Type Of Industry Research Articles

Exploring visual communication in corporate sustainability reporting: Using image recognition with deep learning

AbstractPhotographs and images in sustainability reports can affect readers' impressions of a company. This study conducts an exploratory analysis to investigate the impact of visual content on sustainability reporting and identify corporate reporting strategies. Using image recognition with deep learning technology, we analyse how 1025 global companies, publishing reports in English, incorporate images into their sustainability reports. The study also identifies factors influencing image selection and utilisation, assessing the images based on these factors' impact. Our findings highlight that economic development, cultural preferences and industry type contribute to variations in image usage. Moreover, these influences differ according to disclosure medium (i.e., integrated or sustainability reports), revealing industry‐specific image trends and providing insights into workforce characteristics. Leveraging deep learning, the study delves into facial expressions and demographic differences in the images, providing a comprehensive understanding of corporate communication strategies.

Fractional PID with Genetic Algorithm Approach for Industrial Tank Level Control Process

Process control is used in various types of industries to boost production while using current resources, product quality, and safety, with level control being one of the most important schemes. It is suggested that study be conducted on nonlinear continuous processes, particularly in process control applications such as conical tank level control. Initially, each process receives one set of Proportional-Integral (PI) controller settings and a First Order Plus Dead-Time (FOPDT) model using the Ziegler Nicholas (ZN) step response tuning method. The numerous FOPDT models and parameter sets are also obtained. Responses obtained with a single set of parameters and responses obtained with many sets of parameters are compared. By creating a Fractional PID Controller (FPID) with Genetic Algorithm (GA), a simulation-based tuning strategy is proposed. The step responses are acquired through experimental research using fine-tuned settings. At most operational points, the Fractional PID Controller with Genetic Algorithm-based tuning approach outperforms, with lowest oscillation, extremely tiny overshoot, minimum average ISE, and minimum average IAE. The proposed FPID with GA method provides better setpoint and regulatory tracking performance compared to ZN-PID.

Overview Metode Fitoremediasi Dalam Pengelolaan air Tercemar Timbal (Pb)

Wastewater originating from industry varies greatly depending on the type of industry. Industrial wastewater usually contains many toxic and dangerous chemical compounds (B3) and contains heavy metals. Heavy metals are pollutants that have dangerous effects because they cannot be broken down biologically and stable. Heavy metal elements can be distributed on the earth's surface in water, soil and air. Heavy metal waste pollution containing lead (Pb) is a problem for current environmental conditions. Heavy metals are found in almost all types of industrial waste. The increasing number of industries will cause increased pollution of water sources originating from industrial waste. . Therefore, effective and efficient methods are needed to manage lead-contaminated water. Methods that are easy to apply, low cost and environmentally friendly are highly desirable for managing lead-contaminated water so that the concept of good mining practice can be applied to sustainable development. The most efficient and effective method applied for mining waste management is the phytoremediation method. The results of the research state that several plants have a significant and efficient effect in helping the absorption of the metal lead (Pb) contained in water. Great Duckweed (Spirodela Polyrhiza) and Water Hyacinth (Eichhornia crassipes) are plants that are suitable for implementing phytoremediation methods in managing water contaminated with lead (Pb).

Gearbox Condition Monitoring and Diagnosis of Unlabeled Vibration Signals Using a Supervised Learning Classifier

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics.

Retraction Note: The effect of firm size, industry type and ownership structure on the relationship between firms’ sustainable innovation capability and stock liquidity

Retraction Note: The effect of firm size, industry type and ownership structure on the relationship between firms’ sustainable innovation capability and stock liquidity

Occupational health and safety activities undertaken by physiotherapy students: A retrospective review of industry placement reports.

The University of South Australia's Bachelor of Physiotherapy course, 'Work Health and Safety (WHS) Practice', provides students with introductory practical WHS experience. Students undertake 80-hour effort WHS projects for industry, culminating in a report for hosts. To establish the types of industry that host WHS undergraduate physiotherapy placements and the nature of activities that students conduct. Reports were retrospectively, systematically classified using a descriptive, document content analysis approach. Demographic data were collected: report and student numbers, placement locations and industry types. Selected reports were used as a subset for further analysis with definitions created to classify the nature of placement activities. Most reports were written by a pair of students (88%, n = 269), with placements based in metropolitan Adelaide (91%, n = 284). Various industries hosted students, including healthcare and social assistance (40%, n = 117) and manufacturing (30%, n = 89). Reports primarily included risk management activities as required by WHS legislation (97%, n = 229). Physiotherapy student WHS activities maintain close links with industry, involving stakeholders (workforce, students, University of South Australia and academics). At an entry-level standard, the student WHS activities align well with some criteria set as key competencies for Australian Occupational Health physiotherapy practitioners.

