Hasil untuk "Industrial directories"

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

Wang Qian

Current humanistic design of urban public spaces focuses on specific design elements while ignoring the conflicts and couplings between multiple user needs. This leads to spatial strategies stuck in local optima and lacking overall balance and adaptability. This paper constructs a multi-objective optimization model that integrates user preferences, multidimensional spatial indicators, and behavioral simulation. This model collects field data such as heat maps, path trajectories, and dwell time, identifies user types through K-means clustering, and models their spatial preferences using fuzzy membership functions. Design variables are set in Grasshopper; an optimization function is constructed; the optimal solution is searched using NSGA-III. Finally, pedestrian simulation is performed in AnyLogic, and the optimization results are corrected for function deviation to improve the coordination and adaptability of the design. Experimental results show that this strategy framework significantly improves spatial coordination, increasing weighted average satisfaction from 0.61 to 0.81 (+32.8%), reducing safety risks by 30.8% to 63.2%, and increasing interaction promotion by 71.2%. Multi-dimensional indicators verify the effectiveness of the optimization strategy in balancing user needs, alleviating local conflicts, and enhancing spatial adaptability, providing a quantitative basis and practical path for systematically solving the local optimal problem of humanized design of public spaces.

Ali Nasiri, Esmaeil Salimi, Morteza Delfan Azari et al.

Flood zoning has extensive applications in flood management and is considered one of the fundamental and critical pieces of information in flood risk management. Flood zoning in urban areas is much more challenging than modeling in floodplain and river areas due to the two-dimensional nature of the flow and, on the other hand, the density of urban features such as buildings, streets, boulevards, and public pathways. In this study, flood zoning for districts 21 and 22 of Tehran was conducted under the current conditions, where the area is almost devoid of surface water collection channels, using a physically-based rainfall-runoff model and two-dimensional hydraulic routing which is the novelty aspect of the article. For this purpose, the HEC-HMS model was used to estimate the runoff from the mountains, and the MIKE model was used to simulate urban rainfall-runoff. According to the modeling results, the areas affected by a 50-year flood event were identified using an integrated modeling approach in districts 21 and 22, covering 8% of these areas. In these areas, the maximum flood depth is 11.8 meters in Vardavard river and the highest speed is 4.5 meters per second at the beginning of Hashemzadeh street (south of Kharrazi highway). The results indicate that in the event of extreme events such as a 50-year rainfall, a significant portion of the highways and main communication arteries of Tehran leading westward would be disrupted, and traffic would be impossible. Moreover, various land uses would fall within the flood zone, and due to the absence of a surface water network, waterlogging conditions throughout districts 21 and 22 of Tehran are predictable. Therefore, the development of a surface water collection network is one of the main priorities for reducing flood risk in these areas.

Charlene Magena

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.

Eric Mutua

Purpose: The general objective of the study was to investigate cyber-physical systems and their role in industry 4.0. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cyber-physical systems and their role in industry 4.0. Preliminary empirical review revealed that that CPS played a pivotal role in enhancing industrial practices by integrating physical processes with digital technologies. It was found that CPS significantly improved operational efficiency, quality control, and real-time monitoring, while also addressing challenges in supply chain management. The study highlighted how CPS contributed to more agile, responsive, and sustainable manufacturing systems, ultimately positioning industries to better meet future demands and challenges through enhanced efficiency, quality, and environmental sustainability. Unique Contribution to Theory, Practice and Policy: Cybernetics Theory, Systems Theory and Technology Acceptance Model (TAM) may be used to anchor future studies on cyber-physical systems. The study recommended several key actions to advance the field of Cyber-Physical Systems (CPS). It suggested further theoretical exploration into the integration of CPS with artificial intelligence and blockchain to drive innovation and address industrial challenges. Practically, it advised industries to invest in advanced CPS infrastructure and workforce training to fully realize the benefits. Policy recommendations included establishing standardized guidelines for CPS implementation and providing incentives for research and development. Operational best practices were recommended to ensure effective CPS deployment, and sustainability initiatives were encouraged to align CPS strategies with environmental goals. Future research was advised to focus on long-term impacts and emerging global issues related to CPS.

Graeme Acheson, Eoin McLaughlin, Gill Newton et al.

