bluuebunny/crossref_metadata_2025 huggingface dataset:
The Seroprevalence of Hepatitis A in Patients with Positive Human Immunodeficiency Virus
Authors: Omidifar Navid , Bagheri Lankarani Kamran , Aghazadeh Ghadim Mir Behrad , Khoshdel Nika , Joulaei Hassan , Keshani Parisa , Saghi Seyyed Amirreza , Nikmanesh Yousef
Published: November 2023

Background: Hepatitis A virus (HAV) can have severe manifestations in adult patients with other liver diseases, particularly in those infected with human immunodeficiency virus (HIV). This study aimed to measure immunity against HAV in HIV-positive individuals to determine the necessity of vaccination against HAV in this population. Methods: This cross-sectional study investigated 171 HIV-positive patients aged 18 years or older who were tested for serum IgG anti-viral hepatitis A antibody. The prevalence and its determinants were analyzed based on patient data. Results: The average age of the patients was 44.2 years old. The prevalence of HAV antibody positivity was 97.7%. The prevalence was higher in patients older than 30 years. There was a close association between hepatitis C virus (HCV) infection (P=0.002). There were no significant correlations between antibody levels and sex, marital status, employment status, education level, economic status, smoking status, drug use status, and physical activity level. The mean and median CD4+counts in patients with positive (reactive) antibody (Ab) levels were 458 and 404±294, respectively, while the mean and median CD4+counts in patients with non-reactive antibody levels were 806 and 737±137, respectively, in those who tested negative for anti-HAV Ab (P=0.05). Conclusion: The prevalence of anti-hepatitis A IgG antibodies in people with HIV was very high in Shiraz. There is an increasing trend in the number of older patients and those with HCV infections. The negative association with CD4 was borderline in this study, which needs to be confirmed in larger groups
Hoja de ruta para la capacitación de docentes en neuroeducación
Authors: Briceño Méndez Jesus Hildemaro , Luengo Molero Dilida Anayra , Urbina Velasco Cristian Javier
Published: November 2023

La investigación sobre funciones mentales, relacionadas con la educación, ha generado una nueva visión de estos procesos desde la neurociencia que se ha denominado: neuroeducación. Aprovechar estos resultados puede generar innovaciones educativas para mejorar la praxis del aprendizaje estudiantil. Esto requiere de un profesional dentro del aula de clase que conozca los conceptos de la neuroeducación y traduzca este conocimiento en la mejora de las actividades del aula. En este sentido, el objetivo de este trabajo es presentar una propuesta de hoja de ruta para generar un programa de capacitación para el docente que actualmente está en aula y que carece de formación profesional en la neuroeducación.
Análisis de la zona residencial: urbano-rural en el rendimiento atencional
Authors: Gordón Rogel Jorge , Estévez Narváez Sol , Almendariz Donoso Melany
Published: November 2023

¿Alguna vez te has preguntado cómo el lugar donde vivimos puede afectar nuestra capacidad de atención? En este artículo científico, se exploró en detalle el impacto de la zona residencial urbana y rural en el rendimiento atencional en la provincia de Imbabura. Mediante el instrumento Attentional Network Test (ANT), se descubrieron resultados que revelan diferencias significativas entre entornos. Se involucró a 617 participantes, divididos entre residentes urbanos y rurales, donde se reveló una relación en el rendimiento atencional durante la aplicación del ANT en residentes de diferentes zonas demográficas.
Los resultados revelaron que existe diferencia significativa (p>0,05) en el promedio de aciertos del primer bucle del ANT, área urbana mostró una media menor (0,94708 ±0,037368), mientras área rural obtuvo una media mayor (0,95205±0,033413); esto sugiere que la zona residencial puede tener influencia en el rendimiento atencional, además se descubrió la ausencia de diferencia significativa en el segundo bucle, planteando un posible sesgo de aprendizaje de los estímulos del ANT.
Este estudio contribuye al conocimiento limitado sobre la atención y la zona residencial en América Latina, destacando la importancia de considerar el entorno ambiental al estudiar procesos cognitivos, el presente hallazgo abre nuevas oportunidades para investigar rigurosamente esta relación compleja.
The Relationship Of The Completeness Of Filling In Medical Records With The Quality Of Medical Record Documents In Inpamentation Patients At Grandmed Lubuk Pakam Hospital In 2023
Authors: Siahaan Okta Vealina
Published: November 2023

Medical records are one of the important parts in shaping the implementation of service delivery to patients in hospitals. Completeness of filling out medical records that are accurate, complete, and timely is needed in health services to fulfill the requirements in medical record management. Completeness of filling out medical records with the quality of medical record documents has a very significant role where the higher the completeness of filling out medical records, he higher the quality of medical record documents. This study aims to determine the relationship between the completeness of filling out medical records and the quality of medical records document for inpatients at Grandmed Lubuk Pakam in 2023. The method used in this research is analytic observational with cross sectional research design. The population of thes study were all medical record files of inpatients and sampling using simple random sampling technique as many as 77 files. The study was conducted by checking the observation sheet on the medical record files of hospitalized patients. Data analysis of this study used chi square test with 95% confidence level, (? = 0,05). The results showed that there was a retionship between the completenees of medical record filling and the quality of medical record documents for inpatiens ( p = 0,001 > 0,05 ). It is hoped that hospital will pay more attention to the completeness of filling out medical records on each sheet (form) in order to improve and maintain the quality of hospital services.
THE FIRST RECORD OF HISTER HELLUO TRUQUI, 1852 (COLEOPTERA: HISTERIDAE) IN THE KAMA REGION
Authors: Dedyukhin S.V.
Published: November 2023

