Productivity In Manufacturing Industry Research Articles

Odor impact patterns and health risks of various enterprises in the rubber product manufacturing industry.

The rubber product manufacturing (RPM) industry generates a large number of odor complaints because persistent and distinctive volatile compounds are released during the associated processes. Such compounds represent a nuisance and may pose health risks to nearby residents. Extensive monitoring and sampling identified 146 volatile organic compounds (VOCs) from 20 enterprises across 6 subcategories of the RPM industry. Their odor impact patterns and health risks were assessed via dispersion modeling from both horizontal and vertical perspectives. Nine out of the 20 enterprises were found to cause odor impacts to the surrounding residents, with separation distances ranging from 0.3 to 3.5 km depending on the prevailing wind directions. The separation distances were more subject to odor concentrations than exhaust velocities. The odor impacts peaked at 40 to 60 m due to the horizontal and vertical dispersion as well as the height of exhaust ports. From a health risk perspective, none of the enterprises posed carcinogenic or noncarcinogenic risks to the surrounding areas based on the cumulative hazard quotient and carcinogenic risk values; however, acetaldehyde and benzene required further attention. This study provides important evidence for the management and control of VOCs in the RPM industry from both odor and health perspectives.

Production and current expenditures: A study of CO2 emissions in Ecuador's manufacturing industry

The activities developed in industrialization processes contribute to the deterioration of the environment. In this context, global organizations generate mitigation alternatives through sustainable strategies. Thus, the main aim is to analyze the impact of manufacturing industry production in Ecuador on the environment caused by the total emissions present in the atmosphere. This research has a quantitative approach and empirical level, and the source of information was a structural survey of companies in 2020 issued by the National Institute of Statistics and Census. The methodological process was developed through the description, correlation of variables, and a multiple regression econometric model to explain the relationship between industrial production, current expenditure, and CO2 emissions. The results show that the manufacturing industry generates two million tons of polluting gases annually because of economic activity and the use of fossil fuels. The activities in which fuels are used are first boilers, then motive power, and finally transportation. This study shows a directly proportional relationship between production, current expenditure, and CO2 emissions, where an increase in production implies a greater generation of emissions and, therefore, more current expenditure. This behavior is driven by the dependence on fossil fuels and industrial use. However, it is important to note that air pollution does not depend solely on these factors.

Changeover minimization in the production of metal parts for car seats

We tackle a capacitated lot-sizing and scheduling problem (CLSP) with the main objective of minimizing changeover time in the production of metal parts for car seats. Changeovers occur when a machine (or production line) is reconfigured to produce a different product or part, leading to production downtime and loss of efficiency. In this study, we first provide a mixed-integer programming (MIP) formulation of the problem. We test the limits of solving the problem with commercial mathematical programming software. We also propose two approaches to tackle instances found in practice for which the mathematical programming model is not a viable solution method. Both approaches are based on partitioning the entire production of a part into production runs (or work slots). In the first approach, the work slots are assigned to machines and sequenced by a metaheuristic that follows the search principles of the GRASP (greedy randomized adaptive procedure) and VNS (variable neighborhood search) methodologies. In the second approach, we develop a Hexaly Optimizer (formerly known as LocalSolver) model to assign and sequence work slots. The study provides insights into how to minimize changeovers and improve production efficiency in metal parts manufacturing for car seats. The findings of this study have practical implications for the auto-part manufacturing industry, where efficient and cost-effective production is critical to meet the demands of the market.

Investigating the effects of industrial transformation and agglomeration on industrial eco-efficiency for green development: Evidence from enterprises in the Yangtze River Economic Belt

Improving industrial eco-efficiency (IEE) is significant for green development, particularly in the areas characterized by intense industrial agglomerations, such as the Yangtze River Economic Belt (YREB). This study analyzes IEE in the middle and lower reaches of the YREB (YREBML) from 2011 to 2020 using the Super SBM-ML model. Based on industrial and commercial enterprise database, this study clarifies the effects of industrial transformation and agglomeration on IEE based on spatial Durbin model. From 2011 to 2020, the IEE in the YREBML exhibited an increased trend, with an average annual increasing rate of 1.48%. The high IEE areas predominantly clustered in eastern coastal cities, while lower IEE regions were mostly located in the central and western areas. Malmquist-Luenberger index decomposition analysis suggests that advanced production technology, improved efficient resource allocation, and optimized industrial structure enhance IEE. The spatial econometric analysis revealed industrial structure rationalization (ISR) and textile industry factory scatter index (FSI) had significant positive effect on IEE, while FSI of chemical raw materials and product manufacturing industry had significant negative effect on IEE. These findings highlight the importance of promoting textile industrial clusters and controlling the quantity and distribution density of chemical industries to improve overall IEE in the YREBML.

