bluuebunny/crossref_metadata_2025 huggingface dataset:
Peningkatan Aktivitas dan Hasil Belajar Melalui Picture And Picture Tematik IIID SDN 77 Kota Bengkulu
Authors: Tria Oktarina Rizki , Lusa Herman , Hasnawati
Published: May 2024

This study aims to increase the activity and student learning outcomes in thematic learning through the picture and picture learning model in class IIID SDN 77 Bengkulu City. This research is a Classroom Action Research (CAR). The subjects in this study were class IIID students at SDN 77 Bengkulu City for the 2023/2024 academic year. The number of students in class IIID, a total of 48 students with details of 29 male students and 19 female students. Data collection techniques using observation sheet techniques and test sheets. The data analysis technique used on the observation sheet, namely the average value formula and for learning outcomes uses the average value formula and the percentage of completeness of classical learning. The results showed that there was an increase in activity and student learning outcomes in Thematic learning. It was seen in cycle I that the average value of teacher activity was 26.5, increasing in cycle II with an average value of 34. In cycle I student activity the average value was 27.5 increased in the second cycle with an average value of 34. In the first cycle student learning outcomes of Indonesian language content with an average value of 70.31 with a percentage of 66.66% with a total of 32 students have a complete score. It increased in cycle II with an average score of 77.60 with a percentage of 85.41% with a total of 43 students having a complete score. In the SBdP cycle I learning content with an average score of 74.47 with a percentage of 79.16% with a total of 38 students having a complete score. It increased in cycle II with an average score of 80.20 with a percentage of 89.58% with a total of 43 students having a complete score. It can be concluded that through the picture and picture learning model can increase student learning activities and results in thematic learning class IIID SDN 77 Bengkulu City.
Penerapan Model Pembelajaran Kooperatif Tipe (TGT) untuk Meningkatkan Hasil Belajar Siswa Pada Pembelajaran Matematika
Authors: Santika Rani , Noperman Feri
Published: May 2024

Penelitian ini bertujuan untuk mendeskripsikan penerapan model kooperatif tipe Teams Games Tournament (TGT) berbantuan alat peraga dan menganalisis peningkatan hasil belajar siswa pada pembelajaran matematika melalui penerapan model TGT berbantuan alat peraga. Pendekatan dan jenis penelitian yang digunakan adalah Penelitian Tindakan Kelas (PTK). Subjek penelitian adalah guru dan siswa kelas V SDN 44 Bengkulu Selatan yang berjumlah 12 orang siswa. Instrumen penelitian berupa lembar observasi dan lembar tes. Teknik analisis data hasil observasi menggunakan rumus rata-rata skor, skor tertinggi, skor terendah, selisih skor, dan kisaran nilai untuk setiap kategori. Data tes dianalisis menggunakan rumus rata-rata nilai dan persentase ketuntasan belajar klasikal. Hasil penelitian: keterlaksanaan model pada siklus I 62,5 dengan kategori B (baik), kemudian mengalami peningkatan pada siklus II dan berada pada angka 77 dengan kategori sangat baik (SB). Hasil belajar siswa khususnya pada aspek kognitif. Hal ini dilihat dari nilai rata-rata pada siklus I yaitu 77 dengan kriteria cukup (C) dan ketuntasan belajar klasikal 75% dengan kriteria cukup (C), kemudian pada siklus II mengalami peningkatan menjadi nilai rata-rata 87,5 dengan kriteria baik (B) dan ketuntasan belajar klasikal 92% dengan kriteria baik (B). Dengan demikian dapat disimpulkan bahwa dengan menerapkan model pembelajaran kooperatif tipe Teams Games Tournament (TGT) berbantuan alat peraga dapat meningkatkan hasil belajar siswa pada pembelajaran matematika.
Hubungan Pola Asuh Orang Tua dengan Kepercayaan Diri Kelas V SDN Gugus X Kota Bengkulu
Authors: Rahmawati Hanifa , Dalifa
Published: May 2024

This study aims to determine the relationship between parenting patterns and self-confidence of fifth grade students of SD Negeri Gugus X Bengkulu City. The type of research used is quantitative with correlation research method. The study population was all fifth grade students of SD Negeri Gugus X Bengkulu City, totaling 174 students. The sample of this study was elementary school students of Gugus X Bengkulu City which was taken by simple random sampling technique with a total of 44 students. The research instrument used was a questionnaire. Data collection techniques using a questionnaire. Data analysis techniques in this study using product moment correlation calculations. The results of this study indicate that the correlation coefficient is 0.427 with a significance level of 5% and n = 44, then rtabel is 0.297. So that the obtained rcount> rtable, namely 0.427> 0.297, with the contribution of variable X to variable Y amounting to 18.23%. It can be concluded that there is a relationship between parenting patterns and self-confidence of fifth grade students of SD Negeri Gugus X Bengkulu City with the level of correlation between variables in the moderate category. So that parents must be right in choosing parenting.
Improving Digital Image Clarity: A Study on the Application of Histogram Equalization for Noise Correction
Authors: Surmayanti Surmayanti , Sumijan Sumijan
Published: May 2024

