bluuebunny/crossref_metadata_2025 huggingface dataset:
Organizer Competency and B2B Exhibition Performance: The Mediating Role of Exhibitor Motivation
Authors:   Korea Trade Research Association, You Chi-Yeon , Jung Yeon-Sung , Kim Junggeun , Roh Taewoo
Published: June 2024

Purpose - This study aims to explore the significance of organizer competency in B2B exhibitions in South Korea. Drawing on previous literature in the exhibition context, this research empirically examines the impact of organizer competency on exhibition performance and investigates the mediating role of exhibiting firms’ motivation to participate. Motivation is deconstructed into the quality of attendees and market-oriented motivation for a detailed analysis.
Design/Methodology - The empirical model of this study considers the relationship between organizer competency and exhibition performance in B2B exhibitions from a resource-based view. To accurately reflect the unique characteristics of B2B exhibitions, we surveyed firms that participated in the Korea International Boat Show. Consequently, the hypotheses of this study were tested through a structural equation model with 85 valid samples.
Findings - The findings of this study are twofold: First, we confirm that the competency of exhibition organizers significantly and positively impacts exhibition performance. Secondly, we identify that both motivational factors—quality of attendees and market-oriented motivation— positively influence exhibition performance.
Originality/value - Previous studies have overlooked organizer competency as a driver to enhance exhibition performance. However, this study demonstrates that organizer competency positively influences exhibition performance, mediated by the quality of attendees and market-oriented motivation. Furthermore, unlike most previous studies focused on regions such as China and Hong Kong, we analyze our research within the context of the Korean B2B industry.
Dlaczego warto i jak można rozwijać empatię u dzieci we wczesnym i średnim dzieciństwie
Authors: Filip Anna , Mróz Anna , Ocetkiewicz Iwona
Published: June 2024

Zdolność do reagowania empatią jest jednym z kluczowych elementów rozwoju społeczno-emocjonalnego, determinującym m.in. zachowanie jednostki wobec innych oraz jakość budowanych relacji. Wraz ze wzrostem różnorodności społecznej wzrasta potrzeba, aby dzieci, już od najmłodszych lat, chcąc zrozumieć postępowanie rówieśników i dorosłych różniących się od nich samych pod różnymi względami, potrafiły „wczuć się” w ich sytuację. Celem artykułu jest ukazanie znaczenia procesu i sposobów kształtowania empatii u dzieci. Artykuł rozpoczyna się od krótkiego wyjaśnienia teoretycznego konstruktu empatii oraz nakreślenia jej normatywnego rozwoju we wczesnym i średnim dzieciństwie. Przedstawia wnioski z badań wskazujących na związki zdolności do reagowania empatią z wieloma obszarami funkcjonowania społeczno-emocjonalnego dziecka. Omawia także czynniki środowiskowe, związane głównie z funkcjonowaniem dzieci w ramach grupy rówieśniczej i w relacji z nauczycielem, sprzyjające rozwijaniu empatii. Ukazane sposoby komunikacji, proponowane zabawy czy też elementy treningów, które na podstawie przeglądu literatury omówiono w tekście, mogą posłużyć jako źródło informacji dla praktyków zainteresowanych rozwijaniem empatii u dzieci. Środowisko przedszkolne i szkolne może odegrać kluczową rolę m.in. w wyłaniającej się prospołeczności dziecka, rozwoju jego moralnego rozumowania, budowaniu satysfakcjonujących relacji rówieśniczych i zapobieganiu agresywnym zachowaniom.
Pelatihan Aplikasi Zotero pada Mahasiswa Jurusan Akuntansi Syariah
Authors: Thalib Mohamad Anwar
Published: June 2024

Purpose: This training aimed to enhance the understanding of Sharia Accounting students regarding the use of the Zotero reference management application. The aim of the training stems from the problem of many students needing to help correctly and efficiently make citations and bibliographies.
Methodology/Approach: Training consisted of several stages. The first stage involves installing the Zotero application and Zotero connector. The second stage involved an explanation of the Zotero interface. The third stage involved building a database, the fourth stage involved creating citations and bibliographies, and the final stage was evaluation.
Results/findings: The training results indicated an improvement in Sharia Accounting students' understanding of using the Zotero application. This is demonstrated by the evaluation results of the participants, which show that they were able to input citations and create bibliographies correctly.
Limitations: The limitations of this study include the fact that Zotero training is limited to inputting and creating citations and bibliographies and has yet to reach the stage of data synchronization from local computers/laptops to the Zotero server.
Contribution: Training using the Zotero application assists Sharia Accounting students in efficiently preparing their final assignments (proposals/theses), saving time and minimizing the risk of errors in reference writing.
CONHECIMENTOS E VIVÊNCIAS DE ESTUDANTES DE ENFERMAGEM SOBRE AS QUESTÕES DE GÊNERO E SEXUALIDADE
Authors: Da Silva Julia Machado , Pilger Carolina Heleonora , Lipinski Jussara Mendes , Prates Lisie Alende , Bonetti Alinne de Lima
Published: June 2024

