A low cost, accurate instrument to measure the moisture content of building envelopes <i>in situ</i>

Buildings must be designed and built to achieve a healthy environment, low energy consumption and a predictable service life. In order to achieve these goals the effects of combined heat, air and moisture (HAM) transfer must be understood. A suitable moisture measurement technique is thus required. There is a pressing need for a suitable instrument capable of in situ moisture measurements in building envelopes. Techniques do exist for such moisture measurement but all exhibit deficiencies in at least one critical area. A thermal dual-probe is investigated as a candidate for an appropriate instrument as such an approach offers significant potential benefits over existing methods.It is demonstrated, via extensive finite-element (FE) modelling, that the thermal dual-probe technique is indeed applicable to in situ moisture measurements in typical building fabrics. The thesis then moves on to deal with the optimisation of the design of such a probe. The results of relevant simulations using the proven two and three-dimensional FE models are detailed.Finally, the extensive experimental work undertaken to support the modelling work is described. The measured data obtained from the thermal dual-probes is compared with the results of series of gravimetric analyses. Close agreement between the two methods is obtained.The work, has successfully demonstrated that, depending upon the building fabric material,optimal probe lengths and spacing range from approximately 45-60mm and 12-20mm respectively. The experimental work clearly indicates that the thermal dual-probe is capable of accurate, in situ moisture measurements in building envelopes

Generalized Hilbert Operator Acting on Bloch Type Spaces

Let $\mu$ be a positive Borel measure on the interval [0,1). For $\alpha>0$, the Hankel matrix $\mathcal{H}_{\mu,\alpha}=(\mu_{n,k,\alpha})_{n,k\geq 0}$ with entries $\mu_{n,k,\alpha}=\int_{[0,1)}\frac{\Gamma(n+\alpha)}{n!\Gamma(\alpha)}t^{n+k}d\mu(t)$ formally induces the operator $$\mathcal{H}_{\mu,\alpha}(f)(z)=\sum_{n=0}^{\infty}\left(\sum_{k=0}^{\infty} \mu_{n, k,\alpha} a_{k}\right)z^{n}$$ on the space of all analytic functions $f(z)=\sum_{k=0}^{\infty}a_{k}z^{k}$ in the unit disc $\mathbb{D}$. In this paper, we characterize the measures $\mu$ for which $\mathcal{H}_{\mu,\alpha}$ ($\alpha\geq 2$) is a bounded (resp., compact) operator from the Bloch type space $\mathscr{B}_{\beta}$ ($0<\beta<\infty$) into $\mathscr{B}_{\alpha-1}$. We also give a necessary condition for which $\mathcal{H}_{\mu,\alpha}$ is a bounded operator by acting on Bloch type spaces for general cases

A permeability model for the hydraulic fracture filled with proppant packs under combined effect of compaction and embedment

The authors acknowledge the financial support from Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC060 and No.2462014YJRC059)Peer reviewedPostprin

Pretraining in Deep Reinforcement Learning: A Survey

The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning. Various breakthroughs ranging from games to robotics have spurred the interest in designing sophisticated RL algorithms and systems. However, the prevailing workflow in RL is to learn tabula rasa, which may incur computational inefficiency. This precludes continuous deployment of RL algorithms and potentially excludes researchers without large-scale computing resources. In many other areas of machine learning, the pretraining paradigm has shown to be effective in acquiring transferable knowledge, which can be utilized for a variety of downstream tasks. Recently, we saw a surge of interest in Pretraining for Deep RL with promising results. However, much of the research has been based on different experimental settings. Due to the nature of RL, pretraining in this field is faced with unique challenges and hence requires new design principles. In this survey, we seek to systematically review existing works in pretraining for deep reinforcement learning, provide a taxonomy of these methods, discuss each sub-field, and bring attention to open problems and future directions

GPUMemSort: A High Performance Graphics Co-processors Sorting Algorithm for Large Scale In-Memory Data

In this paper, we present a GPU-based sorting algorithm,GPUMemSort, which achieves high performance insorting large-scale in-memory data by take advantage ofGPU processors. It consists of two algorithms: an in-corealgorithm, which is responsible for sorting data in GPUglobal memory efficiently, and an out-of-core algorithm,which is responsible for dividing large-scale data intomultiple chunks that fit GPU global memory.GPUMemSort is implemented based on NVIDIA’s CUDAframework and some critical and detailed optimizationmethods are also presented. The tests of differentalgorithms have been run on multiple data sets. Theexperimental results show that our in-core sorting canoutperform other comparison-based algorithms andGPUMemSort is highly effective in sorting large-scale inmemorydata

Immediate Antiretroviral Therapy Decreases Mortality Among Patients With High CD4 Counts in China: A Nationwide, Retrospective Cohort Study.

BackgroundClinical trials have demonstrated that immediate initiation of antiretroviral therapy (ART) reduces AIDS-related morbidity and mortality. We tested the hypothesis that initiating ART ≤30 days after human immunodeficiency virus (HIV) diagnosis would be associated with reduced mortality among people living with HIV (PLWH) with CD4 counts &gt;500 cells/μL.MethodsPLWH enrolled in the Chinese National HIV Information System between January 2012 and June 2014 with CD4 counts &gt;500 cells/μL were followed for 12 months. Cox proportional hazards model was used to determine hazard ratios (HRs) for PLWH who initiated ART after HIV diagnosis. ART initiation was treated as a time-dependent variable.ResultsWe enrolled 34581 PLWH with CD4 &gt;500 cells/μL; 1838 (5.3%) initiated ART ≤30 days after diagnosis (immediate ART group), and 19 deaths were observed with a mortality rate of 1.04 per 100 person-years (PY). Fifty-eight deaths were documented among the 5640 PLWH in the delayed ART group with a mortality rate of 2.25 per 100 PY. There were 713 deaths among the 27103 PLWH in the no ART group with a mortality rate of 2.39 per 100 PY. After controlling for potential confounding factors, ART initiation at ≤30 days (adjusted HR, 0.37 [95% confidence interval, .23-.58]) was a statistically significant protective factor.ConclusionsWe found that immediate ART is associated with a 63% reduction in overall mortality among PLWH with CD4 counts &gt;500 cells/μL in China, supporting the recommendation to initiate ART immediately following HIV diagnosis

A New Architecture for Application-aware Cognitive Multihop Wireless Networks

In this article, we propose a new architecture for AC-MWN. Cognitive radio is a technique to adaptively use the spectrum so that the resource can be used more efficiently in a low-cost way. A multihop wireless network can be deployed quickly and flexibly without fixed infrastructure. In our proposed new architecture, we study backbone routing schemes with network cognition, and a routing scheme with network coding and spectrum adaptation. A testbed is implemented to test the proposed schemes for AC-MWN. In addition to basic measurements, we implement a video streaming application based on the proposed AC-MWN architecture using cognitive radios. Preliminary results demonstrate that the proposed AC-MWN is applicable, and is valuable for future low-cost and flexible communication networks