An investigation of the ambient temperature field and thermal response of the Calgary Airport Trail Tunnel

2018 ◽  
Vol 45 (12) ◽  
pp. 1015-1026
Author(s):  
Lomere Mori ◽  
Mustafa Gül ◽  
Roger Cheng ◽  
Ved Sharma
Keyword(s):  
Ambient Temperature ◽  
Thermal Response ◽  
Sensor Data ◽  
Gradient Effect ◽  
Direct Design ◽  
Road Tunnels ◽  
Tunnel Design ◽  
Temperature Loads ◽  
Structural Code

Currently, there is a lack of direct design provisions in structural codes and literature that address design temperatures in tunnel structures and thus it is common practice for designers to resort to bridge codes. The main focus of this paper is the study of the temperature distribution in concrete road tunnels due to ambient temperature. In this context, the temperature distributions of the Airport Trail Tunnel in Calgary, Alberta is studied using numerical modelling and long-term temperature monitoring data collected from the tunnel. The overall aim is to evaluate findings from the numerical analysis and sensor data with current Canadian structural code provisions considered in tunnel design in regards to temperature loads. From the investigation, it is determined that the design temperature range was within CSA S6, however CSA S6 underestimates the temperature gradient effect in the walls and slabs of the tunnel under study.

Physiologic aspects of microwave irradiation of mammals

1961 ◽  
Vol 201 (2) ◽  
pp. 351-356 ◽  
Author(s):  
Sol M. Michaelson ◽  
Roderick A. E. Thomson ◽  
Joe W. Howland
Keyword(s):  
Microwave Irradiation ◽  
Ambient Temperature ◽  
Thermal Effect ◽  
Temperature Increase ◽  
Thermal Response ◽  
Critical Factor ◽  
Long Term Effects ◽  
Microwave Radar ◽  
Physiologic Responses

Dogs, rabbits, and rats exposed under controlled conditions to pulsed 2800 Mcycle/sec microwave (radar) radiation display characteristic physiologic responses, some of which are related to heating of superficial tissues. Specific changes in leukocyte levels occur which are independent of hematocrit or temperature increase. Postexposure lymphocytopenia and eosinopenia appear related to duration of exposure. Anesthetization of the dog results in an increased thermal susceptibility which is not evident in the rabbit or rat. Consumption of water during exposure depresses the thermal response. Exposure at increased ambient temperature results in a synergism of thermal effect reducing the tolerance to microwaves. Vasomotor integrity appears to be a critical factor in regulating the thermal response to microwaves. No specific long-term effects such as cataracts have been observed in animals held for more than 1 yr postexposure.

Long-term measurement of matric suction using thermal conductivity sensors

2003 ◽  
Vol 40 (3) ◽  
pp. 587-597 ◽  
Author(s):  
Craig Nichol ◽  
Leslie Smith ◽  
Roger Beckie
Keyword(s):  
Thermal Conductivity ◽  
Ambient Temperature ◽  
Water Movement ◽  
Mine Waste ◽  
Matric Suction ◽  
Waste Rock ◽  
Sensor Data ◽  
Wetting Front ◽  
Conductivity Sensor

Thermal conductivity (TC) sensors, which provide estimates of matric suction, were used in a field experiment designed to characterize unsaturated water movement through coarse mine waste rock at a mine site in northern Saskatchewan. Two years of monitoring data were used to evaluate long-term TC sensor performance and accuracy. Thermal conductivity sensor output requires corrections of sensor hysteresis and changes in ambient temperature. A correction method for ambient temperature is derived. A comparison of the uncorrected field measurements with the values corrected for both hysteresis and ambient temperature indicates that the magnitude of these corrections can be similar. Corrected TC sensor measurements are compared to measurements of matric suction made using tensiometers. Thermal conductivity sensor response to the initial arrival of a wetting front lagged 1–3 days behind the tensiometer measurements. The TC sensor data tended to overestimate matric suction in the waste rock, when compared to the tensiometer data. Long-term drift in the TC sensors located at depths of 50 cm and below (where the sensors have been continuously exposed to matric suctions less than 20 kPa) has lead to data that are not interpretable using the calibration curves derived prior to sensor emplacement in the waste rock pile.Key words: matric suction, thermal conductivity sensor, hysteresis, temperature.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals Research on HAR-Based Floor Positioning

