bluuebunny/crossref_metadata_2025 huggingface dataset:
USE OF INNOVATIVE TECHNOLOGIES IN PERSONNEL MANAGEMENT
Authors: DYAKIV Olga
Published: November 2023

Introduction. Personnel management is considered to be one of the mechanisms for the implementation of the personnel policy of the managementsubject, including a system of organizational, socio-economic, psychological, moral and other factors founded on the regulatory and legal basis, ensuring the rational use of the abilities of a person both in his own interests and and ininterests of the enterprise. Today, the task of introducing the latest changes in personnel management technology is especially acute. The process of creating innovative approaches in personnel management is no less important than technological innovations. These are the innovative technologies of the personnel management system through recruitment, training, motivation and evaluation of personnel which allow building effective enterprise activity, that confirms the relevance of the research topic.
Goal. Monitoring of innovative approaches in personnel management and substantiation of the expediency of its use by modern enterprises.
Research methods. In order to achieve the defined goal and fulfill the defined tasks, modern research methods were used, in particular: historical and logical analysis - to reveal the process of formation and development of innovative technologies in personnel management; system analysis - to determine the essence of basic categories; analysis and synthesis - to study the existing personnel management system; expert evaluations and the index method – to evaluate the effectiveness of innovative technologies in personnel management.
The results. The latest technologies of HR processes have been identified and analyzed: recruiting, staff training, evaluation and motivation of employees. It was determined that the innovative approach is implemented in the form of new, improved HR processes used in the practical activities of the company's personnel management. Modern technologies of search, choice and selection of personnel, development and motivation of personnel are analyzed. The conditions for the use of innovative technologies in personnel management are substantiated, which will allow developing relationships and ensuring the harmonious satisfaction of the interests of the enterprise’s management and its employees.
Conclusions. The main provisions of the scientific article will contribute to the implementation of innovative technologies in HR processes (formation, training and development, motivation and evaluation of personnel), which will significantly affect the improvement of the formation of qualitative strategic goals of the enterprise.
Keywords: innovative technologies, recruiting, personnel development, personnel motivation, HR processes, personnel evaluation.
FEATURES OF PERSONNEL MANAGEMENT ACTIVITIES IN THE CONDITIONS OF WAR
Authors: KOTSUR Andrii , OSTROVERHOV Viktor , BORTNYK Olga
Published: November 2023

Introduction. The article deals with the main features of personnel management under wartime conditions. Adaptation of life to wartime made some adjustments into personnel management activities as labour resources became the main factor of production. Business entities faced a new problem that is staff retention. External and internal population migration, mobilization of conscripts created a lack of personnel.
The labour legislation also made adjustments and changes in the work of personnel managers. These changes were "dictated" by the need to adapt social and labour relations to current conditions. The main emphasis of the changes in the labour legislation concerned the limitation of the employees’ rights, which were caused by wartime.
The consequences of military actions also affected the organization of work at the enterprises. Curfews, electricity supply schedules, logistical collapse forced enterprises to adapt their work to these circumstances.
Meanwhile, the amount of work for the personnel management specialists has also increased in terms of finding new employees. This task was complicated by population migration and mobilization for military service.
The specified business conditions have made adjustments to the organization's activities. Staff retention, their replenishment and development have become the main tasks of personnel managers.
Goal. Outline of the theoretical and methodological aspects of the peculiarities of the activities of personnel managers in the conditions of martial law and the development of practical recommendations for the stabilization of the enterprise.
In conditions of mass migration of the population, internal displacement, evacuation of enterprises to relatively safe regions, the issue of personnel preservation becomes fundamental. These modern phenomena and trends in the use of personnel force personnel management workers to take measures for keeping the personnel. Under these conditions, appropriate conditions are created for employees not only for labour activity, but also for ensuring safe working conditions, for creating acceptable social and living conditions, the use of various forms of employment and modes of work and rest, which would allow the enterprise to function.
The issue of personnel replenishment and development has become more acute due to the need to train new employees for performing their duties. Such necessity may be caused by dismissal of employees as a result of their migration abroad or to other regions, or the enterprise was evacuated to another region of the country. Moreover, employees may temporarily stop working in connection with mobilization for military service. In these conditions, there is a need to find new employees whose qualification level does not always meet the employer's requirements.
Key words: personnel management, personnel retention, labour legislation, migration, internal displacement, mobilization.
SIBILITY AND HEALTH SUPPORT FOR ELDERLY PEOPLE IN UKRAINE
Authors: SHUSHPANOV Dmytro , DIAKOVICH Lina
Published: November 2023

