Hasil untuk "Industrial relations"

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Donia Touihri-Mebarek

Recent developments such as the Black Lives Matter movement and the COVID-19 pandemic have laid bare persistent racial disparities in the UK, especially in the workplace. Despite the presence of a battery of legislative and regulatory protections, ethnic minority groups are characterised by lower employment levels, occupational segregation in the lowest-paying and most insecure jobs, a substantial pay gap compared to the white majority and overall lower economic achievement. In this context, the role of trade unions in combatting racism and discrimination is worth questioning. This article analyses how issues of race and industrial relations have been and continue to be connected. Drawing on official reports from trade unions and available archives, it underlines the developing position of trade unions regarding issues of racism and discrimination. It first analyses how the labour market became racialised following the advent of significant non-white immigration, with racism pervasive in trade unions. It then highlights the role played by ethnic minority groups’ resistance and mobilisation to oppose racism and discriminatory practices in the workplace. Finally, it scrutinises how the unions’ changing attitudes and policy agenda on racism and discrimination foster renewal through strategic engagement in community unionism.

Marie-Eve Corneau, Martin Lauzier

Violence is common in healthcare settings. Several studies have highlighted the prevalence of lateral violence (i.e violence between colleagues) among healthcare workers. For healthcare organizations, the preferred solution is often to provide workplace training to reduce such violence. However, few studies have evaluated the effectiveness of such training. This scoping review of 19 studies reveals several findings. The main conclusions are that these studies are limited in their ability to provide a clear answer as to the effectiveness of this type of training, given the small number of studies on the subject, their great heterogeneity and their shortcomings on several levels (i.e. conceptual, methodological, and evaluative). The absence of criteria to evaluate the learning transfer that may result from such training is an important shortcoming. In light of these observations, avenues are proposed to guide future research.

Gabriel Oliveira de Alarcão, Eduardo Roberto Zana, Tomás de Siervi Barcellos

Contexto: a definição do mercado relevante no setor de aviação internacional varia entre as autoridades antitruste, com algumas focando nas rotas de origem-destino (O&D) e outras incorporando critérios mais amplos ou mais restritos. Enquanto a Comissão Europeia enfatiza a abordagem O&D e reconhece o impacto do modelo hub-and-spoke na substitutibilidade pelo lado da oferta, persistem debates sobre o papel dos efeitos de rede e a substitutibilidade entre voos diretos e indiretos. Objetivo: o objetivo deste artigo é contribuir para o debate aplicando métodos quantitativos baseados na teoria de redes para identificar a existência de substitutibilidade pelo lado da demanda e da oferta entre rotas internacionais com origem ou destino no Brasil, possibilitando uma análise mais precisa em processos de fusões e aquisições que impactam o mercado de aviação internacional brasileiro. Método: a adoção de métodos quantitativos (utilizando indicadores de rede) em conjunto com abordagens qualitativas para analisar a substitutibilidade tanto do lado da demanda quanto do lado da oferta no setor de aviação internacional. Conclusões: este estudo demonstra a importância de adotar uma abordagem baseada em rede para avaliar a substituibilidade no setor de aviação internacional. Ao analisar tanto os fatores de demanda quanto de oferta, fornece insights sobre como a conectividade de rotas e o comportamento das companhias aéreas influenciam a dinâmica do mercado. Os resultados ressaltam a necessidade de incorporar a dinâmica das redes nas avaliações antitruste, oferecendo uma base para futuras pesquisas sobre as implicações competitivas das estruturas de rotas e das estratégias das companhias aéreas.

Marcela Gonçalves dos Santos, Sylvain Hallé, Fábio Petrillo

Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introducing small faults, or mutants, into the code. However, traditional mutation operators are poorly suited to robotic programs, which involve message-based commands and interactions with the physical world. This paper explores the adaptation of mutation testing to IRS by defining domain-specific mutation operators that capture the semantics of robot actions and sensor readings. We propose a methodology for generating meaningful mutants at the level of high-level read and write operations, including movement, gripper actions, and sensor noise injection. An empirical study on a pick-and-place scenario demonstrates that our approach produces more informative mutants and reduces the number of invalid or equivalent cases compared to conventional operators. Results highlight the potential of mutation testing to enhance test suite quality and contribute to safer, more reliable industrial robotic systems.

