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We evaluate the accuracy of a machine-learning-based path loss model trained on 42 157 324 RSSI samples collected over one year from an environmental wireless-sensor network using 2.4 GHz radios. The 2218 links in the network span a 2000 km 2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms were evaluated in order to find the one with lowest error: 1) Random Forest; 2) AdaBoost; 3) Neural Networks; and 4) K -Nearest-Neighbors. Of the candidate models, Random Forest showed the lowest error. The independent variables used in the model include path distance, canopy coverage, terrain variability, and path angle. We compare the accuracy of this model to several well-known canonical (free space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). Unlike canonical models, machine-learning algorithms are not problem-specific: they rely on an extensive dataset and a flexible model architecture to make predictions. We show how this model achieves a 37% reduction in the average prediction error compared to the canonical/empirical model with the best performance. This paper presents an in-depth discussion on the strengths and limitations of the proposed approach as well as opportunities for further research.

A network of sensors for spatially representative water-balance measurements was developed and deployed across the 2000 km2 snow-dominated portion of the upper American River basin, primarily to measure changes in snowpack and soil-water storage, air temperature, and humidity. This wireless sensor network (WSN) consists of 14 sensor clusters, each with 10 measurement nodes that were strategically placed within a 1 km2 area, across different elevations, aspects, slopes, and canopy covers. Compared to existing operational sensor installations, the WSN reduces hydrologic uncertainty in at least three ways. First, redundant measurements improved estimation of lapse rates for air and dew-point temperature. Second, distributed measurements captured local variability and constrained uncertainty in air and dew-point temperature, snow accumulation, and derived hydrologic attributes important for modeling and prediction. Third, the distributed relative-humidity measurements offer a unique capability to monitor upper-basin patterns in dew-point temperature and characterize elevation gradient of water vapor-pressure deficit across steep, variable topography. Network statistics during the first year of operation demonstrated that the WSN was robust for cold, wet, and windy conditions in the basin. The electronic technology used in the WSN-reduced adverse effects, such as high current consumption, multipath signal fading, and clock drift, seen in previous remote WSNs.

Next-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture, air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring.

This paper introduces SOL, which is both an efficient data representation for sensor measurements and network statistics, and a complete low-power wireless sensor management system that builds around it. A SOL system consists of multiple low-power wireless mesh networks in which motes connected to sensors and actuators send data to a single server. It offers multi-tier data replication and the associated data recovery. SOL is used in 3 pilot deployments for micro-climate monitoring, building automation and agriculture applications. In conjunction with Metronome Systems' NeoMote and Manager devices, SOL is a turn-key ready-to-deploy end-to-end system.

Objective To explore the impact of venous thromboembolism (VTE) on the diagnosis and treatment of lung cancer patients. Methods We retrospectively analyzed the clinical data of lung cancer patients with VTE treated in Peking University Cancer Hospital. The incidence of VTE was calculated and the relation between VTE and lung cancer was analyzed. Results Among 1766 patients with lung cancer, VTE occurred in 115(6.5%) patients. Among 115 VTE cases, there were 78(67.8%) males patients; 82(71.3%) cases were deep venous thrombosis, 21(18.3%) cases were pulmonary embolism, 12(10.4%) cases were deep venous thrombosis with pulmonary embolism; 74(64.3%) cases were symptomatic VTE; 102(88.7%) patients received antithrombotic therapy. Among the patients who received antithrombotic therapy, 97(95.1%) cases chose drug therapy. In drug treatment, 65(67.0%) cases used adequate/standard dose. Among 115 VTE cases, 52(45.2%) patients had improvement or disappearance of thrombus, 32(27.8%) patients had no improvement, and 31(27.0%) patients with unknown results; 11(9.6%) patients had adverse reactions such as bleeding during antithrombotic therapy; 30(26.1%) patients changed antineoplastic treatment plan due to VTE. Conclusion VTE is a common complication of lung cancer patients. Antithrombotic therapy for lung cancer patients with VTE is complex, the treatment effect is not ideal and the risk of adverse reactions such as bleeding is high. VTE has a great impact on the implementation of anti-cancer treatment.

Compacted loess is a special collapsible unsaturated soil and is sensitive to hydraulic conditions. Understanding and modeling the coupled hydromechanical behavior of compacted loess during isotropic compression is essential for establishing a complete constitutive model for unsaturated soils, which is of significance for predicting the deformation of loess fills in engineering. In this research, a series of constant suction isotropic compression tests are conducted to study the behavior of compacted loess. The experimental results show that volume change and soil–water characteristics are mutually affected during compression and that the normal compression curves flatten out and shift downward with the decrease in suction. Based on the experimental observations, the normal compression surface is modified, and the loading–collapse yield curve is deduced for the compacted loess. An equation associated with the void ratio is used to describe the effect of the volume change on the soil–water characteristics. Furthermore, a coupled hydromechanical constitutive model for the compacted loess under isotropic compression conditions is proposed within the framework of the Barcelona basic model. Model predictions of the mechanical and hydraulic behaviors of the compacted loess for constant suction isotropic compression tests and wetting tests at constant net stresses are compared with experimental data to verify the performance and validation of the proposed model. The comparisons indicate that the proposed model can accurately reproduce the coupled hydromechanical behavior of the compacted loess under isotropic compression conditions. [ABSTRACT FROM AUTHOR]