Benefits Of Enterprises Research Articles

A Prediction Model for Methane Concentration in the Buertai Coal Mine Based on Improved Black Kite Algorithm–Informer–Bidirectional Long Short-Term Memory

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R2) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R2 values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications.

Optimal dynamic pricing and green design under carbon-reduction competition considering social influence

The concept of green design is a design idea that focuses on environmental protection and sustainability. Green design has become the core competitiveness of enterprises. So this paper explores competitive pricing and green design strategy of firms in the context of carbon reduction. In the analysis of the effect of social influence, consumer environmental awareness is also considered. And two research scenarios are examined: Price competition in single carbon reduction scenario; Competition in pricing and green level under dual carbon reduction scenario. The results indicate that social influence and consumer environmental awareness interactively and significantly affect corporate decisions; Social influence positively promotes firms’ profit and is not always disadvantageous for competitors’ profit; In equilibrium, when one firm makes a green transformation, it achieves the win-win situation of not only improving environmental performance but also maximizing economic benefits. However, the induced environmental improvements and economic benefits of competitive enterprises are not necessarily ideal. The study results provide optimal strategies for competitive and environmental actions of enterprises, and also provide management insight for the sustainable development of enterprises.

Analisis Regulasi Kawasan Industri Halal dalam Mendukung Pengembangan IKM : Studi Kasus Safe N Lock Sidoarjo, Jawa Timur

The Halal Industrial Estate (HIE) in East Java has enormous potential to capitalize on the global trend of increasing demand for halal products. As a country with the largest Muslim population in the world, Indonesia is expected to seize this opportunity through regulations that support the development of HZs, which can not only improve the competitiveness of local products but also expected to contribute to overall economic growth. However, challenges in the development of HEC need to be overcome through close cooperation between the government, industry players, and the community. Socialization of the importance of halal certification and its benefits for Micro, Small and Medium Enterprises (MSMEs) should also be improved to motivate business actors. This study takes a qualitative approach and literature review to understand how government regulations can support the development of the halal industry in Sidoarjo. The findings show that with the right regulations, KIH can function as a halal industry center that meets local needs and can compete in the global market. The strategic steps recommended by the government include increasing local production capacity, strengthening the halal ecosystem from upstream to downstream, and creating a competitive market environment. With these strategic steps, it is expected that East Java can optimize its role in the halal industry.

The Entrepreneurial Mindset: Views and Perspectives of Cacao Entrepreneurs on Innovation and Entrepreneurship in Bicol Region, Philippines

Innovation and entrepreneurship are fundamental drivers of economic growth. Oftentimes, sustainable development is equated with these two indices where untapped potential and emerging industries are available that present countless opportunities for the people. A research study was conducted composed of cacao farmers, farmers/processors and farmers/processors/traders residing in various towns in the Bicol region, Philippines aimed at determining their perception, innovation and entrepreneurial initiatives and strategies. A survey-questionnaire was designed and administered to 70 cacao farmers and entrepreneurs and qualitative data analysis techniques, such as thematic coding and content analysis was employed to analyze result of the interview and focus group discussions. Findings of the study showed that farming is the primary focus for the majority of respondents (67%), with only a minority involved in processing or trading activities. The most highly recommended innovative cacao-based products with potential market success are primarily in the food and confectionery categories, with substantial interest in beverages and skincare products. Ninety-one percent (91%) are willing to pay a premium for innovative cacao products because they value supporting local entrepreneurship and a good sign of a strong community support for local businesses with high level of willingness to invest in locally developed products. Job creation and empowerment of small-scale farmers are considered as top benefits of a community-based value-added cacao products enterprise. Limited financial investment/capital is the most significant problem faced, followed closely by unstable demand and low and unregulated farm gate price of agricultural commodities.

Machine vision-based recognition of safety signs in work environments.

