Industrial Classification Research Articles

Structuring the Decision-Making Process Using Quantitative Options Valuation

This study focuses on enhancing decision-making processes in the construction industry by investigating quantitative decision-making models. The construction industry is known for its diverse projects and inherent risks. Effective decision-making is crucial for project success, but it faces challenges due to various factors. The research explores biases and heuristics in decision-making, specifically in entrepreneurial and managerial contexts, with a focus on two biases: overconfidence and representativeness. Data collection involved surveys administered to entrepreneurs and managers in prominent industrial sectors. The surveys measured the levels of overconfidence and representativeness in decision-making. Additionally, the study examined commonly used decision-making models in construction, including multi-criteria decision analysis, decision support systems, decision trees, and mathematical optimization techniques. The objective was to gain insights into applying quantitative models and improve the understanding of decision-making processes in construction projects. The survey achieved a response rate of 54%, and participating managers were categorized based on their two-digit Standard Industrial Classification (SIC) codes, specifically in the 1300, 3400, 3500, 3600, and 3800 categories. Rigorous statistical analyses were conducted to evaluate potential response bias. Comparing usable responses to non-respondents using chi-square tests, no significant evidence of bias was found (χ^2 (4) = 3.973, p = .59). Moreover, a further analysis explored potential response bias across the broader set of five two-digit SIC categories, and again, no significant evidence of bias was observed (χ^2 (5) = 1.782, p = .878). The findings of this study contribute to the improvement of decision-making in construction projects and provide valuable insights into the practical application of quantitative models. By addressing biases and exploring effective decision-making approaches, this research aims to enhance project success within the complex construction industry.

Classification of Fall Out Boy Eras

This paper explored the use of machine learning techniques to differentiate between two different musical eras of the same rock band, including the technique of Logistic Regression. Logistic regression (LR) is a widely used statistical modeling method for binary classification in supervised machine learning. It is often used to predict whether a given event belongs to one of two categories. The process helps data scientists understand which variables are good predictors of class membership. Applications of logistic regression include loan classification in the financial industry and predicting susceptibility to disease in the medical field. In this particular project, a dataset was constructed using data from Spotify and Genius consisting of songs and lyrics written by the band Fall Out Boy. A logistic regression model was developed from scratch to classify the songs and lyrics into one of two eras of the band: before their 2009 hiatus and afterward. The study aimed to determine if a computer could differentiate between the two eras. The model was also tested against other binary classification algorithms, including Random Forest and Support Vector Machines.

The identification and efficiency evaluation of industrial parcels by integrating multi-source spatial data in Shenzhen, South China

ABSTRACT Industrial land status, encompassing quantity, distribution and efficiency, is pivotal in urban planning and sustainable development. However, obtaining detailed and up-to-date industrial land maps poses a significant challenge in many developing countries due to rapid urban expansion. Furthermore, the identification and efficiency assessment of industrial land at the parcel scale are rarely undertaken. To address these challenges, our study proposes an innovative approach to map industrial land, categorize its types and assess its efficiency at the parcel scale. We select Shenzhen, one of China’s largest industrial cities, as a case study to demonstrate the effectiveness of our method. Our results unveil several key findings: (1) Shenzhen’s urban area was meticulously segmented into 15,591 multi-size socio-economic parcels, of which 5,738 were identified as industrial parcels (IPs); (2) IPs were further categorized into three distinct types: office industrial parcels at 69.47%, manufacturing industrial parcels at (6.08%) and comprehensive industrial parcels at 24.45%; (3) Their efficiency was classified into four levels: low (20.30%), middle (35.73%), fine (35.97%) and high (8.00%). Efficient IPs were predominately concentrated in the southwest, whereas other IPs clustered in the northwest. Our findings can provide valuable insights into industrial land identification, classification and efficiency evaluation at parcel scale.

