Hasil untuk "Industry"

M. B. Ali, R. Saidur, M. S. Hossain

M. Breuer, K. Ditrich, T. Habicher et al.

R. Grant

Frank M. H. Neffke, M. Henning

Wahyu Rahmaniar, Kenji Suzuki

Traditional deep learning models often lack annotated data, especially in cross-domain applications such as anomaly detection, which is critical for early disease diagnosis in medicine and defect detection in industry. To address this challenge, we propose Multi-AD, a convolutional neural network (CNN) model for robust unsupervised anomaly detection across medical and industrial images. Our approach employs the squeeze-and-excitation (SE) block to enhance feature extraction via channel-wise attention, enabling the model to focus on the most relevant features and detect subtle anomalies. Knowledge distillation (KD) transfers informative features from the teacher to the student model, enabling effective learning of the differences between normal and anomalous data. Then, the discriminator network further enhances the model's capacity to distinguish between normal and anomalous data. At the inference stage, by integrating multi-scale features, the student model can detect anomalies of varying sizes. The teacher-student (T-S) architecture ensures consistent representation of high-dimensional features while adapting them to enhance anomaly detection. Multi-AD was evaluated on several medical datasets, including brain MRI, liver CT, and retina OCT, as well as industrial datasets, such as MVTec AD, demonstrating strong generalization across multiple domains. Experimental results demonstrated that our approach consistently outperformed state-of-the-art models, achieving the best average AUROC for both image-level (81.4% for medical and 99.6% for industrial) and pixel-level (97.0% for medical and 98.4% for industrial) tasks, making it effective for real-world applications.

Tengyue Pan, Chengming Jiang, Xinmin Shen et al.

In today’s data-driven age, the thermal properties of computer transistors play an important role. In this research, finite element simulation is employed to construct the structural model of the primary components within a computer chassis, and the thermal performance is evaluated based on ambient temperature, thermal conductivity, and heat dissipation rate. By combining the particle swarm optimization algorithm with numerical simulation for joint simulation and structural optimization, the component layout was optimized to reduce the working temperature. The results show that when the background temperature, that is, the ambient temperature, rises from −20 °C to 60 °C, the maximum operating temperature of the computer is approximately 88 °C. The maximum temperature is mainly in the transistor core and the minimum temperature is in the intake grille, and the operating temperature of the optimized structure decreases by approximately 10 °C. The research shows that the operating temperature is most sensitive to the change of background temperature, and the transistor core is the main heating source. The maximum temperature can be reduced by rationally adjusting the position of the components. This study provides a reference for analyzing the thermal performance of computers and optimizing structures.

Imene Djaalab, Samia Haffaf, Hadria Mansour-Djaalab et al.

Animal Welfare has a significant impact on the dairy cow’s health, behaviour, productivity and milk quality. By implementing husbandry practices that respect the physical, behavioural and emotional needs of dairy cows, the dairy industry can improve the sustainability of its operations and meet rising expectations. The aim of this study is to evaluate the impact of housing systems (free vs. tied) on dairy cow health through musculoskeletal health indicators and lameness scores. The hypothesis that dairy cows reared in free housing have a better quality of health than cows reared in restrained housing is tested. Thus, 300 dairy cows of the Holstein and Montbeliarde breeds were selected from dairy farms in five municipalities of Constantine province (eastern Algeria). The results showed that the frequency of severe lameness did not exceed 12% in stalls with restraints and more than 42% of light lameness are in free-stall housing (<i>p</i> < 0.001). These results reflect a lack of comfort in restricted housing, with an impact on dairy performances. Moreover, the monitoring of lame cows and the functional trimming of their hooves should be frequent. It is also important to implement a cull policy for unproductive cows. Finally, it is very important to provide adequate training to farmers in order to improve the well-being of dairy cows.

M. Diaz-Piloneta, M. Terrados-Cristos, F. Ortega-Fernandez et al.

Abstract Mine tailings are an unavoidable waste generated during iron ore mining operations, of which millions of tonnes are generated worldwide. Given the importance of steel, and therefore, iron ore mining, solutions are needed to recover this waste. Despite global efforts, the current proposed solutions struggle to reach the market due to cost-effectiveness issues. This study explores a potential solution, presenting iron tailings as a viable, economical, and sustainable material for thermal energy storage systems. Thermal characterization showed a specific heat capacity of 780–990 J/kg·K up to 590 °C. The material remained thermally stable without melting or decomposition up to 1000 °C, and the resulting storage density was estimated up to 450 kWh/m3. The material stands up safety, minimal environmental impact, and favourable thermophysical properties at a low investment cost. This innovative application not only addresses energy challenges but also contributes to resolving the waste management crisis in the iron mining industry.