Introduction. Healthcare for the elderly in Ukraine is currently an urgent societal issue. As our nation ages, it becomes increasingly important to focus on supporting and ensuring the well-being of the older generation. Like many other countries, Ukraine faces various challenges in meeting the healthcare needs of its elderly citizens.
Research on social responsibility and support in this context has the potential to help identify gaps in the current systems and develop effective strategies to ensure the health and quality of life of the elderly population in Ukraine. This will provide a better understanding of the role of public policy, public initiatives, and healthcare services in providing adequate support for the older generation.
A comprehensive study of the state of health of the elderly population in Ukraine under the conditions of war becomes a vital tool for decision-making by policymakers, the provision of healthcare services, and the work of humanitarian organizations. It underscores the unique challenges facing this social group and emphasizes the urgent need for targeted support and intervention. Additionally, it will contribute to addressing the specific needs of the elderly population and directing efforts towards creating a more inclusive and healthy society.
Goal. Identifying specific health issues, social dynamics, and support systems for older people, including those affected by war. The research aims to inform humanitarian efforts, promote social responsibility, and ultimately enhance the health and well-being of older individuals.
Structural methods, comparative analysis, empirical reliance on data, abstract-logical generalization, as well as statistical and sociological methods were used to solve this scientific problem.
The results. he main trends regarding the aging of the population of Ukraine have been identified. The main risks for the health of the elderly in war conditions have been identified and described. The role of social responsibility in maintaining the health of the elderly has been clarified. The main types of support for the elderly, as well as the directions for their implementation in maintaining health, have been determined.
Conclusions. Ensuring a quality life for older people, including health care, social participation, and protection from discrimination, is a moral imperative. Ensuring a quality life for older people, including health care, social participation and protection from discrimination, is a moral imperative.
Given the challenging situation in Ukraine, it is imperative for the government, civil society organizations, and the international community to collaborate in providing comprehensive support to the elderly. This support should encompass access to healthcare and social services while also contributing to the reconstruction of the economy and infrastructure to enhance their quality of life.
Keywords: health, elderly people, population aging, social responsibility, social support. war, Ukraine.
Median and Ulnar Nerve Repair Using Sural Nerve Graft: A Case Report
Authors: Prasetyo Eko , Oley Maximillian C. , Manuhutu Yovanka N. , Panduwinata Dicky
Published: November 2023

Abstract: Most peripheral nerve injuries (PNI) are caused by trauma. Upper extrimities are most likely to get injured, and the radial nerve is the most common part in upper extrimites to be affected. There are different surgical options available to repair PNI. We reported a 48-years-old male consulted to the Neurosurgery Department for right upper limb weakness and numbness. He had history of laceration due to iron sheeting in the right arm. There was an irregular scar, weakness on right lower arm with hypesthesia, but no neurovascular distal (NVD) abnormalities were found. Further examination showed claw hand. EMG examination revealed total lesion of the right median and ulnar nerve, partial lesion of the right radial nerve with the lesion at wrist level, possibly neurotmesis according to Seddon Classification. Initial debridement, suturing, and repairing of radial artery and flexor carpi ulnar tendon were performed. The patient was then diagnosed as total lesion of the right median and ulnar nerve, partial lesion of the right radial nerve, post repair right ulnar and radial artery and flexor carpi ulnar tendon repair with the planning of nerve graft with no complication under monitoring. The patient was scheduled for sural nerve graft procedure. Early nerve repair or reconstruction is needed even though it only results in incomplete recovery for months to years. The sural nerve is great either in diameter or length which can be a huge source for the grafting of nerve and revealed good outcomes. Recent study showed incomplete recovery especially seen when the patient could not fully clench his hand and his thumb did not move properly during the movement. In conclusion, sural nerve graft is an appropriate choice for peripheral nerve injuries with more benefits included. Prognosis depends on how early the treatment performed and how severe the lesion is.
Keywords: median nerve; sural nerve; peripheral nerve injury; nerve repair; nerve grafting
THE EFFECT OF PEOPLE, PHYSICAL EVIDENCE, PROCESS AND PRICE FAIRNESS ON PATIENT SATISFACTION AND REVISIT INTENTION AT THE XYZ HOSPITAL POLYCLINIC, MAKASSAR CITY
Authors: Angelica Vena , Bernarto Innocentius
Published: November 2023

Abstract This study aims to determine the factors of patient satisfaction and the effect on revisit intention. The factors are people, physical evidence, process and price fairness. A written questionnaire is used in collecting respondent data. Total respondents obtained were 277 people. Convenience sampling method is used as a sampling method. The collected data were analyzed using PLS-SEM with SmartPLS software. The research results obtained are people, physical evidence and price fairness that have a positive effect on patient satisfaction. However, for the process variable it was found to have no positive effect on patient satisfaction. Patient satisfaction has a positive effect on revisit intention. The people factor is the most influential factor on patient satisfaction.
 

