Effective Analysis Method Research Articles

Risk Identification on Screw Press Machines Using the Failure Mode and Effect Analysis (FMEA) Method on CV.” Rezeki Bersama

CV. Rezeki Bersama is a producer of crude coconut oil (CCO) based in Alus-alus Village, Teupah Selatan District, Simeulue Regency, Aceh. CV. Rezeki Bersama employs 15 people at various production stations, including press machines. In practice, this research uses the Failure Mode and Effect Analysis (FMEA) method to determine the damage that occurred at the press station of the joint rezeki factory. The FMEA approach is used to assess the likelihood of failure in a manufacturing process, determining its severity, frequency of occurrence, and detectability. This study focuses on collecting machine damage data from January 3 to February 3 2024 and evaluating it to determine the Risk Priority Number (RPN) value which will be used to indicate the priority of repairs needed. This research aims to provide recommendations for repair and prevention of damage in the future, as well as increasing the reliability and efficiency of the company's operations.

Analisis Pengendalian Kualitas Batako di CV. Berkah Pandaan dengan Menggunakan Metode FMEA (Failure Mode and Effect Analysist)

Kholifan fani, Engineering Study Program, Yudharta Pasuruan University Pasuruan, 02 July 2024. Batake Quality Control Analysis, At CV. BERKAH PANDAAN Using the FMEA (Failure Mode and Effect Analysis ) Method. The aim of this research data is to determinan the process of controlling the quality of bricks at CV. Berkah Pandaan using the FMEA (Failure Mode and Effect Analysis) Method. The approaches used are SWOT and FMEA. Failure Mode and Effect Analysis is defined as a method used to identify the causes and consequences of a failure. SWOT is the systematic identification of various factors to formulate company strategy. This analysis is based on logic that can maximize strengths and opportunities, but simultaneously minimize weaknesses and threats. Based on result of this research, the causes of defects that occur at CV. Be identified. BERKAH PANDAAN is caused by two thigs, namely in terms of humans and machiens, form the human perspective it is mosly caused by a lack of work concentration so that the quality of work decreases, this is triggered by working environmental conditions that are less supportive, including room temperatures that are too hot, insufficient lighting. Engine noise that is too loud in terms of the engine usually occurs because the engine is experiencing downtime.

Work Accident Risk Analysis Using Failure Mode and Effect Analysis (FMEA) Method at PT. XYZ

In the production process, the interaction between humans and machines is closely related. From this the higher the risk of accidents along with the higher workload. This study aims to identify the risk of accidents that may arise during the work in progress. In addition, to identify actions that can be taken to reduce these risks. This type of descriptive qualitative research was conducted to assess the risk of work accidents at PT. XYZ. The data used in this study is primary data obtained through interviews using a questionnaire on one of the workers of PT. XYZ. The data were analyzed using the Failure Mode and Effect Analysis (FMEA) method to find the RPN and critical values ​​to find priority risks for action. The results obtained are that there are 4 work objects on the machine being analyzed, namely the stamping machine, lathe, welding machine, and milling machine. Based on the RPN value, it was obtained a value of 264 for a cut hand, 357 for a hand hit by a craft, 148 for a hand exposed to a welding spark, and 371 for a hand-carried by a machine. Based on the highest critical value, a stamping machine was obtained with a value of 66. The suggestion given to anticipate the occurrence of work accidents is by affirming SOPs in the use of Personal Protective Equipment (PPE), as well as workers' understanding of the risks of work accidents and their impacts.

Risk Analysis of Level Crossing Element Failures in a Fuzzy Environment

The concept of risk analysis is especially important because it examines and analyses in a detailed manner the factors that affect the normal functioning of a system. In this paper, the level crossing is considered as one system, composed of several elements. The failures of those elements were analysed with the aim of showing which are the most frequent and most critical failures. A multi-methodological approach was used in the analysis. The failure modes and effects analysis (FMEA) method was used to determine risk factors, after that a multi-criteria model was created in a fuzzy environment, and as output, it gave a ranking list of critical failures in the system. Through the discussion of the results, a comparison of the basic model with two other similar ones was made, and the comparative results were analysed. The main aim of this paper is to present one of the possible ways to analyse the risk of the system of level crossings with the aim of improving traffic safety at the crossing.

