bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Development of Reliability Assessment Method for Industrial Drone
Authors: Park Jong Won , Lee Jin Hee , Choi Byung-Oh
Published: March 2024

In the past, drones have been developed for military use. Recently, however, the use of drones in industrial and civilian markets has been spreading rapidly because of their potential for various applications. The main application areas of industrial drones are aerial photography, logistics transportation, lifesaving, policing, wildfire monitoring and spraying agricultural pesticide. The reliability assessment and performance test methods of drones are not systematically established against such a rapidly growing market situation until these days. Therefore, in this study,we developed reliability assessment methods and test equipment that can evaluate reliability of drone by performance test, environmental test, safety test and life test method, which can be used to ensure safety of individual users and reliability of industrial drones. In the developmentof the reliability assessment method for industrial drones, we tried to verify drones’ reliability and to show the acceptable minimum limit required by manufacturer’s general specification. Furthermore, it is expected that we possibly obtain the degradation measure of the flight performance and the durability through the comprehensive performance and life test via applying the usage history and practical load in the laboratory. The developed methods were set up reflecting international standards, user requirements and field operating conditions. Since the field failure data of industrial drones has been reported rare in the manufacturing companies and research institutes until now. We think that it is necessary to obtain more than thousand's hours of the actual usage history and the failure data in order to correct or supplement the developed reliability assessment method in the future.
Feasibility Study on Image-based Faulty Nozzle Detection Method for Digital Textile Printing
Authors: Baek Dong-Cheon , Park Jong-Won
Published: March 2024

Digital textile printing (DTP) is a production method in which ink droplets are ejected from an inkjet head to print on a fabric. In recent years, the resolution of the DTP head has been increased, the nozzles have become smaller, fabric defects due to nozzle clogging have become easier to occur, and the production speed has also become faster, making it difficult for the human operator to confirm this in real time. In addition, real-time monitoring technology of defective nozzles is needed to break the existing time-based maintenance and implement condition-based maintenance.In this study, we propose a method to identify defective nozzle position by acquiring an image of printed fabric in real time and verify method at prototype level and to examine feasibility. Based on the multi-pass type 1.8m-wide production equipment using a 1200 dpi head module, thespecifications and technical difficulties required for acquiring and analyzing the image are described in detail. It is hoped that these technologies will enable condition-based maintenance and further improve operational efficiency of manufacturing.
Energy Scanning for Designing Self-Generative Power Grid to Operate Wireless Sensors in Health Sensing Modules
Authors: Yoon Heonjun , Youn Byeng D. , Kim Miso , Park Choon-Su
Published: March 2024

The effectiveness of prognostics and health management (PHM) in operation reliability improvement and failure prevention strongly depends on health-relevant information conveyed by a sensory signal. The advances in wireless communications and low power electronics have allowed the deployment of wireless sensor network (WSN) for a health sensing module in the PHM. Energy harvesting (EH) has received much attention as a possible solution of a self-generative power grid to sustainably operate the wireless sensors. Piezoelectric vibration energy harvesting (PVEH) is a technology that converts ambient, otherwise wasted, vibration energy into electric power in response to the mechanical strain. Prior to selecting best sites for installation of the PVEH devices, it is of great importance to preliminary scan the amount of harvestable electric power in a cost-effective manner. This study thus aims at establishing an ‘energy scanning’ concept to provide a guideline on how to optimally design the locationand the number of the PVEH devices for the self-generative power grid in the health sensing module. The backbone idea of the energy scanning is to implement the stochastic electroelastically-coupled analytical model using time-frequency analysis for quantifying the expected output electric power based on a non-stationary random vibration signal acquired from an engineered system. The three-fold steps are systematically organized with an aim to realize the energy scanning as: (1) analysis of ambient vibration sources induced by a target engineered system, (2) optimal design of the PVEH device, and (3) prediction of the expected output electric power in a statistical manner. Finally, the predictive capability of the energy scanning is validated with experimental observations. The main contribution of this study lies in that the energy scanning plays an essential role inscheduling the operation time interval of wireless sensors when the amount of harvestable electric power is larger than the threshold at which it is set to activate the operation
A study on a reliability of a mixed-model assembly line based on complexity
Authors: Oh YeongGwang , Busogi Moise , Ju Ikchan , Lee Wooyeol , Kim Namhun
Published: March 2024

Many manufacturers have made extensive efforts to enhance the flexibility of their manufacturing systems, which can produce various products in small volumes with limited resources. For instance, in the automotive industry, maintaining a high diversity of models and options is a key objective in their business to guarantee market competitiveness. However, this diversification of products frequently causes a dramatic increase in manufacturing complexity in the mixed-model assembly system. Nevertheless, quantitative indices for manufacturing complexity are relatively insufficient due to their high dependency on manual tasks and minimal consideration for task difficulty in complex assembly processes. This research, thus, proposes a reliability-based complexity metric for effective assessment of mixed model assembly lines with consideration for taskdifficulties in manual assembly processes. This research presents a model and analysis of process complexity based on information entropy to estimate the manufacturing complexity of mixed-model production system in manufacturing industries. The proposed model is illustrated with a small-scale assembly line with operators’ manual tasks to verify and validate its applicability and usefulness.
Adaptive SVM-based Real-time Quality Assessment for Primer-Sealer Dispensing Process of Sunroof Assembly Line
Authors: Oh YeongGwang , Ransikarbum Kasin , Busogi Moise , Kwon Daeil , Kim Namhun
Published: March 2024