An automated quantitative investment model of stock selection and market timing based on industry information

As a growth asset, many listed companies have distinct characteristics of their respective industries. The listed companies in the same industry have a link effect, which can provide a wealth of information for stock operations. In this paper, a complete automated quantitative investment model is developed based on statistical models that use industry information to select stocks and time the market, thereby bringing out the synergistic effect of the system and ensuring maximum returns on investment. Risk control is taken into consideration in our model, a risk control factor is designed by measuring volatility of stock prices to determine the buying volume and the timing of stop loss, effectively safeguarding capital security. The latest industry classification results published by the China Securities Regulatory Commission are used as the basis for the industry classification. After data preprocessing, there are 70 sub-categories in 18 major categories of industry. We take the stock price of the 70 sub-categories from January 1, 2012 to January 1, 2022 as our research data. The back testing results show that positive returns are obtained in all industries except for six in our model. The average annualized rate of return is 11.10 %, which is higher than the stock indexes of the same period and far higher than the investment model of bank savings. Additionally, in accordance with a real trading system, the experiment simulates the inclusion of all transaction fees in the trading process, demonstrating the practical application value of our expert system.

Forty years of struggle in North Carolina: Workplace segregation and fatal occupational injury rates.

To assess workplace segregation in fatal occupational injury from 1992 to 2017 in North Carolina. We calculated occupational fatal injury rates within categories of occupation, industry, race, age, and sex; and estimated expected numbers of fatalities among Black and Hispanic male workers had they experienced the rates of White male workers. We also estimated the contribution of workforce segregation to disparities by estimating the expected number of fatalities among Black and Hispanic male workers had they experienced the industry and occupation patterns of White male workers. We assessed person-years of life-lost, using North Carolina life expectancy estimates. Hispanic workers contributed 32% of their worker-years and experienced 58% of their fatalities in construction. Black workers were most overrepresented in the food manufacturing industry. Hispanic males experienced 2.11 (95% CI: 1.86-2.40) times the mortality rate of White males. The Black-White and Hispanic-White disparities were widest among workers aged 45 and older, and segregation into more dangerous industries and occupations played a substantial role in driving disparities. Hispanic workers who suffered occupational fatalities lost a median 47 life-years, compared to 37 among Black workers and 36 among White workers. If Hispanic and Black workers experienced the workplace safety of their White counterparts, fatal injury rates would be substantially reduced. Workforce segregation reflects structural racism, which also contributes to mortality disparities. Root causes must be addressed to eliminate disparities.

Improved stochastic configuration networks with vision patch fusion method for industrial image classification

This paper contributes to the advancement of stochastic configuration neural networks (SCN) in the field of visual applications. The proposed image classification randomized algorithm is an extension of deep stochastic configuration networks (DeepSCN), known as an improved SCN with vision patch fusion (Vi-SCN). Compared to existing two dimensional stochastic configuration networks (2DSCN) and DeepSCN, we have developed an incremental modeling method that employs a stochastic configuration patch fusion method. This method extracts randomly fused image features from three-channel high-resolution image data, improving the network's ability to extract features. Moreover, we have introduced a strategy for dynamically determining the depth structure of the network, enabling flexible adjustments to the network structure and the number of nodes in each layer based on the complexity of image recognition tasks. Through a series of comparisons on four image classification benchmark datasets, we assess the superior learning performance of our design compared to 2DSCN and DeepSCN. Furthermore, to evaluate the performance of Vi-SCN in practical visual application scenarios, we collect three industrial datasets with distinct characteristics. Vi-SCN demonstrate outstanding performance in all three tasks, showing its significant potential in image recognition.

Does enterprise digital transformation affect audit opinion type?—Based on business evidence of Chinese listed companies

AbstractUtilizing text analysis through machine learning techniques, this study investigates the influence of enterprise digital transformation on audit opinions within China's A‐share listed companies spanning from 2011 to 2020. The findings underscore that enterprise digital transformation has the potential to bolster standard unqualified audit opinions through the enhancement of internal control quality and information transparency. Additionally, factors such as marketization level, property rights nature, and industry classification are found to be pivotal in shaping this relationship. These research outcomes not only extend the theoretical horizons of audit opinion studies but also offer valuable insights and recommendations for auditors to optimize the efficacy of audit opinions.

