Hasil untuk "Industrial relations"

Nermeen Arastu, Linda Bosniak, Barbara Buckinx et al.

In the aftermath of the 2018 migrant caravans, the Mexican government arrested two migrants’ rights activists,1 but not because they gave food or donated clothes to the caravaneros. The transgressive nature of their activism consisted of walking and organizing alongside people whose presence in the country was unauthorized. They were charged with smuggling-related crimes; but they were really “guilty” of solidarity. In this essay, we outline what solidarity entails, what compels various actors to join in, and to what end. From an interdisciplinary perspective, we discuss the “what,” “where,” “who,” and “why” of solidarity. The purpose is to open a new epistemological horizon, providing tools to collectively reflect on the complex issues at the intersection between solidarity, migration, and law.

Bree Hadley

In the last decade, #BlackLivesMatter, #MeToo, and other social justice campaigns have increased awareness of the decades-long work of activists to address the structural exclusion of D/deaf, disabled, and neurodiverse, First Nations, Culturally and Linguistically Diverse, and LGBTIQA+ artists, in the arts industry, and the institutions that train for it. Increased commitment to individual, institutional, and industry-wide allyship is a sign of the success of years of activism. However, it also highlights gaps between rhetoric and day-to-day lived reality in the industry and the institutions that train for it. Strategies, policies, funding programmes, and initiatives notwithstanding, the statistical data suggests that the arts industry has considerable work to do to achieve full inclusivity in representation and industrial relations. In this article, I seek to assist artists and would-be allies in navigating the complex web of personal, social, professional, artistic, and financial motivators that drive different approaches to allyship relationships by examining them in terms of six Spectrums of Allyship. The six spectrums, axes, or motivators of allyship I identify – attitudes to labour, capital, community, power, risk, and safety – are interconnected drivers which, laid over one another, consciously or unconsciously direct would-be allies towards more or less appreciated modes of artist–ally collaboration. Together, these form a tool that can help reflect on why, despite stated commitment to inclusion, it remains difficult for arts workers, the arts industry, and the institutions that train for it, to enact the good allyship that leads to improved education, employment, leadership, and economic opportunities for disabled and otherwise diverse artists.

Yifan Li, Yuhang Chen, Anh Dao et al.

Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to industrial settings. This drawback limits the evaluation of agent readiness for real-world industrial applications. To bridge this, we introduce IndustryEQA, the first benchmark dedicated to evaluating embodied agent capabilities within safety-critical warehouse scenarios. Built upon the NVIDIA Isaac Sim platform, IndustryEQA provides high-fidelity episodic memory videos featuring diverse industrial assets, dynamic human agents, and carefully designed hazardous situations inspired by real-world safety guidelines. The benchmark includes rich annotations covering six categories: equipment safety, human safety, object recognition, attribute recognition, temporal understanding, and spatial understanding. Besides, it also provides extra reasoning evaluation based on these categories. Specifically, it comprises 971 question-answer pairs generated from small warehouse and 373 pairs from large ones, incorporating scenarios with and without human. We further propose a comprehensive evaluation framework, including various baseline models, to assess their general perception and reasoning abilities in industrial environments. IndustryEQA aims to steer EQA research towards developing more robust, safety-aware, and practically applicable embodied agents for complex industrial environments. Benchmark and codes are available.

BOLARINWA Taiwo-Agosu, ANOCHIRIONYE Elssy-Chidinma

This research investigates the relationship between exchange rates and economic growth in Nigeria during the period from 1981 to 2019. The research conducted an analysis of Nigerian data using the Ordinary Least Square (OLS) technique and identified that Exchange Rates have a favourable impact on Economic Growth. This paper affirmed the regression's validity with stationary residuals and found no long-term equilibrium using Bound Cointegration. The ARDL modelling revealed short-term connections between exchange rates and economic growth. Positive correlations were observed between exchange rates, GDP per capita growth, interest rates, and total exports, while negative relationships were noted with inflation and total imports, highlighting the importance of exchange rate stability in sustaining economic growth. This research provides a novel viewpoint on the connection between exchange rates and economic growth in Nigeria during the period from 1981 to 2019. It uncovers that more than 98% of the fluctuations in exchange rates can be accounted for by the included variables. The noteworthy discovery of the absence of a long-term equilibrium relationship within the specified time frame sets this study apart. Furthermore, it underscores the crucial role of exchange rate stability in promoting enduring economic growth, emphasising the necessity for well-designed policies that consider the intricate economic landscape.