Risk Management of Green Bean Farming Production in Gluranploso Village, Gresik District

Green beans are the most important food crop in Indonesia and production continues to increase to meet domestic demand. However, production is still insufficient due to several risks faced by farmers. This research analyzes the risk management of green bean production in Gluranploso Village using the Coefficient of Variation (KV) and Failure Mode and Effect Analysis (FMEA) methods. The results showed that yield variation was relatively low with a KV value of 17%, indicating good yield stability. The FMEA analysis identified four main sources of risk affecting production: seed aspect with an RPN value of 5.808, weather with an RPN value of 7.04, pests and diseases with an RPN value of 12.936, and technical aspects with an RPN value of 47.88, particularly delays in pest and disease control spraying. In addition, the market aspect is also a source of risk with the largest value in the increase in production input prices at 12.288. These sources require special attention in risk management. The study concludes that the implementation of appropriate risk management strategies, focusing on the identified key risk sources, can help farmers improve yield stability and farm profitability. These strategies include improving seed quality, mitigating weather risks, controlling pests and diseases, and improving technical procedures in cultivation and market risks. Thus, effective risk management can be key in increasing the productivity and sustainability of green bean farming in Gluranploso village.

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

Fault diagnosis of bearings under variable operating conditions based on domain adaptation

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.

Comparison of 35 trace elements content in malignant breast tumors with their content in the normal female mammary gland: Original data and a mini-review

Objective: In many countries, including Russia, breast cancer ranks first in the incidence of cancers in women. The etiology of this disease remains largely unclear, but there is evidence indicating that disturbances in the somatic homeostasis of trace elements may be involved in the process of oncogenesis. Therefore, this study was aimed at identifying changes in the content of trace elements during malignant transformation of breast tissue.Methods: For this purpose, an effective method of small sample analysis by means of inductively coupled plasma mass spectrometry was developed. The method makes it possible to determine the content of 35 trace elements in microsamples (with mass ≥ 10 mg) of breast tissue obtained by puncture biopsy. With the help of this technique, the samples of cancerous (n = 43) and normal (n = 38) breast tissue were studied.Results: In malignant breast tissue, the content of Al, As, B, Cd, Co, Cs, Cu, Mg, Mn, Mo, Ni, Rb, Se, Sr, Ti, Tl, U, V, Zn, and Zr was higher, while the content of Ge, Pb, Sb and Th was lower than in healthy gland tissue. All the identified differences were statistically significant.Conclusions: The significant disruption of somatic homeostasis of trace elements resulting from malignant transformation of breast tissue has been described, but its cause has not been determined, so additional research is required. Further the method we employ, which we have developed and described here, requires tissue samples weighing only a few milligrams, so it is possible to use it with tissue obtained from puncture tissue biopsies.

Dynamic Modeling and Analysis of Multi-Span Timoshenko Beams Under Moving Loads

Dynamic response analysis of multi-span beams is one of the important topics in the field of highway and railway engineering, which provides valuable guidance for the design, operation and maintenance of multi-span bridges. It is worth considering how to calculate the dynamic characteristics of multi-span beams quickly and conveniently. In this study, an effective analytical method is developed for the dynamic modeling and investigations of multi-span Timoshenko beams under moving loads by employing the assumed mode method (AMM). Based on Hamilton’s principle, the equation of motion of the Timoshenko multi-span beam subjected to moving loads is formulated and the natural frequencies are calculated. A comprehensive investigation is conducted on the dynamic responses of the multi-span Timoshenko beam considering different factors, including the length-to-thickness ratio, disorder degree, arrangement order, number of spans and related parameters of moving loads. In addition, the influence of the interval and velocity of the moving load series on the dynamic responses, including displacement, velocity and acceleration of the multi-span beam, are analyzed in detail. The theoretical calculation results of multi-span Timoshenko beams under moving loads are compared with numerical results obtained from ANSYS software, and the results are in good agreement. This demonstrates that the developed method, which utilizes the AMM to analyze the dynamic characteristics of multi-span beams subjected to moving loads, significantly simplifies the calculations and is well-suited for practical engineering applications.

