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The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network's ability to recognize each strength level is more stable.Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station's (CHIRPS) precipitation and the Global Land Data Assimilation System's (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016-2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period.Every day, vehicle accidents occur and many of them might be avoided if the drivers demonstrated excellent driving without mistakes. This paper presents a novel prototype applied in a real transportation system, particularly for buses, to avoid accidents, which may involve numerous victims, and even occasionally cause death. This system consists of a wearable device and embedded system with several sensors connected via Bluetooth, similar to the Internet of Things (IoT). Wearable devices are made to monitor the driver's heart rate and alert the driver if they are in a state of sleep deprivation to prevent any potential accidents. The embedded system includes a Global Positioning System (GPS), accelerometers, and gyroscopes attached to a Smart Box mounted on the bus. Selleckchem Cefodizime The embedded system alert function will be triggered if an accident occurs and automatically sends the geolocation of the accident to the registered phone number through a message using a mobile phone. The results for all scenarios were significant when measured by an automatic accident trigger via the smart box if the value of measured values in each axis exceeded 583. In conclusion, the implementation of this innovative solution at the system-level was shown to be satisfactory in terms of the safety mechanism used by the nominated drivers.This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo State Network (ESN) input and reservoir weights, in the context of human action recognition (HAR). To ensure stability and echo state property in the reservoir, Recurrent Plots (RPs) and Recurrence Quantification Analysis (RQA) techniques are exploited for explainability and characterization of the reservoir dynamics and hence tuning ESN hyperparameters. The optimized self-organizing reservoirs are cascaded with a Convolutional Neural Network (CNN) to ensure that the activation of internal echo state representations (ESRs) echoes similar topological qualities and temporal features of the input time-series, and the CNN efficiently learns the dynamics and multiscale temporal features from the ESRs for action recognition. The hyperparameter optimization (HPO) algorithms are additionally adopted to optimize the CNN stage in SO-ConvESN. Experimental results on the HAR problem using several publicly available 3D-skeleton-based action datasets demonstrate the showcasing of the RPs and RQA technique in examining the explainability of reservoir dynamics for designing stable self-organizing reservoirs and the usefulness of implementing HPOs in SO-ConvESN for the HAR task. The proposed SO-ConvESN exhibits competitive recognition accuracy.Various types of motion blur are frequently observed in the images captured by sensors based on thermal and photon detectors. The difference in mechanisms between thermal and photon detectors directly results in different patterns of motion blur. Motivated by this observation, we propose a novel method to synthesize blurry images from sharp images by analyzing the mechanisms of the thermal detector. Further, we propose a novel blur kernel rendering method, which combines our proposed motion blur model with the inertial sensor in the thermal image domain. The accuracy of the blur kernel rendering method is evaluated by the task of thermal image deblurring. We construct a synthetic blurry image dataset based on acquired thermal images using an infrared camera for evaluation. This dataset is the first blurry thermal image dataset with ground-truth images in the thermal image domain. Qualitative and quantitative experiments are extensively carried out on our dataset, which show that our proposed method outperforms state-of-the-art methods.This study aims to introduce a resistance training protocol (6 repetitions × 70% of 1 maximum repetition (1RM), followed by 6 repetitions × 50% of 1RM within the same set) specifically designed for postmenopausal women with osteopenia/osteoporosis and monitor the effect of the protocol on bone mineral density (BMD) in the lumbar spine, assessed by dual-energy X-ray absorptiometry (DEXA). The subjects included in the study were 29 postmenopausal women (56.5 ± 2.8 years) with osteopenia or osteoporosis; they were separated into two groups the experimental group (n = 15), in which the subjects participated in the strength training protocol for a period of 6 months; and the control group (n = 14), in which the subjects did not take part in any physical activity. BMD in the lumbar spine was measured by DEXA. The measurements were performed at the beginning and end of the study. A statistically significant increase (Δ% = 1.82%) in BMD was observed at the end of the study for the exercise group (0.778 ± 0.042 at baseline vs. 0.792 ± 0.046 after 6 months, p = 0.018, 95% CI [-0.025, -0.003]); while an increase was observed for the control group (Δ% = 0.14%), the difference was not statistically significant (0.762 ± 0.057 at baseline vs. 0.763 ± 0.059, p = 0.85, 95% CI [-0.013, 0.011]). In conclusion, our strength training protocol seems to be effective in increasing BMD among women with osteopenia/osteoporosis and represents an affordable strategy for preventing future bone loss.High-precision, real-time, and long-range target geo-location is crucial to UAV reconnaissance and target strikes. Traditional geo-location methods are highly dependent on the accuracies of GPS/INS and the target elevation, which restricts the target geo-location accuracy for LRORS. Moreover, due to the limitations of laser range and the common, real time methods of improving the accuracy, such as laser range finders, DEM and geographic reference data are inappropriate for long-range UAVs. To address the above problems, a set of work patterns and a novel geo-location method are proposed in this paper. The proposed method is not restricted by conditions such as the accuracy of GPS/INS, target elevation, and range finding instrumentation. Specifically, three steps are given, to perform as follows First, calculate the rough geo-location of the target using the traditional method. Then, according to the rough geo-location, reimage the target. Due to errors in GPS/INS and target elevation, there will be a re-projection error between the actual points of the target and the calculated projection ones. Third, a weighted filtering algorithm is proposed to obtain the optimized target geo-location by processing the reprojection error. Repeat the above process until the target geo-location estimation converges on the true value. The geo-location accuracy is improved by the work pattern and the optimization algorithm. The proposed method was verified by simulation and a flight experiment. The results showed that the proposed method can improve the geo-location accuracy by 38.8 times and 22.5 times compared with traditional methods and DEM methods, respectively. The results indicate that our method is efficient and robust, and can achieve high-precision target geo-location, with an easy implementation.Physical training at home by making individuals play active video games is a new therapeutic strategy to improve the condition of patients with cystic fibrosis (CF). We reviewed studies on the use of video games and their benefits in the treatment of CF. We conducted a systematic review with data from six databases (PubMed, Medline, Scopus, Web of Science, PEDro, and Cochrane library plus) since 2010, according to PRISMA standards. The descriptors were "Cystic Fibrosis", "Video Game", "Gaming Console", "Pulmonary Rehabilitation", "Physiotherapy", and "Physical Therapy". Nine articles with 320 participants met the inclusion criteria and the study objective. Patients who played active video games showed a high intensity of exercise and higher ventilatory and aerobic capacity compared to the values of these parameters in tests such as the cardiopulmonary stress test or the six-minute walk test. Adequate values of metabolic demand in these patients were recorded after playing certain video games. A high level of treatment adherence and satisfaction was observed in both children and adults. Although the quality of the included studies was moderate, the evidence to confirm these results was insufficient. More robust studies are needed, including those on evaluation and health economics, to determine the effectiveness of the treatment.This paper proposes the procedure for minimising the dynamic error in the time and frequency domains, based on the example of a second-order sensor. Our procedure includes three main steps modelling of the sensors using the Monte Carlo (MC) method; determination of the maximum value of the dynamic error using the integral-square criterion (ISC); and optimisation of the parameters of the sensor model by minimising the ISC. The uncertainties associated with the modelling procedure and the MC method are also considered. The mathematical formulae necessary for implementation in a given programming language (MathCad, MATLAB, C, etc.) are presented in detail. The proposed procedure was implemented in the frequency domain, using MathCad 15, and applied to the example of the Althen 731-207 accelerometer. Validation of the proposed procedure was carried out using a digital signal processor of type TMS320C6713. The proposed procedure can increase the accuracy of the signal processing obtained at the output of sensors applied to a wide range of measurements.