Fulvia D'Aloisio

The article proposes an analysis following a fieldwork inside an enterprise, Automobili Lamborghini (Sant’Agata Bolognese) and reflects on some public consequences of the research aftermaths. It shortly focuses on fieldwork, starting from the post-modernist debate, its outcomes, also in the Italian contest, that progressively leads to consider that the ‘fieldwork is not what it used to be’. Afterwards the case-study focuses on the construction of the fieldwork dialogue, on the industrial relations that, based on the German model so-called mitbestimmung, facilitated the researcher’s entrance and her presence in the company, even with an independent project. In addition to detecting the objective difficulty to do ethnography inside the factory, it reflects on the public outcomes that show Anthropology and HR and trade-union representatives increasingly side by side, for the dissemination of the research results. So the Lamborghini research help us to rethink the traditional splits among independent and founded-by-company research, public anthropology and public engagement, identifying useful intersection points, with an usefulness configured differently for the parties in the fieldwork, researcher and research actors.

Lennon Ó Náraigh

We introduce two models of industrial drying - a simplified one-equation model, and a detailed three-equation model. The purpose of the simplified model is rigorous validation of numerical methods for PDE-constrained optimal control. The purpose of the detailed model is to be able to predict and control the behaviour of an industrial disk drier. For both models, we introduce a fully validated numerical method to compute the optimal source term to maintain the outlet temperature as close as possible to the set-point temperature. By performing simulations using realistic parameters for industrial driers, we illustrate potential applications of the method.

Roman Bögli, Leandro Lerena, Christos Tsigkanos et al.

TLA+ is a formal specification language used for designing, modeling, documenting, and verifying systems through model checking. Despite significant interest from the research community, knowledge about usage of the TLA+ ecosystem in practice remains scarce. Industry reports suggest that software engineers could benefit from insights, innovations, and solutions to the practical challenges of TLA+. This paper explores this development by conducting a systematic literature review of TLA+'s industrial usage over the past decade. We analyze the trend in industrial application, characterize its use, examine whether its promised benefits resonate with practitioners, and identify challenges that may hinder further adoption.

Konrad Wolsing, Eric Wagner, Frederik Basels et al.

With the escalating threats posed by cyberattacks on Industrial Control Systems (ICSs), the development of customized Industrial Intrusion Detection Systems (IIDSs) received significant attention in research. While existing literature proposes effective IIDS solutions evaluated in controlled environments, their deployment in real-world industrial settings poses several challenges. This paper highlights two critical yet often overlooked aspects that significantly impact their practical deployment, i.e., the need for sufficient amounts of data to train the IIDS models and the challenges associated with finding suitable hyperparameters, especially for IIDSs training only on genuine ICS data. Through empirical experiments conducted on multiple state-of-the-art IIDSs and diverse datasets, we establish the criticality of these issues in deploying IIDSs. Our findings show the necessity of extensive malicious training data for supervised IIDSs, which can be impractical considering the complexity of recording and labeling attacks in actual industrial environments. Furthermore, while other IIDSs circumvent the previous issue by requiring only benign training data, these can suffer from the difficulty of setting appropriate hyperparameters, which likewise can diminish their performance. By shedding light on these challenges, we aim to enhance the understanding of the limitations and considerations necessary for deploying effective cybersecurity solutions in ICSs, which might be one reason why IIDSs see few deployments.