A comunidade de Lésbicas, Gays, Bissexuais, Transexuais, Travestis, Queer, Intersexo, Assexuais, outros grupos e variações (LGBTQI+) ainda vivencia situações de discriminação e preconceito. No processo de saúde e doença, essas situações podem ser capazes de afetar a busca pela assistência à saúde. Este estudo objetivou analisar os conhecimentos e as vivências de estudantes de enfermagem sobre as questões de gênero e sexualidade. Trata-se de uma pesquisa qualitativa, desenvolvida com 15 estudantes de enfermagem de uma instituição de ensino superior, localizada na Fronteira Oeste do Rio Grande do Sul, durante agosto e outubro de 2021. Os dados foram obtidos a partir de formulário eletrônico e submetidos à análise de conteúdo temática. Os estudantes sinalizaram a necessidade de maior aprofundamento teórico sobre as questões de gênero e sexualidade, considerando que esses temas são abordados de forma pontual no contexto familiar e universitário. Eles indicaram o desejo de fornecer um cuidado digno, respeitando os direitos sexuais e reprodutivos dos indivíduos. Conclui-se que são necessárias estratégias de ensino e aprendizagem no ambiente universitário e nos serviços de saúde, envolvendo as questões de gênero e sexualidade, a fim de ampliar o debate em diferentes segmentos, permitindo o respeito aos direitos sexuais e reprodutivos.
Pengaruh Pelatihan Keterampilan, Regulasi Pra Pengakhiran Dinas, dan Motivasi Kerja terhadap Masa Persiapan Pensiun Anggota POLRI Satuan Kerja Mabes POLRI
Authors: Setyawati Erny , Gumilar Aris , Hadiwijaya Dudung
Published: June 2024

Penelitian ini bertujuan untuk mengetahui pengaruh pelatihan keterampilan (Latram), regulasi pra pengakhiran dinas (prakhirdin), dan motivasi kerja terhadap masa persiapan pensiun (MPP) secara parsial dan simultan. Lokasi penelitian di Satuan Kerja (Satker) Markas Besar (Mabes) Kepolisian Negara Republik Indonesia (Polri). Metode penelitian kuantitatif diterapkan dalam kasus ini. Responden sebanyak 320 anggota Polri satker Mabes Polri. Teknik pengumpulan data menggunakan kuesioner dan hasil pengolahan data diolah secara analisis statistik penelitian. Hasil penelitian menunjukkan bahwa Latram, regulasi prakhirdin, dan motivasi kerja berpengaruh terhadap MPP secara parsial dan simultan.
Strategi Pemertahanan Eksistensi Pondok Pesantren Raudhatut Thalibin Amuntai Kalimantan Selatan
Authors: Musyarapah Musyarapah , Hidayati Noorazmah
Published: June 2024

Sebagai pondok pesantren salafiyah yang belum terlalu lama berdiri di Kabupaten Hulu Sungai Utara – di mana banyak terdapat pondok pesantren besar dan ternama – Pondok Pesantren Raudhatut Thalibin mampu bersaing dan menandingi pondok pesantren lainnya, sehingga eksistensinya terus meningkat. Fenomena ini patut untuk dikaji, kontestasi apa yang menarik bagi masyarakat sehingga mendapatkan kepercayaan yang begitu besar kepada pondok pesantren Raudhatut Tahlibin Amuntai. Penelitian ini memfokuskan terkait dengan bagaimana stretegi pemertahanan eksistensi pondok pesantren Raudhatut Thalibin Amuntai. Penelitian ini menggunakan metode dan teknik analisis kualitatif dengan pendekatan studi kasus dengan teknik pengumpulan data melalui observasi, wawancara dan dokumentasi. Teknik analisis data dilakukan dengan reduksi data, display dan verifikasi data. Adapun strategi pemertahanan eksistensi pondok pesantren Raudhatut Thalibin, yaitu: 1) Membentuk jaringan habaib dan ulama dalam kemitraan; 2) Corak pemikiran yang diajarkan sesuai dengan religiusitas masyarakat sekitar yang cenderung ta’dzim kepada para habaib dan ulama; 3) Bangunan fisik ponpes yang ditata dengan menyesuaikan kebutuhan para santri putra dan putri dilengkapi dengan sarana dan prasarana yang memadai; dan 4) Menerapkan prinsip almuhafadzatu ‘alal qadimis shaleh wal akhdzu bil jadidil ashlah.
Kehidupan Pluralisme Sosial Agama Masyarakat Transmigrasi Sebamban Kebupaten Tanah Bumbu Kalimantan Selatan: Perspektif Pendidikan Humanisme
Authors: Riady Fahmi , Nida Najminnur Hasanatun
Published: June 2024