Simultaneous detection of myostatin-targeting monoclonal antibodies in dried blood spots and plasma using liquid chromatography-tandem mass spectrometry with field asymmetric ion mobility spectrometry

Transforming growth factor-β superfamily members, such as myostatin, growth/differentiation factor 11, and activin A, negatively regulate skeletal muscle mass. Inhibitors targeting these cytokines or activin receptor type IIB have the potential to treat muscular diseases and enhance physical performance. However, because of their effects on muscle mass and potential misuse, they are strictly prohibited in sports. Given the high potential for misuse as a doping agent in sports, effective analytical methods for these prohibited antibodies targeting these specific cytokines or their receptor are critically needed. In this study, we aimed to develop and validate a multitarget method to detect the prohibited transforming growth factor-β superfamily-targeting monoclonal antibodies, such as landogrozumab, domagrozumab, and the activin receptor type IIB-targeting antibody, bimagrumab, in human plasma and dried blood spot (DBS) samples using liquid chromatography-tandem mass spectrometry. Antibodies were purified from both the DBS and plasma samples using protein G magnetic beads and field-asymmetric ion mobility spectrometry (FAIMS) to minimize interference, followed by liquid chromatography-tandem mass spectrometry analysis. The validation process included tests for specificity, selectivity, linearity, limit of detection (LOD), limit of identification, precision, recovery, carryover effect, and matrix effect. The LODs for the target antibodies were identical in both DBS and plasma samples at 0.1 µg/mL for landogrozumab heavy and light chains, as well as 0.25 µg/mL for the domagrozumab light chain and 0.25 µg/mL for the bimagrumab heavy chain. However, the heavy chain of domagrozumab exhibited an LOD of 0.5 µg/mL in DBS and 1 µg/mL in plasma. The analytical method demonstrated strong linearity, with R² values greater than 0.99 for both plasma and DBS, and no carryover effect. Precision (CV%) was below 15 % at both middle (1 or 5 µg/mL; specific to the heavy chain of domagrozumab in plasma) and high (10 µg/mL) concentrations and was less than 20 % at the LOD. The selectivity and specificity indicated no interference in the analysis of target mAbs in different blood samples. Recovery was 31.6–49.8 % for DBS and 51.4–85.3 % for plasma, with no significant matrix effect. This study provides an effective method for doping analysis and novel protein detection.

Reliability Analysis of Steam Turbine Instrumentation Using the Failure Mode and Effect Analysis (FMEA) Method at PT. PLN Nusantara Power UP Tenayan

PT. PLN Nusantara Power Unit Generation Tenayan is a company operating in the Steam Power Plant sector in Pekanbaru, Indonesia. One of the main components of a steam power plant (PLTU) is a steam turbine. A steam turbine is a machine that functions to convert kinetic energy into electrical current. Operational failures are often caused by less than optimal instrumentation in the steam turbine. This research uses the failure mode and effect analysis (FMEA) method with the aim of identifying the type of failure, the cause of the failure, the effect of the failure, and determining the RPN value. The analysis results from this research show that the turbine instrumentation components still meet performance standards because the risk priority number (RPN) value is less than 200. The conclusion from this research is that errors in steam turbine instrumentation are inaccurate sensor readings, switches cannot deactivate the equipment, and readings control room does not match local readings, the instrumentation component with the highest risk priority number (RPN) is the solenoid valve with a value of 140, and the instrumentation component with the lowest risk priority number (RPN) is the pressure indicator with a value of 30. The component given top priority in action The recommendation is for the solenoid valve component, namely to carry out maintenance every 6 months and add an overspeed protection system

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

A service failure assessment model for smart product consumption experience based on customer perception

Customer perception is an important consideration factor in evaluating the quality of human-computer interaction services. Sustainable user experiences and marketing strategies can be created by analyzing customer perception. By understanding consumer satisfaction with product services in the customer perception area, appropriate product service failure prevention strategies can be formulated. A service failure evaluation model is proposed in this study, which considers the customer tolerance area to accurately evaluate consumers’ behavioral experiences from purchasing to using products. The concept of tolerance area is introduced, and a combination of the fuzzy Failure Mode and Effect Analysis (FMEA) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to construct a human-computer interaction service failure evaluation model. Potential service failure factors of smart speakers are accurately evaluated by this model, and these service failure factors are ranked within the tolerance area. The research identifies voice misinterpretation and signal connectivity issues as the primary risk factors impacting the quality of human-computer interaction for smart speakers. The application of this method not only enhances the evaluation of smart speaker human-computer interaction services quality but also aids in the precise identification and prioritization of critical failure modes. The proposed service failure prevention strategies can reduce consumer dissatisfaction and provide innovative references for smart product design and marketing. The findings bolster empirical evidence for service failure prevention strategies in smart products and pave the way for novel perspectives on enhancing the quality of human-computer interaction services.

