Processing Methods Research Articles

The Power of Culture: Business Nationalist Culture and ESG Performance

High-quality development is the theme of China’s economic and social development in the new era, and corporate ESG performance is a comprehensive indicator for evaluating the level of corporate environmental responsibility, social responsibility and governance, as well as an important yardstick for identifying the high-quality development of enterprises. This paper takes Chinese non-financial listed companies from 2011 to 2022 as the research sample and empirically examines the impact of corporate nationalism culture on corporate ESG performance and its mechanism by quantifying corporate nationalism culture using the text of corporate annual reports, natural language processing and text analysis methods. The results of the study show that corporate nationalism culture significantly enhances corporate ESG performance. The mechanism analysis suggests that corporate nationalism culture, as an internal informal system, can play a governance role and promote corporate ESG practices by changing attention allocation and mitigating agency problems. The positive effect of corporate nationalism culture on corporate ESG performance is more pronounced in the grouping of firms with lower institutional investor shareholding, fewer analysts’ attention and embedded party organisations. A heterogeneity analysis reveals that the corporate nationalism culture driving effect on corporate ESG performance is more significant in the subsample of firms with weak financing constraints, in the growth period and in the decline period. This study reveals the positive role of soft cultural factors in enhancing corporate ESG performance, providing useful managerial evidence for companies to integrate ESG concepts at the strategic level for high-quality economic development.

Cost-Effective Thermomechanical Processing of Nanostructured Ferritic Alloys: Microstructure and Mechanical Properties Investigation.

Nanostructured ferritic alloys (NFAs), such as oxide-dispersion strengthened (ODS) alloys, play a vital role in advanced fission and fusion reactors, offering superior properties when incorporating nanoparticles under irradiation. Despite their importance, the high cost of mass-producing NFAs through mechanical milling presents a challenge. This study delves into the microstructure-mechanical property correlations of three NFAs produced using a novel, cost-effective approach combining severe plastic deformation (SPD) with the continuous thermomechanical processing (CTMP) method. Analysis using scanning electron microscopy (SEM)-electron backscatter diffraction (EBSD) revealed nano-grain structures and phases, while scanning transmission electron microscopy (STEM)-energy dispersive X-ray spectroscopy (EDS) quantified the size and density of Ti-N, Y-O, and Cr-O fine particles. Atom probe tomography (APT) further confirmed the absence of finer Y-O particles and characterized the chemical composition of the particles, suggesting possible nitride dispersion strengthening. Correlation of microstructure and mechanical testing results revealed that CTMP alloys, despite having lower nanoparticle densities, exhibit strength and ductility comparable to mechanically milled ODS alloys, likely due to their fine grain structure. However, higher nanoparticle densities may be necessary to prevent cavity swelling under high-temperature irradiation and helium gas production. Further enhancements in uniform nanoparticle distribution and increased sink strength are recommended to mitigate cavity swelling, advancing their suitability for nuclear applications.

A novel application of laser speckle imaging technique for prediction of hypoxic stress of apples

BackgroundFruit storage methods such as dynamic controlled atmosphere (DCA) technology enable adjusting the level of oxygen in the storage room, according to the physiological state of the product to slow down the ripening process. However, the successful application of DCA requires precise and reliable sensors of the oxidative stress of the fruit. In this study, respiration rate and chlorophyll fluorescence (CF) signals were evaluated after introducing a novel predictors of apples' hypoxic stress based on laser speckle imaging technique (LSI).ResultsBoth chlorophyll fluorescence and LSI signals were equally good for stress detection in principle. However, in an application with automatic detection based on machine learning models, the LSI signal proved to be superior, due to its stability and measurement repeatability. Moreover, the shortcomings of the CF signal appear to be its inability to indicate oxygen stress in tissues with low chlorophyll content but this does not apply to LSI. A comparison of different LSI signal processing methods showed that method based on the dynamics of changes in image content was better indicators of stress than methods based on measurements of changes in pixel brightness (inertia moment or laser speckle contrast analysis). Data obtained using the near-infrared laser provided better prediction capabilities, compared to the laser with red light.ConclusionsThe study showed that the signal from the scattered laser light phenomenon is a good predictor for the oxidative stress of apples. Results showed that effective prediction using LSI was possible and did not require additional signals. The proposed method has great potential as an alternative indicator of fruit oxidative stress, which can be applied in modern storage systems with a dynamically controlled atmosphere.