Bioeconomy firms and where to find them

The bioeconomy represents a transformative approach to economic development and sustainability by harnessing biological resources and knowledge to produce goods, services, and energy while reducing dependence on non-renewable resources. In order to understand and support the bioeconomy, scholars and policymakers rely on an accurate measurement and monitoring of biobased economic activities. However, existing statistical frameworks and industry classifications often fall short in capturing the unique characteristics and complexities of the bioeconomy. This article addresses this challenge by developing a methodological approach for comprehensive measurement and mapping of biobased economic activities. We build a novel data set of bioeconomy firms in Germany using web-mining and machine learning techniques. This data set enables detailed analysis of biobased economic activities, providing valuable insights into the spatial organization of the bioeconomy. The paper demonstrates the applicability of the data set by testing several stylized facts about the bioeconomy. Our research contributes to a better understanding of the bioeconomy's regional impacts and offers a valuable resource for policymakers and researchers interested in understanding the geography of biobased economic activities. We make an aggregated version of the data set freely available online.

Digital intermediate product imports and firms’export quality: evidence from China

Based on the ‘Statistical Classification of Digital Economy and Its Core Industries (2021)’ published by the National Bureau of Statistics of China, this paper proposes a more accurate method to identify digital intermediate products by matching China Industrial Classification code with Harmonized System code, and investigates the connection between digital intermediate product imports and firms’ export quality using China’s firm-level data from 2000 to 2013. Our estimations show that digital intermediate product imports can significantly promote the quality of firms’ export products through both the productivity channel and the quality production capacity channel, and the conclusion still holds after a series of endogeneity tests and robustness tests. Moreover, considering the heterogeneity effect of digital intermediate product imports with different characteristics, we find that high-quality and diversified digital intermediate product imports will strengthen the quality upgrading effect of firms’ export products, while the effect of improving the technical content is not significant. This paper provides a new path for firms, especially those in developing countries, to upgrade export quality.

A Study on Motivational Factors of Entrepreneurial Performance

Entrepreneurship is a key factor in the national economy. While understanding about entrepreneurship, it is vital to realize the achievement motivation among the entrepreneurs and its relationship with the success of their organisations. In this study, the factors influencing entrepreneurial performance are analysed using the correlation method. Totally 112 entrepreneurs from five categories of industries are considered for the survey. Interview-schedule is designed with the parameters of achievement motivation and entrepreneurial performance. It is found that achievement motivation is significantly correlated with entrepreneurial performance.

IBISWorld

In fall 2023, business librarian stalwart IBISWorld underwent a platform renovation to improve discoverability, usability, and shareability of its Industry Reports while retaining its dependable industry data and analyses. These changes have, by and large, been successful, concentrating financial data, improving mobile navigation, adding download and sharing options, and revising the reporting style to be quicker and easier for users to understand. While there are other database options for Industry Reports, IBISWorld remains both the most exhaustive, covering more than 700 North American Industry Classification System (NAICS) industries, and, with the new-look launch, the most intuitive to use, making it a key addition for institutions with business, advertising, marketing, or finance departments.

Combining zip code-based population data and pharmacy administrative claims data to create measures of social determinants of health.

