Reduce Labor Costs Research Articles

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.

Economic efficiency of the use of means of mechanization of intensive horticulture in Belarus

This article considers the issue of using means of mechanization of the most labor-intensive processes of intensive horticulture in Belarus. It has been established that the level of mechanization of the basic technology of cultivating an apple orchard is only 12,5 %, and requires financial resources – 6163 rubles/ha. A set of machines developed by RUE “SPC NAS of Belarus for agricultural mechanization” consists of an ASU-6 unit for harvesting fruits and pruning trees, a KUV-1,8 complex for swathing and chopping cut branches of fruit trees and a technological line LSP-4 for sorting and packing apples allows you to mechanize these processes. The use of the proposed train of machines in a promising garden cultivation technology allows for annual cost savings on the technological map in the amount of 1018,5 rubles/ha, a reduction in labor costs by 278,5 man-hours/ha, and the level of mechanization of garden cultivation – 50,1 %.

Spatiotemporal layout schemes of exclusive lanes for connected automated container trucks

Connected automated container trucks (CACTs) have been widely used at container ports in China to reduce labor costs and save energy. CACT lanes, as components of intelligent infrastructure, can accelerate the application of CACTs. However, the current layout schemes cannot effectively solve the spatiotemporal imbalance issues among traffic volumes during container transportation. In this study, optimal spatiotemporal layout schemes for exclusive CACT lanes are analyzed. Considering emission, construction and travel costs, a spatiotemporal optimization model of CACT lanes is established. A solution framework that combines the improved artificial bee colony algorithm and the simulation of urban mobility (SUMO) method is proposed. Finally, the road network of the Tianjin Port Container Terminal is used as a case study. The results indicate that establishing reasonable layout schemes for CACT lanes is important for enhancing traffic efficiency and reducing pollutant emissions.

Влияние отраслевой структуры на динамику производительности труда в экономике США

The importance of labor productivity growth increases in the context of economic regionalization, which requires an economy-wide restructuring. In the United States of America, which continues its policy of reindustrial development, protectionism, and the suppression of competing countries by all means, the role of the structural factor in the dynamics of labor productivity is not uniform. The paper presents a theoretical analysis and methodological possibility of calculating the relationship between such economic categories and processes as labor productivity and its decline in the US economy, on the one hand, and structural changes in the American economy, on the other hand. Based on the use of the index method with the conversion of material resources into labor costs, an attempt was made to empirically identify the different impacts of specific industries (sectors) of the US economy on changes in labor productivity. The chronological period for calculating the system of necessary indices takes into account 2005 as the base year used for comparison with 2022. The results obtained indicate the beginning of a “reindustrial turn” in the US economy, which created the basis for the gradual recovery of the lost growth rates of labor productivity, together with the sector of information and communication services. It is noted that the movement of workers with relatively low qualifications from the manufacturing sectors of the American economy to the service sector, along with an increase in working hours, contributes to a decrease in labor productivity in a number of service industries. An increase in the role of the structural factor in the dynamics of labor productivity in those industries that most actively respond to the possibility of reducing labor costs in the context of growing technologization of economic activity is revealed. The conclusion is made about the information-industrial orientation of the structural changes observed in the United States over the period under study, which provides for a structural transformation of the labor market in the direction of increasing the efficiency of labor costs per unit of working time.

Machine learning applications in predictive maintenance: Enhancing efficiency across the oil and gas industry

The oil and gas industry faces constant pressure to enhance operational efficiency, reduce costs, and minimize equipment downtime. Machine learning (ML) applications in predictive maintenance have emerged as a transformative approach to achieving these goals. This review explores the role of machine learning in predictive maintenance, highlighting its potential to revolutionize maintenance strategies and improve asset management across the industry. Predictive maintenance leverages advanced algorithms to analyze historical and real-time data from equipment and sensors, enabling the identification of patterns and anomalies that precede equipment failures. By utilizing techniques such as supervised learning, unsupervised learning, and reinforcement learning, organizations can forecast equipment malfunctions and schedule maintenance activities proactively, thereby reducing unexpected downtimes and extending asset lifecycles. The implementation of ML in predictive maintenance provides several key benefits. Firstly, it enhances operational efficiency by optimizing maintenance schedules and minimizing unplanned outages, which are critical in the capital-intensive oil and gas sector. Secondly, it enables cost savings through more efficient resource allocation and reduced labor costs associated with reactive maintenance strategies. Thirdly, machine learning algorithms can continuously learn from new data, refining their predictive capabilities and improving accuracy over time. Several case studies illustrate the successful application of machine learning in predictive maintenance within the oil and gas industry. For example, ML models have been employed to predict pump failures, optimize drilling operations, and improve pipeline integrity monitoring. These applications not only lead to significant financial savings but also enhance safety by reducing the risk of catastrophic failures. In conclusion, machine learning applications in predictive maintenance represent a crucial advancement for the oil and gas industry. By harnessing the power of data-driven insights, organizations can enhance operational efficiency, reduce costs, and ultimately drive sustainable growth. This review emphasizes the transformative potential of machine learning in predictive maintenance, establishing it as a key strategy for success in the ever-evolving oil and gas landscape.

