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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.

Background: Currently there is no standard therapy recommended for second‐line treatment for thymic carcinoma. Our study compared multidrug chemotherapy, single‐agent chemotherapy, and PD‐1 inhibitors in patients diagnosed with advanced thymic carcinoma who had previous platinum‐based chemotherapy at the clinic. Methods: The study included patients with thymic carcinoma who failed first‐line platinum‐based chemotherapy. Kaplan–Meier methods were applied in the study for estimating the progression‐free survival (PFS) and overall survival (OS) curves. Pearson chi‐square or Fisher's exact chi‐square test was adopted to make comparisons of the objective response rate (ORR) between treatment groups. Cox regression was used for the multivariate analyses in PFS and OS. Results: Among the 92 patients enrolled, multidrug chemotherapy was used in 51 (55.4%) patients for second‐line therapy. Thirty‐six patients (35.9%) received single‐agent chemotherapy, and eight patients (8.7%) underwent PD‐1 inhibitors. The multidrug chemotherapy group showed better efficacy than the other two groups, with an ORR of 35.3% (p = 0.006). The median PFS of multidrug chemotherapy, single‐agent chemotherapy and PD‐1 inhibitors were 5.0 months, 3.0 months, and 4.0 months, respectively (p = 0.008). Patients in the multidrug chemotherapy group also showed an advantage in OS in comparison with the other two treatment groups (p = 0.045), with a median OS of 30.4 months. Multivariate analysis showed that second‐line treatment was independent factor for both PFS (p = 0.035) and OS (p = 0.037). Grade 3–4 AEs were mostly detected in patients receiving multidrug chemotherapy and were primarily hematologic. Treatment‐related mortality was not found in any of the groups. Conclusions: Multidrug chemotherapy had a trend toward a more positive response rate and outcomes in longer survival time than single‐agent chemotherapy and PD‐1 inhibitors. Multidrug chemotherapy is a choice worth considering for second‐line therapy in patients with thymic carcinoma if tolerable. [ABSTRACT FROM AUTHOR]

In the study, context-creativity of Teresa M. Amabile was used as The foundation to apply it in the bags & luggage design course. Moreover, the sectional creative training education mode of prior heuristic task and postpositional algorithmic task was proposed. 26 junior students in product design were used as the trial objects. The Consensual Technique for Creativity (CAT) was considered as the scoring standard of creative performance. In the end, the sectional theoretical framework of effective creative training in product design was finally proposed.

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817–0.892) and 0.793 (95% CI, 0.725–0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12–97% for the training group and 17–82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population. [ABSTRACT FROM AUTHOR]