Hasil untuk "Industrial relations"

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas et al.

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

Ali Bakhshi Movahed, Hamed Nozari, Aminmasoud Bakhshi Movahed

Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.

Changheon Han, Yuseop Sim, Hoin Jung et al.

Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation Sounds), a large-scale open-access dataset. DINOS comprises over 74,149 audio samples (exceeding 1,093 hours) collected from various industrial acoustic scenarios. We also present IMPACT (Industrial Machine Perception via Acoustic Cognitive Transformer), a novel foundation model for industrial machine sound analysis. IMPACT is pretrained on DINOS in a self-supervised manner. By jointly optimizing utterance and frame-level losses, it captures both global semantics and fine-grained temporal structures. This makes its representations suitable for efficient fine-tuning on various industrial downstream tasks with minimal labeled data. Comprehensive benchmarking across 30 distinct downstream tasks (spanning four machine types) demonstrates that IMPACT outperforms existing models on 24 tasks, establishing its superior effectiveness and robustness, while providing a new performance benchmark for future research.

Antoine Houssard

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.

Pablo Otálora, Sigurd Skogestad, José Luis Guzmán et al.

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.

Azmine Toushik Wasi, Enjamamul Haque Eram, Sabrina Afroz Mitu et al.

Industry 5.0 marks a new phase in industrial evolution, emphasizing human-centricity, sustainability, and resilience through the integration of advanced technologies. Within this evolving landscape, Generative AI (GenAI) and autonomous systems are not only transforming industrial processes but also emerging as pivotal geopolitical instruments. We examine strategic implications of GenAI in Industry 5.0, arguing that these technologies have become national assets central to sovereignty, access, and global influence. As countries compete for AI supremacy, growing disparities in talent, computational infrastructure, and data access are reshaping global power hierarchies and accelerating the fragmentation of the digital economy. The human-centric ethos of Industry 5.0, anchored in collaboration between humans and intelligent systems, increasingly conflicts with the autonomy and opacity of GenAI, raising urgent governance challenges related to meaningful human control, dual-use risks, and accountability. We analyze how these dynamics influence defense strategies, industrial competitiveness, and supply chain resilience, including the geopolitical weaponization of export controls and the rise of data sovereignty. Our contribution synthesizes technological, economic, and ethical perspectives to propose a comprehensive framework for navigating the intersection of GenAI and geopolitics. We call for governance models that balance national autonomy with international coordination while safeguarding human-centric values in an increasingly AI-driven world.

Athina Ditsiou, Konstantia Darvidou, Evangelos Siskos

The EU and China are among the largest economies affecting the global economy and each other. The paper aims to determine the structure and trends in the trade relations between the EU and China from the perspective of trade imbalances. Net export index (–29% in 2021) and the difference between export and import growth rates (–9% in 2016-2021) were calculated as the indicators of competitiveness of the economies relative to each other. Correlation coefficients and regression models were used to estimate the effects of several factors on the net export index. The EU has a surplus in services trade with China (21% of the trade), but it does not cover a much larger bilateral merchandise trade deficit (–36%), which exists in most member states. Machinery and vehicles are the most important traded items. The net export index shows that the European Union is more competitive than China in nonfuel minerals, food, vehicles, pharmaceutical products, intellectual property, computer, travel, and sea transport services. The effect of the real exchange rates on the trade imbalances is not robust due to the large difference in regression coefficients for the real exchange rates based on consumer prices and unit labor costs. In recent years, the trade balance was not significantly affected by industrial output growth trends in the EU and China (except for the COVID-19 pandemic crisis when the relative competitiveness of China in its trade with the EU improved at least in the short run).

Babkin Aleksandr, Mikhailov Pavel, Shkarupeta Elena et al.

