Hasil untuk "Industrial relations"

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian et al.

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

Claudio Pirrone, Stefano Fricano, Gioacchino Fazio

The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.

Sangbum Choi, Kyeongryeol Go, Taewoong Jang

Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To bridge this gap, Superb AI introduces ZERO, an industry-ready vision foundation model that leverages multi-modal prompting (textual and visual) for generalization without retraining. Trained on a compact yet representative 0.9 million annotated samples from a proprietary billion-scale industrial dataset, ZERO demonstrates competitive performance on academic benchmarks like LVIS-Val and significantly outperforms existing models across 37 diverse industrial datasets. Furthermore, ZERO achieved 2nd place in the CVPR 2025 Object Instance Detection Challenge and 4th place in the Foundational Few-shot Object Detection Challenge, highlighting its practical deployability and generalizability with minimal adaptation and limited data. To the best of our knowledge, ZERO is the first vision foundation model explicitly built for domain-specific, zero-shot industrial applications.

Sanjukta Ghosh

Industrial diagrams such as piping and instrumentation diagrams (P&IDs) are essential for the design, operation, and maintenance of industrial plants. Converting these diagrams into digital form is an important step toward building digital twins and enabling intelligent industrial automation. A central challenge in this digitalization process is accurate object detection. Although recent advances have significantly improved object detection algorithms, there remains a lack of methods to automatically evaluate the quality of their outputs. This paper addresses this gap by introducing a framework that employs Visual Language Models (VLMs) to assess object detection results and guide their refinement. The approach exploits the multimodal capabilities of VLMs to identify missing or inconsistent detections, thereby enabling automated quality assessment and improving overall detection performance on complex industrial diagrams.

Moritz Mock, Thomas Forrer, Barbara Russo

Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.

Andrea Komlosy

This article discusses processes of core formation and peripheralization in the Habsburg Monarchy, focusing on political, economic and cultural factors and their interplay in various lands of the empire between the regional, the provincial and sub-state level in the aftermath of the Austro-Hungarian Dual Settlement or Compromise (1867). The formula of the Compromise offered a way to find a political balance between the Austrian and the Hungarian sub-empires in spite of an unequal economic division of labor between industrial and agrarian provinces. The common market benefited Hungarian big agriculture as well as Austrian and Bohemian big industry, offering graduated perspectives for different business and national elites to accept the dual framework. K.u.k. unity was challenged at countless occasions, but it did not collapse before the defeat in World War I. The article also discusses Austro-Hungary’s international relations, which are characterized by a semi-peripheral position vis-a-vis West European great powers on the one hand and European as well as non-European colonized or dependent regions on the other hand. Although not possessing proper colonies, Austria-Hungary participated in “colonial complicity” with European colonial powers, especially on the Balkans, Africa and Asia. Moreover, the entangled imbalances between the sub-empires, the lands and the provinces showed similarities with colonial relations and compensated for Austria-Hungary’s lack of oversee colonies, hence contributing to imperial cohesion.

Chen Li, Ruijie Ma, Xiang Qian et al.

Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.

F. Adetunji, A. Karukayil, P. Samant et al.

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.

M. Henderson, J. P. Edelen, J. Einstein-Curtis et al.

Industrial particle accelerators typically operate in dirtier environments than research accelerators, leading to increased noise in RF and electronic systems. Furthermore, given that industrial accelerators are mass produced, less attention is given to optimizing the performance of individual systems. As a result, industrial accelerators tend to underperform their own hardware capabilities. Improving signal processing for these machines will improve cost and time margins for deployment, helping to meet the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging. Our work focuses on using machine learning techniques to reduce noise in RF signals used for pulse-to-pulse feedback in industrial accelerators. Here we review our algorithms and observed results for simulated RF systems, and discuss next steps with the ultimate goal of deployment on industrial systems.

