bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Koneksi Politik, Capital Intensity, dan Corporate Social Responsibility Disclosure Terhadap Tax Avoidance
Authors: Ishak Andi Chaerunnisa , Asalam Ardan Gani
Published: October 2023

Based on the central government's financial report regarding targets and realization of tax revenues for the last 4 years, it is known that in 2018 to 2020 tax realization did not reach the set targets. This shows that in that year there was a shortfall condition. Tax avoidance can be caused by several factors including political connections, capital intensity, and corporate social responsibility disclosure. The purpose of this study is to find out and explain how tax avoidance is influenced by political connections, capital intensity, and corporate social responsibility disclosure in manufacturing companies listed on the Indonesia Stock Exchange in 2018-2021.
The research method used is the sampling technique with a descriptive approach and deductive in nature with the research strategy used is a case study. The data used in this research is 10 samples of companies with an observation period of 4 (four) years so that 40 data were used in this study. The data in this study were analyzed using a panel data regression model with the help of excel and eviews 12 software. Results research shows that political connections, capital intensity, and corporate social responsibility disclosure simultaneously affect tax avoidance. Partially, the capital intensity variable has a negative effect on tax avoidance. While political connections and corporate social responsibility disclosure has no effect on tax avoidance.
Evaluasi Implementasi Analisis Kesebandingan atas Sengketa Transfer Pricing PT OCI
Authors: Tambunan Rafael , Abbas Yulianti
Published: October 2023

Transfer pricing is one of the most popular drivers of tax disputes between taxpayers and tax authorities around the world. To lessen the probability of disputes, the OECD proposes procedures for comparability analysis. These procedures help taxpayers and tax authorities to determine the arm's length values of related-party transaction, which is the main cause of many transfer pricing disputes. This study uses a case study method, focusing on PT OCI, to evaluate a firm’s comparative analysis practice and its conformity to OECD Transfer Pricing Guidelines. Data collection through interviews and documentation. The results of this study indicate that PT OCI had conducted a comparability analysis of transfer pricing transactions in conformity with the OECD guidelines, while the tax authorities have not carried out a comparability analysis in carrying out transfer pricing audits. However, specifically for trademark utilization transactions, PT OCI must accept the results of inspection corrections. Fulfillment of the arm’s length principle for transactions utilizing intangible assets must comply with two additional provisions, namely related to the existence of transactions and economic benefits from the utilization of intangible assets. With the results of the benefit test, it was concluded that the trademark used by PT OCI did not contribute to increasing the success of PT OCI's sales so that transactions for the use of intangible assets did not meet the arm’s length principles.
Analisis Pengetahuan Investasi, Return, dan Risiko Terhadap Minat Berinvestasi Online di Aplikasi Bibit
Authors: Amanda Keisya Tri , Tanjung Ahmad Albar
Published: October 2023

This study will evaluate and assess investment knowledge, as well as returns and risks to an interest in investing online in BIBIT applications. The goal of this research is to measure and analyze investment knowledge. The amount of interest one has in making investments is serving as the dependent variable for this investigation. Because of the more rigorous and planned approach that this research method takes to the collection and analysis of numerical data, it can be classified as a type of quantitative research. For both the collecting and the analysis of data, this study relied on primary sources. Whereas the primary data is made up of information that was acquired through the direct and online distribution of questionnaires using a Google form, the secondary data is made up of information that was gathered through other means. The survey was completed by one hundred individuals, and the researchers collected a total of fifty samples from those individuals. The sample determination technique used a purposive sampling technique. The young people who take part in seed applications are the ones who are targeted and targeted for this activity. According to the data, having knowledge in investing led to an increase in the level of investment interest displayed in BIBIT applications. Increased investing activity is typically encouraged when a higher rate of return is offered. In a similar vein, there is a connection between the risk of an investment and the interest in that investment.
Comparison of NB and SVM in Sentiment Analysis of Cyberbullying using Feature Selection
Authors: Riadi Selamet , Utami Ema , Yaqin Ainul
Published: October 2023

In the past few decades, the internet has become an inseparable part of human life. It provides ease of access and permeates almost every aspect of human existence. One of the internet platforms that is widely used by people around the world is social media. Apart from being spoiled with the convenience and efficiency offered by social media to support daily life, it has gained popularity among a wide audience. This has positive implications when utilized effectively, but it cannot be denied that there are negative consequences if not utilized properly. One such consequence is the prevalence of cyberbullying activities on social media. Cyberbullying has become a major concern for the public and social media users, prompting researchers to leverage information technology in developing technologies that can identify the elements of cyberbullying, particularly on social media platforms. Sentiment analysis has been employed by researchers to identify the components of cyberbullying in online platforms. Sentiment analysis involves the application of natural language processing techniques and text analysis to identify and extract subjective information from text. This study aims to compare the Naive Bayes algorithm and the Support Vector Machine algorithm, while utilizing feature selection, specifically chi-square, to enhance the accuracy of both algorithms in classifying Instagram comments. The experimental results indicate that the Multinomial Naive Bayes (MNB) algorithm outperforms the Support Vector Machine (SVM) algorithm, achieving an accuracy of 83.85% without feature selection and 90.77% with feature selection. Meanwhile, SVM achieves an accuracy of 82.31% without feature selection and 90% with feature selection. Evaluation through the confusion matrix and classification report reveals that MNB exhibits better precision and recall rates compared to SVM in identifying bullying and non-bullying classes. The use of feature selection enhances the performance of both algorithms in classifying Instagram comments related to cyberbullying.
Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based
Authors: Vinardo Vinardo , Wasito Ito
Published: October 2023

