The global effect of COVID-19 is no longer simply a public health issue;it is causing an economic crisis that has a significant impact on the job market and people's lives. The disease has led to 43% of businesses...
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For some rodent mammals when they foraging or looking for a target,the positions and headings in their brain cells are distributed as a hexagonal raster ***-grid maps used in the navigation of the enhanced learning ro...
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For some rodent mammals when they foraging or looking for a target,the positions and headings in their brain cells are distributed as a hexagonal raster ***-grid maps used in the navigation of the enhanced learning robots may have a better performance than the traditional squaregrid *** this paper,a new reinforcement learning based robot navigation method is proposed using hex-grid *** hex-grid map-building methods and the reinforcement learning based navigation approach in a hex-grid environment are introduced *** groups of comparative simulation experiments are carried out and the results demonstrate the advantages and efficiency of the proposed method.
Virtual hospitals empower traditional hospitals to deliver more accessible, affordable, and comprehensive patient-centered (PC) care services. However, the legacy information systems of traditional hospitals are ill-e...
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ISBN:
(纸本)9781665480468
Virtual hospitals empower traditional hospitals to deliver more accessible, affordable, and comprehensive patient-centered (PC) care services. However, the legacy information systems of traditional hospitals are ill-equipped to support the needs of virtual hospitals. We propose a holistic virtual hospital ecosystem design that addresses these issues. We have developed two models. The first is a VHealth-CNN model that extracts PC knowledge from multi-sourced biomedical big data by (1) extracting disease health-related features; (2) structuring the relevant health-related features as per the pre-identified factors; (3) training a convolutional neural network (CNN) double-layer structure, where we select significant health-related features in the first layer, and classify the positively and negatively correlated features in the second one; and (4) generating disease class outputs representing the PC knowledge. The second model is a granular VHealth-AC model that seamlessly grants healthcare practitioners at a hub hospital remote access to PC knowledge at the right point of care. We have deployed a granular 5-tier PC information classification scheme to enforce information security rules across hospitals. In addition, we examined the feasibility of the proposed design through a tele-monitoring service experimental case study for predicting obesity, hypertension, and diabetes. The experimental results show that the proposed model predicts obesity, hypertension, and diabetes diagnoses with 91.3%, 93.5%, and 95% accuracy, respectively. Finally, our ecosystem design should encourage the adoption of virtual hospitals and the adoption of virtual healthcare services as a new norm.
To enhance useful gained energy by solar thermal collector, two software programs are used for optimizing tilt and incidence angle of the collector. TRNSYS program is used to predict solar useful energy gained by the ...
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ISBN:
(数字)9780738111391
ISBN:
(纸本)9781665403818
To enhance useful gained energy by solar thermal collector, two software programs are used for optimizing tilt and incidence angle of the collector. TRNSYS program is used to predict solar useful energy gained by the collector. The Design-Expert (DOE) tool with response surface methodology (RSM) program is used to optimize the tilt angle. Results show that tilt angle and the incidence angle have important effect on the gain of the useful energy. In addition, it is shown that the optimum tilt angle varies monthly or seasonally. Moreover, to get maximum useful energy, the incidence angle has small angle around 10° and almost stable throughout the seasons. The optimal tilt angle has lowest value in summer and the highest value in winter. The calculated values are of 39.57°, 26.72°, 20.15°, and 32.2° and the corresponding maximum monthly averaged useful energy gained by the collector in January, April, July, and October are 223.52 kWh, 482.61 kWh, 596.56 kWh, 386.51 kWh respectively.
XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such ...
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Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emiss...
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Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit
Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve t...
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Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter.
The objective of this paper is to report the results of a generalized additive model used to predict local particulate matter concentrations at a Washington, DC department of Energy and Environment (DOEE) federal regu...
