Efficiency Of Industrial Production Research Articles

The role of financial inclusion, urbanization, and energy efficiency on environmental performance in belt and roads initiative (BRI) economies

The primary objective of the study is to determine how environmental performance is affected by several factors, including trade freedom, industrial production, urbanization, financial inclusion, and energy efficiency. This research provides a novel perspective on the environmental performance of Belt and Roads Initiatives (BRI) member states by employing panel data from 1991 to 2021. It contributes to our understanding of the role of several factors in improving environmental performance. Panel data analysis also incorporates the CD, CIPS, FMOLS, DOLS, and PMG-ARDL tests. This research possesses substantial theoretical ramifications, as it might enable the expansion and refinement of current economic and environmental theories. As per the findings of this research, each factor significantly affects environmental performance (ecological foot-point). This contributes to short-term environmental issues by conserving resources and lowering emissions, creating the framework for long-term environmentally friendly procedures and innovation. Strong environmental policies that may cause effective environmental performance in Belt and Roads Initiatives (BRI) countries.

Evaluating the Impact of 3D Printing Technology Innovations on Industrial Production Efficiency and Economic Growth in the Automotive Sector in China

This paper evaluates the impact of 3D printing technology innovations on industrial production efficiency and economic growth in the automotive sector in China. The study explores the historical development and current market trends of 3D printing in China, highlighting its adoption and integration into automotive manufacturing. The applications of 3D printing, including the production of complex geometric parts, rapid prototyping, and customization, are examined for their economic benefits and production efficiency gains. The paper also addresses the technological and market challenges, such as material limitations, printing speed, quality control, regulatory compliance, and market readiness. Emerging technologies and future research directions, including advancements in materials, multi-material printing, large-scale 3D printing, and the integration with IoT and AI, are discussed. Strategic recommendations are provided to maximize the potential of 3D printing in the automotive industry, focusing on investments in R&D, partnerships, workforce training, agile manufacturing practices, sustainability, IP protection, and supply chain optimization. The findings suggest that while 3D printing technology offers significant advantages, addressing existing challenges and leveraging future innovations are crucial for its successful adoption and impact on economic growth in the Chinese automotive sector.

Deep learning-based phenotype classification of three ark shells: Anadara kagoshimensis, Tegillarca granosa, and Anadara broughtonii

The rapid and accurate classification of aquatic products is crucial for ensuring food safety, production efficiency, and economic benefits. However, traditional manual methods for classifying ark shell species based on phenotype are time-consuming and inefficient, especially during peak seasons when the demand is high and labor is scarce. This study aimed to develop a deep learning model for the automated identification and classification of commercially important three ark shells (Tegillarca granosa, Anadara broughtonii, and Anadara kagoshimensis) from images. The ark shells were collected and identified using a polymerase chain reaction method developed in a previous study, and a total of 1,400 images were categorized into three species. Three convolutional neural network (CNN) models, Visual Geometry Group Network (VGGnet), Inception-Residual Network (ResNet), and SqueezeNet, were then applied to two different classification sets, Set-1 (four bivalve species) and Set-2 (three ark shell species). Our results showed that SqueezeNet demonstrated the highest accuracy during the training phase for both classification sets, whereas Inception-ResNet exhibited superior accuracy during the validation phase. Similar results were obtained after developing a third classification set (Set-3) to classify six categories by combining Set-1 and Set-2. Overall, the developed CNN-based classification model exhibited a performance comparable or superior to that presented in previous studies and can provide a theoretical basis for bivalve classification, thereby contributing to improved food safety, production efficiency, and economic benefits in the aquatic products industry.

A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding

Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

Exploring the Impact of the Digital Economy on Green Total Factor Productivity—Evidence from Chinese Cities

The digital economy promotes economic development, industrial upgrading, and environmental protection. In this study, we calculated green total factor productivity (GTFP) based on the SBM-DDF model and used the entropy method and principal component analysis to calculate a digital economy index. We used panel data from 282 Chinese cities to measure the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP. We then assessed the heterogeneity of this impact. We also explored the mechanisms by which the digital economy promotes green development and found that the digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.