Aydin Homay

Design is a fundamental aspect of engineering, enabling the creation of products, systems, and organizations to meet societal and/or business needs. However, the absence of a scientific foundation in design often results in subjective decision-making, reducing both efficiency and innovation. This challenge is particularly evident in the software industry and, by extension, in the domain of industrial control and automation systems (iCAS). In this study, first we review the existing design definitions within the software industry, challenge prevailing misconceptions about design, review design definition in the field of design theory and address key questions such as: When does design begin? How can design be defined scientifically? What constitutes good design? and the difference between design and design language by relying on advancements in the field of design theory. We also evaluate the distinction between ad-hoc and systematic design approaches, and present arguments on how to balance complementary operational concerns while resolving conflicting evolutionary concerns.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Xuanming Zhang

Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.

Mikhail Bychkov, Matey Yordanov, Andrei Kuchma

This paper introduces AURA, a novel hybrid spatiotemporal-chromatic framework designed for robust, real-time detection and classification of industrial smoke emissions. The framework addresses critical limitations of current monitoring systems, which often lack the specificity to distinguish smoke types and struggle with environmental variability. AURA leverages both the dynamic movement patterns and the distinct color characteristics of industrial smoke to provide enhanced accuracy and reduced false positives. This framework aims to significantly improve environmental compliance, operational safety, and public health outcomes by enabling precise, automated monitoring of industrial emissions.

Zongyun Zhang, Jiacheng Ruan, Xian Gao et al.

Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. This decoupling is achieved through independent optimization objectives and tailored learning strategies. Additionally, we contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA), encompassing a wide range of defect types and industrial scenarios. Unlike conventional datasets that rely on GPT-generated data, DDQA ensures authenticity and reliability and offers a robust foundation for model training. Experimental results demonstrate that our proposed method, Explainable Industrial Anomaly Detection Assistant (EIAD), achieves outstanding performance in defect detection and localization tasks. It not only significantly enhances accuracy but also improves interpretability. These advancements highlight the potential of EIAD for practical applications in industrial settings.

Haoran Yang, Yinan Zhang, Wenjie Zhang et al.

Nowadays, industrial hybrid modeling which integrates both mechanistic modeling and machine learning-based modeling techniques has attracted increasing interest from scholars due to its high accuracy, low computational cost, and satisfactory interpretability. Nevertheless, the existing industrial hybrid modeling methods still face two main limitations. First, current research has mainly focused on applying a single machine learning method to one specific task, failing to develop a comprehensive machine learning architecture suitable for modeling tasks, which limits their ability to effectively represent complex industrial scenarios. Second, industrial datasets often contain underlying associations (e.g., monotonicity or periodicity) that are not adequately exploited by current research, which can degrade model's predictive performance. To address these limitations, this paper proposes the Recurrent Perceptron-based Channel Attention Transformer Encoder (RP-CATE), with three distinctive characteristics: 1: We developed a novel architecture by replacing the self-attention mechanism with channel attention and incorporating our proposed Recurrent Perceptron (RP) Module into Transformer, achieving enhanced effectiveness for industrial modeling tasks compared to the original Transformer. 2: We proposed a new data type called Pseudo-Image Data (PID) tailored for channel attention requirements and developed a cyclic sliding window method for generating PID. 3: We introduced the concept of Pseudo-Sequential Data (PSD) and a method for converting industrial datasets into PSD, which enables the RP Module to capture the underlying associations within industrial dataset more effectively. An experiment aimed at hybrid modeling in chemical engineering was conducted by using RP-CATE and the experimental results demonstrate that RP-CATE achieves the best performance compared to other baseline models.