ISBN:
(数字)9781728171456
ISBN:
(纸本)9781728171463
The objective of this paper is to report the results of a generalized additive model used to predict local particulate matter concentrations at a Washington, DC department of Energy and Environment (DOEE) federal regulatory monitoring station. While the DOEE uses state-of-the-art federal equivalent method (FEM) equipment to demonstrate compliance with the clean air act for regulatory purposes, these measurements reflect regional, not neighborhood air quality. A GW student-led living lab project-Fresh Air DC-has been testing uRAD INDUSTRIAL low-cost air quality sensors that can be used to collect air quality data at the neighborhood level using LoRaWAN based smart city technology. Because low-cost sensors often lack the accuracy and sensitivity of FEM equipment, research indicates that low-cost sensor (LCS) monitoring networks require post- processing and data modelling in order to apply findings to educational and policy goals. Although LCS data processing has been conducted using linear and nonlinear models, nonlinear models tend to have a greater ability to capture the nuanced relationships between air pollutants and meteorological influences. In this paper, we post-process uRAD PM 2.5 sensor data using DOEE FEM equipment as a reference instrument in the development of three models to adjust uRAD data to the DOEE FEM data-ordinary least squares linear regression, generalized linear models (GLMs), and generalized additive models (GAMs). Our model includes meteorological variables such as temperature, humidity, and wind speed. Our statistical models for post-processing are evaluated on the basis of deviance and Akaike Information Criterion (AIC). We expect that the GLM and GAM will be useful for capturing nonlinear relationships between the PM 2.5 measurements and meteorological variables.
Unmanned aerial vehicles (UAVs) are widely applied in various industries and fields. Because they use a variety of rotor types, from single rotors to multi-rotors, UAVs offer a wide array of functions. However, contin...
Unmanned aerial vehicles (UAVs) are widely applied in various industries and fields. Because they use a variety of rotor types, from single rotors to multi-rotors, UAVs offer a wide array of functions. However, continuously rotating rotors can be dangerous if they accidentally encounter foreign objects or bare hands. Therefore, non-rotor UAVs are the focus of discussion and modification in the present study. Non-rotor UAVs do not contain visible rotor mechanical components. Compared with the development of popularized multirotor UAVs, that of non-rotor UAVs is challenging in terms of structure and flight control. To address this challenge, wind tunnel structure models were developed in this study for different levels of aerodynamic force, and computer-aided engineering was used to conduct structural and flow field analyses to determine the key elements that reinforce non-rotor UAV structures. The research results revealed that the thickness and fillet design at the joint of the non-rotor UAV aerodynamic wind tunnel system were crucial factors influencing the system's performance. Motor rotors can be embedded inside support structures to reduce airflow turbulence in the wind tunnel.
In this study, unidirectional carbon fiber prepregs that contain long carbon nanofiber (CNF) z-threads as a through-thickness (z-directional) reinforcement were manufactured. The CNF z-threads are long enough to threa...
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ISBN:
(纸本)9781934551301
In this study, unidirectional carbon fiber prepregs that contain long carbon nanofiber (CNF) z-threads as a through-thickness (z-directional) reinforcement were manufactured. The CNF z-threads are long enough to thread through multiple carbon fiber (CF) arrays, which creates a multi-scale CNF/CF/resin-composite. The CNF z-threaded prepregs were manufactured using an electric-field aligned flow-transferring process. It was hypothesized that the CNF z-threads with the zig-zag threading pattern reinforces the interlaminar and intralaminar regions of the CFRP laminate thus improve the compressive strength by reducing the chance of carbon fiber buckling. Compressive testing was performed per modified version of ASTM D695 (i.e., SACMA SRM 1R-94) to evaluate the compressive strength of the CNF z-threaded CFRP (ZT-CFRP) laminates. The samples were manufactured using AS4 carbon fibers, EPON 862/Epikure-W resin and a 1wt% CNF content. ZT-CFRP testing results were compared with unaligned CNF-modified CFRP (UA-CFRP) and unmodified CFRP samples to investigate the impact of the CNF z-threads on the compressive strength. Results showed an increase of ~15% for the compressive strength of ZT-CFRPs, whereas the UA-CFRPs experienced a decrease of ~8% when compared to unmodified CFRPs. It was concluded that CNF/carbon fiber interlocking stops and delays crack growth, and helps to stabilize carbon fibers from further buckling. Copyright 2019 by Sebastian Kirmse et al. Published by Society for the Advancement of Material and Process engineering with permission.
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