Cold storage condensation heat recovery system with a novel composite phase change material

© 2016 Elsevier Ltd. Using condensation heat from cold storage refrigeration systems to provide heat for domestic hot water preparation and industrial hot water supply promotes energy conservation. However, few studies have investigated cold storage condensation heat recovery using phase change materials (PCMs). In this study, a cold storage condensation heat recovery system that uses PCMs has been designed and analysed. According to the principle of energy cascade recycling, different operation modes could be effectively switched to recycle condensation heat. Furthermore, a novel and suitable phase change composite material is developed for cold storage condensation heat recovery, which has a relatively large latent heat, high thermal conductivity, and an appropriate phase change temperature (i.e. 80 °C). With carnauba wax (CW) as the PCM and expanded graphite (EG) as the additive, a composite was developed with an optimal mass ratio of CW:EG = 10:1. The thermal and physical properties and the interior structure of the composite were then investigated using a scanning electron microscope (SEM), thermal constants analyser (Hot Disk), differential scanning calorimeter (DSC), and Fourier transform infrared spectrometer (FT-IR). Furthermore, experiments on the melting and solidification processes and accelerated thermal cycling were also conducted. It was found that at the optimal mass ratio of 10:1, the temperatures of the CW/EG composite in the melting and solidification processes were 81.98 °C and 80.43 °C, respectively, while the corresponding latent heats were 150.9 J/g and 142.6 J/g, respectively. During both processes, CW could retain its original worm-like structure after being completely adsorbed by EG. Compared to only CW, the melting and solidification time of the CW/EG composite were reduced by 81.7% and 55.3%, respectively, while its thermal conductivity was 16.4 times higher. After 1000 runs of accelerated thermal cycling, the endothermic/exothermic phase change temperatures of CW and the CW/EG composite increased by only 0.42%/0.42% and 0.23%/0.27%, respectively, while their endothermic/exothermic latent heats decreased by 4.96%/4.78% and 2.05%/3.44%, respectively. These results indicate that both CW and the CW/EG composite have excellent thermal reliability, while the CW/EG composite exhibits a slightly better performance. Finally, the experiments show that the CW/EG composite has desirable thermal and physical properties such as high thermal conductivity and reliability; Hence, it has good potenti al as a material for facilitating condensation heat recovery from cold storage refrigeration systems

Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

Performance Evaluation of Coordinated Intersections with GPS Devices

This paper presents methods for applying GPS devices to evaluate the performance of coordinated intersections in terms of traffic delays. It illustrates that GPS-recorded data provides detailed information on traffic delays of individual intersections as well as traffic delays of coordinated intersections as a whole system, including travel-time delay, stopped-time delay, time-in-queue delay, approach delay, and total delay. Most of these intersection delays are difficult to measure manually; however, with GPS-collected vehicle-positioning data, they can be either directly identified or indirectly derived. Methods for obtaining different types of intersection delays with GPS devices are introduced

