Effective Analysis Method Research Articles

Analysis of Repetitive Positioning Accuracy of Parallel Mechanisms Based on Monte Carlo Method

Based on the inverse kinematics model of the Stewart parallel mechanism, a motion platform pose error model was constructed through differential transformation. The error model shows that the motion platform pose error comes from the length error of 6 struts and the position error of 12 hinge centers, with a total of 42 error terms. Monte Carlo simulation was used to analyze the effects of struts length error on the pose error of the moving platform. The results show that when the length error of the struts follows a normal distribution, the posture error of the moving platform approximately follows a normal distribution. Monte Carlo simulation is a highly effective error analysis method that can analyze the error distribution pattern of the mechanism before processing and assembly, which provides a method for error analysis of Stewart parallel mechanisms.

The Recommendations to Improve Quality of Kitchen Set Products Using New Failure Mode and Effect Analysis and Fault Tree Analysis Methods at a Furniture Company in Bandung

The company had a problem regarding product quality, especially on kitchen set products. Based on data for the last six months, reveals a consistent surpassing of acceptable limits for product defects every month. This causes negative consequences, encompassing both time and cost setbacks, along with a decline in consumer confidence in the company. Therefore, there was a compelling necessity to enhance the overall quality of the products. Two methodologies, New FMEA and FTA, were used to pursue the objective. A New FMEA was used to indicate which type of defect will be prioritized (using Action Priority (AP)) and a FTA was used to identify the root cause. The process of creating a kitchen set involved 11 steps, with defect occurrences noted in 8 of these steps. Based on AP high criteria, there were 6 failure modes (from 20) that will be singled out for root cause analysis through FTA. Based on the FTA, 14 root causes require attention. The proposed recommendations involved training, material inspection, material protection, periodic maintenance, operational procedures, replacement of measuring tools, and redesign of the workstation environment. Simulations were carried out to compare before and after (if the recommendations are applied) of AP by changing the occurrence and detection values. The simulation showed a decrease in the AP classification from high to moderate, suggesting a positive impact on product quality.

A DECISION MODEL FOR THE ASSESSMENT OF ENVIRONMENTAL SUSTAINABILITY RISKS OF THE AUTOMOTIVE INDUSTRY

The automotive industry is increasing its competitive power by undergoing more and more technological and digital changes every day. Along with this competitiveness, the concepts of sustainability, ethics, and reputation come to the fore. Sustainability includes social, economic, and environmental issues that meet all the needs of future generations and use limited resources efficiently. In this study, the environmental and sustainable supply chain risks of the automotive industry are examined through automotive industry reports, expert opinions, and a literature review. After nine risks were identified, 14 alternatives were developed to prevent them. In the study, a multi-level risk assessment method using Failure Modes and Effects Analysis and Entropy-based EDAS method was introduced and an application was carried out in the automotive sector. According to the entropy-based EDAS method, the most important alternative has been “The top management's determination of sustainability commitments and targets.

Ground temperature monitoring at the meteorological observatory site of the Lomonosov Moscow State University

According to international climate reports, global warming is not slowing down, and the summer of 2024 will be the hottest on record in the Northern Hemisphere, with an anomalous temperature increase of 0.68°C compared to the 1991-2020 average. The summer of 2024 is particularly marked by an increase in temperature in northern Russia, causing permafrost to thaw. To monitor the response of permafrost to climate change, a monitoring system based on Roshydromet meteorological stations is being established in the Russian Federation, with more than 140 sites planned to be installed by 2025. The monitoring methodology will comply with Russian GOSTs and international standards. The MSU meteorological observatory is also equipped with thermometric boreholes to collect data on ground temperature at the site with natural cover and at the site without natural cover. The results of observations at these thermometric boreholes are compared with mathematical models of the thermal state of the ground at these sites. The results of the observations and mathematical modeling have shown effective methods of analysis and will make it possible to obtain meaningful results on the thermal state of the ground depending on the presence of natural cover.

Molecular aggregation by hydrogen bonding in cold-crystallization behavior of mixed nucleobases analyzed by temperature-controlled infrared spectroscopy.

