Hasil untuk "Industrial productivity"

C. Luo, S. Stoyanov, S. Stoyanov et al.

T. Deelen, C. H. Mejía, K. P. Jong

Jiancheng Guan, N. Ma

Shweta Singh, D. Tripathi, Swati Singh et al.

U. Hölker, M. Höfer, J. Lenz

Huwei Wen, Chien‐Chiang Lee, Ziyu Song

C. Turner, J. Oyekan, L. Stergioulas et al.

In recent years, a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This article discusses the current state of the art in the adoption of Industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of Industry 4.0 technologies. This article discusses the relevance of the following key Industry 4.0 technologies to construction: data analytics and artificial intelligence, robotics and automation, building information management, sensors and wearables, digital twin, and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This article also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector, a three-phase use of intelligent assets from the point of manufacture up to after build, and a four-staged R&D process for the implementation of smart wearables in a digital enhanced construction site.

Fengsheng Chien, Muhammad Sadiq, Hafiz Waqas Kamran et al.

This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic’s severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

A. Zanchettin, N. Ceriani, P. Rocco et al.

B. B. Ashoor, S. Mansour, A. Giwa et al.

Rifki Maulana Iqbal Taufik, Tri Kurnia Revul Andina

This research aims to examine the impact of automation on future work in Indonesia's agriculture sector. Internet and digital platforms have altered many aspects of life and resulted in automation in several fields, including agriculture. The emergence of new technologies is often perceived as a threat and a challenge for this sector and its workers. Using a desk study method, however, the result shows that the automation of agriculture is beneficial to Indonesia’s economy. This is not only in terms of gross domestic product (GDP) but also in terms of enhancing the quality of smallholder employment and the interest of youth in working in this sector. Farmers' use of the internet and smartphones have become catalysts in adopting automation in the present and emerging. Digital technology has become an important intermediate factor that helps farmers improve their productivity. This has become the mainstream of the adoption of the technology in agriculture rather than mechanization. Job replacement due to automation will possibly not happen in Indonesia due to labour market conditions, disparity of land ownership, and the high cost of implementing automation. Furthermore, this research addresses aspects that deserve attention, including job quality, social protection, industrial relations, inequality, gender, and participation.

Xin Wen, Fange Meng, Yali Liu

Abstract The human capital structure changes as the industrial structure evolves. In order to achieve the matching development of human capital structure and industrial structure, this study, based on the theoretical framework of the dynamic evolution of the human capital structure, selects the panel data of 284 prefecture-level and above cities in China from 2003 to 2019 to explore the impact of China’s human capital structure on the upgrading of the industrial structure and the dynamic evolution of the marginal effect of the human capital structure. The results of the study reveal that the optimization of human capital structure can significantly promote the upgrading of the industrial structure, but this promotion is not significant during the industrialization stage and in small cities. The results of the quantile regression show that as the adjustment of the industrial structure deepens, the marginal effect of the human capital structure gradually increases. The mechanism test shows that the human capital structure can promote the upgrading of the industrial structure by facilitating technological progress, increasing total factor productivity, and boosting technology transactions. In the further examination, this study measures the human capital structure from the dimension of skill training, and finds that there is an obvious substitution effect between training and education. This substitution effect gradually weakens as the industrial structure upgrades. Therefore, it is recommended that local governments formulate policies for talent cultivation and introduction in combination with the current development situation of the industrial structure, strike a proper balance between talent introduction policies and industrial structure adjustment plans, and promote the coordinated development of human capital structure and industrial structure.

Mukhtar Sama, Amit Sata, Gaurang Joshi et al.

