Hasil untuk "Industrial productivity"

Yiran Ma, Jerome Le Ny, Zhichao Chen et al.

In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications.

Ioannis Adamopoulos, Niki Syrou, George Mpourazanis et al.

The relationship between the global climate crisis, which is associated with environmental risks, and occupational hygiene has not been extensively studied. This study develops a framework for identifying how climate change and the climate crisis could impact the workplace environment, workers, and occupational morbidity, mortality, and injury. A framework is used in this paper that is based on a review of the scientific literature published from 2014 to 2024, addressing climate risks, their interaction with occupational hazards, and their effects on the workforce. Eight categories of climate-related hazards are identified: increasingly high temperatures, dust and air pollution, sun and cosmic ultraviolet exposure, pandemics and infectious diseases, diseases transmitted by insects and changes in ecosystems, industrial occupational diseases, changes and crises in the built environment, and extreme weather events. Policies need to consider the gaps in the possibility of interactions between known hazards and new conditions and the productivity of workers, especially those who are most at risk of heat-related illnesses.

Solar Jin

Accelerating the deep transformation and upgrading of industrial structure and forming new quality productive forces are essential components for China to achieve the great rejuvenation of the Chinese Dream. After more than 40 years of rapid development, China has entered the "new normal" of development, making the advancement of new quality productive forces an urgent task. This paper reviews the evolution of China's industrial structure, argues the necessity for a new round of deep industrial transformation, and explores the impact of industrial structure transformation and upgrading on the level of new quality productive forces using various methods. The research findings are as follows:(1)The deep transformation and upgrading of the industrial structure can significantly promote the development of new quality productive forces, but there are obvious regional differences.(2)The core indicator of the improvement in the level of new quality productive forces is the enhancement of total factor productivity. Furthermore, this paper summarizes past industrial development processes and the challenges faced, and analyzes and discusses the potential challenges that may arise in promoting the development of new quality productive forces through deep industrial structure transformation, based on empirical research results.

Alper Saricioglu, Mujde Erol Genevois, Michele Cedolin

The Bullwhip Effect, describing the amplification of demand variability up the supply chain, poses significant challenges in Supply Chain Management. This study examines how the COVID-19 pandemic intensified the Bullwhip Effect across U.S. industries, using extensive industry-level data. By focusing on the manufacturing, retailer, and wholesaler sectors, the research explores how external shocks exacerbate this phenomenon. Employing both traditional and advanced empirical techniques, the analysis reveals that COVID-19 significantly amplified the Bullwhip Effect, with industries displaying varied responses to the same external shock. These differences suggest that supply chain structures play a critical role in either mitigating or intensifying the effect. By analyzing the dynamics during the pandemic, this study provides valuable insights into managing supply chains under global disruptions and highlights the importance of tailoring strategies to industry-specific characteristics.

Chen Yizhe, Wang Qi, Hu Dongxiao et al.

In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Effectively representing dynamic environmental states through multimodal sensor data fusion remains a critical challenge in current robotic datasets. To address this, an industrial-grade multimodal interference dataset is presented, designed for robotic perception and control under complex conditions. The dataset integrates multi-dimensional interference features including size, color, and lighting variations, and employs high-precision sensors to synchronously collect visual, torque, and joint-state measurements. Scenarios with geometric similarity exceeding 85\% and standardized lighting gradients are included to ensure real-world representativeness. Microsecond-level time-synchronization and vibration-resistant data acquisition protocols, implemented via the Robot Operating System (ROS), guarantee temporal and operational fidelity. Experimental results demonstrate that the dataset enhances model validation robustness and improves robotic operational stability in dynamic, interference-rich environments. The dataset is publicly available at:https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP.

Nikolaos E. Karkalos, Panagiotis Karmiris-Obratański

Non-conventional processes are considerably important for the machining of hard-to-cut alloys in various demanding applications. Given that the surface quality and integrity, dimensional accuracy, and productivity are important considerations in industrial practice, the prediction of the outcome of the material removal process should be able to be conducted with sufficient accuracy, taking into consideration the computational cost and difficulty of implementation of the relevant models. In the case of AWJ, various types of approaches have been already proposed, both relying on analytical or empirical models and developed by solving partial differential equations. As the creation of a model for AWJ pocket milling is rather demanding, given the number of parameters involved, in the present work, it is intended to compare the use of three different types of efficient modeling approaches for the prediction of the dimensions of pockets milled by AWJ technology. The models are developed and evaluated based on experimental results of AWJ pocket milling of a titanium workpiece by an eco-friendly walnut shell abrasive. The results indicate that a semi-empirical approach performs better than a two-step hybrid analytical/semi-empirical method regarding the selected cases, but both methods show promising results regarding the realistic representation of the pocket shape, which can be further improved by a probabilistic approach.