Social determinants of health (SDoH) are key factors that impact health outcomes. However, there are many barriers to collecting SDoH data (eg, cost of data collection, technological barriers, and lack of standardized measures). Population data may provide an accessible alternative to collecting SDoH data for patients. To explain how population data can be leveraged to create SDoH measures, assess the association of population SDoH measures with diabetic medication adherence, and discuss how understanding a patient's SDoH can inform care plans and patient engagement. A nationally representative commercial sample of patients who were aged 18 years and older and met Pharmacy Quality Alliance inclusion criteria for diabetes mellitus were analyzed (N = 37,789). US Census and North American Industry Classification System data were combined with pharmacy administrative claims data to create SDoH measures. Derived measures represent 2 SDoH domains: (1) economic stability (housing density, housing relocation, jobs per resident, and average salary) and (2) health care access and quality (urban/rural classification, distance traveled to prescriber and pharmacy, use of a primary care provider [PCP], and residents per PCP). The association of population SDoH measures with diabetic medication adherence (proportion of days covered) was assessed via logistic regression, which included covariates (eg, sex, age, comorbidities, and prescription plan attributes). As housing density (houses per resident) increased, so did the likelihood of adherence (odds ratio = 1.54, 95% CI = 1.21-1.97, P = 0.001). Relative to patients who did not move, patients who moved once had 0.87 (95% CI = 0.81-0.93, P < 0.001) the odds of being adherent, and patients who moved 2 or more times had 0.82 (95% CI = 0.71-0.95, P = 0.008) the odds of being adherent. Compared with areas with fewer jobs per resident, patients living within a zip code with 0.16 to 0.26 jobs per resident were 1.12 (95% CI = 1.04-1.20, P = 0.002) times more likely to be adherent. Patients who lived in an urban cluster were 1.11 (95% CI = 1.01-1.22, P = 0.037) times more likely to be adherent than patients living in a rural area. Patients who travel at least 25 miles to their prescriber had 0.82 (95% CI = 0.77-0.86, P < 0.001) the odds of being adherent. Community pharmacy users had 0.65 (95% CI = 0.59-0.71, P < 0.001) the odds of being adherent compared with mail order pharmacy users. Patients who had a PCP were 1.26 (95% CI = 1.18-1.34, P < 0.001) times more likely to be adherent to their medication. Leveraging publicly available population data to create SDoH measures is an accessible option to overcome barriers to SDoH data collection. Derived measures can be used to increase equity in care received by identifying patients who could benefit from assistance with medication adherence.

How digital government mediate the influence of fossil fuels on green innovation: Evidence from China's a-share industrial enterprises

China's rapid economic growth has resulted in a heavy reliance on fossil fuels, necessitating effective government intervention to facilitate their green transformation within the industrial sector. Therefore, a comprehensive investigation into the impact of mineral resource and fossil fuel dependence (ED) on the green transformation of Chinese enterprises, alongside the role of digital governance systems, holds paramount importance. This study delves into the influence of ED on firms' green technology innovation (FGI) using data sourced from A-share industrial enterprises listed on China's Shanghai and Shenzhen stock exchanges during the period from 2011 to 2021. Additionally, it examines the internal mechanisms facilitated by digital government, and provides insights into the heterogeneity observed from the perspectives of enterprises' industry classifications and sizes. The following conclusions are drawn: (1) ED significantly impedes the enhancement of green technology innovation levels among listed industrial enterprises, and this finding remains robust across multiple testing methods. (2) ED demonstrates varying inhibitory effects on firms' green technology innovation, with the most pronounced impact observed in the energy production and supply industry and the mining industry, and a relatively weaker effect observed in manufacturing enterprises. Furthermore, the inhibitory effect of fossil energy dependence on green technology innovation is observed to be stronger in small-scale enterprises compared to their larger counterparts. (3) The advancement of the digital economy notably mitigates the inhibitory effect of fossil energy dependence on firms' green technology innovation. (4) ED primarily diminishes the level of green innovation by reducing the stringency of government environmental regulations, hindering regional marketization, and impeding the industrial structure's upgrade.

Exposure of polychlorinated biphenyls via indoor dust particles and their health risks in Dhaka City, Bangladesh

This study is a pioneer evaluation of Polychlorinated Biphenyls (PCBs) within the indoor environment of the megacity Dhaka, Bangladesh. It assessed the occurrence, distribution and associated human health risks of selected 6 PCB congeners in four types of buildings (commercial, industrial, institutional, and residential). Dust samples were collected from the air conditioner filters and subsequently analyzed by Gas Chromatography- Electron Capture Detector (GC-ECD). ∑6 PCB concentrations in commercial, industrial, institutional, and residential buildings ranged from 168 ± 59.42 to 3419 ± 1307, 590 ± 217 to 4669 ± 1731, 244 ± 89.08 to 2538 ± 779, and 161 ± 60.54 to 5413 ± 2136 ng/g, respectively. Considering the total PCB loads from each building type, the industrial category revealed the maximum concentration (19176 ± 1141 ng/g), followed by commercial, residential, and institutional, meaning industrial buildings are more susceptible to PCBs exposure. The heavy PCBs (hexa- and hepta-CBs) were dominant over the light PCBs (di-, tri-, and tetra-CBs). All hazard index (HI) values, except PCB-180, were below 1, suggesting a lower risk of non-carcinogenic effect. The incremental lifetime cancer risk (CR) values were below 10−03, indicating no high carcinogenic risk of the human population from PCB-contaminated dust through the exposure routes.