In the south of Udmurtia, a rare species of the clown beetle, Hister helluo Truqui, 1852, was found. The beetles were collected in the Ust-Belsk Nature Park on the banks of the Nizhnekamsk Reservoir on the leaves of gray alder ( Alnus incana ), where they preyed on the larvae of the leaf beetle Agelastica alni . The distribution of the species is not well understood. The find of the species in the Vyatka-Kama region is the most eastern of those previously known in Europe.
CAPTURE EFFICIENCY OF TWO SNAP TRAP MODELS FOR SMALL MAMMALS
Authors: Tolkachev O.V. , Maklakov K.V. , Malkova E.A. , Gorshkolepova A.V.
Published: November 2023

The capture of small mammals (mouse-sized rodents and shrews) is widely used in zoological and ecological research. The most common traps are snap traps, which allow you to quickly get the desired sample of animals and / or cover the required number of points. Researchers have access to different models of snap traps, differing in design and materials used, which may result in unequal efficiency of the devices, which makes it difficult to compare the results obtained with their help. The purpose of this work was to compare two modifications of snap traps with hook (on a wooden or steel base) in terms of catchability and the spectrum of animals considered differed by species, weight or sex. No statistically significant differences were found for both parameters, therefore the tested models of traps are interchangeable. Other features of traps that can affect the convenience and success of their use are discussed. The conclusion is made about the preference of using a model on a steel base.
DYNAMICS OF PERMAFROST HEAVING HILLS IN THE SOUTHERN PERMAFROST ZONE OF WESTERN SIBERIA
Authors: Korkin S.E. , Korkina E.A. , Taran I.M.
Published: November 2023

The article presents an analysis of the annual dynamics of the temperature regime of the permafrost heaving mound in the southern permafrost zone of the West Siberian Plain. The study area belongs to the Tagrinskoye oil and gas field. The use in the study of the data of the automatic measuring system of temperatures within the heaving mounds made it possible to record the following parameters in time: temperature and moisture of the air at an altitude of 2 m, temperature and moisture of the soil at different depths. Modern automatic systems that record temperature data in permafrost make it possible to monitor changes in permafrost processes and predict undesirable technogenic hazards. The temperature regime of the permafrost heaving mounds is differentiated into an active layer with the dynamics of positive and negative temperatures and into the core of the heaving mounds, where long-term negative temperatures remain. Temperature study in permafrost is an important engineering and geological data that allows assessing the plasticity of permafrost in heaving mounds. It was determined that the active layer at a depth of 0,3-0,6 m has the highest dynamics of thawing and freezing, which refers to seasonal thawing. Permafrost rocks are plastic-frozen rocks, since at a depth of 1,6 m the temperature of permafrost increased to -0,3 °C and at a depth of 3 m it is underlain by melted sand with an average temperature of 0,66 °C. The dynamics of geomorphological changes depends on a complex of factors: the amount of precipitation in the summer-autumn period, the transition to zero temperatures and the amount of snow in the autumn-winter period.
HYDRO-ECOLOGICAL PROBLEMS OF USE AND PROTECTION OF WATER RESOURCES OF THE REGION
Authors: ANDRUSYSHYN Tetiana , FAIFURA Vasyl , BYTSYURA Leonid
Published: November 2023

Introduction. The article examines the issue of the hydroecological state of water resources of Ukraine in a regional section. The situation and trends in the field of water supply, some aspects of financing, administrative and management vectors in this field are characterized.
The need to implement a set of measures to improve the ecological situation, optimize approaches to nature use, preservation and protection of hydroecosystems is emphasized.
The purpose of the article is to study the hydro-ecological state of water resources, factors affecting water quality, approaches to the protection of hydro-ecosystems at the regional level (on the example of the Ternopil region).
Research methods. The methods of analysis and synthesis, systematic generalization were used in the preparation of the scientific publication.
Results: Ukraine's water resources require comprehensive measures to improve their ecological status, optimize approaches to nature management and protection. An analysis of the ecological problems of the hydroecosystems of the Ternopil region was carried out, and the main reasons for their degradation were clarified. Installed. the satisfactory state of water bodies and the main areas of work to improve the ecological state of the region's rivers are recommended.
It is necessary to clear the riverbeds, optimize the industrial and communal water treatment system, establish and expand sanitary protection zones, strengthen the banks, stimulate the responsible attitude of the population and individuals to the preservation of water bodies and the improvement of their condition through environmentalization, education and financial regulation mechanisms , implement measures to prevent unauthorized interference in channel processes, increase funding for the restoration of water resources with strict control of the expenditure part and strict reporting.
Keywords: water bodies; protection; hydroecosystem; ecological condition; degradation.
MANAGER'S WORK WITH PERSONNEL AS THE BASIS OF CONFLICT PREVENTION IN АN ORGANIZATION
Authors: BABIAK Halyna , KOTYS Nataliya
Published: November 2023