Industrial IoT-Based Energy Monitoring System: Using Data Processing at Edge

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day.

Zero Produced Water Discharge Innovation in Production Fluid Management for Sustainability and Financial Efficiency

This paper will present the sustainability initiatives of PHI Tanjung Field obtained from the evaluation of existing oil and gas production activities. Through the Plan-Do-Check Action (PDCA) approach, based on the results of life cycle assessment and focus group discussions from managers and engineers in Tanjung Field, the identification of priority problems is related to the management of production fluid generated from outside the existing production facility process. Through a fishbone diagram approach to find out the root of the problem followed by Failure Mode Effect Analysis (FMEA) including Pareto diagrams and risk map analysis to get priority risk problems in the form of production fluid processing methods. Furthermore, through the Analytic Hierarchy Process (AHP) approach, the alternative solution taken is to modify the production fluid processing with CSSR (Contain, Sediment, Separate, Recovery) innovation. The results of this CSSR innovation are no more produced water is discharged into the environment so that the Tanjung Field Manager’s policy of moving towards zero produced water discharge is achieved. With this innovation, the processing efficiency of the production fluid in 2nd year (prediction) after innovation is 97.5%, up from 66.6% in initial year before innovation. By optimizing the use of produced water, the need for clean water for the injection program can be minimized. The reduction of clean water usage from Initial year to 2nd year (prediction) is 83.1%. The efficiency ratio of this innovation is 11.10 > 1 indicates that the program generates more savings and revenue than its cost, making it a worthwhile investment. In energy reduction, the effectiveness of this innovation from Initial year to 2nd year (prediction) is reducing energy by 94.96%. In emission reduction, the reduction of CO2 emission load from Initial year to 2nd year (prediction) is 95%. This innovation contributes to the 6th Sustainable Development Goal (SDGs) “Clean Water and Sanitation,” the 7th SDGs “Clean and Affordable Energy,” the 12th SDGs “Responsible Consumption and Production,” 13th SDGs “Handling Climate Change,” and the 14th SDGs “Ocean Ecosystems.”

Gradation optimization of AC-20 asphalt mixture based on the fuzzy analytic hierarchy process and comprehensive evaluation method

The aggregate gradation of asphalt mixture is one of the most important factors affecting the service life of asphalt pavement. In order to study the gradation of asphalt mixture with the best comprehensive performance, this study puts forward a new method of mineral aggregate gradation optimization based on the fuzzy analytic hierarchy process and comprehensive evaluation method, aiming at the multi-level and multi-index evaluation of the road performance of asphalt mixture. First, the orthogonal test design method is applied to design nine target gradations with coarse aggregate (>4.75 mm) and fine aggregate (<4.75 mm) of AC-20 (asphalt concrete) mixture serving as two factors and the upper, middle, and lower positions of the gradation curve as three levels, and then the road performance test research is carried out. Second, a comprehensive model for evaluation of road performance of mineral aggregate gradation is established. A fuzzy complementary judgment matrix is constructed, and the index weights of each level and the hierarchical total ranking weight are calculated. Then, the membership function is introduced into the comprehensive evaluation model for the road performance of mineral aggregate gradation, and the membership values of each index of asphalt mixture road performance are obtained. Finally, the fuzzy comprehensive evaluation method is used to find out the comprehensive evaluation value of the road performance of the nine graded asphalt mixtures, and the mineral aggregate gradation is optimized. The research results show that the 1# gradation of the asphalt mixture has the highest comprehensive road performance evaluation value, and the combination of the fuzzy hierarchical analysis process and comprehensive evaluation method can more objectively and comprehensively evaluate the comprehensive road performance of the asphalt mixture and provide a useful reference for the optimal selection of asphalt mixture mineral gradation.

Bacterial Cellulose Production within a Circular Economy Framework: Utilizing Organic Waste.