The cold-crystallization behaviors of dodecyl-substituted nucleobases (adenine, uracil, and thymine) were analyzed. The dodecyl derivative from uracil alone did not exhibit cold crystallization; however, a mixture of adenine and uracil derivatives at a molar ratio of 1 : 1 exhibited cold crystallization. These results are similar to the thermal behavior of dodecyl derivatives of adenine and thymine alone and in mixtures reported in a previous study. Temperature-controlled infrared spectroscopy was used to observe the molecular assembly states of the liquid, supercooled state, and cold-crystallized compounds. Hydrogen-bonded molecular pairs in the high-temperature liquid state, multiple hydrogen-bonded networks in the supercooled state, and reverse Hoogsteen-type complementary hydrogen bonds in cold-crystallized compounds were observed using infrared spectroscopy. The heterogeneity of the system, due to multiple types of hydrogen bonding, retarded the crystallization rate, resulting in supercooling and cold crystallization. Infrared spectroscopy, which can be used to measure the aggregation state of molecules, including the liquid and supercooled states, is an effective analytical method for clarifying the process of cold crystallization.

Reduced order calculation method of subsynchronous oscillation mode of PMSG based wind farm connected with power grid based on Arnoldi method

Large scale wind turbine connected to the power grid is easy to induce subsynchronous oscillation. “Curse of dimensionality” will be encountered when the subsynchronous oscillation mode is analysed and evaluated based on the full-order model of the wind farm. It is urgent to research the fast and effective analysis method of subsynchronous oscillation mode when the wind farm is connected to the power grid. Therefore, a reduced order calculation method of the subsynchronous oscillation mode of PMSG based wind farm connected with a power grid based on the Arnoldi method is proposed. This method can quickly obtain the eigenvalues corresponding to the subsynchronous oscillation mode through iterative approximation when PMSG-based wind farm connected to the power system. This can be used to analyze the subsynchronous oscillation of large-scale wind farms connected to the power grid and provide an effective means for the evaluation of it. By changing system stability, collector network structure, and wind farm interconnection scale, the accuracy of the eigenvalues distribution between the full-order system and the reduced order system are compared. Moreover, the reduced order and computation time are considered. The results present that the proposed method can quickly and accurately obtain the subsynchronous oscillation mode in the system.

Cost-effectiveness of dialysis and kidney transplantation to treat end-stage renal disease in Ukraine.

Aim: To determine the economic feasibility of using kidney transplantation compared to hemodialysis in end-stage renal disease in the long term in countries with a low and medium level of economic development using the example of Ukraine. Materials and Methods: The cost effectiveness analysis method was used. Conducted Markov modeling and comparison of the consequences of kidney transplantation and hemodialysis in terms of treatment costs and the number of added years of life for a cohort of 1,675 patients were carried out. The incremental cost-effectiveness ratio is defined. Results: Based on the results of modeling, it was determined that among 1,675 patients with end-stage kidney disease in Ukraine, 1,248 (74.5%) will remain alive after 10 years of treatment when kidney transplantation technology is used. The highest costs will be in the first year ($25,864), and in subsequent years - about $5,769. With the use of hemodialysis technology, only 728 patients (43.5%) will be alive after 10 years, the cost of treating one patient per year is $11,351. The use of kidney transplantation adds 3191 years of quality life for 1675 patients compared with hemodialysis (1.9 years per patient). Conclusions: Kidney transplantation is an economically feasible technology for Ukraine, as the incremental cost-effectiveness ratio is $4694, which is 1.04 times higher than Ukraine's GDP per capita. The results of the study allow us to recommend that decision-makers in countries with a low and medium level of economic development give priority in financing to renal transplantation.

Research on the Mechanism and Intervention of Cross-cultural Psychological Adaptation of International Students in the Context of Digitization

Abstract The management and social problems caused by the psychological problems of international students have become more and more serious, and cross-cultural adaptation has a very important influence on the integration of international students into new environments and cultures, which has been increasingly concerned by the education circles at home and abroad. In this paper, we investigated the psychological adjustment ability and demographic characteristics of international students and analyzed the correlation between different factors and cross-cultural psychological adjustment ability based on the statistical results. After analyzing different groups of international students with different types of psychological adjustment abilities using the LPA method, a reasonable intervention plan is formulated. The mediation effect analysis method was also utilized to explore the impact and mechanism of interventions on psychological adaptation. The study’s results indicate that metacognition and cross-cultural psychological adjustment have a high correlation coefficient (0.773), which suggests that this factor can have a significant impact on the psychological adjustment of international students. It was also found that the intercultural psychological adjustment interventions for international students positively and significantly affected the intercultural psychological adjustment ability (B=0.085, P=0.032), indicating that the intervention program can improve the intercultural psychological adjustment ability of international students through psychological capital. This study provides a reference for the later targeted construction of intervention strategies to improve international students’ cross-cultural psychological adjustment and also paves the way for future studies to specifically analyze the mechanisms of international students’ cross-cultural psychological adjustment.