T. A. Balina , L. S. Batalova , M. A. Pospishenko

Light industry is one of the oldest sectors of the world economy, which has developed rapidly under the influence of industrial revolutions, the introduction of technological innovations, the development of trade, increased competition in consumer goods markets and other factors, which formed special areas of the industry with its centers. Global trends and features of the development of light industry are of interest for spatio-temporal analysis necessary for understanding the problems of domestic production. The relevance of the study is due to the need for a scientific analysis of the development of key sectors of the light industry in the context of modern geopolitical and macroeconomic realities in the context of constituent entities and federal districts. Having a relatively small share in the structure of manufacturing, light industry plays an important role in the country’s economy, provides all its spheres with various types of products, and the population with consumer goods. Russia’s modern light industry has complex technological chains, relies on a diverse raw material base, it is focused on the growth of consumer demand, which requires the modernization of production. Radical changes in the sectoral and spatial structure of light industry in the world, as well as import substitution requirements have had a great impact on the state of the industry in the regions of Russia. Geopolitical challenges have shown that it is necessary to make maximum use of the existing potential by creating new production facilities, introducing modern technologies, forming our own raw material bases, and training personnel for the sustainable development of the industry. The post-Soviet crisis slowed down the development of light industry for a long time, but at present it is being renovated and transformed into a creative industry. The retrospective analysis reveals positive dynamics in the development of key sectors of light industry, despite the aggravation of a number of problems. Changes in the sectoral and territorial structure of the industry were identified, a typology of the subjects of the Russian Federation was made up by the share of light industry in the economy of the regions. Measures are proposed to bring the industry to a qualitatively new level of development.

K. L. van der Enden, R. Kirschner, M. Krumtünger et al.

A future quantum internet brings promising applications related to security, privacy and enabling distributed quantum computing. Integration of these concepts into the future trends of the automotive sector is of considerable interest, as it enables both the development of practical quantum internet use cases and the adoption of innovative technologies in the automotive sector. In this work we analyze cross-platform megatrends in both the quantum internet and the automotive industry, identifying mutually beneficial regions of interest. In the short-term ($<10$ years) hardware miniaturization and automation of quantum internet technology provides a synergy interface between the two domains. For the long-term ($\geq10$ years) we develop a comprehensive list of use cases for the quantum internet within the automotive sector. We find considerable relevancy of augmenting autonomous driving, vehicle ad hoc networks and sensor fusion with blind quantum computing, anonymous transmission and quantum cryptographic tools. These results can be used to target future research, engineering and venture developments for both domains. Furthermore, our approach can be applied to other industries, enabling a structured methodology for identifying and developing feasible use cases for the quantum internet in diverse domains.

Chaoran Zhang, Chenhao Zhang, Zhaobo Xu et al.

The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR

Ting-Yen Wang

This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data from Bangladesh, Colombia, Egypt, and India. The study normalizes entropy values to quantify heterogeneity and compares these measures with a Zonotope-based Gini index. Results demonstrate the ME method's superiority in capturing nuanced, functional heterogeneity often missed by traditional techniques. Furthermore, the study incorporates a "Tangent Against Input Axes" method to dynamically assess technical change within industries. By integrating information theory with production economics, this unified framework quantifies structural and functional differences across industries using firm-level data, advancing both methodological and empirical understanding of heterogeneity. A numerical simulation confirms the ME regression functions can approximate actual industrial heterogeneity. The research also highlights the superior ability of the ME method to provide a precise and economically meaningful measure of industry heterogeneity, particularly for longitudinal analyses.

Mansoor Shafi, Erik G. Larsson, Xingqin Lin et al.

6G standardization is to start imminently, with commercial deployments expected before 2030. Its technical components and performance requirements are the focus of this article. Our emphasis is on the 6G radio access, especially MIMO, AI, waveforms, coding, signal constellations and integration with non-terrestrial networks. Whilst standardization has not yet formally started, the scope of the 6G study items has been defined. Our predictions in this paper are speculative as there are no results of the study yet, but our views are guided by implementation and deployment aspects. We expect that the views here will guide researchers and industry practitioners.

Oscar Adamuz-Hinojosa, Felix Delgado-Ferro, Núria Domènech et al.

The digital transformation driven by Industry 4.0 relies on networks that support diverse traffic types with strict deterministic end-to-end latency and mobility requirements. To meet these requirements, future industrial automation networks will use time-sensitive networking, integrating 5G as wireless access points to connect production lines with time-sensitive networking bridges and the enterprise edge cloud. However, achieving deterministic end-to-end latency remains a challenge, particularly due to the variable packet transmission delay introduced by the 5G system. While time-sensitive networking bridges typically operate with latencies in the range of hundreds of microseconds, 5G systems may experience delays ranging from a few to several hundred milliseconds. This paper investigates the potential of configuring the 5G time division duplex pattern to minimize packet transmission delay in industrial environments. Through empirical measurements using a commercial 5G system, we evaluate different TDD configurations under varying traffic loads, packet sizes and full buffer status report activation. Based on our findings, we provide practical configuration recommendations for satisfying requirements in industrial automation, helping private network providers increase the adoption of 5G.