The article discusses the main stages of digitalization of economic entities, including industrial enterprises and ecosystems, starting with automation, due to which routine tasks are removed from personnel and there is an opportunity to focus on more complex or creative processes, and ending with full digital transformation, which involves transformations at all levels of the economic entity’s work. As the object of the study, the authors considered industrial enterprises and ecosystems in the context of digitalization and digital transformation of the economy and industry. The subject of the study was the scientific and methodological tools for assessing the digital potential and digital maturity of industrial enterprises and ecosystems, as well as organizational and economic relations, arising in the process of its application. The purpose of the study was to develop a scientific and methodological approach for assessing the digital potential of an industrial enterprise and the coevolutionary potential of an industrial ecosystem, based on which it is necessary to propose a methodology for assessing the digital maturity of an industrial enterprise and ecosystem. To assess the digital potential and digital maturity of enterprises and ecosystems, methods of hierarchical complexing of integral indicators were used. The materials of the research were information from open electronic sources, statistical and scientific reports on the development of Russian industry, enterprises and ecosystems. In the course of the study, the following main results were obtained. The terminological apparatus was clarified in terms of the definitions of “ecosystem”, “industrial ecosystem”, “digital coevolutionary potential”, “digital maturity”. A scientific and methodological approach for assessing the digital potential of an industrial enterprise and the coevolutionary potential of an industrial ecosystem was presented. A methodology for assessing the digital maturity of an industrial enterprise and a methodology for assessing the digital maturity of an industrial ecosystem based on digital coevolutionary potential were developed. Various approaches to the concept of digital maturity of an enterprise as a cumulative assessment of digital potential and digital foresight were considered. For the developed methodology for assessing the digital maturity of an industrial enterprise, the results of its testing were given and the results of calculations of the digital maturity of the enterprise under the initial conditions, as well as when the initial data were changed. The stages of implementation of the methodology for assessing the digital maturity of the industrial ecosystem based on coevolutionary potential were outlined.

E. Jakobson, L. Jakobson

<p>This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing.</p> <p>The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas.</p> <p>The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration.</p> <p>We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, <span class="inline-formula"><i>R</i>²</span>, was highest for winter: for <span class="inline-formula"><i>T</i>_2 m</span>, <span class="inline-formula"><i>R</i>²=0.64</span>; for sea level pressure (SLP), <span class="inline-formula"><i>R</i>²=0.44</span>; and for precipitation (PREC), <span class="inline-formula"><i>R</i>²=0.35</span>. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest <span class="inline-formula"><i>R</i>²=0.09</span> being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak.</p> <p>Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.</p>

Jiayi He, Jiao Chen, Qianmiao Liu et al.

The Industrial Internet of Things (IIoT) integrates interconnected sensors and devices to support industrial applications, but its dynamic environments pose challenges related to data drift. Considering the limited resources and the need to effectively adapt models to new data distributions, this paper introduces a Continual Learning (CL) approach, i.e., Distillation-based Self-Guidance (DSG), to address challenges presented by industrial streaming data via a novel generative replay mechanism. DSG utilizes knowledge distillation to transfer knowledge from the previous diffusion-based generator to the updated one, improving both the stability of the generator and the quality of reproduced data, thereby enhancing the mitigation of catastrophic forgetting. Experimental results on CWRU, DSA, and WISDM datasets demonstrate the effectiveness of DSG. DSG outperforms the state-of-the-art baseline in accuracy, demonstrating improvements ranging from 2.9% to 5.0% on key datasets, showcasing its potential for practical industrial applications.

Lei Ren, Haiteng Wang, Jinwang Li et al.

With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

Wei Zhang, Xianfu Cheng, Yi Zhang et al.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.

Sergey Malenko / Сергей Анатольевич Маленко, Andrey Nekita / Андрей Григорьевич Некита

