https://www.selleckchem.com/products/dwiz-2.html By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM). Spices, i.e., curcumin, ginger, saffron, and cinnamon, have a thousand-year history of medicinal use in Asia. Modern medicine has begun to explore their therapeutic properties during the last few decades. We aimed to perform a systematic literature review (SLR) of randomized controlled trials (RCTs) assessing the effect of spice supplementation on symptoms and disease activity in patients with chronic inflammatory rheumatic diseases (rheumatoid arthritis (RA), spondylarthritis, or psoriatic arthritis). An SLR of RCTs, reviews, and meta-analyses was performed, searching for articles in MEDLINE/PubMed. Abstracts from international rheumatology and nutrition congresses (2017-2020) were also scrutinized. The risk of bias of the selected studies was evaluated using the Cochrane Collaboration's tool and the Jadad scale. Altogether, six studies, assessing the use of spice supplementation only in RA patients, were included one on garlic supplementation,