Determinan Overpricing Saham pada Saat Initial Public Offering di Bursa Efek Indonesia

Purpose: This study aimed to prove the effect of financial leverage, company size, company age and underwriter’s reputation on stock overpricing. Method: The population of this research was the companies which conducted initial public offering (IPO) which were listed on the Indonesia Stock Exchange in 2011 - 2020. This research used purposive sampling technique and the number of samples obtained was 62 companies. Results: The results of this study indicate that the variable company size, company age, and underwriter reputation have a positve effect on overpricing, financial leverage have a negative effect on overpricing. Limitations: This study does not test the type of industry and the level of industrial saturation of companies conducting an IPO. This study does not look at the agreement between the issuer and the underwriter whether it is full commitment or not. Contribution: This research is expected to increase knowledge about the factor that influence overpricing of share during the initial public offering on the Indnesia Stock Exchange.

Research on load curve fitting method based on K-means algorithm

Some areas of China are affected by the fluctuation of new energy output and load, and the power supply as a whole presents an unbalanced phenomenon of abundant electricity supply but shortage of power supply. To understand the characteristics of the load electricity consumption curve more accurately, this paper proposes a method of fitting the load electricity consumption curve based on K-mea ns algorithm. First, analyze the characteristics of the load electricity consumption based on the industry subdivision, and build the load electricity consumption characteristic index system. Then, the k-mea ns cluster analysis is carried out based on the characteristics of the load power consumption curve of the subdivided industries to determine the number of clusters, and the curve fitting and deviation test are carried out for each cluster group. This paper carries out practical analysis and theoretical research based on the data of typical industry load power consumption curve in Ningxia.

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.

Prevalence of ocular morbidity among industrial workers of Eastern Nepal

Objectives: The study aimed to investigate the prevalence of ocular morbidity among industrial workers. Materials and Methods: A descriptive cross-sectional study was carried out in four industries of Koshi Province. The demographic information gathered encompassed details such as age and gender. Employment-related factors included the nature of work, years of experience, and the type of industry. Economic considerations, such as monthly pay scale and job responsibilities, were also explored. Educational background and awareness of ocular hazards in the workplace, along with knowledge about safety devices, constituted crucial components of the survey. Comprehensive ocular examinations, along with assessments for color vision, contrast sensitivity, and stereopsis, were carried out on-site in collaboration with the outreach team of Biratnagar Eye Hospital. A structured schedule was administered through face-to-face interviews and documented. Results: In the present study, A total of 359 industrial workers were screened; among them, 321 (89%) were male, and 38 (11%) were Female, with a mean age of 43.72 ± 10.18. The majority of them were from 40 to 49 years of age group 135 (37.6%). The overall prevalence of ocular morbidity among the workers was 289, (80.50%). The highest rate of ocular morbidity was seen in sugar industry 98, (89.9%) followed by the jute industry 85, (80.18%), the soap industry 59, (74.68%), and the dairy industry 47, (72.30%). Presbyopia 166, (46.23%) emerged as the most common ocular morbidity among all workers. A history of ocular injuries was found in 115, (32%) of workers, and only 19% of workers used ocular safety devices. The main reason for not using ocular safety devices was due to unavailability (65%). Conclusion: The research highlights a significant prevalence of ocular morbidity among industrial workers, with presbyopia emerging as the most common ocular disorder.