Abstrak Penelitian ini bertujuan untuk mengetahui faktor kepuasan pasien dan pengaruhnya terhadap niat berkunjung kembali. Faktor tersebut adalah manusia, bukti fisik, proses dan keadilan harga. Kuesioner tertulis digunakan dalam mengumpulkan data responden. Total responden yang diperoleh adalah 277 orang. Metode convenience sampling digunakan sebagai metode pengambilan sampel. Data yang terkumpul dianalisis menggunakan PLS-SEM dengan perangkat lunak SmartPLS. Hasil penelitian yang diperoleh adalah people, physical evidence dan price fairness yang berpengaruh positif terhadap kepuasan pasien. Namun untuk variabel proses ditemukan tidak berpengaruh positif terhadap kepuasan pasien. Kepuasan pasien berpengaruh positif terhadap niat berkunjung kembali. Faktor people merupakan faktor yang paling berpengaruh terhadap kepuasan pasien
PENGARUH KEADILAN PROSEDURAL TERHADAP KEPUASAN KERJA DENGAN PENGEMBANGAN KARIER SEBAGAI VARIABEL INTERVENING
Authors: Tanjung Olivia , Surjanti Jun
Published: November 2023

Kualitas karyawan bergantung pada manajemen karyawan. Pengembangan karir merupakan salah satu strategi pengelolaan karyawan. Untuk mendapatkan pengembangan karir yang efektif maka diperlukan keadilan prosedural dalam menentukan kegiatan pengembangan karir sehingga karyawan mendapatkan kepuasan kerja. Tujuan penelitian ini adalah menganalisis pengaruh keadilan prosedural terhadap kepuasan kerja. Dan peran pengembangan karir sebagai variabel intervening dalam pengaruh keadilan prosedural terhadap kepuasan kerja. Sampel dari penelitian ini adalah seluruh karyawan di bagian SDM yang berjumlah 34 orang. Teknik analisis data yang digunakan adalah analisis metode alternatif Structural Equation Model Partial Least Square. Hasil penelitian menjelaskan bahwa fairness prosedural berpengaruh positif dan signifikan terhadap kepuasan kerja. Keadilan prosedural berpengaruh positif dan signifikan terhadap pengembangan karir. Pengembangan karir berpengaruh positif dan signifikan terhadap kepuasan kerja. Pengembangan karir memediasi pengaruh keadilan prosedural terhadap kepuasan kerja.
Failure Prediction of a Motor-driven Gearbox in a Pulverizer Under External Noise and Disturbance
Authors: Park Jungho , Jeon Byungjoo , Park Jongmin , Cui Jinshi , Kim Myungyon , Youn Byeng D.
Published: November 2023

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.
Condition-based Maintenance of Brake Pads and Tires in Shared Vehicles using Cloud-based Health monitoring and prognostics.
Authors: Lee Jeong Hae , Oh Jaewook , Lee Jeongwoo , Park Seungyoung , Lee Jihyeon , Kim Namsu
Published: November 2023

Cloud-based prognostics and health management is a centralized method for monitoring the condition of individual shared vehicles and determining their maintenance schedules.In this study, we focused on monitoring the condition of brake pads and tires, as these crucial components require frequent and regular maintenance for safety. We developed a data acquisition system to transmit data from acoustic and vibration sensors to the cloud server. Useful and efficient features were extracted and selected from time and frequencydomains to assess the degradation of brake pads and tires. Moreover, based on feature extraction using the KruskalWallis method, we confirmed that diagnosing brake pad conditions with support vector machines (SVM) provides consistent result for classification of sevierities.. Our preliminary results suggest that cloud-based conditionmonitoring can be an effective approach to managing shared vehicles.
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
Authors: Srinivasan Abhishek , Andresen Juan Carlos , Holst Anders
Published: November 2023

A core part of maintenance planning is a monitoring system that provides a good prognosis on health and degradation, often expressed as remaining useful life (RUL). Most of  the current data-driven approaches for RUL prediction focus on single-point prediction. These point prediction approaches do not include the probabilistic nature of the failure. The fewprobabilistic approaches to date either include the aleatoric uncertainty (which originates from the system), or the epistemic uncertainty (which originates from the model param-eters), or both simultaneously as a total uncertainty. Here, we propose ensemble neural networks for probabilistic RUL predictions which considers both uncertainties and decouples these two uncertainties. These decoupled uncertainties are vital in knowing and interpreting the confidence of the predictions. This method is tested on NASA’s turbofan jet engine CMAPSS data-set. Our results show how these uncertainties can be modeled and how to disentangle the contribution of aleatoric and epistemic uncertainty. Additionally, our approach is evaluated on different metrics and compared against the current state-of-the-art methods.
Data-driven Pump Performance Analysis Using Online Monitoring Data Accumulated with Supervisory Control and Data Acquisition System
Authors: Namba Ryo , Hokari Hiroyuki , Komine Hideaki
Published: November 2023

