Hasil untuk "Construction industry"

Yuxiu Bai, Yingying Lin, Daokai Sun et al.

Abstract Construction industry migrant workers (CIMWs) face high-intensity labor and harsh working environments, experiencing significant health issues. Studying these health issues can help to protect workers’ rights, improve labor efficiency, and promote sustainable development in the construction industry. The objective of this study is to analyze the relationships among health status, social support, and marital adjustment in construction migrant workers. Data were collected from 446 migrant workers who completed the Self-rated Health Measurement Scale (SRHMS), Marital Adjustment Test (MAT), and Social Support Rating Scale (SSRS). The results showed that (1) CIMWs were in poor social health, with low social support but good marital adjustment. (2) The higher the education level and monthly income, the better the social health. (3) The scores on the SRHMS had a significant positive correlation with the scores on the MAT and SSRS, and there were significant differences in SRHMS scores among the different levels of marital adjustment. (4) Education level, subjective social support, utilization of support, and marital adjustment significantly predicted overall health status. (5) Marital adjustment directly predicted mental health and social health, and also had an indirect relationship through social support. Education level had direct and indirect predictive effects via social support on social health. Social support fully mediated the association between education level and mental health. (6) Education level, marital adjustment, and social support were all protective factors contributing to the health status of CIMWs.

Igor A. Potapov , Vasiliy L. Erokhin

The transformation of industrial zones in cities during the transition of economy to post-industrial development is reflected in the emergence of abandoned industrial areas on the sites of closed inefficient enterprises. At the same time, the city budget ceases to receive taxes from these areas. For a more rational use of such lands, their revitalization and renovation are necessary. The problem of using former industrial zones is typical for all cities in the Arctic. Using Arkhangelsk as an example, a retrospective analysis of the formation and transformation of its industrial zone was conducted. It does not represent a continuous space, but is located separately in several parts of the city. Based on the peculiarities of the geographical location and specialization of production, three industrial areas have been identified: Northern, Central and Southern. Analysis of the public cadastral map has determined the current state of the industrial territories of enterprises that existed in Arkhangelsk during the Soviet period. It was revealed that most of the closed enterprises are located in districts with poor transport accessibility in the northern area on island territories and partially in the southern area. These are mainly enterprises of the timber industry complex. This process contributed to the degradation of the outskirts of Arkhangelsk, especially on the island territories. At the same time, most of the remaining timber industry enterprises are located in the central part of the northern area, near the main highway connecting it to the city center and the seaport. This area is the most promising for the revitalization of production. The central industrial area has undergone the least transformation. Here, residential construction is taking place on the site of industrial sites, and public and business spaces are being created. In the southern industrial area, there are promising territories for the creation of logistics zones (the port areas of Zharovikha, Bakaritsa, and Levy Bereg). Former industrial zones on island territories can be used to create landscape parks and to develop recreation.

Sergey S. Vopilovskiy

The analysis of the influence of actual external and internal economic factors on the work of the fishing industry in general and in the Arctic zone in particular shows the stability and ability of the Russian fishery complex to fulfil the tasks of implementing the Food Security Doctrine of the Russian Federation and other strategic regulatory documents. Timely work of the state legislative bodies in decision-making at all levels of management in the current situation is noted. Key economic factors (export and import, supply and demand, shipbuilding and ship repair, logistics, etc.) that have direct and indirect impact after the in-troduction of sanctions are considered. The paper analyses the key performance indicators of the Russian fishery complex, provides an analytical review of the demand for fresh-frozen fish in the North-West re-gion, the relationship between the population’s income and the consumption of fish and fish products in the country. The primary role of scientific support of the fishing industry in the successful realization of the general goals and achievement of the set tasks is emphasized. An assumption about opening of new logistic routes and expansion of geography of fish and fish products supplies to African and Latin American countries, countries of Asia-Pacific region is made on the basis of assessment of modern international relations. It is determined that the construction and repair of the fishing fleet in modern conditions is of concern to the state structures and fishing business community. The measures of state support of shipbuilding plants of the country are considered, the proposal on creation of ship repair cluster in the Arctic zone of the Russian Federation is substantiated.

Lingfei Chen, Kai Zhang, Xueying Yang

While higher interest rates increase the cost of credit financing for businesses, this study finds that the direct impact of this traditional credit transmission mechanism on corporate bankruptcy risk is limited. Instead, our research reveals that changes in corporate behavior induced by rising debt financing costs are the root cause of bankruptcy risk. In the short term, an increase in interest rates drives businesses to substitute supply chain financing for credit financing in pursuit of profit maximization. This mismatch of short-term debt and long-term investments undermines the sustainability of the supply chain, ultimately reducing financial security-sacrificing safety for profitability. In the long term, higher interest rates exacerbate the overcapacity problem in industries, increasing the unsustainability of the production and sales balance. Using data from China's construction industry, this study empirically tests these findings and, based on the main conclusions, provides policy suggestions regarding the long- and short-term effects of monetary policy on the sustainable development of China's construction industry: (1) focus on short-term interest rate risks and be vigilant against commercial credit bubbles; (2) long-term monetary policy should prioritize industrial structure optimization.