This study aims to improve the clarity of digital images by examining the application of the histogram equalization method for noise correction. Noise in digital images is often a major challenge in maintaining the clarity and authenticity of visual information. Histogram equalization has been recognized as an effective method in improving image contrast and reducing the effects of noise. In this research, we conducted experiments by applying histogram equalization techniques to various types of digital images that are affected by noise. We analyzed the results by comparing the clarity and quality of the images before and after applying this method. The results of this research show that histogram equalization is able to significantly improve the clarity of digital images by reducing the effects of noise without sacrificing important details in the image. The implication of this discovery is the potential use of the histogram equalization method as an effective tool in improving the quality of digital images that are affected by noise.
Uncovering Blockchain's Potential for Supply Chain Transparency: Qualitative Study on the Fashion Industry
Authors: Hindarto Djarot , Alim Syariful , Hendrata Ferial
Published: May 2024

With the capacity to increase security and transparency, blockchain technology is being used as an interesting subject of investigation in the fashion industry. This underscores the importance of this current research endeavour. In terms of supply chain transparency, the fashion industry faces considerable barriers, thus requiring new approaches such as blockchain that can address issues such as child labour, unethical payment practices, and environmental impact. Main objective of this research is to identify how blockchain technology can improve transparency, accountability, and compliance with ethical standards. However, knowledge of the specific ways in which blockchain technology can improve transparency in the fashion supply chain, including the drivers and barriers, needs to be improved. The research method is described through a qualitative approach that includes in-depth interviews, participatory observation, and document analysis to collect data from various stakeholders in the industry, including manufacturers, distributors, and consumers. Explanation provides an overview of how the researcher collected and analysed data to achieve the research objectives. Blockchain increases transparency through the provision of verifiable and durable product records and fosters consumer-brand trust. Blockchain facilitates accountability and compliance with environmental and ethical standards, according to key findings. Research detected significant barriers, including exorbitant costs for implementation, limited knowledge of technology, and difficulties in fostering collaboration among relevant parties. Results of this study have far-reaching consequences, providing valuable insights to fashion industry stakeholders on how to overcome barriers to blockchain adoption. Long-term benefits of enhanced supply chain transparency and strategic recommendations ensure a smooth implementation process.
Application of Decision Tree Method in ECG Signal Classification For Heart Disorder Detection
Authors: Banjarnahor Jepri , Sinaga Friska , Sitorus Dedi Setiadi , Sitanggang Wahyu Adventus Andreas , Turnip Mardi
Published: May 2024

Cardiovascular Disease (CVD) is a group of diseases that affect the heart and blood vessels, and it is the leading cause of death globally. In Indonesia, Coronary Heart Disease (CHD) is one of the most prevalent CVDs. However, due to the high cost of drugs, lengthy treatment duration, and various supporting examinations required, treating CHD can be very expensive. An obstacle to treating heart disease in Indonesia is the insufficient number of cardiologists and experts experienced in interventional cardiology. Along with technological developments, the computer science community is encouraged to contribute to the medical field. For instance, using an electrocardiogram (ECG) can help prevent and minimize problems arising from heart disease. An Electrocardiogram (ECG) is a medical test that measures and records the heart’s electrical activity using a machine that detects electrical impulses. The use of Artificial Intelligence (AI) in ECG is rapidly increasing and has shown to have great potential in improving the diagnosis and treatment of cardiac patients. AI has become a valuable tool in helping doctors diagnose, predict risk, and manage heart disease with greater accuracy, speed, and precision. One of the machine learning methods used in this research is the decision tree method, which is often employed to make decisions. The decision tree method exhibited promising results, with an accuracy rate of 99% in identifying heart defects at an early stage. This method has significant potential to assist doctors in diagnosing heart defects at an early stage with high accuracy.
Chatbot Design for Interview Questions Using Neural Network Models on the CarTech Website
Authors: Sihotang Diko Pradana , Harahap Syaiful Zuhri , Irmayanti Irmayanti
Published: May 2024