Pump degradation has significant impacts on the operation of water treatment plants since pumps are widely used as basic facilities to supply and distribute water. The authors develop a method for quantifying pump performance to monitor pump degradation by using online monitoring data accumulated with a supervisory control and data acquisition (SCADA) system. The developed method estimates the performance of each pump from the measured values at the confluence of pipes with the least-squares method and respective on-off signals. From the viewpoint of practicality, the usefulness of the developed method was evaluated.
Bridging the Gap: A Comparative Analysis of Regressive Remaining Useful Life Prediction and Survival Analysis Methods for Predictive Maintenance
Authors: Rahat Mahmoud , Kharazian Zahra , Sheikholharam Mashhadi Peyman , Rögnvaldsson Thorsteinn , Choudhury Shamik
Published: November 2023

Regressive Remaining Useful Life Prediction and Survival Analysis are two lines of research with similar goals but different origins; one from engineering and the other from survival study in clinical research. Although the two research paths share a common objective of predicting the time to an event, researchers from each path typically do not compare their methods with methods from the other direction. Given the mentioned gap, we propose a framework to compare methods from the two lines of research using run-to-failure datasets. Then by utilizing the proposed framework, we compare six models incorporating three widely recognized degradation models along with two learning algorithms. The first dataset used in this study is C-MAPSS which includes simulation data from aircraft turbofan engines. The second dataset is real-world data from streamed condition monitoring of turbocharger devices installed on a fleet of Volvo trucks.
Multi-Label Fault Diagnosis of Rotary Machine via Domain Adversarial Neural Network-Based Domain Generalization Targeting High Range Rotating Speed
Authors: Hong Sukeun , Lee Jongsoo
Published: November 2023

Research on deep learning has been increasing in recent years in the field of fault diagnosis in rotary machine. However, compared to training data, real world data is collected from different system conditions and environments. Therefore, real world data has different datadistribution and various noise with the training data, leading to domain shift between data. Due to the problem mentioned above, deep learning often fails to apply on industrial data.Domain generalization is an emerging deep learning technique to generalize domain discrepancy. In this study, domain adversarial neural network (DANN)-based domaingeneralization is proposed for multi-label fault diagnosis of rotary machine. Frequency domain image data were generated via implementing short time fourier transform(STFT) to the sensor data collected from the test rig. Then, the features are utilized as training data to diagnosis multi-label fault via DANN-based domain generalization.  Moreover, the upper boundary of rotating speed domain of the rotary machine where domain generalization can effectively diagnosis multi-label fault is suggested.
Imbalanced Anomaly Detection Using Augmented Deeper FCDDs for Wooden Sleeper Deterioration Prognostics
Authors: Yasuno Takato , Fujii Junichiro , Okano Masahiro
Published: November 2023

Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top or side-view cameras and GPS positioning systems have facilitated progress toward automating periodic inspections of defective features and assessing the deteriorating status of railway components. However, collecting data on deteriorated status can be time-consuming and requires repeated data acquisition because of the extreme temporal occurrence imbalance. In supervised learning, thousands of paired data sets containing defective raw images and annotated labels are required. Concretely, the one-class classification approach offers the advantage of requiring quite a few anomalous images to optimize parameters for training large normal images. The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters. However, it is not yet known to feasible to railway components. In this study, we devised a prognostic discriminator pipeline to automate one class classification using the augmented deeper FCDDs for defective railway components. We also performed sensitivity analysis of the mixture and erasing augmentations, and the deeper backbone rather than the shallow baseline of convolutional neural network (CNN) with 27 layers. Furthermore, we visualized defective railway features by using transposed Gaussian upsampling. We demonstrated our application to railway inspection using a video acquisition dataset that contains wooden sleeper deterioration. Finally, we examined the usability of our approach for prognostic monitoring and fu ture work on railway component inspection.
Pipe Corrosion Inspection System based on Human-in-the-Loop Machine Learning
Authors: Shimbo Toshihiro , Okada Yousuke , Matsubara Hitoshi
Published: November 2023

The aim of this study was to improve the efficiency of external corrosion inspection of pipes in chemical plants. Currently, the preferred method involves manual examination of images of corroded pipes; however, this places significant workload on human experts owing to the very high number of such images. To address this issue, we developed an artificial intelligence (AI)-based corrosion diagnosis system and implemented it in a factory.
Initially, interviews were conducted to understand the decision-making processes of human experts. Subsequently, we converted their tacit knowledge into explicit knowledge, which was used to define the training data for the machine learning (ML) model. The predictions of the ML model were compared with the manually obtained results, exhibiting an accuracy of 70 %.
The proposed architecture was based on human-in-the-loop ML. It included a process to retrain the ML model using manual results gathered during operation. It was operated using a collaborative approach, in which human experts supported the ML model under development.
The proposed model enhanced the efficiency of the inspection process successfully.
Novel Ensemble Domain Adaptation Methodology for Enhanced Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets
Authors: Minami Takanobu , Suer Alexander , Kundu Pradeep , Siahpour Shahin , Lee Jay
Published: November 2023

