertainty modeling is introduced to ensure accurate knowledge representation. A semantic-similarity-driven knowledge-fusion method improves event abstraction and consistency, and the resulting graph is stored in Neo4j for efficient querying and analysis. By integrating the eventic graph with RAG, we created a Q&A system that significantly outperforms baseline models and traditional reasoning methods. A case study of the 8·12 Tianjin Port explosion demonstrates the framework’s ability to represent accident evolution patterns and causal chains. This integrated approach provides a practical tool for accident investigation, risk assessment, and emergency decision-making, contributing to improved safety management in industrial processes.

Alper Saricioglu, Mujde Erol Genevois, Michele Cedolin

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.

Chen Yizhe, Wang Qi, Hu Dongxiao et al.

In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Effectively representing dynamic environmental states through multimodal sensor data fusion remains a critical challenge in current robotic datasets. To address this, an industrial-grade multimodal interference dataset is presented, designed for robotic perception and control under complex conditions. The dataset integrates multi-dimensional interference features including size, color, and lighting variations, and employs high-precision sensors to synchronously collect visual, torque, and joint-state measurements. Scenarios with geometric similarity exceeding 85\% and standardized lighting gradients are included to ensure real-world representativeness. Microsecond-level time-synchronization and vibration-resistant data acquisition protocols, implemented via the Robot Operating System (ROS), guarantee temporal and operational fidelity. Experimental results demonstrate that the dataset enhances model validation robustness and improves robotic operational stability in dynamic, interference-rich environments. The dataset is publicly available at:https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP.

Yelizaveta Chernysh, Viktoriia Chubur, Iryna Ablieieva et al.

The migration of heavy metals and radionuclides is interrelated, and this study focusses on the interaction and complex influence of various toxicants. The rehabilitation of radioactively contaminated territories has a complex character and is based on scientifically supported measures to restore industrial, economic, and sociopsychological relations. We aim for the achievement of pre-emergency levels of hygienic norms of radioactive contamination of output products. This, in its sum, allows for further economic activity in these territories without restrictions on the basis of natural actions of autoremediation. Biosorption technologies based on bacterial biomass remain a promising direction for the remediation of soils contaminated with radionuclides and heavy metals that help immobilise and consolidate contaminants. A comprehensive understanding of the biosorption capacity of various preparations allows for the selection of more effective techniques for the elimination of contaminants, as well as the overcoming of differences between laboratory results and industrial use. Observation and monitoring make it possible to evaluate the migration process of heavy metals and radionuclides and identify regions with a disturbed balance of harmful substances. The promising direction of the soil application of phosphogypsum, a by-product of the chemical industry, in bioremediation processes is considered.

Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas et al.

The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.

Bjarke Madsen, Ramoni Adeogun

Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters.

A. A. Satybaldin, B. E. Agniyazov, B. S. Myrzalyiev

The article deals with the problems of increasing the efficiency of enterprises of the agro-industrial complex, the feasibility and possibility of using the outsourcing model in the business organization, the advantages of increasing labor efficiency and forms of management using an outsourcing system for agro-industrial structures are studied. In the post-crisis period, the negative results and principles of agribusiness organization were considered as a new direction for ensuring the sustainable development of agriculture by revising the outsourcing service. At the same time, the problem of increasing the efficiency of the economy of the agro-industrial complex of Kazakhstan was formulated, and much attention was paid to the study of the regional direction and the introduction of outsourcing processes. The aim of the study is to analyze the organizational and economic essence of outsourcing and increase the efficiency of agricultural enterprises using this tool in rural business. When studying outsourcing relations in agricultural enterprises, such methods of research methodology as systemic and situational analysis were applied when introducing outsourcing the theory of the synergetic concept, comparative analysis and synthesis, methods of institutional economic analysis and generalization. The results of the study on the development of outsourcing activities are aimed at the use by small and medium-sized agricultural enterprises, market organizations in business restructuring, as well as management bodies of associations, unions, and support centers interested in providing real assistance to rural entrepreneurs.

Tri Astuti, Avin Fadilla Helmi, Bagus Riyono

Two business models differ significantly between an established company and a startup in the industrial world. There are notable distinctions between the two business models in several areas that, upon closer examination, can offer entrepreneurs, practitioners, researchers, and the government guidance in devising the most appropriate business model approach. The subsequent goal of this research is to conduct a more thorough analysis of the organizational behavior variations between startups and larger businesses. Because numerous research sources have been completed but have yet to be more thoroughly consolidated, this study uses a literature review approach to address the research issues posed. This approach is also capable of identifying variations from the given context. According to the study, established businesses and startups differ in three ways. The study's findings, to be more precise, revealed variations in the two business models' definitions and life cycles. Second, the social relations system and structure derived from these two company models differ. Finally, these two categories of businesses must deal with varying degrees of uncertainty in the workplace. This research suggests that by examining the distinctions between large and small companies which are just starting, business actors can benefit from this research and use it as a guide to improve organizational performance.