Quality assessment in many production processes typically relies on manual inspections due to a lack of reference data and an effective method to classify defects in a systematic way. Recently, the real-time, automated approach for product quality assessment has been regarded an important aspect for smart manufacturing applications, such as in the automotive industry. In this research, we develop and implement the self-evolving quality assessment system based on the adaptive support vector machine (ASVM) model in the real production system. An adaptive process is a feedback control that ensures the effectiveness of the support vector machine (SVM) algorithm over time and enables the real-time improvement of SVM-based quality assessment. Next, an industrial case study of a primer-sealer dispensing process in a sunroof assembly line of an automobile isillustrated to verify and validate the applicability and effectiveness of the proposed ASVM-based quality assessment system. Defective patterns are then analyzed using an infrared thermal image of primer-sealer dispensing in a manufacturing process, which contains multi-modal data of dimensional information and temperature deviation from the dispending patterns in our study.
An Analysis of Quality Monitoring and Control System using Real- Time, Integrated Cost Effectiveness and Support Vector Machine
Authors: Oh YeongGwang , Ransikarbum Kasin , Busogi Moise , Kwon Daeil , Kim Namhun
Published: March 2024

The quality monitoring and control (QMC) has been an essential process in the manufacturing industries to ensure product reliability. With the advancements in big-data analytics, machine-learning based QMC has become more and more popular in various manufacturing industries, such as automotive and electronic companies. At the same time, the cost effectiveness (CE) of the QMC is perceived as a main decision criterion that explicitly accounts for inspection efforts and has a direct relationship with the QMC capability. In this paper, the integrated support vector machine (SVM)-based automated QMC system with the adjusted CE, CEadj, model is proposed. Unlike existing models, the proposed model explicitly incorporates inspection-related expenses (i.e., warranty cost, rework cost, inspection cost) and error types (i.e., type-I and -II errors) in the CEadj framework to guide the SVM algorithm. The proposed automated QMC system is verified and validated using a door-trim manufacturing process of an automotive industry. Next, a designed experiment is performed to assess the sensitivity analysis of the proposed framework. The proposed model is found to be effective and could be exploited as an alternative or complementary tool for the traditional quality inspection system.
Lifetime Prediction of Optocouplers in Digital Input and Output Modules based on Bayesian tracking approaches
Authors: Shin Insun , Kwon Daeil
Published: March 2024

In recent years, reliability of DIO modules has been drawing much attention from manufacturing companies under the growing complexity of automation systems for smart factory establishment. In manufacturing systems, DIO modules have been widely used to pass sensor measurements and configuration input signals for controlling actuators. Because sensor measurement and control signals pass through DIO modules, the faults of DIO modules would cause malfunctions or failures of the smart manufacturing systems and eventually lead to unexpected downtime in the manufacturing process. For predictive maintenance of DIO modules, this paper proposes a method of predicting the remaining useful life of a critical component in DIO modules based on the Bayesian tracking approaches. Optocouplers are one of the critical components in DIO modules that uses a short optical transmission path including light sources and photo-sensors to transfer an electrical signal. The performance of optocouplers may be degraded overtime with damages in a light source or a photo-sensor and eventually cause the faults of control systems. Extended Kalman Filter and Particle Filter are used in nonlinear degradation modeling to predict the lifetime of optocouplers, evaluating those filters by accuracy-based prognostic metrics and showing the effectiveness of Bayesian tracking approaches for lifetime prediction of optocouplers.
A Digital Signal-based Prognostic Approach to Factory Automation
Authors: Lee Jinwoo , Kwon Daeil
Published: March 2024

A data network from a control system is usually consist of complex wiring systems to communicate with manufacturing lines. The electrical connections by the wiring systems can be deteriorated by wire faults such as chafing in the wiring system. During the life cycle of the wiring system, field stress conditions such as mechanical stress condition often cause and intensify the wire faults. The progress of wire fault can cause other wire failures such as cutoff or arcing. Furthermore, failures in the connected wires may eventually cause malfunctions in facilities of manufacturing lines. In order to prevent serious failures, health of the wiring system can be monitored in real time to repair or replace the damaged wires before the time to failure. However, the conventional approaches to wire health monitoring often require additional connections to the wiring system due to additional monitoring devices. Thus, the operation of the facilities may be interfered when the monitoring devices monitor wires. As a result, the conventional approaches, which require additional devices, have difficulty detecting the extent of wire damages in real time, and may end up neglecting the progress of chafing. In this study, a method for wire health monitoring is developed to prevent wire failures by monitoring wiredamages in real time. Digital signal is affected adversely by impedance discontinuity on the transmission line. By monitoring the integrity of digital signal continuously, time to failure can be predicted in real time depending on the extent of wire damage. In addition, an accelerated wire abrasion test was designed to damage wires gradually. During the abrasion test, the integrity of the transmitted digital signal was continuously deteriorated. The monitored signal makes it possible to extrapolate the degradation pattern of the signal parameters depending on the extent of wire damage. Thus, the results in this study validate that the proposed method is capable of monitoring the wire damage to diagnose a wiring system with real-time information.
Deep Neural Network for Fault Diagnosis of Power Transformers using Dissolved Gas Analysis
Authors: Kim Sunuwe , Jang Beomchan , Youn Byeng D. , Kwon Daeil , Park Byeong-Cheol
Published: March 2024