Effective augmentation of front opening unified pod filter images

The front opening unified pods (FOUPs) are popular carriers in semi-conductor industry, and their automatic optical inspection relies on the sufficiency of images. Filter papers at the bottom of FOUP require frequent inspection, while obtaining their images is time-consuming. This work thus investigates augmenting images of filter papers using conventional interpolation and a recent generative adversarial network with variations, including numbers of epochs, usage of progressive learning, loss functions, and cross frame calibration. Drawbacks of interpolated images are exhibited. Results of sixteen models are quantitatively evaluated using the Fréchet inception distance (FID). The best FID among all models is fid = 0.102, and the distribution of generated images is close to that of real images. A user study is also conducted to investigate the authenticity of generated images. Our experiments reveal that given a limited number of training images, after proper geometric calibration, images synthesized by a generative adversarial network are qualified for data augmentation and substantially benefit industrial image classification tasks.

The Analytic Network Process for Identifying the Potential Sub-Sectors of Creative Industry: Empirical Finding from Semarang, Indonesia

Objective: The aims of the present study is to identify the most potential sub-sector of creative industry in Semarang, Indonesia. Theoretical Framework: The creative industry is considered to promote human development while promoting the economic growth. It maintains and protects cultural and historical heritage, foster individual and communities’ creativities, as well as improve cultural capital. Although there is widespread classification of creative industry, the Indonesian Ministry of Tourism has classified the creative industry into 16 sub-sectors, namely, (1) advertising, (2) application and game developers, (3) architecture, (4) craft, (5) fashion, (6) film, animation, and video, (7) fine arts, (8) gastronomy/culinary, (9) interior design, (10) music, (11) performing arts, (12) photography, (13) product design, (14) publishing, (15) TV and radio (broadcasting), and (16) visual communication design. Methodology: The analytic network process (ANP) is used to identify the most potential sub-sector of creative industry. A case study is conducted in Semarang, a capital city of Central Java Province, Indonesia. There are four criteria to be considered, i.e., economic, technology, social, and cultural indicator. Data are collected from in-depth interviews with experts in the field of creative industry. Results and Conclusion: According to the four criteria, it is revealed that the social indicator has the greatest weight among others with value of 0.338. Next, the fashion sub-sector is considered as the most potential sub-sector of creative industry in Semarang with the weight of 0.428. A consistency checking is also performed to test whether the answers of the experts contradict each other or not. The result shows that the value of consistency ratio is 0.049 or less than 0.1, indicating consistency. Implication of Research: The findings of this study can provide local government with valuable insights to prioritize the most potential sub-sector to be developed to actualized Semarang as a creative city. Originality/value: Decision-making is of vital importance as it can help management to make good decision.

Regional short-term load forecasting method based on power load characteristics of different industries

With the gradual increase in the proportion of end-user electricity consumption, the electrical load has become a crucial issue in carbon emission studies. Precise load forecasting will benefit the treatment, calculations, and markets of carbon emission. To explore the characteristics of industrial power load in depth and improve the accuracy of regional short-term load forecasting, this paper proposes a regional short-term load forecasting method based on the power load characteristics of different industries. First, a power load characteristic index system is established to obtain a fine industry classification. Considering the influence of external factors on the power load, the industries are then further classified depending on their sensitivity to external factors using a combination of grey correlation analyses. For the non-external factor-sensitive industries, an integrated model based on the Shapley value method is adopted; for the external factor-sensitive industries, a factor analysis-bidirectional gated recurrent unit-attention mechanism model is proposed. The final combination of the two is the output of the total regional load forecasting results. The combined forecasting method is tested using power load data from a city in Liaoning, China. The results show that the proposed method has apparent advantages over the traditional single forecasting model in terms of forecasting accuracy.