Tingyi Chai, Chang Liu, Yichuan Xu et al.

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Hassan Al Garni, Arunachalam Sundaram, Anjali Awasthi et al.

A major design challenge for a grid-integrated photovoltaic power plant is to generate maximum power under varying loads, irradiance, and outdoor climatic conditions using competitive algorithm-based controllers. The objective of this study is to review experimentally validated advanced maximum power point tracking algorithms for enhancing power generation. A comprehensive analysis of 14 of the most advanced metaheuristics and 17 hybrid homogeneous and heterogeneous metaheuristic techniques is carried out, along with a comparison of algorithm complexity, maximum power point tracking capability, tracking frequency, accuracy, and maximum power extracted from PV systems. The results show that maximum power point tracking controllers mostly use conventional algorithms; however, metaheuristic algorithms and their hybrid variants are found to be superior to conventional techniques under varying environmental conditions. The Grey Wolf Optimization, in combination with Perturb & Observe, and Jaya-Differential Evolution, is found to be the most competitive technique. The study shows that standard testing and evaluation procedures can be further developed for comparing metaheuristic algorithms and their hybrid variants for developing advanced maximum power point tracking controllers. The identified algorithms are found to enhance power generation by grid-integrated commercial solar power plants. The results are of importance to the solar industry and researchers worldwide.

Lanhang Ye, Toshiyuki Yamamoto

Modular autonomous vehicles (MAVs) represent a transformative paradigm in the rapidly advancing field of autonomous vehicle technology. The integration of modularity offers numerous advantages, poised to reshape urban mobility systems and foster innovation in this emerging domain. Although publications on MAVs have only gained traction in the past five years, these pioneering efforts are critical for envisioning the future of modular mobility. This work provides a comprehensive review of industry and academic contributions to MAV development up to 2024, encompassing conceptualization, design, and applications in both passenger and logistics transport. The review systematically defines MAVs and outlines their technical framework, highlighting groundbreaking efforts in vehicular conceptualization, system design, and business models by the automotive industry and emerging mobility service providers. It also synthesizes academic research on key topics, including passenger and logistics transport, and their integration within future mobility ecosystems. The review concludes by identifying challenges, summarizing the current state of the art, and proposing future research directions to advance the development of modular autonomous mobility systems.

Yuechen Wu

The analysis of determinants of a company's financial performance has aroused significant attention, particularly, the environmental, social, and governance (ESG) has been the research focus in recent years. In addition to increasing revenue, the cruise industry has actively embraced the initiative of "green shipping". This study investigates the relationship between ESG and corporate financial performance (CFP) in the global cruise sector. This paper utilizes the sample data from the world's largest cruise companies over 2012-2023, to examine the ESG-CFP relationship by a regression model. The results indicate that ESG practices in cruise companies negatively influence CFP, which is further impacted by financial constraints. Furthermore, the heterogeneity analysis suggests that the high time interest earned (TIE) ratios and low total annual greenhouse gas (GHG) emissions worsen the adverse impacts of ESG on CFP. These findings contribute to the theoretical research on ESG and provide practical guidance for cruise industry operators and investors in their decision-making.

Arit Kumar Bishwas, Mousumi Sen

As quantum computing continues to advance, its ability to compromise widely used cryptographic systems projects a significant challenge to modern cybersecurity. This paper outlines a strategic roadmap for industries to anticipate and mitigate the risks posed by quantum attacks. Our study explores the development of a quantum-resistant cryptographic solutioning framework for the industry, offering a practical and strategic approach to mitigating quantum attacks. We, here, propose a novel strategic framework, coined name STL-QCRYPTO, outlines tailored, industry-specific methodologies to implement quantum-safe security systems, ensuring long-term protection against the disruptive potential of quantum computing. The following fourteen high-risk sectors: Financial Services, Banking, Healthcare, Critical Infrastructure, Government & Defence, E-commerce, Energy & Utilities, Automotive & Transportation, Cloud Computing & Data Storage, Insurance, Internet & Telecommunications, Blockchain Applications, Metaverse Applications, and Multiagent AI Systems - are critically assessed for their vulnerability to quantum threats. The evaluation emphasizes practical approaches for the deployment of quantum-safe security systems to safeguard these industries against emerging quantum-enabled cyber risks. Additionally, the paper addresses the technical, operational, and regulatory hurdles associated with adopting quantum-resistant technologies. By presenting a structured timeline and actionable recommendations, this roadmap with proposed framework prepares industries with the essential strategy to safeguard their potential security threats in the quantum computing era.

Cassandra Crowe, Belinda Middleweek, Laura Ryan et al.

We surveyed Australian finance professionals and tested whether there are statistically significant differences in promotional propensity according to gender identity. The findings indicate men and women are equally likely to ask for promotion, however, 'gifted advancements' account for the higher statistical frequency of promotions among men. These gender-based differences in behaviors have been overlooked in existing research on promotion. We call for a standardized framework for the development of promotion policies to address this industry-wide problem.