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown.
This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.
Performance Comparison between Signature Cryptography: A Case Study on SNAP Indonesia
Authors: Ramadhoni Moehammad , Santoso Handri
Published: October 2023

SNAP (Standar Nasional OPEN API Pembayaran) was submitted by several sub-working groups formed jointly by ASPI and the Bank of Indonesia for encouraging digital transformation in the banking industry. In the document Pedoman Tata Kelola (Bank of Indonesia, n.d.), there is the use cryptographic algorithms that are used as validation for third parties to use the Open API. The algorithms used in the document are HMAC and RSA. The third party will send the signature in the API header along with the sent API payload. The signature describes the body payload, the endpoint URL that was called by the third party, and the time when the API call was made, so the signature will change all the time. However, there are other algorithms that can be used as a form of validation, such as ECC and ZK-SNARK. In this journal, the performance of the four cryptographic algorithms is compared. The performance we compare is overall speed when creating the signature and verifying it. The result is that HMAC is the most efficient algorithm, but for financial data, it is better to use ECC which uses asymmetric keys and is faster than RSA contained in the SNAP document, especially when 256 bits security level that ECC could be 10 times faster then RSA.
Ontology-Based Food Menu Recommender System for Patients with Coronary Heart Disease
Authors: Adila Najla Nur , Baizal Z. K. A.
Published: October 2023

Coronary heart disease is one of the leading causes of death. Knowledge of dietary patterns and proper food selection is an effort to address the risk and support coronary heart disease's healing process. Therefore, this study developed a food menu recommender system as a reference for patients with coronary heart disease. The recommender system is crucial in creating a proper dietary pattern for managing personalized meal plans. The system calculates the required nutritional needs of users. Ontology is used to represent knowledge about nutrition data and food intake. The ontology base with Semantic Web Rule Language (SWRL) enables the system to identify the most suitable foods for patients with coronary heart disease. We use SWRL rules to generate recommendation conclusions based on the existing ontology. Using this language enhances the descriptive logic capabilities, as the rules can overcome the limitations of the ontology language. Therefore, the system is built to find food menu options that match the required nutrition for patients. The nutritionist knowledge will be used to measure the system's performance compared to the recommendations made by nutritionists. From the user data sample, 150 recommended food menu data were obtained. The validation performance results obtained a precision 0.893, recall 1, and F_Score 94.3%.
Image Augmentation for BreaKHis Medical Data using Convolutional Neural Networks
Authors: Isthigosah Maie , Sunyoto Andi , Hidayat Tonny
Published: October 2023

In applying Convolutional Neural Network (CNN) to computer vision tasks in the medical domain, it is necessary to have sufficient datasets to train models with high accuracy and good general ability in identifying important patterns in medical data. This overfitting is exacerbated by data imbalances, where some classes may have a smaller sample size than others, leading to biased predictive results. The purpose of this augmentation is to create variation in the training data, which in turn can help reduce overfitting and increase the ability of the model to generalize. Therefore, comparing augmentation techniques becomes essential to assess and understand the relative effectiveness of each method in addressing the challenges of overfitting and data imbalance in the medical domain. In the context of the research described, namely a comparative analysis of augmentation performance on CNN models using the ResNet101 architecture, a comparison of augmentation techniques such as Image Generator, SMOTE, and ADASYN provides insight into which technique is most suitable for improving model performance on limited medical data. By comparing these techniques' accuracy, recall, and overall performance results, research can identify the most effective and relevant techniques in addressing the challenges of complex medical datasets. This provides a valuable guide for developing better CNN models in the future and may encourage further research in developing more innovative augmentation methods suitable for the medical domain.
Search Optimization of PIP Scholarship Recipients In Web-Based Student Data Application Using The Levenshtein Distance Algorithm
Authors: Agustin Yoga Handoko , Septiana Yosep , Aspahany Arbi Yuan
Published: October 2023