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim et al.

Industry 5.0's increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber-physical attack surface. Industrial Control Systems (ICS) face escalating security challenges as traditional siloed defences fail to provide coherent, cross-domain threat insights. We present BRIDG-ICS (BRIDge for Industrial Control Systems), an AI-driven Knowledge Graph (KG) framework for context-aware threat analysis and quantitative assessment of cyber resilience in smart manufacturing environments. BRIDG-ICS fuses heterogeneous industrial and cybersecurity data into an integrated Industrial Security Knowledge Graph linking assets, vulnerabilities, and adversarial behaviours with probabilistic risk metrics (e.g. exploit likelihood, attack cost). This unified graph representation enables multi-stage attack path simulation using graph-analytic techniques. To enrich the graph's semantic depth, the framework leverages Large Language Models (LLMs): domain-specific LLMs extract cybersecurity entities, predict relationships, and translate natural-language threat descriptions into structured graph triples, thereby populating the knowledge graph with missing associations and latent risk indicators. This unified AI-enriched KG supports multi-hop, causality-aware threat reasoning, improving visibility into complex attack chains and guiding data-driven mitigation. In simulated industrial scenarios, BRIDG-ICS scales well, reduces potential attack exposure, and can enhance cyber-physical system resilience in Industry 5.0 settings.

Joseph Le Bihan, Thomas Lapi, José Halloy

This study proposes a new model based on a classic S-curve that describes deployment and stabilization at maximum capacity. In addition, the model extends to the post-growth plateau, where technological capacity is renewed according to the distribution of equipment lifespans. We obtain two qualitatively different results. In the case of "fast" deployment, characterized by a short deployment time in relation to the average equipment lifetime, production is subject to significant oscillations. In the case of "slow" deployment, production increases monotonically until it reaches a renewal plateau. These results are counterintuitively validated by two case studies: nuclear power plants as a fast deployment and smartphones as a slow deployment. These results are important for long-term industrial planning, as they enable us to anticipate future business cycles. Our study demonstrates that business cycles can originate endogenously from industrial dynamics of installation and renewal, contrasting with traditional views attributing fluctuations to exogenous macroeconomic factors. These endogenous cycles interact with broader trends, potentially being modulated, amplified, or attenuated by macroeconomic conditions. This dynamic of deployment and renewal is relevant for long-life infrastructure technologies, such as those supporting the renewable energy sector and has major policy implications for industry players.

Nico Baumgart, Markus Lange-Hegermann, Mike Mücke

In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale training datasets. While image synthesis from 3D CAD models is a common solution, the individual techniques of domain and rendering randomization to create rich synthetic training datasets have been well studied mainly in simple domains. Hence, their effectiveness on complex industrial tasks with densely arranged and similar objects remains unclear. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection, carefully combining randomization and domain knowledge. We describe step-by-step the creation of our image synthesis pipeline that achieves high realism with minimal implementation effort and explain how this approach could be transferred to other industrial settings. Moreover, we created a dataset comprising 30.000 synthetic images and 300 manually annotated real images of terminal strips, which is publicly available for reference and future research. To provide a baseline as a lower bound of the expectable performance in these challenging industrial parts detection tasks, we show the sim-to-real generalization performance of standard object detectors on our dataset based on a fully synthetic training. While all considered models behave similarly, the transformer-based DINO model achieves the best score with 98.40 % mean average precision on the real test set, demonstrating that our pipeline enables high quality detections in complex industrial environments from existing CAD data and with a manageable image synthesis effort.