This paper proposes a novel methodology for enhancing multi-class classification accuracy in fault diagnosis problems among domains with highly-connected fleets of assets using time series data. The approach involves appending specially tailored models to an initial model and incorporating domain adaptation techniques to account for domain variations. The methodology is demonstrated through a case study on fault diagnosis of a fleet of hydraulic rock drills, which presents challenges due to variations in sensor data between different fault classes and individual machines. Results show significant improvements in classification accuracy, both in validation and testing, upon employing ensemble models and applying domain adaptation. While the study is limited to one case study, it lays the groundwork for exploring the applicability of the proposed methodology in other contexts.
Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors
Authors: Liu Dehong , Varatharajan Anantaram , Goldsmith Abraham
Published: November 2023

An effective way to detect broken-bar faults of squirrel-cage induction motors is to extract the characteristic frequency component in the stator current as a fault signature, or so-called motor current signature analysis (MCSA). However, for inverter-fed motor drive systems, the motor is typically operating under varying-speed, varying-load, and noisy environments, which makes the fault signature extraction a very challenging problem. In this paper, we propose a sparsity-driven and graph-based method to extract the fault signature effectively, where the fault signature is modeled as a sparse component in the frequency domain for each short-time window measurement while gradually changing from window to window in the time-domain. Compared to the conventional short-time Fourier transform-based method, our method is more robust to noise and varying speed operations. Experiments are carried out to demonstrate the effectiveness of the proposed method.
Advancing Predictive Maintenance: A Study of Domain Adaptation for Fault Identification in Gearbox Components
Authors: Tsuruta Shinya , Wakimoto Koji , Nakamura Takaaki , Siahpour Shahin , Miller Marcella , Taco John , Lee Jay
Published: November 2023

This paper explores the use of machine learning in predictive maintenance, which has been increasingly demanded in recent years to reduce downtime and maintenance burden. The challenge of different data distributions between training and test data in machine learning is common in predictive maintenance where equipment operation patterns can change, leading to reduced operational efficiency. The authors validate a domain-adaptive anomaly detection method combining CNN and MMD, which achieves similar accuracy with PCA, SVD, and other dimensionality reduction methods. The study also shows that the method maintains accuracy even when the number of normal data in the target domain is 1/10 of the source domain.
Anomaly detection of propulsion system of spacecrafts with Light GBM
Authors: Iino Shota , Nomoto Hideki , Hirose Takayuki , Michiura Yasutaka , Fujii Go , Uchiyama Takashi
Published: November 2023

Future spacecrafts require robust operations for long-term missions to the Moon or Mars. Automatic anomaly detection with machine learning, in this context, plays a significant role because it enables early symptom detection and proactive redundant switching which preserves components in the long mission. In this research, we adopted Light GBM, one of the machine learning models, to investigate such anomaly. We especially focused on the telemetry data of propulsion system of H-II Transfer Vehicle (HTV) to resolve typical problems of deep-space mission spacecrafts, a thruster failure. The data was collected from multiple types of thruster maneuvers performed at simulator training. The results showed the effectiveness of the proposed method.
Physics Informed Machine Learning Approach for Performance Degradation Monitoring of Gas Turbine
Authors: Liu Yiyang , Jiang Xiaomo , Ge Xin , Wei Manman
Published: November 2023

The heavy-duty gas turbine is playing an increasingly significant role on power generation due to its lower-emission, higher flexibility and thermo-efficiency. Main subsystems of the gas turbine like compressor, combustor and turbine degrade over the operating time under the harsh environmental conditions, which largely impacts the efficiency and productivity of the system. Therefore, it is critical to develop effective approaches to monitor performance degradation of a heavy-duty gas turbine for system predictive maintenance thus improving the efficiency and productivity of the machine. This paper presents a new physics informed machine learning methodology to predict the degradation of gas turbine by seamlessly integrating thermodynamic heat balancing mechanism, component characteristics, multi-source data and artificial neural network model. The mechanism-based thermodynamic model is established for multiple subsystems considering the balance of flow, mass and energy, and then integrated to a system level for performance simulation of the gas turbine under different conditions. The system model is able to effectively simulate values for those parameters that are not measurable (e.g. GT exhaust flow) or inaccurately measured (e.g. fuel flow). Machine learning based data cleaning approach is employed to preprocess the multivariate raw data of the gas turbine. The difference between design performance data and corrected value obtained from the physics-informed model under ISO conditions is utilized to assess the performance degradation. A Long Short-Term Memory (LSTM) model is established from the fusion of the actual and simulation data to predict the performance degradation of the gas turbine. A comparison study with the classical Nonlinear Autoregressive Network with External Input (NARX) neural network is conducted to demonstrate the advantage of the proposed method. Key Word: Gas Turbine, Thermodynamic Balance, Performance Degradation Predict, Machine Learning, LSTM
Effect of Aircraft Health Management on Aircraft Maintenance Program Development by Aircraft Manufacturer
Authors: Koizumi Takuro , Kogiso Nozomu
Published: November 2023