ABSTRACT Data are most important for any manufacturing processes in decision‐making. It is important to monitor this data to improve the productivity of processes. Industrial Internet of Things (IIoT) is a very effective tool for capturing process information. This work focus on implement of IIoT in TIG welding processes. We developed an IIoT‐enabled TIG welding setup that tracks key parameters like current, travel speed, gas flow rate, and arc gap in real time. This data is sent to a cloud platform and displayed through an easy‐to‐use mobile app, giving welders and engineers clear visibility into the process. This work also explore the potential application of IIoT in inspection to improve the inspection process. By capturing and processing weld images, we could measure bead width and detect any visible surface defects using edge detection and contour analysis. Also, Mobile based application is developed to store the inspection results of LPT, UT and metallography for proper documentation and analysis. The implementation of machine learning using XGBoost algorithm is discussed to predict the mechanical properties like, Ultimate tensile strength and hardness of HAZ and weld. The model performed very well, achieving over 95% accuracy, and was further explained using SHAP tools so we could understand not just what the model predicted, but why. For example, we could see how changing travel speed or gas flow affected the final weld quality. In short, this work demonstrates how combining IIoT, machine learning, and image processing can make TIG welding smarter, more reliable, and easier to control. It turns raw.

T. Hermann

Navruzov Sobir, Khudayberdieva Umida, Abdikayumova Nigora et al.

This article highlights the results of testing new complex and simple industrial hybrids of silkworms, focusing on their improved productive and technological properties in comparison with foreign analogues. Various reproductive, biological, productive, and technological indicators were used to evaluate the performance of these hybrids. The research demonstrated that local complex and simple hybrids outperformed the control variants in terms of these indicators, indicating their high performance and potential for silk production. These findings suggest that the local silkworm hybrids have significant advantages over foreign analogues, particularly in terms of cocoon quality and quantity. Based on the positive results obtained from the testing, the article proposes increasing the volume of preparation of local silkworm hybrids for the production of industrial cocoons. This recommendation is supported by the superior performance of these hybrids in comparison with foreign analogues, indicating their suitability for large-scale silk production. This study provides valuable insights into the potential of local silkworm hybrids for improving silk production in Uzbekistan. By utilizing these hybrids, the silk industry in the country could enhance its productivity and competitiveness in the global market, while also promoting the development of local sericulture.

Sameh Samir Ali, Savvas Giannis Mastropetros, Michael Schagerl et al.

Rapidly expanding industrialization and the depletion of non-renewable fossil fuels have necessitated the discovery of feasible renewable alternatives to meet the rising energy demand while reducing carbon dioxide (CO2) emissions. The present global energy strategy is built on cost-effective and environmentally friendly alternatives; and production of microalgae has the ability to meet these requirements. Microalgae have been found as a promising and sustainable alternative for treating wastewater (WW) concurrently with biofuel production. One potential strategy, which uses microalgae for lowering the level of contamination in WW is called bioremediation. There are substantial gains to be made for both the economy and the environment through the integration of microalgae-based biofuel production with wastewater treatment (WWT). The use of microalgae that have a short life span, a high growth rate, and a high CO2 usage efficiency is one of the promising approaches for producing biomass from WW nutrients that involves the utilization of renewable resources. Microalgae are one of the most promising biomass resources for use in thermochemical conversion processes for the production of liquid and gaseous biofuels due to their advantages over other biomass feedstocks, such as sustainability, renewability, and productivity. Currently, technology and cost are the primary obstacles limiting industrial applicability, which necessitates an optimum downstream process to minimize production costs. Consequently, the concurrent utilization of microalgae for WWT and biofuel production has made these challenges practical and economically viable. This review provides an overview of microalgae and their bioremediation and bioenergy production applications. It also provides insight for future research to investigate additional possible applications of microalgal biomass. These applications could include not only the bioremediation process, but also the generation of revenues from microalgae through the incorporation of clean and green technology, which would provide long-term sustainability and environmental benefits.

Gorelik O.V., Gorelik A.S., Lopaeva N.L. et al.