Prevalence of COVID-19 and Long COVID by Industry and Occupation: Behavioral Risk Factor Surveillance System 2022.

Workers in healthcare and other essential occupations had elevated risks for COVID-19 infection early in the pandemic. No survey of U.S. workers to date has comprehensively assessed the prevalence of both COVID-19 and Long COVID across industries and occupations (I&O) at a detailed level. Behavioral Risk Factor Surveillance System data for 2022 from 39 states, Guam, and the U.S. Virgin Islands were used to estimate prevalence of self-reported history of COVID-19 and Long COVID, as well as the prevalence of Long COVID among those reporting prior COVID-19, by broad and detailed I&O. Adjusted prevalence ratios were used to compare outcome prevalence in each I&O to prevalence among all other workers combined. By broad I&O, workers in healthcare, protective services, and education had elevated prevalences of COVID-19. The prevalence of Long COVID was elevated in healthcare and protective service but not education workers. Detailed I&O with significantly elevated prevalences of COVID-19 but not Long COVID included Dairy Product Manufacturing industry workers and subsets of mining workers. Both COVID-19 and Long COVID were elevated among bartenders/drinking places and personal care and appearance workers. The prevalence of Long COVID was elevated among farmworkers who reported having had COVID-19. Industries and occupations with elevated levels of COVID-19 or Long COVID in this study may warrant increased measures to prevent transmission of airborne respiratory viruses. Accommodations are a key component for supporting workers in all workplaces. This new information about the distribution of Long COVID by I&O suggests where employer understanding and implementation of tailored workplace supports and accommodations are most needed to support continued employment of affected workers.

Industry 4.0 and Sustainability: Analysis of Impacts and Proposed Guidelines

Objective: To survey the impacts of implementing new technologies with the Industry 4.0 concept in a large building materials and related products company and propose sustainable guidelines to minimize negative impacts. Theoretical Framework: Industry 4.0 technologies provide improvements in the product manufacturing process and industry performance through the use of data, but it is necessary to consider the integration of business with social, environmental and governance issues. Method: The methodological approach is a single case study, qualitative in nature, applied and exploratory in purpose. In developing the method, a list of impacts caused by the implementation of Industry 4.0 technologies was created from the theoretical framework, which gave rise to a questionnaire applied to the building materials and related products company by means of a field survey. Results and Discussion: 11 impacts were “observed” by the interviewee, 45 impacts, i.e. the majority of them, were classified as “not observed” and 10 impacts were considered to be “not applicable” to existing technologies due to the fact that the company is a retailer. After analyzing the impacts, sustainable guidelines were suggested for each negative impact. Research Implications: It can be concluded that in most of the literature searched, the economic pillar of sustainability was the most commonly found, thus highlighting the lack of analysis focused on environmental and social issues in the implementation of Industry 4.0 technology in the building materials industry. Originality/Value: It was observed that with regard to sustainability, the building materials and related products company has several very important practices, however, no studies applied to other companies were found in the scientific literature, a fact that makes the proposed work very important.