The relevance of the country and sector effect in global equity returns around COVID-19 and developed and emerging markets

This study explores the historical and current explanatory power of country classification and Global Industry Classification Standard sector classification on global equity returns in the equally weighted MSCI All Country World Index. R2 is a crucial statistical tool for risk and portfolio management professionals because it serves as a measure of how much of a portfolio's movements can be explained by factors. Its significance lies in its ability to quantify how dependent a portfolio's risk is on those factors. The country classification's adjusted R2 is dominant in global equities, driven by its dominance within emerging markets (EM). Within developed markets (DM), the country effect's slight dominance over the sector effect has decreased over time — the two have become more balanced since 2015, with the sector effect having a slightly higher adjusted R2 value. There was a drop in the R2 for all classifications during the COVID-19 pandemic. This drop was most significant in the country classification, with a sharp decline to 8.3 per cent, well below its previous historical minimum of 12 per cent, before it rebounded to its historical range. This change drove further analysis into the country effect across EM and DM, where the large drop and rebound were primarily within EM. During this time, the sector effect dropped but remained within its historical range, implying a larger loss of diversification benefit in country effect than in sector effect. The impact of Chinese stocks on country effect within EM is also investigated. In 2018, there was a large influx of Chinese companies into the index, which caused a decline in country diversification and hence a reduction in explanatory power within EM. China's rapid and strict COVID-19 response, early tight monetary policy and regulatory crackdown have since caused it to become increasingly differentiated from the rest of EM, increasing country diversification within EM and causing the accentuated rebound in explanatory power of the country effect post-COVID-19. Risk managers can use these results to validate the use of sector and country classifications in portfolio construction.

Reconstruction of CES time series: implementing the NAICS 2022 redefinitions

With the release of January 2023 data on March 13, 2023, the Current Employment Statistics program revised the basis for industry classification for states and areas from the 2017 North American Industry Classification System (NAICS) to NAICS 2022. The conversion to NAICS 2022 resulted in revisions to data that reflected content and coding changes within the retail trade, information, mining and logging, manufacturing, wholesale trade, financial activities, and other services sectors. In the revision process, the chief aim was to maintain the integrity of data and comparability across the redefined series.

Detection of bruises on red apples using deep learning models

The detection and sorting of bruised apples after harvest play a crucial role in improving their economic value by eliminating surface defects. This also reduces the risk of contamination of infected apples during transport and storage. It can be done by using manual detection or machine vision techniques in red, green, and blue (RGB) colors to detect bruises on apples of various skin colors; however, in the early stages of bruising, it is challenging. Therefore, the main purpose of this study is determin of the effectiveness of Deep Learning models combined with the Near Infrared (NIR) imaging system for naturally bruised Super Chief red apples immediately after harvest. In total, 1000 images for the healthy class and 500 images for the bruised class were acquired from 500 apples. After the images were acquired with the RGB and NIR cameras, the data sets were divided into training (70 %), validation (15 %), and testing (15 %) sets. The Alexnet, the Inceptipon-V3, and the VGG16 network structures were trained using the training and validation data sets, and the trained network was evaluated using the test dataset. The VGG16 model achieved the highest test accuracy (86 %) when trained on the RGB data set, while the AlexNet model exhibited the lowest test accuracy (74.6 %). When the models were trained and tested with NIR datasets, 99.33 %, 100 % and 100 % accuracy rates were obtained for AlexNet, Inception V3, and VGG16, respectively. During the experiments, the VGG16 model trained with the NIR dataset achieved the lowest loss rate of 0.0002, whereas when trained and tested with the RGB dataset, the same VGG16 model also recorded the lowest loss rate of 0.353.These findings indicate that the deep learning models, particularly when trained with NIR data, demonstrate high accuracy rates in classifying apples as healthy or bruised, making them suitable for industrial classification applications. Therefore, the NIR data set is recommended for precise and reliable apple classification in industrial settings.