Hasil untuk "Industrial directories"

Mykola Dumenko

Мета статті. Розробити методику оцінювання можливостей системи кадрових органів Збройних Сил України щодо виконання завдань у воєнний час. Методи дослідження. У статті розглядаються три взаємопов’язані методи. Метод обґрунтування складу системи кадрових органів Збройних Сил України (n), який базується на математичній моделі системи кадрових органів, що являє собою мережу масового обслуговування та передбачає вирішення оптимізаційної задачі знаходження оптимальної чисельності кадрових органів, які задовольняють різним критеріям функціонування системи кадрових органів Збройних Сил України та дає змогу отримати: детальну ієрархічну модель системи кадрових органів Збройних Сил України з визначеним рівнем декомпозиції та відобразити наочно взаємозв’язок між кадровими органами; проаналізувати систему кадрових органів Збройних Сил України та обґрунтувати раціональний варіант її побудови залежно від заданої ймовірності виконання кадрових завдань. Метод оптимального розподілу людських ресурсів між військовими формуваннями ( ) враховує темпи мобілізаційного розгортання, обсяги надходження мобілізаційного людського ресурсу та безповоротні й санітарні втрати під час мобілізації і бойових дій. Він ґрунтується на оптимізації рішень шляхом вирішення багатокрокової задачі оптимізації розподілу людських ресурсів (метод динамічного програмування) на основі даних щодо комплектування військових формувань у попередніх періодах комплектування та вимог до укомплектованості їх у  послідуючі періоди, що дає можливість знайти оптимальний розподіл людських ресурсів між військовими формуваннями і забезпечить досягнення максимальної їх укомплектованості на кінець визначеного терміну комплектування. Метод оцінювання ефективності функціонування системи кадрових органів Збройних Сил України дав змогу оцінити ефективність функціонування цієї системи з урахуванням інтенсивності надходження кадрових завдань та раціонального складу кадрових органів і розрахувати загальний показник ефективності функціонування системи, здійснити зважену адитивну згортку показників часткової ефективності, які розраховані на основі показників оперативності, якості забезпечення та ефективності структури й організації роботи системи кадрових органів. Для визначення ступеня важливості завдань застосовано метод аналізу ієрархій, який дає змогу оперативно оцінити ефективність функціонування системи кадрових органів для подальшого прийняття управлінських рішень щодо її удосконалення (W ско). Отримані результати дослідження. Запропонована методика дає змогу комплексно врахувати часткові показники ефективності системи кадрових органів, які характеризують якість окремих її властивостей. Водночас, прогнозування укомплектованості органів військового управління, військових частин і підрозділів, а також оцінювання впливів зазначених складових на ефективність системи кадрових органів надає можливість досліджувати вплив окремих її елементів на загальний рівень ефективності. Це, в свою чергу, дасть змогу оцінити діяльність системи на підставі аналізу структури і змісту роботи підрозділів системи кадрових органів й системи у цілому, як на етапі їх функціонування у мирний час, так і планування їх розвитку на період воєнного стану. Елементи наукової новизни. Використання запропонованого методичного апарату оцінювання ефективності функціонування системи кадрових органів Збройних Сил України дало змогу розробити методику оцінювання можливостей системи кадрових органів Збройних Сил України щодо виконання завдань у воєнний час. Теоретичне й практичне значення викладеного у статті. Методика передбачає: оцінювання показників ефективності системи кадрових органів; аналіз та комплексування показників оцінювання ефективності системи кадрових органів для різних варіантів її побудови, їх перевірку за відповідними критеріями; вироблення пропозицій щодо вибору раціональної структури системи кадрових органів на підставі аналізу значень вказаних показників та перевірки критеріїв для різних варіантів формування системи кадрових органів у воєнний час. Практична значущість: під час розгляду варіантів замислу операції – використовувати розроблену методику для оптимального розподілу людських ресурсів між військовими формуваннями стосовно кожного варіанту, що розглядається, замислу застосування військ; під час розрахунку потрібного складу чисельності кадрових органів Збройних Сил України – використовувати запропоновану методику для укомплектування військ (сил) з урахуванням прогнозованих втрат особового складу, які виникатимуть в ході операції (бойових дій) як унаслідок впливу противника, так і недостатньої кількості особового складу кадрових органів та оцінити можливості існуючого складу системи кадрових органів та її вплив на рівень боєздатності військ; під час підготовки необхідних даних для директивних і планових документів – спиратися на результати проведеного обґрунтування складу та чисельності системи кадрових органів Збройних Сил України; в процесі оцінювання ефективності функціонування системи кадрових органів Збройних Сил України; під час підготовки пропозицій командуванню щодо укомплектованості військ (сил) у визначені періоди та можливості системи кадрових органів Збройних Сил України з перерозподілу наявних людських ресурсів.