Near-infrared spectroscopy combined with deep convolutional generative adversarial network for prediction of component content in melt-cast explosive

Rapid and nondestructive prediction of component content is the key to improve industrial production efficiency. However, limited data sets also result in low generalization capabilities of the model, and it is time-consuming to obtain a large amount of content reference values and costly. Here, near infrared (NIR) spectroscopy technique combined with deep convolutional generated countermeasure network (DCGAN) was used to predict the trinitrotoluene (TNT) content of the melt-cast explosive. DCGAN was used to simultaneously extend its spectral data and content data. After several iterations, fake data were produced, which was very similar to the experimental data. The partial least squares (PLS) regression model was established and the performance was compared before and after data enhancement. The results showed that this method not only improved the performance of regression model, but also solved the problem of requiring large number of training data.

An efficient targeted design for real-time defect detection of surface defects

In practical industrial applications, the inference speed of deep learning models directly affects the efficiency of industrial production. Therefore, the lightweight real-time detection method of surface defects is an essential task in the industrial process. We need to achieve a favorable balance between efficiency and accuracy since the rising demand for production efficiency. However, most of the existing pixel-level detection methods 1) often adopt huge computational overhead to learn rich features, resulting in slow inference speed and 2) show a performance degradation when applied to different industrial surface defect scenarios. To this end, we propose an efficient targeted design (ETD) for real-time defect detection of surface defects. It consists of two branches: (i) an efficient feature enhancement branch, with global aggregation module (GAM) and cross-scale guide module (CGM) to gradually enhance defect features, and (ii) an edge posterior branch, with verification module (VM) and scale interaction module (SIM) to implicitly guide the boundary details of defects. Specifically, while inheriting this framework, we reconsider the relationship between precision, parameters, and speed so that our model can be applied to different industrial scenarios. Extensive experimental results on four datasets indicate that ETD outperforms other leading saliency detection methods. Meanwhile, our method ETD-S achieves 347 FPS on ESDIs-SOD dataset, 254 FPS on Crack500 dataset, 227 FPS on NRSD-MN dataset and 273 FPS on DAGM dataset. Additionally, we conduct real-time analysis of ETD on an intelligent paradigm for industrial surface defect detection, further demonstrating its efficacy in practical scenarios. ETD demonstrates effective detection performance while achieving a lightweight architecture, which can be implemented using various deep learning frameworks, showcasing substantial potential for real-time surface defect detection. The source code and dataset are publicly available at https://github.com/VDT-2048/ETD.

Identifying Causes of Pressure Drop in Heat Exchanger E-204

This study investigates the efficacy of preventive maintenance measures on the E-204 Plate Heat Exchanger (PHE) to mitigate pressure drop issues affecting production efficiency and operational costs in the petrochemical industry. The research identifies gasket leaks and plate blockages as primary contributors, attributed to wear and dirt deposits. Through periodic inspections, gasket replacements, and mechanical and chemical cleaning, maintenance interventions were executed. Results demonstrate a significant reduction in gasket blockages and leaks, leading to improved fluid flow and operational efficiency. The findings underscore the importance of regular preventive maintenance in ensuring optimal PHE performance and cost-effective operations. Highlight : Maintenance Intervention: Periodic inspections, gasket replacements, and mechanical and chemical cleaning were implemented to address gasket leaks and plate blockages. Enhanced Efficiency: The study resulted in a significant reduction in gasket blockages and leaks, leading to improved fluid flow and operational efficiency. Implications for Industry: Regular preventive maintenance is crucial for optimal performance and cost-effective operations in petrochemical production processes. Keywords: Plate Heat Exchanger, Preventive Maintenance, Efficiency Improvement, Gasket Leaks, Petrochemical Industry