Leonhard Faubel, Klaus Schmid

As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes into play. MLOps refers to the processes, tools, and organizational structures used to develop, test, deploy, and manage ML models reliably and efficiently. However, there is currently a lack of information on the practical implementation of MLOps in industrial enterprises. To address this issue, we conducted a multiple case study on MLOps in three large companies with dedicated MLOps teams, using established tools and well-defined model deployment processes in the Industry 4.0 environment. This study describes four of the companies' Industry 4.0 scenarios and provides relevant insights into their implementation and the challenges they faced in numerous projects. Further, we discuss MLOps processes, procedures, technologies, as well as contextual variations among companies.

Valentina Zaccaria, Chiara Masiero, David Dandolo et al.

While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. This paper addresses this need by testing the applicability of AcME-AD in industrial settings. This recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. Thus, it seems suitable for seamless integration with industrial Decision Support Systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Davide Arcidiacono, Giorgio Piccitto

A great deal of the literature has underlined how job quality is a key element in individual well‐being. However, the rise in platform work challenges this issue, since not only do “plat‐firms” play an increasingly important role in job matching, work organization, and industrial relations, but they also increase the risks of a poorly inclusive socio‐technical system in terms of the quality of working conditions and accessibility. In this sense, the platform economy is intertwined with multiple forms of social exclusion by acting on pre‐existing inequalities that stratify workers within the labor market. This is particularly true in Italy, a country with a strongly dualistic labor market, which leads to a remarkable gap between insider and outsider workers. Therefore, the goal of our analysis is to evaluate the impact of the platform model on job quality in the Italian context. This will be accomplished by adopting an integrated and multidimensional perspective through the application of the OECD Job Quality Framework. The analysis identifies how job quality is differently affected by the type of platform work involved in terms of creating differentiated patterns of social inclusion/exclusion in the case of platform workers.

F. F. Sharipov, Man Xu

Today due to the development of technologies a new economy, network economy is formed. The network business is an economic activity carried out through digital networks. The introduction of the network economy into production at the present stage is conditioned by a special economic category or form of industrial relations in cooperation with the development of productive forces and economic phenomena. In the context of digitalization, contradictory requirements are beginning to be placed on production. In the first case, it is about low costs and high variability, in the other case, the life cycle of a product is beginning to shorten rapidly. All of these problems can be solved at different stages of digital transformation. Continued innovation is needed today, as progress in the existence and effectiveness of some organizations becomes almost impossible without technological innovation. Flexibility of reaction of people to changes and development of new methods of activity are the leading conditions of competitiveness of modern enterprise. The object of this study is new opportunities, various aspects of digitalization of production, optimization of enterprise resources. The key aspect is to increase the productivity of the enterprise by reducing the time of the development of new products marketed and delivered to the consumer. As a result, the reduction contributes to overall efficiency. The authors substantiate that digitalization of production is the consolidation of all business processes at the enterprise using computer and information technologies. The basis of the introduction of the latest technologies is the desire of any company to comprehensively increase the efficiency of production, demand among customers and competitiveness. Keywords: economy, production, process, technology, digitalization, labor productivity, efficiency.

Samidh Pal

This paper investigates the inter-regional intra-industry disparity within selected Indian manufacturing industries and industrial states. The study uses three measures - the Output-Capital Ratio, the Capital-Labor Ratio, and the Output-Labor Ratio - to critically evaluate the level of disparity in average efficiency of labor and capital, as well as capital intensity. Additionally, the paper compares the rate of disparity of per capita income between six major industrial states. The study finds that underutilization of capacity is driven by an unequal distribution of high-skilled labor supply and upgraded technologies. To address these disparities, the paper suggests that policymakers campaign for labor training and technology promotion schemes throughout all regions of India. By doing so, the study argues, the country can reduce regional inequality and improve economic outcomes for all.

Mahbubul Islam, Hossain Md. Mubashshir Jamil, Samiul Ahsan Pranto et al.

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the suitable device for the suitable usage. So far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. Moreover, most of them are wireless protocols due to the sheer number of device requirements for IoT. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of critical features, including power consumption, coverage, data rate, security, cost, and Quality of Service (QoS). This comparative study shows that Low Power Wide Area Network (LPWAN) based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M ) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable protocol for an industrial and production field.