Fei Ren, Ming Liu, Yanxiang Liu et al.

Whole grains are a crucial part of healthy and sustainable diets, attracting great attention. Tianpei is a popular traditional fermented whole grain food and beverage from China. It is suitable for all ages with lots of health benefits. However, its microflora and their functions, relations between microbial taxa and functions with Tianpei properties, were still little informed, limiting the fermentation optimization and quality improvement. In this study, the characteristics and distribution of the microbial flora taxa and their functions of the fermented whole grain Tianpei from typical regions of China were mainly deciphered through metagenomic methods. Phyla Mucoromycota, Firmicutes, Ascomycota, and Proteobacteria were the most abundant. <i>Rhizopus</i>, <i>Limosilactobacillus</i>, and <i>Lactobacillus</i> were the most abundant genera. Microbial COG functions carbohydrate transport and metabolism (mainly including fructose, galactose, glucose, L-arabinose, and mannose) and amino acid transport and metabolism (mainly including arginine, asparagine, glutamine, and glycine) kept a high abundance. PCA (Principal Component Analysis) illustrated that the microbial community and their functions of every Tianpei sample clustered individually based on the analysis, related with the factors of raw material and sources. The microbial taxa, microbial functions, and the Tianpei properties were significantly correlated. <i>Rhizopus</i>, <i>Limosilactobacillus</i>, and <i>Lactobacillus</i> contributed most COG functions in Tianpei samples. Analysis of quorum sensing, pfam, secretion protein, probio, and cytochromes P450 were also annotated and found among Tianpei microbial communities. A sum of 105 probiotics were classified, mainly belonging to <i>Lactobacillus</i>, <i>Leuconostoc</i>, <i>Acetobacter</i>, <i>Bacillus</i>, <i>Bifidobacterium</i>, <i>Pediococcus</i>, etc. Tianpei samples made in the library with the most abundant and functional microbial key taxa strains—<i>Rhizophus oryzae</i>, <i>Lactobacillus plantarum</i>, and <i>Limosilactobacillus fermentum</i>—showed rich nutrient chemicals. The results indicate that microbial taxa and their functions could determine Tianpei properties. Thus, the quality, nutrients, flavor, and industrial production of Tianpei could be further investigated, promoted, and improved in the future based on the characteristics of these microbial taxa and their functions, such as the regulations of the main carbohydrate and AA. The study will also lay a foundation for the fermentative characteristics and condition technology of fermented whole grain food.

Eduard Hirsch, Simon Hoher, Stefan Huber

Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

Johannes G. Hoffer, Sascha Ranftl, Bernhard C. Geiger

We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.

Abdülkadir Şenkal

The Great Transformation, published in 1944 by Karl Polanyi, brought a new dimension to the relationship between market, state, and welfare. Polanyi considered the relation between markets and societies as a central feature of any social order; according to him, while the market destabilizes society, the commodification of labor, land, and money creates a reaction or “counter-movement.” For this reason, he describes market society as being a dominant principle for social organization. Social relations are embedded within the economic system instead of the economy being embedded in social relations. Polanyi also claims that market society is a political and social construct rather than a natural phenomenon. Yet, the rapid growth of government bureaucracy and interference in the private sphere has challenged many traditional notions related to the nature of capitalist society, especially since the 1940s. Therefore, the state plays an important role in both the establishment and regulation of the private market economy. This article proposes an interpretation, in the context of the contemporary welfare state based on Polanyi’s The Great Transformation, which discusses the distinction between market, welfare, and labor. The institutions, that once contributed to embedding the market economy within society, now play an important role in situations that have potential consequences for those seeking help from the welfare state.

V. M. Tayar

Since integration of the Spanish economy into the EU (since January 1, 1986), 35 years have already passed. During this period of time political and economic European map has undergone significant changes. Economic sectors were deeply transformed by globalization and regionalization. For Spain, the process of joining European structures mainly meant rebuilding of national economic system in accordance with the necessary criteria and norms, which set the Spanish economy on the path of diversification and modernization. In recent years, Brussels has been focusing on the growing importance of external economic and export component for the EU member–states. It means redistributing production and technological chains within the Eurozone and, furthermore, diversification of the EU trade and industrial external relations, including the demand and needs of third parties, i.e. on the markets outside the European Union. In this regard, for Spain it is of particular importance to build up its export potential, develop export clusters and extend internationalization of business, to reduce the cost of production, and intensify country’s participation in global price chains. At the same time, it is necessary to admit, that the Spanish economy and its external sector are under the pressure of both internal and external factors today. These are: protracted crisis phenomena in the European Union (Brexit, financial and economic difficulties in the Eurozone, price volatility in the energy market, etc.), the worsening conditions for interaction with the United States, trade wars, slowing global economic growth. Finally, the impact of the COVID-19 has been a major social and economic challenge for Spain.