The field of image recognition is extensively researched, with applications addressing numerous challenges posed by the scientific community. Notably among these challenges are those related to individual safety. This article presents a system designed for the application of image recognition in the realm of Occupational Risk Prevention-a concern of paramount importance due to the imperative of preventing workplace accidents as falls, collisions, or other types of accidents for the benefit of both workers and enterprises. In this study, convolutional neural networks are employed due to their exceptional efficacy in image recognition. Leveraging this technology, the focus is on the recognition of safety signs used in Occupational Risk Prevention. The primary objective is to enable the recognition of these signs regardless of their orientation or potential degradation, phenomena commonly observed due to regular exposure to environmental elements or deliberate defacement. The results of this research substantiate the feasibility of integrating this technology into devices capable of promptly alerting individuals to potential risks. However, to improve classification capabilities, especially for highly degraded or complex images, a larger and more diverse data set might be needed, including real-world images that introduce greater entropy and variability. Implementing such a system would provide workers and companies with a proactive measure against workplace accidents, thereby enhancing overall safety in occupational environments.

Financing of the company's investment activity in the face of growing risks

The article examines the impact of globalization and economic reforms on enterprise management, focusing on the need to modernize production facilities, automate processes and use information technology. The role of investment as a key tool for promoting economic growth of enterprises, increasing their competitiveness and adaptation to new challenges of the globalized economy is considered. Particular attention is paid to the analysis of investment risks, their classification and methods of minimization, which allows enterprises to reduce negative impacts in the implementation of investment projects. Effective management of investment risks and determination of optimal investment strategies are critical factors for attracting new investments and achieving sustainable economic growth. The legal framework governing investment activity in Ukraine is defined, and approaches to investment management in accordance with international financial reporting standards are summarized. Thus, investment activity is considered not only as a source of economic benefit for enterprises, but also as a factor contributing to the economic stability of the national economy.

Research on High-Frequency Isolated NPC Three-Level Inverter for Frequency Conversion and Speed Regulation

Mining frequency converters are the primary means for achieving variable frequency speed regulation of electromechanical equipment in coal mines, offering energy-saving benefits for coal mining enterprises. The common power supply method involves converting high voltage to low voltage using power frequency transformers before supplying equipment. However, this integration of power frequency transformers with supply devices occupies significant space, making it unsuitable for confined underground environments. Additionally, they suffer from poor output waveform quality and high harmonic content. To tackle these challenges, this paper presents a three-stage topology for high-frequency isolated frequency conversion and speed regulation, utilizing three-phase uncontrolled rectification, a single active isolated DC/DC converter, and an NPC three-level inverter. The control strategies for each stage are discussed in detail. Simulations and experimental results confirm the validity and feasibility of the proposed design, demonstrating enhanced stability and dynamic performance of the three-stage high-frequency isolated frequency converter.

Optimizing VAT threshold: The key to improving VAT performance

The low tax ratio in Indonesia is an issue often associated with suboptimal tax revenue performance. This study examines the role of the threshold for Value Added Tax (VAT), which is suspected to significantly influence the optimization of VAT revenue, while also exploring the effect of VAT rate and Gross Domestic Product (GDP) per capita. The study analyze data from member countries of the Organization for Economic Co-operation and Development (OECD) in 2022 using multiple linear regression methods. The results show that increases in the VAT threshold and VAT rate positively impact VAT revenue performance, while an increase in per capita GDP has a negative effect. A higher VAT threshold allows the country to focus on larger taxpayers, while the Micro, Small, and Medium Enterprises (MSMEs) benefit from reduced administrative burdens. These findings can serve as a basis for government evaluation in determining the appropriate threshold and VAT rates to enhance tax revenue performance optimally.

Evolutionary game analysis of multi-agent cooperation strategy analysis in agricultural water conservancy PPP project under digitization background

This study constructed a system dynamics (SD) evolutionary game model, from which we analyzed the conditions that need to be met by multi-agents to achieve cooperation in agricultural water conservancy (AWC) Public-Private Partnership (PPP) projects. Simultaneously, this study used numerical simulation to depict the impact of initial participation willingness, reward, punishment, income, and other parameters of local governments, project enterprises, and farmers on the evolution strategy. The results firstly showed that the basic conditions for tripartite cooperation are to strengthen the rewards and punishments for project enterprises and improve the participation benefits of farmers. Secondly, it showed that increasing the rewards for farmers and improving the project dividend benefits can effectively motivate farmers to participate. Thirdly, it showed that enhancing the incentive subsidies and reputation benefits for project enterprise’s participation, as well as strengthening the supervision of local government, boosts the motivation of project enterprises significantly. However, it is vital to ensure that the reward amounts are not too high to allow local government to play its guiding role efficiently. Lastly, the results indicated that digital technology can help reduce the participation cost of local government, project enterprises and farmers, and improve the external reputation benefits of each participant, thus forming a win-win situation for all three participants. Analyzing the cooperation strategies of the three parties in AWC PPP projects provides theoretical support and a decision-making basis for the efficient operation of AWC PPP projects under the digital background, and also provides practical policy recommendations for the high-quality development of AWC.