Yubo Xiao, Muxi Lin, Lu Wang

Abstract To investigate the impact of Green Digital Finance (GDF) policies on sustainable regional development goals, this study exploits the implementation of China’s green finance reform and innovation pilot zones as a quasi-natural experiment to examine the theory and impact of policy channels on sustainable development. A difference-in-differences model was applied to evaluate the impact of policies in these zones based on data from 285 cities in China from 2014 to 2020. Research has shown that the GDF is conducive to achieving sustainable development goals through the effects of financial inclusion and energy transitions, which promote the transformation and upgrading of industrial structures. The impact of the GDF pilot-zone policies on the sustainable development of cities at different levels, locations, resource endowments, and green total factor productivity is heterogeneous. This study provides accurate empirical evidence of the effects of the extensive implementation of the policies adopted in the pilot zones and the expansion of the scale of these zones, and it provides policy recommendations for the GDF.

S. Tanchyk, O. Pavlov, A. Babenko

Intensive farming has caused soil degradation, including the loss of humus, soil structure breakdown, compaction, and a decrease in both potential and effective fertility. Therefore, research into farming systems is highly relevant. In this regard, the purpose of this study is to scientifically substantiate, develop, and implement an ecologically friendly modern farming system under Ukrainian conditions. The primary methods used to determine the effectiveness of various farming systems and ensure the accuracy and reliability of experimental data were field, laboratory, and statistical methods. The study substantiated that an industrial farming system with the input of approximately 12.0 tonnes per hectare of crop rotation area of organic matter (8.0 tonnes per hectare of manure and 4.0 tonnes per hectare of plant residues) produces about 0.81 tonnes per hectare of humus, although 1.33 tonnes per hectare of it is mineralised, leading to a negative humus balance in the soil. The output of grain units in this system is 8.21 tonnes per hectare, feed units – 9.63, and digestible protein – 0.86 tonnes per hectare, with stability at 91.2% and profitability at 88.0%. The organic farming system, which includes the use of 24 tonnes per hectare of organic fertilisers and biological products to control weeds, diseases, and pests in agrocenoses, does not ensure a positive humus balance in the soil (-0.14 tonnes per hectare) and has significantly lower productivity. The no-till system, which involves the application of 12 tonnes per hectare of organic fertilisers in the form of root and stubble residues, by-products of crop production, and mineral fertilisers during sowing and foliar feeding, ensures a positive humus balance (+0.12 tonnes per hectare) but has productivity levels comparable to the organic system. The ecological system provides stable, economically viable, and resource-adequate productivity of arable land, enhances the quality indicators of products, and preserves and restores soil fertility. It increased the output of grain units by 8.9%, feed units by 7.2%, and digestible protein by 8.1%, ensuring high stability at 94.1% and an increase in production profitability by 8.5% compared to the control. The materials in this study are of practical value for agricultural enterprises of various ownership forms and will serve as technological guidelines for the implementation of modern, ecologically safe, economically and energetically justified agricultural production