Disorder Effects in Charge Transport and Spin Response of Topological Insulators

Topological insulators are a class of solids in which the non-trivial inverted bulk band structure gives rise to metallic surface states that are robust against impurity backscattering. First principle calculations predicted Bi2Te3, Sb2Te3 and Bi2Se3 to be three-dimensional (3D) topological insulators with a single Dirac cone on the surface. The topological surface states were subsequently observed by angle-resolved photoemission (ARPES) and scanning tunneling microscopy (STM). The investigations of charge transport through topological surfaces of 3D topological insulators, however, have faced a major challenge due to large charge carrier densities in the bulk donated by randomly distributed defects such as vacancies and antisites. This bulk disorder intermixes surface and bulk conduction channels, thereby complicating access to the low-energy (Dirac point) charge transport or magnetic response and resulting in the relatively low measured carrier mobilities. Moreover, charge inhomogeneity arising from bulk disorder can result in pronounced nanoscale spatial fluctuations of energy on the surface, leading to the formation of surface \u27puddles\u27 of different carrier types. Great efforts have been made to combat the undesirable effects of disorder in 3D topological insulators and to reduce bulk carriers through chemical doping, nanostructure fabrication, and electric gating. In this work we have developed a new way to reduce bulk carrier densities using high-energy electron irradiation, thereby allowing us access to the topological surface quantum channels. We also found that disorder in 3D topological insulators can be beneficial. It can play an important part in enabling detection of unusual magnetic response from Dirac fermions and in uncovering new excitations, namely surface superconductivity in Dirac \u27puddles\u27. In Chapter 3 we show how by using differential magnetometry we could probe spin rotation in the 3D topological material family (Bi2Se3, Bi2Te3 and Sb2Te3), and describe our detection of paramagnetic singularity in the magnetic susceptibility at low magnetic fields that persists up to room temperature, and which we have demonstrated to arise from the surfaces of the samples. The singularity is universal to the entire family, largely independent of the bulk carrier density, and consistent with the existence of electronic states near the spin-degenerate Dirac point of the 2D helical metal. The exceptional thermal stability of the signal points to an intrinsic surface cooling process, probably of thermoelectric organ, and establishes a sustainable platform for the singular field-tunable Dirac spin response. In Chapter 4 we describe our discovery of surface superconductivity in a hole-conducting topological insulator Sb2Te3 with transition to zero resistance induced through a minor tuning of growth chemistry that depletes bulk conduction channels. The depletion shifts Fermi energy towards the Dirac point as witnessed by over two orders of magnitude reduced bulk hole density and by the largest carrier mobility (~ 25,000 cm2 V-1s-1) found in any topological material. Direct evidence from transport, the unprecedentedly large diamagnetic screening, and the presence of up to ~ 25 meV gaps in differential conductance detected by scanning tunneling spectroscopy (STM) reveal the superconducting condensate to emerge first in surface puddles at unexpectedly high temperature, near 50 K. Percolative Josephson paths mediated by diffusing quasiparticles establish global phase coherence around 9 K. Rich structure of this state lends itself to manipulation and tuning via growth conditions and the topological material\u27s parameters such as Fermi velocity and mean free path. In Chapter 5 we describe a new approach we have developed to reaching stable charge neutrality in 3D topological materials. The technique uses swift (~ 2.5 MeV energy) electron beams to compensate charged bulk defects and bring the Fermi level back into the bulk gap. By controlling the beam fluence we could tune bulk conductivity from p- (hole-like) to n-type (electron-like), crossing the Dirac point and back, while preserving the robust topological signatures of surface channels. We establish that at charge neutrality conductance has a two-dimensional (2D) character with a minimum value on the order of ten conductance quanta G0 = e2 / h. From quantum interference contribution to 2D conductance we demonstrate in two systems, Bi2Te3 and Bi2Se3, that at charge neutrality only two quantum channels corresponding to two topological surfaces are present. The charge neutrality point achieved using electron irradiation with long penetration range shows a route to intrinsic quantum transport of the topological states unconstrained by the bulk size

A survey of methane point source emissions from coal mines in Shanxi province of China using AHSI on board Gaofen-5B

Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.</p

Cortical and medullary oxygenation evaluation of kidneys with renal artery stenosis by BOLD-MRI

Aim: Blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) can measure deoxyhemoglobin content. This study aims to evaluate the capacity of BOLD-MRI, which is possible to evaluate the oxygenation state of kidneys with renal artery stenosis (RAS). Materials and methods: We performed BOLD-MRI for 40 patients with RAS and for 30 healthy volunteers. We then performed post-scan processing and analysis of manually drawn regions of interest to determine R2* values (relaxation rates) for the renal cortex and medulla. We compared R2* values in patients with RAS with those in the control group, and also compared these values for subgroups with varying degrees of stenosis. Results: Medulla R2* values were higher than cortex R2* values in the control group. There was no significant difference in R2* values for different segments (upper, middle, lower) of the kidneys. Both cortex and medulla R2* values in patients with RAS were significantly higher than corresponding R2* values in the control group (P < 0.05), and BOLD-MRI was more sensitive to changes in the R2* values in the medulla than in the cortex. Among different subgroups in the RAS group, the medulla R2* values were significantly higher in kidneys with severe stenosis than in those with no obvious obstruction, mild stenosis, or moderate stenosis (P < 0.05). Conclusion: BOLD-MRI is an effective, noninvasive method for evaluating kidney oxygenation, which is important for proper treatment in RAS. It is sufficiently sensitive for detecting medulla ischemia and anoxia of the kidneys