The dissolved gas analysis, produced by deterioration of insulating oil, is the most popular diagnostic tool to detect various incipient faults in power transformers. So far, the handcrafted DGA features, such as DGA composition ratios (i.e., C2H2/C2H4, C2H4/C2H6, CH4/H2), have been often used as the input features of shallow learning or used to identify diagnostic criteria (i.e., Dornenburg Ratio, Rogers Ratio, IEC ratio) for the fault diagnosis of power transformers. However, a false alarm rate is relatively large due to the limitations of the handcrafted features because they are made up of two or three gas combinations that can classify the fault types in a low dimensional space that can be analyzed by the human inspection. To enhance DGA-based diagnostic accuracy, a novel method using deep neural network (DNN) is proposed to determine high-level features without relying on the handcrafted features. Specifically, many layers of nonlinear transforms in a DNN convert the raw DGA data into a highly invariant and discriminative representation without losing high-dimensional information that human cannot analyze in high dimensional space. This makes health classification more effective. A proposed method is validated from the reference database of IEC TC 10, which is the visual inspection data of transformer faults. The results indicate that the proposed DNN approach achieves higher accuracy than the existing methods based on shallow learning with the handcrafted features.
Unsupervised Feature Learning Using Domain Knowledge Based Autoencoder
Authors: Kim Hyunjae , Youn Byeng D.
Published: March 2024

Autoencoder is an unsupervised feature engineering technique, which is an emerging technique in the prognostics and health management (PHM) domain. However, since neural network based techniques such as auto-encoder have many hyper-parameters belonging to the number of hiddenunits, the number of layers and activation functions, substantial efforts are required in a heuristic way to make the autoencoder learn proper features. In this paper, we propose a novel method to regularize an auto-encoder by exploiting domain knowledge, such as mechanical engineeringexpertise. The proposed autoencoder learns robust features in a fast and efficient manner, therefore resulting in minimal consideration for the hyper-parameters. In the proposed method, some of the hidden units of the autoencoder are forcibly pre-trained by back-bone signals, such as 1Xsinusoidal wave of vibration, and the remaining hidden units efficiently learn the features of fault signals by minimizing the redundancy of learned features. The domain knowledge based regularization reduces the degree of freedom (DOF) of the autoencoder model as well as guide the model to learn the more physically reasonable features. Various fault data measured from a journal bearing rotor testbed are used for demonstration of the proposed method.
Four-Stage Degradation Physics of Rolling Element Bearings
Authors: Kim Keunsu , Hwang Taewan , Kim Su J. , Jeon Byungjoo , Youn Byeng D.
Published: March 2024

Rolling element bearings are a critical component of rotating machinery. Timely prediction of bearing faults become of great importance to minimizing unscheduled machine downtime. Most of the bearings experience gradual condition degradation due to repeated mechanical loads.Vibration signals are often used for bearing diagnosis and prognosis with a predefined threshold. However, false (positive/negative) alarms are often observed, thus leading to unnecessary downtime and expensive corrective maintenance. This is mainly because the thresholds are defined without accounting for bearing physics and a great deal of uncertainty in manufacturing and operation condition. To resolve this difficulty, this study aims at investigating the degradation physics of rolling element bearings using a vibration signal, while accounting for bearing physics and a substantial amount of uncertainty in manufacturing and operation condition. First, bearing feature engineering is thoroughly studied through timedomain and frequency domain analyses. This study proposes the features that are most sensitive to the change in bearing physics. Second, bearing degradation physics is investigated so that the bearing degradation process can be modeled into four degradation stages. To the end, theproposed idea is demonstrated with vibration data measured from rolling element bearings, which experience accelerated life tested to simulate naturally induced degradation. This study will benefit to enhance physical understanding for bearing faults in various engineering applications.
Evaluation of a Knock Sensor for Gearbox Diagnosis
Authors: Kim Keon , Ha Jong M. , Park Jungho , Youn Byeng D.
Published: March 2024