ВИЗНАЧЕННЯ ТЕНДЕНЦІЙ ЗМІН КАПІТАЛЬНИХ ІНВЕСТИЦІЇ ПРОМИСЛОВОГО ВИРОБНИЦТВА В УМОВАХ НЕВИЗНАЧЕНОСТІ, ЗАГРОЗ ТА РИЗИКІВ

The article examines the trends of changes in capital investment in industry and determines the trends of further investment in conditions of uncertainty, threats and risks. Capital investments in industry for 2010-2022 were analyzed. It was determined that the general level of capital investments in the industry of Ukraine in the period 2010-2022 increased, with the exception of 2020. The number of industrial enterprises that introduced innovations for the period 2013-2020 was analyzed as a percentage of the total number. During the period 2013-2016, there was an increase in enterprises that implemented innovative processes in their activities, in 2017 and 2019, there was a slight decrease in innovative activity compared to the previous period. It is substantiated that the main vectors of development among science-intensive types of industry and mechanical engineering should become: the development of the aviation industry, rocket and space technology, the production of new types of rolling stock, the development of the energy industry, the production of electronic equipment and the modernization of the defense complex. To ensure the security and competitiveness of the state, it is necessary to develop modern types of weapons and military equipment. Among the priorities: radar systems, high-precision weapons and means of their destruction, missile complexes of various ranges. In order to maintain competitive positions in the field of mining and metallurgical production, it is necessary to develop directions for the diversification of production, optimization of the structure according to the nomenclature range, development of new types of goods and services on the basis of existing production capacities. It is necessary to develop and attract new, more effective methods of development of natural reserves of mineral raw materials.

Measuring the Synergistic Effect of Pollution and Carbon Reduction in China’s Industrial Sector

The industrial sector is a major source of CO2 and atmospheric pollutants in China, and it is important to promote industrial pollution reduction and carbon reduction to improve the quality of China’s atmospheric environment and meet CO2 peak targets. In this paper, based on 2005 to 2021’s panel data from the industrial sector, we construct a computational model of the synergistic effect of pollution reduction and carbon reduction, quantitatively evaluate the synergistic effect of industrial CO2 emissions and air pollutants, and explore its evolutionary mechanism. The results showed that between 2005 and 2021, there was a clear synergistic effect between CO2 and air pollutants in China’s industrial sector, and the synergistic effect is increasing. For different pollutants, CO2 and SO2 have the strongest synergies, and CO2 and particulate matter have relatively weak synergies. For different energy types, the synergies between coal-related carbon emissions and air pollutants gradually increase, while gas-related carbon emissions and pollutants tend to decrease. From different industry types, the synergies between CO2 and air pollutants are weaker in high-polluting and high-emission industries than in other industries. These results have strong policy implications. First, the focus of synergistic measures should be on source reduction. The second is to make high-polluting and high-emission industries the focus of pollution reduction and carbon reduction. Third is harmonized management of air quality standards and carbon peaking should be promoted. The formulation of relevant policies from the above three aspects will help synergize pollution reduction and carbon reduction in the industrial sector.

Do China's pilot emissions trading schemes lead to domestic carbon leakage? Perspective from the firm relocation

The limited geographical coverage of pilot emissions trading schemes (ETS) raises domestic carbon leakage risks. Since relocation represents a primary channel that causes carbon leakage, this paper investigates the impact of China's pilot ETSs on firms' cross-provincial relocation with a method combining propensity score matching and staggered difference-in-differences. The empirical results, based on the data of Chinese listed industrial firms from 2009 to 2020, show that the ETS has accelerated firm relocation by increasing negative attention and social capital investments. According to the threshold analysis, firms with sufficient abatement efforts do not undertake relocation strategies. The theoretical analysis can also be confirmed by further investigations on the price signal distortions and the negative impact of relocation on carbon prices. Moreover, the study also demonstrates the heterogeneous effects across firm characteristics, industry types, and policy design features. Overall, these findings provide a reference for China's government to prioritize the establishment of domestic leakage prevention mechanisms for ETS.

Exploring the Formation of Sino-Japan Third-Party Market Cooperation Through the Lens of Industrial Strengths

With the intensification of the wave of anti-globalization and the prevalence of unilateralism, multilateral economic cooperation is urgently needed, due to the delicate bilateral relations between China and Japan, the field and strength of cooperation are limited, in this case, the third-party market cooperation has been explored. This paper focuses on the establishment of Sino-Japan third-party market cooperation, the impact of comparative advantages of different industries on the establishment of cooperation between the two sides, calculates the RCA indices of SITC classified products and some typical industries in recent years, searches for the complementary zone of the two countries' advantages, and evaluates the expected benefits of Japan; At the same time, it compares and discusses the trade and industrial structure of ASEAN countries as third-party market partners, and concludes that third-party cooperation is in line with the economic development needs and cooperation strategy of ASEAN. Finally, the article concludes that the establishment of Sino-Japan third-party market cooperation is in line with Japan's expected benefits and ASEAN regional development strategy, so it is necessary to make full use of the advantages of cooperation, take advantage of the industrial strengths of different regions to carry out a new type of industrial cooperation, and also reduce the negative competition in cooperation, so as to contribute to the recovery of the world economy.