Abstract: This research focuses on analyzing interview questions using a neural network model, implemented on the CarTech website. With the main aim of optimizing the interaction between users and the system through the questions asked, this research takes an innovative step by utilizing Google Collab as a development platform. For this research, several paragraphs were carried out, namely problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. These stages were carried out so that this research could get good results, plus the integration between Google Collab and chatbot which made it possible for this research to get good results. Google Collab makes it easy to use neural network models and integrate with chatbots, enabling efficient and effective testing and deployment of models. The results of this study are quite impressive, with an accuracy of 92%, demonstrating the model's ability to process and understand interview questions with high precision. The aim of this research is not only to explore the potential of neural network models in automatically understanding questions and providing accurate responses, but also to show how this technology can be integrated into web applications to improve the quality of user interactions, making AI-based chatbots a viable solution and effective in improving user experience on the CarTech website. In conclusion, by utilizing AI you will also get good results. As in this research, AI can help analyze interview questions with neural network models.
Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection
Authors: Agustina Triya , Masrizal Masrizal , Irmayanti Irmayanti
Published: May 2024

As the volume and complexity of computer network traffic continue to increase, network administrators face a growing challenge in monitoring and discovering unusual activity. To keep the network safe and functioning, detecting anomalies is essential. Machine learning-based anomaly detection techniques have become increasingly popular in recent years. This is due to the fact that conventional anomaly detection methods make it difficult to detect unknown and complex attacks. This research aims to conduct a performance analysis of two feature selection methods using the random forest algorithm using the UNSW-NB15 dataset to determine which model is most effective in detecting network traffic anomalies. The models evaluated were random forest with the filter method and random forest with the wrapper method. A number of metrics used for model performance assessment are accuracy, F1-score, receiver operating characteristic curve, and precision-recall. Dataset collection, data pre-processing, feature selection, model construction, and evaluation are the main components of the research methodology. The research results show that the Random Forest approach with the Filter method has an accuracy of 0.8950, F1-score of 0.8333, ROC score of 0.8928, and a precision-recall value of 0.8347. Meanwhile, the approach using the Wrapper method obtained an accuracy of 0.9151, F1-score of 0.8510, ROC score of 0.9136, and a precision-recall value of 0.8637. This shows that the performance of Random Forest with the Wrapper method is superior in all assessment metrics. Random Forest with the Wrapper Method is the right choice of model for detecting network traffic anomalies because of its stable performance and ability to handle complex patterns
Analysis of Student Excellence Classes in Data Mining Using the KNN Method
Authors: Ritonga Arvida , Masrizal Masrizal , Irmayanti Irmayanti
Published: May 2024

Excellent classes are programs designed to maximize the academic and non-academic potential of students and girls, with the aim of improving their overall achievement. This program aims to provide more intensive learning and a curriculum tailored to students' needs and abilities, so that they can develop their talents and competencies optimally. In order to evaluate the effectiveness of the superior class program and to identify students who are most suitable for the program, this research was conducted using the K-Nearest Neighbors (KNN) method in data mining. The research process includes several critical stages, namely determining relevant data, designing a machine learning model, testing the model to ensure its effectiveness, and evaluating the model to assess the accuracy and reliability of the results. This research used sample data consisting of 92 male and female students, where the results of the analysis showed that 42 of them met the criteria to enter the superior class, while 50 other students did not. These criteria are determined based on various factors, including academic achievement, participation in extracurricular activities, and other individual characteristics assessed through the KNN method. The accuracy results obtained from the model evaluation show excellent performance, confirming that the approach used is effective in classifying students based on their potential to excel in superior class programs. The conclusion of this research shows that the use of the KNN method in data mining can accurately identify students who will benefit most from superior class programs. Thus, this approach offers a valuable tool for educational institutions to optimize student potential and raise overall standards of achievement.
Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm
Authors: Melyani Sri , Harahap Syaiful Zuhri , Irmayanti Irmayanti
Published: May 2024

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.
Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time
Authors: Muliani Sonia Sri , Sihombing Volvo , Munthe Ibnu Rasyid
Published: May 2024

Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.
Scale Development and Validation for Assessing University Faculty’s Knowledge, Attitudes, and Practices in Special Education
Authors: Chao Pen-Chiang , Chou Yu-Chi , Hu Shin-Tzu , Huang Chiu-Hsia
Published: May 2024

This study aims to develop and validate a scale to assess university faculty’s knowledge, attitudes, and practices in special education. A total of 210 faculty members in Taiwan participated in this study. The proposed Faculty Perspectives on Special Education Scale (FPSES), a 32-item scale, includes three subscales: Special Education Knowledge (SEK), Attitudes Towards Students (ATS), and Provision of Services (POS). Data were analyzed using descriptive statistics, item analysis, correlation analysis, t tests, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Results showed that FPSES has satisfactory internal consistency reliability, with Cronbach’s α ranging from .93 to .96 and test-retest reliabilities, with coefficient ranging from .88 to .96. The FPSES also exhibited favorable content validity. Furthermore, the CFA confirmed a good fit between the proposed model and the sample data, thereby ensuring the construct validity. In summary, FPSES is a favorable assessment tool with good psychometric properties, which provides insights into faculty perspectives, attitudes, and practices in the provision of special education services for university students with disabilities.
How to Attract Learners Using the Content of Online Interactive Courses? A Case Study
Authors: Ahmad Amal , Abduljawad Mohammad
Published: May 2024