The article is devoted to the problems of the manager's activities with personnel as the basis of conflict prevention in the organization (on the example of LLC SPA "Perlyna Podillya").
Goal. The study of modern trends in the manager's work with personnel as a basis for the prevention of conflicts in the organization, as well as the development of recommendations and substantiation of directions to improve personnel work.
The results. It is stated that the current stage of social and economic development is characterized by the maximum commercialization of social relations, formed under the influence of the market economy and fierce competition, reinforced by periodically occurring crisis phenomena, which exacerbates conflicts and creates conflict situations.
The authors emphasize that personnel policy in the organization as a tool for managing it and conflicts makes it possible to create a competitive policy if there is high-quality personnel potential.
The article states that today the LLC pays considerable attention to the study and diagnosis of conflicts. Personnel policy is always aimed at work with the personnel in conflict situations.
The analysis of personnel work and personnel policy was carried out in the context of prevention and resolution of conflict situations in the conditions of the latest challenges.
The article emphasizes the global experience of conflict resolution. It confidently shows that based on the achievements of joint decisions and coordination of interests, any conflict situations and problems of organizational life at the LLC can be resolved.
The recommendations for improving the personnel policy of the LLC, aimed at conflict prevention, have been developed. And it, in turn, should minimize the possibility of conflict situations at the enterprise.
Keywords: personnel work, personnel policy, personnel management, conflict, conflict situation, conflict prevention.
PENGARUH BRAND IMAGE, PERSEPSI HARGA, DAN ELECTRONIC WORD OF MOUTH KEPADA KEPUTUSAN PEMBELIAN PRODUK MAD FOR MAKEUP DI BANDUNG
Authors: Hendriyani Putry , Saputri Marheni Eka
Published: November 2023

Saat ini minat terhadap produk makeup sangat meningkat. Produk makeup lokal pun semakin berkembang dan banyak melaksanakan inovasi. Salah satu merek makeup lokal yaitu Mad For Makeup. Tujuan dari penelitian ini adalah untuk menganalisis bagaimana keputusan pembelian pada produk Mad For Makeup di Bandung yang dipengaruhi oleh citra merek, persepsi harga, dan e-word of mouth. Menggunakan pendekatan kuantitatif dengan analisis deskriptif serta kausal. Sampel diambil secara nonprobabilistik, dengan sampling purposive. Analisis dalam penelitian ini menggunakan deskriptif dan regresi linear berganda. SPSS versi 25 digunakan dalam mengolah data. Hasil penelitian menampilkan bahwasanya variabel brand image (X1), persepsi harga (X2), dan electronic word of mouth (X3) berimplikasi signifikan secara parsial dan simultan kepada keputusan pembelian produk Mad For Makeup di Bandung. Disarankan agar Mad For Makeup memastikan bahwa merek nya unik, membuat produk yang sesuai antara harga yang ditawarkan dengan manfaat yang diberikan produk tersebut, meningkatkan keanggotaan komunitas “Rebels”, serta menjelaskan melalui konten sosial medianya mengenai fungsi produk yang ditawarkan.
FINANCIAL LITERASI, PERILAKU KEUANGAN SEBAGAI PEMBENTUK FINANCIAL CAPABILITY PADA UKM KREATIF DI SULAWESI SELATAN
Authors: Sarira Simeon , Kadir Abd Rahman , Saudi Nur Dwiana Sari
Published: November 2023

Penelitian ini bertujuan untuk menganalisis dan menjelaskan bagaimana Financial Literasi, Perilaku Keuangan sebagai pembentuk .financial capability pada UKM Kreatif di Sulawesi Selatan, Jenis penelitian yang akan digunakan dalam penelitian ini adalah explanatory Tipe. Penelitian ini merupakan penelitian kausalitas dimana terdapat hubungan kausal antara dua variabel atau lebih. Kuesioner yang disebar dalam penelitian ini adalah 100 dan tingkat pengembalian kuesioner (response rate) adalah 100% dan kuesioner yang layak untuk dianalisis adalah 100 kuesioner. Metode analisis data dalam penelitian ini adalah analisis regresi linier berganda (Multiple Regression Analysis). Data penelitian diolah dengan menggunakan program Statistical Package for Social Science (SPSS). Dengan menggunakan teknik pengumpulan data menggunakan kuesioner. Hasil dari peneltian ini menunjukkan bahwa Financial Literasi dan Perilaku Keuangan memiliki pengaruh dalam membentuk financial capability pada UKM Kreatif di Sulawesi Selatan.
PENGARUH ELECTRONIC WORD OF MOUTH (eWOM) DAN BRAND IMAGE TERHADAP MINAT BELI PADA SKINCARE AVOSKIN BEAUTY MELALUI PLATFORM MEDIA SOSIAL TIKTOK
Authors: Hermita Putri  , Riski Taufik Hidayah
Published: November 2023