Tiago M. Fernández-Caramés, Paula Fraga-Lamas

The Metaverse is a concept that proposes to immerse users into real-time rendered 3D content virtual worlds delivered through Extended Reality (XR) devices like Augmented and Mixed Reality (AR/MR) smart glasses and Virtual Reality (VR) headsets. When the Metaverse concept is applied to industrial environments, it is called Industrial Metaverse, a hybrid world where industrial operators work by using some of the latest technologies. Currently, such technologies are related to the ones fostered by Industry 4.0, which is evolving towards Industry 5.0, a paradigm that enhances Industry 4.0 by creating a sustainable and resilient world of industrial human-centric applications. The Industrial Metaverse can benefit from Industry 5.0, since it implies making use of dynamic and up-to-date content, as well as fast human-to-machine interactions. To enable such enhancements, this article proposes the concept of Meta-Operator: an Industry 5.0 worker that interacts with Industrial Metaverse applications and with his/her surroundings through advanced XR devices. This article provides a description of the technologies that support Meta-Operators: the main components of the Industrial Metaverse, the latest XR technologies and the use of Opportunistic Edge Computing communications (to interact with surrounding IoT/IioT devices). Moreover, this paper analyzes how to create the next generation of Industrial Metaverse applications based on Industry 5.0, including the integration of AR/MR devices with IoT/IIoT solutions, the development of advanced communications or the creation of shared experiences. Finally, this article provides a list of potential Industry 5.0 applications for the Industrial Metaverse and analyzes the main challenges and research lines. Thus, this article provides useful guidelines for the researchers that will create the next generation of applications for the Industrial Metaverse.

Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas et al.

The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.

Bjarke Madsen, Ramoni Adeogun

Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters.

Klaudia Arauzo-Aguilera, Luisa Buscajoni, Karin Koch et al.

Abstract Introduction In the biopharmaceutical industry, Escherichia coli is one of the preferred expression hosts for large-scale production of therapeutic proteins. Although increasing the product yield is important, product quality is a major factor in this industry because greatest productivity does not always correspond with the highest quality of the produced protein. While some post-translational modifications, such as disulphide bonds, are required to achieve the biologically active conformation, others may have a negative impact on the product’s activity, effectiveness, and/or safety. Therefore, they are classified as product associated impurities, and they represent a crucial quality parameter for regulatory authorities. Results In this study, fermentation conditions of two widely employed industrial E. coli strains, BL21 and W3110 are compared for recombinant protein production of a single-chain variable fragment (scFv) in an industrial setting. We found that the BL21 strain produces more soluble scFv than the W3110 strain, even though W3110 produces more recombinant protein in total. A quality assessment on the scFv recovered from the supernatant was then performed. Unexpectedly, even when our scFv is correctly disulphide bonded and cleaved from its signal peptide in both strains, the protein shows charge heterogeneity with up to seven distinguishable variants on cation exchange chromatography. Biophysical characterization confirmed the presence of altered conformations of the two main charged variants. Conclusions The findings indicated that BL21 is more productive for this specific scFv than W3110. When assessing product quality, a distinctive profile of the protein was found which was independent of the E. coli strain. This suggests that alterations are present in the recovered product although the exact nature of them could not be determined. This similarity between the two strains’ generated products also serves as a sign of their interchangeability. This study encourages the development of innovative, fast, and inexpensive techniques for the detection of heterogeneity while also provoking a debate about whether intact mass spectrometry-based analysis of the protein of interest is sufficient to detect heterogeneity in a product.

Johannes G. Hoffer, Sascha Ranftl, Bernhard C. Geiger

We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.

Seyedeh Motahareh Hosseini, Mohammad Aghdasi, Babak Teimourpour et al.

The importance of process analysis in engineering, procurement and construction companies (EPC), due to the complexity of the measures, the high level of communication between people, different and non-integrated information systems, as well as the amount of capital involved in these projects is much higher and more challenging. Limited research has been done on exploring business processes in these companies. In this study, in order to better and more accurately analyze the company's performance, three perspectives of process mining (process flow, case and organizational) is analyzed by using the event logs recorded in the supplier selection process. The results of this study led to the identification of challenges in the process, including repetitive loops, duplicate activities, survey of factors affecting the implementation of the process and also examining the relationships between people involved in the project, which can be used to improve the future performance of the company.