This study aimed to get input on how learners felt about and preferred online course options. According to the study, teachers’ prior experience instructing online courses, applications’ practicality in course delivery, learner interactions, the quality and timeliness of learner-teacher interactions, consistency of course design across courses, and session duration are all crucial for learners’ satisfaction with online instruction. The convenience, availability of technical support, and flexibility of online courses compared to face-to-face courses a group of students were surveyed after completing online training courses in various specializations to evaluate the quality of the courses, which were selected from reputable and well-known sites in the field of providing online training courses. The results, according to students’ feedback, showed that the courses that attracted students’ interest were those that took into account the student’s needs in terms of interaction with professors and colleagues, course content, and duration, especially if there was a practical aspect. The findings also indicate the need to develop a coherent course structure so that students’ needs and attractions are considered before starting the course.
MACHINE LEARNING FOR PREDICTING SPREAD OF COVID-19 IN INDONESIA
Authors: Hayati Nur , Mardiani Eri , Fauziah Fauziah , Haryanto Toto , Repi Viktor Vekky Ronald
Published: May 2024

In previous research, we carried out an analysis using the FBProphet model to predict the COVID-19 outbreak in Indonesia. The application of the FBProphet model to time series data is considered quite good because it produces a MAPE of 22.60% with a linear distribution. Additionally, based on the pattern in the previous dataset and the total number of active cases currently stands at 2,606, in this research we tried to use the Linear Regression (LR) model as a comparison with the FBProphet model by using additional data from the same data source, KAWALCOVID19 website. Data collection started from March 2, 2020 to December 19, 2021. The aim of this research is the same as previous research, namely predicting the spread of COVID-19. The analysis process is carried out by preprocessing the data by validating missing data and validating the format of the data variables. Then carry out descriptive analysis and data visualization so that it can be seen that in this 657 data there is a fluctuates data that non-periodically from July to August 2021. Next, model analysis is carried out using FBProphet and LR and validating the results of each model. The research results are in the form of evaluation metrics where the LR model gets better RMSE, MAE and MAPE values compared to FBProphet, namely 292.91; 178, 81 and 12.79%.
CLASSIFICATION OF CUSTOMERS’ REPEAT ORDER PROBABILITY USING DECISION TREE, NAÏVE BAYES AND RANDOM FOREST
Authors: Dewi Amelia Citra , Hermawan Arief , Avianto Donny
Published: May 2024

Limited customer information in sales data on e-commerce in Indonesia hinders companies in determining targeted marketing strategies, especially in targeting groups of potential customers to make repeat purchases. Sales data in the form of customers' names and cellphone numbers has been hidden by e-commerce, and only data is available in the form of products purchased, number of purchases, and customer addresses. So far, the methods used to determine potential customers mostly use more complete data features. Research that uses limited e-commerce data to determine potential customers is scarce. Several algorithms for predicting repeat purchases in e-commerce also have been widely used. However, the comparison of the performance of these methods in the context of e-commerce in Indonesia with limited data has yet to be discovered. In this research, the Decision Tree, Naive Bayes, and Random Forest methods were compared to classify potential customers using Maschere brand sales data from two e-commerce sites, namely Tokopedia and Shopee. The research results show that the Decision Tree algorithm achieved an accuracy of 90.91%, Naive Bayes achieved an accuracy of 37.50%, and Random Forest achieved the best level of accuracy, namely 93.94%. These results show that the Random Forest method is the best method for classifying customers' probability of repeat purchases. In the future, the results of this research can be developed again as a decision-making system to determine potential customers.
Chitosan-based Materials
Authors:   , Schnabl Kordula Barbara
Published: May 2024

This Thesis aims to extend the knowledge on the effective utilization of chitosan for potential industrial applications and to show its path towards functional materials.
One of these applications is an effective, highly porous, biobased support, which can efficiently hold active phases such as metal organic frameworks (MOFs) and zeolites in its structure. We discovered a new synthesis route for these highly porous chitosan structures and investigated different ways to incorporate MOFs while keeping them active for CO2 conversion.
Another potential application is to synthesize chitosan-based film materials, whose properties can be widely tuned with biobased green additives. Hence, we investigated a set of seven biobased additives which can vary the film’s properties from soft to stiff, and from clear to opaque, in their uptake behaviour into the chitosan matrix. Further we analysed the film properties of the final material in its fresh state, as well as followed them over the period of 400 days in order to investigate their long-term stability and early signs of decay such as yellowing and enhanced brittleness.
We further also investigated the possibilities to vary the properties of chitosan on its own by depolymerization of the polymer with a set of heterogeneous supported bimetallic nanoparticles. With these catalysts, we were able to roughly half the initial polymer chain length. We propose a rough depolymerization mechanism on the catalyst surface and propose the role of the individual catalyst components.
Finally, we show the huge potential of chitosan in future industrial applications and potential future projects.
Clinical and diagnostic substantiation for computed tomography of internal organs in acute lung injury in intensive care unit
Authors: Bormyshev A. V. , Morozova T. G.
Published: May 2024