Penelitian ini dilakukan karena penjualan produk Avoskin mengalami penurunan pada bulan Agustus 2022. Dalam kategori produk perawatan kulit, Avoskin menempati peringkat kelima di antara pesaing lokal. Akibatnya, minat dan penjualan konsumen untuk Avoskin menurun karena masih kalah dengan kompetitor sejenis. Tujuan penelitian ini adalah untuk mengetahui pengaruh yang signifikan dari hasil variabel Electronic Word of Mouth dan Brand Image terhadap Minat Beli Konsumen. Pengumpulan data dilakukan dengan menyebar kuesioner kepada 115 responden berusia 16-29 tahun, pengguna aktif media sosial TikTok yang mengetahui akun TikTok @avoskinbeauty, berdomisili di JABODETABEK dan Kota Bandung. Pengujian data dilakukan menggunakan metode SEM (Structural Equation Modelling) menggunakan software SmartPLS 3.0 for Windows. Hasil dari penelitian mengindikasikan bahwa terdapat pengaruh yang positif dan signifikan antara Electronic Word of Mouth dan Brand Image terhadap Minat Beli. Rekomendasi penelitian untuk Avoskin yaitu meningkatkan minat beli konsumen dengan bekerja sama dengan influencer TikTok yang lebih populer. Selain itu, apabila penelitian selanjutnya menggunakan topik yang berhubungan dengan variabel electronic word of mouth, brand image, dan minat beli diharapkan dapat melakukan pengembangan penggunaan media sosial lainnya dan objek yang berbeda.
Survey of Prognostics and Health Management for Transformers: From Statistical Analysis to Condition-Based Diagnostics
Authors: Cheng Jiaxiang , Cho Sungin , Tan Yap Peng , Hu Guoqiang
Published: November 2023

Power transformers are one of the key network components for reliable and efficient operation of power grids. Over the past few decades, there have been growing research efforts in improving the prognostics and health management (PHM) for transformers, including failure analysis using time-to-event data and condition-based diagnostics for both single and multiple components. In this paper, we survey recent literature and relevant works, focusing on widely used statistical models and advanced diagnostic techniques that leverage on condition data and maintenance history. Additionally, we examine the role of artificial intelligence (AI) applications in PHM for power transformers. Finally, we summarize the current limitations and future opportunities to support new research efforts for improving the monitoring of power transformers.
Algorithmic Study for Power Restoration in Electrical Distribution Networks
Authors: Kawahara Jun , Yamaoka Chuta , Ito Takehiro , Suzuki Akira , Iioka Daisuke , Sugimura Shuhei , Goto Seiya , Tanabe Takayuki
Published: November 2023

We study the automated power restoration in electrical distribution networks, from the algorithmic viewpoint. During power outages, blackout sections without faults may be able to be recovered early using the capacity margins of surrounding supply sources. However, remote supply sources must be utilized in cases where the capacity margins of the neighboring supply sources are insufficient for the scale of the power outage, which is called multi-stage power restoration. In multi-stage power restoration, the distribution network subject to control becomes broader, and in addition, even healthy sections are subjected to control. In this study, we give an efficient algorithm which determines whether multi-stage power restoration is needed for power restoration, and in either case, the algorithm calculates the switching procedure which recovers power with the minimum number of switch operations. Our algorithm employs a novel algorithmic technique of combinatorial reconfiguration, which enables to maintain power supply to the healthy sections.
Expert Knowledge Transfer from CAE Models to CNN Models Using Enhanced Adversarial Domain Adaptation
Authors: Choi Minseok , Lee Dongmin , Hwang Mikyung , Yoo Dongwhi , Oh Hyunseoh
Published: November 2023

It is a common belief that convolutional neural networks (CNN) are incapable of acquiring knowledge from domain experts for fault detection and diagnosis. To address the challenge, this paper proposes a knowledge-transfer scheme from computer-aided engineering (CAE) models to CNN models. Domain experts build the CAE models that emulate the faulty behavior of rotating machines by incorporating fault symptom and controlling the degree of fault severity. Fault data are hardly acquired from rotating machines in the field, while a sufficient number of fault data can be generated using the CAE models. Then, a domain adaption model is trained using synthetic data (i.e., normal and fault data) from the CAE models and real data (i.e., normal data only) from rotating machines. To evaluate the validity of the proposed method, a small-scale testbed is regarded as the target system that does not have any fault data. This study contributes to resolve the dearth of fault data from most safety-related engineering assets such as power plant steam turbines, wind turbines, and urban air mobility.
RUL Estimation for Package Failure of Power Electronic Devices Using Integral Mean of Precursor Signal
Authors: Lu Zhonghai , Guo Chao , Ghesquiere Pol , Kriegel Kai , Mitic Gerhard
Published: November 2023

Package failure like bond-wire lift-off is one common cause of failure for discrete power electronic devices such as Schottky diodes. To estimate their Remaining Useful Lifetime (RUL), forward voltage drop is often used as the precursor signal. Prior researches use the direct forward voltage feature and its derived features to construct neural networks for RUL prediction. These features can reflect the instant health condition of the device in the current time or time window, but miss to represent the accumulated effect of gradually decreasing health conditions. In the paper, we formulate the integral mean feature of forward voltage drop and propose to use it to conduct RUL estimation. By the integral mean feature, we are able to capture the device's health condition in an accumulated fashion. Our experiments show that our approach is superior in generalization performance when compared to the forward voltage feature and its statistical features based neural networks for RUL estimation.
Anomaly Detection in Airliner Centrifugal Compressor Using Sensor Data during the Climb Phase
Authors: Shigetomi Sadanari , Imamura Makoto , Kaido Naoya , Taniguchi Makoto , Nishiwaki Masaru , Kaya Junichiro
Published: November 2023

In recent years, aircraft have been able to quickly acquire vast and diverse sensor data, leading to the expansion of predictive maintenance applications. Centrifugal compressors are crucial to aircraft air conditioning systems, which has a high need for anomaly detection due to the impact of failures. However, due to non-stationary behavior, there is a challenge in anomaly detection of the air conditioning system. In this study, we propose a method of detecting anomalies by comparison of actual and predicted behavior of centrifugal compressors for non-stationary time-series data. Multiple failure cases confirmed that bearing deterioration results in changes in behavior.
Advanced condition-based monitoring of CFRP under multiple impacts using monte carlo based prognostics and real-time self-sensing data
Authors: Lee Yong , Oh So Young , Juhyeong Jang , Park Young-Bin
Published: November 2023