데이컴 기법을 활용한 지역자활센터 종사자 직무분석

The purpose of this study is to analyze jobs to strengthen the capabilities of local self-sufficiency center workers as the self-sufficient project environment changes rapidly. To this end, the effective DACUM job analysis method was applied to curriculum development based on field expert opinions. In addition, a survey was conducted on 102 people to determine the importance and difficulty of the identified jobs. As a result of the analysis, a total of 10 missions, 24 tasks, and 46 required competencies were derived, and priority training required competencies were presented. Based on these results, the following suggestions were made. First, the derived missions and tasks must be reflected within the self-sufficient project guidelines. Second, tasks that are difficult for individual self-sufficiency centers to handle require scale-up or specialized approaches. Third, if a task is difficult to carry out with the current workforce composition of the self-sufficiency center, additional manpower support is required. Fourth, a system must be established in which practical knowledge can be accumulated and educated. Lastly, education priorities must be determined considering the importance and the need of competency.

A product performance rapid simulation approach driven by digital twin data: Part 1. For variable product structures

In the product design stage, the effective performance analysis method is the premise to guarantee the application performance of products. However, digital simulation, as a widely used performance analysis method, has a long simulation cycle and high requirements for computer hardware performance, which leads to long product design and iteration time. Facing this problem, a product performance rapid simulation approach driven by digital twin (DT) data for variable product structures is proposed to replace the digital simulation method. Firstly, the framework for variable product structures is designed, which includes a digital simulation module, a data-driven module, and a surrogate-based simulation module. Then, module implementation of the proposed framework is introduced in detail, in which the twin data-driven product performance rapid simulation model (RSM) for variable product structures is studied based on the makeTwin reference architecture. Finally, a case study is conducted on the iterative design of a connecting rod, focusing on equivalent stress evaluation. The result shows that the proposed method is feasible, as well as costs a shorter simulation time than traditional digital simulation.

Ethylene oxide: methodological approaches to the determination of residues in food products (literature review)

Introduction. Ethylene oxide is a prohibited fumigant in the Russian Federation and the states of the European Union (EU), however, the substance is used in a number of other countries to disinfect food before they are sold, so there is a risk of such products entering the Russian market, which can lead to negative consequences for public health. The residues of the fumigant in consumed products can cause neurological disorders and the development of cancer in humans. In addition, the presence of 2-chloroethanol in foodstuffs, the main transformation product of ethylene oxide, which is formed during the interaction of the fumigant with the matrix, is of concern. 
 Material and methods. The analysis of domestic and foreign databases and regulatory legal acts on the regulation of the dangers of ethylene oxide and 2-chloroethanol was carried out.
 Results. The toxicological characterization of ethylene oxide and 2-chloroethanol shows the need to establish acceptable levels of contaminants in food, and, as a result, to create a simple and effective analytical control method. 
 Limitations.The study is limited to a review of open literature sources in describing the toxicological characterization and methods of analysis of ethylene oxide in food products.
 Conclusion. The analysis of open literature sources has shown the need for scientific substantiation of hygienic standards and the development of a method for controlling the content of ethylene oxide and 2-chloroethanol in food products.

STATE LAND MANAGEMENT: CASE STUDY IN VIET TRI CITY, PHU THO PROVINCE

The purpose of this study is to examine the existing state land management practices in Viet Tri City, Phu Tho province, with the aim of identifying potential solutions to enhance operational efficiency. The research findings have employed statistical analysis methods, comparison methods, and cause and effect analysis methods to assess the current state management of land in Viet Tri City from 2020 to 2022. This evaluation aims to identify the limitations and underlying causes associated with the management practices. Based on these findings, the study suggests six categories of solutions to enhance the effectiveness of state land management in this area: (1) Strengthening leadership, direction, close collaboration, and synchronization between levels and sectors for state land management; (2) Enhancing the scrutiny and supervision of land administration and utilization; (3) Establishing a system to publicly and openly disseminate information regarding rules and policies in the local land sector; (4) Better land dispute settlement, complaints, and denunciations; (5) Reforming administrative procedures and use science and technology in land management; (6) Boosting land management staff quality; (7) Promote the public's awareness of land policies and laws through propaganda and other means.