Realizing that education is very important, the government supports every citizen to get education. One of the government programs is the Smart Indonesia Program. PlP is a scholarship designed to help school-age children from poor/vulnerable families to continue to receive education services, both through formal elementary to high school/vocational schools and non-formal pathways from package a to package c and special education. SDN II Babakanloa has not been touched by technology for processing student data. So that the student section has difficulties in recording and updating student data. Student names have unique identities and errors often occur in typing the keywords to be searched. This results in an information that is desired or sought can not be found. Therefore we need a web-based data application that can provide keyword corrections in searching for student names. This study aims to create a web-based student data application by optimizing corrections to typing keywords searched by implementing the Levenshtein Distance Algorithm and also making it easier to process and search student data. The development method used is the Rational Unified Process (RUP) with the stages of Inception, Elaboration, Construction, and Transition. Designed using the CodeIgniter Framework with the PHP and JavaScript programming languages. The application of the Levenshtein Distance Algorithm can optimize the search for student data and reduce the occurrence of search errors by School Operators. The application of the Levenshtein Distance Algorithm produces a very good accuracy rate of 94% of the results of student data correction. accordance with the expectations of the School Operator. So it shows that the application of the Levenshtein Distance Algorithm is appropriate to use in optimizing the search.
Satellite Images Classification using MobileNet V-2 Algorithm
Authors: Wijaya Bayu Angga , Gea Perisman Jaya , Gea Areta Delano , Sembiring Alvianus , Hutagalung Christian Mitro Septiano
Published: October 2023

Satellite imagery is an invaluable source of visual information for environmental monitoring and land mapping with high resolution and wide coverage. In this modern technological era, advances in Deep Learning technology have brought great benefits in utilizing satellite images for various purposes. One of the efficient Deep Learning models for satellite image classification is MobileNet V-2, which is specifically designed for devices with limited resources such as smartphones. This study aims to develop an accurate satellite image classification model using Convolutional Neural Network algorithm and MobileNet V-2 model. The data used is taken from the RSI-CB256 dataset developed through crowdsourcing data. This research resulted in the performance of three deep learning models, namely ResNet50, MobileNet V-2, and VGG-16. ResNet50 is the highest model performed best during the training phase, achieve an accuracy of 98.40%. MobileNet V-2 and VGG-16 followed with 95.64% and 96.62% accuracy, respectively. The evaluation results demonstrate the model's strong ability to accurately classify satellite imagery and strengthen the model's ability to generalize well. With high accuracy and the ability to run on smartphone devices, this model has the potential to provide valuable information for governments and scientists in preserving the earth and better responding to environmental changes.
Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model
Authors: Firdaus M. Haikal , Utami Ema , Ariatmanto Dhani
Published: October 2023

This research is motivated by the urgent need to improve the capability of detecting diseases in citrus plants, which play a crucial role in maintaining agricultural sector productivity. Diseases such as blackspot, canker, and greening can have a serious impact on harvest yields and overall plant health. Therefore, this research aims to enhance the accuracy in classifying diseases in citrus plants by applying a Deep Learning approach. In this study, we chose to adopt the DenseNet-169 architecture and conducted experiments with two different scenarios: one using original features and the other using a combination of features. This method was employed to classify four different classes, namely blackspot, canker, greening, and healthy plants, using an LDI dataset consisting of 3,000 images. This dataset was divided into three parts, namely training, testing, and validation sets. The experimental results indicate that the DenseNet-169 model with the use of feature combination achieved the highest accuracy rate at 96.66%, whereas the model using only original features achieved 91.33%. This significant improvement of 5.33% in accuracy provides strong evidence that the feature combination approach has a highly meaningful positive impact on the model's ability to identify and classify diseases in citrus plants. These findings confirm that the use of feature combinations is a highly effective strategy in improving the model's performance in disease classification tasks in citrus plants.
PyTorch Deep Learning for Food Image Classification with Food Dataset
Authors: Iswahyudi Iswahyudi , Hindarto Djarot , Santoso Handri
Published: October 2023

Classification of food images is crucial in today's increasingly digitally connected world. In the rapidly evolving mobile applications and social media era, the demand for an automated system that can recognize food types from an image is intensifying. This study employs deep learning and the PyTorch framework to develop a dependable and efficient solution for classifying food images. This research is motivated by the growing complexity of food introduction challenges. The primary challenge is improving the accuracy of food type recognition and overcoming variations in the visual presentation of food, such as lighting, shooting angles, and proportional and textural differences. Convolutional Neural Networks (CNN) are effective for image classification and are incorporated into the methods utilized. In addition, we employ ResNet101 transfer learning techniques to capitalize on the knowledge of trained models for large image datasets. The primary objective of this study is to develop a food image classification model that is accurate, training-efficient, and capable of accurately recognizing various types of food. In testing and evaluation, the developed model could realize multiple types of food with satisfactory accuracy. The accuracy of training reached 99.35%, while the accuracy of testing reached 94.65%. This study also reveals how Resnet101 transfer learning is utilized by deep learning technology.
Identification of 10 Regional Indonesian Languages Using Machine Learning
Authors: Nugraha Azhar Baihaqi , Romadhony Ade
Published: October 2023