Studies on self-sensing system under multiple impacts are limited. Furthermore, real-time prognostics research using electromechanical behavior for impact-damage growth is rare and the impact-damaged area analysis has limited in self-sensing. In this paper, the health state of the carbon-fiber-reinforced plastic samples were monitored in real time utilizing self-sensing data. In-depth damage analysis using C-scan and cross-sectional analysis were conducted to investigate the correlation between the electromechanical behavior analysis. Moreover, the relationship between electromechanical behavior and the impact-damaged area was investigated. The damage propagation during multiple impacts was identified in real time. Furthermore, the electromechanical behavior was predicted to prognosticate the damage propagation in the samples under multiple impacts using a particle filter. The impact damage area was determined based on the predicted electromechanical behavior. Moreover, the prediction accuracy according to data acquired was investigated. An advanced condition-based monitoring methodology can monitor current and future health states and damage propagation under multiple impacts that overcomes the previous self-sensing research. Therefore, this study showed high applicability and guidelines for future self-sensing research fields.
To Trust or Not: Towards Efficient Uncertainty Quantification for Stochastic Shapley Explanations
Authors: Cohen Joseph , Byon Eunshin , Huan Xun
Published: November 2023

Recently, explainable AI (XAI) techniques have gained traction in the field of prognostics and health management (PHM) to enhance the credibility and trustworthiness of data-driven nonlinear models. Post-hoc model explanations have been popularized via algorithms such as SHapley Additive exPlanations (SHAP), but remain impractical for real-time prognostics applications due to the curse of dimensionality. As an alternative to deterministic approaches, stochastically sampled Shapley-based approximations have computational benefits for explaining model predictions. This paper will introduce and examine a new concept of explanation uncertainty through the lens of uncertainty quantification of stochastic Shapley attribution estimates. The proposed algorithm for estimating Shapley explanation uncertainty is efficiently applied for the 2021 PHM Data Challenge problem. The uncertainty in the derived explanation for a single prediction is also illustrated through personalized prediction recipe plots, improving post-hoc model visualization. Finally, important practical considerations for the implementation of Shapley-based XAI for industrial prognostics are provided.
Evaluation of Multi-Modal Learning for Predicting Coolant Pump Failures in Heavy Duty Vehicles
Authors: Fan Yuantao , Atoui Amine , Nowaczyk Slawomir , Rognvaldsson Thorsteinn
Published: November 2023

Coolant Pump failures in heavy-duty vehicles can cause severe collateral damage if they are not detected and resolved in time; the engine will overheat quickly, rendering the vehicle inoperable. Nowadays, a vast amount of heterogeneous sensor data from different sources is being collected in the automotive industry. Such multi-modal data include onboard signals reflecting the overall usage of the vehicle, multi-dimensional histograms that capture the relation between physical quantities, and categorical variables that encode the physical configuration of the vehicle. This work evaluates several multi-modal learning approaches leveraging this diverse data to build a prognosis and health management system for coolant pumps in commercial heavy-duty vehicles. Four auto-encoder architectures are examined to extract features from 2D histograms. These trained models are anticipated to capture key characteristics of the healthy system operation and yield large reconstruction errors when applied on faulty, or near end-of-life samples. Such learned representations are then combined with expert-engineered features. Both early and intermediate fusion are evaluated on a real-world coolant pump replacement dataset. Results indicate that the combination of diverse features was the most effective approach, thereby motivating further research on multimodal methods.
Feature Selection Method for Machine Tool Wear Diagnosis
Authors: Homma Yuji , Nakamura Takaaki
Published: November 2023

We propose an algorithm for estimating the wear condition of tools. We have previously developed a method for predicting machining dimensions by learning features of waveform shapes such as torque during machining as explanatory variables and measured machining dimensions as objective variables. In this method, the features do not fully explain the machining dimensions because including data other than the machining operation such as tool change. In this paper, we propose a method to improve explanatory power and prediction accuracy by selecting subsequences from the machining waveform that are highly related to machining dimensions as explanatory variables. The effectiveness of the proposed method was confirmed through an evaluation using data of machining product part.
Fault Detection of Rotor Bars in Inverter-Fed Induction Motors Based on Current Signature Analysis Technique
Authors: Iwawaki Tomoyuki , Kanemaru Makoto , Yasuhara Yuto , Miyauchi Toshihiko
Published: November 2023