Optimizing the System Life Cycle in Manufacturing Industry Through Implementation of IoT

This paper aims to explore how the implementation of the Internet of Things (IoT) can optimize the system life cycle in the manufacturing industry, enhancing efficiency, sustainability, and profitability. A qualitative research method with a case study approach was employed. Data collection techniques included observation, documentation, and in-depth interviews with management, supervisors, engineers, and operators. The data were analyzed using an interactive model, consisting of data reduction, data presentation, and conclusion drawing/verification. It was found that the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization. It was found that. the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization

Is fair carbon mitigation practicable in China? Insights from digital technology innovation and carbon inequality

The uneven matching of distribution and targets in practicing carbon emission reduction has led to emission reduction inequality, while digital technology innovation (DTI) is expected to reduce these gaps. Therefore, this study casts new insights into modeling the nexus between DTI and carbon inequality using China's 286 prefecture-level cities from 2000 to 2021. Remarkably, city-level DTI performance is innovatively proxied by the related patent application volume, while the carbon inequality index within the region is measured using the environmental Gini coefficient. The main findings of this study include: (1) DTI robustly mitigates China's carbon inequality. (2) The effect of DTI is asymmetrically more significant in regions with a higher carbon inequality index. (3) DTI heterogeneously reduces carbon inequality more obviously in the eastern and central regions, and technology innovation in digital product manufacturing and digital technology application industries reduces carbon inequality more significantly. (4) More precisely, DTI strengthens carbon justice through income equalization, spatial agglomeration, and digital inclusion effects. Specifically, DTI significantly reduces income inequality and the digital economy's spatial agglomeration level and enhances digital financial inclusion, thereby reducing carbon inequality. This study also provides the policy implications for China.

GVCs Embeddedness and Its Effects on Industrial Productivity: Evidence from Turkish Manufacturing Industries

This study examines the impact of global value chain (GVC) embedding degree and position on productivity in Turkish manufacturing industries from 2003 to 2018 using a two-step system generalized method of moments (GMM). It explores variations in these effects between low-tech and high-tech industries. Results indicate a significant positive productivity effect for low-tech industries and an insignificant effect for high-tech and overall manufacturing industries. Furthermore, our findings suggest that embedding position has limited effects in all three contexts. This study reveals a statistically significant productivity impact of FDI, financial development, subsidies, and openness for overall manufacturing industries, with significant positive effects observed only for FDI and trade openness in low-tech and high-tech industries.

The Effect of IoT Technology, Real-Time Analytics, and Digital Asset Management on Energy Efficiency and Productivity in Indonesia's Manufacturing Industry

This research investigates the impact of IoT (Internet of Things) technology, real-time analytics, and digital asset management on energy efficiency and productivity in the Indonesian manufacturing industry. A quantitative approach, employing structural equation modeling (SEM) with Partial Least Squares (PLS) 3 software, is utilized to analyze survey data collected from manufacturing firms across different sectors and sizes in Indonesia. The results reveal significant positive relationships between digital technologies (IoT, real-time analytics, digital asset management) and energy efficiency/productivity. Specifically, Digital Asset Management, IoT Technology, and Real-Time Analytics are found to have statistically significant effects on enhancing energy efficiency and productivity levels in Indonesian manufacturing operations. These findings underscore the transformative potential of digital technologies in driving operational performance and sustainability in the manufacturing sector. Policymakers, industry stakeholders, and practitioners can leverage these insights to inform strategic decision-making and investments in digital infrastructure, workforce development, and regulatory frameworks to foster innovation and competitiveness in the Indonesian manufacturing landscape.

Markov Method Revolutionizes Paper Industry, Slashing Maintenance Costs Globally

Paper manufacturing industries rely on efficient machinery for production processes, yet neglecting maintenance can lead to substantial costs. This study addresses the maintenance cost optimization challenge in a Pasuruan paper company, where maintenance expenses exceed 20% of the standard. Utilizing the Markov chain method, the research identifies optimal paper machine maintenance costs, achieving a 32% cost reduction. By analyzing data from company documents and literature reviews, specific machine states are identified, recommending overhaul strategies to enhance operational efficiency. The study's implications underscore the method's effectiveness in reducing costs and improving productivity for paper manufacturing industries, offering valuable insights for similar sectors. Highlights: 1. Neglecting maintenance in paper manufacturing leads to substantial costs.2. Markov chain method optimizes paper machine maintenance, reducing expenses.3. Implementation of optimal maintenance strategies significantly enhances financial sustainability. Keywords: Paper manufacturing, Maintenance optimization, Markov chain method, Cost reduction, Operational efficiency.