Artikel ini bertujuan untuk menjelaskan kehidupan pluralisme masyarakat transmigrasi Sebamban perspektif pendidikan humanisme. Fokus penelitian adalah penggambaran pluralisme sosial agama masyarakat transmigrasi Sebamban dan bagaimana pendidikan humanisme memotret kehidupan sosial agama masyarakat transmigrasi Sebamban yang plural. Penelitian ini berjenis penelitian lapangan dengan menggunakan pendekatan kualitatif dengan menjadikan masyarakat transmigrasi Sebamban sebagai subjek penelitian. Adapun teknik pengumpulan data dengan menggunakan observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa masyarakat transmigrasi Sebamban Tanah Bumbu, khususnya mereka yang tinggal di desa Sari  Utama  blok B1 dan blok G dan desa Dwi Marga Utama blok B, berasal dari daerah yang berbeda-beda (Jawa Timur, Jawa Barat, Jawa Tengah, DKI, Lombok, dan Bali). Mereka memiliki keragaman suku, budaya, dan agama yang cukup tinggi. Dalam kehidupan plural semacam ini mereka mengembangkan kemampuan alami manusia untuk hidup bersama. Di mana dari sudut pandang pendidikan humanisme mereka adalah orang-orang yang memiliki kedaulatan dan bertanggung jawab; dapat menemukan identitas diri, mengembangkan sistem nilai, dan mampu beraktualisasi dengan aktif berpartisipasi.
Memorias del refugio: la (re)construcción identitaria de una venezolana en Brasil
Authors: Rohling Nívea , Ábila Fernanda , Remenche Maria de Lourdes Rossi
Published: June 2024

Este artículo presenta un análisis del processo de (re)construcción identitaria en el discurso de una refugiada venezolana, por medio de un diario publicado en el periódico Folha de S. Paulo, en diciembre 2020. El diario presenta relatos escritos por la refugiada, entre agosto y septiembre del mismo año, sobre su experiencia de refugio en la ciudad de São Paulo - Brasil. Se há aclado el análisis teórico y metodológico en conceptos como dialogismo, alteridad y exotopia, en la concepción del Círculo de Bajtín. El análisis pone en evidencia trazos de identificaciones y la recreación de los procesos identitarios, dada por medio de las relaciones de alteridad. Además, la escritura autobiográfica posibilitó un tom más asertivo en el sentido de asumir una postura axiológica ante la propia vida, en un proceso de exotopia por medio del cual la refugiada se há convertido en otra para narrarse a sí misma.
Machinery Fault Detection using Advanced Machine Learning Techniques
Authors: Neupane Dhiraj , Bouadjenek Mohamed Reda , Dazeley Richard , Aryal Sunil
Published: June 2024

Manufacturing industries are expanding rapidly, making it essential to detect early signs of machine faults for safety and productivity. With the extension of machines' runtime due to industrial automation, breakdown risks have increased, leading to economic and productivity consequences and sometimes even causalities. The surge in industrial big data from low-cost sensing technologies has enabled the development of intelligent data-driven Machinery Fault Detection (MFD) systems based on machine learning techniques in recent years. However, most existing methods are based on supervised pattern classification techniques to detect previously known fault types, which have limitations such as lack of generalization across different operational settings, focusing only on specific machinery and/or data types, and considering the identical and independent distribution of training and testing data. Therefore, my PhD research aims to develop a robust MFD framework for practical use by addressing these limitations.I will explore the potential of ensemble learning, unsupervised and semi-supervised anomaly detection, reinforcement learning, transfer learning, and cross-domain adaptation approaches in MFD. My PhD research will contribute to the field of data-driven MFD by proposing novel, effective solutions that can be applied across various manufacturing applications.
Prognostics of Remaining Useful Life for Aviation Structures Considering Imperfect Repairs
Authors: Salinas Mariana , Eleftheroglou Nick , Zarouchas Dimitrios
Published: June 2024

Maintenance plays an important role in fulfilling the goals ofthe Prognostics and Health Management (PHM) field. As ofnow, no publication has addressed the impact of imperfectrepair actions from the prognostics perspective. Imperfectrepairs introduce complexities, altering system degradationprocesses and increasing prediction uncertainties, thereby impactingthe accuracy of Remaining Useful Life (RUL) predictions.To fill this gap in the literature, the study proposes developinga robust prognostic model adaptable to post-repairoperations. The prognostic model that will be developed isstochastic since stochastic models have already proven theiradaptability to unseen test data. However, further developmentof such models is needed to deal with data on repairedsystems. In addition to that, the implementation of a BayesianExtension allows uncertainty interpretability to be consideredto account for the uncertainty coming from the repair actionitself but also from the different sources of uncertainties thathave not been studied in the field of prognostics.
A Physics-Inspired and Data-Driven Approach for Temperature-Based Condition Monitoring
Authors: Garegnani Giacomo , Hencken Kai , Kassubek Frank
Published: June 2024

System overheating is a common problem in electric equipment, as degradation of contacts lead to an increase in Ohmic resistance and increased thermal losses. Temperature measurements are widely employed to monitor a device's health status, to estimate its remaining useful life, and to inform maintenance strategies. An issue that is special to electrical distribution networks is the varying heating power, which is in turn due to changes in the current. This results in varying temperatures, which in addition can often be delayed compared to the currents. Simple threshold-based diagnostics approaches are therefore not reliable for detecting faults in contacts. It is common to analyze the thermal behavior of electric devices using thermal networks, for both design and diagnostic purposes. Unfortunately, identifying the parameters of thermal networks from measured temperature data is a challenging problem, mainly due to identifiability issues and to numerical instabilities in parameter estimation. We propose an alternative data-driven strategy to compute the state-of-health of electrical devices which does not resort to thermal networks. Our approach consists in informing physics-based reduced models of the thermal response with sensor data. We show that our method is linked to the thermal network approach but avoids the full identification of the system, leading to better stability as well as less computational effort in the determination of its parameters. Rigorous testing with synthetic and experimental data confirms the efficacy of our methodology.
Development of a Feature Extraction Methodology for Prognostic Tasks of Aerospace Structures and Systems
Authors: Orru Antonio , Kontogiannis Thanos , Falcetelli Francesco , Di Sante Raffaella , Eleftheroglou Nick
Published: June 2024