Ms Bokang Sithole, Charles Tony Simphiwe Ngwenya

This study investigates the practical implementation and effectiveness of risk management tools and techniques in construction projects, with a particular focus on how organizational culture influences their success. Through qualitative research, including semi-structured interviews with professionals in the construction industry, the study explores the challenges and best practices in risk identification, assessment, and mitigation. Findings reveal that traditional tools such as checklists, risk registers, and brainstorming remain widely used, though their application is often limited by a lack of formal education and training in risk management. While quantitative methods, such as Monte Carlo simulations, are recognized for their predictive capabilities, they are underutilized, particularly for non-financial risks. The study highlights significant gaps between theory and practice, particularly in the integration of advanced data-driven approaches, which could improve the accuracy of risk assessments for high-risk tasks. Furthermore, the research identifies the absence of structured knowledge transfer mechanisms as a barrier to effective risk management. The study concludes that while risk management frameworks are theoretically sound, their practical implementation is often hindered by organizational barriers, including inadequate training, poor communication, and the failure to incorporate emerging technologies. The results suggest that future research should focus on developing standardized, technology-integrated risk management frameworks and fostering a culture of continuous improvement to enhance project success.

Eugenia Obidiegwu, Munirat Ayomide BALOGUN, Paul Adedeji AJAYI

The need for environmentally friendly and affordable building materials has sparked researches for materials to use in the construction industry. This paper therefore, studied the characterization of ceiling boards manufactured from plaster of Paris (POP) reinforced with banana fibres (BF) and coconut shell (CS) particulates. The samples were produced by adding different ratios of BF and CS to the POP matrix. To evaluate the effectiveness of the samples, several tests, were conducted. The results demonstrated that the addition of reinforcements improved the properties. The sample with a mixture of POP, BF, and CS demonstrated superior properties with the lowest water absorption (2.77%), high compressive strength (7.74 MPa), least thermal conductivity (0.2157 kW/mK) and hardness value of (23.2 HVN), these are within the standard range. The Scanning Electron Microscopy (SEM) analysis confirmed the results obtained. This study established the possibility of using local accessible materials to produce high quality ceiling boards.

Hui Wang, Yu Huang, Guojun Zhang et al.

Vaporizers have a wide range of applications in industry, and the welding of finned tubes is an important step in the manufacturing of vaporizers. The obstruction of U-shaped tubes poses a huge challenge to the detection of weld seams by visual sensors and robot welding, making it difficult to achieve automatic welding of finned tubes. Therefore, this paper designed an automatic robot welding system for finned tubes based on laser vision sensors, and innovatively proposed a point cloud construction method for dissimilar complex weld seams, which can achieve fast and high-precision collection of finned tube point clouds. Furthermore, a finned tube point cloud processing method was proposed, which can complete the calculation of circular weld seam parameters by extracting the fin end face and tubes. Finally, precise positioning of finned tube weld seams was achieved. Experimental results showed that the welding seam positioning accuracy of the method proposed in this paper is better than 0.22 mm, which meets the welding requirements of finned tubes.

Fengbo Yu, Mingze Li, Jigang Zhang et al.

To reduce the computational cost associated with traditional moving heat source methods, a segmented approach is proposed for simulating the welding process of T-joints in large-scale infrastructure steel modules. Firstly, the hole-drilling method was employed to measure the welding residual stresses in a 2400 mm T-joint. Subsequently, a three-dimensional finite element model was established in ABAQUS, and a user-defined subroutine for the segmented moving heat source was developed in Fortran to calculate the welding residual stresses. The numerical simulation results were compared with experimental data, showing high consistency and further validating the accuracy of the finite element model. Furthermore, the distribution patterns of residual stresses along the thickness direction and the effects of different welding sequences on temperature, stress, and deformation were investigated to optimize the welding sequence. The results indicated that the residual stresses along the weld seam exhibited a compressive–tensile–compressive distribution, with the maximum tensile stress reaching approximately 347 MPa. Additionally, the simulation results demonstrated that the double-ellipsoidal heat source method was computationally intensive and failed to provide accurate results for long weld seams, whereas the segmented moving heat source approach reduced the computation time to only 38 h. Moreover, different welding sequences had a significant impact on residual stresses and deformation. Through comprehensive analysis, it was found that Case 1 (sequential welding in the forward direction) achieved the best performance in minimizing welding residual stresses and deformation.