The widespread militarization of civilizational spaces naturally affected not only official, but also informal social environments. In the context of modern media culture, for the first time in the history of civilization, it is possible to directly introduce these forms of communication into the everyday environment of ordinary people. It is here, thanks to American horror films, that the fundamental ideological substitution of everyday forms of communication with bipolitical patterns of feelings, thoughts and behavior takes place. The visual appeal of the imposed media content contributes to the widespread displacement of family and clan relations by their institutional and industrial surrogates. By making this substitution, modern biopolitics provokes ordinary people to destroy the traditional system of values and accept militarized everyday life as the natural way of human existence. Повсеместная милитаризация цивилизационных пространств закономерно затронула не только официальные, но и неформальные социальные среды. В контексте современной медиакультуры впервые в истории цивилизации появилась возможность непосредственного внедрения этих форм коммуникации в среду повседневности обывателей. Именно здесь, благодаря американским фильмам ужасов, осуществляется принципиальная идеологическая подмена повседневных форм коммуникации биополитическими шаблонами чувств, мыслей и поведения. Визуальная привлекательность навязываемого медиаконтента способствует повсеместному вытеснению семейно-родовых отношений их институциональными и производственными суррогатами. Осуществив такое замещение, современная биополитика провоцирует обывателей к разрушению традиционной системы ценностей и принятию милитаризованной повседневности как естественного способа человеческого бытия.

Mthunzi Mndela, Julius T. Tjelele, Ignacio C. Madakadze et al.

Abstract Background Atmospheric CO2 may double by the year 2100, thereby altering plant growth, photosynthesis, leaf nutrient contents and water relations. Specifically, atmospheric CO2 is currently 50% higher than pre-industrial levels and is projected to rise as high as 936 μmol mol−1 under worst-case scenario in 2100. The objective of the study was to investigate the effects of elevated CO2 on woody plant growth, production, photosynthetic characteristics, leaf N and water relations. Methods A meta-analysis of 611 observations from 100 peer-reviewed articles published from 1985 to 2021 was conducted. We selected articles in which elevated CO2 and ambient CO2 range from 600–1000 and 300–400 μmol mol−1, respectively. Elevated CO2 was categorized into < 700, 700 and > 700 μmol mol−1 concentrations. Results Total biomass increased similarly across the three elevated CO2 concentrations, with leguminous trees (LTs) investing more biomass to shoot, whereas non-leguminous trees (NLTs) invested to root production. Leaf area index, shoot height, and light-saturated photosynthesis (A max) were unresponsive at < 700 μmol mol−1, but increased significantly at 700 and > 700 μmol mol−1. However, shoot biomass and A max acclimatized as the duration of woody plants exposure to elevated CO2 increased. Maximum rate of photosynthetic Rubisco carboxylation (V cmax) and apparent maximum rate of photosynthetic electron transport (J max) were downregulated. Elevated CO2 reduced stomatal conductance (g s) by 32% on average and increased water use efficiency by 34, 43 and 63% for < 700, 700 and > 700 μmol mol−1, respectively. Leaf N content decreased two times more in NLTs than LTs growing at elevated CO2 than ambient CO2. Conclusions Our results suggest that woody plants will benefit from elevated CO2 through increased photosynthetic rate, productivity and improved water status, but the responses will vary by woody plant traits and length of exposure to elevated CO2.

Mohammed Alnour, Maysam Ali, Abdelaziz Abdalla et al.

Despite the overwhelming research and useful outcomes on the relationship between hydropower consumption, urban population growth, and environmental quality, the empirical studies have mostly relied on partial indicators of environmental quality and employed conventional econometric models in which structural shocks are not controlled. Therefore, this study aims to offering a new perspective for the dynamic connection between hydropower consumption, urban population growth, natural resources rent, and ecological footprint within the context of Sudan during the period spanning 1990Q12018Q1. The study applied SVAR model to monitor the structural shocks, by decomposing shocks through relevant matrices and composite shocks through recursive impulse responses via a triangular matrix. In addition, the study employed the wavelet coherence technique to explore the lead-lag relations among the purposed variables. The findings of SVAR model uncover that hydropower consumption and economic growth significantly reduce the pollution in the long run, while urban population growth can do so in the short run. Moreover, natural resources rent, and industrial production are found to have detrimental effects on environment quality. The wavelet coherence analysis discloses that ecological footprint is lagging in hydropower consumption and natural resources rent. Meanwhile, ecological footprint leads the rest of the variables. The findings clearly reveal that hydropower plays an essential role in the fight against the environmental pollution in Sudan. Accordingly, the study suggests that throughout the energy sector reforms, more emphasis should be placed on the expansion of hydropower plants accompanied by strong environmental measures. Furthermore, the study recommends implementing efficient and sustainable urban policies by coordinating actions across state and regional urban institutions.