The objective of the research is to propose a system modeling method for aircraft maintenance program development applying the condition-based maintenance using AHM (Aircraft Health Management) from the viewpoint of aircraft manufacturer. The proposed model based on the MSG-3 (Maintenance Steering Group - 3) considers the uncertainty of aircraft maintenance environment related to the airline operation and assumed system degradation levels. Then, the proposed model is formulated by using the concept of a robust optimization method.
Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System
Authors: He Yuning , Schumann Johann
Published: November 2023

Autonomous Centerline Tracking (ACT) enables an unmanned aircraft to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. The EASA Guidelines for machine-learning based systems list numerous assurance objectives that must be met toward certification and V&V. We extend our analysis framework SYSAI to provide feedback on performance of system and AI component to the designer and describe a combination with a runtime monitoring architecture that also supports advanced risk mitigation to support safety assurance of a complex AI-based aerospace system.
new approach to multivariate statistical process control and its application to wastewater treatment process monitoring
Authors: Yamanaka Osamu , Hiraoka Yukio
Published: November 2023

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate Statistical Process Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. A conventional MSPC based on Principal Component Analysis (PCA) is firstly adjusted to have an easy-to-understand user interface and then a new yet simplified decomposable diagnostic model is introduced. The developed user interface is designed to seamlessly connect MSPC to existing process monitoring system adopting the so-called trend graphs. The proposed diagnostic model is derived in a constructive way by aggregating small size models with one input or two inputs to improve tractability of the diagnostic model. The effectiveness of the modified MSPC is illustrated through some offline and on-line experiments by using a set of real multivariate process data at a municipal wastewater treatment plant.
Few-Shot Learning for Full Ceramic Bearing Fault Diagnosis with Acoustic Emission Signals
Authors: He David , He Miao , Taffari Alessandro
Published: November 2023

Full ceramic bearings are critical components in many full ceramic and oil-free food processing and medical equipment. Developing effective full ceramic fault diagnostic methods is important. Supervised deep learning approaches have been considered promising for fault diagnosis in the era of big data where abundantly labelled datasets are available. However, in many industrial applications, datasets with fault labels are rare. This challenge has motivated the task for developing deep learning approaches for fault diagnosis with few training examples. To meet the challenge, one attractive direction is to use available pre-trained deep learning architectures to do fault diagnosis with only few examples. Specifically, this paper investigates the effectiveness of using pre-trained deep learning architectures successfully used in natural language processing to achieve few-shot learning for full ceramic bearing fault diagnosis using acoustic emission signals.
Observation Maintenance for Bridges Using Early Detection of Deterioration Progress
Authors: Ito Hitoshi , Mizobuchi Toshiaki
Published: November 2023

In Japan, the results of inspections show that many bridges require repairs. However, Japanese municipalities do not have sufficient time and budget, and the progress of bridge repair is insufficient. Almost all the bridges managed by municipalities are short-span bridges. Therefore, a management system that selects and focuses on bridge repair based on performance evaluation would be effective. This paper presents an overview of a bridge management system that applies observational maintenance based on the early detection of deterioration, and its effectiveness is demonstrated.
Explainable multimodal learning for predictive maintenance of steam generators
Authors: Nguyen Duc An , Jose Sagar , Nguyen Thi Phuong Khanh , Medjaher Kamal
Published: November 2023

Prognostics and Health Management (PHM) is identified as an important lever for enhancing the development of predictive maintenance to ensure the reliability, availability, and safety of industrial systems. However, the efficiency of data- driven PHM approaches is dependent on the quality and quantity of data. Therefore, exploiting multiple data sources can provide additional, useful information than single-modal data. For instance, by incorporating multiple data sources, including condition monitoring data, images from cameras, and texts from maintenance technicians’ reports, multi-modal learning can provide a more comprehensive and accurate understanding of the system’s health. However, multi-modal deep learning is complex to understand. To address this complexity, it is crucial to incorporate explainable artificial intelligent techniques to provide clear and interpretable insights into how the model makes decisions. In this light, this paper proposes the application of the model-agnostic-explanation approach, i.e., SHAP, to explain the working mechanism of multimodal learning for the prediction of industrial steam generator degradation. Particularly, we determine the important features of each data modality and investigate how multimodal learning can overcome the issues of low-quality data from a single modality due to the additional information from other data modalities.
Capacitated Network Traffic Assignment using Lagrange Neural Networks
Authors: DALMAN Hasan
Published: November 2023