Low temperature phase transition mechanism of amorphous Al-oxalate to nano α-alumina

α-Alumina (α-Al2O3), widely used as a raw material or production in advanced manufacturing industry, has attracted much attention in terms of its preparation temperature. A simple dissolution-evaporation-precipitation process was employed to obtain the amorphous Al-oxalate (AAO) precursor, serving as the starting material for the low temperature preparation of nano α-Al2O3. The crystalline structure and thermal behavior of the AAO were compared with commercially available aluminum hydroxide (Al(OH)3) powder using the X-ray powder diffraction (XRD) instrument, the thermal gravity analysis and differential scanning calorimetry (TG-DSC). The phase transition mechanism of AAO was investigated by the fourier transform infrared spectroscopy (FTIR), transmission electron microscope (TEM), scanning electron microscope (SEM), and 27Al magic angle spinning nuclear magnetic resonance (27Al-MAS-NMR). The DSC results indicated that AAO underwent a phase transformation at 982 °C. The α-alumina diffraction peaks appeared in AAO at 1000 °C, while α-Al2O3 phase was observed for Al(OH)3 at 1200 °C. Furthermore, an absorption peak at 452 cm−1, corresponding to Al–O bond in α-alumina was detected at 1000 °C. The proposed low-temperature preparation mechanism of α-alumina from AAO is as follows: four coordination Al-oxalate species entities coexist within the AAO sample; with increasing temperature, organic acid functional groups oxidize and volatilize, resulting in the formation of tetra-coordinate [AlO4], penta-coordinate [AlO5], and hexa-coordinate [AlO6] atomic groups; these atomic clusters further polymerize to form a structurally dense α-alumina crystal at the nano-scale (∼50 nm) at around 1000 °C. The experimental findings present a novel approach and method for the low-temperature preparation of nano-sized alumina powder. Additionally, a feasible theoretical reference for the low-temperature phase transition mechanism of amorphous Al-oxalate was provided.

Turkey Paper and Paper Products Sector Import Forecasting with Artificial Neural Networks

The paper and paper products sector is a crucial component of the Turkish economy, characterized by significant interactions with various other sectors. Türkiye imports substantial amounts of paper, playing a vital role in the growth and sustainability of this sector. Accurate import forecasting is essential for strategic planning and resource management. This study aims to forecast the imports of the Turkish paper sector for the period from April 2023 to March 2024 using two artificial neural network (ANN) models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset, obtained from the Turkish Statistical Institute (TurkStat), covers 219 months of data from 2005 to 2023. The dependent variable is Türkiye’s monthly import value of paper and paper products, while the independent variables include the monthly average US Dollar exchange rate, monthly imports of Türkiye, the Manufacturing Industry Production Index, the Paper Production Index, and the monthly exports of paper and paper products from Türkiye. The MLP model forecasts that the monthly imports of paper and paper products will range between 270 to 300 million USD, while the RBF model predicts values between 268 and 321 million USD. These findings underscore the efficacy of ANNs in providing accurate and reliable forecasts. This study addresses a gap in the literature by applying ANN methods to forecast imports in the paper and paper products sector, presenting a novel approach that can assist companies in making better-informed decisions regarding inventory management, production planning, and marketing strategies. By leveraging the advanced computational power and pattern recognition capabilities of ANNs, the study aims to enhance the strategic planning processes in the paper and paper products industry. The traditional methods often used in trade data analysis and forecasting are limited in capturing the complex and non-linear relationships present in economic data. This study's application of ANNs offers a significant advancement by utilizing models that can better handle such complexities. The accuracy of the MLP and RBF models highlights their potential as valuable tools for economic forecasting, providing insights that are crucial for optimizing supply chain operations and improving market responsiveness. The results indicate that companies can achieve better operational performance and increased customer satisfaction by effectively forecasting future import requirements. The originality of this study lies in its methodological approach, utilizing ANN models to forecast import values in a sector where traditional methods have been predominant. This innovative approach not only contributes to the existing body of knowledge but also offers practical applications for businesses within the sector. The detailed analysis of the data, combined with the robust modeling techniques employed, provides a comprehensive framework for understanding the dynamics of paper imports and making strategic decisions based on accurate predictions. In conclusion, the study demonstrates the significant success of artificial neural networks in predicting import values for the Turkish paper and paper products sector. The findings provide valuable information that can aid companies in strategic planning, enhancing their ability to manage inventory, plan production, and develop effective market strategies. The research contributes to the literature by filling a gap with its innovative approach, offering new perspectives and practical applications for improving decision-making processes in the industry.