Xuejiao Li, Cheng Yang, Charles Møller et al.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

Kristin A. Oliver, Victoria Borish, Bethany R. Wilcox et al.

As quantum technologies transition out of the research lab and into commercial applications, it becomes important to better prepare students to enter this new and evolving workforce. To work towards this goal of preparing physics students for a career in the quantum industry, a senior capstone course called "Quantum Forge" was created at the University of Colorado Boulder. This course aims to provide students a hands-on quantum experience and prepare them to enter the quantum workforce directly after their undergraduate studies. Some of the course's goals are to have students understand what comprises the quantum industry and have them feel confident they could enter the industry if desired. To understand to what extent these goals are achieved, we followed the first cohort of Quantum Forge students through their year in the course in order to understand their perceptions of the quantum industry including what it is, whether they feel that they could be successful in it, and whether or not they want to participate in it. The results of this work can assist educators in optimizing the design of future quantum-industry-focused courses and programs to better prepare students to be a part of this burgeoning industry.

Haoyuan Jiang, Xuantang Xiong, Ziyue Li et al.

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limited than simulation-based RL methods. For real-world solutions, only flow can be reliably collected, whereas common RL methods need more. For the output action, most RL methods focus on acyclic control, which real-world signal controllers do not support. Most importantly, industry standards require a consistent cycle-flow relationship: non-decreasing and different response strategies for low, medium, and high-level flows, which is ignored by the RL methods. To narrow the gap between RL methods and industry standards, we innovatively propose to use industry solutions to guide the RL agent. Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance. We theoretically prove that such guidance can largely decrease the sample complexity to polynomials in the horizon when searching for an optimal policy. Our rigid experiments show that our method has good cycle-flow relation and superior performance.

Xiang Li, Hu Yang

Since the chemical industry index is one of the important indicators to measure the development of the chemical industry, forecasting it is critical for understanding the economic situation and trends of the industry. Taking the multivariable nonstationary series-synthetic material index as the main research object, this paper proposes a new prediction model: DeepVARMA, and its variants Deep-VARMA-re and DeepVARMA-en, which combine LSTM and VARMAX models. The new model firstly uses the deep learning model such as the LSTM remove the trends of the target time series and also learn the representation of endogenous variables, and then uses the VARMAX model to predict the detrended target time series with the embeddings of endogenous variables, and finally combines the trend learned by the LSTM and dependency learned by the VARMAX model to obtain the final predictive values. The experimental results show that (1) the new model achieves the best prediction accuracy by combining the LSTM encoding of the exogenous variables and the VARMAX model. (2) In multivariate non-stationary series prediction, DeepVARMA uses a phased processing strategy to show higher adaptability and accuracy compared to the traditional VARMA model as well as the machine learning models LSTM, RF and XGBoost. (3) Compared with smooth sequence prediction, the traditional VARMA and VARMAX models fluctuate more in predicting non-smooth sequences, while DeepVARMA shows more flexibility and robustness. This study provides more accurate tools and methods for future development and scientific decision-making in the chemical industry.

Simon Alexander Wiese, Johannes Lehmann, Michael Beckmann

Using novel establishment-level observational data from Switzerland, we empirically examine whether the usage of key technologies of Industry 4.0 distinguishes across firms with different types of organizational culture. Based on the Technology-Organization-Environment and the Competing Values framework, we hypothesize that the developmental culture has the greatest potential to promote the usage of Industry 4.0 technologies. We also hypothesize that companies with a hierarchical or rational culture are especially likely to make use of automation technologies, such as AI and robotics. By means of descriptive statistics and multiple regression analysis, we find empirical support for our first hypothesis, while we cannot con-firm our second hypothesis. Our empirical results provide important implications for managerial decision-makers. Specifically, the link between organizational culture and the implementation of Industry 4.0 technologies is relevant for managers, as this knowledge helps them to cope with digital transformation in turbulent times and keep their businesses competitive.

Vahid Garousi, Alper Buğra Keleş

Alongside software testing education in universities, a great extent of effort and resources are spent on software-testing training activities in industry. For example, there are several international certification schemes in testing, such as those provided by the International Software Testing Qualifications Board (ISTQB), which have been issued to more than 914K testers so far. To train the highly qualified test engineers of tomorrow, it is important for both university educators and trainers in industry to be aware of the status of software testing education in academia versus its training in industry, to analyze the relationships of these two approaches, and to assess ways on how to improve the education / training landscape. For that purpose, this paper provides a pragmatic overview of the issue, presents several recommendations, and hopes to trigger further discussions in the community, between industry and academia, on how to further improve the status-quo, and to find further best practices for more effective education and training of software testers. The paper is based on combined ~40 years of the two authors' technical experience in test engineering, and their ~30 years of experience in providing testing education and training in more than six countries.