A gearbox is one of the critical components in rotating machinery. Timely prediction of gearbox faults become of great importance to minimizing unscheduled machine downtime. Most of gearbox diagnosis studies are focused on the development of gearbox diagnosis algorithms using costly vibration sensors. However, vibration sensor cost matters in some applications, thus pushing to the use of a low-cost accelerometer, such as a knock sensor. This study develops a sensor evaluation process for the purpose of diagnosis. This study uses a planetary gearbox with a knock sensor, known to be cheap and good for high frequency applications (i.e., diesel engines). First, gearbox feature engineering is thoroughly studied through time domain and frequency domain analyses. This study uses the features that are most sensitive to gearbox faults, such as pitting and surface damages. Second, some sensor evaluation metrics (i.e., signal-to-noise ratio (SNR)) are overviewed for the purpose of diagnosis. The sensor evaluation study suggests a frequency range of the knock sensor that can be reliably used for fault diagnosis. Two case studies are presented to demonstrate the effectiveness of the proposed sensor evaluation process and metric: 1) one-stage planetary gearbox and 2) a swing reduction gear (two-stage planetary gearbox) in an excavator. It is concluded that a knock sensor can be used for the fault diagnosis of a gearbox.
A Time-varying Energy Residual (TVER) Method for Fault Detection of a Planetary Gear Under Variable Speed Conditions
Authors: Park Jungho , Hamadache Moussa , Ha Jong M. , Kim Yunhan , Youn Byeng D.
Published: March 2024

In recent decades, many methods have been developed to detect faults in the gear system. In particular, techniques for fault detection of planetary gears have gained considerable attention because planetary gears are often exposed to harsh operating conditions, such as heavy loads or high speeds. However, many previous studies are limited to detection of the gear faults under a constant speed condition. In real-world settings, geared systems often operate under a variable speed condition. This study thus proposes a time-varying energy residual (TVER) method to detect faults in a planetary gear under a variable speed condition. In the proposed method, the wavelet transform is exploited to extract energy-related features that can express timevarying and faulty behaviors of the gear vibration signals in the time-frequency domain. Then, the Gaussian process is used with the wavelet coefficients to calculate energy residuals that represent the fault severities of transient signals. The effectiveness of the proposed method was demonstrated through a case study of simulated vibration signals of the planetary gear. From the case study, it can be concluded that the proposed TVER method can successfully differentiate a faulty gear from a normal one in a variable speed condition.
Does PHM Make an Engineered System Resilient under Sensor Faults?
Authors: Yoo Minji , Yoon Joung Taek , Kim Yunhan , Youn Byeng D.
Published: March 2024

The use of Prognostics and Health Management (PHM) technology enables engineered systems resilient under adverse events. Adverse events include unexpected system failures, anomaly operation, manufacturing defects, etc. As engineered systems become more complex, resilience thus becomes an emerging engineering feature, which offers an attractive ability to resist and recover from adverse events. However, most resilience studies were conducted under the assumption of no false alarms. This study thus concerns how engineering resilience can be formulated while taking into account false alarm due to sensor faults. Sensor faults can be interpreted in various manners: sensor breakdown, calibration change, sensor noise, etc. This study proposes a new formulation of engineering resilience measure considering sensor faults. Sensor faults can be implemented by changing a sensor gain. The effectiveness of the proposed resilience measure is demonstrated by implementing the PHM into the electro-hydrostatic actuator (EHA) and assessing the resilience under sensor faults. This study is also conducted to see how the proposed resilience measure behaves in the course of the sensor degradation.
A Probabilistic Approach to Maintenance Cost Analysis Considering Time-dependent False Alarms for PHM Design
Authors: Yoon Joung Taek , Yoo Minji , Kim Yunhan , Youn Byeng D.
Published: March 2024

Prognostics and health management (PHM) aims at predicting system failures in advance and timely conducting maintenance so as to minimize operation and maintenance costs. One of the challenges in PHM is how to deal with a false alarm. A false alarm in PHM implies incorrect estimation about a health state of an engineered system. This could incur unnecessary overhaul and maintenance or unexpected costly system failures. That is, the incurred costs due to false alarms should be analyzed prior to deciding the implementation of PHM. This paper presents the framework of life-cycle maintenance cost analysis considering false alarms. First, the framework estimates false alarm rates for a given PHM algorithm. In this study, two types of false alarms (i.e., false healthy and false faulty) are taken into account and health diagnostics is considered for a PHM algorithm. The false alarm rates are calculated according to their probabilistic definition while considering the time-dependent (or time-dependent) property of false alarm rates. Then, three maintenance probabilities (unnecessary, corrective and preventive) are calculated by multiplying the probability of a true health state (reliability or failure rate) by the conditional probability from the detectability matrix. For instance, the probability of the unnecessary maintenance is calculated as the conditional probability of system faulty estimation given a true healthy system (false faulty) times the probability of a true healthy system (reliability). Lastly, the expected costs of three maintenances are calculated with consideration of the calculated maintenance probabilities and their costs. An electro-hydrostatic actuator simulation model is employed for demonstration. Its expected maintenance cost during life-cycle is estimated, to demonstrate the validity of the proposed framework.
Online Temperature Estimation of Li-ion Battery Pack Using Principal Component Analysis
Authors: Kim Taejin , Kim Sunuwe , Youn Byeng D.
Published: March 2024