THE ADOPTION OF BUSINESS DIGITALIZATION BY SMEs: A CASE STUDY IN BANDUNG

The objective of this study is to investigate the the existing practice of business digitalization by SMEs in Bandung. There were qualitative techniques employed in conducting the research, which mainly consisted of several interviews with small scale enterprise owners cutting across different sectors. The findings show that digital enterprises are responsible for significant improvements in business operations and financial results. However, there are still some challenges including high costs at initiation stage and inadequate employee skills required. According to research affordable technology solutions and training employees can be effective approaches to mitigate these obstacles thereby improving Small Medium Enterprises benefits from going digital.

Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry.

To apply machine learning models based on air conduction thresholds of pure-tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH). Retrospective study. Tertiary medical center. Gadolinium-enhanced magnetic resonance imaging sequences and pure-tone audiometry data were collected. Subsequently, basic and multiple analytical features were engineered based on the air conduction thresholds of pure-tone audiometry. Later, 5 classical machine learning models were trained to diagnose MD using the engineered features. The models demonstrating excellent performance were also selected to predict EH. The model's effectiveness in MD diagnosis was compared with experienced otolaryngologists. First, the winning light gradient boosting (LGB) machine learning model trained by multiple features demonstrates a remarkable performance on the diagnosis of MD, achieving an accuracy rate of 87%, sensitivity of 83%, specificity of 90%, and a robust area under the receiver operating characteristic curve of 0.95, which compares favorably with experienced clinicians. Second, the LGB model, with an accuracy of 78% on EH prediction, outperformed the other 3 machine learning models. Finally, a feature importance analysis reveals a pivotal role of the specific pure-tone audiometry features that are essential for both MD diagnosis and EH prediction. Highlighted features include standard deviation and mean of the whole-frequency hearing, the peak of the audiogram, and hearing at low frequencies, notably at 250 Hz. An efficient machine learning model based on pure-tone audiometry features was produced to diagnose MD, which also showed the potential to predict the subtypes of EH. The innovative approach demonstrated a game-changing strategy for MD screening and promising cost-effective benefits for the health care enterprise.

Research on Frequency Control Technology in Electrical Automation Control

With the continuous improvement of production efficiency, quality and flexibility, electrical automation control technology plays an increasingly important role in industrial production. As the core technology of electrical automation control, frequency conversion speed regulation is of great significance to improve production efficiency, reduce production costs, ensure stable operation of equipment and reduce energy consumption of electrical automation control system. Frequency conversion speed regulation refers to the frequency and voltage adjustment of motor power supply, so as to achieve accurate control of motor speed. The economy of this technology is mainly manifested in its ability to effectively reduce production costs, while its versatility is mainly manifested in its high degree of functional modularity and wide adaptability. Simply put, frequency conversion speed control technology is essential in the electrical automation control system. It reflects the scientific and technological progress of the enterprise, and also plays a decisive role in the enhancement of enterprise benefits. This paper provides a more comprehensive discussion on the application of frequency conversion speed control technology in electrical automation control for relevant personnel.

Impact of the carbon market on investment benefits of power-grid enterprises in China: A system dynamics analysis

The power grid, as the hub connecting the power supply and consumption sides, plays an important role in achieving carbon neutrality in China. In emerging carbon markets, assessing the investment benefits of power-grid enterprises is essential. Thus, studying the impact of the carbon market on the investment and operation of power- grid enterprises is key to ensuring their efficient operation. Notably, few studies have examined the interaction between the carbon and electricity markets using system dynamics models, highlighting a research gap in this area. This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model. First, an index system for benefit evaluation was constructed from six aspects: financing ability, economic benefit, reliability, social responsibility, user satisfaction, and carbon-emissions. A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises. The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises. This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market.