2021 ◽  
Vol 10 (7) ◽  
pp. 437
Author(s):  
Hongxia Qi ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongji Cao ◽  
Shenglei Xu
Keyword(s):  
Barometric Pressure ◽  
Air Pressure ◽  
Location Based Services ◽  
Sensor Data ◽  
Reference Station ◽  
Position Change ◽  
Radio Signals ◽  
Positioning Method ◽  
Pressure Changes

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. It is more stable than methods based on wireless signals. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.

scholarly journals Field Test and Numerical Simulation on the Long-Term Thermal Response of PHC Energy Pile in Layered Foundation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3873
Author(s):  
Guozhu Zhang ◽  
Ziming Cao ◽  
Yiping Liu ◽  
Jiawei Chen
Keyword(s):  
Thermal Conductivity ◽  
Heat Exchange ◽  
Temperature Variation ◽  
Thermal Response ◽  
Ground Temperature ◽  
Short Term ◽  
Energy Pile ◽  
Backfill Soil ◽  
Short And Long Term

Investigation on the long-term thermal response of precast high-strength concrete (PHC) energy pile is relatively rare. This paper combines field experiments and numerical simulations to investigate the long-term thermal properties of a PHC energy pile in a layered foundation. The major findings obtained from the experimental and numerical studies are as follows: First, the thermophysical ground properties gradually produce an influence on the long-term temperature variation. For the soil layers with relatively higher thermal conductivity, the ground temperature near to the energy pile presents a slowly increasing trend, and the ground temperature response at a longer distance from the center of the PHC pile appears to be delayed. Second, the short- and long-term thermal performance of the PHC energy pile can be enhanced by increasing the thermal conductivity of backfill soil. When the thermal conductivities of backfill soil in the PHC pile increase from 1 to 4 W/(m K), the heat exchange amounts of energy pile can be enhanced by approximately 30%, 79%, 105%, and 122% at 1 day and 20%, 47%, 59%, and 66% at 90 days compared with the backfill water used in the site. However, the influence of specific heat capacity of the backfill soil in the PHC pile on the short-term or long-term thermal response can be ignored. Furthermore, the variation of the initial ground temperature is also an important factor to affect the short-and-long-term heat transfer capacity and ground temperature variation. Finally, the thermal conductivity of the ground has a significant effect on the long-term thermal response compared with the short-term condition, and the heat exchange rates rise by about 5% and 9% at 1 day and 21% and 37% at 90 days as the thermal conductivities of the ground increase by 0.5 and 1 W/(m K), respectively.

scholarly journals A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
Author(s):  
Hanbo Yang ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Zheng Sun ◽  
Xuesong Mei
Keyword(s):  
Time Windows ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Time Interval ◽  
Kernel Module ◽  
Initial Stage ◽  
Research Task ◽  
Consecutive Time ◽  
Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

Influence of Alkalinity and Ambient Temperature on Long-Term Properties of GFRP Reinforcement

2018 ◽  
Vol 760 ◽  
pp. 213-218
Author(s):  
František Girgle ◽  
Lenka Bodnárová ◽  
Ondřej Januš ◽  
Vojtěch Kostiha
Keyword(s):  
Tensile Strength ◽  
Ambient Temperature ◽  
Mechanical Characteristics ◽  
Current Problem ◽  
Concrete Structures ◽  
Experimental Program ◽  
Glass Fibres ◽  
Metallic Reinforcement ◽  
Gfrp Reinforcement

The article deals with the current problem of determining long-term reliability of non-metallic reinforcement in concrete structures. The alkaline environment of concrete with a pH higher than 12.0 affects the glass fibres degradative, whereas this degradation presents by reduction of their mechanical characteristics, resulting in a decrease in the tensile strength of the whole composite. The article summarizes the results of the ongoing experimental program so far, which aims to quantify this influence.

Learning routines over long-term sensor data using topic models

2013 ◽  
Vol 31 (4) ◽  
pp. 365-377 ◽  
Author(s):  
Federico Castanedo ◽  
Diego López- de-Ipiña ◽  
Hamid K. Aghajan ◽  
Richard Kleihorst
Keyword(s):  
Topic Models ◽  
Sensor Data
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