Jiaqi Liu, Guoyang Xie, Jinbao Wang et al.

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

Miguel Ángel Sanjuán, Cristina Argiz

After years of decline, coal consumption has risen significantly in the last year (2021), driven mainly by the ever-increasing demand in fast-growing Asian countries and fostered by rising gas prices in Europe and the United States. Coal is both the largest electricity production source and the largest source of carbon dioxide emission. Coal-fired plants produce electricity by generating steam by burning coal in a boiler, but also large amounts of coal fly ash. Coal fly ash contains essential constituents for cement production, such as Ca, Si, Al, and Fe. Application of coal-fired ash to produce clinker at high doses may reduce the limestone content in the raw mix. Furthermore, coal fly ash is one of the industrial source materials utilized in the development of low-carbon cements and concretes on account of its chemical characteristics. The monitoring methodology is based fundamentally on the analysis of a set of variables (Na₂O_e, Na₂O, K₂O, free CaO, and reactive silica content and fineness) over time. Weak relations between Na₂O and K₂O, and Na₂O_e, and reactive silica content were found. This applied research has been done to verify previously done research. The scope of this paper is to assess the alkaline content of coal fly ash over a period of 10 years. The Na₂O-equivalent of coal fly ash ranged from 0.35% to 2.53%, with an average value of 0.79%. These values should be taken into account producing concretes made with potentially reactive aggregates in order to mitigate the alkali–silica reaction (ASR).

Tan Chen, Zhe Huang, James Motes et al.

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies.

Bu XuSong, Nie Hao, Zhang Zhan et al.

The working conditions of large-scale industrial systems are very complex. Once a failure occurs, it will affect industrial production, cause property damage, and even endanger the workers' lives. Therefore, it is important to control the operation of the system to accurately grasp the operation status of the system and find out the failure in time. The occurrence of system failure is a gradual process, and the occurrence of the current system failure may depend on the previous state of the system, which is sequential. The fault diagnosis technology based on time series can monitor the operating status of the system in real-time, detect the abnormal operation of the system within the allowable time interval, diagnose the root cause of the fault and predict the status trend. In order to guide the technical personnel to troubleshoot and solve related faults, in this paper, an industrial fault diagnosis system is implemented based on the cubic DUCG theory. The diagnostic model of the system is constructed based on expert knowledge and experience. At the same time, it can perform real-time fault diagnosis based on time sequence, which solves the problem of fault diagnosis of industrial systems without sample data.

Man Zhang, Andrea Arcuri, Yonggang Li et al.

Remote Procedure Call (RPC) is a communication protocol to support client-server interactions among services over a network. RPC is widely applied in industry for building large-scale distributed systems, such as Microservices. Modern RPC frameworks include for example Thrift, gRPC, SOFARPC and Dubbo. Testing such systems is very challenging, due to the complexity of distributed systems and various RPC frameworks the system could employ. To the best of our knowledge, there does not exist any tool or solution that could enable automated testing of modern RPC-based services. To fill this gap, in this paper we propose the first approach in the literature, together with an open-source tool, for white-box fuzzing modern RPC-based APIs with search. To assess our novel approach, we conducted an empirical study with two artificial and four industrial APIs selected by our industrial partner. The tool has been integrated into a real industrial pipeline, and could be applied to real industrial development process for fuzzing RPC-based APIs. To further demonstrate its effectiveness and application in industrial settings, we also report results of employing our tool for fuzzing another 50 industrial APIs autonomously conducted by our industrial partner in their testing processes. Results show that our novel approach is capable of enabling automated test case generation for industrial RPC-based APIs (i.e., two artificial and 54 industrial). We also compared with a simple grey-box technique and existing manually written tests. Our white-box solution achieves significant improvements on code coverage. Regarding fault detection, by conducting a careful review with our industrial partner of the tests generated by our novel approach in the selected four industrial APIs, a total of 41 real faults were identified, which have now been fixed. Another 8,377 detected faults are currently under investigation.