Fluorescence labeling-based differential scanning fluorimetry, an effective method for protein thermal stability and protein-compound binding analysis

Differential scanning fluorimetry (DSF) is widely used to assess protein thermal stability and protein-ligand interaction. However, its utility is often limited by the presence of detergents, which can affect hydrophobic binding. To tackle this issue, we developed an effective fluorescence-labeled DSF (FL-DSF) technique that tracks protein denaturation by monitoring the labeling fluorescence decrease, thus overcoming challenges typically encountered with traditional DSF methods. In this research, FL-DSF was first validated using Peroxisome Proliferators-Activated Receptor γ (PPARγ), Retinoid X Receptor α (RXRα), and Lysozyme, confirming its accuracy in determining melting curves. Expectedly, FL-DSF also exhibited strong compatibility with detergents in our investigations. Besides this, a new calculation method was proposed to characterize the protein denaturation process and evaluate protein-ligand binding. This mathematical model goes beyond traditional approaches, which simply treated the melting temperature (TM) shift as a concentration-dependent variable. Instead, it comprehensively incorporates the influence of irreversible denaturation-induced native protein loss on the equilibrium of protein-ligand binding. This methodology was successfully applied into the evaluation of binding affinity for 2 classical binding systems of PPARγ-Rosiglitazone and RXRα-CD3254. It was also utilized for the following binding screening studies, leading to the discovery of promising ligands for PPARγ, RXRα, and Lysozyme.

Investigation of Surge Phenomena in a Centrifugal Compressor by 3D-1D Coupling Approach

Abstract Critical instability, such as surge phenomena, can occur at low flow rates for a compressor system, leading to flow rate fluctuations and potential damage. To study this, traditional numerical analysis requires a comprehensive approach encompassing the compressor and its piping system. This demands extensive computational resources and time due to the complexity of capturing internal flow during surge, including separation and vortex generation. To overcome these limitations, this research proposes a computational strategy combining 3D and 1D analysis for enhanced efficiency and reduced computational demands, which employs 1D analysis for more straightforward flow regions like piping and 3D analysis for the more complex compressor section. This approach is facilitated using AMESim for 1D and STAR-CCM+ for 3D simulations, enabling seamless integration and data exchange between different dimensional analyses. The method improves computational efficiency and reasonably approximates surge phenomena compared to experimental data. The 3D and 1D coupled analysis closely aligns with experimental data, providing a viable and more effective alternative method for surge analysis in compressor systems. This methodology promises to streamline the study of compressor surge phenomena, providing a practical tool for engineers and researchers.

Polymer-metal film induced ultrahigh output TVNG and self-powered intelligent sensor system assisted by EEMD

Tribovoltaic nanogenerators (TVNGs) based on tribovoltaic effect can output DC current directly, and has the advantages of high output current density and low internal resistance. While TVNGs have been restricted by its low output voltage and power. Herein, we have developed a metal-polymer film with a micro-uneven structure on the surface, much higher corrosion resistance and wear resistance than metal. On this basis, a centimeter-level sliding structure metal-EP/GaN-TVNG has been fabricated, which shows a high and stable long-term DC electrical output (a maximized power output of 4.19 W/m2 when matched with a 0.07 MΩ resistor, a stable output current of 2.25 A/m2 and a peak Voc of 27 V). Further, a TVNG vehicle travelling intelligent sensor and system which can integrate electrical signals collected by TVNG into digital signal processing algorithms and then feed backing to the driving system, has been designed and manufactured. As an effective method for deep analysis of signals, Ensemble Empirical Mode Decomposition (EEMD) has been used for the first time for deep processing of nanogenerator signals in this work. This work provides a new model of further development of high output TVNG and a new deep signal processing methods of TVNG/TENG signal.

Analytical modelling techniques for enhancing tacrolimus dosing in solid organ transplantation: a systematic review protocol.

Tacrolimus is an immunosuppressant commonly administered in transplant recipients. Given its narrow therapeutic range and susceptibility to various influencing variables, determining its optimal dosage is challenging. This systematic review seeks to identify effective analytical modelling techniques and methods for optimal tacrolimus dose prediction in solid transplant recipients. This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The study will review the literature published from database inception to 11 March 2024, that assesses predictive models of tacrolimus dosing through analytical modelling techniques. We will include both randomised and non-randomised, as well as cross-sectional, qualitative and before-and-after studies and will perform our searches in four main databases-Ovid/MEDLINE, PubMed/MEDLINE, Scopus, Embase and Web of Science, and search engines including Centers for Disease Control (CDC) and Google Scholar. Papers that are not published in English or French are excluded from this study. A narrative synthesis and meta-analysis will be done if the extracted information permits such analysis. Conference abstracts will be ignored unless they are recent (published within 2 years of the search date). Ethics clearance is not required for this study as no primary data will be collected. The completed manuscript will be published, and the results of the study will be presented at conferences. International Prospective Register of Systematic Reviews (PROSPERO), CRD42024537212.

