Use of hydrogel in the irrigation management of white pitaya (Hylocereus undatus) seedlings: Biometrics and accumulation of organic and inorganic solutes

The initial development of pitayas may be limited by a few factors, among them, water deficit. Agricultural hydrogels can be used as an alternative to enhance the retention and availability of water and nutrients in the soil. Therefore, this study aimed to evaluate the influence of irrigation frequency and hydrogel doses on the development of white pitaya (Hylocereus undatus) seedlings to establish a time interval in days between irrigations that provides better seedling development and determine the hydrogel dose that provides a reduction of water consumption without damaging seedling development. The experimental design consisted of randomized blocks in a 4 x 4 factorial arrangement, in which the treatments corresponded to 4 hydrogel doses (0, 2, 4, and 6 g/plant of Biogel Hidro Plus) incorporated into the substrate and four irrigation frequencies (1, 3, 5, and 7 days of interval). The biometric characteristics, photosynthetic pigments, and organic and inorganic solutes of the plants were evaluated after 120 days. The use of daily irrigation negatively influenced the growth and biomass accumulation of the aerial part of the seedlings and, consequently, provided the lowest values of cladodes of the pitaya seedlings. Pitaya seedlings had greater development when using an irrigation frequency of around 3 days. The application of 6 g/plant of hydrogel provided the highest averages for accumulation of dry biomass, photosynthetic pigments, and organic and inorganic solutes at irrigation levels of 3.6, 4, and about 3.8 days of intervals, respectively. Hydrogel incorporation allowed increasing the interval between irrigations by 1 day without damages to the seedling development.

Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach

Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion preventable expenses to the U.S. health systems annually and often indicates suboptimal patient care. To alleviate those severe financial and health consequences, it is crucial to proactively predict patients’ readmission risk. Such prediction is challenging because the evolution of patients’ medical history is dynamic and complex. The state-of-the-art studies apply statistical models which use static predictors in a period, failing to consider patients’ heterogeneous medical history. Our approach – Trajectory-BAsed DEep Learning (TADEL) – is motivated to tackle the deficiencies of the existing approaches by capturing dynamic medical history. We evaluate TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals in the United States, reaching an F1 score of 87.3% and an AUC of 88.4%. Our approach significantly outperforms all the state-of-the-art methods. Our findings suggest that health status factors and insurance coverage are important predictors for readmission. This study contributes to IS literature and analytical methodology by formulating the trajectory-based readmission prediction problem and developing a novel deep-learning-based readmission risk prediction framework. From a health IT perspective, this research delivers implementable methods to assess patients’ readmission risk and take early interventions to avoid potential negative consequences.

Secured and Privacy-Based IDS for Healthcare Systems on E-Medical Data Using Machine Learning Approach

Existing methods use static path identifiers, making it easy for attackers to conduct DDoS flooding attacks. Create a system using Dynamic Secure aware Routing by Machine Learning (DAR-ML) to solve healthcare data. A DoS detection system by ML algorithm is proposed in this paper. First, to access the user to see the authorized process. Next, after the user registration, users can compare path information through correlation factors between nodes. Then, choose the device that will automatically activate and decrypt the data key. The DAR-ML is traced back to all healthcare data in the end module. In the next module, the users and admin can describe the results. These are the outcomes of using the network to make it easy. Through a time interval of 21.19% of data traffic, the findings demonstrate an attack detection accuracy of over 98.19%, with high precision and a probability of false alarm.

State of charge estimation for lithium-ion batteries connected in series using two sigma point Kalman filters

This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with 𝑁 series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of 𝑁 times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%.

Phytoremediation of Domestic Wastewater

Phytoremediation is fresh, well organized, low priced and recycled method for control of environmental pollution. In this phytoremediation technology, plants are used to enhance the status of environment. By using this method, organic and inorganic pollutant can easily eliminate from domestic. An aquatic plant culture was grown in regimented cement tank. Domestic waste Water was filled in this cement tank for specified interval of seven days. Before growth of aquatic plant culture quality of domestic waste water was evaluated. After specified time interval domestic waste water quality was again evaluated to check improvement of quality of waste water. The result of analysis indicates that phytoremediation process improves the quantity of waste water. For this phytoremediation process Canna, Hyacinth colocasia Arabica, Typha etc. aquatic plants are used. These aquatic plants absorb organic and inorganic parameters from waste water.

Nine-Axis Sensor for Athlete Physical Training Load Characteristics

In order to understand the characteristic data of athletes’ training load, a method based on nine-axis sensor was proposed. Twenty-seven male college athletes were tested twice with a time interval of more than 48 hours. In part 1, participants take the 1 Repetition Maximum (1RM) test. The results show that maximum strength is one of the basic factors to develop the output power of athletes. In the process of skeletal muscle contraction, the curve of speed, force, and power is closely related. When the external load is 10%∼70%, the average power increases with the increase in the average force, it increases with the decrease in the average speed, and at 70%1RM, the average power reaches the peak and then decreases at an inflection point. It is proved that the accurate weight ratio of strength training is the basis of winning athletes, the focus of high level physical coach, and the premise of scientific sports training.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

A Lightweight Flow Feature-Based IoT Device Identification Scheme

Internet of Things (IoT) device identification is a key step in the management of IoT devices. The devices connected to the network must be controlled by the manager. For this purpose, many schemes are proposed to identify IoT devices, especially the schemes working on the gateway. However, almost all researchers do not pay close attention to the cost. Thus, considering the gateway’s limited storage and computational resources, a new lightweight IoT device identification scheme is proposed. First, the DFI (deep/dynamic flow inspection) technology is utilized to efficiently extract flow-related statistical features based on in-depth studies. Then, combined with symmetric uncertainty and correlation coefficient, we proposed a novel filter feature selection method based on NSGA-III to select effective features for IoT device identification. We evaluate our proposed method by using a real smart home IoT data set and three different ML algorithms. The experimental results showed that our proposed method is lightweight and the feature selection algorithm is also effective, only using 6 features can achieve 99.5% accuracy with a 3-minute time interval.