Thermal management is one of the important function of the battery management system (BMS). The thermal management system monitors and equalizes the temperature distribution of the battery pack to prevent the different cell degradation rate and to keep the battery on its bestperformance. In this study, as a part of the thermal management system the in-situ temperature estimation method is developed based on the principal component analysis (PCA) reinforced with the measured temperatures. To begin with, the PCA is used for finding the basis vectorsof the battery thermal system, which is the eigenvectors of the covariance matrix of the training data set. Then an arbitrary thermal map can be expressed as the linear combination of these basis vectors and their amplitudes. The amplitude for each basis vectors is estimated from themeasured temperatures. The performance of the thermal map reconstruction depends on the accuracy of this amplitude estimation which again is related to the temperature measurement locations. The measured locations are determined considering two aspects: the prediction accuracyand the robustness of the sensor network. To find the sensor location satisfying both criteria, the sensor network optimization problem is accordingly formulated, and solved by the genetic algorithm. The proposed study is validated for various operating conditions including the distributed heat generation condition and different cooling conditions.
Design of Service Model & System Architecture for Maintenance Support System based on PHM
Authors: Jung Hoon , Choi Yong Hoon
Published: March 2024

Led by certain European countries, rolling stock (railway car) maintenance technology is undergoing a paradigm shift from preventive maintenance based on the inspection period to predictive maintenance in a bid to reduce damages to railroad components that cause interruptions to railroadoperation and incur unnecessary maintenance costs. This has led to increasing demand for fault diagnosis and remaining- useful-life-prognosis technologies in order to simultaneously satisfy the need for greater reliability and lower maintenance costs to cope with faster systems. This study aimed to design the functions and architecture of a rolling stock maintenance support system that analyzes the status data collected from sensors installed at onboard and wayside in order to automatically evaluate and prognosticate the likelihood of parts failures, so as to manage railroad car parts more efficiently.
Fault Detection and Diagnosis of Rolling Element Bearing based on Neural Network
Authors: Lim Chaeyoung , Kim Seokgoo , Choi Joo-Ho
Published: March 2024

In this paper, a neural network based diagnosis technique is developed to detect fault and estimate the severity of the spalls at the inner race, outer race, roller and cage. To this end, a bearing test rig is developed, in which the normal and faulted bearings with differing spall size at different locations are operated under accelerated loading conditions. Features are extracted from the bearings, which include the time-based indicators such as RMS, peak, crest and kurtosis, frequency based indicators obtained by envelope analysis, and timefrequency based ones like wavelet decomposition. Neural network model is constructed using the features for the classification. The model is then applied to diagnose the fault in the new bearings, which includes the identification of the fault and severity. Particular attention is made to the study of statistical significance to check the validity of the model.
A Study on Necessity to Introduce Prognostic Maintenance of Rolling Stock
Authors: Kim Ju-Won , An Chan-Hoi , Lee Seong-Hee , Han Jung-Hwa
Published: March 2024

The easiest way to prevent the railway vehicle failure is to heavily invest money and labor into maintenance and advance the vehicle replacement and maintenance cycles. However, the actual operational maintenance resources (cost and time) are limited, making it difficult to predict when and where sudden failures would occur. Therefore, many studies have examined how to build an effective maintenance system that can minimize failures based on limited resource availability. In this paper, I will examine the necessity of predictive maintenance and research trends.
Point-of-care CD4 Cell Counting for Monitoring HIV/AIDS Patients in Resource-confined Regions
Authors: Kim Jung Kyung , Cho Myoung-Ock , Kim Subin , Shourav Mohiuddin Khan
Published: March 2024

We developed a prototype of optical imaging-based pointof-care (POC) devices that can detect CD4+ T-lymphocytes in human blood for HIV/AIDS monitoring. The proposed portable cell-counting system, Helios CD4, can acquire sample images and analyze particles or cells automatically, by using a simple imaging module and a sample cartridge with a three-dimensional (3D) helical mini-channel. This device has advantages over the existing devices because of its small size and simple scanning mechanism. A performance evaluation was conducted by comparing the cell count obtained using Helios with that obtained using PIMA, one of the most widely used POC CD4+ cell counter.
Clinical Observation on the Life Expectancy of Artificial Pacemakers Implanted into Humans
Authors: Bae Han-Jun , Heo Yun Seok , Park Hyoung-Seob , Han SeongWook , Kim Yoon-Nyun
Published: March 2024

As the life span has been extended, heart disease has also become increasingly common, making it the second leading cause of death in Korea. Advances in treatment include the use of cardiac devices, especially artificial pacemakers, which are mainly used for arrhythmias. Recently, the use of cardiac devices has been increasing, but there are insufficient data on their use in Korea. The life expectancy of cardiac devices is often not consistent with laboratory results and clinical outcomes. There has been little research on the actual life expectancy of cardiac devices implanted into the human body. Patients who had implanted cardiac device for treatment of the cardiac arrhythmia at Keimyung University Dongsan Medical Center until August 2014 were collected the duration until replacement. There were a total of 402 patients. 367 (91/3%) had only replacement of generator, and 32 (8%) patients were replaced with lead and generator. In case of the pacemaker, the duration of first and second replacement were 7.6 years (2778 days), 6.2 years (2256 days). There was the significant statistically reduction in second replacement. In case of defibrillator, the first and second replacement was 5.8 years (2124 days), 5.3 years (1940 days). The longer the implanted period, the shorter the replacement period.
Identification and Quantification of Leptin in Serum by using a Disposable electrochemical Immunosensor
Authors: Seong Ryong , Heo Yun Seok
Published: March 2024