Borys Ioshchikhes, Michael Frank, Matthias Weigold

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1692 results, of which 54 articles published between 1987 and 2023 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Ramil N. Safiullin, Julia V. Torkunova

The purpose of this article is to analyze modern approaches and technologies for creating voice assistants based on artificial intelligence, as well as to present the results of mobile development of a virtual voice assistant. The article discusses key aspects of the development, including the choice of algorithms for natural language processing, machine learning and speech recognition technologies. The architecture and functionality of the developed voice assistant are described, as well as examples of its application. Materials and methods: modern methods of visual modeling and programming, the capabilities of the Dart language and the Flutter framework are used to solve the problems of developing a virtual assistant. Results: a cross-platform mobile application has been developed that combines the capabilities of voice recognition, text mining, voice and image playback. In conclusion, conclusions are drawn about the future prospects of development, integration and implementation into the modern digital educational ecosystem.

Kai ZHANG, Xiaonan LI, Kaikai BAO et al.

The ecological restoration of mine dump is a major environmental problem faced by open-pit mining, and it is an important factor restricting the construction of green open-pit coal mine. Soil reconstruction is an important step in the ecological restoration of dumps. The northwest coal base, represented by Xinjiang, is characterized by water scarcity and salinization. Soil water and salt migration is a key indicator to determine the success of soil reconstruction. At present, the research focuses on the surface soil reconstruction to improve soil nutrients and promote plant growth. There are few studies on the functional soil reconstruction of water and salt control, and the mechanism of water and salt transportation under different soil reconstruction methods is still unclear. Based on the characteristics of coal resource endowment in Xinjiang, from the perspective of coal circular economy, this study used coal gasification slag (CGS), a by-product of energy and chemical industry, as a reconstruction material. Through a capillary water rising-evaporation experiment, the vertical migration of water and salt and water supply capacity after CGS reconstruction were analyzed. The Van Genuchten model was used to fit the soil water characteristic curve, analyzed the soil water holding capacity after CGS reconstruction, and studied the feasibility of CGS as an aquifer reconstruction material. The red mudstone associated with coal mining was used as the reconstruction material. Through the soil column infiltration evaporation experiment, the water and salt changes at different soil depths after the reconstruction of red mudstone were analyzed, and the feasibility of mudstone as the reconstruction material of aquiclude was studied. The results showed that the CGS reconstruction improved soil texture, optimized pore structure, promoted soil water and salt transport, enhanced capillary action, promoted the upward supply of water in the lower layer, and also increased salt surface accumulation. The reconstruction changed the parameters of soil water characteristic curve, increased θs, decreased a and n, and improved soil water holding capacity. The higher the amount of CGS added, the greater the fine slag content, the more obvious the effect. The CGS was feasible as a material for reconstructing aquifer. The red mudstone had high clay and secondary mineral content, rich pore structure and good physical adsorption. After reconstruction, the soil water content at 0−24 cm depth was higher than that of the control group, and the salt reached the highest value at 20−24 cm after evaporation. The red mudstone effectively blocked the upward movement of salt. Red mudstone was feasible as a material for reconstructing aquifuge. The research explores a suitable soil reconstruction model for the waste dump in western coal base.

Luigi Capogrosso, Alessio Mascolini, Federico Girella et al.

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

Will Aitken, Mohamed Abdalla, Karen Rudie et al.

Impressive performance of pre-trained models has garnered public attention and made news headlines in recent years. Almost always, these models are produced by or in collaboration with industry. Using them is critical for competing on natural language processing (NLP) benchmarks and correspondingly to stay relevant in NLP research. We surveyed 100 papers published at EMNLP 2022 to determine the degree to which researchers rely on industry models, other artifacts, and contributions to publish in prestigious NLP venues and found that the ratio of their citation is at least three times greater than what would be expected. Our work serves as a scaffold to enable future researchers to more accurately address whether: 1) Collaboration with industry is still collaboration in the absence of an alternative or 2) if NLP inquiry has been captured by the motivations and research direction of private corporations.

Isaac Kwan Yin Chung, Minh Tran, Eran Nussinovitch

In this industry talk at ECIR 2022, we illustrate how we approach the main challenges from large scale cross-domain content-based image retrieval using a cascade method and a combination of our visual search and classification capabilities. Specifically, we present a system that is able to handle the scale of the data for e-commerce usage and the cross-domain nature of the query and gallery image pools. We showcase the approach applied in real-world e-commerce snap and search use case and its impact on ranking and latency performance.