Optimal local content requirement under export share requirement consideration

This paper considers a Cournot model with a local content requirement and export share requirement. The analysis investigates the conditions under which the local content requirement may promote an intermediate industry and explores the optimal local content requirement and the key factors that affect it. The results show that while a small local content requirement has a promotion effect when it is first introduced, while if the local content requirement is further increased, the effect will depend on the intermediate industry's production efficiency. Furthermore, the optimal local content requirement should be higher as the domestic market expands or the export price increases. Finally, the export share requirement can be treated as a policy instrument that is supplementary to the local content requirement.

Effect of ultrasonic power density on the quality of fresh wet noodles

In order to enhance the production efficiency in the flour products industry by reducing dough resting time, this study investigated the impact of different ultrasonic densities on starch, protein hydration, and gluten network formation in fresh wet noodles using the ultrasound. The sensory quality, secondary structure, and cross-sectional morphology of the dough were also analyzed. Compared to the control group, the results demonstrate that treatment with an ultrasonic power density of 66 W/L improves the texture of wheat dough, as well as the hydration of starch and protein, the hardness of fresh wet noodles decreased by 14.19 %, and tensile force significantly increased by 33.43 %. Furthermore, Fourier-transform infrared spectroscopy (FTIR) analysis indicated a significant increase in the content of α-helix and an overall increase in the content of β-sheet due to ultrasonic vibration treatment. To summarize, the implementation of ultrasonic treatment exhibits promising potential in reducing the resting processing period while concurrently elevating the overall quality of freshly prepared wet noodles. This is primarily achieved by augmenting the texture, heightening the hydration levels of starch and protein constituents, as well as modifying the gluten network structure.

Gender Control of Mouse Embryos by Activation of TLR7/8 on X Sperm via Ligands dsRNA-40 and dsRNA-DR.

Gender control technologies are promising for enhancing the production efficiency of the farm animal industry, and preventing sex-linked hereditary diseases in humans. It has been shown that the X sperm of mammalian animals specifically expresses X-chromosome-derived toll-like receptor 7/8 (TLR7/8), and the activation of TLR7/8 on the X sperm by their agonist, R848, can separate X and Y sperm via the specific inhibition of X sperm motility. The use of R848-preselected sperm for fertilization resulted in sex-ratio-skewed embryos or offspring. In this study, we aimed to investigate whether two other TLR7/8 ligands, double-stranded RNA-40 (dsRNA-40) and double-stranded RNA-DR (dsRNA-DR), are also effective in the separation of mouse X and Y sperm and the subsequent generation of gender-ratio-skewed in vitro fertilization (IVF) embryos. Our results indicated that cholesterol modification significantly enhances the transfection of dsRNA-40 and dsRNA-DR into sperm cells. dsRNA-40 and dsRNA-DR incubation with mouse sperm could separate X and Y sperm by the specific suppression of X sperm motility by decreasing its ATP level and mitochondrial activity. The use of a dsRNA-40- or dsRNA-DR-preselected upper layer of sperm, which predominantly contains high-motility Y sperm, for IVF caused a male-biased sex ratio shift in resulting embryos (with 65.90-74.93% of embryos being male). This study develops a simple new method for the efficient separation of mammalian X and Y sperm, enabling the selective production of male or female progenies.

The role of biologization of processes in increasing the technological and economic efficiency of viticulture

The necessity of increasing the technological and economic efficiency of viticulture is substantiated. The assessment of the influence of negative manifestations of chemical and technogenic intensification factors in viticulture on the stability of agrocenoses with the participation of grape plantations is given. The priority role of biotechnologies in ensuring the stability of grape agrocenoses to negative manifestations caused by an increase in chemical and man-made loads, which are a set of industrial methods that use living organisms and biological processes for the production of industrial products, is substantiated. The basic principles on which the biologization of protective measures of vineyards is based are considered. The effectiveness of the main methods of biological protection of industrial vineyards is assessed, the use of which will reduce the negative impact of xenobiotics on the environment, material costs for the purchase of insecticides in the cultivation of products and ensure the development of optimal agrotechnological solutions for the protection of perennial plantations, which will contribute to the growth of technological and economic efficiency of industrial production.