Induction motors, which are one key piece of equipment for power plants, waterworks facilities, and factories, must be maintained appropriately for reliable operation. A motor current signature analysis (MCSA) technique, which monitors and detects problems in motors and diagnoses devices, has already been marketed by some companies. Recently, applications of inverter-fed motors have increased for greater energy conservation. However, a fault-detection method in inverter-fed motors has been inadequately studied despite the risk of misdetection due to inverter noise. This paper shows our results that detected a broken rotor bar in an inverter-fed motor based on a MCSA technique. An abnormal motor with a broken rotor bar and a normal motor are driven by an inverter. The current supplied to both motors is measured and the frequency spectra results are compared. In the measurements, the inverter’s drive frequency is varied from 120 to 10 Hz in 10 Hz increments. In each drive frequency, the slip is varied in a range of 0-5% by adjusting the load connected to the motor. The results of comparing the current spectra show significant reinforcement of the signal intensities in abnormal motors. Some signals are reinforced by both inverter noise and a component that originated in the broken bar. The superposition that may lead to a misdiagnosis in inverter-fed motors is avoided by identifying the normal spectrum shapes of the target equipment in advance.
Building The Health Monitoring and Fault Diagnosis Models For Stamping Press
Authors: Chang Yuan-Jen , Chen Lin-Jie , Tu Yuta , Yang Hung-Pin , Lee Chen-Kang
Published: November 2023

A stamping press is widely used for the metal forming process. To achieve continuous automation and high precision forming, monitoring the press's health and diagnosing faults during stamping is necessary. The three primary types of faults that may occur in the stamping press are lack of lubrication oil, quality variation of lubrication oil, and clearance variation, which can lead to a decline in workpiece quality and reduced lifespan of the dies and presses. This study adopted the Prognostics and Health Management (PHM) technique to implement a predictive maintenance system for the stamping press. To extract relevant data, the National Instrument (NI) DAQ was used to acquire the three-phase currents and X, Y, and Z vibration signals. Six signals provided a total of seventy-two features, and the top three key features were selected for building a health assessment model using the Logistic regression and PCA algorithms. An early warning is triggered when the health indicator drops below the threshold, alerting the operators. Additionally, fault diagnosis was achieved using classification algorithms such as Support Vector Machine (SVM), K Nearest Neighbors (K-NN), and eXtreme Gradient Boosting (XGBoost). The fault diagnosis model achieved high accuracies of up to 99%.
Simple Hybrid Model for Estimating Remaining Useful Life of SiC MOSFETs in Power Cycling Experiments
Authors: Eng Mattias P. , Lövberg Andreas , Misiorny Maciej , Söderkvist Vermelin Wilhelm , Brinkfeldt Klas , Mishra Madhav
Published: November 2023

Recording and prediction of the accumulated damage, which will eventually lead to the failure of power electronic modules, is an aspect of high importance for power electronic systems design and, in particular, for development of Prognostic and Health Management (PHM) schemes for in-field applications. To this end, this paper presents a simple and cost-effective prognostic method for predicting the remaining useful life (RUL) of TO-247 packaged silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs) subjected to power cycling experiments. The model assumes that the major failure mode is bond-wire lift-off and uses a damage accumulation scheme based on Paris’ crack law. The only inputs to the model are historical data on the average junction temperature swing and the temperature-compensated drain-source ON-state resistance at the peak temperature of the current cycle. Using only these two input values, the model is shown to predict RUL with surprising accuracy for the range of constant current loads determining cycling conditions under which the test data series have been acquired. This work is a first step in an ongoing project towards building more elaborate prognostic schemes for RUL-determination of SiC power MOSFETs in actual working conditions, using physics-informed neural networks (PINNs).
Multiphysics-informed DeepONet of a lithium-ion battery to predict thermal runaway
Authors: Jeong Jinho , Kwak Eunji , Kim Jun-Hyeong , Oh Ki-Yong
Published: November 2023

This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) to predict the thermal runaway of lithium-ion batteries (LIBs) under a variety of thermal operational and abuse conditions. Specifically, this study aims to address the functional mapping from a heating curve to predict the evolution of the temperature of a LIB and dimensionless concentration of dominant components of the LIB including an anode, cathode, electrolyte, and solid electrolyte interphase. The proposed method has two key characteristics. First, the MPI-DeepONet is supervised by using ordinary and partial differential equations, which govern highly complex and nonlinear phenomena of thermal runaway of a LIB, including the chemical reaction degradation of the dominant four components and thermodynamics. This feature enables to train of the proposed neural network with a small amount of data available, suggesting that the proposed neural network is accurate and robust even though the proposed method is trained even with limited data. Second, the proposed neural network is trained with the data that is generated from high-fidelity finite element analysis under a variety of thermal operational and abuse conditions because measurements for the thermal runaway of a LIB are limitedly available. Hence, the MPIDeepONet does not require actual measurements, which is extremely difficult in field experiments. Finally, the accuracy and robustness of the proposed architecture are verified through actual measurements and other scenarios, which are different from the data trained. The analysis of results reveals that the MPI-DeepONet secures higher accuracy and robustness than purely data-driven DeepONet. The proposed surrogate model, which is faster than existing surrogate models, suggesting that this model contributes to developing a digital twin model of a LIB, which can be deployed on a battery thermal management system and provides sufficient information for effective power and energy management.
Damage-Based Lifetime Modeling for Power Electronic Devices
Authors: Guo Chao , Guo Chao , Lu Zhonghai
Published: November 2023

Lifetime modeling is an essential tool for ensuring the reliability of systems. The purpose is to estimate the time before the power electronic device failure so that downtime can be reduced and costly failures can be avoided in industry. This paper will first quantify the cumulative damage in the power cycling test using Junction Temperature Swing and Maximum Junction Temperature, and then formulate the cumulative damage-based lifetime model of power electronic devices. This model assumes that the lifetime is linear to the inverse of the cumulated damage, and shows superior performance in experiments compared with the well-known LESIT model.
Machine learning model for detecting hydrogen leakage from hydrogen pipeline using physical modeling
Authors: Suzuki Yuki , Nakayama Jo , Suzuki Tomoya , Soma Tomoya , Izato Yu-ichiro , Miyake Atusmi
Published: November 2023