A cooperative model to lower cost and increase the efficiency of methane leak inspections at oil and gas sites

Methane is a potent greenhouse gas that tends to leak from equipment at oil and gas (O&G) sites. Conventional leak detection and repair methods for fugitive methane emissions are labor-intensive and costly because they involve time-consuming close-range, component-level inspections at each site. This has prompted duty holders to examine new methods and strategies that could be more cost-effective. We examined a cooperative model in which multiple duty holders of upstream O&G sites in a region use shared services to inspect on-site equipment using optical gas imaging camera or Method 21. This approach was hypothesized to be more efficient and cost-effective than independent inspection programs by each duty holder in the region. To test this hypothesis, we developed a geospatial simulation model using empirical data from 11 O&G-producing regions in Canada and the United States. We used the model to compare labor cost, transit time, mileage, vehicle emissions, and driving risk between independent and co-op leak inspection programs. The results indicate that co-op leak inspection programs can generate relative savings in labor costs (1.8%–34.2%), transit time (0.6%–38.6%), mileage (0.2%–43.1%), vehicle emissions (0.01–4.0 tCO2), and driving risk (1.9%–31.9%). The largest relative savings and efficiency gains resulting from co-op leak inspection programs were in regions with a high diversity of duty holders, which was confirmed with simulations of fictitious O&G sites and road networks spanning diverse conditions. We also found reducing leak inspection time by 75% with streamlined methods can additionally reduce labor cost by 8.8%–41.1%, transit time by 5.6%–20.2%, and mileage by 2.60%–34.3% in co-op leak inspection programs. Overall, this study demonstrates that co-op leak inspection programs can be more efficient and cost-effective, particularly in regions with a large diversity of O&G duty holders, and that methods to reduce leak inspection time can create additional savings.

The Application Status and Trends of Machine Vision in Tea Production

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

INVESTIGATION OF THE EFFECT OF CLAPS ON THE STRESS-STRAIN STATE OF TANK STRUCTURES

The need to study the problems of reliable operation of the reservoir system is relevant. Special attention should be paid to the specifics of the operation of oil storage tanks and their equipment. The Russian tank farm was formed in the 1970s, a significant part of it has a service life of more than 50 years. The experience of examining such structures with a long service life indicates the presence, in most cases, of the physical aging of their structural elements.
 The article presents the results of processing a cloud of points created using a ground laser scanner to determine the actual geometric deviations of the reservoir and their combined effect on the reliability of the reservoir.
 During the survey, a method of work was used that differs from the standard. In the field, a laser scanner was used instead of a theodolite; in office processing, software systems were used that simplify the tasks.
 In the process of research in the 3DR software package, a triangulation mesh model was created from a cloud of points from a set of finite elements to identify defects located on the wall. The data obtained can be compared with previous and future survey results. Also, the model can be used to create a calculation scheme in PC LIRA 10.
 We set the actual characteristics of the tank wall to the finite elements (the thickness of the first belt is 12 mm, the next ones 10 mm). The main load combinations were applied to the tank. The calculations were carried out for a combination of loads, including the own weight of the structures, the pressure of the stored product and the overpressure in the gas space. Circular and equivalent stresses according to the 4 (energy) theory of strength were analyzed.
 The use of a laser scanner and software systems reduces labor costs for tank inspection, improves the quality of work performed, makes it possible to see the real surface of the tank and take into account defects in the aggregate.
 This method is of practical importance and is currently used by the Department of Inspection of Buildings and Structures.