This paper examines the prevalence of business partnerships in a late‐nineteenth‐century British city, using individual‐level data from post office directories and censuses. Focusing on Glasgow, we present a detailed picture of partnership number and type, demographic characteristics of the entrepreneurs who ran them, and how these businesses persisted over time. We show that partnerships were a key business grouping in the city and demonstrate that the partnership form was advantageous in manufacturing and that the majority of partnerships were formed between individuals without family ties. Furthermore, we offer new insight into business longevity, showing that partnership business survival broadly matched corporate survival rates in this period, with persistence data also suggesting that kinship partnerships were better able to deal with the perceived hold‐up problems associated with the partnership form.

Qarani Shuokr

Global warming, climate change, greenhouse gasses, floods, drought years, and desertification have an impact on the environment. Naturally, the environment of Bnaslawa district (Dashti Hawler) in Erbil City-Iraqi Kurdistan region is affected by the global environmental changes. This research focused on the assessment of environmental parameters, planning, and environmental modelling in Bnaslawa district. A series of site visits, interviews, collection of documented data from directories and literature were conducted for data collection. Environmental factors such as wind direction, topography, water sources, soil type, distance, archeology, esthetics, air pollution, noise pollution, and disease spread were selected. The selected points for environmental planning were landfill, gas factory, slaughterhouse, quarry, cemetery, wastewater treatment plant, green area, animal shelters, industrial area, commercial area, institutional area, and service area. The points for the environmental factors were changed from zero (low impact) to 10 (strong/high impact). The ratio of points and twelve mathematical models for the elements were determined. Based on the scoring and mathematical models, wind direction, topography, water sources, archeology, esthetics, air pollution, noise pollution, and disease spread had an excessive impact on the planning and management of environmental parameters. In contrast, soil type and distance had less influence.

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Laura Filosa, Vanni Lopresto

This paper outlines the specific provisions of Italian legislation regarding workers’ exposure to electromagnetic fields (EMFs) from 0 Hz to 300 GHz compared to the minimum health and safety requirements set in European Directive 2013/35/EU. In particular, the path to be followed to assess and manage occupational exposure to EMFs is outlined in relation to the distinction between ‘professional’ and ‘non-professional’ exposure of workers, as well as to the precautionary limits regarding exposures from power lines (50 Hz) and broadcast and telecommunication fixed systems (100 kHz–300 GHz) established by Italian regulations. The reasons underlying such an approach—mainly relying on the intent to reconcile scientific evidence with risk perception in public opinion—are analysed and discussed with the aim of increasing the knowledge of national regulatory provisions on occupational risk assessment, which may be more stringent than the requirements envisaged by international guidelines and community regulations.

Sentagi Sesotya Utami, Winny Setyonugroho, Moch Zihad Islami et al.

Introduction: Ship-to-shore (STS) crane operators strive for efficiency in their work, but they must take a hard look at their high-risk jobs. It is necessary to learn how to improve occupational safety and health. This study aims to investigate the problems faced by STS crane operators working in container ports and to understand the importance of fit-for-work monitoring procedures, particularly for individuals working in high-risk industries such as STS operators. Methods: This study used a qualitative approach, and data were collected through interviews and observations of STS operators and in-house clinic staff. Nine STS operators, two in-house clinic staff, and two safety, health, and environment (SHE) staff were interviewed. Results: This study found that container terminal companies emphasise two critical aspects for STS operators: productivity and occupational safety and health. STS operators face health problems, including physical and psychological problems, due to the fast-paced work system, sleep patterns, daily activities, and thoughts that are difficult to control. Employees have coping mechanisms to deal with fatigue, and stakeholders have effectively communicated the company's safety and health culture. Most stakeholders in a container terminal company want a fit-for-work monitoring system to make the business efficient and sustainable. Conclusion: The STS industry faces a significant problem with operator fatigue, which can negatively impact safety and productivity. This issue requires a comprehensive strategy, including legislation to regulate working hours and shift patterns, technology to combat fatigue, and operator education and training.

Shahzeb Ansari, Haiping Du, Fazel Naghdy et al.