Research Progress on Bacteria-Reducing Pretreatment Technology of Meat.

Reducing the initial bacteria number from meat and extending its shelf life are crucial factors for ensuring product safety and enhancing economic benefits for enterprises. Currently, controlling enzyme activity and the microbial survival environment is a common approach to reducing the rate of deterioration in raw meat materials, thereby achieving the goal of bacteria reduction during storage and preservation. This review summarizes the commonly used technologies for reducing bacteria in meat, including slightly acidic electrolyzed water (SAEW), organic acids, ozone (O3), ultrasound, irradiation, ultraviolet (UV), cold plasma, high-pressure processing (HPP), and biological bacterial reduction agents. This review outlines the mechanisms and main features of these technologies for reducing bacteria in meat processing. Additionally, it discusses the status of these technologies in meat storage and preservation applications while analyzing associated problems and proposing solutions. The aim is to provide valuable references for research on meat preservation technology.

Genome-wide detection of multiple variants associated with teat number in French Yorkshire pigs

BackgroundTeat number is a vital reproductive trait in sows, crucial for providing immunity and nutrition to piglets during lactation. However, “missing heritability” in Single Nucleotide Polymorphism (SNP)-based Genome-Wide Association Studies (GWAS) has led to an increasing focus on structural variations in the genetic analysis of complex biological traits.ResultsIn this study, we generated a comprehensive CNV map in a population of French Yorkshire pigs (n = 644) and identified 429 CNVRs. Notably, 44% (189 CNVRs) of these were detected for the first time. Subsequently, we conducted GWAS for teat number in the French Yorkshire pig population using both 80K chip and its imputed data, as well as a GWAS analysis based on CNV regions (CNVR). Interestingly, 80K chip GWAS identified two SNPs located on Sus scrofa chromosome 5 (SSC5) that were simultaneously associated with Total Teat Number (TTN), Left Teat Number (LTN), and Right Teat Number (RTN). The leading SNP (WU_10.2_5_76130558) explained 3.33%, 2.69%, and 2.67% of the phenotypic variance for TTN, LTN, and RTN, respectively. Moreover, through imputed GWAS, we successfully identified 30 genetic variants associated with TTN located within the 73.22 -73.30 Mb region on SSC5. The two SNPs identified in the 80K chip GWAS were also located in this region. In addition, CNVR-based GWAS revealed three significant CNVRs associated with TTN. Finally, through gene annotation, we pinpointed two candidate genes, TRIM66 and PRICKLE1, which are related to diverse processes such as breast cancer and abnormal vertebral development.ConclusionsOur research provides an in-depth analysis of the complex genetic structure underlying teat number, contributing to the genetic enhancement of sows with improved reproductive performance and, ultimately, bolstering the economic benefits of swine production enterprises.

Utilizing Enterprise Economic Benefit Evaluation Methods in Edge Intelligent Neural Network Applications

The core of enterprise economic benefit evaluation lies in the development of a quantitative identification model. The Back Propagation (BP) neural network possesses robust parallel computing, adaptive learning, and error correction capabilities, which can effectively reveal the economic benefits of enterprises and their relationship with influencing factors. This study establishes an economic benefit evaluation model for express delivery enterprises based on the BP neural network. The model takes the annual profit rate of enterprises as the quantitative index of economic benefits and selects 13 factors, both external and internal, influencing the annual profit rate of express delivery enterprises as inputs for the BP neural network model. The economic benefit evaluation model based on BP neural network meets the requirement of objective mean square error in the 300th training cycle. The research results demonstrate that the BP model significantly saves computing time and enables rapid, comprehensive, and objective evaluation of the economic benefits of industrial enterprises.

Insulation life prediction of stacking multi-model fusion motors based on Conv1d-LSTM

Abstract The insulation system of the mine motor is key to ensuring its stable operation, which affects the efficiency of mine production and transportation and the benefit of enterprises. The prediction of motor insulation life can effectively prevent the deterioration of motor performance. Due to the complex underground environment, many factors affect the insulation of coal mine motors, which are not conducive to the selection of feature parameters. The insulation life prediction efficiency and accuracy of motors need to be improved. To solve this problem, this paper proposes a prediction method based on Conv1d (one-dimensional convolve) and LSTM (long short-term memory neural network), which integrates multiple models and the Stacking integrated algorithms. The operating data and environmental parameters of a coal mine motor under different conditions are analyzed. Three Conv1d-LSTM hybrid models with batch normalization, residual network, and dense number are used as the base learner, and the results are weighted by K-fold to verify the accuracy of the three models. The linear regression model is used as a meta-learner to prevent overfitting of the model and effectively improve the speed and accuracy of single network optimization. Compared with the traditional method, the results show that the optimization of the LSTM neural network by the multi-feature model has a higher accuracy in predicting the remaining life of motor insulation, which provides a new way for the prediction of motor insulation life.