Nursafa G. Khairullina

The Tyumen region plays an important strategic role among Russian regions, remaining a leading domestic oil and gas producer for six decades. The complex nature of Russia’s current foreign policy situation poses new challenges for society, authorities, and residents of the country in various spheres of life, in particular, in the field of ethno-confessional development and interaction. To assess the impact of ongoing activities, it is necessary to systematically conduct sociological research taking into account the main provisions of the national policy of the state. An example of such research is the monitoring of interethnic and interfaith relations, carried out since 2013 in Russian regions, including the cities of the south of the Tyumen region. Questionnaire surveys conducted by scientists from the Tyumen Industrial University among residents of two provincial cities (Yalutorovsk, Zavodoukovsk) revealed the positive dynamics of the interethnic and interfaith situation in the region. According to the survey results, the number of residents who positively assess the relationships that develop between people of different nationalities and religions increased during the study period by 1–4% and amounted to 90–95%. For comparison, countrywide, three-quarters of Russians give such assessment. 93.3–94.0% of residents of these cities indicated the absence of hostility towards them on the basis of nationality and religion; over five years their number increased by 3.2–5.3%. In the course of the research, it is concluded that in the provincial cities of the south of the Tyumen region, a stable interethnic and interfaith situation has developed, which determines a positive direction in the areas under study. The goodwill of the existing relationships is not influenced by the nationality and religion of the respondents.

Vasily Kashin, Ekaterina Kosevich

Introduction. China started to develop military-to-military ties and defense-industrial cooperation with Latin America and Caribbean countries in the early 2000s as an element of its strategy of comprehensive cooperation with the region. Defense cooperation was mentioned in the Chinese documents and statements on the policy in the region from the early 2000s. However, China has always considered this defense cooperation to be just a secondary, subordinate element of its overall strategy in the region. Methods. The article is based on Chinese publications, statements, and documents concerning China’s policies in defense cooperation with the region, as well as on Latin American and other Western publications on specific cooperation projects. The author compares the Chinese approach to defense cooperation with Latin America to similar Chinese cooperation policies in other regions, especially in Africa. Analysis.China views defense cooperation with Latin America as a subordinate element of its general strategy of economic and political presence in the region. Cultivating ties with the local military elites of Latin America is especially emphasized, and the exchange of military delegations started in the early 2000s. To boost defense cooperation, China is sometimes ready to provide significant military assistance to the region. China has established permanent mechanisms of defense dialogue with the regional countries in the form of regular forums and conferences. Other important venues of military cooperation include personnel training, joint exercises, and the region’s visits by Chinese warships. China has managed to establish a presence on the arms market in Latin America in the 2000s but has failed to become a major weapons provider in the region. Currently, Chinese arms sales in Latin America are in decline. However, China has achieved major results in the development of dual-use technology cooperation with the LAC countries, especially in the fields of space and internal security. That may create preconditions for faster development of military and military technical cooperation between China and Latin America in the future. So far, the Chinese approach to defense cooperation in the region remains more cautious and gradual compared to cooperation with Africa. China is reluctant to challenge the US red lines here. That may change in the future as China-US relations continue to deteriorate. Authors contributions. Ekaterina Kosevich was responsible for researching and covering the development of political relations between China and Latin America. Vasily Kashin touched on cooperation in defense, the security industry, and dual-use technology.

Yue Wang, Jinlong Peng, Jiangning Zhang et al.

2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multimodal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code is available at https://github.com/nomewang/M3DM.

Francesco Ragusa, Rosario Leonardi, Michele Mazzamuto et al.

ENIGMA-51 is a new egocentric dataset acquired in an industrial scenario by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and equipments (e.g., oscilloscope). The 51 egocentric video sequences are densely annotated with a rich set of labels that enable the systematic study of human behavior in the industrial domain. We provide benchmarks on four tasks related to human behavior: 1) untrimmed temporal detection of human-object interactions, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human behavior in industrial scenarios. We publicly release the dataset at https://iplab.dmi.unict.it/ENIGMA-51.

