The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

The grid map, representing area information with the number of cells, is a widely used mapping scheme for mobile robots and simultaneous localization and mapping (SLAM) processes. However, the tremendous amount of cells in a grid map for a detailed map representation results in overheads for memory space and computing paths in mobile robots. Therefore, to overcome the overhead of the grid map, this study proposes a new low overhead mapping scheme which the authors call as the Rmap that represents an area with variable sizes of rectangles instead of the number of cells in the grid map. This mapping scheme also provides an exploration path for obtaining new information for the unknown area. This study evaluated the performance of the Rmap in real environments as well as in simulation environments. The experiment results show that the Rmap can reduce the overhead of a grid map. In one of our experimental environments, the Rmap represented an area with 85% less memory than the grid map. The Rmap also showed better coverage performance compared with other previous algorithms.

Background and Objectives: Previous studies regarding tranexamic acid (TXA) in total knee arthroplasty (TKA) investigated only symptomatic deep vein thrombosis (DVT), or did not include high risk patients. The incidence of DVT including both symptomatic and asymptomatic complications after applying topical TXA has not been evaluated using ultrasonography. Materials and Methods: The medical records of 510 patients who underwent primary unilateral TKA between July 2014 and December 2017 were retrospectively reviewed. Because TXA was routinely applied through the topical route, those who had a history of venous thromboembolism, myocardial infarction, or cerebral vascular occlusive disease, were not excluded. Regardless of symptom manifestation, DVT was examined at 1 week postoperatively for all patients using ultrasonography, and the postoperative transfusion rate was investigated. The study population was divided according to the use of topical TXA. After the two groups were matched based on the propensity scores, the incidence of DVT and the transfusion rate were compared between the groups. Results: Of the 510 patients, comprising 298 patients in the TXA group and 212 patients in the control group, DVT was noted in 22 (4.3%) patients. Two patients had DVT proximal to the popliteal vein. After propensity score matching (PSM), 168 patients were allocated to each group. In all, 11 patients in the TXA group and seven patients in the control group were diagnosed with DVT, which did not show a significant difference (p = 0.721). However, the two groups differ significantly in the transfusion rate (p < 0.001, 50.0% in the TXA group, 91.7% in the control group). Conclusions: The incidence of DVT, whether symptomatic or asymptomatic, was not affected by the use of topical TXA. The postoperative transfusion rate was reduced in the TXA group. Topical TXA could be applied safely even in patients who had been known to be at high risk.

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Global Positioning Systems (GPS) are successfully used in many fields such as navigation, meteorology, military tasks, mapping, virtual fencing, and more. Smart collars are currently the most convenient device for determining animal location in virtual fencing systems, however; these systems are still suffering from environmental effects and propagation in direct visibility. These types of side effects may reduce the work of GPS receivers. The current article defines a method for improving animal location accuracy using a virtual fence smart collar worn around the animal’s neck by the aid of maximum probability of movement from one point to another. The proposed approach first checks the current position of the animal, and after receiving a GPS signal from satellites it calculates the distance between the two GPS signals. Secondly, the method checks the animal’s behavior for the receiving period of the two points. Finally, the approach calculates a probability of maximum animal movement for the two-point receiving period. If the animal can pass the distance in the time frame of the two signals, then the second signal is taken as the correct position; otherwise, the point is taken which the animal could pass. Real-time animal behavior is classified using Support Vector Machines (SVM). The proposed method was verified within seven days of experiments. Consequently, the proposed approach experiments were sufficiently successful. The recreated locations from our approach appeared very close to the real point. The mean average of passed distance by the marked line decreased to 16.2, 5, 0 m for running, walking, and resting conditions, respectively. On the other hand, the unfiltered geolocations of the GPS receiver, give results significantly further from the animal’s actual position such as 148.8, 182.7, 136.2 m for running, walking, and resting conditions.