E. K. Sadanandan Nambiar

Our ideas and investments for landscape restoration should be broadened to include sustainably managed plantation forestry, especially those owned by small growers in forest-rural landscapes, as a part of the solution. Small growers play seminal roles in tropical forestry; for example, they provide about 90% and 60% of the industrial wood in India and Vietnam, respectively, and are central for countries such as Ethiopia and Uganda. They contribute to restoration of degraded landscapes in large areas. Wood production and use of wood products as a carbon positive, renewable, recyclable material should be a part of climate change mitigation actions. Forestry and wood-based business is providing livelihoods for millions of rural households and probably helping hundreds of thousands of families out of poverty.Tropical countries are facing widening wood supply- demand gaps. Substantial growth of wood production from small-scale plantations via both higher productivity per unit area and carefully managed land expansion, may be the only options for closing this gap. Nevertheless, there is no reliable inventory of the number of small-scale growers and households involved in any tropical country. Similarly, there is a serious lack of attention given to productivity in small -scale plantations, and exploring how sustainable management practices can improve productivity, product value, economic outcomes, and environmental benefits, at appropriate spatial and temporal scales.Many projects carried out in the name of small growers within country are fragmented, donor-driven, and uncoordinated. They will be more effective for providing benefits to small-growers if each recipient country developed a holistic and coherent strategy with priorities for advancing small- scale plantation forestry, within which, projects from international groups are harnessed. Creation of a cooperative (public-private), action and impact driven organisation(centre) is proposed. For success, the private sector should engage small growers as co-investing partners for advancing Green-growth and landscape restoration, and thus foster a direct conduit for channelling the global interests in tree-forest based solutions for improving the environment and reducing rural poverty.Small-scale plantation forestry can be substantially strengthened by three opportunities: the growing demand for wood, the role of forests and land restoration in the global carbon cycle and for low-emissions economy, and the urgent need to uplift rural economies and reduce poverty. These challenges are interwoven, and sustainably managed forestry offers integrated solutions for addressing them.

Yong Joon Jang

This paper empirically examines whether and how inward foreign direct investment (FDI) affected industrial productivity in Korea during the 2000-2016 period, based on dynamic panel data of inflow FDI on an arrival basis from 427 manufacturing industries. The paper adds to the literature by analyzing whether both technology spillovers and industrial restructuring from inward FDI can differ according to industrial characteristics such as capital intensity, imported intermediate inputs, and tariffs. The empirical results show that the overall effects of inward FDI on total factor productivity (TFP) were statistically insignificant in general. However, the positive effects of inward FDI on productivity became statistically significant for industries with lower tariffs. Capital intensity were not involved in the relationship between inward FDI and productivity. Thus, the paper highlights that the results in previous studies with inward FDI on a notification basis were overestimated and inward FDI policies in Korea should focus on channels such as trade liberalization and the redistribution of production factors rather than capital accumulation.

Chee Sheng Tan, Rosmiwati Mohd-Mokhtar, Mohd Rizal Arshad

The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

Stamatis Karnouskos, Roopak Sinha, Paulo Leitão et al.

Agent-technologies have been used for higher-level decision making in addition to carrying out lower-level automation and control functions in industrial systems. Recent research has identified a number of architectural patterns for the use of agents in industrial automation systems but these practices vary in several ways, including how closely agents are coupled with physical systems and their control functions. Such practices may play a pivotal role in the Cyber-Physical System integration and interaction. Hence, there is a clear need for a common set of criteria for assessing available practices and identifying a best-fit practice for a given industrial use case. Unfortunately, no such common criteria exist currently. This work proposes an assessment criteria approach as well as a methodology to enable the use case based selection of a best practice for integrating agents and industrial systems. The software product quality model proposed by the ISO/IEC 25010 family of standards is used as starting point and is put in the industrial automation context. Subsequently, the proposed methodology is applied, and a survey of experts in the domain is carried out, in order to reveal some insights on the key characteristics of the subject matter.

Chandan Sharma, Roopak Sinha, Paulo Leitao

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.

Dennis Jarvis, Jacqueline Jarvis, Chen-Wei Yang et al.

The benefits that arise from the adoption of a systems engineering approach to the design of engineered systems are well understood and documented. However , with software systems, different approaches are required given the changeability of requirements and the malleability of software. With the design of industrial cyber-physical systems, one is confronted with the challenge of designing engineered systems that have a significant software component. Furthermore, that software component must be able to seamlessly interact with both the enterprise's business systems and industrial systems. In this paper, we present Janus, which together with the GORITE BDI agent framework, provides a methodology for the design of agent-based industrial cyber-physical systems. Central to the Janus approach is the development of a logical architecture as in traditional systems engineering and then the allocation of the logical requirements to a BDI (Belief Desire Intention) agent architecture which is derived from the physical architecture for the system. Janus has its origins in product manufacturing; in this paper, we apply it to the problem of Fault Location, Isolation and Service Restoration (FLISR) for power substations.