Aminet Magametovna Siyukhova, Zukhra Khadzhimuratovna Mamizheva, Angela Askerovna Kubova et al.

The article considers various aspects of a sociological study on the preferences of the population in fashionable clothes. Based on the well-known scientific literature on fashion as a social phenomenon, Remarks have been made on the need for reconsidering some theoretical position in the post-industrial era, as most of the public life is transferred to the media space, the Internet, and the way celebrities dress becomes the object of intent attention of the mass audience. It has been pointed out that the modern information age has changed the ratio of the importance of individuals by gender in the direction of some increase in the status of the social category of women in public relations, but at the same time their need for self-presentation through fashionable clothes has not decreased. It has been substantiated that applied sociological research in order to study preferences in fashionable clothes of different categories of society can clearly illustrate many characteristics of social life, such as economic, activity, psychological, ideological, etc. Also, the level of interest in fashion can indirectly inform about the level of social consolidation, or the abnormal state of society. In addition to the analysis of theoretical sources, a sociological survey method has been used in the form of a questionnaire (100 women of two age categories have been interviewed). The survey included questions about their preferences for clothing styles in everyday life and about the interest in aesthetics in the clothing of media persons. One of the main results of research is the conclusion that the respondents’ conscious attitude to fashion illustrates a high level of their socialization and socially active attitude to life.

Aleksandr V. Babkin, Luiza R. Batukova

Background. Since the beginning of the 21st century, the world system of financial, economic and economic relations has been objectively evolving towards the formation of a post-industrial integral cyber-formation production method. This serves as a basis for talking about the onset of the era of the eponymous Integral Cyber Formation Society. The complexity of the tasks to be solved by the state and society in the course of the upcoming changes requires new approaches to organizing industry, including the use of cyber-social industrial ecosystems and mechanisms for sustainable ESGC development. The purpose of the study is to develop in the system paradigm the conceptual foundations of the mechanism of sustainable ESGC-development of the cluster-type cybersocial industrial ecosystem, as well as the justification of the need for multidimensional system modeling of this mechanism for the design of its relevant digital image. Materials and methods. The study was conducted in a system paradigm based on system theory and a system approach. To study the causes of historical evolutionary changes in economic and economic mechanisms, general scientific methods of analysis and synthesis, and system-organizational analysis tools were used. During the analysis of the actual material and to present the results, the method of structural and logical modeling and visualization of the conceptual foundations of economic and economic systems was used. Results. The main results are: a model of the organizational mechanism of the cybersocial industrial cluster, which determines the conceptual essence of the multidimensional system modeling of the mechanism of sustainable ESGC-development of the cybersocial industrial ecosystem of the cluster type; the concept of a new, engineering approach to the design and modeling of cluster-type cybersocial industrial ecosystems based on the theoretical and methodological basis of the system paradigm. Conclusions. The proposed conceptual foundations of multidimensional system modeling of the mechanism of sustainable ESGC-development of a cluster-type cybersocial industrial ecosystem will contribute to the development of the digital industry economy and increase its efficiency.

Naim Göktaş

Workers make up the vast majority of societies. Hence, wage policies are an important item on the agenda. This study examines wage policies implemented in Türkiye in recent years. Our findings reveal the main facts that stand out in our country’s wage policies. Moreover, the Turkish Statistical Institute’s dominant role in these policies has been evaluated. In addition, the increasing distrust toward the inflation measurements of the Turkish Statistical Institute and the concrete reasons for this distrust have been disclosed. After these assessments, the wage policies in the collective bargaining agreements were investigated. This study also used document analysis method and examined the articles regulating wage increases in the agreements. Accordingly, questions such as whether trade unions will be considered part of general wage policies or whether trade unions have constructed policies that can change current general wage policies were answered. Finally, the study emphasized that unions should introduce new methods in collective bargaining wage policies to protect the welfare of employees.