Bacterial cellulose (BC) has emerged as a sustainable biomaterial with diverse industrial applications. This paper examines BC production through a circular economy framework, focusing on organic waste as a primary feedstock. It compares static and agitated cultivation methods for BC production, highlighting their advantages and limitations. Static cultivation using Gluconacetobacter xylinum yields high-quality cellulose films but is constrained by lower yields and longer incubation times. Agitated cultivation accelerates production but may affect fiber uniformity. This paper emphasizes sustainability by exploring organic waste materials such as coffee grounds, tea leaves, and food scraps as cost-effective nitrogen and carbon sources. These materials not only lower production costs but also support circular economy principles by converting waste into valuable products. BC produced from these waste sources retains key properties, making it suitable for applications in the textile and other industries. In addition, BC production can align with vegan principles, provided that all additives and processing methods are free of animal-derived components. The paper discusses BC's potential to replace synthetic fibers in textiles and reduce environmental impact. Case studies show successful BC integration into textile products. In conclusion, this paper calls for more research to optimize BC production processes and explore new industrial applications. Using organic waste in BC production can help industries adopt sustainable practices, reduce environmental footprints, and create high-value materials.

Analysis of the Effects of Different Synthesis and Processing Methods on Circular RNA Integrity, Protein Expression, and Removal of Immunogenic Impurities

Analysis of the Effects of Different Synthesis and Processing Methods on Circular RNA Integrity, Protein Expression, and Removal of Immunogenic Impurities

Development of a Quality Evaluation Method for Allii Macrostemonis Bulbus Based on Solid-Phase Extraction–High-Performance Liquid Chromatography–Evaporative Light Scattering Detection Chromatographic Fingerprinting, Chemometrics, and Quantitative Analysis of Multi-Components via a Single-Marker Method

As a traditional Chinese medicine (TCM), Allii Macrostemonis Bulbus (AMB) is a key herb for the treatment of thoracic paralytic cardiac pain, but its quality evaluation method has not yet been fully clarified. In this study, chromatographic fingerprints of AMB were developed using solid-phase extraction–high-performance liquid chromatography–evaporative light scattering detection (SPE-HPLC-ELSD) to evaluate the quality of AMB from various origins and processing methods. This was achieved by employing chemical pattern recognition techniques and verifying the feasibility and applicability of the quality evaluation of AMB through the quantitative analysis of multi-components via a single-marker (QAMS) method. Through the analysis of the fingerprints of 18 batches of AMB, 30 common peaks were screened, and 6 components (adenosine, syringin, macrostemonoside T, macrostemonoside A, macrostemonoside U, and macrostemonoside V) were identified. Moreover, three differential markers (macrostemonoside A, macrostemonoside T, and macrostemonoside U) were screened out using chemometrics techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Subsequently, a QAMS method was established for macrostemonoside T and macrostemonoside U using macrostemonoside A as an internal reference. The results demonstrate the method’s accuracy, reproducibility, and stability, rendering it suitable for the quality evaluation of AMB. This study provides a theoretical basis for drug quality control and the discovery of quality markers for AMB.

Значение руководителя служебного коллектива органа внутренних дел в формировании нормативного поведения его участников: эмпирическое исследование

Introduction. Increasing the effectiveness of the activity of the internal affairs bodies of the Russian Federation in modern conditions focuses on psychological factors of its implementation both individually (by particular employees) and in the process of cooperative actions (by service staffs). Special consideration should be given to the role of the head as an organiser and regulator of the activities of subordinate employees, who is responsible for the quality and legality of their actions. Existing ideas on the interaction of service staff members characterise the role of the head in different ways, but empirically insufficiently verify its significance in teams with different levels of functional relations and normative behaviour. The prerequisite of the present research is the replenishment of scientific and practical knowledge of the role of the head of the service staff as a regulator of functional relations and normative behaviour of internal affairs officers. The purpose of the research is to reveal the significance of the attitude to the head of the service staff when assessing the influence of functional characteristics of relations in the service staff on the understanding normative behaviour among its members. Research methods. A set of psychodiagnostic questionnaires was used: ‘Subjective assessment of intragroup relations’ (S. V. Dukhnovsky, K. V. Zlokazov), ‘Attitude of an employee to the performed activity, his direct supervisor and his reputation’ (A. A. Sarsenova), ‘Normativity. Intelligence. Regulation’ (S. V. Dukhnovsky, V. M. Kofanov). The sample of the research consisted of 160 employees of internal affairs bodies (112 males and 48 females aged from 29 to 43 years). Regression analysis was used as a method of data processing. Results. The authors found out that the normative behaviour of service team members is determined by functional-negative characteristics of their interaction and is an independent cause of service discipline violations. This fact testifies that negative forms of interaction of service team members (aimlessness, uncontrollability, connivance) as well as relations (hostility, negativism and conflict) reduce the level of normative service behaviour. It is determined that the attitude of employees to the head of the service team mediates the influence of functional-negative characteristics, acting as a regulator of their service behaviour. The results of the research expand scientific ideas on psychology of management of formal groups (by the example of service staffs) and can be used in preparation of employees of internal affairs bodies for positions of junior and middle supervising staffs.