Language Identification plays a pivotal role in deciphering the rich tapestry of Indonesia's diverse regional languages, encompassing a wide spectrum of scripts, and spoken forms. Language Identification, an integral component of Natural Language Processing, is frequently addressed through Text Classification. In this study, we embark on the task of identifying 10 Indonesian languages, leveraging the NusaX dataset, with the overarching objective of contextual language determination. To achieve this, we harness a diverse array of machine learning techniques, including Support Vector Machine, Naïve Bayes Classifier, Decision Tree, Rocchio Classification, Logistic Regression, and Random Forest. We complement these methods with two distinct feature extraction approaches: N-gram and TF-IDF. This comprehensive approach enables us to construct robust models for language identification. Our findings unveil the strong efficacy of these models in discerning Indonesian languages, with the Naïve Bayes Classifier emerging as the frontrunner, achieving an impressive accuracy rate of 99.2% with TF-IDF and an even more remarkable 99.4% with N-Gram. To gain deeper insights, we delve into error analysis, revealing that misclassifications often stem from shared words across different languages. This research is underpinned by the necessity for a robust language identification model, underscoring its critical role within the complex linguistic landscape of Indonesian regional languages. These results hold great promise for applications in automated language processing and understanding within this diverse and multifaceted linguistic context.
Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes
Authors: Alam Alam , Alana Divi Adiffia Freza , Juliane Christina
Published: October 2023

Diabetes mellitus is an urgent global health problem and has a major impact on people around the world. This disease is characterized by high levels of sugar (glucose) in the blood due to disturbances in the production or use of the hormone insulin by the body. This study aims to carry out accurate early detection of diabetics so that they can be treated as soon as possible to reduce the risk of death and to compare the two algorithms that have the best level of accuracy. The algorithms used in this study are the C4.5 and Naïve Bayes Decision Tree Algorithms. The results of the experiments carried out in this study the Decision Tree Algorithm C4.5 and Naïve Bayes can be used in modeling the early detection of diabetes. The highest average accuracy results were obtained at 90.835% using the Decision Tree C4.5 Algorithm. As for the Naïve Bayes Algorithm, an average accuracy rate of 90.745% is obtained. The pruning process was carried out using the Decision Tree Algorithm C4.5, the accuracy performance increased to 91.30%. There were 18 patterns or rules for the early detection of diabetics from the built model. The determination of attributes, the number of attribute dimensions, and the number of samples greatly affect the performance of the model built.
COMPARISON OF ANTI-DIARRHEAL ACTIVITY OF METHANOL AND ETHANOL EXTRACTSS OF WATERMELON RIND(CITRULLUS LANATUS L.) IN WHITE MICE (MUS MUSCULUS)
Authors: Arifianto Nasruhan , Munawaroh  , Ermawati  , Wahyuni
Published: October 2023

Watermelon (Citrulluslanatus L.) is one of the most abundant fruit-producing plants in Indonesia. Watermelon is generally only consumed on the red flesh, while the rind is less desirable so that it becomes waste. The purpose of this study was to comparison of anti-diarrheal activity of  methanol and 70% ethanol extractss of watermelon rind (citrulluslanatus l.) in white miice (musmuscucolus. This study used a protection method with the induction of MgSO4 (Magnesium Sulfate) by calculating the frequency of diarrhea, changes in stool consistency and stool weight. The data obtained from the activity test results of the watermelon rind methanol extracts against white mice were statistically analyzed using the one-way ANOVA test.Methanolic and 70% ethanolicextractss of watermelon rind (Citruluslanatus L.) contain tannin compounds that have antidiarrheal activity against mice (Musmusculus). From the results of the one-way ANOVA test, the weight of feces, the frequency of diarrhea and the consistency of the stools in the methanol extracts of watermelon rind at a dose of 100 mg/KgBW gave an optimal effect, because the weight of the feces obtained a value of p = 0.633, the frequency of diarrhea obtained a value of p = 0.899 and the consistency of the fecesobtained a value of p = 0.899 p-value = 0.525 compared to the positive control (loperamide). Ethanol extracts of Citrulluslanatus L. had antidiarrheal effect at a dose of 100 mg/kgBW, 200 mg/kgBW, and 300 mg/kgBW. Based on the results of the One Way ANOVA statistical test, the ethanolicextracts of Citrulluslanatus L. had an antidiarrheal effect at a dose of 100 mg/kgBW and the best dose was a dose of 300 mg/kgBW which was significant with the positive control group loperamide P value = 0.000, meaning that the ethanolicextracts of Citrulluslanatus L. at a dose of 300 mg/kgBW had almost the same effect as the loperamide positive control group based on the parameters of stool weight, frequency of diarrhea, and stool consistency. From this data 70% Ethanolic extracts better than methanolicextracts for antidiarrheal activity in mice.
Cardiac effects of endothelin-1 (ET-1) and related C-terminal peptide fragment: increased inotropy or contribution to heart failure?
Authors: Dřímal J , Knezl V , Dřímal J , Dřímal D , Bauerová K , Kettmann V , Doherty AM , Štefek M
Published: October 2023