Farzana Yeasmin, Aaron J. E. Bach, Jean P. Palutikof et al.

Objective: The ready-made garment (RMG) sector is pivotal to Bangladesh’s economy, providing export opportunities and employment. To ensure sustained productivity and a thriving workforce, workplace hazards like heat must be acknowledged, assessed and managed. This paper explores heat impacts on health and productivity of production-line workers in two RMG factories of Bangladesh. Methods: Focus group discussions and in-depth interviews were conducted with the workers of two RMG factories in Dhaka in 2022 to identify perceived heat-related health and productivity impacts and explore barriers to workers accessing heat-related medical care. Key informant interviews were conducted with factory officials, onsite health professionals, government officials, the RMG peak body, and non-government organisation professionals with expertise in industry and workplace issues. Results: Workers and health professionals attributed symptoms like headaches, dizziness, fatigue and nausea to heat. Factory health professionals observed changes in cardiovascular strain (eg, altered blood pressure responses) in workers during summer. Other key informants identified higher absenteeism across summer. Heat was identified as an impediment to overall productivity by workers themselves and others working across the sector. Conclusion: This qualitative study identified how heat exposure in indoor work environments of RMG in Bangladesh influences health of workers and how productivity is influenced directly by heat but also indirectly via necessary cooling measures to reduce heat strain that take workers away from the production line. Despite knowledge of access to hydration as an important heat health risk mitigation strategy, quota pressures inherent in these factories restrict the use of this vital measure.

P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula et al.

Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.

Emily L. Tucker, Mohammadhossein Mohammadisiahroudi

Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.

Alice Di Bella, Toni Seibold, Tom Brown et al.

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.

Ahmed Jalil Al-Bayati, Mason Ali, Fadi Alhomaidat et al.

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types.

Crystal May BROWN, Rene OOSTHUIZEN, Roman TANDLICH

Adaptation and changes to the management of infectious diseases result from human research, knowledge gathering, interpretation and applications. Findings from the current study clearly point to the nature of the policy and disaster risk management response to COVID19, as having characteristics of a super-wicked problem. This provides an explanation for the sometimes diverging strategies in tackling the impacts of the coronavirus pandemic on humans and the surrounding socio-ecological systems. Solutions to the pandemic and its long-term outcomes will have to take into account the disparity of impacts and pre-disaster conditions.

Rohit Konda, Jordan Prescott, Vikas Chandan et al.

The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide efficient heat extraction. By adopting tools from inventory control, we characterize the structure of these optimal control policies, exploring the impact of different energy cost-rate structures such as time-of-use (TOU) pricing and peak pricing. While classical threshold policies are optimal under TOU costs, introducing peak pricing challenges their optimality, emphasizing the need for carefully designed control strategies in the presence of significant peak costs. We provide theoretical findings and simulation studies on this phenomenon, offering insights for more efficient industrial refrigeration management.

Aimira Baitieva, David Hurych, Victor Besnier et al.

Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.

Ziyan Yao

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.

Pietro Iob, Mauro Schiavo, Angelo Cenedese

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.