Evaluating energy cost performance in China's thermal power industry: A global cost Malmquist approach

AbstractWith the inclusion of carbon neutrality in the Government Work Report for the first time at the two sessions held in March 2021 and the strengthening of environmental regulation as well as the increase of limits on polluting gas emissions, it has become increasingly important for China to effectively save energy and reduce carbon for sustainable growth under the “dual carbon” target. Since thermal power generation is the most important source of carbon emissions in China, the thermal power industry is the key to achieving the goal of carbon peaking and carbon neutrality. Therefore, it is of great theoretical and practical importance to study the performance of thermal power generation from the micro perspective of power plants. This paper provides a new perspective on the environmental efficiency measurement of thermal power plants by introducing the cost factors of inputs into the model and constructing a global cost Malmquist productivity index, which can effectively solve the problem of the infeasibility of linear programming and different measures of productivity change, to obtain the productivity performance of China's thermal power industry from 2013 to 2017. The empirical results indicate that: (1) The significant decrease in price effect (PE) is the main driving factor of this situation. (2) Except for the central region, the production technology, technical efficiency, and allocative efficiency related to thermal power generation have increased in the eastern and western regions. Therefore, improving CMG productivity in the central region is more challenging than in the other two regions and improving the PE can significantly improve CMG productivity in China's thermal power industry. This study reveals the spatiotemporal characteristics and influencing factors of production efficiency in China's thermal power industry, which can provide targeted policy guidance for managers to improve the sustainable development of power enterprises.

Choline regulation of triglycerides synthesis through ubiquintination pathway in MAC-T cells.

This study aims to investigate the regulatory mechanism of choline (CH) on triglyceride (TG) synthesis in cows, with a specific focus on its potential association with high milk fat percentage in the gut of the Zhongdian yak. By employing combined metagenomics and metabolomics analysis, we establish a correlation between CH and milk fat production in yaks. Bovine mammary epithelial cells (MAC-T) were exposed to varying CH concentrations, and after 24 h, we analyzed the expression levels of key proteins (membrane glycoprotein CD36 (CD36); adipose differentiation-related protein (ADFP); and ubiquintin (UB)), cellular TG content, lipid droplets, and cell vitality. Additionally, we evaluated the genes potentially related to the CH-mediated regulation of TG synthesis using real-time qPCR. CH at 200 μM significantly up-regulated CD36, ADFP, UB, and TG content. Pathway analysis reveals the involvement of the ubiquitination pathway in CH-mediated regulation of TG synthesis. These findings shed light on the role of CH in controlling TG synthesis in MAC-T cells and suggest its potential as a feed additive for cattle, offering possibilities to enhance milk fat production efficiency and economic outcomes in the dairy industry.

Current changes and strategical directions of modernization of oilfield services market