The performance of prognostic models used for prognostic health management (PHM) applications heavily depend on the quality of features extracted from raw sensor data. Traditionally, feature extraction criteria such as monotonicity, prognosability, and trendability are selected intuitively. However, this intuitive selection may not be optimal. This research introduces an innovative approach to transform raw data into 'high-scoring' data without the need for predefined feature extraction criteria. Our methodology involves generating a set of synthetic high-scoring time series. These synthetic time series, resembling the length and amplitude of target features, are created through Monte Carlo sampling (MCS) of a user-defined hidden semi-markov model (HSMM). We pair these synthetic time series with raw data/features from the signals and use them as targets to train a convolutional neural network (CNN). As a result, the trained CNN can convert input features into high-scoring ones, irrespective of their initial characteristics. So, this study provides the following contribution to PHM frameworks: it transforms raw data/features into high-scoring ones without relying on predefined criteria, rather on stochastically generated labels that resemble the nature of the degradation processes. It is worth noting, that the proposed FE technique is independent of the prognostic model that will be utilised, thus making it applicable to the established prognostic models.We demonstrate and validate the effectiveness of this approach using acoustic emission (AE) sensor data for remaining useful life (RUL) estimation of open-hole CFRP specimens. We compare prognostic performance using cumulative AE features with their transformations via our proposed framework. The transformed features exhibit superior prognostic performance, underscoring the value of our innovative feature extraction framework.
Enhancing Data-driven Vibration-based Machinery Fault Diagnosis Generalization Under Varied Conditions by Removing Domain-Specific Information Utilizing Sparse Representation
Authors: Latil David , HOUE NGOUNA Raymond , MEDJAHER Kamal , Lhuisset Stéphane
Published: June 2024

This paper introduces a novel approach to machinery fault diagnosis, addressing the challenge of domain generalization in diverse industrial environments. Traditional methods often struggle with domain shift and the scarcity of balanced, la- beled datasets, limiting their effectiveness across varied oper- ational conditions. The proposed method leverages the abun- dance of healthy machinery signals as a reference for extract- ing domain-specific information. By doing so, it removes the domain-related variances from the observation signals, focus- ing on the intrinsic characteristics of faults. The methodol- ogy is validated with a case study, demonstrating enhanced diagnosis accuracy and generalization capabilities in unseen domains. This research contributes to the field of vibration- based intelligent fault diagnosis by providing a robust solu- tion to a long-standing problem in machine condition moni- toring.
Anomaly Detection of a Cooling Water Pump of a Power Plant Based on its Virtual Digital Twin Constructed with Deep Learning Techniques
Authors: Sanz-Bobi Miguel A. , Orbach Sarah , Bellido-López F.  Javier , Muñoz Antonio , González-Calvo Daniel , Álvarez-Tejedor Tomás
Published: June 2024

This paper aims to explore the use of recent approaches of deep learning techniques for anomaly detection of potential failure modes in a cooling water pump working in a gas-combined cycle in a power plant. Two different deep learning techniques have been tested: neural networks and reinforcement learning. Two virtual digital twins were developed with each family of deep learning techniques, able to simulate the behavior of the cooling water pump in the absence of pump failure modes. Each virtual digital twin consists of several models for predicting the expected evolution of significant behavior variables when no anomalies exist. Examples of these variables are bearing temperatures or vibrations in different pump locations. All the data used comes from the SCADA system. The main features and hyperparameters in the virtual digital twins are presented, and demonstration examples are included.
Applying Prognostics and Health Management to Optimize Safety and Sustainability at the First Adaptive High-Rise Structure
Authors: Efinger Dshamil , Mannone Giuseppe , Dazer Martin
Published: June 2024

Prognostics and Health Management (PHM) offers the potential to increase the acceptance of adaptive structures and to operate them in an optimal way. With suitable design and proper operation, adaptive high-rise structures enable significant increases in sustainability and service life extensions compared to passive high-rise buildings. The control loop for PHM provides a systematic overview of the contents related to PHM and their sequence. However, a framework is required for application to a complex adaptive system. Such a framework is presented in this paper. The framework is divided into the areas of system analysis and modeling as well as the PHM solution. A systematic approach is used to analyze the system and create the basis for full integration of all functional domains. This is then used in modeling to develop an adapted model structure. Finally, the PHM solution looks at the details of the approaches for diagnosis, prognosis, and health management.
Active learning for gear defect detection in gearboxes
Authors: Liao Wenzhi , De Geest Roeland
Published: June 2024