Novel numerical simulation method for quasi-continuous wave laser processing of carbon-carbon composites and its application in parameter inversion

Novel numerical simulation method for quasi-continuous wave laser processing of carbon-carbon composites and its application in parameter inversion

A systematic risk assessment approach for urban roadside infrastructure assets

Revealing potential risks and the influence of these risks on urban roadside vulnerable/risky zones has been a great deal for the last decades owing to increasing vehicular mobility. This study aimed to develop a risk assessment methodology for urban roadside infrastructure assets. In this context, seven risk parameters that are likely to affect hazardous aboveground assets installed on urban roadsides were determined, and weights of each parameter (order of importance) were presented based on expert surveys and literature information using the Analytic Hierarchy Process (AHP) method. Mathematical models were set up for each risk parameter using real field data, The American Association of State Highway and Transportation Officials (AASHTO), literature, related standards, and expert surveys by linear, nonlinear, and binary logistic regression analyses. Risk groups were created, and precautions were offered. The efficiency of the models was verified based on the 28 accident records and field observations in 172 assets. When considering the assets that undergo traffic accidents, 71% of the total assets are ranked in the critical and high-risk group. This ratio is consistent with the risk group definitions assigned in this study; thus, the model proposed is accurate and can be applied reliably. The verification process of the total risk model was sufficiently successful to be used in practice and can be generalized by considering the road and traffic characteristics, infrastructure facilities, and future requirements of the regions or countries that reflect similar historical and cultural concerns. Practitioners, governmental institutes, and researchers can apply this methodology to provide their own data with high transparency and reliability.

Transparent MXene Microelectrode Arrays for Multimodal Mapping of Neural Dynamics.

Transparent microelectrode arrays have proven useful in neural sensing, offering a clear interface for monitoring brain activity without compromising high spatial and temporal resolution. The current landscape of transparent electrode technology faces challenges in developing durable, highly transparent electrodes while maintaining low interface impedance and prioritizing scalable processing and fabrication methods. To address these limitations, we introduce artifact-resistant transparent MXene microelectrode arrays optimized for high spatiotemporal resolution recording of neural activity. With 60% transmittance at 550 nm, these arrays enable simultaneous imaging and electrophysiology for multimodal neural mapping. Electrochemical characterization shows low impedance of 563 ± 99 kΩ at 1 kHz and a charge storage capacity of 58 mC cm⁻² without chemical doping. In vivo experiments in rodent models demonstrate the transparent arrays' functionality and performance. In a rodent model of chemically-induced epileptiform activity, we tracked ictal wavefronts via calcium imaging while simultaneously recording seizure onset. In the rat barrel cortex, we recorded multi-unit activity across cortical depths, showing the feasibility of recording high-frequency electrophysiological activity. The transparency and optical absorption properties of Ti₃C₂Tx MXene microelectrodes enable high-quality recordings and simultaneous light-based stimulation and imaging without contamination from light-induced artifacts.

Current Status of Research on Fault Diagnosis Using Machine Learning for Gear Transmission Systems

Gear transmission system fault diagnosis is crucial for the reliability and safety of industrial machinery. The combination of mathematical signal processing methods with deep learning technology has become a research hotspot in fault diagnosis. Firstly, the development and status of gear transmission system fault diagnosis are outlined in detail. Secondly, the relevant research results on gear transmission system fault diagnosis are summarized from the perspectives of time-domain, frequency domain, and time-frequency-domain analysis. Thirdly, the relevant research progress in shallow learning and deep learning in the field of fault diagnosis is explained. Finally, future research directions for gear transmission system fault diagnosis are summarized and anticipated in terms of the sparsity of signal analysis results, separation of adjacent feature components, extraction of weak signals, identification of composite faults, multi-factor combinations in fault diagnosis, and multi-source data fusion technology.