Analysis of the Potential Hazards of Work Accidents Department of Production Using Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA) Methods in PT. XYZ

PT. XYZ is a company engaged in the business of white crystal sugar with raw sugar cane in Jombang. As the industrial world develops, companies are required to produce products both in terms of quality and quantity. Therefore, the production process has the potential to experience work accidents. To minimize this from happening, this research identified the potential hazards of work accidents in the production division. The methods used are the Failure Mode Effect Analysis (FMEA) and Fault Tree Analysis (FTA) methods. Using the FMEA method produced 3 potential hazards with the highest RPN values namely potential for exposed hot ashes at the boiler station with an RPN of 33,716. In second place is the potential collapse of the crane at the mill station with RPN 27,192. Then in third place is the potential Explosion of pipes and tanks at the evaporation station with an RPN value of 23.549. With the FTA method, the 3 potential hazards that have been obtained are used as top events and an analysis is carried out to find out the root causes of the potential accidents that occur. The analysis is grouped from human, equipment, and environmental aspects. From the human factor, it is known that the cause was workers who were not concentrating, were negligent, and did not comply with SOPs. The equipment factor comes from the age of the old tool and lack of maintenance. As well as environmental factors, namely high temperature pressure and humidity which can have an impact on corrosion of iron equipment. So that the suggestions that can be given to companies are imposing sanctions on employees who do not comply with the rules, providing job training according to their respective fields, and scheduling regular machine maintenance.

Defect Analysis of ZA Fertilizer Products with Fault Tree Analysis (FTA) and Failure Mode Effect Analysis (FMEA) Methods at PT. XYZ

PT XYZ is one of the fertilizer producers in Indonesia. The problem that often occurs in the company is that there are product defects that cause the production process to be ineffective. One of the products that has a high percentage of defects is non-subsidized ZA fertilizer. Research using Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods to find out the causes of rejects or rejects in products and can provide preventive solutions so that these defects do not occur. In this study, 4 types of defects were found, namely failed weighing with a chance of defect occurrence of 0.138%, mixed color defect with a chance of defect occurrence of 0.115%, crystal clumping defect with a chance of defect occurrence of 0.115%, and defect mixed with dirt with a chance of defect occurrence of 0.116%, mixed color, crystal clumping, and mixed with dirt. The root causes of these defects include less careful operators, hasty operators, operators do not check the fertilizer bag machine needs maintenance, machine trouble, operators do not check the level on the machine regularly, poor air circulation, lack of cleaning facilities, operators work not according to SOP, lack of supervision, lazy operators, no barrier with other fertilizers, lack of lighting.

Local dynamic neural network for quantitative analysis of mixed gases

The Gas sensor array is commonly used in combination with quantitative analysis method for detecting mixed gases. Artificial neural network (ANN) is usually employed to achieve a quantitative analysis of the mixed gases. However, the current ANN models typically require a large number of test samples to obtain low relative errors. In this work, we fabricated a gas sensor array composed of four gas sensors (i.e., In2O3: NO2, Pd-ZnO: NH3, Au-SnO2: CH4, Pd-LaFeO3: CO2) and proposed a local dynamic neural network (LDNN) model for quantitative analysis of four mixed gases. By constructing and extracting features through a pre-trained autoencoder network, only a small sample size (25 local points) is input into the LDNN for training to realize the concentration prediction of four gases. The results show that the MAEs (mean absolute error) of the predicted concentrations of NO2, NH3, CH4, and CO2 are 0.01 ppm, 0.04 ppm, 0.13 ppm, and 42.67 ppm, while the MREs (mean relative error) are 0.19%, 0.85%, 1.17%, and 1.06%, respectively. Moreover, the MREs of the predicted concentrations for four gases are less than 2% within the 95% confidence interval. This work provides an effective quantitative analysis method with small sample size, simple structure and high-precision for the detection of four mixed gases.

Defect Analysis of Ceramic Products with Statistical Quality Control (SQC) and Failure Mode Effect Analysis (FMEA) Methods at PT. XYZ

Ceramic product company is one of the companies engaged in the ceramic industry. Ceramic product company produces various types of floor tiles with various sizes including sizes 40x40 cm, 50x50 cm, and 60x60 cm. Problems that arise at ceramic product company is that there are still defects in the 50x50 cm ceramic production line which is quite high, this certainly cannot be separated from the company's losses. Therefore, to reduce the level of defects in products, it needs to be supported by maintenance management and employee expertise. The purpose of this study is to determine product defects and propose improvements to reduce product defects. This research uses the Statistical Quality Control (SQC) method to control quality from the initial process to the finished product and Failure Mode Effect Analysis (FMEA) is used to identify and prevent problems that occur in products and processes. Based on the results of the analysis from October 2022 to March 2023, with a total production of 327,600 products, there are 56,543 defective products. Based on the results of the Pareto diagram for chipping before firing with a percentage of (34.3%), crack defects of (25.6%), chipping before vela of (21.2%), and ink drop defects of (18.9%). The calculation of RPN for ceramic production measuring 50x50 cm obtained several risks with the highest priority level, obtained the highest RPN of 288 from the type of crack defect with a proposed improvement, which emphasizes the operator to always pay attention to the running machine settings.