Modification of the Method for the Determination of Zinc Content in Insulins by Atomic Absorption Spectrometry

Scientific relevance. Zinc quantification in insulin active substances and preparations by atomic absorption spectrometry requires a sample preparation procedure using hydrochloric acid, a narcotic and psychotropic precursor. The exclusion of precursors from laboratory practice significantly reduces labour costs during analysis.Aim. This study aimed to modify the pharmacopoeial method for quantifying zinc in insulins with a view to eliminating drug precursors and to evaluate the applicability of the modified method across different insulin preparations.Materials and methods. The study considered insulin active substances and preparations of various types, dosage forms, and strengths. Zinc content determination involved using an Agilent 240FS atomic absorption spectrometer at a characteristic wavelength of 213.86 nm.Results. The authors selected the conditions for elemental analysis (nitric acid concentration, monochromator slit width, and flame stabilisation time) corresponding to the pharmacopoeial requirements for quantifying zinc in insulins (RSD ≤1.4%, standard zinc solution with a concentration of 0.8 mg/dm3, and calibration curve correlation coefficient ≥0.99). The study compared zinc content measurements in the studied insulin samples with hydrochloric acid or nitric acid as a solvent; it did not reveal any significant differences between the results obtained by the pharmacopoeial method and the modified method.Conclusions. The modified method enables analysts to quantify zinc in insulin preparations in various dosage forms without drug precursors.

Cost and quality of operational larviciding using drones and smartphone technology

BackgroundLarval Source Management (LSM) is an important tool for malaria vector control and is recommended by WHO as a supplementary vector control measure. LSM has contributed in many successful attempts to eliminate the disease across the Globe. However, this approach is typically labour-intensive, largely due to the difficulties in locating and mapping potential malarial mosquito breeding sites. Previous studies have demonstrated the potential for drone imaging technology to map malaria vector breeding sites. However, key questions remain unanswered related to the use and cost of this technology within operational vector control.MethodsUsing Zanzibar (United Republic of Tanzania) as a demonstration site, a protocol was collaboratively designed that employs drones and smartphones for supporting operational LSM, termed the Spatial Intelligence System (SIS). SIS was evaluated over a four-month LSM programme by comparing key mapping accuracy indicators and relative costs (both mapping costs and intervention costs) against conventional ground-based methods. Additionally, malaria case incidence was compared between the SIS and conventional study areas, including an estimation of the incremental cost-effectiveness of switching from conventional to SIS larviciding.ResultsThe results demonstrate that the SIS approach is significantly more accurate than a conventional approach for mapping potential breeding sites: mean % correct per site: SIS = 60% (95% CI 32–88%, p = 0.02), conventional = 18% (95% CI − 3–39%). Whilst SIS cost more in the start-up phase, overall annualized costs were similar to the conventional approach, with a simulated cost per person protected per year of $3.69 ($0.32 to $15.12) for conventional and $3.94 ($0.342 to $16.27) for SIS larviciding. The main economic benefits were reduced labour costs associated with SIS in the pre-intervention baseline mapping of habitats. There was no difference in malaria case incidence between the three arms. Cost effectiveness analysis showed that SIS is likely to provide similar health benefits at similar costs compared to the conventional arm.ConclusionsThe use of drones and smartphones provides an improved means of mapping breeding sites for use in operational LSM. Furthermore, deploying this technology does not appear to be more costly than a conventional ground-based approach and, as such, may represent an important tool for Malaria Control Programmes that plan to implement LSM.

Improved artificial potential field method for local minima

With the development of autonomous driving technology, its applications permeate many aspects of work and life, providing convenience while reducing labor costs. Path planning has always been important for autonomous driving, where APF is widely used thanks to its simplicity of calculation and effectiveness. However, therere still problems existing such as local minima, influenced by initial positions and parameters, and so on. In this study, a better approach to solving the local minimum issue is suggested. Firstly, the odometry method is used for the determination of falling into local minima by saving and computing the relationships between adjacent steps. Subsequently, a variable step length method is designed for escaping local minimum points and bypassing obstacles in front. The feasibility and robustness of the method were verified by simulations, and this method proved capable of solving the local minima and planning a reasonable trajectory.