The contrasting pattern of cardiac inotropy induced by human peptide endothelin-1 (ET-1) has not been satisfactorily explained. It is not clear whether ET-1 is primarily responsible for increased myocardial ET-1 expression and release with resultant inotropic effects, or for the induction of myocardial hypertrophy and heart failure. There are at least two subtypes of endothelin receptors (ETA and ETB) and the inotropic effects of ET-1 differ depending on the receptor involved. Along with some other groups, we reported significant subtype-ETB endothelin receptor down-regulation in human cardiac cells preincubated with endothelin agonists (Dřímal et al. 1999, 2000). The present study was therefore designed to clarify the subtype-selective mechanisms underlying the inotropic response to ET-1 and to its ETB-selective fragment (8-21)ET-1 in the isolated rat heart. The hearts were subjected to (1-21)ET-1 and to (8-21)ET-1, or to 30 min of stop-flow ischemia followed by 40 min of reperfusion, both before and after selective blockade of endothelin receptors.The present study revealed that both peptides, ET-1 and its (8-21)ET-1 fragment, significantly reduced coronary blood flow in nmolar and higher concentrations. The concomitant negative inotropy and chronotropy were marked after ET-1, while the infusion of the ET-1(8-21) fragment produced a slight but significant positive inotropic effect. Among the four endothelin antagonists tested in continuous infusion only the non-selective PD145065 and ETB1/B2-selective BQ788 (in mmolar concentrations) slightly reduced the early contractile dysfunction of the heart induced by ischemia, whereas ETA-selective PD155080 partially protected the rat heart on reperfusion.
Diabetes-induced biochemical changes in central and peripheral catecholaminergic systems.
Authors: Gallego M , Setién R , Izquierdo MJ , Casis O , Casis E
Published: October 2023

A great variety of alterations have been described in the nervous system of diabetic animals. They are named as diabetic neuropathy and affect the brain, spinal cord and peripheral nerves. In diabetic animals, plasma and tissue catecholamine levels have been reported to be increased, decreased or unchanged, and these disparities have been explained by differences in the tissues selected, severity or duration of diabetes. Dopamine, norepinephrine and epinephrine from different tissues were extracted by absorption onto alumina, and measured by high performance liquid chromatography with electrochemical detection. We found that diabetes alters catecholaminergic systems in a highly specific manner. The dopamine content is reduced in the dopaminergic nigrostriatal system only. Norepinephrine is differently altered in several areas of the sympathetic nervous system. It is increased in cardiac ventricles, and decreased in stellate ganglia and the blood serum. However, it is not altered in the central nervous system. Finally, epinephrine is only altered in the adrenal gland where it is increased, and in the serum where it is reduced. Our results suggest that diabetes reduces the activity of the nigrostriatal dopaminergic system. Changes found at the sympathoadrenal level could be explained by reduced norepinephrine and epinephrine synthesis, with increased storage due to a reduced release from synaptic vesicles.
Activity of the laryngeal abductor and adductor muscles during cough, expiration and aspiration reflexes in cats
Authors: Poliaček I , Stránsky A , Jakuš J , Baráni H , Tomori Z , Halašová E
Published: October 2023

We studied the temporal relationships and the patterns of electromyographic activities of the posterior cricoarytenoid and thyreoarytenoid muscles (laryngeal abductor and adductor), the diaphragm and abdominal muscles in anesthetized cats during mechanically induced tracheobronchial and laryngopharyngeal coughs, expiration and aspiration reflexes. The posterior cricoarytenoid muscle activity reached the maxima just before the peak of diaphragmatic activity in both types of cough and aspiration reflexes and slightly before the top of abdominal muscle activity in coughs and the expiration reflex. Thus, this muscle contributes to the inspiratory phase of coughs and aspiration reflex and also to the expulsive phase of coughs and the expiration reflex. The thyreoarytenoid muscle presented strong discharges in the compressive phase of coughs and expiration reflex (during the rising part of the abdominal muscle activity) and in the subsequent laryngoconstriction (following the diaphragmal and/or abdominal muscle activity) in all four reflexes. This muscle was also slightly activated at the beginning of the aspiration reflex. The existence of four phases of the cough reflex is also discussed.
Kynurenic acid prevented social recognition deficits induced by MK-801 in rats.
Authors: Hliňák Z , Krejčí I
Published: October 2023

MK-801 impaired social recognition potency of adult male rats when given immediately after the initial interaction with a juvenile rat. Administration of kynurenic acid prior to the initial interaction protected the adults against recognition deficits induced by MK-801. When re-exposed at a delay of 30 min to the familiar juvenile, social investigation in the adults was significantly reduced. Thus, the adults are able to remember olfactory stimuli emitted by juvenile con-specifics.
ABCA3 and LZTFL1 Polymorphisms and Risk of COVID-19 in the Czech Population
Authors: Hubacek JA , Philipp T , Adamkova Adamkova, , Majek O , Dusek L
Published: October 2023