Obesity has already become one of the most common medical condition in the world. Leptin has been reported that it has essential role in obesity. The identification and quantification procedure of hormone leptin using disposable electrochemical immunosensor is described in this paper. Serum has been prepared from diet-induced obesity (DIO) model (C57BL/6J, male). DIO model is induced by high fat feed for over 2 months with above 30% of body weight. We used immune- assay method involving in the target analyte (leptin) and polyclonal anti-leptin antibody which has been immobilized on disposable electrodes by chemical linkers, 4-mercaptobenxoic acid (4-MBA), N-hydroxysuccinimide (NHS) and N-ethyl-N′-(3-dimethyl aminopropyl) carbo diimide (EDC). A linear calibration plot has also suggested with a specific concentration range between 100pg/mL and100ug/mL by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The usefulness of the immune sensor was evaluated by comparing with enzyme-linked immunosorbent assay (ELISA). This attractive method with the simplicity and miniaturization of the required instrumentation would be promising alternative in the development of portable devices for clinical analysis.
Statistical Modeling for Reliability Assessment Using Rubber Stiffness Data of the Automotive Engine Mount
Authors: Joo Minho , Doh Jaehyeok , Lee Jongsoo , Jang Yongsok , Jang Hongsok , Park Jongchan
Published: March 2024

Input variables used in mechanical system analysis or Prognostics and Health Management (PHM) are various, such as life of material, size and property of product. It has uncertainties (Aleatory uncertainty) due to various noises like temperature, mechanical error and various environmental differences during experiments in real field. For example, obtained data by conducting repeatedly the experiment in the identical environment condition have to be same theoretically. However, experimental data have the variation due to the generated error or noise by the various factors and uncertainties. In this study, the improved algorithm how to determine proper distribution of input variable containing uncertainties is proposed using the probabilistic and statistical method. Also, the validity of this improved algorithm is verified using the automotive engine mounts rubber stiffness data.
Bayesian Analysis of Two-Phase Degradation Data Based on Change-Point Wiener Process
Authors: Wang Pingping , Tang Yincai , Bae Suk Joo , He Yong
Published: March 2024

In degradation test of some products, such as plasma display panels (PDPs) and organic light emitting diodes (OLEDs), observed degradation paths tend to exhibit multi-phase patterns over testing period. In this paper, we propose a change-point Wiener process (CPWP) model to fit the degradation data with two-phase patterns, mainly in Bayesian framework. Hierarchical Bayesian approach is employed to estimate the parameters in this model. Considering distinct degradation behaviors between testing items, degradation rates and change-points are specified as random variables, varying from item to item in the hierarchical Bayesian CPWP. The proposed model is illustrated by a simulation study along with real application to OLED degradation data. The simulation results show that the hierarchical Bayesian approach is superior to the maximum likelihood method. From the analysis of the OLED degradation data, the CPWP model outperforms other existing models in reliability prediction.
Effective Single Twymeeg Administration for Elderly Patient with Type 2 Diabetes (T2D), Arthralgia and Depression as Common Medical Problems
Authors: OKADA Masaki , BANDO Hiroshi , IWATSUKI Noboru , SAKAMOTO Kazuki , OGAWA Tomoya
Published: March 2024

Background: Imeglimin (Twymeeg) has been effective for patients with type 2 diabetes (T2D).
Case Presentation: The patient is an 82-year-old female with T2D, arthralgia, and depression.
Results: She was given duloxetine hydrochloride (Cymbalta) and Twymeeg, leading to improved symptoms of low back pain (LBP), knee osteoarthritis, osteoporosis, depression, and possible mild cognitive impairment (MCI). HbA1c decreased from 8.8% to 7.1% for 4 months with a single administration of Twymeeg 2000mg/day.
Discussion: In recent clinical practice, elderly cases often tend to have simultaneously these problems, which are medical and social crucial problems. Twymeeg and Cymbalta may be indispensable agents for future practice.
Novel Nomenclature of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) for Future Hepatology
Authors: Bando Hiroshi
Published: March 2024

Type 2 diabetes (T2D) has close relationships with nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH), and others. The European Hepatology Society International Hepatology Conference (EASL-ILC) 2023 presented the announcement of novel nomenclatures for metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). Furthermore, metabolic dysfunction-associated steatotic liver disease (MetALD) is used to describe those who consume greater amounts of alcohol, defined as more than 210g per week for males and more than 140g per week for females. MASLD may affect about one quarter of adults worldwide. Its criteria include the evidence of hepatic steatosis in three situations: obesity/overweight, T2DM, or the presence of metabolic dysfunction.
Beneficial Japanese Seasoning Sprinkle (Furikake) Leading to Satisfactory Cuisine and Life
Authors: Bando Masahiro , Ogawa Hiroko , Bando Hiroshi
Published: March 2024

