Hasil untuk "Industrial relations"

Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl et al.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Dan Peng

The automation of resume screening is a critical component of modern recruitment processes, particularly in large organizations. Automated systems for resume screening typically involve various NLP tasks to streamline candidate evaluation. This paper investigates the application of LLM models in automating labor education and skill assessment, focusing on optimizing workforce development through advanced language models. We propose a comprehensive framework for automating resume screening and grading, utilizing SOTA LLM models to enhance recruitment processes. The proposed system integrates information extraction and summarization tasks, leveraging LLMs for decision-making throughout the hiring process. Our experiments, conducted on a publicly available resume dataset, demonstrate significant improvements in efficiency and accuracy. The LLaMA2-13B model, achieves a ROUGE-1 score of 37.31, ROUGE-2 of 15.04, ROUGE-L of 36.99, and BLEU score of 13.82, significantly outperforming the baseline models such as FLAN-T5 and GPT-NeoX. These results highlight the potential of LLM-based systems in automating labor-related assessments, with the fine-tuned LLaMA2-13B model delivering up to 27% better performance than zero-shot models.

Varvara Borisova, Tereza Stöckelová

Abstract This article explores the notion of rational nutrition that constituted the core of nutritional expertise in socialist Czechoslovakia and the modes of nutritional modernity it encompassed. The study draws on an analysis of issues published between 1946 and 1986 of the journal People and Nutrition, which was founded by the Czechoslovak Society for Rational Nutrition to disseminate expert knowledge among both the expert community and the public. Drawing on Hannah Landecker’s notion of industrial metabolism and Annemarie Mol’s concept of ontonorms, the study focuses on the complex relations and tensions present in socialist expertise between the individual and the collective and between reason and pleasure. First, the article zooms in on the epistemological shift from the body as a “human engine”, which functions according to the “calories in – calories out” principle, towards a more complex understanding of metabolism as a regulatory system shaped by social factors and environmental exposure. Second, the article explores how dietary recommendations took pleasure into account and what relationship was construed between rationality and pleasure. Finally, the article examines the articulation of individual responsibility for one’s metabolic health and the collective, state-led efforts to implement “rational nutrition”. Exploring expertise in the state-socialist era, the study aims to contribute to a more nuanced understanding of nutritional modernity across different socio-political contexts.

Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula et al.

The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.

Pietro Chiavassa, Stefano Scanzio, Gianluca Cena

Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.

Changheon Han, Yun Seok Kang, Yuseop Sim et al.

Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud infrastructure or high-end hardware that is impractical for on-site, real-time deployment. We address this gap with LISTEN (Lightweight Industrial Sound-representable Transformer for Edge Notification), a kilobyte-sized industrial sound foundation model. Using knowledge distillation, LISTEN runs in real-time on low-cost edge devices. On benchmark downstream tasks, it performs nearly identically to its much larger parent model, even when fine-tuned with minimal datasets and training resource. Beyond the model itself, we demonstrate its real-world utility by integrating LISTEN into a complete machine monitoring framework on an edge device with an Industrial Internet of Things (IIoT) sensor and system, validating its performance and generalization capabilities on a live manufacturing shop floor.

Xiaochao Dang, Xiaoling Shu, Fenfang Li et al.

Hyper-relational knowledge graphs can enhance the intelligence, efficiency, and reliability of industrial production by enabling equipment collaboration and optimizing supply chains. However, the construction of knowledge graphs in industrial fields faces significant challenges due to the complexity of hyper-relational data, the sparsity of industrial datasets, and limitations in existing link prediction methods, which struggle to capture the nuanced relationships and qualifiers often present in industrial scenarios. This paper proposes the HyLinker model, designed to improve the representation of entities and relations through modular components, including an entity neighbor aggregator, a relation qualifier aggregator, MoE-LSTM (Mixture of Experts Long Short-Term Memory), and a convolutional bidirectional interaction module. Experimental results demonstrate that the proposed method performs well on both public datasets and a self-constructed hoisting machine dataset. In the Mine Hoist Super-Relationship Dataset (MHSD-100), HyLinker outperforms the latest models, with improvements of 0.142 in MRR (Mean Reciprocal Rank) and 0.156 in Hit@1 (Hit Rate at Rank 1), effectively addressing the knowledge graph completion problem for hoisting machines and providing more accurate information for equipment maintenance and fault prediction. These results demonstrate the potential of HyLinker in overcoming current challenges and advancing the application of hyper-relational knowledge graphs in industrial contexts.

William Thériault, William Thériault, Guillaume Blanchet et al.

Current strategies for teaching evacuation methods in private seniors’ residences (PSR) and long-term care (LTCH) homes may pose risks to people with disabilities (PWD) and seniors' physical and psychological health. This study aimed to address the following questions: (1) Which are the current fire evacuation learning strategies used with PWD or seniors? (2) What are the barriers and facilitators for PWD and seniors' during fire evacuation and learning strategies in PSR and LTCH? (3) What is the existing equipment that could be used with PWD seniors?. A scoping review of grey and scientific literature was done in six databases and Google scholar. Additional information was found on Québec government websites. This review identified 13 scientific papers and 22 documents. Twenty barriers (personal = 9, environmental = 11), and 14 facilitators (personal = 4, environmental = 10) were extracted. The current fire evacuation learning strategies currently used can be grouped into three categories: drills; training; promotion of a fire safety plan. Six types of evacuation equipment were found; however, their use has been scarcely documented. Safety for seniors during fire evacuation is still an important issue to be improved. Increasing awareness and creating new practices and tools that consider the strengths and difficulties of seniors seems to be a promising avenue for improving evacuation.

Gurjot Singh Bhatia, Yoann Corre, Linus Thrybom et al.

Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP's channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.