SARS-CoV-2 infection, which causes the respiratory disease COVID-19, has spread rapidly from Wuhan, China, since 2019, causing nearly 7 million deaths worldwide in three years. In addition to clinical risk factors such as diabetes, hypertension, and obesity, genetic variability is an important predictor of disease severity and susceptibility. We analyzed common polymorphisms within the LZTFL1 (rs11385942) and ABCA3 (rs13332514) genes in 519 SARS-CoV-2-positive subjects (164 asymptomatic, 246 symptomatic, and 109 hospitalized COVID-19 survivors) and a population-based control group (N = 2,592; COVID-19 status unknown). Rare ABCA3 AA homozygotes (but not A allele carriers) may be at a significantly increased risk of SARS-CoV-2 infection [P = 0.003; OR (95 % CI); 3.66 (1.47 9.15)]. We also observed a borderline significant difference in the genotype distribution of the LZTFL1 rs11385942 polymorphism (P = 0.04) between the population sample and SARS-CoV-2-positive subjects. In agreement with previous studies, a nonsignificantly higher frequency of minor allele carriers was detected among hospitalized COVID-19 subjects. We conclude that a common polymorphism in the ABCA3 gene may be a significant predictor of susceptibility to SARS-CoV-2 infection.
Comparation of Federated and Centralized Learning for Image Classification
Authors: Mar'i Farhanna , Supianto Ahmad Afif , Bachtiar Fitra Abdurrachman
Published: October 2023

Federated Learning (FL) is a new approach in machine learning or it can also be called collaborative learning, which is a machine learning method that includes client devices to carry out the training process, so that clients do not need to send training data to the server but directly conduct training on their respective devices. respectively. The models generated from local training will be sent to the server for further global aggregation. Therefore, FL is referred to as machine learning which can maintain the privacy of the data owner, because the data is not submitted to the server and is still stored in each client's device. In this study, a performance comparison will be carried out to prove whether the latest approach which is Federated Learning, can produce the same accuracy performance as the traditional approach, that is Centralized Machine Learning, in the case of Image Classification. The comparison of two approaches would be conducted by using the Open Source Image Classification dataset, namely MNIST. The performance of two approaches would be presented by evaluation that is Accuracy. The result shows that Federated Learning almost overcome the performance of Centralized Learning in the case of Image Classification by provided Accuracy 76%.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUASAN KONSUMEN PRODUK SUSU MURNI KPUB SAPI JAYA MENGGUNAKAN METODE REGRESI LINEAR BERGANDA
Authors: Aziz Muslih  , Sumarsono  , Andhika Mayasari
Published: October 2023

Customer satisfaction became one major priority for business agent to attract customer buying interest. Customer satisfaction is the feeling of happiness  or  sadness  which  appear  after  buying  and  comparing  the product with other brands. The process of getting good Multiple Linear Regression analysis result in this research, then it has to be examined in these following steps: (1) Testing the validity and reliability of questionnaire measuring instruments. (2) Describing variables of the research. (3) Testing the classical assumptions such as heterokedasticity, multicollinearity, collinearity and normality test. (4) Analyzing Multiple Linear Regression, which includes the F_test (joint test), t_test (partial test), and the coefiicient of detemination (R-squared) test. Identification values of  the  factors that affect customer satisfaction towards KPUB Sapi Jaya pure milk products obtained from the result of the linear regression equation, where the value of the influence constituent is 2,117 units, the value of the influence of X1. of 0.367 units, the value of influence X3. Product features of 0.671 units, the value of influence X4. The product beauty is 0.259, the value of influence X5. The perceived quality is 0.814 units and the value of influence X6. Service is 0.211 units. Furthermore, the results of the significance  test,  which  significantly  affects  customer  satisfaction  of KPUB Sapi Jaya pure milk products, it is concluded that there are 2 factors, the first is product durability factor (X1) and the second is perceived quality factor (X5). Meanwhile 4 other factors are not significant, such as product suitability factor (X2); product features (X3); beauty products (X4) and services (X6).
ANALISIS PENGEMBANGAN KUALITAS PRODUK KANTONG SEMEN JENIS PASTED KRAFT DENGAN PENDEKATAN SIX SIGMA DI PT. XYZ
Authors: Kharis Mandha Imawan  , Andhika Mayasari  , Sumarsono
Published: October 2023

The quality development is the most important thing that every company must do to minimize the defective products. One of the methods that can be used to analyze the defective products is Six Sigma which means as a method that can achieve, maintain, and maximize the success of business in a comprehensive and flexible way. The steps to be formulated by implementing the Six Sigma management cycle are DMAIC (Define, Measure, Analyze, Improve and Control). PT. XYZ which is engaged in manufacturing cement bags with one of its products is pasted kraft bags. The purpose of this study was to calculate the sigma score of the current conditions in the company, to analyze the causes of defective product that exceed the company's tolerance standards. The results showed three defective products. Those are printing defects, wrinkled bags and cutting defects. The average sigma score in the production process of Pasted kraft bags was 4.03 sigma, which means that out of 1 million products, there were around 5,858 defective products. The analysis of the core causes of product defects are the poor control of machine component conditions, the less attention of operators to standardize the machine settings, and the worn machine components during the production process. The repair step which is based on the 5W + 1H method are the recommendations for making a form to control the condition of engine components and good SOPs (Standard Operating Procedure), the procurement of field supervisors and regular training to operators.
Non-tuberculous mycobacterial disease: progress and advances in the development of novel candidate and repurposed drugs
Authors: Gu Yuzhen , Nie Wenjuan , Huang Hairong , Yu Xia
Published: October 2023