Mairi Bowdler, Wouter Martinus Petrus Steijn, Dolf van der Beek

For years, the connection between safety behaviours and injury and illness in high-risk industries has been recognised, but the effectiveness of this link has been somewhat overlooked. Since there is still a significant amount of injury within high-risk workplaces, this systematic review aims to examine the effectiveness of behavioural interventions to decrease fatal and non-fatal injuries within high-risk industries. Scopus and Google Scholar were used to find relevant systematic reviews and meta-analyses on this topic. In total, 19 articles met the inclusion criteria. Of these articles, 11 suggested that their reviewed interventions revealed some evidence of being effective in reducing injury/accident rates. Additionally, seven of the papers found that the interventions affected certain determinants, such as safety knowledge, health and safety behaviours, attitudes, efficacy, and beliefs. One of the papers found no effect at all. It must be noted that a significant amount of the articles (<i>n</i> = 10) reported methodological quality or quantity issues, implying that the results should be approached with caution. Nonetheless, it was found that certain components, such as multi-faceted interventions tailored to the target group, contribute to either reducing injury/accident rates or improving the specific aforementioned determinants. There is a need for additional safety interventions in high-risk industries that are based on methodologically sound structural elements and theoretical frameworks. Existing approaches, such as Intervention Mapping, can assist safety professionals in achieving this goal.

Kazunori Ikegami, Hajime Ando, Yasuro Yoshimoto et al.

Objectives: This study examined the relationship of potential advisors — human resources or services that advise workers when they experience health issues that affect their work and work-related health — with psychological distress and analyzed which human resources have a greater impact on improving workers’ mental health. Methods: An Internet-based survey using a self-administered questionnaire was conducted. The target population was workers between the ages of 20 and 69 years. Among a total of 5,111 participants, 4,540 were included in the present analysis. Participants were asked questions regarding potential advisors on work-related health issues. The Kessler 6-item Psychological Distress Scale (K6) was used to assess psychological distress. We used a generalized linear model with a binomial response for assessing the relationship between K6 scores and each potential advisor on work-related health issues. Results: Participants without potential advisors on work-related health issues were significantly more likely to score both K6 ≥5 (cutoff for mild psychological distress) and K6 ≥13 (cutoff for severe psychological distress) than the participants with potential advisors (all p&lt;0.001). The participants for whom a supervisor was the potential advisor on work-related health issues were significantly less likely to score K6 ≥13 than their counterparts (p=0.005). Those for whom an occupational physician or family members was the potential advisor on work-related health issues were significantly less likely to score K6 ≥5 than their counterparts (p=0.011 and p=0.001, respectively). Conclusions: Having potential advisors could be important for workers’ mental health improvement. Specifically, having supervisors, occupational physicians, or family members as potential advisors may be effective in reducing workers’ psychological distress.

Eric Wagner, David Heye, Martin Serror et al.

Industrial control systems increasingly rely on middlebox functionality such as intrusion detection or in-network processing. However, traditional end-to-end security protocols interfere with the necessary access to in-flight data. While recent work on middlebox-aware end-to-end security protocols for the traditional Internet promises to address the dilemma between end-to-end security guarantees and middleboxes, the current state-of-the-art lacks critical features for industrial communication. Most importantly, industrial settings require fine-grained access control for middleboxes to truly operate in a least-privilege mode. Likewise, advanced applications even require that middleboxes can inject specific messages (e.g., emergency shutdowns). Meanwhile, industrial scenarios often expose tight latency and bandwidth constraints not found in the traditional Internet. As the current state-of-the-art misses critical features, we propose Middlebox-aware DTLS (Madtls), a middlebox-aware end-to-end security protocol specifically tailored to the needs of industrial networks. Madtls provides bit-level read and write access control of middleboxes to communicated data with minimal bandwidth and processing overhead, even on constrained hardware.