Domenico Fiorenza, Omid Hurson

In "Homfly polynomial via an invariant of colored plane graphs", Murakami, Ohtsuki, and Yamada provide a state-sum description of the level $n$ Jones polynomial of an oriented link in terms of a suitable braided monoidal category whose morphisms are $\mathbb{Q}[q,q^{-1}]$-linear combinations of oriented trivalent planar graphs, and give a corresponding description for the HOMFLY-PT polynomial. We extend this construction and express the Khovanov-Rozansky homology of an oriented link in terms of a combinatorially defined category whose morphisms are equivalence classes of formal complexes of (formal direct sums of shifted) oriented trivalent plane graphs. By working combinatorially, one avoids many of the computational difficulties involved in the matrix factorization computations of the original Khovanov-Rozansky formulation: one systematically uses combinatorial relations satisfied by these matrix factorizations to simplify the computation at a level that is easily handled. By using this technique, we are able to provide a computation of the level $n$ Khovanov-Rozansky invariant of the 2-strand braid link with $k$ crossings, for arbitrary $n$ and $k$, confirming and extending previous results and conjectural predictions by Anokhina-Morozov, Beliakova-Putyra-Wehrli, Carqueville-Murfet, Dolotin-Morozov, Gukov-Iqbal-Kozcaz-Vafa, Nizami-Munir-Sohail-Usman, and Rasmussen.

Alaa A. Abdelrahman, Mohamed S. Abdelwahed, Hani M. Ahmed et al.

This article investigates the size dependent on piezoelectrically layered perforated nanobeams embedded in an elastic foundation considering the material Poisson’s ratio and the flexoelectricity effects. The composite beam is composed of a regularly squared cut-out elastic core with two piezoelectric face sheet layers. An analytical geometrical model is adopted to obtain the equivalent geometrical variables of the perforated core. To capture the Poisson’s ratio effect, the three-dimensional continuum mechanics adopted to express the kinematics are kinetics relations in the framework of the Euler–Bernoulli beam theory (EBBT). The nonlocal strain gradient theory is utilized to incorporate the size-dependent electromechanical effects. The Hamilton principle is applied to derive the nonclassical electromechanical dynamic equation of motion with flexoelectricity impact. A closed form solution for resonant frequencies is obtained. Numerical results explored the impacts of geometrical and material characteristics on the nonclassical electromechanical behavior of nanobeams. Obtained results revealed the significant effects of the mechanical, electrical, and elastic foundation parameters on the dynamic behavior of piezoelectric composite nanobeams. The developed procedure and the obtained results are helpful for many industrial purposes and engineering applications, such as micro/nano-electromechanical systems (MEMS) and NEMS.

Carolina Trevizo

Contextualização: Agências reguladoras e autoridades concorrenciais possuem uma posição de poder com relação aos regulados e agentes econômicos, vez que têm o poder de controlar o acesso a um mercado específico, alterar comportamentos, proporcionar concessões, etc. Observa-se, pois, a exploração desse leverage para atingir outros objetivos/obter vantagens, dentro ou fora da sua competência de atuação, que talvez fosse inviável ou requeresse recorrer a outras ferramentas mais caras ou arriscadas, não fosse a utilização dessa influência. Objetivo: Analisar em que medida o instituto do leverage regulatório do CADE é compatível com a sua atuação na promoção da defesa da concorrência no Brasil. Método: A pesquisa analisou o uso (ou tentativa) do leveraging do CADE mediante aos poderes de gatekeeper atrelados as suas atribuições, à luz da teoria dos atos administrativos, especificamente mediante o cumprimento dos requisitos de competência e finalidade do ato administrativo. Resultados: Identificou-se 3 (três) casos em que o CADE utilizou (ou tentou utilizar) seus poderes de gatekeeper, inerentes às suas funções preventivas e repressivas, para obtenção de objetivos adicionais ou mais amplos. Em 1 (um) dos casos, a autarquia atingiu a competência de outra autoridade ao tentar utilizar o poder de gatekeeper de imposição unilateral de restrições, visando tão-somente operacionalizar um ato de concentração inviável – mesmo agindo para maximizar um objetivo inerente à defesa da livre concorrência. Já nos outros 2 (dois) casos, o leverage regulatório do CADE parece ter sido utilizado de forma correta, pois a autarquia celebrou os acordos no exercício legítimo da sua competência, e impôs obrigação mais ampla que àquele atribuído à celebração de TCC pela lei concorrencial de forma coerente com as finalidades do antitruste brasileiro. Conclusões: Como o leverage regulatório não é regulamentado, seu uso desgovernado pode transformar o CADE em um executor secundário de um conjunto potencialmente grande de outras leis. Portanto, por ora, seu exercício deve estar aos objetivos do direito antitruste brasileiro (mesmo que os objetivo sejam difusos) e ao escopo de atuação da autarquia de acordo com as suas atribuições, sob pena de incorrer em algum tipo de abuso de poder. Caso haja interesse em expandir seu uso para além da zona de atuação da autarquia, ou visando atingir objetivos alheios àqueles consagrados na legislação concorrencial, é fundamental que sua legitimação se dê de forma expressa.