Objective. To conduct a clinical and diagnostic substantiation of computed tomography (CT) of internal organs in acute lung injury (ALI) in intensive care.

Methods. 96 patients of the intensive care unit of the Clinical hospital No 1(Smolensk) were examined. CT scans of the lungs, liver, and brain were performed using a tomograph GE Revolution EVO64. Clinical structure of the patients: 49 – with pneumonia, 15 – septic condition, 12 – Inhalation of toxic substances, 8 – aspiration of toxic liquids, 3 – aspiration of disosmolar liquids, 3 – disseminated intravascular coagulation, 3 – pulmonary contusion, 3 – shock condition. Statistical processing of the results was carried out in the Statistica 6.0 program.

Results. Of the 15 patients without changes of the lungs on CT scan, in 12 patients’ attenuation value of liver parenchyma was 14–28HU, brain attenuation value was 24–30HU. In patients with established changes in lung tissue (n=81), the density of liver tissue less than 54HU, brain density less than 32HU in 80.2 % of cases allowed us to predict an unfavorable course of ALI. 10 deaths were recorded, 6 patients were in critical condition for a long period of time. Treatment of hemostasis disorders, normalization of water-electrolyte homeostasis, elimination of hypoxemia, prescription of hepatoprotective, antibacterial, detoxification therapy helped to avoid an unfavorable outcome.

Conclusions. 1. It is recommended to consider of criteria for an unfavorable course in patients with acute lung injury the attenuation value of liver parenchyma less than 54HU and brain tissue attenuation value less than 32HU on CT (r=0.969 and r=0.988). 2. CT scan of the liver and brain allows the resuscitator to correct therapy and decide whether it is necessary to involve other specialists. 3. Diagnostic and prognostic significance of a complex CT scan in patients with acute lung injury, at the patient’s admitting AUROC 0.998; CI 0.901–0.999; at follow-up AUROC 0.835; CI 0.822–0.847.
The challenges faced by (bio-)designers in contributing to sustainable material development
Authors: Luecking Malu
Published: May 2024

Prototyping plays a pivotal role in the development of new materials for a circular industry, serving as a crucial bridge between innovation and sustainable production by enabling experimentation, refinement, and validation of novel material concepts. The process of prototyping also reveals problems and opportunities at a systemic level, leading to deep interconnected sustainable thinking, provided that the material life cycle is consistently considered. Designers, particularly in the fields of material, product, and textile design, are in a position to mediate between the material and system approaches, the hands-on material design practice, and industrial production. Therefore, this abstract suggests the urgent need to involve designers in the early stage development of novel materials and strategies. The premises for this abstract arise in response to the challenges faced by the Conscious Textile Group at Moholy-Nagy University of Art and Design during a collaborative research project with the fashion brand Nanushka. The project aimed to develop circular textile materials based on down-to-fiber recycling from pre-consumer textile waste. Throughout the project, we identified a mismatch between the project's goals and the possibilities of execution due to the lack of recycling infrastructure that would allow for rapid prototyping and material experimentation. The product and textile sectors encounter hurdles in materialising proof-of-concept stages for next-gen materials, primarily due to the industry's lack of agility, which continues to function as a closed system and leaves little room for cooperation. To nurture a generation of designers with inherited sustainable thinking, a paradigm shift is urgently needed.
Data-driven Parameters Tuning for Predictive Performance Improvement of Wire Bonder Multi-body Model
Authors: Cheng Xiaodong , Di Bucchianico Alessandro , Javanmardi Najmeh , de Jong Matthijs , Diget Emil Lykke , Please Colin , Lahaye Domenico , Peng Qiyao , Reisch Cordula , Sclosa Davide
Published: May 2024