Japanese cuisine revolves around rice as a staple, accompanied by various innovations. Among these, Japanese seasoning sprinkle (furikake) has garnered attention due to its rich history, delightful taste, versatility in cooking applications, clinical nutrition, and research contributions. Furikake is crafted from ingredients such as fish, seaweed, eggs, meat, beans, sesame seeds, fermented soybeans (natto), vegetables, fruits, and others.
There are four main types of furikake: dry, soft, freeze-dried, and oil-based. It also plays a role in research, particularly in improving taste abnormalities. In Japanese nursing homes, residents frequently pair furikake with rice, supplying essential trace elements like Fe, Zn, Mg, and Mn, thereby promoting both physical and mental health.
Effect of Preoperative Accurate Evaluation and Intervention on Prognosis and Outcome in Elderly Patients with Painless Gastroenteroscopy: Protocol for A Single-Centre Randomized Controlled Trial
Authors: Chen Hongzhou , Wang Rurong
Published: March 2024

Background: Factors such as abstinence from drinking, fasting, intestinal preparation, and stress responses can contribute to issues like inadequate blood volume, hypoglycemia, and electrolyte imbalances. The ASA classification presently serves as a primary assessment standard globally. In response to prevailing clinical challenges, we propose enhancing the preoperative assessment for elderly patients undergoing painless gastroenteroscopy. This enhancement involves incorporating the Frailty score, bedside ultrasound, and bedside laboratory results alongside the ASA classification, to establish a comprehensive assessment system. This holistic approach aims to monitor patients’ fluid balance, electrolyte levels, and blood glucose changes, facilitating precise interventions and optimized anesthesia strategies. The ultimate goal is to diminish perioperative adverse events, especially those related to the cardiovascular system, thereby enhancing prognosis, outcomes, and patient satisfaction among the elderly.
Methods: This study is a randomized controlled trial conducted at a single center. It includes 204 patients scheduled for painless gastroenteroscopy. Eligible subjects will be randomly assigned to either Group A or Group B. Pre-anesthesia assessments will be conducted twice – during the preoperative visit and upon entry to the endoscopy center on the examination day. Evaluation parameters will encompass the Edmonton Frail Scale (EFS), bedside ultrasound measurements including inspiratory IVCDmax, expiratory IVCDmin, and calculation of the inferior vena cava collapse index (IVC-CI). Additionally, serum electrolyte (potassium) and blood glucose levels will be measured. Patients in Group A will receive specific interventions based on predetermined criteria. The primary endpoint is anesthesia-related adverse events. The measurements will be performed perioperatively, post-treatment, and at 1 day, 3 days, and 7 days after the end of treatment.
Stress and Folate Impact Neurodevelopmental Disorders
Authors: Ahmavaara Kai , Ayoub George
Published: March 2024

Autism Spectrum Disorder (ASD) is one of several developmental disabilities that can create significant communication and behavioral challenges in affected individuals. Several studies have found that children with ASD have high levels of Folate Receptor Antibody (FRA), which blocks the transport of folate across the Blood-Brain Barrier (BBB) and leads to Cerebral Folate Deficiency (CFD). Supplementation with folate in its reduced form, such as with folinic acid, has been found to improve communication in autistic children with folate receptor antibodies. Here, we provide an overview of the role of folate in nervous system development, effects of FRA on brain folate levels, and clinical trials that have examined the efficacy of folate supplementation in reducing the symptoms of developmental disabilities. Further, we highlight the importance of prenatal folate supplementation in reducing the risk and severity of developmental disorders and the need for additional research to explore optimal dietary interventions to aid in managing them. The results suggest that supplementing with reduced folate may offer a promising treatment approach for individuals with neurodevelopmental disorders, particularly those with FRA.
EDUCAÇÃO INFANTIL DURANTE O PERÍODO DA PANDEMIA DA COVID-19 NO BRASIL E EM PORTUGAL:
Authors: Marks Elenise , Zucchetti Dinora Tereza , Belchior Ferreira Barreto Maria Antónia , Gonçalves de Oliveira Luís Miguel
Published: March 2024

Este ensaio acadêmico, de caráter documental, insere-se nos estudos que vêm sendo realizados no âmbito do Mestrado em Diversidade Cultural e Inclusão Social. A pesquisa investiga, por meio da metodologia educação comparada (FERREIRA, 2008), a função social das escolas de educação infantil nas camadas populares, no Brasil e em Portugal, durante a Pandemia da covid-19. Aqui, busca-se compreender as adequações realizadas em legislações e normativas educacionais para a educação infantil durante esse período. Entre os resultados, tem-se que, enquanto em Portugal, por meio do seu governo central, as escolas foram amparadas com suporte legal, ágil e objetivo, no Brasil, foram especialmente os estados e municípios que, na ausência de orientação nacional, buscaram suprir os desacertos do governo federal.
ESTUDOS SOBRE FORMAÇÃO CONTINUADA NA EDUCAÇÃO INFANTIL E A RELAÇÃO COM A PRÁXIS DOCENTE:
Authors: Bento de Oliveira Francisca Fabiana , Vieira Bôto Anne Heide , Gonçalo do Nascimento Bruna , Dias Cavalcante Maria Marina
Published: March 2024