Condition monitoring of gears in gearboxes is crucial to ensure performance and minimizing downtime in many industrial applications including wind turbines and automotive. Monitoring techniques using indirect measurements (i.e. accelerometers, microphones, acoustic emission sensors and encoders, etc.) face challenges, including the defect interpretation and characterization. Vision-based gear condition monitoring, as a direct method to observe gear defects, has the capability to give a precise indication of the starting point of a potential surface failure, but suffers from the image annotations (to train a reliable vision model for automatic defect detection of gears). In this paper, we propose an active learning framework for vision-based condition monitoring, to reduce the human annotation effort by only labelling the most informative examples. In particular, we first train a deep learning model on limited training dataset (annotated randomly) to detect pitting defects. To select which samples have the highest priority to be annotated, we compute the model's uncertainty on all remaining unlabeled examples. Bayesian active learning by disagreement is exploited to estimate the uncertainty of the unlabeled samples. We select the samples with the highest values of uncertainty to be annotated first. Experimental results from defect detection of gears in gearboxes show that with less than 6 times image annotations, we can achieve similar performances.
Data Scarcity in Fault Detection for Solar Tracking Systems: the Power of Physics-Informed Artificial Intelligence
Authors: Lüscher Mila Francesca , Zgraggen Jannik , Guo Yuyan , Notaristefano Antonio , Goren Huber Lilach
Published: June 2024

Combining physical and domain knowledge in artificial intelligence (AI) models has been gaining attention in various fields and applications. Applications in machine prognostics and health management (PHM) are natural candidates for such hybrid approaches. In particular, they can be efficiently exploited for high fidelity anomaly detection in technical and industrial systems. A natural way for hybridization is using physical models to generate representative data for the training of AI models. Depending on the level of domain knowledge availability, data augmentation can compensate for scarcity of real data from the field. This is particularly attractive for anomaly detection tasks, in which data from the abnormal regimes is limited by definition. On top of this inherent data limitation, many real-world systems suffer from data limitations even within the normal regimes.In this paper we propose a physics-informed deep learning algorithm for fault detection in grid scale photovoltaic power plants. We focus on a common data scarce scenario that emerges from a low asset monitoring granularity: instead of monitoring the power production of each solar string, the power output is monitored only at combiner-box or even inverter level (monitoring a large number of strings with a single sensor). As a result, the signatures of single local faults can become very subtle and challenging to detect. We show that in this case a physics-informed AI approach significantly outperforms the alternative of a purely data-driven anomaly detection model. This enables high fidelity fault detection in larger solar power plants, that are often limited in the granularity of their condition monitoring data.
Damage Detection using Machine Learning for PHM in Gearbox Applications
Authors: Binanzer Lisa , Schmid Tobias , Merkle Lukas , Dazer Martin
Published: June 2024

Early damage detection in gearbox applications enables the implementation of Prognostics and Health Management (PHM). On the one hand, the earliest possible damage detection provides a precise in-sight into the state of health of a gearbox. In addition, early damage detection offers the possibility to slow down the damage progress and extend the remaining useful life (RUL) by intervening in the operating state at an early damage stage. The main contribution of this work is that existing Machine Learning tools are applied to the challenge of very early damage detection in gearboxes. Thus, the need for complex physically based data evaluation is avoided. The aim of this investigation is a comparison of two different machine learning approaches. To investigate the detection possibilities test bench experiments were conducted with a single stage spur gearbox. For a comprehensive investigation, i.e. to detect damage under different operating conditions, the test runs are carried out at different damage sizes, speeds and torques. Based on the recorded vibration data, the damage detection is examined. Two machine learning approaches of anomaly detection are considered: An encoding approach and a loss approach. The same sparse autoencoder is developed for both approaches Both machine learning approaches are able to detect even the smallest damage of about 0.5 % in most operating states. The loss approach allows the different damage stages to be recognized much more clearly than the encoding approach. The comparison of the different approaches provides valuable insights for the further development of robust damage detection algorithms.
Towards Efficient Operation and Maintenance of Wind Farms: Leveraging AI for Minimizing Human Error
Authors: Keprate Arvind , Kilskar Stine , Andrews Pete
Published: June 2024

To effectively compete with other renewable energy sources, there remains a critical need to further decrease the Levelized Cost of Energy of Wind Farms (WFs). A promising way to achieve this objective is by minimizing the downtime of wind turbines (WTs) through effective Inspection and Maintenance (I&M) activities. Conventionally, I&M plans have predominantly relied on CM/SCADA data obtained from the physical components of turbines, with data analytics and machine learning (ML) techniques being employed to predict their performance and maintenance needs. However, statistics indicate that nearly 40% of WT failures can be traced back to HFs. These include aspects such as skills, knowledge, communication, and even the broader organizational culture. This paper delves into the importance of integrating HFs in the I&M of WFs to optimize turbine performance, enhance safety, and reduce downtime.
Firstly, we briefly discussed various Human Reliability Analysis (HRA) methods with special emphasis on Performance Shape Factors (PSFs). We then identify key human factors (HFs) that are vital for performing O&M tasks. For this, we have prepared a questionnaire to get qualitative input from technicians and also done a thorough literature review. E.g., some of the HFs that stand out include the ergonomics of tools and workspace designs tailored to technicians' needs, the cognitive load placed on operators during system monitoring and diagnostics, continuous training to handle evolving challenges, effective communication channels, and safety protocols designed with human behavior in mind. We then propose a novel framework for developing a computer vision-based recommendation system that can guide the technicians to perform the maintenance effectively thus minimizing the HE.
Defect Data Augmentation Method for Robust Image-based Product Inspection
Authors: Choi Youngwoon , Lee Hyunseok , Lee Sang Won
Published: June 2024

