A study of porous membrane evaporation for desalination in a flow system

The purpose of this investigation was to study the simultaneous mass and heat transfer mechanism in evaporation through a porous membrane with a non-wettable surface. Such water repellent membranes permit the passage of water vapor, but not liquid water. The investigation concerned the mass transfer rate through the membrane pores with flow on one or both sides of the membrane. The water-repellent membrane separated a hot salt solution from the fresh water, and a copper sheet separated the fresh water from a cold salt solution. A three-channel evaporator-condenser was used, and the membrane consisted of glass fiber paper treated with a teflon dispersion. The temperature range studied was from 93 to 190⁰F. A temperature difference and a corresponding vapor pressure difference maintained across the membrane provided the driving force both for mass and heat transfer through the membrane and heat recovered through the copper sheet to cold salt solution. Theoretical and empirical correlations were employed to fit the experimental data. It was observed that heat transfer resistance and diffusion in the membrane pores were the major resistances to total mass transfer. The correlation predicted rates of mass transfer resistance close to the experimental values. The heat transfer coefficient was affected by the mass diffusion. The ratio of heat transfer coefficient with diffusion to that without diffusion was 1.5, and was slightly dependent on flow. The mass transfer coefficient varied form 0.22 to 0.516 lb/(hr)(ft²)(in-Hg). The overall heat transfer coefficient for the membrane varied from 48 to 104 BTU/(hr(ft²)(⁰F), and the overall heat transfer coefficient for the copper sheet varied from 54 to 84 BTU/(hr)(ft²)(⁰F) --Abstract, page ii-iii

Regional institutions, financial analysts and stock price informativeness

This paper investigates the impact of legal institutions on the external governance role of equity analysts in enhancing the corporate information environment. By analysing a sample of Chinese listed firms between 2003 and 2013, we find that analyst coverage is positively related to stock price informativeness. Firms located in provinces where legal institutions are stronger, as indicated by better development of market intermediaries and lower levies and charges on firms, are less likely to withhold value-relevant information. Financial analysts play a more effective role in improving stock informativeness in provinces with less developed legal institutions

Reversible Data Hiding Scheme with High Embedding Capacity Using Semi-Indicator-Free Strategy

A novel reversible data-hiding scheme is proposed to embed secret data into a side-matched-vector-quantization- (SMVQ-) compressed image and achieve lossless reconstruction of a vector-quantization- (VQ-) compressed image. The rather random distributed histogram of a VQ-compressed image can be relocated to locations close to zero by SMVQ prediction. With this strategy, fewer bits can be utilized to encode SMVQ indices with very small values. Moreover, no indicator is required to encode these indices, which yields extrahiding space to hide secret data. Hence, high embedding capacity and low bit rate scenarios are deposited. More specifically, in terms of the embedding rate, the bit rate, and the embedding capacity, experimental results show that the performance of the proposed scheme is superior to those of the former data hiding schemes for VQ-based, VQ/SMVQ-based, and search-order-coding- (SOC-) based compressed images

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

<p>Abstract</p> <p>Background</p> <p>The hierarchical clustering tree (HCT) with a dendrogram <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> and the singular value decomposition (SVD) with a dimension-reduced representative map <abbrgrp><abbr bid="B2">2</abbr></abbrgrp> are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.</p> <p>Results</p> <p>This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen <abbrgrp><abbr bid="B3">3</abbr></abbrgrp> as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.</p> <p>Conclusion</p> <p>We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at <url>http://gap.stat.sinica.edu.tw/Software/GAP</url>.</p

C-reactive Protein Positively Correlates With Metabolic Syndrome in Kidney Transplantation Patients

ObjectiveC-reactive protein (CRP) is an independent risk factor for renal allograft loss and predicts all-cause mortality in kidney transplantation patients. Metabolic syndrome has also been associated with increased mortality in kidney transplantation patients. The aim of this study was to investigate the relationship between CRP and metabolic syndrome in kidney transplantation patients.Materials and MethodsFasting blood samples were obtained from 55 kidney transplantation patients. Metabolic syndrome and its components were defined using diagnostic criteria from the International Diabetes Federation.ResultsIn total, 13 kidney transplantation patients (23.6%) had metabolic syndrome. Fasting CRP levels positively correlated with metabolic syndrome (p = 0.001). Univariate linear regression analysis indicated that fasting serum CRP values were positively correlated with body weight (p = 0.001), waist circumference (p = 0.008), body mass index (p < 0.001), and body fat mass (p = 0.042). Multivariate forward stepwise linear regression analysis of the significant variables showed that body mass index (β = 0.455, R2 = 0.207, p < 0.001) was an independent predictor of serum CRP levels in kidney transplantation patients.ConclusionCRP level positively correlated with metabolic syndrome in kidney transplantation patients. Body mass index was an independent predictor of serum CRP levels in kidney transplantation patients

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.Comment: Camera-ready for 23, ACM M

Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model

Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and failed on more complex sequence generation tasks. Besides, adapter tuning is primarily applied with a focus on encoder-only self-supervised models. Our experiments show that prompting on Wav2Seq, a self-supervised encoder-decoder model, surpasses previous works in sequence generation tasks. It achieves a remarkable 53% relative improvement in word error rate for ASR and a 27% in F1 score for slot filling. Additionally, prompting competes with the FT method in the low-resource scenario. Moreover, we show the transferability of prompting and adapter tuning on Wav2Seq in cross-lingual ASR. When limited trainable parameters are involved, prompting and adapter tuning consistently outperform conventional FT across 7 languages. Notably, in the low-resource scenario, prompting consistently outperforms adapter tuning.Comment: Accepted to IEEE ASRU 202