This report describes work performed during SWI 2023 at the University of Groningen in relation with Problem 1 posed by the company ASMPT. They have detailed simulation software of a machine and they compare the results of this with physical experimental results. There is a significant difference between the simulated and measured data, and it is the goal of this work to study how to estimate the parameters in the simulation model using the ex-perimentally measured frequency response. First, two toy models are studied to understand the challenges of pa- rameter estimation in the frequency domain. Later, optimization methods are applied. Several different approaches of reducing the dimensionality of the parameter space are explored, including determining the parameter sensitivity. A suggestion for increasing the detail of the model, specifically related to the machine base, is also outlined. In the summary, we supply a discussion of the key insights we gained.
Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence
Authors: Nagahara Megumi , Tatemoto Satoshi , Ito Takumi , Fujimoto Otoha , Ono Tetsushi , Taniguchi Masayasu , Takagi Mitsuhiro , Otoi Takeshige
Published: May 2024

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.
Is gestational diabetes mellitus in lean women a distinct entity warranting a modified management approach?
Authors: Wadivkar Pradnyashree , Hawkins Meredith
Published: May 2024

During pregnancy, insulin resistance and impaired insulin secretion may lead to the development of Gestational Diabetes Mellitus (GDM). Although a higher Body Mass Index (BMI) is often cited as a risk factor for the development of GDM, lean pregnant women are also at risk of developing GDM based on evidence from several studies. It is proposed that insulin deficiency (more than insulin resistance) leads to the development of GDM in women with low BMI (BMI <18.5 kg/m2). Neonates of these women are more at risk of preterm birth and small-for-gestational-age. Given this unique pathophysiology and phenotype, this entity needs a modified management approach. This article aims to raise awareness of GDM in lean women to encourage more research on this topic and create a modified management approach.
Imaging analysis for muscle stem cells and regeneration
Authors: Karthikeyan Smrithi , Asakura Atsushi
Published: May 2024

Composed of a diverse variety of cells, the skeletal muscle is one of the body’s tissues with the remarkable ability to regenerate after injury. One of the key players in the regeneration process is the muscle satellite cell (MuSC), a stem cell population for skeletal muscle, as it is the source of new myofibers. Maintaining MuSC quiescence during homeostasis involves complex interactions between MuSCs and other cells in their corresponding niche in adult skeletal muscle. After the injury, MuSCs are activated to enter the cell cycle for cell proliferation and differentiate into myotubes, followed by mature myofibers to regenerate muscle. Despite decades of research, the exact mechanisms underlying MuSC maintenance and activation remain elusive. Traditional methods of analyzing MuSCs, including cell cultures, animal models, and gene expression analyses, provide some insight into MuSC biology but lack the ability to replicate the 3-dimensional (3-D) in vivo muscle environment and capture dynamic processes comprehensively. Recent advancements in imaging technology, including confocal, intra-vital, and multi-photon microscopies, provide promising avenues for dynamic MuSC morphology and behavior to be observed and characterized. This chapter aims to review 3-D and live-imaging methods that have contributed to uncovering insights into MuSC behavior, morphology changes, interactions within the muscle niche, and internal signaling pathways during the quiescence to activation (Q-A) transition. Integrating advanced imaging modalities and computational tools provides a new avenue for studying complex biological processes in skeletal muscle regeneration and muscle degenerative diseases such as sarcopenia and Duchenne muscular dystrophy (DMD).
An altered gut microbiome in pre-eclampsia: cause or consequence
Authors: Deady Clara , McCarthy Fergus P. , Barron Aaron , McCarthy Cathal M. , O’Keeffe Gerard W. , O’Mahony Siobhain M.
Published: May 2024

Hypertensive disorders of pregnancy, including pre-eclampsia, are a leading cause of serious and debilitating complications that affect both the mother and the fetus. Despite the occurrence and the health implications of these disorders there is still relatively limited evidence on the molecular underpinnings of the pathophysiology. An area that has come to the fore with regard to its influence on health and disease is the microbiome. While there are several microbiome niches on and within the body, the distal end of the gut harbors the largest of these impacting on many different systems of the body including the central nervous system, the immune system, and the reproductive system. While the role of the microbiome in hypertensive disorders, including pre-eclampsia, has not been fully elucidated some studies have indicated that several of the symptoms of these disorders are linked to an altered gut microbiome. In this review, we examine both pre-eclampsia and microbiome literature to summarize the current knowledge on whether the microbiome drives the symptoms of pre-eclampsia or if the aberrant microbiome is a consequence of this condition. Despite the paucity of studies, obvious gut microbiome changes have been noted in women with pre-eclampsia and the individual symptoms associated with the condition. Yet further research is required to fully elucidate the role of the microbiome and the significance it plays in the development of the symptoms. Regardless of this, the literature highlights the potential for a microbiome targeted intervention such as dietary changes or prebiotic and probiotics to reduce the impact of some aspects of these disorders.
Untargeted metabolomics in gastric and colorectal cancer patients – preliminary results
Authors: Kaźmierczak-Siedlecka Karolina , Muszyński Damian , Styburski Daniel , Makarewicz Jakub , Sobocki Bartosz Kamil , Ulasiński Paweł , Połom Karol , Stachowska Ewa , Skonieczna-Żydecka Karolina , Kalinowski Leszek
Published: May 2024

Introduction

Recent years, microbiota-associated aspects have been analysed in multiple disorders regarding cancers. Existing evidence pints that gut microorganisms might take part in tumour origin and therapy efficacy. Nevertheless, to date, data on faecal metabolomics in cancer patients is still strongly limited. Therefore, we aimed to analyse gut untargeted metabolome in gastrointestinal cancer patients (i.e., gastric and colorectal cancer).