In this paper, we develop a model for detecting defects in fabric products based on an object segmentation algorithm, including a novel image data augmentation method to enhance the robustness. First, a vision-based inspection system is established to collect image data of the fabric products. The three types of fabric defects, such as a hole, a stain, and a dyeing defect, are considered. To enhance defect detection accuracy and robustness, a novel image data augmentation method, referred to as the defect-area cut-mix, is proposed. In this method, the shapes that are the same as each defect are extracted using the masks, and then they are added to non-defective fabric images. Second, an ensemble process is implemented by combining the results of two models, one with high sensitivity in defect diagnosis and the other with lower sensitivity. The results demonstrated that the model trained on the augmented dataset exhibits improved metrics such as intersection over union and classification accuracy in defect detection on the test dataset.
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
Authors: Srinivasan Abishek , Singapura Ravi Varun , Andresen Juan Carlos , Holst Anders
Published: June 2024

The complexity of modern electro-mechanical systems require the development of sophisticated diagnostic methods like anomaly detection capable of detecting deviations. Conventional anomaly detection approaches like signal processing and statistical modelling often struggle to effectively handle the intricacies of complex systems, particularly when dealing with multi-variate signals. In contrast, neural network-based anomaly detection methods, especially Auto-Encoders, have emerged as a compelling alternative, demonstrating remarkable performance. However, Auto-Encoders exhibit inherent opaqueness in their decision-making processes, hindering their practical implementation at scale. Addressing this opacity is essential for enhancing the interpretability and trustworthiness of anomaly detection models. In this work, we address this challenge by employing a feature selector to select features and counterfactual explanations to give a context to the model output. We tested this approach on the SKAB benchmark dataset and an industrial time-series dataset. The gradient based counterfactual explanation approach was evaluated via validity, sparsity and distance measures. Our experimental findings illustrate that our proposed counterfactual approach can offer meaningful and valuable insights into the model decision-making process, by explaining fewer signals compared to conventional approaches. These insights enhance the trustworthiness and interpretability of anomaly detection models.
Integration of Condition Information in UAV Swarm Management to increase System Availability in dynamic Environments
Authors: Dingeldein Lorenz
Published: June 2024

The approach of prognostics and health management (PHM) focuses on the real-time health assessment of a system under its actual operating condition and even extending this by the prediction of the future state based on up-to-date system information. This pursues the aim to derive more advanced maintenance or asset deployment strategies in order to keep the operation of the system safe and reliable. In this context, the outcome of a PHM system is often used as a decision support. For a high fidelity system where the actual state is considered at every timestep and a decision is executed immediately based up on this  information, Reinforcement Learning (RL) becomes a tool to find an optimized solution. Therefore the paper presents a methodology that integrates health and operational data into a RL approach in order to derive immediate operational strategies for lower degradation and higher safety and reliability. The approach is  evaluated on the basis of a swarm of unmanned aerial vehicles (UAVs) that performs a complete-area path-coverage (CAPC) mission. It can be shown that the integration of health information as well as environmental data describing dynamic operating conditions lead to lower degradation and result in more reliable operations of the swarm while achieving a more flexible mission performance compared to pre-divided swarm-missions. Varying states are also taken into account, which emphasises this approach to be a highly dynamic PHM system application.
Exploring a Knowledge-Based Approach for Predictive Maintenance of Aircraft Engines: Studying Fault Propagation through Spatial and Topological Component Relationships
Authors: HAFSI Meriem
Published: June 2024

Predictive maintenance has become a highly favored application in Industry 4.0, particularly in complex systems with requirements for reliability, robustness, and performance. Aircraft engines are among these systems, and several studies have been conducted to try to estimate their remaining lifespan. The C-MAPSS dataset provided by NASA has greatly served the scientific community, and several works based on physical models and data-driven approaches have been proposed. However, several limitations related to data quality or data availability of failures persist, and integrating domain knowledge can help address these challenges. In this article, we are currently implementing a new approach based on knowledge coupled with qualitative spatial reasoning to study the propagation of faults within system components until complete shutdown. Region Connection Calculus (RCC8) formal model will be used to describe the component relationships, drawing inspiration from the C-MAPSS dataset.
Automated Fault Diagnosis Using Maximal Overlap Discret Wavelet Packet Transform and Principal Components Analysis
Authors: Gougam Fawzi , Soualhi Moncef , Soualhi Abdenour , Afia Adel , Touzout Walid , Aitchikh Mohamed Abdssamed
Published: June 2024