The Ural type of the domestic black-and-white breed is distinguished by high productivity indicators, good suitability for use in the conditions of industrial milk technology. To obtain modern dairy cattle, related breeds were used in the herds, a significant number of animals obtained as a result of closely related breeding were revealed. the study of the influence of the degree of inbreeding on the growth and development of repair young is relevant and has practical significance. It was found that the live weight of heifers in all accounting periods practically did not differ, that is, it can be said that the method of obtaining heifers using unrelated or related selection did not affect the dynamics of live weight during their cultivation. The average daily gains in live weight differed slightly by growth periods and by groups. So they were higher in the periods from the first to the first fruitful insemination and from 10 to 12 months, lower gains were noted in the period from the moment of fruitful insemination to 18 months of age. The highest milk yield for lactation was obtained from the first heifers with a remote degree of inbreeding.

Tengku Nazmi Tengku Asmawi, Azlan Ismail, Jun Shen

Abstract Cloud failure is one of the critical issues since it can cost millions of dollars to cloud service providers, in addition to the loss of productivity suffered by industrial users. Fault tolerance management is the key approach to address this issue, and failure prediction is one of the techniques to prevent the occurrence of a failure. One of the main challenges in performing failure prediction is to produce a highly accurate predictive model. Although some work on failure prediction models has been proposed, there is still a lack of a comprehensive evaluation of models based on different types of machine learning algorithms. Therefore, in this paper, we propose a comprehensive comparison and model evaluation for predictive models for job and task failure. These models are built and trained using five traditional machine learning algorithms and three variants of deep learning algorithms. We use a benchmark dataset, called Google Cloud Traces, for training and testing the models. We evaluated the performance of models using multiple metrics and determined their important features, as well as measured their scalability. Our analysis resulted in the following findings. Firstly, in the case of job failure prediction, we found that Extreme Gradient Boosting produces the best model where the disk space request and CPU request are the most important features that influence the prediction. Second, for task failure prediction, we found that Decision Tree and Random Forest produce the best models where the priority of the task is the most important feature for both models. Our scalability analysis has determined that the Logistic Regression model is the most scalable as compared to others.

Eko Fajar Cahyono, Lina Nugraha Rani, M. Fariz Fadillah Mardianto

Depositor trust plays an essential role in the banking sector. The main objective of this study is to test several factors that significantly affect depositors’ confidence in conventional and Islamic banks in Indonesia during the COVID-19 pandemic. We conducted qualitative research with a sample of 217 customers who had a minimum of two bank accounts, one conventional, and one Islamic. In a questionnaire, customers were asked their opinions related to indicators of the variables studied, such as depositor trust, and their perceptions of inflation, conventional bank interest, the equivalent yield rate of Islamic banks, and industry perception Productivity Index. The results of the questionnaire were analysed using the partial least squares (PLS) method. The PLS analysis results show that the indicators related to conventional bank interest and the equivalent yield rate of Islamic banks significantly affected depositors’ trust and hands. In other words, customers were influenced when making bank deposits by the factors related to conventional bank interest and the equivalent yield rate of Islamic banks. The external aspect of the industrial production index based on the PLS test had a significant effect on depositors’ trust in both types of bank. In contrast, the external factor of inflation did not significantly affect depositors’ trust in either conventional or Islamic banks. Therefore, based on the PLS-SEM results, conclusions can be drawn regarding the factors influencing depositor trust.

Alexander Dmitriev, Ildar Mustafin

This article deals with the problem of the digital revolution, which in recent decades has led to the rapid pace of innovation not only in the global industrial sector, but also in science, medicine, education and other areas of human activity, providing them with a significant increase in productivity, profitability, labor productivity, and safety for the environment. However, this has hardly affected the construction industry, which has not undergone significant changes over the past 50 years. This article reveals the content of the main reason for the extremely slow pace of innovative processes in construction, due to its global scale and decentralized nature. The construction industry accounts for approximately 6 % of global GDP (and for developing countries it reaches 8 % of GDP) and continues to grow. Special attention is paid to the fact that construction is the largest consumer of resources, which annually uses about 50 % of the total volume of steel produced and more than 3 billion tons of raw materials. Therefore, any innovation that leads, for example, to increased productivity in construction, on a global scale could save $100 billion a year. Also, this paper shows the factors that make construction a difficult business, which is not amenable to the necessary transformations. The article presents the basic principles of building an automated information support system.