FUZZY MODEL OF ELECTRICITY CONTROL WITH WIRELESS INFORMATION PROCESSED ON GPU

Methods of processing information transmitted through wireless networks with software development are investigated in the work. Innovative methods of data transmission such as optical technologies, quantum data transmission and wireless data transmission technologies are disclosed. It is noted that in the modern understanding, the concept of distributed computing defines the process of convergence (convergence) of distributed processing methods, such as GRID, cloud and fog computing, with the combination of virtual cluster systems (grid clusters, cloud clusters and fog clusters) into a single information communication and computing system . It is emphasized that, unlike cellular modems, ZigBee technology nodes have microcontrollers with a pre-installed operating system and flash memory, which allows solving simple computational tasks in real time before sending data. It is advisable to solve such tasks within the framework of a multi-agent approach, which will increase the efficiency of the use of sensor nodes and the entire sensor network. The advantages of the multi-agent technology of fog computing based on sensor nodes of the wireless network of the ZigBee standard are revealed. The method of multi-agent processing of sensory information and its main components are described. The architecture of the system of distributed sensor data processing is outlined, which includes 4 hardware and software levels: Terminal sensor nodes and controllers of measuring devices and automation devices that implement fuzzy calculations; Coordinators, sensor segment routers and cellular modems that collect, protect and transmit sensor data to the processing center; A data processing center that includes a cluster of servers for GRID calculations and a cloud data storage server; Client devices to access cloud storage, computing cluster servers, and distributed fog computing terminals. It is emphasized that indicators and forecast results can be stored on distributed sensor nodes or transmitted for accumulation in cloud storage for further extraction and intelligent processing in the GRID cluster of the data center.

Effects of different processing methods on the lipid composition of seabuckthorn fruit oil based on lipidomics.

Employing lipidomics, this study investigated the lipid composition of seabuckthorn fruit oil processed via supercritical CO2 extraction and centrifugal separation. Qualitative analysis showed that a total of 2861 lipid molecules were identified in seabuckthorn fruit oil. Quantitative analysis showed that the content of lipids in seabuckthorn fruit oil extracted by supercritical CO2 extraction (927,539.84µg/mL) was significantly higher than that in centrifugal-separated seabuckthorn fruit oil (735,717.63µg/mL), with 17 distinct lipid classes and 215 lipid molecules differentiated through multivariate statistical analysis. Lipid molecules, such as diacylglycerol (DG), ceramides (Cer), monohexosyl ceramide, phosphatidylglycerol, phosphatidylinositol, phosphatidylethanolamine, and monogalactosyl DG, were predominantly found in the oil extracted using supercritical CO2. In contrast, monogalactosyl monoacylglycerol, diglycosyl ceramide, and Cer phosphate were significantly present in the oil extracted by centrifugal separation. These findings contribute new insights into how processing methods affect the quality and composition of seabuckthorn fruit oil and provide a basis for detecting oil adulteration.

Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Local Mean Suppression Filter for Effective Background Identification in Fluorescence Images.

We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.

Determining the best alternative to build and operate a new coal crushing facilities: Analytical hierarchy process approach

Purpose: This study examines the increase in national coal production in Indonesia and its impact on PT. KLM is one of the largest coal producers in the country. With rising production and shifting global market needs, this study aims to analyze the necessity of building a new Coal Crushing Facility next to the South Pinang Extension #2 area, including evaluating costs and the best strategies for its implementation. Methods: The research methodology involves analyzing problem trees and stakeholders to uncover business complexities and identify the root causes of issues. Qualitative data were collected using semi-structured interviews. All alternatives were evaluated using Value-Focused Thinking (VFT), and the Analytic Hierarchy Process (AHP) method was employed to determine the best alternative assisted by the Super Decision application. Results: Based on interviews conducted with subject matter experts (SMEs) and the analysis, three funding solutions were identified: 1. owned by self-financing, and 2. Own by Leasing 3. Rental Scheme. Limitations: The limitations of this study are that it was sourced from an internal company and gathered from outside the company. An alternative option was produced based on extensive collaborative discussions and interview sessions conducted with subject matter experts within the company. Contributions: This study provides valuable guidelines for selecting alternative financing to build and operate new coal crushing facilities at South Pinang Extension #2.