Overviewing Challenges and Solutions in Lean Manufacturing Implementation for Domestic Enterprises

Companies keep focus on enhancing the quality, efficiency, and customer satisfaction of their products and services while at the same time increasing their profits. Lean Manufacturing emerges as a potent strategy to address these imperatives via identifying and eliminating losses. Hence, it leads to optimized production processes and enhances overall performance of enterprises. This study presents an overview and analysis of lean manufacturing principles, implementation strategies, challenges, and economic benefits for Ukrainian enterprises. The main hypostatize of the paper was to investigate whether the Lean manufacturing approach is associated with different scope of businesses and whether it brings efficient outcomes for all of them. Based on conducted structured literature review the study highlights the versatility of lean methodologies across various industries and administrative processes. Employing scientific methods like comparative analysis and theoretical generalization, the research offers insights into the outcomes of lean implementations, including cost savings, productivity enhancements, and improved product quality for business organizations. The main outcomes of study underscore the critical role of strong leadership commitment and organizational culture in driving successful lean transformations. Through overviewing case studies and practical insights, the article illustrates real-world applications of lean manufacturing principles from different industries, exemplified by organizations AeroStar, Nova Poshta and Boeing. The paper is theoretical. However, the study advocates the practical importance of adoption of lean principles to achieve operational excellence for companies regardless of their scope of operating activities. Also, it offers suggestions for future research to explore emerging technologies' impact and cross-cultural comparisons in lean manufacturing practices.

Optimization of Flotation Conditions for Long-Flame Coal Mud by Response Surface Method

With the application of modern coal mining technology and the fact that there are fewer and fewer high-quality coal seams, the quality enhancement and utilization of low-rank coal are gaining more and more attention. To solve the problems of high consumption of chemicals and low recovery of refined coal in the flotation separation process of low-rank coal, the long-flame coal from the Inner Mongolia Autonomous Region of China was selected as the research object, and the factors affecting the flotation process were analyzed and optimized by adopting the response surface method and establishing a regression model with high precision and reliability. The test results showed that the primary and secondary relationships of the factors on the fine coal yield were as follows: slurry concentration > frother dosage > collector dosage; and the primary and secondary relationships of the factors on the flotation refinement index were as follows: slurry concentration > collector dosage > frother dosage. The optimal conditions for flotation were 2453.09 g/t of collector, 795.84 g/t of frother, and 50.04 g/L of slurry concentration. Under these conditions, the fine coal yield was 51.51%, and the relative error of 53.71% was 4.27%. The flotation refinement index was 21.34%, and the relative error with the predicted value of 21.58% was 1.12%. The relative error of the experimental results was within a reasonable range, which indicated that the regression model obtained by the response surface method was highly reliable. The research results are of great significance to strengthen the comprehensive utilization of long-flame coal in full particle size and improve the economic benefits of coal enterprises.

Analysis of Safety Management Issues and Countermeasures for Construction Operations of Petroleum Refining and Chemical Equipment

The economic operation in modern society is closely related to petroleum, and petroleum refining products play a great role in various industries such as military, transportation, agriculture, light industry, and textile industry. Refining and chemical enterprises belong to fixed asset intensive enterprises, among which the equipment used for oil refining is the most important asset in fixed assets. Improving the operational safety of equipment plays an extremely important role and significance in improving and enhancing the economic benefits of enterprises. This article starts with the analysis of safety hazards at construction sites and explores potential risk factors such as human factors and equipment failures. A series of safety management measures are proposed to address safety issues, improving the effectiveness of safety management by establishing a sound safety management system, applying advanced safety equipment and technology, and developing effective emergency response and post-disaster recovery plans.