Simone Romagnoli

The essay analyzes the evolution of the Italian legislation on agile work, during the Sars Cov-2 emergency period. The author analyses in detail the case of ELT Group, an Italian company, worldwide leader providing technological solutions in the field of electronic defence, and examines how agile working has been addressed and regulated, during and after the pandemic. Finally, the author analyzes the missing aspects, within the industrial relations and the Italian labor law systems, to facilitate the agile work of the future.

Li Han

Based on the statistical yearbook data and related patent data of 287 cities in China from 2000 to 2020, this study regards the policy of establishing the national high-tech zones as a quasi-natural experiment. Using this experiment, this study firstly estimated the treatment effect of the policy and checked the robustness of the estimation. Then the study examined the heterogeneity in different geographic demarcation of China and in different city level of China. After that, this study explored the possible influence mechanism of the policy. It shows that the possible mechanism of the policy is financial support, industrial agglomeration of secondary industry and the spillovers. In the end, this study examined the spillovers deeply and showed the distribution of spillover effect.

Liana M. Petranek

This article discusses the current precarious state of the US economy vis-à-vis the rise of China and the US proxy war in Ukraine. It discusses the problematic of a capitalist economy and the fundamental requirement of capital accumulation to avert and circumvent capitalist cyclical crises. It also discusses the methodology of accumulation, the law of value and the necessity of geographic hegemonic control in order to sustain homeostasis within a capitalist economy. Brought into the discussion is the effect such a dynamic imposes upon not only the hegemons (US, EU, Russia, and China) within the global economy but also the population and environment that such an unrelenting mission imposes upon the ecology and geopolitical state of affairs. In response to the endogenous exigencies of capital accumulation and the exogenous threats that such projects as China’s Belt and Road Initiative (BRI) and Russia’s interests as an Eastern European hegemon present, US foreign policy is designed as strategies (“strategy of containment” in reference to Russia after World War II and now the “strategy of denial” to contain the influence China presents as a world hegemon). Also, policy changes that are codified, such as the Bush Doctrine and the National Security Strategy of the United States of America provided legal rationalization and protection for the US to pursue practices that contravened international law i.e. the Geneva Conventions. China has emerged as a world hegemon and is ubiquitously engaging on the world stage especially in Eastern, Western and South Asia, the Pacific, the Middle East, Africa, South and Central America, and Mexico. The country is becoming a threat to US capitalist interest with their “natural partnerships,” with their China–Arab States Cooperation Forum (CASCF), with their 1+2+3 Cooperation Frameworks, and with their 4 Action Plans within these countries. These Chinese initiatives as actions are seen to be destabilizing what the US calls “the balance of power” and “the rules-based order” within the global economy. The US is counteracting the threat to US capital accumulation with its neoliberal and neocon agenda, mostly formulated by the main actors within the Council on Foreign Relations (CFR) who play an important role in formulating US foreign policy in the interest of US capital and the US military industrial complex. The US is implementing this with the postulates outlined in the 2021 book The Strategy of Denial: American Defense in an Age of Great Power Conflict by Eldridge A. Colby and his fellow cohorts within the CFR who formulated the theory and particulars of the strategy that the US is currently pursuing.