Non-tuberculous mycobacteria (NTM) are opportunistic pathogens that can infect all body tissues and organs. In particular, the lungs are the most commonly involved organ, with NTM pulmonary diseases causing serious health issues in patients with underlying lung disease. Moreover, NTM infections have been steadily increasing worldwide in recent years. NTM are also naturally resistant to many antibiotics, specifically anti-tuberculosis (anti-TB) drugs. The lack of drugs targeting NTM infections and the increasing drug resistance of NTM have further made treating these mycobacterial diseases extremely difficult. The currently recommended NTM treatments rely on the extended indications of existing drugs, which underlines the difficulties of new antibiotic discovery against NTM. Another challenge is determining which drug combinations are most effective against NTM infection. To a certain extent, anti-NTM drug development depends on using already available antibiotics and compounds. Here, we aimed to review new antibiotics or compounds with good antibacterial activity against NTM, focusing on their mechanisms of action,in vitroandin vivoantibacterial activities.
Comparison of female rat reproductive effects of pubertal versus adult exposure to known endocrine disruptors
Authors: Boberg Julie , Li Tianyi , Christiansen Sofie , Draskau Monica K. , Damdimopoulou Pauliina , Svingen Terje , Johansson Hanna K. L.
Published: October 2023

A prevailing challenge when testing chemicals for their potential to cause female reproductive toxicity is the lack of appropriate toxicological test methods. We hypothesized that starting a 28-day in vivo toxicity study already at weaning, instead of in adulthood, would increase the sensitivity to detect endocrine disruptors due to the possibility of including assessment of pubertal onset. We compared the sensitivity of two rat studies using pubertal or adult exposure. We exposed the rats to two well-known human endocrine disruptors, the estrogen diethylstilbestrol (DES; 0.003, 0.012, 0.048 mg/kg bw/day) and the steroid synthesis inhibitor ketoconazole (KTZ; 3, 12, 48 mg/kg bw/day). Specifically, we addressed the impact on established endocrine-sensitive endpoints including day of vaginal opening (VO), estrous cyclicity, weights of reproductive organs and ovarian histology. After 28 days of exposure, starting either at weaning or at 9 weeks of age, DES exposure altered estrous cyclicity, reduced ovary weight as well as number of antral follicles and corpora lutea. By starting exposure at weaning, we could detect advanced day of VO in DES-exposed animals despite a lower body weight. Some endpoints were affected mainly with adult exposure, as DES increased liver weights in adulthood only. For KTZ, no effects were seen on time of VO, but adrenal and liver weights were increased in both exposure scenarios, and adult KTZ exposure also stimulated ovarian follicle growth. At first glance, this would indicate that a pubertal exposure scenario would be preferrable as timing of VO may serve as sensitive indicator of endocrine disruption by estrogenic mode of action. However, a higher sensitivity for other endocrine targets may be seen starting exposure in adulthood. Overall, starting a 28-day study at weaning with inclusion of VO assessment would mainly be recommended for substances showing estrogenic potential e.g., in vitro, whereas for other substances an adult exposure scenario may be recommended.
The prevalence of thyroid dysfunction and hyperprolactinemia in women with PCOS
Authors: van der Ham Kim , Stekelenburg Karlijn J. , Louwers Yvonne V. , van Dorp Wendy , Schreurs Marco W. J. , van der Wal Ronald , Steegers-Theunissen Régine P. M. , Laven Joop S. E.
Published: October 2023

Introduction

Ovulatory dysfunction is usually caused by an endocrine disorder, of which polycystic ovary syndrome (PCOS) is the most common cause. PCOS is usually associated with estrogen levels within the normal range and can be characterized by oligo-/anovulation resulting in decreased progesterone levels. It is suggested that decreased progesterone levels may lead to more autoimmune diseases in women with PCOS. In addition, it is often claimed that there is an association between hyperprolactinemia and PCOS. In this large well-phenotyped cohort of women with PCOS, we have studied the prevalence of thyroid dysfunction and hyperprolactinemia compared to controls, and compared this between the four PCOS phenotypes.

Methods

This retrospective cross-sectional study contains data of 1429 women with PCOS and 299 women without PCOS. Main outcome measures included thyroid stimulating hormone (TSH), Free Thyroxine (FT4), and anti-thyroid peroxidase antibodies (TPOab) levels in serum, the prevalence of thyroid diseases and hyperprolactinemia.

