Analytical and Numerical Investigation on Depth and Pipe Configuration for Coaxial Borehole Heat Exchanger, A Preliminary Study

Existing research on the performance of shallow geothermal systems are prone to investigate the ground as a large thermal mass at a constant temperature despite possible temperature increase at depths - otherwise commonly known as the geothermal gradient. Most of the existing analytical models that predict the heat exchange between a borehole heat exchanger with the soil does not allow for the geothermal gradients to be accounted for. The few models that actually does account for the geothermal gradients, on the other hand, does so by enforcing a pre-existing temperature gradient only. We are presenting a bottom up approach in this paper to solve the temperature distribution by accounting for both the convective heat transfer from the working fluid and the conductive heat transfer through both the pipe and the soil. Assuming the heat transfer is entirely axisymmetric, we approach the problem by solving the Navier-stokes equation and energy equation with a finite difference solver that calculates the temporal change of temperature with given diameter, depth of borehole and geothermal gradient. The heat transfer through the pipe and into the ground can therefore be further calculated. We were able to determine a CBHE configuration that allows maximized thermal output by assuming a synthetic heating/cooling load for year-round production of heat

Visualizing the exergy destructed in exergy delivery chain in relation to human thermal comfort with ExFlow

Exergy analysis is an important tool to fully appreciate the usability of energy at different levels and has been widely applied in the building system analysis domain. It has became more useful as low temperature heating and high temperature cooling began to attract more attention both in Europe and the United States. Using low-grade energy to supply for these systems have, in return, led to an increase in awareness of low exergy (LowEx) system designs. The possibility of modeling the last missing link in the system that is to delivery thermal comfort, the human body, have therefore became a topic that increasingly draws the attention of many more researchers. Due to the complexity of these human body exergy models, it is very rare for these models to be linked back to building systems and produce an exergy efficiency for occupants’ thermal comfort. Attempting to fill in the blanks of overall system exergy efficiency on delivery occupant thermal comfort, we have developed a visualization algorithm that could visually assess the exergy efficiency in comfort delivery. Using the ExFlow tool, it is much clearer and easier to determine the relationship of how much primary energy input is eventually converted to the energy that is used to condition for the occupants’ comfort

Impacts of spatial mismatch on commuting time of urban residents in China

In much of studies on spatial mismatch between residential and employer locations, job accessibility has been measured. However, the apparent disadvantages of the traditional measurement methods on the studies of Chinese cities have been noted.  This paper proposed an optimized method for job accessibility measurement by introducing the weigh coefficient of job opportunity, which quantifies the degree of uneven distribution of job opportunity in the Chinese cities. Take Nanjing city for example, this new method was used to measure the spatial distribution of job opportunity, investigate the spatial patterns and analyze the influences of job accessibility on commuting behavior. The results show that the distribution of job accessibility in Nanjing exhibits the different spatial patterns and mechanisms compared with US cases. <! [endif] --

AdaCompress: Adaptive Compression for Online Computer Vision Services

With the growth of computer vision based applications and services, an explosive amount of images have been uploaded to cloud servers which host such computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the {\em de facto} compression and encapsulation method before one uploads the images, due to its wide adaptation. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model, e.g., the standard quality level of JPEG leads to 50\% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models including InceptionNet, ResNet, etc. Knowing this, designing a better JPEG configuration for online computer vision services is still extremely challenging: 1) Cloud-based computer vision models are usually a black box to end-users; thus it is difficult to design JPEG configuration without knowing their model structures. 2) JPEG configuration has to change when different users use it. In this paper, we propose a reinforcement learning based JPEG configuration framework. In particular, we design an agent that adaptively chooses the compression level according to the input image's features and backend deep learning models. Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size. In our real-world evaluation on Amazon Rekognition, Face++ and Baidu Vision, our approach can reduce the size of images by 1/2 -- 1/3 while the overall classification accuracy only decreases slightly.Comment: ACM Multimedi

Thermoheliodome Testing: Evaluation Methods for Testing Directed Radiant Heat Reflection☆

Abstract The Thermoheliodome is a prototype experimental pavilion that produces comfort through the manipulation of the mean radiant temperature generated by a combination of evaporative cooling and radiant heat reflection. We present the development of a sensing and analysis method for measuring the impact on radiant temperature and other performance data for the space, along with the initial system measurements. This is an environmental control station through which low cost microcontrollers enable distributed networked nodes to take measurements of relevant system parameters. The system measurements show a reduction of mean radiant temperature by 2-3 °C using evaporative cooling and strategic reflection

Caenorhabditis elegans RIG-I Homolog Mediates Antiviral RNA Interference Downstream of Dicer-Dependent Biogenesis of Viral Small Interfering RNAs.