Localization of innovative technologies of industrial production as a tool for intensification of import substitution policy

The main criterion for the successful implementation of import substitution policy is the location of domestic production on the domestic territory of the state, in order to reduce the negative economic consequences of the industrial policies of unfriendly countries, with the orientation of domestic industrial enterprises towards their own intensification of production, including the production of analogous products to imported ones, for subsequent replacement of imported technologies with domestic ones and supply of domestic products to the domestic market – production and sale of own products. Localization of production is the placement of manufacturing or component industrial production facilities on its own, separate, local territory, in order to reduce the cost of products and bring them closer to assembly sites or sales markets. The term «localization of production», in the scientific literature, is represented by a special case of territorial organization (localized enterprises in separate territories) of production processes for the creation of material wealth, which has a number of features: in the organization of production, a company with a higher level of technological development forms the “conditions of the game”, organizing the distribution of production of certain products among all participants; localization is often carried out on the territory of another state, which has a lower level of development of technology and technology and a higher demand for final products in the markets of this state; An additional benefit of localization is the optimization of process logistics, bringing it closer to consumers. The goal of localization is to reduce production, transaction and logistics costs and increase the level of investment. The article presents the author's position on the development of intensification of the processes of industrial import substitution, in modern conditions, the main idea of which is the creation of localized production, a full technological cycle in the activities of Russian industrial enterprises, representing: at the initial stage, a full line of production of components and import substitution of missing parts, to meet demand in the domestic market, and in the future, the development of processes of substitution of imported technologies, the formation and use of advanced development technologies, increasing export potential and strengthening their presence of Russian industry in world markets. The work explores the essence of localization of production, which is determined by the production of parts and components manufactured in Russia, at national production facilities, using its own technologies, in order to increase the intensity of import substitution processes.

Design and fabrication of circular sheet metal shearing machine

The growing use of sheet metal in various industries such as automotive, packaging, medical appliances, cylinder production, Apparatus construction, Tank construction, Aircraft bodies, Missile production, Satellite dishes, road building and road signs, and household appliances, is attributed to its ease of manufacturing, handling, and use. To meet customer demands, sheet metal product manufacturing industries are working towards producing circular feature products of good quality at a large scale and low cost. To this end, this research emphasis the design, analysis, and fabrication of an electric circular sheet metal shearing machine, this would replace the punching and blanking operations and reduce scrap value from the stock material. The research includes concept design, detail design and analysis, assembly, 3D modelling, and fabrication of the Machine.

Assessment of opportunities for socio-economic sustainable development of small towns in Jambyl oblast

Achieving sustainable development of small towns as a special system is directly related, first of all, to maintaining socio-economic stability and creating a favourable environment for human life. Indeed, as a basis for the sustainability of small towns, the development of services, effective utilisation of local characteristics and diversification of socio-economic spheres are very important. Therefore, the purpose of this study is to assess the possibilities of socio-economic sustainable development of small towns in Zhambyl region. The study consisted of two stages: at the first stage a systematic analysis of statistical indicators of socio-economic development of Zhambyl region was conducted, the dynamics of sustainable development was assessed, at the second stage a survey was conducted and the information necessary for analysis was collected. The survey was conducted in the cities of Zhanatas, Karatau and Shu, which have the status of small towns in Zhambyl region. As a result of the survey, positive dynamics was noted for all indicators of socio-economic development of Zhambyl region. The volume of production in the region's manufacturing industry totalled 521.7 billion tenge, with a physical volume index of 111.1% compared to 2022. At the same time, the production of food products increased by 17.8%, chemical industry - by 5.5%, oil products - by 19.6%, finished metal products - by 31.9%, machine building - by 7.7%. However, according to the results of a survey conducted in the small towns of Zhanatas, Karatau and Shu, it became known that 31 per cent of respondents earn within 100-200 thousand tenge and 28 per cent within 60-100 thousand tenge. In addition, 66% of residents of small towns ask to increase the number of new jobs. Thus, the study revealed the need to modernise the socio-economic plan for sustainable development of small towns in Zhambyl region, the need to diversify the economy and improve the effective use of the environment.