Bearings and gears are components most susceptible to failure in electromechanical systems, especially rotating machines. Therefore, fault detection becomes a crucial step, as well as fault diagnosis. Over decades, substantial progress in this field has been observed and numerous methods are now proposed for feature extraction from monitoring data. Among these data, vibration signals are most used. However, in the presence of non-Gaussian noise, most conventional methods may be inefficient. In this paper, a hybrid methodology is proposed to address this potential issue. The proposed methodology uses a combination of the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) and Principal Component Analysis (PCA) techniques. First, the MODWPT technique decomposes the vibration signal with uniform frequency bandwidth, facilitating effective signal processing and introducing diversity for enhanced time-frequency signals. Then, to identify significant patterns and characteristics related to faults, PCA is used for 3D dimensional representation of system health state by capturing the variance in the extracted features. Subsequently, a self-organizing map (SOM) is used for system state classification for diagnostics. This technique is applied to open-access test bench data containing vibration signals with non-Gaussian noise.
Process Quality Monitoring Through a LSTM Network Derived from a Rule-Based Approach
Authors: Bernroitner Andreas , Eckerstorfer Roland
Published: June 2024

The railway infrastructure condition is a crucial factor for thesafe and efficient operation of trains. Regular maintenanceis inevitable as the track geometry degrades over time dueto traffic and environmental effects. To restore the ideal positionand provide sufficient durability of ballasted track socalled tamping machines are used. These machines lift thetrack, correct the longitudinal level and the alignment of thetrack panel and tamp the ballast. During the tamping processthe tamping tines penetrate the ballast bed, fill voids and compactthe ballast underneath the sleepers by a squeezing movementwith superimposed vibration. A detailed description ofthe tamping cycle can be found on section 2. Monitoring andevaluating this tamping process is essential for maintainingprocess quality. This can be achieved through a variety ofsensors, such as incremental encoders, angle encoders, temperature,pressure, and acceleration sensors, coupled with ameasurement unit (DAQ and edge device) to collect, locallystore and transmit the data to a cloud. This paper explores thedevelopment of a rule-based algorithm for assessing the qualityof the tamping process execution in reference to its nominalchronological sequence. The focus is on identifying tampingoccurrences and classifying them into acceptable (OK)or non-acceptable (NOK) categories. This involves selectingrelevant measurement parameters and processing them,considering the inherent imprecision in real-world processes.Empirical thresholds are established to differentiate betweengood and bad outcomes. The classification approach has tobe sufficiently generic in order to cover a high variety of customizedtamping machine types. As each machine is individuallydesigned, the process of generalization is challengingand complex. The paper demonstrates the accuracy and universalapplicability of the developed rule set across differenttamping machines. The model’s effectiveness is validated usingthe Hold-Out-Test-Set method. Furthermore, the rule-set-achieved outcomes are compared with results gained from anLSTM network. Both the rule-based approach and the neuralnetwork demonstrate precision, but the latter requires significantlymore effort.
Data-Driven Remaining Useful Life Estimation Approach for Neutron Generators in Multifunction Logging-While-Drilling Service
Authors: Sobczak-Oramus Karolina , Mosallam Ahmed , Shen Nannan , Youssef Fares Ben
Published: June 2024

This paper introduces a data-driven approach for estimating the remaining useful life of the neutron generator component in logging-while-drilling tools. The approach builds on identification of the incipient failure modes of the neutron generator and constructing a health indicator that serves as a statistical representation of the component’s deterioration over time. Afterwards, a K-nearest neighbors algorithm is trained to establish the relationship between the extracted health indicator values and the corresponding remaining useful life. The effectiveness of the presented approach is verified through the utilization of real-world data gathered from oil well drilling operations. The study is part of a long term project aimed at developing a digital fleet management system for drilling tools.
Determinants of brand love in wine experiences
Authors: Dias Alvaro , Sousa Bruno , Santos Vasco , Ramos Paulo , Madeira Arlindo
Published: June 2024

Wine tourism provides a multisensory experience through various wine tourism activities. It also adds value to the regions and their producers by enhancing the wine knowledge and sensory experience of the visitors. This study explores the determinants of brand love in wine tourism. Using survey data from wine tourists in Portugal, a structural equation modelling was performed to test the conceptual model. An Importance-performance matrix analysis was also used to obtain additional insights. The results show that the wine tourism enhances brand love. This implies that emotions (i.e., a sense of belonging) towards the overall location is associated with the satisfaction and the quality perception that the winery exhibits. Furthermore, although the consumer’s active participation in wine tourism does not impact brand love directly, its influences occur through the mediating effect of the relation quality. From an interdisciplinary managerial perspective, the present study provides insights into tourism management (i.e., wine tourism) and marketing (i.e., brand management and consumer behaviour).
Enriching product exposure in e-commerce through a hedonistic and utilitarian cue
Authors: Szymkowiak Andrzej , Garczarek-Bąk Urszula , Faganel Armand
Published: June 2024