Relevance. Innovative-technological development of oilfield service business is associated with the need of vertically integrated companies (VINCs) in services, which are largely dependent on fluctuations in demand for hydrocarbon raw materials, so the model of strategic planning of innovative-technological development of oilfield service, taking into account the current realities, will be in wide demand. Purpose of the research – Justification of the need to implement the model of strategic planning in innovation and technological development in oilfield service companies based on the analysis of current problems and trends in the oilfield service market. Main results. The analysis of problems and trends in the oilfield services market proves that the growing demand for hydrocarbons is the key factor determining the market growth and forming the outstripping demand for geological exploration, drilling and geophysical support. Increasing sanctions pressure may reduce the volume of high-tech service operations due to the withdrawal of multinationals from the Russian market, which may lead to stagnation of the oilfield services market and cause risks of lower oil and gas production levels. The pandemic-related drop in oil quotations on world stock exchanges has caused an increase in competition in both the Russian and global oilfield services markets, where two opposite trends have manifested themselves: on the one hand, there is a decline in demand for these services, while on the other – data on the growth of drilling volumes indicate the industry’s development prospects. New technological solutions increase the production efficiency of the oilfield service industry, which requires companies to focus on innovative development and use a model of strategic planning for the introduction of modern technologies together with classical market instruments, which determines the practical significance of the results of this study. Conclusion. The analysis of opportunities, trends and problems of oilfield service market development, as well as strategic priorities of oilfield service companies at the present stage confirms the significance and necessity of introducing innovative technologies to form and maintain competitive advantages in the existing global market. The strategic approach to modelling the development of oilfield service companies allows to increase the sustainability and stability of their development, as well as to reduce the negative impact on the environment in the changing energy landscape.

Defect segmentation with local embedding in industrial 3D point clouds based on transformer

Three-dimensional (3D) defect detection provides an effective method for improving industrial production efficiency. However, the 3D dataset is scarce, which is valuable for the industrial production field. This study proposes a new approach for detecting defect point clouds, which can provide an end-to-end 3D defect detection model. A self-attention mechanism is used to enrich the semantic relationships between local neighborhood features and global features based on the connection between them. Through adding multi-channel features, the rich structural features of the target point cloud are obtained, and the defect areas are accurately segmented to finally complete the 3D point cloud defect detection task. Furthermore, the multi-feature fusion in the model makes the segmented defect regions closer to the ground truth. Our method outperforms four state-of-the-art point cloud segmentation methods in terms of both segmentation region accuracy and defect detection point cloud accuracy. In the field of 3D defect detection, it provides an effective method to detect 3D information of industrial products.

Motility-related microRNAs identified in pig seminal plasma exosomes by high-throughput small RNA sequencing

Boar fertility is a key determinant of the production efficiency of the whole pig breeding industry and boar sperm motility is the seminal parameter with the greatest impact on the fecundity of a sow. Exosomes are small, extracellular vesicles found in many body fluids. Seminal plasma exosomes, which are secreted by the epididymis, prostate, seminal vesicles, and testes, contain a large number of miRNAs, the types and levels of which can reflect the physiological state of source cells. It has been shown that the expression profile of seminal plasma exosomal miRNA differs between low-motility semen and normal semen. The aim of this study was to investigate the relationship between semen motility and exosomal miRNA profiles to obtain information that would allow to predict boar fertility, as well as contribute to the understanding of the mechanisms by which exosomal miRNAs regulate semen motility. Three high-motility (semen motility >90 %) and three low-motility (semen motility <80 %) semen samples were collected from Landrace and Yorkshire boars, respectively, and seminal plasma exosomes were extracted by ultracentrifugation. Exosome characterization was performed using transmission electron microscopy, NTA, and Western blot. The expression profiles of exosomal miRNAs associated with semen motility in the two boar breeds were subsequently determined by small RNA sequencing. The results showed that 297 known miRNAs and 295 novel RNAs were co-expressed in the four groups. Notably, six miRNAs (ssc-miR-122–5p, ssc-miR-486, ssc-miR-451, ssc-miR-345–3p, ssc-miR-362, and ssc-miR-500–5p) were found to be differentially expressed in both boar breeds. Enrichment analysis of the target genes of the differentially expressed miRNAs showed that they were mainly involved in biological processes such as regulation of transcription from RNA polymerase II promoter, regulation of gene expression, and intracellular signal transduction and signaling pathways such as the PI3K-Akt, MAPK, and Ras signaling pathways. The six differentially expressed miRNAs identified in this study have significant potential as noninvasive markers of boar semen motility. Meanwhile, the results of the enrichment analysis provide novel insights into the mechanisms underlying the regulation of semen motility.