Life Cycle Assessment of Energy Production from Solid Waste Valorization and Wastewater Purification: A Case Study of Meat Processing Industry

The meat processing industry is a very energy-intensive and water-demanding industry that produces large amounts of solid and aqueous wastes. Therefore, methods for the effective treatment of the produced wastes have been studied in order to treat and reuse water within the industry and valorize the solid wastes for the production of energy and value-added products. The primary aim of this work is to evaluate the overall sustainability of energy produced from solid waste valorization and wastewater treatment in the meat processing industry via Life Cycle Assessment (LCA). For this purpose, the total environmental impact of a typical meat industry that utilizes conventional waste management methods (Scenario A) was evaluated and compared with two different industries with appropriate waste treatment/valorization processes. In the first studied valorization scenario (Scenario B), waste management is conducted using anaerobic digestion, composting, membrane bioreactors, and ultraviolet (UV) treatment, whereas in the second studied valorization scenario (Scenario C), aeration treatment, chlorination, and hydrothermal carbonization (HTC) are the selected treatment techniques. As expected, it is evident from this LCA study, that both Scenarios B and C exhibited a significantly improved environmental footprint in all studied indicators compared with Scenario A, with the reduction in certain environmental impact categories reaching up to 80%. Between the two studied alternative scenarios, the biggest improvement in the environmental footprint of the meat industry was observed in Scenario C, mainly due to the substantial quantity of the produced thermal energy. According to the results of the present case study, it is evident that the incorporation of appropriate methods in the meat industry can result in the efficient generation of energy and a significant improvement in the environmental footprint contributing to environmental safety and sustainability.

Dyes Degradation Using Cooper-Nickel Ferrite and Its Tunable Structural and Photocatalytic Properties

Water pollution is a serious worldwide problem. Among its pollutants, dyes that are overused by various types of industries and are resistant to conventional effluent treatments stand out. In this study, mixed copper and nickel ferrites NixCu(1-x)Fe2O4 (x = 0, 0.2, 0.4, 0.6, 0.8 e 1.0), were produced by the EDTA-Citrate complexation method, characterized and applied in photocatalysis with methylene blue (MB) and rhodamine B (RhB) dyes, varying the reaction pH between 2, 6 and 10. The ferrites with the highest percentages of copper had a tetragonal structure, while those with the highest percentages of nickel had a cubic structure, all with inverse spinel and all presenting bandgap values lower than 2 eV. Samples with higher percentages of copper (x = 0 and 0.2) at pH 10 showed degradation of approximately 55% for RhB and 40% for MB. A mixture of MB and RhB was also evaluated, showing a greater removal of methylene blue due to its preferential adsorption on the surface of the material. In this way, mixed copper and nickel ferrites proved promising as catalysts in photocatalytic processes.

Risk of bladder, kidney and prostate cancer from occupational exposure to welding fumes: a systematic review and meta-analysis.

Our aimed to conduct a meta-analysis of cohort studies on risk of genitourinary (GU) cancers in workers exposed to welding fumes (WF). We performed a systematic review of studies published on Pubmed, Scopus and Embase following PRISMA criteria. Two researchers selected cohort studies on WF exposure. From 2582 articles, 7 non-overlapping studies were included. Quality of studies was scored according to CASP. We run a random effects meta-analysis to calculate the relative risk (RR) and 95% confidence intervals (CI) of GU cancer, overall and stratified by cancer, country, and quality score. We included seven studies reporting results on GU cancers, including prostate, bladder and kidney cancer (PC, BC, and KC). The RR was 1.19 (95% CI = 1.07-1.32, 16 risk estimates) for GU cancer; 1.13 (95% CI = 0.90-1.42, 4 risk estimates) for PC; 1.26 (95% CI = 0.98-1.60, 7 risk estimates) for BC and 1.28 (95% CI = 1.12-1.47, 5 risk estimates) for KC. Heterogeneity was present in all meta-analyses (p < 0.001). The increased risk was more pronounced in North American than in European studies (respectively, OR = 1.35, 95% CI = 1.18-1.55; OR = 1.13, 95% CI = 1.01-1.27 p heterogeneity = 0.03). There was no heterogeneity according to quality score (p = 0.4). Data were insufficient to investigate associations by industry or welding type. Publication bias for each cancer was excluded. This meta-analysis suggests increased risk of KC and BC, but not of PC, in workers exposed to WF. Confounding by other occupational and non-occupational risk factors could not be excluded. Data were not adequate to address the risk of specific exposure circumstances.