In this study, we utilize a neural network methodology to obtain user equilibrium for network traffic assignment problems with capacity constraints. The optimization problem associated with network traffic assignment is first transformed into a Lagrange problem. By considering the gradient method, a system of differential equations is obtained. Subsequently, the system of differential equations is solved using the Runge-Kutta method. The effectiveness of the proposed neural network approach is demonstrated through a numerical example.
PENDIDIKAN KELUARGA SEBAGAI FONDASI PERTAMA PENDIDIKAN KARAKTER ANAK PERSPEKTIF ISLAM
Authors: Ramdhani Khalid , Hermawan Iwan , Muzaki Iqbal Amar
Published: November 2023

Al Quran dan hadits bagi umat Islam adalah tuntunan dalam hidup. Begitu pula dalam mendidik anak menjadi anak baik yang mampu membedakan hal mana yang baik dan buruk. Mendidik adalah salah satu langkah penting untuk membentuk manusia yang sempurna aqlan, jisman, khuluqan. Pendidikan keluarga melibatkan semua anggota keluarga yang dimulai oleh kakek dan nenek sebagai keluarga, ayah dan ibu tertua sebagai orang tua dan anak-anaknya sendiri. Yang lebih penting adalah pendidikan harus diberikan oleh orang tua kepada anak-anak. Menurut hukum sistem pendidikan nasional, tanggung jawab mendidik anak merupakan tanggung jawab tiga elemen penting dalam pendidikan, keluarga, masyarakat dan pemerintah. Sayangnya, pada pandangan publik bahwa bertanggung jawab dalam mendidik anak sepenuhnya dipercaya oleh pemerintah dan guru di sekolah yang mulai pra sekolah ke universitas. Namun, nampak bahwa peran yang dimainkan oleh ketiga unsur tersebut belum secara sinergis maksimal terintegrasi, terutama dalam pendidikan di rumah sebagai dasar pendidikan anak sebelum memasuki wilayah luar rumah mereka. Jadi pembentukan karakter dasar pendidikan karakter dalam keluarga tidak optimal. Metode yang digunakan pada penulisan artikel ini ini adalah studi literature (library research). Temuannya menunjukkan bahwa dalam Islam, keluarga memiliki peran penting yang sangat penting sebagai lembaga sosial pertama bagi anak dalam membentuk karakter mereka yang mampu bertahan dalam kehidupan sosial secara luas dengan agama mengkristalkan dengan kuat pada diri mereka sendiri.
ADAPTASI PENDIDIKAN BERBASIS TEKNOLOGI DALAM MENGHADAPI ERA REVOLUSI INDUSTRI 4.0
Authors: Ismunandar Arif
Published: November 2023

Di era revolusi industri 4.0 Perguruan tinggi sebagai salah satu penyelenggara pendidikan tinggi serta sebagai pusat penggembangan ilmu dan teknologi diharapkan mampu meningkatkan perannya dalam memajukan dan mengembangkan seluruh potensi yang dimiliki. Penguatan dalam bidang teknologi menjadi sasaran utama dalam menghadapi era tersebut, pendidikan mulai mengontruksi ulang sistem yang sudah ada menjadi sistem yang terpusat pada teknologi.    Tulisan ini memaparkan tentang adaptasi pendidikan dalam teknologi, sebagai peluang, tantangan, dan analisis strategi dalam menghadapi era revolusi industri 4.0. Pemaparan tulisan ini didasarkan pada analisis dari data pustaka dengan model deskriptif. Dari hasil pembahasan dapat diperoleh kesimpulan bahwa mahasiswa dan teknologi pada era revolusi industri 4.0 ini memang tidak dapat dipisahkan, dengan efek pandemi covid-19 pemenuhan kebutuhan akan teknologi dalam setiap pembelajaran mutlak diperlukan sehingga sistem pembelajaran berbasis teknologi dapat dilaksanakan. Beberapa peluang dalam penerapan teknologi sangat berarti bagi mahasiswa dalam memperoleh pengalaman pembelajaran. Sedangkan tantangan muncul dari SDM kurang memadai dalam pengaplikasian. Analisis SWOT mampu diterapkan sebagai langkah-langkah dalam mengahadapi era revolusi industri 4.0 sehingga menjadi lebih terukur dalam pengambilan langkah atau strategi.
STRATEGI KEPALA MADRASAH DALAM MEMBANGUN BUDAYA KOMPETITIF DI MADRASAH ALIYAH NEGERI (MAN) BATU
Authors: Nawawi Muhammad Latif
Published: November 2023