Kun Qian, Tianyu Sun, Wenhong Wang

Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industrial settings. CLAD aligns visual and textual features into a shared embedding space using contrastive learning, ensuring that normal instances are grouped together while anomalies are pushed apart. Through extensive experiments on two benchmark industrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization. Additionally, we provide ablation studies and human evaluation to validate the importance of key components in our method. Our approach not only achieves superior performance but also enhances interpretability by accurately localizing anomalies, making it a promising solution for real-world industrial applications.

Yuchen Xia, Nasser Jazdi, Jize Zhang et al.

Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.

Hambur Wang

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.

Anna M. Krol, Marvin Erdmann, Ewan Munro et al.

In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem of industrial shift scheduling in manufacturing and the Quantum Industrial Shift Scheduling algorithm. We base our figures of merit on current technology, as well as theoretical high-fidelity scenarios for superconducting qubit platforms. We find that the execution time of gate and measurement operations determines the overall computational runtime more strongly than the system error rates. Moreover, achieving a quantum speedup would not only require low system error rates ($10^{-6}$ or better), but also measurement operations with an execution time below 10ns. This rules out the possibility of near-term quantum advantage for this use case, and suggests that significant technological or algorithmic progress will be needed before such an advantage can be achieved.

Danny Weyns, Thomas Vogel

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community.

O. N. Misko, P. I. Sysoev

The article describes transformation of the ideology of liberalism and the associated practices of economic domination at the transition stage from the socio-philosophical Modern paradigm to Postmodern paradigm.Aim. Identify the link between the rhetoric of modern liberalism, Postmodern paradigms and trends in the evolution of the world economic system.Tasks. Consider the historical conditions for the formation of liberal and neo liberal ideology; determine the place of neoliberal discourse in the ideological landscape of our time; correlate it with other relevant discourses that claim to explain economic, social and cultural processes; to analyze the features of modern practices of developed countries economic domination in relations with the others world countries.Methods. The article, using logical analysis, establishes the main features of modern liberal ideology, allowing us to talk about a new stage in the history of the liberalism development. Comparative analysis allows us to find out the points of liberalism convergence at this stage with other ideological directions in present time. However, statistical analysis reveals important economic factors impeding the reduction of the distance between developed and developing countries.Results. The study showed that to date, the liberal discourse has undergone significant changes, which allows us to talk about a completely new stage in the development of liberal ideology. On the one hand, it was significantly influenced by a general paradigm shift that affected the entire spectrum of socio-economic thought in the 20th century, which we define as an intensifying transition from the Modern paradigm to the Postmodern paradigm. On the other hand, the analysis of the historical path of imperialism and its inherent practices of domination also allows us to talk about its sig nificant transformation in the conditions of the post-industrial economy and the information society. Parallel consideration of these aspects (ideological and practical), the continuous correlation of theory with economic realities allows the authors to define such a complex concept as hyper imperialism. This term is proposed to be used to refer to a specific form of imperialism in a post-industrial manner. At the same time, the intrinsic inseparability of various types of expansion — economic, ideological and, more broadly, civilizational — is taken as its key feature.Conclusion. In the context of a large-scale socio-economic and ideological transformation of a global nature, the need for new forms and means of confronting economic hegemony and finding a path to independent economic and social development is increasing.

Gregor Thiele, Theresa Johanni, David Sommer et al.

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system.

Tim Lackorzynski, Sebastian Rehms, Tao Li et al.

Trends like Industry 4.0 will pose new challenges for future industrial networks. Greater interconnectedness, higher data volumes as well as new requirements for speeds as well as security will make new approaches necessary. Performanceoptimized networking techniques will be demanded to implement new use cases, like network separation and isolation, in a secure fashion. A new and highly efficient protocol, that will be vital for that purpose, is MACsec. It is a Layer 2 encryption protocol that was previously extended specifically for industrial environments. Yet, it lacks the ability to bridge local networks. Therefore, in this work, we propose a secure and efficient Layer 3 tunneling scheme for MACsec. We design and implement two approaches, that are equally secure and considerably outperform comparable state-of-the-art techniques.

Xian Tao, Xinyi Gong, Xin Zhang et al.

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Siddhartha Paul Tiwari

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.

D. Smirnov, L. Botasheva, A. Leonov

The article deals with the issues of legal support of active digitalization processes, including the economic sphere. The state policy on active digitalization of economic relations found expression in the national program "Digital Economy of the Russian Federation", which includes six federal projects: "Law regulation of digital environment", "Information infrastructure", "Personnel for the digital economy", "Information security", "Digital Technologies" and "Digital Public Administration". The provisions of this national program were the subject of research in the article. The current stage of development is characterized by processes of technologization and digitalization of social relations. The emergence of new phenomena of digital economy: big data, machine law, neurotechnology and artiicial intelligence; distributed registry systems (blockchain); quantum technologies; new production technologies; industrial internet; components of robotics and sensorics; wireless technology); technologies of virtual and augmented reality, LegalTech, FinTech require new approaches to the universal regulator - the law. Innovations in the financial and banking sectors, developing outside the legal regulation, are accompanied by serious risks in the economic sphere. Last year, the public movement "Cryptovolya" released Cryptomani-fest, where a requirement is claimed for the state to legalize cryptocurrency and not to hinder its circulation. Unfortunately, today's turnover of digital inancial assets is almost not subject to the laws, the law on cryptocurrency promised for adoption in December has been "safely" rejected. The Russian Federation and other countries are still not in a hurry to mediate new social relations by law, but it should be noted that a certain activity of the domestic legislator over the past few years has resulted in the adoption of a number of important digital rights regulations. The authors of the article paid attention to the problems of giving legal status to such digitalization tools as: blockchain, bitcoin, cryptocurrency.