ELECTRONIC DOCUMENT MANAGEMENT AND DIGITAL SIGNATURE IN THE BUSINESS ENVIRONMENT

The article examines the main aspects of the essence and significance of electronic document management and its types. Electronic document management remains a very relevant and important tool for document management for many organizations and institutions from various industries. It is proved that electronic document management can improve the speed of document circulation, reduce the time for their processing and increase the overall productivity of the organization. This is especially important in the conditions of the modern rhythm of life and business processes. The use of electronic document management allows you to spend less time searching and processing documents, which leads to reduced labor costs and a reduced need for physical resources such as the paper and printing. Electronic documents can be easily stored and backed up electronically. This allows you to increase the reliability and security of data storage. In the era of working with remote teams and remote work, electronic office work makes it easy to share the documents and information without being physically present in the office. It is substantiated that the introduction of paperless accounting by enterprises is an urgent problem, the solution of which requires the adoption of comprehensive measures to optimize accounting processes, which will contribute to the simplification of management initiatives and the definition of strategic decisions. A comparison of software products for the creation of electronic document management at the enterprise, which will help automate the process of processing and exchanging documents, is carried out. The possibilities of creating and using the digital signatures are disclosed, as well as the main advantages and disadvantages of its use are analysed. It is proved that the use of electronic digital signatures in the entrepreneurial activity can reliably protect the transmitted information from external interference and distortion and give it a legal status. This article highlights the features of displaying an electronic digital signature in accounting.

Hotel Human Resource Management Strategy During the Pandemic---A Case Study of Hilton Hotel

As COVID-19 spreads, an increasing number of industries have become stagnant, especially the hotel industry. Considering safety hazards, lots of hotel employees tend to make a career change. Therefore, the labor shortage and rising operating costs of human resources have gradually become the major issues for the hotel industry under the pandemic. In this circumstance, it is of great significance for the human resource department to think about how to adjust the hotel business strategy, optimize the management structure and reduce labor costs. Taking the renown Hilton Hotel as an example, this paper describes how the hotel should adopt human resources strategies to effectively cope with the crisis brought by COVID-19 and seize new development opportunities in this context. Hilton Hotels is working hard to ensure guaranteed quality of service during the pandemic, lowering corporate labor costs, and earning higher profits.The specific practice is to improve employee satisfaction, launch new employment mode, use artificial intelligence and so on.

Digital Transformation and Innovation Strategies for Traditional Retail Stores

Due to the global pandemic and world economy recession, the passenger flow of the retail industry is still relatively weak. Many countries have strengthened measures to prevent and control the epidemic, and then temporarily closed their stores. This reminds businesses that COVID-19 is still an ongoing challenge. This paper aims to identify the possible digital solutions for traditional offline retail stores to embrace digital innovation to become more smart and efficient. This research theme is to explore the feasibility of introducing a digitized business model into traditional retail stores using JD sports as the case. Business Model Canvas is used to identify the existing business model of JD retail shops, while Innovation Ambition Matrix is designed for making the innovation plan. The latest business model is presented with the help of the Customer Journey Map. By implementing the model, JD's traditional retail shop will reduce labour costs and increase operational efficiency, strengthening its competitive advantages.

Effect of Cost Control on Corporate Performance: Evidence from Selected Listed Brewery Firms in Nigeria

The main objective of this study is to determine the effect of cost control on corporate performance of listed brewery firms in Nigeria for the period of 2011 to 2021. To achieve this objective, material cost (MATC), labour cost (LABC) and overhead cost (OHDC) were used as proxy for cost control while the return on assets (ROA) was used as the proxy for the dependent variable, corporate performance. The study adopted ex post facto design and the data was obtained from the sampled firms’ annual reports and World Bank annual data official publications. The study was anchored on the Kaizen Costing Theory. The ordinary least square (OLS) regression technique was employed to analyze the data and test the hypotheses formulated at 5% level of significance. The findings revealed that cost control proxies such as material cost and overheads have positive and significant impact on corporate performance of brewery firms in Nigeria while labour cost has positive and insignificant impact on corporate performance of brewery firms in Nigeria. The study concluded that the elements of cost control used in the study such as material cost and overheads have positive and significant impact on corporate performance while labour cost has positive and insignificant impact on corporate performance of brewery firms in Nigeria. The study therefore recommends among other things that the management of brewery firms in Nigeria should embark on cost control strategies that emphasized on material cost, labour cost and overheads reduction in order to improve on their corporate performance indices.

A Pure Electric Driverless Crawler Construction Machinery Walking Method Based on the Fusion SLAM and Improved Pure Pursuit Algorithms.

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery.

Examining AI and Systemic Factors for Improved Chatbot Sustainability

ABSTRACT Chatbots link companies and users, increase conversions, reduce labor costs, and provide answers based on big data. Since COVID-19, demand for non-face-to-face services has increased. Despite expectations, chatbot use is inconsistent and satisfaction is low. This study identifies factors for improving the sustainability of chatbot services by considering artificial intelligence factors (personalization, anthropomorphism, social presence) and systemic factors (responsiveness, compatibility). The confirmatory factor analysis and structural equation model of the measurement model were analyzed using Smart PLS 3.3. Two hypotheses were rejected because the effect on expectation-confirmation was not statistically significant. This study presents implications for future chatbot research and development.