Este ensaio apresenta o caminho percorrido em uma disciplina sobre a construção do Estado da Questão (EQ) no curso de mestrado. Para subsidiar a investigação, adotamos os seguintes questionamentos: o que os trabalhos científicos revelam sobre a formação continuada na Educação Infantil? Quais as contribuições e lacunas do inventariado em relação à práxis docente na creche? Propomos como objetivos específicos identificar os trabalhos publicados que se aproximam das seguintes categorias: formação continuada, educação infantil e práxis docente. A metodologia adotada configura-se em uma pesquisa quali-quantitativa de abordagem reflexiva respaldada pelo levantamento bibliográfico. O levantamento foi realizado no Portal de Periódicos da CAPES. Consultamos os artigos científicos publicados nessa base, adotando um recorte temporal de cinco anos, compreendido entre 2017 e 2021. O referencial teórico ancorou-se nos estudos de Nóbrega-Therrien e Therrien (2004), Imbernón (2010) e Gomes (2009). Os resultados da pesquisa revelaram que a formação continuada dos professores da educação infantil vem ganhando notoriedade no campo científico e na sociedade. Constatamos ainda, através dos artigos lidos, uma pequena quantidade de trabalhos referentes à educação das crianças que frequentam as creches. Concluímos que os estudos realizados por meio do EQ, mediados pela reflexão e diálogo, possibilitaram-nos fazer interlocuções com o objeto de estudo, auxiliando tanto na confirmação das categorias teóricas como também dos objetivos e do problema da pesquisa.
PERCEPÇÕES DE PROFESSORES DE FÍSICA SOBRE ASPECTOS DA IMPLEMENTAÇÃO DA BASE NACIONAL COMUM CURRICULAR
Authors: Ribeiro de Figueirêdo Filho Jafé , Mac Cole Tavares Santos Jean
Published: March 2024

Com a homologação do texto final da Base Nacional Comum Curricular (BNCC) para a etapa do Ensino Médio em dezembro de 2018, estamos ante os primeiros movimentos do processo de implantação. Diante de uma política educacional que será responsável por balizar a reelaboração dos currículos em todo o país e que apresentou, em sua trajetória de consolidação, diversas disputas por sentidos entre grupos ligados à educação, propõe-se neste artigo investigar as percepções de professores de Física acerca da temática, contribuindo para o debate sobre a Base, delineando aspectos do processo de implementação e destacando posicionamentos dos professores. O estudo, de cunho exploratório e base qualitativa, teve seus dados produzidos por meio do uso de questionários fechados com perguntas no formato de escala de Likert, aplicados a um grupo de professores que lecionam a disciplina de Física em instituições de ensino públicas, no município de Mossoró, estado do Rio Grande do Norte. Concebemos a Base dentro do estudo como uma política analisada por meio da Abordagem do Ciclo de Políticas, com ênfase nos contextos da prática e dos resultados/efeitos. A partir da análise dos dados produzidos, foi possível inferir, dentre outros apontamentos, que as ações de formação continuada sobre a temática ainda se apresentam incipientes e que os docentes não estão certos quanto a uma possível equidade no processo de ensino-aprendizagem vinda da implementação da Base.
Ninfoplastia: Uma Análise Completa das Técnicas Cirúrgicas e seus Desdobramentos Emocionais
Authors: Filho Carlos Augusto da Conceição Sena , Nascimento Washington Luiz do , Carvalho William Ferreira de , Jales João Gilberto Mendonça Bezerra , Batista Leonardo Nascimento de Sousa , Costa Adrianna Torres da , Carvalho Leonardo Cavalcante de , Azevedo Maria Carolina Oliveira , Baseman Guilherme Tambosi
Published: March 2024

Introdução: A ninfoplastia, também conhecida como labioplastia, é um procedimento cirúrgico que tem ganhado cada vez mais destaque na área da cirurgia plástica genital feminina. Neste contexto, é fundamental uma análise completa das técnicas cirúrgicas envolvidas na ninfoplastia e de seus impactos emocionais, a fim de compreender melhor os benefícios e as potenciais repercussões psicológicas dessa intervenção cirúrgica. Objetivo: Explorar e compreender as técnicas cirúrgicas envolvidas na ninfoplastia, analisando os impactos emocionais pré e pós-operatórios nas pacientes. Metodologia:  Foram utilizadas as bases de dados Google Scholar, Scielo e Medline, buscando artigos publicados entre os anos de 2015 e 2023, nos idiomas Português ou Inglês. Considerações Finais: Com base na análise das técnicas cirúrgicas envolvidas na ninfoplastia e em seus desdobramentos emocionais, é possível concluir que essa cirurgia representa uma opção eficaz para mulheres que buscam melhorar sua autoimagem genital e reduzir sintomas de desconforto ou insatisfação. Portanto, uma abordagem holística, que leve em consideração não apenas os aspectos técnicos da cirurgia, mas também o bem-estar psicológico das pacientes, é crucial para garantir resultados satisfatórios e duradouros.