Patients and methods

There were 12 patients with either gastric (n=4) or colorectal cancer (n=8) enrolled and 8 analysed (n=4 each). Stool samples were collected prior to anti-cancer treatments. Untargeted metabolomics analyses were conducted by means of mass spectrometry.

Results

A plethora of metabolites in cancer patients we analysed were noted, with higher homogenity in case of gastric cancer patients. We found that the level of Deoxyguanosine,m/z 266.091,[M-H]-, Uridine,m/z 245.075,[M+H]+, Deoxyguanosine,m/z 268.104,[M]+, 3-Indoleacetic acid,m/z 176.07,[M+H]+, Indoxyl,m/z 132.031,[M-H]-, L-Phenylalanine,m/z 164.073,[M-H]-, L-Methionine,m/z 150.058,[M+NH4]+, was significantly higher in colorectal cancer patients and Ethyl hydrogen malonate,m/z 133.031,[M+H]+ in gastric cancer.

Conclusion

The overall insights into untargeted metabolomics showed that most often higher levels of analysed metabolites were detected in colorectal cancer patients compared to gastric cancer patients. The link between gut metabolome and both local and distal metastasis might exist, however it requires confirmation in further multi-centre studies regarding larger sample size.
Case Report: Three cases of clinically suspected viral myocarditis with recovery of left ventricular dysfunction
Authors: Brown Jonathan , Rashid Hytham , Sarva Siva T. , Tatapudi Suhas , Kalathoor Jeby , Srinivasan Aswin , Daniel Michael , Raza Syed
Published: May 2024

Viral myocarditis is an important cause of non-ischemic cardiomyopathy. Multiple clinical manifestations have been reported, including acute heart failure, cardiogenic shock, and ventricular arrhythmias. We present three patients with clinically suspected viral myocarditis causing acute heart failure. Serum coxsackievirus B antibodies were positive in all three patients. Each case resulted in significant clinical improvement with hemodynamic support and acute recovery of left ventricular ejection fraction. Despite an initial critical presentation concerning for cardiogenic shock, we highlight three cases of clinically suspected coxsackie myocarditis with an excellent short-term prognosis.
H3K27Me3 abundance increases fibrogenesis during endothelial-to-mesenchymal transition via the silencing of microRNA-29c
Authors: Fledderus Jolien , Brouwer Linda , Kuiper Timara , Harmsen Martin C. , Krenning Guido
Published: May 2024

Objective

Endothelial-to-mesenchymal transition (EndMT) is a transdifferentiation process in which endothelial cells (ECs) adopt a mesenchymal-like phenotype. Over the past few years, it became clear that EndMT can contribute to several cardiovascular pathologies. However, the molecular pathways underlying the development of EndMT remain incompletely understood. Since the epigenetic enzyme Enhancer of Zeste Homolog 2 (EZH2) and its concomitant mark H3K27Me3 have been shown to be elevated in many cardiovascular diseases that associate with EndMT, we hypothesized that H3K27Me3 is a determinant for the susceptibility of EndMT.

Methods

To study the association between H3K27Me3 and EndMT, a knockdown model of EZH2 in human endothelial cells (HUVEC) was utilized to reduce H3K27Me3 abundance, followed by induction of EndMT using TGFβ1. The expression of molecular markers of EndMT and fibrogenesis were analysed.

Results

In cultured HUVECs, a reduction of H3K27Me3 abundance facilitates EndMT but mitigates fibrogenesis as shown by a decreased expression of collagen I and III. In HUVEC, H3K27Me3 abundance directly affects the expression of miR29c, a collagen-targeting miRNA. Additionally, knockdown of miR-29c in HUVEC with low H3K27Me3 abundance partly restored the expression of collagen I and III. Expectedly, in rats with perivascular fibrosis an increased abundance of H3K27Me3 associated with a decreased expression of miR-29c.