Multi-Objective Optimization of a Long-Stroke Moving-Iron Proportional Solenoid Actuator.

In this study, the performance of a long-stroke moving-iron proportional solenoid actuator (MPSA) was improved by combining numerical simulations and experiments. A finite element model of the MPSA was developed; its maximum and mean relative absolute errors of electromagnetic force were 4.3% and 2.3%, respectively, under typical work conditions. Seven design parameters including the cone angle, cone length, depth of the inner hole of the coil skeleton, cone width of the armature, inner cone diameter, and initial position of the moving-iron core were selected for developing the model, and the coefficient of the variation in electromagnetic force, nominal acceleration, 95% of the maximum stable output electromagnetic force, and corresponding response time were used as the performance indicators. The constraint relation between each performance indicator and the influence of each design parameter on the performance indicators were revealed using the uniform Latin hypercube experiment design, correlation analysis, and the main effect analysis method. A multi-objective optimization mathematical model of the MPSA was developed by combining traditional surrogate and machine learning models. The Pareto solution set was obtained using the nondominated sorting genetic algorithm II (NSGA-II), and three decision schemes with different attitudes were determined using the Hurwicz multi-criteria decision-making method. The results showed that a strong contradiction exists among the 95% of the maximum stable output electromagnetic force and its corresponding response time and the coefficient of the variation in electromagnetic force. The cone angle considerably influenced the performance indicators. Compared with the initial design, the coefficient of the variation in electromagnetic force was reduced by 54.08% for the positive decision, the corresponding response time was shortened by 15.65% for the critical decision, and the corresponding acceleration was enhanced by 10.32% for the passive decision. Thus, the overall performance of the long-stroke MPSA effectively improved.

Bankruptcy Analysis with the Altman Z-Score Method Case Study on Ubamie Restaurant

Financial statements are documents that present the revenues, costs, and net income or loss of a company over a period of time. This report is an important tool in financial analysis for company performance evaluation and strategic decision making. This journal examines the role of financial statement analysis through company cash flow as an effective tool in making business decisions engaged in the F&B sector, to be precise a case study of Ubamie restaurant. This research uses a quantitative descriptive method with a case study approach that includes analyzing historical data on the company's financial statements. This research aims to provide a better view of effective financial statement analysis methods to support proper decision making at various levels of the company or business. The results of this study can provide valuable insights for stakeholders, such as business owners, investors, and management, to take appropriate steps to improve financial health and reduce the risk of bankruptcy

Uncovering Malware Families Using Convolutional Neural Networks (CNN)

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently classifies malware into families by analyzing visual representations of malicious software as greyscale images using a Convolutional Neural Network (CNN). The Malwizard is a flexible Python tool suitable for both organizations and end-users enabling automated and rapid malware analysis within email system. Malwizard could be use as an Outlook Email’s add-in and an API service for SOAR platforms. The study evaluates this novel approach using the Microsoft Classification Challenge dataset, where image representations are encrypted to address privacy concerns. Experimental results show that the proposed approach performs comparably to the best existing model on plain text data, accomplishing the task in one-third of the time. For the encrypted dataset, adjustments to classical techniques are necessary for improved efficiency.

Identification of pollution source and prediction of water quality based on deep learning techniques

Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely informed and effective management strategies. In this study, we collected water quality monitoring data from a typical semi-arid river. By water quality inter-correlation mapping, we identified the regularity and abnormal fluctuations of pollutant discharges. Combining the association rule method (Apriori) and characterized pollutants of different industries, we tracked major industrial pollution sources in the Dahei River Basin. Meanwhile, we deployed the integrated multivariate long and short-term memory network (LSTM) to forecast principal contaminants. Our findings revealed that (1) biological oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen, total phosphorus, and ammonia nitrogen exhibited high inter-correlations in water quality mapping, with lead and cadmium also demonstrating a strong association; (2) The main point sources of contaminant were coking, metal mining, and smelting industries. The government should strengthen the regulation and control of these industries and prevent further pollution of the river; (3) We confirmed 4 key pollutants: COD, ammonia nitrogen, total nitrogen, and total phosphorus. Our study accurately predicted the future changes in this water quality index. The best results were obtained when the prediction period was 1 day. The prediction accuracies reached 85.85%, 47.15%, 85.66%, and 89.07%, respectively. In essence, this research developed effective water quality traceability and predictive analysis methods in semi-arid river basins. It provided an effective tool for water quality surveillance in semi-arid river basins and imparts a scientific scaffold for the environmental stewardship endeavors of pertinent authorities.