Results

The prevalence of thyroid disease in PCOS women was similar to that of controls (1.9% versus 2.7%; P = 0.39 for hypothyroidism and 0.5% versus 0%; P = 0.99 for hyperthyroidism). TSH levels were also similar (1.55 mIU/L versus 1.48 mIU/L; P = 0.54). FT4 levels were slightly elevated in the PCOS group, although within the normal range (18.1 pmol/L versus 17.7 pmol/L; P < 0.05). The prevalence of positive TPOab was similar in both groups (5.7% versus 8.7%; P = 0.12). The prevalence of hyperprolactinemia was similarly not increased in women with PCOS (1.3%% versus 3%; P = 0.05). In a subanalysis of 235 women with PCOS and 235 age- and BMI-matched controls, we found no differences in thyroid dysfunction or hyperprolactinemia. In according to differences between PCOS phenotypes, only the prevalence of subclinical hypothyroidism was significantly higher in phenotype B (6.3%, n = 6) compared to the other phenotypes.

Conclusion

Women with PCOS do not suffer from thyroid dysfunction more often than controls. Also, the prevalence of positive TPOab, being a marker for future risk of thyroid pathology, was similar in both groups. Furthermore, the prevalence of hyperprolactinemia was similar in women with PCOS compared to controls.
Metabolic dysfunction-associated fatty liver disease is an early predictor for testosterone deficiency in aging men without metabolic syndrome
Authors: Liu Chia-Chu , Huang Shu-Pin , Lee Yung-Chin , Lee Cheng-Hsueh , Huang Tsung-Yi , Geng Jiun-Hung , Chang Che-Wei , Lin Chung-Yu , Juan Yung-Shun , Wu Wen-Jeng , Hsieh Tusty-Jiuan
Published: October 2023

Background and aims

Metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a valuable marker for identifying individuals at increased risk of metabolic dysfunction, liver-related complications, and cardiovascular disease. However, the association between MAFLD and testosterone deficiency (TD) in aging men remains poorly understood. This study aimed to investigate the association between MAFLD and the risk of TD in aging Taiwanese men, with a specific focus on those without metabolic syndrome (MetS).

Methods

A free health screening program was conducted for Taiwanese men aged over 40 years in Kaohsiung, Taiwan. Participants underwent physical examinations, completed questionnaires regarding demographics, medical history, and clinical symptoms of TD, and provided 20-mL whole blood samples for biochemical, adipocytokine, and hormonal evaluations. Fatty liver index was used to evaluate the risk of fatty liver. Diagnostic criteria for MAFLD included fatty liver along with overweight/obesity, type 2 diabetes, or evidence of metabolic dysregulation.

Results

A total of 631 men (mean age: 54.4 ± 8.4 years) were enrolled. The prevalence rates of TD and MetS were significantly higher in men with MAFLD compared to those without (both p < 0.001). Additionally, the presence of MAFLD showed a significant correlation with adipocytokines associated with insulin resistance, such as adiponectin, leptin, and retinol-binding protein-4 (RBP-4) levels (all p < 0.001). Among men without MetS, those with MAFLD had a 3.89- and 4.74-fold higher risk of total testosterone < 300 ng/dL and TD, respectively, after adjusting for potential covariates.

Conclusion

MAFLD is associated with an elevated risk of TD in aging Taiwanese men, particularly in the absence of MetS. This finding suggests that MAFLD could serve as an early predictor of TD, facilitating the identification of high-risk individuals and enabling timely interventions. Further research is needed to validate these findings and explore the underlying mechanisms linking MAFLD, TD, and MetS in diverse populations.
The impact of obstructive sleep apnea on nonalcoholic fatty liver disease
Authors: Tang Haiying , Lv Furong , Zhang Peng , Liu Jia , Mao Jingwei
Published: October 2023

Obstructive sleep apnea (OSA) is characterized by episodic sleep state-dependent collapse of the upper airway, with consequent hypoxia, hypercapnia, and arousal from sleep. OSA contributes to multisystem damage; in severe cases, sudden cardiac death might occur. In addition to causing respiratory, cardiovascular and endocrine metabolic diseases, OSA is also closely associated with nonalcoholic fatty liver disease (NAFLD). As the prevalence of OSA and NAFLD increases rapidly, they significantly exert adverse effects on the health of human beings. The authors retrieved relevant documents on OSA and NAFLD from PubMed and Medline. This narrative review elaborates on the current knowledge of OSA and NAFLD, demonstrates the impact of OSA on NAFLD, and clarifies the underlying mechanisms of OSA in the progression of NAFLD. Although there is a lack of sufficient high-quality clinical studies to prove the causal or concomitant relationship between OSA and NAFLD, existing evidence has confirmed the effect of OSA on NAFLD. Elucidating the underlying mechanisms through which OSA impacts NAFLD would hold considerable importance in terms of both prevention and the identification of potential therapeutic targets for NAFLD.