Supply Chain Resilience Digitalization, and Localization

The epidemic of COVID-19 exposed companies to the failure of typical manufacturing and consumption patterns and their long-term effects on supply chains. This conceptual paper discusses localization, agility, and digitalization factors that make the supply chain more resilient. From a theoretical perspective, this research intends to investigate how these characteristics interact to assist the supply chain in becoming resilient by using digitalization, localization, and agility as enablers through conceptual models. The methodology used in this study is an exploratory conceptual investigation to cover the theoretical gaps. A critical evaluation of the literature was conducted to develop a conceptual model to ascertain the current state of knowledge regarding the relationship between supply chain resilience, localization, agility, and digitization in management practice. The study's primary data are the responses to questionnaires issued to respondents, analyzed, and hypotheses were formed and tested using the structural equation modeling technique. The result indicates that agility, localization, and digitalization positively impact supply chain resilience. Additionally, the industry's size and type positively impact the supply chain resilience as a control variable. As a result of the research, a new understanding of supply chain resilience emerges that can assist firms to gain new possibilities to develop new ways to do business under stress.

Mapping the Lobbying Footprint of Harmful Industries: 23 Years of Data From OpenSecrets.

Commercial lobbying is often a barrier to the development and implementation of public health policies. Yet, little is known about the similarities and differences in the lobbying practices of different industry sectors or types of commercial actors. This study compares the lobbying practices of four industry sectors that have been the focus of much public health research and advocacy: tobacco, alcohol, gambling, and ultraprocessed foods. Data on lobbying expenditures and lobbyist backgrounds were sourced from the OpenSecrets database, which monitors lobbying in the United States. Lobbying expenditure data were analyzed for the 1998-2020 period. We classified commercial actors as companies or trade associations. We used Power BI software to link, analyze, and visualize data sets. We found that the ultraprocessed food industry spent the most on lobbying ($1.15 billion), followed by gambling ($817 million), tobacco ($755 million), and alcohol ($541 million). Overall, companies were more active than trade associations, with associations being least active in the tobacco industry. Spending was often highly concentrated, with two organizations accounting for almost 60% of tobacco spending and four organizations accounting for more than half of alcohol spending. Lobbyists that had formerly worked in government were mainly employed by third-party lobby firms. Our study shows how comparing the lobbying practices of different industry sectors offers a deeper appreciation of the diversity and similarities of commercial actors. Understanding these patterns can help public health actors to develop effective counterstrategies.

PM2.5-bound heavy metals in a typical industrial city of Changzhi in North China: Pollution sources and health risk assessment

Heavy metal contamination in fine particulate matter (PM2.5) emitted in industrial cities is extremely severe. Hence, determining their pollution sources apportionment is critical to inform effective pollution control strategies for reducing the risks associated with heavy metals in PM2.5. This study comprehensively investigated the characteristics, source apportionment, and health risks associated with heavy metals in PM2.5 at three monitoring stations—Jiance, Qinghua, and Shenjiju—in Changzhi, a typical industry city in China, during three seasons: autumn and winter of 2017 and summer of 2018. The daily average concentrations of PM2.5 (65.68 μg m−3) during the study period were lower than the second-grade limit value established in China (75 μg m−3). The highest mean PM2.5 concentrations were observed during winter (72.45 μg m−3) and at the Jiance station (64.51 μg m−3), respectively. At Qinghua station, vehicle emissions contributed more V to PM2.5, whereas Jiance station showed higher levels of Pb and Zn from coal combustion. In autumn, there was an increased in the Fe content in PM2.5 from vehicle emissions, whereas elevated concentrations of Cr and Ni from industry sources were observed in summer. According to the results of a positive matrix factorization, the main sources of PM2.5-bound heavy metals in Changzhi were coal combustion (34.3%), vehicle emissions (30.9%), fugitive dust (21.8%) and industry (13.0%). The major sources during autumn, winter, and summer were vehicle emissions (39.0%), coal combustion (35.0%), and industrial emissions (61.7%), respectively, showing a clear seasonal pattern. At both the Jiance station and Shenjiju station, extensive coal-based heating was the primary source of emissions, whereas vehicle emissions were the main source of emissions at QH owing to its high levels of traffic. The total hazard quotient (HQ) values for heavy metals were below the safe level (HQ = 1); but the carcinogenic risks (CR) exceed the lower limit of tolerance (1E-6). Stronger emissions from industrial sources in summer and the large amount of coal combustion for heating at the Jiance station are possible reasons for the higher health risk of heavy metals. Identification the source of PM2.5-bound heavy metals could support the targeted reduction of associated emissions, consequently helping alleviate heavy metals contamination and control associated risks to human health.