Strengthening exercise combined with blood flow restriction potentially increases muscle strength. This type of exercise does not require heavy weight liftings and is a feasible method to be performed by persons suffering illnesses. However, strengthening exercise may induce inflammatory responses due to muscle and vascular endothelial damage. This study aimed to investigate alterations of high-sensitivity C-reactive protein (hsCRP) and fibrinogen levels in healthy subjects after five weeks of low intensity resistance training (LIRT) with blood flow restriction (BFR) on increasing strength in comparison with high intensity resistance training (HIRT) and LIRT alone, and to evaluate aspects related to the relative safety of LIRT + BFR. Eighteen healthy subjects were randomized into 3 groups. The HIRT group: 70% of One-Repetition Maximum (1-RM); LIRT + BFR group: 30% of 1-RM with BFR (a modified 13-cm wide cuff was used); LIRT group: 30% of 1-RM. The peak torque of isokinetic contraction of the left elbow flexor in each subject was measured before and after 5 weeks of resistance exercises to determine any increases in the left biceps brachii muscle strength. Blood markers of homeostasis (fibrinogen) and inflammation (hsCRP) were also measured before and after five weeks of training. Significant increases of strength were demonstrated between the five weeks of resistance exercises in the HIRT group (P = 0.003) and the LIRT + BFR group (P = 0.001). Peak torque of isokinetic contraction of the left flexor elbow joint at 60° per second angular velocity showed that the LIRT + BFR group produced the greatest peak torque increase than the HIRT group. There were no significant changes in the hsCRP levels in all the groups (P > 0.05) after five weeks of intervention. No significant differences of fibrinogen levels were found in the HIRT group (P = 0.500) and the LIRT + BFR group (P = 0.405), but significant decreases were found in the fibrinogen levels in the LIRT group (P = 0.017). The LIRT + BFR increases in the muscle strength were as significant as in HIRT without altering the fibrinogen and hsCRP levels in the healthy subjects. In this study, LIRT + BFR showed increase muscle strength without any vascular problems.

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Emas adalah salah satu bentuk logam mulia yang memiliki nilai berharga di zaman sekarang ini. Oleh karena itu, banyak orang yang mulai untuk berinvestasi dengan emas. Seseorang yang ingin berinvestasi di emas, harus memperhatikan perubahan pada harga jual beli dari emas tersebut. Salah satu situs yang dapat dijadikan acuan untuk melihat perubahan harga jual beli emas adalah gold.org. Ada beberapa faktor yang mempengaruhi perubahan harga dari emas ini yaitu perubahan nilai kurs US Dollar, jumlah produksi emas dunia, dan kenaikan permintaan dari emas itu sendiri. Hal ini berarti harga dari emas ini cenderung tidak stabil karena sering terjadi perubahan. Metode LSTM atau Long Short Term Memory dapat diimplementasikan untuk melakukan prediksi harga emas berdasarkan harga emas sebelumnya. Model prediksi yang dibangun pada penelitian ini memprediksi harga emas kedepan berdasarkan 60 data harga emas sebelumnya. Berdasarkan hasil pengukuran akurasi yang dilakukan, didapatkan akurasi sebesar 87,84% dengan nilai rentang selisih antara harga asli dengan prediksi sebesar 5 dan jumlah epoch yaitu 100.

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.