В. Чорній, Є. Дергачов

The article considers the trends of the COVID-19 pandemic impact on the economic activity of certain sectors of the economy, the consequences of quarantine restrictions in the national and world economy. The introduction of quarantine in the economic system provoked a decrease in purchasing power and income, there was a halt in transport and financial operations and communications, there was a need to develop social and medical spheres. There was a reduction in industrial production, small business and trade. Sectors of transport, tourism, hotel and restaurant business underwent crisis changes. This required a redistribution of capital and increased reserves. As a result, it was possible to reduce the rate of decline in key macroeconomic indicators. Some sectors of the economy in global challenges to refocus on digital technologies and successfully apply them. This provoked the development of the markets of online education, gambling, and e-commerce. Such changes have allowed the preservation of international contracts and economic relations. In addition, the current economic crisis has the specifics of a cognitive economy. this involves building direct links between the producer or seller and the end consumer. In the future, the management of international contracts in the post-crisis period will be based on digital communications, availability of IT technologies and logistics of product distribution, socio-psychological and institutional influences.

Bonno Pel, Julia M. Wittmayer, Flor Avelino et al.

Society is transforming through a whirlpool of innovations. This includes technological as well as social innovations, i.e. changes in social relations involving new ways of doing, organizing, framing and knowing. Especially the potentials for transformative social innovation (TSI) are gaining the interest of progressive political actors and critical scholars. Occurring in the form of new modes of governance and alternative ways of working and living together, TSI involves the challenging, altering or replacing of dominant institutions. As documented in various strands of critical social inquiry and innovation research, TSI praxis is pervaded with contradictions, anomalies and paradoxes. This methodological contribution addresses the challenge that tends to remain: How to elaborate this general critical awareness into more operational ‘strategies of inquiry’? The paper discusses paradoxes of a) system reproduction, b) temporality, and c) reality construction. Identifying distinct kinds of contradictions and distinct empirical phenomena, this differentiation also calls attention to the associated differences between realist, processual and constructivist research philosophies. Gathering the empirical analyses, theoretical interpretations and methodological advances that have been made on these paradoxes, this contribution opens up the scope for critical and practically relevant innovation research: It is important to bridge the divide between rigorous but sterile methodological know-how, and critical-reflexive theorizing that lacks operational insights.

Qifan Jin, Li Chen

The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based industrial product surface defect detection methods and deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data. The traditional image processing-based industrial product surface defect detection methods are divided into statistical methods, spectral methods and model methods. Deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data are divided into based on data augmentation, based on transfer learning, model-based fine-tuning, semi-supervised, weak supervised and unsupervised.

RODE Sanjay

Every municipal corporation must prove continuous quality infrastructural services to people within its jurisdiction. But due to rise in population and paucity of financial resources, the Kalyan Dombivali Municipal corporation is not able to provide quality basic infrastructural facilities such as transportation, roads and traffic management, sewage treatment, solid waste management, education and health care. Kalyan Dombivali municipal corporation is declared as smart city. But funds are inadequate to provide quality infrastructure to people. Already the capital expenditure is very low for various services and economically weaker section and poor people of city. Municipal corporation is spending very less on education as far as revenue expenditure is concerned. Slums are increasing due to lack of affordable housing scheme. There is need to invest more money on infrastructural facilities such as safe and sustainable drinking water, complete sanitation, sewage treatment, technological investment in road traffic management and environment sustainability. Municipal corporation should not depend on central and state government resources to finance capital expenditure. It must raise capital through issue of municipal infrastructure debt bonds. It must increase the property tax, water tariff, tax on registration of private vehicles, registration and transfer fees on private properties. Such measures will certainly improve the financial condition of the Kalyan Dombivali Municipal Corporation and it will able to provide the better quality infrastructural facilities to its population.