Hunterdickinson0812

Further, we train multiple ML classifiers to classify a person into high risk or low risk based on labeled data of RSSI and distance. We analyze the accuracy of the ML classifiers in terms of F-score, receiver operating characteristic (ROC) curve, and confusion matrix. Lastly, we debate future research directions and limitations of this study. We complement the study with open source code so that it can be validated and further investigated.Evaluation of arterial carbon dioxide pressure (PaCO2) and dead space to tidal volume ratio (VD/VT) during exercise is important for the identification of exercise limitation causes in heart failure (HF). However, repeated sampling of arterial or arterialized ear lobe capillary blood may be clumsy. The aim of our study was to estimate PaCO2 by means of a non-invasive technique, transcutaneous PCO2 (PtCO2), and to verify the correlation between PtCO2 and PaCO2 and between their derived parameters, such as VD/VT, during exercise in HF patients. 29 cardiopulmonary exercise tests (CPET) performed on a bike with a ramp protocol aimed at achieving maximal effort in ≈10 min were analyzed. PaCO2 and PtCO2 values were collected at rest and every 2 min during active pedaling. The uncertainty of PCO2 and VD/VT measurements were determined by analyzing the error between the two methods. The accuracy of PtCO2 measurements vs. PaCO2 decreases towards the end of exercise. Therefore, a correction to PtCO2 that keeps into account the time of the measurement was implemented with a multiple regression model. PtCO2 and VD/VT changes at 6, 8 and 10 min vs. 2 min data were evaluated before and after PtCO2 correction. PtCO2 overestimates PaCO2 for high timestamps (median error 2.45, IQR -0.635-5.405, at 10 min vs. 2 min, p-value = 0.011), while the error is negligible after correction (median error 0.50, IQR = -2.21-3.19, p-value > 0.05). The correction allows removing differences also in PCO2 and VD/VT changes. In HF patients PtCO2 is a reliable PaCO2 estimation at rest and at low exercise intensity. At high exercise intensity the overall response appears delayed but reproducible and the error can be overcome by mathematical modeling allowing an accurate estimation by PtCO2 of PaCO2 and VD/VT.An ammonia gas (NH3) generator was developed to maintain a set concentration of ammonia gas in a controlled environment chamber to study poultry physiological responses to sustained elevated levels of ammonia gas. The goal was to maintain 50 parts per million (ppm) of ammonia gas in a 3.7 m × 4.3 m × 2.4 m (12 ft × 14 ft × 8 ft) controlled environment chamber. The chamber had a 1.5 m3/s (3000 cfm) recirculation system that regulated indoor temperature and humidity levels and a 0.06 m3/s (130 cfm) exhaust fan that exchanged indoor air for fresh outdoor air. The ammonia generator was fabricated by coupling ultrasonic humidifiers with an Arduino-based microcontroller and a metallic oxide MQ-137 ammonia gas sensor. Preliminary evaluation under steady conditions showed the average MQ-137 gas sensor accuracy was within 1.4% of the 65.4 ppm concentration measured using a highly accurate infrared gas analyzer. Further evaluation was performed for a setpoint concentration of 50 ppm where ammonia generator reservoirs were filled with 2% or 10% ammonia liquid. For the system tested, it was found that two generators operating at the same time filled with 3.8 L (1.0 gallon) of 2% ammonia cleaning liquid each (7.6 L or 2.0 gallons total) could maintain a gas level of 49.45 ± 0.79 ppm in the chamber air for a duration of 30 h before refilling was required. One generator filled with 3.8 L of 10% ammonia cleaning liquid could maintain 51.24 ± 1.53 ppm for 195 h. Two ammonia generators were deployed for a six-week animal health experiment in two separate controlled environment chambers. The two ammonia generators maintained an average ammonia concentration of 46.42 ± 3.81 ppm and 45.63 ± 4.95 ppm for the duration of the experiment.The reliability of the ultrasonic phased array total focusing method (TFM) imaging of parts with curved geometries depends on many factors, one being the probe standoff. Strong artifacts and resolution loss are introduced by some surface profile and standoff combinations, making it impossible to identify defects. This paper, therefore, introduces a probe standoff optimization method (PSOM) to mitigate such effects. Based on a point spread function analysis, the PSOM algorithm finds the standoff with the lowest main lobe width and side lobe level values. Validation experiments were conducted and the TFM imaging performance compared with the PSOM predictions. The experiments consisted of the inspection of concave and convex parts with amplitudes of 0, 5 and 15 λAl, at 12 standoffs varying from 20 to 130 mm. Three internal side-drilled holes at different depths were used as targets. To investigate how the optimal probe standoff improves the TFM, two metrics were used the signal-to-artifact ratio (SAR) and the array performance indicator (API). The PSF characteristics predicted by the PSOM agreed with the quality of TFM images. A considerable TFM improvement was demonstrated at the optimal standoff calculated by the PSOM. The API of a convex specimen's TFM was minimized, and the SAR gained up to 13 dB, while the image of a concave specimen gained up to 33 dB in SAR.This paper contains a detailed description of the design and validation of a measurement stand for testing the airborne sound insulation of specimens made at a small scale. The stand is comprised of two coupled reverberation rooms in which the geometry represents the full-size reverberation rooms used at the AGH University of Science and Technology at a 18 scale. The paper proves that both the scaled measurement stand and the testing methodology conform to the ISO 10140 standards, and that the obtained measurement uncertainty does not exceed the maximum values specified in ISO 12999-1. Moreover, the calculated uncertainty of measurements obtained for the 18 scale stand is comparable with the typical uncertainty given in ISO 12999-1 and the uncertainty obtained on the full-scale measurement stand. In connection with the above, the authors have proved that by using the scaled-down measurement stands, one can obtain reliable and repeatable results of measurements of airborne sound insulation.Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a "rational" player who continuously updates its own "action" through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground-background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.Solid-contact ion-selective electrodes for histamine (HA) determination were fabricated and studied. Gold wire (0.5 mm diameter) was coated with poly(3,4-ethlenedioxythiophene) doped with poly(styrenesulfonate) (PEDOTPSS) as a solid conductive layer. The polyvinyl chloride matrix embedded with 5,10,15,20-tetraphenyl(porphyrinato)iron(iii) chloride as an ionophore, 2-nitrophenyloctyl ether as a plasticizer and potassium tetrakis(p-chlorophenyl) borate as an ion exchanger was used to cover the PEDOTPSS layer as a selective membrane. The characteristics of the HA electrodes were also investigated. The detection limit of 8.58 × 10-6 M, the fast response time of less than 5 s, the good reproducibility, the long-term stability and the selectivity in the presence of common interferences in biological fluids were satisfactory. The electrode also performed stably in the pH range of 7-8 and the temperature range of 35-41 °C. Additionally, the recovery rate of 99.7% in artificial cerebrospinal fluid showed the potential for the electrode to be used in biological applications.