Conclusion

our data shows that endothelial fibrogenesis underlies an epigenetic regulatory pathway and we demonstrate that a decreased abundance of H3K27Me3 in ECs blunts fibrogenesis in part in a miR-29c dependent manner. Therefore, a reduction of H3K27Me3 could serve as a novel therapeutical strategy to mitigate fibrogenesis and may prove to be beneficial in fibrogenic diseases including atherosclerosis, cardiac fibrosis, and PAH.
Multi-vessel coronary artery grafting: analyzing the minimally invasive approach and its safety
Authors: Ushioda Ryohei , Hirofuji Aina , Yoongtong Dit , Sakboon Boonsap , Cheewinmethasiri Jaroen , Lokeskrawee Thanin , Patumanond Jayanton , Lawanaskol Suppachai , Kamiya Hiroyuki , Arayawudhikul Nuttapon
Published: May 2024

Introduction

At our institution, we perform off-pump coronary artery bypass (OPCAB) as a standard procedure. Moreover, patients with favorable coronary anatomy and condition are selected for minimally invasive cardiac surgery (MICS)-OPCAB. We retrospectively compared early outcomes, focusing on safety, between MICS-OPCAB and conventional off-pump techniques for multivessel coronary artery bypass grafting (CABG).

Methods

From August 2017 to September 2022, 1,220 patients underwent multivessel coronary artery grafting at our institution. They were divided into the MICS-OPCAB group (MICS group = 163 patients) and the conventional OPCAB group (MS group = 1057 patients). Propensity score matching (1 : 1 ratio) was applied to the MICS-OPCAB and MS groups (149 patients per group) based on 23 preoperative clinical characteristics.

Results

After matching, there were no significant differences in preoperative characteristics between the groups. The MICS group had a lower total graft number (2.3 ± 0.6 vs. 2.9 ± 0.8, p < 0.001) and fewer distal anastomoses (2.7 ± 0.8 vs. 3.2 ± 0.9, p < 0.001). There were no significant differences in hospital stay, intensive care unit stay, postoperative complications, and 30-day mortality. The MICS group had less drain output (MICS 350 ml [250–500], MS 450 ml [300–550]; p = 0.013). Kaplan–Meier analysis revealed no significant differences in postoperative MACCE (major adverse cardiac or cerebrovascular events)-free and survival rates between the groups (MACCE-free rate p = 0.945, survival rate p = 0.374).

Conclusion

With proper patient selection, MICS-OPCAB can provide good short to mid-term results, similar to those of conventional OPCAB.
Does PAD and microcirculation status impact the tissue availability of intravenously administered antibiotics in patients with infected diabetic foot? Results of the DFIATIM substudy
Authors: Fejfarová Vladimíra , Jarošíková Radka , Antalová Simona , Husáková Jitka , Wosková Veronika , Beca Pavol , Mrázek Jakub , Tůma Petr , Polák Jan , Dubský Michal , Sojáková Dominika , Lánská Věra , Petrlík Martin
Published: May 2024

Aims/hypothesis

The aim of this substudy (Eudra CT No:2019-001997-27)was to assess ATB availability in patients with infected diabetic foot ulcers(IDFUs)in the context of microcirculation and macrocirculation status.

Methods

For this substudy, we enrolled 23 patients with IDFU. Patients were treated with boluses of amoxicillin/clavulanic acid(AMC)(12patients) or ceftazidime(CTZ)(11patients). After induction of a steady ATB state, microdialysis was performed near the IDFU. Tissue fluid samples from the foot and blood samples from peripheral blood were taken within 6 hours. ATB potential efficacy was assessed by evaluating the maximum serum and tissue ATB concentrations(Cmax and Cmax-tissue)and the percentage of time the unbound drug tissue concentration exceeds the minimum inhibitory concentration (MIC)(≥100% tissue and ≥50%/60% tissue fT>MIC). Vascular status was assessed by triplex ultrasound, ankle–brachial and toe–brachial index tests, occlusive plethysmography comprising two arterial flow phases, and transcutaneous oxygen pressure(TcPO2).

Results

Following bolus administration, the Cmax of AMC was 91.8 ± 52.5 μgmL-1 and the Cmax-tissue of AMC was 7.25 ± 4.5 μgmL-1(P<0.001). The Cmax for CTZ was 186.8 ± 44.1 μgmL-1 and the Cmax-tissue of CTZ was 18.6 ± 7.4 μgmL-1(P<0.0001). Additionally, 67% of patients treated with AMC and 55% of those treated with CTZ achieved tissue fT>MIC levels exceeding 50% and 60%, respectively. We observed positive correlations between both Cmax-tissue and AUCtissue and arterial flow. Specifically, the correlation coefficient for the first phase was r=0.42; (P=0.045), and for the second phase, it was r=0.55(P=0.01)and r=0.5(P=0.021).

Conclusions

Bactericidal activity proved satisfactory in only half to two-thirds of patients with IDFUs, an outcome that appears to correlate primarily with arterial flow.