Daniel Agbaji, Brady Lund, Nishith Reddy Mannuru

The Fourth Industrial Revolution, particularly Artificial Intelligence (AI), has had a profound impact on society, raising concerns about its implications and ethical considerations. The emergence of text generative AI tools like ChatGPT has further intensified concerns regarding ethics, security, privacy, and copyright. This study aims to examine the perceptions of individuals in different information flow categorizations toward AI. The results reveal key themes in participant-supplied definitions of AI and the fourth industrial revolution, emphasizing the replication of human intelligence, machine learning, automation, and the integration of digital technologies. Participants expressed concerns about job replacement, privacy invasion, and inaccurate information provided by AI. However, they also recognized the benefits of AI, such as solving complex problems and increasing convenience. Views on government involvement in shaping the fourth industrial revolution varied, with some advocating for strict regulations and others favoring support and development. The anticipated changes brought by the fourth industrial revolution include automation, potential job impacts, increased social disconnect, and reliance on technology. Understanding these perceptions is crucial for effectively managing the challenges and opportunities associated with AI in the evolving digital landscape.

Nag Meetu, Pratap Bhanu, Kumar Ajay

This paper investigates the effect of diaphragm thickness, dimensions of piezoresistors, doping profile and temperature compatibility on sensitivity and non-linearity of graphene MEMS pressure sensor. Taguchi method is used for maximizing the sensitivity and minimizing the nonlinearity of the designed pressure sensor. L27 orthogonal array is utilized for five input factors with three levels. Output voltage is obtained from simulation in COMSOL for different combinations of the input parameters as per L27 orthogonal array. It was found that diaphragm thickness and length of the sensing element shows maximum contribution in increasing the sensitivity of the pressure sensor. Similarly, interaction of diaphragm thickness with piezoresistors thickness and doping concentration shows a major contribution in reducing the non-linearity of the pressure sensor. Other factors such as operating temperature affects both sensitivity and nonlinearity of the pressure sensor with a very low contributing percentage of 0.40% and 2.16%, respectively. Pareto Analysis of variance (ANOVA) was employed to validate the predicated results of the designed pressure sensor. The result indicated that the optimum design shows a sensitivity of 4.10 mV/psi with very low non linearity of 0.1%.

T. Taleb, I. Afolabi, M. Bagaa

Industry 4.0 aims at shaking the current manufacturing landscape by leveraging the adoption of smart industrial equipment with increased connectivity, sensing, and actuation capabilities. By exploring access to real-time production information and advanced remote control features, servitization of manufacturing firms promises novel added value services for industrial operators and customers. On the other hand, industrial networks would face a transformation process in order to support the flexibility expected by the next-generation manufacturing processes and enable inter-factory cooperation. In this scenario, the 5G systems can play a key role in enabling Industry 4.0 by extending the network slicing paradigm to specifically support the requirements of industrial use cases over heterogeneous domains. We present a novel 5G-based network slicing framework which aims at accommodating the requirements of Industry 4.0. To interconnect different industrial sites up to the extreme edge, different slices of logical resources can be instantiated on-demand to provide the required end-to-end connectivity and processing features. We validate our proposed framework in three realistic use cases which enabled us highlight the envisioned benefits for industrial stakeholders.

Sanjay Adhikesaven

Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Petr Kostka, Bruno Rossi, Mouzhi Ge

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.

Reyhaneh Sadeghi, Floris Goerlandt

Even though validation is an important concept in safety research, there is comparatively little empirical research on validating specific safety assessment, assurance, and ensurance activities. Focusing on model-based safety analysis, scant work exists to define approaches to assess a model’s adequacy for its intended use. Rooted in a wider concern for evidence-based safety practices, this paper intends to provide an understanding of the extent of this problem of lack of validation to establish a baseline for future developments. The state of the practice in validation of model-based safety analysis in socio-technical systems is analyzed through an empirical study of relevant published articles in the <i>Safety Science</i> journal spanning a decade (2010–2019). A representative sample is first selected using the PRISMA protocol. Subsequently, various questions concerning validation are answered to gain empirical insights into the extent, trends, and patterns of validation in this literature on model-based safety analysis. The results indicate that no temporal trends are detected in the ratio of articles in which models are validated compared to the total number of papers published. Furthermore, validation has no clear correlation with the specific model type, safety-related concept, different system life cycle stages, industries, or with the countries from which articles originate. Furthermore, a wide variety of terminology for validation is observed in the studied articles. The results suggest that the safety science field concerned with developing and applying models in safety analyses would benefit from an increased focus on validation. Several directions for future work are discussed.