Dicer enzymes process virus-specific double-stranded RNA (dsRNA) into small interfering RNAs (siRNAs) to initiate specific antiviral defense by related RNA interference (RNAi) pathways in plants, insects, nematodes, and mammals. Antiviral RNAi in Caenorhabditis elegans requires Dicer-related helicase 1 (DRH-1), not found in plants and insects but highly homologous to mammalian retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), intracellular viral RNA sensors that trigger innate immunity against RNA virus infection. However, it remains unclear if DRH-1 acts analogously to initiate antiviral RNAi in C.&nbsp;elegans Here, we performed a forward genetic screen to characterize antiviral RNAi in C.&nbsp;elegans Using a mapping-by-sequencing strategy, we uncovered four loss-of-function alleles of drh-1, three of which caused mutations in the helicase and C-terminal domains conserved in RLRs. Deep sequencing of small RNAs revealed an abundant population of Dicer-dependent virus-derived small interfering RNAs (vsiRNAs) in drh-1 single and double mutant animals after infection with Orsay virus, a positive-strand RNA virus. These findings provide further genetic evidence for the antiviral function of DRH-1 and illustrate that DRH-1 is not essential for the sensing and Dicer-mediated processing of the viral dsRNA replicative intermediates. Interestingly, vsiRNAs produced by drh-1 mutants were mapped overwhelmingly to the terminal regions of the viral genomic RNAs, in contrast to random distribution of vsiRNA hot spots when DRH-1 is functional. As RIG-I translocates on long dsRNA and DRH-1 exists in a complex with Dicer, we propose that DRH-1 facilitates the biogenesis of vsiRNAs in nematodes by catalyzing translocation of the Dicer complex on the viral long dsRNA precursors.IMPORTANCE The helicase and C-terminal domains of mammalian RLRs sense intracellular viral RNAs to initiate the interferon-regulated innate immunity against RNA virus infection. Both of the domains from human RIG-I can substitute for the corresponding domains of DRH-1 to mediate antiviral RNAi in C.&nbsp;elegans, suggesting an analogous role for DRH-1 as an intracellular dsRNA sensor to initiate antiviral RNAi. Here, we developed a forward genetic screen for the identification of host factors required for antiviral RNAi in C.&nbsp;elegans Characterization of four distinct drh-1 mutants obtained from the screen revealed that DRH-1 did not function to initiate antiviral RNAi. We show that DRH-1 acted in a downstream step to enhance Dicer-dependent biogenesis of viral siRNAs in C.&nbsp;elegans As mammals produce Dicer-dependent viral siRNAs to target RNA viruses, our findings suggest a possible role for mammalian RLRs and interferon signaling in the biogenesis of viral siRNAs

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile Computing and Networking (MobiCom), 202

Energy Delivery Reconditioned for Thermal Comfort

Energy has been delivered to buildings and households to make sure rooms are comfortable for occupants. We currently think of this delivery in the isolation as it heats and cools individual spaces. The heating and cooling of buildings have consequently become more about hitting a setpoint instead of aligning the delivery of comfort to humans with the effective and readily available upstream sources. The thesis of this dissertation is finding the most effective pathway to supply comfort to people. The work begins with the examination of the fundamental assumptions of thermal comfort models using an exergetic approach to insert the occupants into the energy delivery chain. Second, the research examines how environmental parameters such as relative humidity and mean radiant temperatures are oversimplified in energy analyses. Finally, this is connected with the analysis of sourcing the supplied exergy accordingly was also investigated within the scope of this dissertation. The research is organised into three corresponding sections, each containing three projects. In the first section, a conceptual energy delivery framework was proposed, upon which the feasibility of using human body exergy model was investigated and a new analytical human body exergy model proposed. Varying certain inputs to the model, such as the relative humidity and the mean radiant temperature, can cause the results of the human body exergy model to vary significantly. Unlike relative humidity, mean radiant temperature is more challenging to measure and model. This radiant connection between the occupants and their surroundings is the focus of the second section. Beginning with a critique on the spatial limitations of conventional measurement techniques of mean radiant temperature, a comprehensive review of the complexity and challenges of modeling and measuring mean radiant temperature is presented. Its coupled relationship with air temperature was also investigated in a subsequent project. Finally, the third section begins by tracing back to the thermal sources such as geothermal for optimal temperatures. It then attempts to rearrange the energy delivery system by matching the supply and demand of energy concerning not only their amounts but usabilities - or exergy - to create a flow model from source to comfort