Hydrogen pipelines (HPL) are one of the hydrogen transportation systems for realizing a hydrogen society. Hydrogen leakage from HPL is a challenge because hydrogen has a wide flammable range and low minimum ignition energy. Thus, hydrogen leakage from the HPL must be rapidly detected, and appropriate actions should be taken. Leakage detection is important for safe operation of HPL. The basic leakage detection method for HPL involves monitoring the pressure and flow rate values of the sensors. However, in some cases, it is difficult to distinguish between non-leakage and leakage conditions using this method. In this study, we focus on a leakage detection method using machine learning (ML) based on the relationship between pressure and flow rate data. There are two challenges in applying the ML- based leak detection method to an HPL. First, there are insufficient operational data for ML during the process- design stage. Secondly, it is difficult to obtain the pressure and flow rate behaviors during hydrogen leakage because leakage does not occur frequently. Consequently, this study employed an unsupervised ML method based on data simulated using a physical model of the HPL. First, a physical model of the HPL (HPL model) was constructed, and an ML model for leak detection was constructed based on the data simulated by the HPL model. The leak detection capability of the ML model was verified by comparing the anomaly scores of the non-leakage and leakage conditions. From the results, the ML model can distinguish between non-leakage and leakage behaviors and identify leakage points under certain conditions. This method can contribute to the optimization of the sensors required for leak detection during the process design stage.
Fusion with Joint Distribution and Adversarial Networks: A New Transfer Learning Approach for Intelligent Fault Diagnosis
Authors: Li Xueyi , Yu Tianyu , He David , Xie Zhijie , Kong Xiangwei
Published: November 2023

Bearings and gears are important components in rotating machinery, and the diagnosis of faults in bearings and gears has always been an important topic. Currently, data-driven fault diagnosis is a better method. However, under actual working conditions, domain shift can easily occur due to different operating conditions, leading to difficulties in transfer learning and significantly reducing the diagnostic performance of the model. Re-labeling the fault types of the model is time-consuming and costly. To overcome these difficulties, a new unsupervised transfer learning framework based on the fusion of joint distribution and adversarial networks has been introduced for the fault diagnosis of bearings and gears in rotating machinery. The joint adaptation network learns the transfer network by aligning the joint distribution of multiple specific domain layers across domains, based on Joint Maximum Mean Discrepancy (JMMD) to achieve domain alignment. At the same time, the domain classifier in the adversarial network is used to minimize the domain classification loss as domain distribution difference to minimize domain shift. The fusion of these two methods achieves domain alignment, reduces model training time, and improves the accuracy and stability of the model. The experimental results demonstrate that the proposed model framework exhibits excellent performance in detecting and classifying different types of faults. The new model framework also demonstrates outstanding performance across various fault detection and classification tasks.
Digital Twin of Built Structures assisted by Computer Vision Techniques: Overview and Preliminary Results
Authors: Narazaki Yasutaka
Published: November 2023

Digital Twin is an effective platform for analyzing, visualizing, and interpreting the condition of as-built structures based on sensor measurement data. Based on the data inflow from the as-built structures, the associated models (typically finite element models) are updated, and/or the full behaviors of the structures are replicated in the virtual space. Despite its potential, field implementation of digital twin concepts often faces challenge, because the specific forms of digital twin, such as sensor types/locations, structural model development, and data fusion algorithms, depend strongly on the case-specific objectives. Focusing on the digital twin concepts assisted by computer vision techniques, this research aims at facilitating the implementation of those concepts by presenting the definition, setting, and preliminary results of digital twin in different application contexts, including post-earthquake building assessment and structural health monitoring based on multiple types of measurements. This research is expected to contribute to the broader impact of digital twin concepts in the structural engineering community.
MEDIA PEMBELAJARAN PENDIDIKAN AGAMA ISLAM (PAI) PADA SISTEM PEMBELAJARAN DALAM JARINGAN (DARING) DI MASA PANDEMI COVID-19
Authors: Rohani Rohani , Kurnuawati Eka , Nurbaiti Syarifah
Published: November 2023

Pandemi Covid-19 telah mengubah banyak aspek kehidupan salah satunya Pendidikan. Transformasi dan akselerasi Pendidikan menjadi tantangan yang juga harus dihadapi oleh pemerintah dan institusi pendidikan. Artikel ini bertujuan untuk mengetahui ragam media ajar yang dapat digunakan untuk disampaikan dalam kegiatan belajar mengajar daring/online. Penelitian ini menggunakan metode penelitian kajian pustaka yang relevan dan survei terhadap peserta didik dan guru SMP di salah satu sekolah di Kota Bandar Lampung dengan menggunakan google form yang diolah dan diinterpretasikan sebagai hasil hasil. Berdasarkan penelitian, beragam media ajar digunakan seperti youtube, platform Pendidikan seperti whatsapp, google classroom, google meet, zoom cloud meeting, the teacherscorner.net dan lain-lain. Berdasarkan hasil penelitian dapat disimpulkan bahwa penggunaan media ajar maupun platform Pendidikan kiranya dapat menjadi alternatif solusi dalam pembelajaran, demi kelangsungan pembelajaran pada setiap satuan Pendidikan.
PENDIDIKAN ISLAM ORDE REFORMASI (PENDEKATAN POLITIK DAN KEBIJAKAN PENDIDIKAN)
Authors: Hasbullah Hasbullah , Putra Arizal Eka
Published: November 2023