Novel titanium-containing precursors for the fabrication of hierarchical TS-1 and the enhanced catalytic performance for olefins

Hierarchical TS-1 zeolites with high catalytic activities toward 1-hexene epoxidation were successfully synthesized by using novel titanium-containing precursors. The precursors were prepared by o/m/p-dihydroxybenzene with tetrabutyl titanate, which not only promote the uniform distribution of titanium sources and reduce the aggregation of Ti species, but also contribute to regulating the coordination state of Ti species and increasing the frameworks Ti contents. TS-m/p-DHB-48h zeolites display excellent catalytic performance in the epoxidation reaction of 1-hexene, and the conversion improved by 41.9 % and 34.7 %, respectively. The hierarchical zeolites also exhibit superior 1,2-epoxyhexane selectivity, which will greatly improve the efficiency of industrial production.

Green production efficiency of China's hog breeding industry: Spatial divergence and its driving factors.

This paper analyzes the green production efficiency (GPE) and spatial divergence of the hog breeding industry, with the aim of providing a foundation for the rational layout of hog breeding and promoting the industry's high-quality development. The paper uses the SBM model to estimate GPE in 29 provinces, cities, and districts of China from 2006 to 2019. Furthermore, it analyzes the spatial divergence of GPE and its driving factors using divergence indexes and the Geodetector. The results show that China's GPE of the hog breeding industry increased from 0.409 to 0.496 between 2006 and 2019. The highest efficiency occurred during the I-period, while the lowest efficiency was observed during the II-period. The highest efficiency was in the key development region, and the lowest efficiency was in the potential growth region. The spatial divergence of GPE in the hog breeding industry expanded, with labor input, non-point source pollution, resource endowment, and environmental load bearing being the main driving factors for the expansion in each period. The potential growth region had the largest spatial divergence, mainly affected by resource endowment. In contrast, the constrained development region had the smallest spatial divergence, mainly affected by resource endowment and pollutant emissions. The spatial divergence of moderate and key development regions was considerable, mainly affected by environmental investment, environmental load bearing, and pollutant emissions. Therefore, the key to improving the GPE of the hog breeding industry is to promote the adoption of advanced technology, such as labor-saving, material-saving, and emission reduction technologies. Moreover, several actions should be taken to reduce the spatial divergence among different regions, such as integrated breeding, clean standards, large-scale breeding, and high-end boutique.

Designing Simulation to Improve Production Efficiency of Batik Industry

Background The study deals with evaluating the production system for manufacturing batik, then developing a scenario for improving the system. Purpose The study aims to point out the possibilities of computerized simulations in designing, testing, and exploring scenario models of real production plants of Small-Medium enterprises (SMEs). Method The research is carried out as a case study, using a simulation approach to achieve the objectives. This study uses three data collection techniques: direct observation, qualitative interviews, and time study. The simulated model of the system is the basis for building improvement strategies. Results The simulation model yields more than 70% of the overall process time mainly consisting of idle time and waiting for the operator. It leads to an inefficient production system. Significant differences in processing time and assigning operators to workstations that are unbalanced cause inefficiency. Although most of the time was not efficient, the average utilization of the operator was 63.96%. The system has a good operator’s utility level because the average total utility value is higher than idle time. The operators are also responsible for other tasks that were included in the simulation. Discussion The study proves that simulation is a very promising approach that allows industrial owners to mimic the production process and resource assignments within the model. The production efficiency with existing resources can be enhanced with the balancing procedure between the workstations and the operators. The suggested system runs on the concept of assigning operators with lower utility to processes with high WIP queues. The operator can interact with multiple workstations. The proposed model reduces the total time required to produce the batik. Contribution The simulation helps the analyzed organization to evaluate the efficiency of the manufacturing process. Changing the parameters in the model can generate new utilization and efficiency for the system.