Identification of key genes promoting stamen formation induced by silver nitrate in gynoecious melon

AbstractThe gynoecious melon shows a high degree of hybrid seed production advantage, and exploitation of this property could improve both seed production and breeding, as well as reduce labor costs in melon production. The mechanism responsible the formation of hermaphroditic flowers by exogenous AgNO3 spraying is unclear. In the present study, the gynoecious inbred melon line WI998 was treated with AgNO3 solution to produce hermaphrodite flowers. Seedlings were investigated at the two, three, four, and five true‐leaf stages, and the AgNO3 concentrations were 150, 300, 450, and 600 mg/L. We found that the optimum spray concentration of AgNO3 was 450 mg/L with an optimum application time at the four true leave‐leaf stage. Transcriptome sequencing was performed on four‐true‐leaf stage samples sprayed with 450 mg/L AgNO3 and water control to identify differentially expressed genes (DEGs). Twenty‐seven DEGs were identified and one, Cucumis melo epidermal pattern factor‐like protein 4 (CmEPFL4), was involved in stamen development gene. Phenotypic analysis showed that CmEPFL4 played a key role in stamen induction, and heterologous expression of CmEPFL4 was found to interfere with stamen development and reduce the amount of pollen produced. Overall, the results demonstrated the importance of CmEPFL4 in AgNO3‐induced hermaphroditic flower production and provide insight into AgNO3 induction of stamen formation in the melon.

FINANCIAL AND ECONOMIC IMPACTS OF AUTONOMOUS VEHICLES IN AZERBAIJAN

Implementation of autonomous vehicles could change the finance and economics of the companies. Autonomous vehicles can operate around the clock, without the need for breaks or rest periods, which can increase efficiency and productivity. There could be no fatigue or tiredness for autonomous vehicles. This can lead to cost savings and increased revenue for companies that use autonomous vehicles. Autonomous vehicles can also help reduce labour costs, as they do not require a human operator. Autonomous vehicles are one of the main players in the market. For this reason, they are significant in Azerbaijan too. Autonomous vehicles could 92% safer in case of human error. If autonomous vehicles could be implemented in Azerbaijan by the year of 2030 number of accidents could be 87. Autonomous vehicles could save from 13.3% to 26.7% of fuel. If I compare 2021 fuel costs to 2021 AV fuel costs, it will give us a difference of 301283 USD. Autonomous vehicles could also save 80-90% labour costs. With the implementation of the autonomous vehicles the labour costs could decrease from 1038 AZN to 156 AZN in 2017. Those numbers could decrease from 1451 AZN to 218 AZN in 2021. Overall, autonomous vehicles could increase safety and impact the economy positively in Azerbaijan and worldwide. Autonomous vehicles could have a positive effect on the economy of Azerbaijan

Four-DOF Robotic Arm System for Goods Transport

Paper presents the design, development, and implementation of a Four-Degree-of-Freedom (Four-DOF) Robotic Arm System tailored for the purpose of efficient goods transport. The increasing demand for automation in logistics and industrial sectors has prompted the need for versatile robotic solutions capable of manipulating objects in various environments. The proposed robotic arm system addresses this need by offering four independent degrees of freedom, enabling it to perform intricate tasks involved in goods transport. The system's mechanical design incorporates four joints, each contributing to a specific movement: lifting, lowering, rotating, and extending. These movements provide the necessary flexibility to manoeuvre in complex spaces, making it suitable for applications within warehouses, factories, and distribution canters. The design process involved careful consideration of structural integrity, weight distribution, and motion optimization to ensure safe and precise operation. In conjunction with the mechanical design, the robotic arm is equipped with advanced sensing and control mechanisms. Sensor arrays, including proximity sensors and cameras, enable the arm to perceive its environment and detect objects, ensuring accurate object detection and collision avoidance. The control system, driven by a combination of microcontrollers and software algorithms, orchestrates the movement of each joint to achieve seamless coordination and efficient goods transport. Real-world implementation of the Four-DOF Robotic Arm System showcases its ability to streamline goods transport operations. By automating tasks that previously required manual intervention, the system enhances operational efficiency, reduces labour costs, and minimizes the risk of human error. Furthermore, its adaptability to diverse environments underscores its potential to revolutionize the logistics industry.