Reformasi merupakan peristiwa bersejarah bangsa Indonesia. Peristiwa ini telah mempengaruhi semua kebijakan negara, termasuk di dalamnya adalah dunia pendidikan Islam. Refomasi bagi pendidikan Islam telah memberikan keluasan dan ruang intergasi keilmuan sehingga terjadi multikultual keilmuan serta pengetahuan. Dalam tulisan ini penulis akan menyajikan bagaimana kondisi pendidikan Islam pada masa orde reformasi dengan pendekatan politik dan kebijakan pendidikan. Adapun penelitian ini menggunakan jenis penelitian kualitatif non interaktif dengan menggunakan metode analisis konsep/isi (teks). Berdasarkan analisis yang telah dilakukan dapat disimpulkan bahwa reformasi telah melahirkan sejumlah kebijakan strategis dalam dunia pendidikan Islam. Hal ini pengaruhnya langsung serta dapat dirasakan oleh masyarakat secara luas dan menyeluruh. Sehingga ada perubahan yang signifikan dalam dunia pendidikan Islam baik itu tujuan, pendidikan, peserta didik, materi, metode dan evaluasi.Pendidikan Islam secara politik dan kebijakan sangat diuntungkan dengan kelahiran undang-undang sistem pendidikan Nasional No. 20 tahun 2003.
Pengelolaan Manajemen Keuangan pada Lembaga Pendidikan SD Negeri 5 Jaten di Karanganyar Jawa Tengah
Authors: Kurnia Aang
Published: November 2023

mendukung kualitas pendidikan. Untuk mewujudkan pendidikan yang berkualitas maka dibutuhkan manajemen keuangan yang professional, sehingga masalah terkait dengan keuangan sekolah dapat dikelola secara efektif dan seefesien mungkin untuk mendukung proses pembelajaran yang berkualitas. Tujuan penelitian ini adalah untuk mengetahui bagaimana penerapan manajemen keuangan pada lembaga pendidikan di SD Negeri 5 Jaten. Tehnik pengumpulan data yang digunakan dalam penelitian ini melalui observasi, wawancara, dan dokumentasi. Teknik analisis data menggunakan analisis deskriptif. Hasil analisis bahwa SD Negeri 5 Jaten sudah menerapkan pengelolaan keuangan yang profesional dan sesuai dengan teori  yaitu menerapkan perencanaan pengannggaran pembiayaan pendidikan, sumber pembiayaan pendidikan, implementasi atau pelaksanaan pembiayaan pendidikan, pertanggungjawaban dan evaluasi pembiayaan pendidikan. Dengan hasil tersebut, secara keseluruhan kegiatan yang berkaitan dengan pengelolaan manajemen keuangan di SD Negeri 5 Jaten sudah sesuai dengan peraturan yang ditetapkan oleh pemerintah.Key Words: Manajemen, manajemen keuangan, lembaga pendidikan
Hakikat Evaluasi Pendidikan Makna Kata Fatana Dalam Al-Qur’an Perspektif Tafsir Tarbawi
Authors: Zahra Zahra
Published: November 2023

Kedudukan Evaluasi pendidikan sangat strategis dikatakan demikian karena dalam Al-Qur’an banyak memuat ayat-ayat yang berhubungan dengan evaluasi pendidikan. Adapun istilah evaluasi dalam Al-Qur’an antara lain, Al-Hisab, Al-hafiddh, Tazkirah, Al-Fitnah, Bala’, Al-Inba’ Al-Wazn, danAt-Takdir. Namun dalam penelitian ini penulis akan membahas Al-Fitnah, (aftana) artinya diserang, memalingkan, menguji. Metode penelitian yang digunakan adalah metode Library Research Adapun Fungsi evaluasi yaitu; mengetahui tercapai tidaknya tujuan instruksional secara komprehensif, sebagai umpan balik, mengukur keberhasilan proses belajar mengajar Sedangkan evaluasi Allah SWT hakikatnya juga bersifat mendidik hamba-Nya agar sadar terhadap fungsinya sebagai hamba-Nya, menghambakan diri hanya kepada-Nya.. Adapun hasil penelitian bahwa kata “Aftana sebanyak 81 ayat dalam 34 Surah di dalam Al-Qur’an. Setelah mengkaji dari Al-Qur’an dan beberapa tafsir, Kemenag, Al-Misbah,Ibnu kasir Berkaitan dengan pemaparan penulis terdapat banyak surah, ayat yang menjelaskankan hakikat evaluasi diantaranya Q.S Al-Ankabut Ayat 2-3. Ujian yang Allah timpakan bagi orang beriman adalah sarana pendidikan Allah bagi mereka. dalam pandangan manusia ujian adalah sarana untuk memberikan balasan yang setimpal, seandainya tidak teruji seorang hamba dengan benar maka akan muncul ketidak adilan dalam hal pembalasan. Ujian yang Allah berikan memiliki beberapa tujaun diantaranya: 1. Menghapuskan kesalahan, 2.Mengangkat derajat 3. Sarana pendidikan, 4. Membersihkan barisan orang-orang beriman 5. Menjadikan sebagai sebuah keteladanan.