A Bibliometric Review of Green Technology- Related Research in the Textile Industry

Despite being an impressive contributor to the world economy, traditional production and processing methods in the textile manufacturing industry cause significant waste and pollution. Green technology has emerged as a viable approach for mitigating environmental consequences. Numerous scholars have investigated green technologies; nonetheless, a more thorough analysis of the development and characteristics of green technology research in the textile industry is needed. This bibliometric study examined the growth patterns and trends of green technology research in the clothing industry from 2000 to 2023. The data were collected from the Scopus database, and the bibliometric analysis tool VOSviewer was used to visualise the data. The study results demonstrated the exponential growth trends of green textile technology research since 2020. The study also uncovered productive journals, countries, and institutions researching green textile technology. The results additionally showed that the United States (US), the United Kingdom (UK), and China exhibit substantial publications, and the UK is the leading country for collaborative research in green textile technology research. This study identified five primary research areas in the green textile technology literature: environmental impact assessment, life cycle assessment of textiles, sustainable design of textiles, sustainable fashion, and circular fashion supply chains. This analysis can assist academics in identifying novel research methodologies.

Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality

To promote energy saving, high efficiency and quality production in mechanical manufacturing industry, a large number of studies have been conducted on parameters optimisation, especially for turning. However, most of the approaches have focused on single objective model and unidirectional turning (UDT). This paper presents a multi-objective parameter optimisation method for energy consumption, cutting time and surface roughness using new process of forward-and-reverse multidirectional turning (MDT). Firstly, the composition characteristics of cutting parameters for energy consumption, cutting time and surface roughness of MDT are analyzed to establish the corresponding models through nonlinear regression fitting, respectively. Then taking the minimum energy consumption, cutting time and surface roughness as the optimisation objectives, the improved fireworks algorithm can generate nearly 100 groups of optimal solutions. The most suitable processing parameters solution needs to be selected according to different machining requirements. In application scenarios, most relative errors of the models are within 10%. Comparing the machining performance of MDT and UDT under the same cutting parameters, the processing efficiency is increased by 25.97% and energy consumption is reduced by 12.38%, using the MDT. This study provides a multi-objective optimisation approach and models for MDT to reduce energy consumption, improve production efficiency and quality.

Energy efficient smart manufacturing of pharmaceutical solid oral dosage forms

Background: The global pharmaceuticals market is a trillion-dollar industry which grows more than 5% annually. However, in comparison to other manufacturing industries (e.g., oil refining, automotive), the pharmaceutical sector lags in manufacturing innovation and automation. In the production of pharmaceutical solid dosage forms, the use of energy utilization as a performance measure of production efficiency has neither been implemented extensively, nor been optimized to maximize efficiency. This study will focus on the development and implementation of a smart manufacturing platform to optimize energy productivity whilst maintaining tablet quality via the consideration of different manufacturing scenarios.
 Methods: This study will consider three main unit operations (wet granulation, drying and milling) which are relatively more energy intensive in pharmaceutical downstream processing, used to produce solid dosage forms, such as tablets. Four case-studies will be considered, which are 1: baseline batch, 2: baseline continuous, 3: optimized batch and 4: optimized continuous. Smart manufacturing is implemented to present optimized cases 3: and 4: Improvements in the energy and performance metrics are quantified and compared to the baseline cases. 
 Results and conclusions: The smart manufacturing platform used in this study, integrates advanced process model development, optimization, technoeconomic analysis and data integration. The utilization of this framework contributed to a ~70% and ~80% improvement in energy utilization in the optimized batch and continuous cases, respectively, when compared to the baseline batch case. In the optimized cases, tablet quality was maintained within targeted specifications and was comparable to the baseline batch case. This smart manufacturing framework can be generalized for drug product manufacturing and other particulate-based industries such as food, agriculture, and fine chemicals.

Recalculation of Manufacturing Industry Production Function with Trade Openness and Human Capital: Multi-Dimensional Panel Data Application

Recalculation of Manufacturing Industry Production Function with Trade Openness and Human Capital: Multi-Dimensional Panel Data Application