Driver mental fatigue is considered a major factor affecting driver behavior that may result in fatal accidents. Several approaches are addressed in the literature to detect fatigue behavior in a timely manner through either physiological or in-vehicle measurement methods. However, the literature lacks the implementation of hybrid approaches that combine the strength of individual approaches to develop a robust fatigue detection system. In this regard, a hybrid temporal approach is proposed in this paper to detect driver mental fatigue through the combination of driver postural configuration with vehicle longitudinal and lateral behavior on a study sample of 34 diverse participants. A novel fully adaptive symbolic aggregate approximation (<i>faSAX</i>) algorithm is proposed, which adaptively segments and assigns symbols to the segmented time-variant fatigue patterns according to the discrepancy in postural behavior and vehicle parameters. These multivariate symbols are then combined to prepare the bag of words (text format dataset), which is further processed to generate a semantic report of the driver’s status and vehicle situations. The report is then analyzed by a natural language processing scheme working as a sequence-to-label classifier that detects the driver’s mental state and a possible outcome of the vehicle situation. The ground truth of report formation is validated against measurements of mental fatigue through brain signals. The experimental results show that the proposed hybrid system successfully detects time-variant driver mental fatigue and drowsiness states, along with vehicle situations, with an accuracy of 99.6% compared to state-of-the-art systems. The limitations of the current work and directions for future research are also explored.

Alka Verma, Amit Goel, Priyank Yadav et al.

Introduction: Limited information is available regarding effect of vaccination on protection against Covid-19 infections and their severity as well. Objectives: In the present study, we assessed the effect of Covid-19 vaccination on incidences and severity of break through Covid-19 infections. Method: This retrospective study was conducted at a tertiary care center in Northern India during one calendar year, 1st August 2021 to 31st July 2022. The study population included Health-care workers (HCWs) who were treated for Covid 19 infection and had already received at least 1 dose of Covishield TM (AZD1222) Covid-19 vaccine. Results: Out of 1868 health care workers enrolled for the study, 513 contracted Covid-19 infections. Amongst infected HCWs, number of single and double doses of CovishieldTM (AZD1222) recipients were 112 and 401 respectively. Out of the 513 covid positive HCWs, 459 (89.4%) had mild disease, whereas 54 (10.6%) had moderate disease. None of the HCWs developed severe disease and no mortality was noted in either group. Conclusion: In this study, we found that immunization with two doses of CovishieldTM (AZD1222) vaccine was associated with decline in number of cases with moderate or severe Covid-19. Moreover, immunization with even single dose of CovishieldTM (AZD1222) vaccine prevented development of severe disease. Henceforth, it is concluded that although, immunization with CovishieldTM (AZD1222) could not protect all recipients from SARS-Cov-2 infection, it did prevent the progress of disease to severe grades.

Diego Moya, Rafael Manzanera, Jordi Ortner et al.

<b>Background:</b> Given the lack of previous studies on adverse events (AEs) in the area of occupational healthcare in Spain, it is very important to begin to understand this phenomenon in order to act on it. The objective was to accurately quantify AE occurring in occupational healthcare in MC Mutual during May 2021. <b>Methods:</b> We conducted a review of a representative random sample of 250 clinical records to identify AEs through an active search audit, focused on the frequency, type, severity, and preventability of these events, categorized using standardized scales. <b>Results:</b> We detected seven AEs in the sample of clinical records, representing 3% AEs per clinical record, while in the APEAS Spanish Study, they were detected in 10% of patients. The most frequent AE type was postoperative, followed by medication and diagnostic delay. The AEs were of intermediate severity and high severity and with a variable degree of being preventable. <b>Conclusions:</b> The detection of AEs has been useful in the development of projects and action plans such as specific training courses, safety patient newsletters, ambulatory risk maps, and treatment plans framed in the official certification of patient safety. These results should be evaluated in other companies similar to MC Mutual.

Todabi Ajabi

Purpose: The study sought to analyze the impacts of industrial revolution on the family structure in Nigeria Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The results show that there has been a change of family structure since the pre industrialization era and the post industrialization era. The historical process of industrialization changed the ways in which families were structured and interacted. Family bonding are decreasing and divorce rate is increasing rapidly. These shift not only affected the roles of spouses and parents but also those of children. Industrialization changed gender roles and Enlightenment philosophies that inspired new ideals of equality, personal freedom, and individualism. Unique Contribution to Theory, Practice and Policy: The modernization theory, Talcott Parsons’ theory and the classic sociological theory may be used to anchor future studies in the sociology sector. The study results will also benefit other stakeholders such as the policy makers as well as researchers and scholars from different parts of the world. The top management of both public and private industries in the country will also use the study findings to improve families and ensure high and stable performance in all their activities and programs. The study recommends that the adoption of effective social protection development policies in the family structure will help to improve efficiency in their major operations and activities.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen et al.

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.