With the projected growth in the global wine market, the ongoing impact of the COVID-19 pandemic, and the increasing prevalence of e-commerce, a pressing need arises to devise unique and engaging ways to present product offerings. While prior research has shed light on the potential of online sales platforms for wineries and the role of wine bottle labels in influencing consumer purchasing behavior, scant attention has been given to the presentation of wine products in the realm of e-commerce. Therefore, the primary objective of this study is to examine the impact of including visual cues (symbols) in online product displays on individuals’ perceptions of the product and their likelihood to make a purchase. Specifically, we aim to investigate how the placement of elements related to wine consumption, such as a ‘Glass’, and the representation of the product’s raw materials, such as a bunch of ‘Grapes’, affect consumer purchasing choices. The results from a simulated online wine store demonstrate the substantial influence of including a ‘Glass’ symbol on consumers’ selections. Furthermore, a series of eye-tracking laboratory experiments conducted in Poland, involving a total of 140 participants, provides deeper insight into underlying mechanisms. These findings reveal that augmenting a wine product with a hedonic symbol, such as a ‘Glass’, significantly enhances consumer perception of the product. Additionally, it exerts influence on their ‘Product Attitude’ and ‘Product Taste’ assessment, both of which are integral components of product perception. This study has the potential to offer valuable insights for wine marketers, e-commerce retailers and researchers specializing in the field of consumer behavior and marketing. Beyond the wine industry, the implications of this research extend to other sectors that rely on e-commerce platforms for sales.
Waar kom je het best tot je recht? Kansen en uitdagingen voor de beschermd wonen sector
Authors: Huber Max , van Vugt Maaike
Published: June 2024

Deze bijdrage werd geplaatst als reactie op het oorspronkelijke discussiestuk van Koopmans et al. (beide op de website van Valente) dat wij in deze aflevering in een bewerkte versie opnemen. De discussie wordt hiermee heropend.
De Nederlandse naastenorganisaties in de ggz Een kleine geschiedenis
Authors: Geerts Irene
Published: June 2024

In deze bijdrage gaat de auteur in op de opkomst en ontwikkeling van organisaties van naasten van cliënten in de geestelijke gezondheidzorg. Een stukje achtergrondgeschiedenis van de ggz dat de redactie met plezier voor het voetlicht brengt. Begin jaren 1980 kwamen in Nederland bijna tegelijkertijd verschillende naastenorganisaties op in de ggz. Dat was deels een reactie op en deels een voortzetting van de cliëntenbeweging, die tien jaar eerder grotendeels op gang was gebracht door naasten. Zij waren in de ggz echter steeds meer buitenspel komen te staan: enerzijds door de voortschrijdende cliëntenemancipatie, anderzijds door wetenschappelijke ontwikkelingen in de psychiatrie. Wat zette naasten toentertijd precies aan om hun eigen organisaties op te richten? En in hoeverre zijn de scheidslijnen van toen nu nog actueel?
’De kroonjuwelen van de cliëntenbeweging’
Authors: Hunsche Petra
Published: June 2024

Passend bij het artikel over de geschiedenis van de naastenorganisaties in de ggz in deze aflevering, is dit interview met Titia Feldmann, oud-huisarts en (familie-) ervaringsdeskundige, betrokken bij diverse cliënten- en naastenorganisaties en – inmiddels – oud-lid van de Raad van Toezicht van Mind. Haar reflecties geven het perspectief van een persoon die er zelf bij was op de in die bijdrage beschreven ontwikkelingen. Inleiding De meest radicale stem die ik ooit in de cliëntenbeweging hoorde, was die van Dunya Breur (1942-2009). Na een psychiatrische opname begin jaren ‘70 werd zij naar eigen zeggen ‘als te gestoord en een gevaar voor mijn medepatiënten het gekkenhuis uitgegooid’. De onverzettelijke Breur, slaviste, schrijfster en vertaalster, sloot zich van 1973 tot 1981 aan bij de Cliëntenbond, lange tijd de grootste ggzcliënten- en familieorganisatie, en maakte daar het landelijk goed gelezen ledenblad Het Bulletin. Haar onafhankelijke geest was een verademing, ik vond het een groot voorrecht haar te portretteren voor mijn boek De strijdbare patiënt (2008). Nadat ze het psychiatrisch ziekenhuis Santpoort was uitgezet, vertelde Dunya Breur, logeerde zij enige tijd bij een solidaire (familie) huisarts. Dit adres: Oranje Nassaulaan 67, is nog steeds een begrip bij veteranen van de cliëntenbeweging. En tot mijn verrassing leven deze herinneringen ook nog in het huidige bestuur van de ggz-koepel Mind!
The Benefits of Nurturing EFL Teachers’ Agency at the Beginning of their Professional Development Journeys
Authors: Kristian Wijaya
Published: June 2024

EFL educators in this modern age are vulnerable to stressful and anxiety-evoking situations while dealing with their daily teaching demands such as heavy administrative workloads, unyielding students’ behaviors, and external pressure from the educational stakeholders. To better mitigate all these inevitable educational issues, it is of key importance for second language educators to instill a higher level of agency to nurture their commitment in giving their best teaching performances for their learners. For this reason, the researcher embarked this small-scale library study by exploring the potential benefits promoted by agency contributable for the further professional development within EFL educationalists. By doing so, the forthcoming research results can potentially promote a glimpse of enlightenment for globalized ELT stakeholders to better support the continual development of EFL teachers’ agency influential for the significant advancement of the whole second language exposure quality.  A library analysis methodology was selected to generate more credible, relatable, and applicable research results for the targeted educational parties. To fulfill this major specific research objective, the researcher particularly chose 20 previous EFL teachers’ agency literature having been published worldwide in varied reputable journal article platforms ranging from 2017 until 2023 year to yield more relevant research outcomes for the predetermined research fields. The thematically clustered research results highly encouraged globalized educational institutions to start prioritizing the constant development of EFL teachers’ agency to gradually transform them into more adaptable and highly dedicated educationalists.