Firth-Type Penalized Methods of the Modified Poisson and Least-Squares Regression Analyses for Binary Outcomes.

The modified Poisson and least-squares regression analyses for binary outcomes have been widely used as effective multivariable analysis methods to provide risk ratio and risk difference estimates in clinical and epidemiological studies. However, there is no certain evidence that assessed their operating characteristics under small and sparse data settings and no effective methods have been proposed for these regression analyses to address this issue. In this article, we show that the modified Poisson regression provides seriously biased estimates under small and sparse data settings. In addition, the modified least-squares regression provides unbiased estimates under these settings. We further show that the ordinary robust variance estimators for both of the methods have certain biases under situations that involve small or moderate sample sizes. To address these issues, we propose the Firth-type penalized methods for the modified Poisson and least-squares regressions. The adjustment methods lead to a more accurate and stable risk ratio estimator under small and sparse data settings, although the risk difference estimator is not invariant. In addition, to improve the inferences of the effect measures, we provide an improved robust variance estimator for these regression analyses. We conducted extensive simulation studies to assess the performances of the proposed methods under real-world conditions and found that the accuracies of the point and interval estimations were markedly improved by the proposed methods. We illustrate the effectiveness of these methods by applying them to a clinical study of epilepsy.

A three-dimensional nitrogen-rich conjugated microporous polymer for solid-phase microextraction of nitroaromatic compounds from environmental water and soil samples

Nitroaromatic compounds (NACs), as a kind of important chemical intermediates, are widely used in industrial productions. However, NACs have carcinogenic effect and their residues may pose harm to human health. Therefore, there is necessity to set up effective analytical method to monitor them in some environmental samples. However, because of their low concentrations in real samples, they need to be enriched by an effective adsorbent before the subsequent instrumental detection. In this work, a three-dimensional nitrogen-rich conjugated microporous polymer (DCT-Try-CMP) was synthesized with 3,6-dichloro-1,2,4,5-tetrazine and triptycene as monomers via Friedel-Crafts reaction. It presented a good adsorption capability for the NACs. After optimization, a solid-phase microextraction with DCT-Try-CMP fiber combined with gas chromatography-flame ionization detection for the quantitation of trace NACs in environmental water and soil samples was developed. The limits of detection of the method (S/N = 3) for environmental water and soil samples were 0.08–0.30 μg L-1 and 1.00–5.00 ng g-1, and the limits of quantification (S/N = 9) were 0.27–0.90 μg L-1 and 3.00–15.0 ng g-1, respectively. The linear quantification response ranges for the analytes were 0.27–200 μg L-1 and 3.00–1000 ng g-1 for water and soil samples, respectively. For water samples at the analytes concentrations of 5.00, 50.0 and 100 μg L-1, the method recoveries ranged from 82.2% to 120% and for soil samples at the concentrations of 15.0, 200 and 400 ng g-1, the method recoveries fell in the range from 80.2% to 120%. The method provides a sensitive and effective approach for monitoring trace NACs in environmental water and soil samples.

Application Of FMEA Method In Halal Risk Determination On Halal Poultry Slaughtering Operations

The importance of halal slaughtered chicken has become crucial in the era of globalization and complex supply chains. The rising awareness among Muslim communities regarding the integrity of slaughtered chicken products has led to a focus on adhering to halal compliance standards and Islamic law. This study aims to qualitatively analyze the halal risk points in chicken slaughtering operations, by using the Failure Mode and Effect Analysis (FMEA) method, in order to identify potential halal risks and ensure the integrity of halal chicken products. This qualitative study uses a analysis approach by examining secondary sources including journals, articles, guidelines and standards related to halal slaughtering process. In this study, seven Halal Risk Points (HRP) have been identified in the entire halal slaughtering process of broilers. Based on the results of the risk assessment through the FMEA method, each determined HRP will be assessed based on the severity, occurrence and detection rating scale. The final result of this study found that only six HRPs have a Risk Priority Number (RPN) value that exceeds 100. Therefore, corrective action recommendations are proposed to prevent any halal non-compliance from occurring on the process involved. Overall, the FMEA framework in this study is a new approach in guaranteeing the integrity of chicken slaughter products and is able to have a positive impact on the halal chicken slaughter industry players and also the implementers of the halal management system.