Krakovskaia Irina, Korokoshko Yulia, Slushkina Yuliana

The industry of the Russian Federation can become a driver of the digital economy and information society by creating a basis for ensuring technological sovereignty and establishing conditions for the development of business models for industrial enterprises. Thus, the executive and legislative authorities of the Russian Federation should accelerate the development of a comprehensive regulatory framework for state policy in this area. The purpose of the study is to systematize foreign and Russian approaches and directions of state regulation of industry digitalization, as well as to analyze their impact on the speed and efficiency of the transformation of industrial business models. The authors analyzed scientific publications on the research topic presented in the system of the Russian Science Citation Index and in Scopus, Taylor & Francis databases. The systematization of regulatory documents and technical regulations that support the digital transformation of Russian industry and state policy in this area was carried out. The results of the study are supplemented with the results of a questionnaire survey of industrial enterprises in a number of Russian regions conducted in April–June of 2022 using the Google Forms service. It was found that the state policy of digital industrialization of the Russian Federation and its regulatory support are focused on the best world practices and claim to be an integrated approach. But so far a number of problems remain: insufficient consistency, high variability of the legal field, insufficient development of technical regulation, the presence of contradictions in legislation, standards and technical regulations. The regulatory and legal framework for the digital transformation of business models in the Russian industry, despite the measures taken, has not yet been formed at a level sufficient to solve the tasks set. This preserves a number of barriers to digital industrialization and makes it difficult to plan and implement business model transformation projects at industrial enterprises. An integrated approach to the regulation of digital legal relations is important. Such an approach should go beyond focusing only on technology, it should be based on an understanding of digital business models as complex socio-economic and technical systems.

Yidan Feng, Biqi Yang, Xianzhi Li et al.

Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances. Particularly, industrial objects can have irregular shapes, that is, thin and concave, whereas in bin-picking scenarios, objects are often closely packed with strong occlusion. To address these challenges, we formulate a novel part-aware instance segmentation pipeline. The key idea is to decompose industrial objects into correlated approximate convex parts and enhance the object-level segmentation with part-level segmentation. We design a part-aware network to predict part masks and part-to-part offsets, followed by a part aggregation module to assemble the recognized parts into instances. To guide the network learning, we also propose an automatic label decoupling scheme to generate ground-truth part-level labels from instance-level labels. Finally, we contribute the first instance segmentation dataset, which contains a variety of industrial objects that are thin and have non-trivial shapes. Extensive experimental results on various industrial objects demonstrate that our method can achieve the best segmentation results compared with the state-of-the-art approaches.

Benjamin Maschler, Tim Knodel, Michael Weyrich

Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer's results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches' applicability caused by its results' reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.

Richard Ohmann

            In the fall of 1978 I was consultant to a video project called "The Unemployment Tapes," designed to explore through talks with local people the human costs, the causes, and the possible cures of unemployment in an old industrial area of Connecticut. At the time, I was also reading and thinking about class, language, and the theories of Basil Bernstein. I began to notice in the taped interviews a close correspondence to Bernstein's cen­tral distinction between "restricted" and "elaborated" codes: almost all the peo­ple interviewed on the streets spoke in the restricted code that Bernstein attrib­utes to the working class, while managers and officials used the elaborated code of what Bernstein calls the "middle class." . . . The power relations of a society permeate speech and shape it, while speech reproduces or challenges the pow­er relations of the society. The way we talk is not just an artifact of class, any more than class is an artifact of the ways we talk. Speech takes place in society, but society also takes place "in" speech. The point is well illustrated, I believe, by what happened in those two interviews. A Bernsteinian explanation of their con­trasts badly misrepresents the social forces at work in them, assigning to static "class," differences in speech that ex­press dynamic and changeable power relations. . . . Movements toward worker self-management, co-ops, progressive credit unions, consumer movements, union organizing, populist movements of many kinds, are all fertile soil in which elaborated codes (put to better use than by the mayor, I hope) may grow along with the habit of democracy.