Budaya Kompetitif dalam Islam sering disebut dengan fastabiqul khoirot yang memiliki arti bersaing atau berlomba-lomba dalam kebaikan. Budaya semacam ini harus dimiliki oleh setiap orang dalam sebuah lembaga jika ingin lembaganya berkembang dengan baik. Penelitian ini bertujuan untuk mendeskripsikan tentang langkah atau strategi Kepala Madrasah untuk merangsang atau membangkitkan budaya kompetitif pada lembaga yang dipimpinnya dan menganalisa beberapa faktor kendala dalam penerapan strategi tersebut. Metode penelitian ini menggunakan kajian Deskriptif-Kualitatif. Pengumpulan data menggunakan metode observasi, interview dan dokumentasi. Setelah data terkumpul dianalisis dengan tahapan reduksi data, penyajian data, dan verifikasi atau menarik kesimpulan. Hasil atau temuan yang dipaparkan pada penelitian ini yakni budaya kompetitif tetap dilestarikan oleh Kepala Madrasah. Adapun strategi Kepala Madrasah meliputi (1) membagi tugas yang jelas untuk setiap unsur; (2) menghargai setiap pendapat atau usulan yang membangun dari setiap unsur; (3) memberi penghargaan untuk setiap prestasi yang dicapai; (4) membangun mentalitas juara dan sikap pantang menyerah. Adapun analisa dari Kepala Madrasah tentang faktor yang menjadi kendala serta solusinya meliputi (1) Guru maupun tenaga kependidikan yang baru direkrut belum bisa menjalankan jobdis dengan maksimal, sehingga pihak sekolah harus melakukan pendampingan; (2) semua unsur rata-rata masih malu dalam mengajukan sebuah gagasan atau usulan tentang perkembangan sekolah, solusinya Kepala Madrasah mendekati dengan pendekatan persuasive; (3) masih ada sikap iri antar anggota untuk terus bersaing, belum adanya solusi yang efektif; (4) terkendala dengan anggapan bahwa tugas adalah beban yang mengakibatkan patah semangat jika tugas tidak berhasil di kerjakan, solusinya membangun motivasinya dengan pengarahan dan keteladanan agar merubah mainsaid seseorang tentang hal tersebut.
Complicated Trichotillomania of a Trichobezoar about a Clinical Case
Authors: Maataoui Z. El , Belhadga H. , Kisra H.
Published: November 2023

Trichotillomania refers to a repetitive behavior which consists of pulling out one's own hair, sometimes to the point of depilation of entire areas of the scalp, resulting in obvious alopecia. People with trichotillomania may play with and/or ingest the pulled hairs; This is trichophagia. The prevalence of this disorder is difficult to estimate, varying depending on the authors and the criteria used in the studies. This disorder is quite common and affects, according to studies, approximately 1 to 2% of the population, preferably women, children and adolescents. We illustrate a clinic for trichotillomania, associated with trichophagia, through the case of a 7-year-old girl. She had consulted a pediatrician and dermatologist previously and was treated as a case of alopecia areata because a mother would conceal her history due to the stigma attached. Due to severe abdominal pain she was referred to a surgeon where detailed examinations revealed the trichobezoar, caused by hair agglomeration linked to repeated hair pulling and ingestion behaviors. The initial management of the disorder is surgical, before referral to child psychiatry. Overall, various psychosocial factors that precipitate and perpetuate the pathology of trichotillomania must be taken into consideration and treated appropriately. The importance and severity of the medical complications of trichotillomania should not be underestimated. Awareness of the disorder as well as greater interdepartmental collaboration would help in early diagnosis and appropriate management.
Acute Intestinal Occlusion: Think About Supra Bladder Internal Hernia
Authors: Hebbezni A. , Akhiyat I. , Yassine N. , Elhejjami A. , Boutakioute B. , Idrissi M. Ouali , Ganouni N. Cherif Idrissi El
Published: November 2023

Internal hernia of the supravesical fossa is a rare cause of intestinal obstruction [1]. This pathology arouses great interest among radiologists and surgeons due to its diagnostic difficulty, even with the advancement of different imaging techniques, particularly computed tomography [2]. We present the case of a patient without a history of laparotomy who presented with acute intestinal obstruction. Computed tomography examination revealed a strangulated left supravesical internal hernia. Diagnostic confirmation was obtained by laparoscopic exploration.
Unusual Intra-Bronchial Foreign Body: About a Case Report
Authors: Salma Msika , Khattabi W. El , Arfaoui H. , Cherkaoui R. , Jabri H. , Afif H.
Published: November 2023

Tracheobronchial foreign bodies Inhalation is a frequent reason for consultation in pediatrics, but rarely seen in adults. We report the case of a 40-year-old woman, without any particular pathological history, presented for a blood- streaked purulent sputum, right subclavicular tingling chest pain and dyspnea on exertion among two days following accidental inhalation of a turkey bone. The clinical examination and the chest X-ray were normal, the chest CT scan showed a calcifield foreign body in the right lower lobar bronchus. Flexible bronchoscopy allowed an extraction of the turkey bone without complications, which was located in the initial part of the intermediate bronchus. The evolution was marked by a complete recovery. Foreign body inhalation is a real emergency and its management must be fast, based on delicate extraction by endoscopy to avoid complications.