Effects of nitrogenous substances on heat transfer fouling using model thin stillage fluids

Fouling is unwanted deposition of materials on surfaces of processing equipment, which leads to additional investment, lower processing efficiency and potential fluid contamination. In the corn ethanol industry, fouling occurs when thin stillage is concentrated into condensed distillers solubles. Several researchers have investigated operating conditions and constituents’ influence on fouling characteristics. However, understanding protein effects on fouling is limited despite its high concentration in thin stillage (17 to 33% db). Protein contributions to fouling have been investigated in the dairy industry. Whey proteins and calcium phosphate interact with each other or other proteins to form aggregates on heated surfaces. Maillard browning is another potential factor influencing fouling since amino acids in thin stillage are able to react with reducing sugars and form brown pigments. Proteins, their hydrolyzed products of amino acids and residual sugars in thin stillage contribute to fouling. Due to complex components in commercial thin stillage, it is difficult to study a single effect on fouling without interference from other factors. The objective was to investigate effects of nitrogenous substances and protease on fouling using model and commercial thin stillage fluids. Nitrogenous substances urea and yeasts, as model protein sources in thin stillage, were mixed with glucose. Thermocouples in an annular probe were used to monitor surface temperature; fouling resistance was obtained by using overall heat transfer coefficients of fouled and unfouled surfaces. Fouling was characterized by maximum fouling resistance (Rmax), induction period and fouling rate during 5 hr test periods. Urea addition did not lead to fouling while glucose-yeast model fluids displayed fouling tendency that had a positive correlation with yeast protein concentration. Protease from pineapple stem (bromelain) incubation increased fouling in both model and commercial fluids, which were indicative that hydrolyzed molecules such as peptides, amino acids or protease itself can be involved in deposit formation. Adjustment of pH in model fluids during incubation reduced Rmax and fouling rate and extended induction periods longer than 300 min. Fouling resistance profiles varied as the amount of bromelain changed from 1 to 3 g, showing a reduction in induction periods. Total suspended solids (TSS) of commercial thin stillage were measured during 14 days of storage; TSS was affected more by sample batch than storage time

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version

Unified Framework for the Effective Rate Analysis of Wireless Communication Systems over MISO Fading Channels

This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple inputs single output (MISO) fading channels, which uses moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared to the probability density function (PDF) based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox’s H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma and generalized K fading channels, are readily available. In addition, asymptotic approximations are provided for the proposed H transform and MGF based approach as well as for the effective rate over i.n.i.d. MISO hyper Fox’s H fading channels. Simulations under various fading scenarios are also presented, which support the validity of the proposed method

NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions

Structural and biochemical insights into small RNA 3' end trimming by Arabidopsis SDN1.

A family of DEDDh 3'→5' exonucleases known as Small RNA Degrading Nucleases (SDNs) initiates the turnover of ARGONAUTE1 (AGO1)-bound microRNAs in Arabidopsis by trimming their 3' ends. Here, we report the crystal structure of Arabidopsis SDN1 (residues 2-300) in complex with a 9 nucleotide single-stranded RNA substrate, revealing that the DEDDh domain forms rigid interactions with the N-terminal domain and binds 4 nucleotides from the 3' end of the RNA via its catalytic pocket. Structural and biochemical results suggest that the SDN1 C-terminal domain adopts an RNA Recognition Motif (RRM) fold and is critical for substrate binding and enzymatic processivity of SDN1. In addition, SDN1 interacts with the AGO1 PAZ domain in an RNA-independent manner in vitro, enabling it to act on AGO1-bound microRNAs. These extensive structural and biochemical studies may shed light on a common 3' end trimming mechanism for 3'→5' exonucleases in the metabolism of small non-coding RNAs

Students' Intention of Visiting Urban Green Spaces after the COVID-19 Lockdown in China.

This study addresses students' perceptions of using urban green spaces (UGSs) after the easing of COVID-19 lockdown in China. We questioned whether they are still mindful of the risks from the outdoor gathering, or conversely, starting to learn the restoration benefits from the green spaces. Online self-reported surveys were distributed to the Chinese students aging from 14 to 30 who study in Hunan and Jiangsu Provinces, China. We finally obtained 608 complete and valid questionnaire forms from all participants. Their intentions of visiting UGSs were investigated based on the extended theory of planned behavior model. Structural equation modeling was employed to test the hypothesized psychological model. The results have shown good estimation performance on risk perception and perceived knowledge to explain the variances in their attitudes, social norms, and perceived behavior control. Among these three endogenous variables, the perceived behavior control owns the greatest and positive influence on the behavioral intention, inferring that controllability is crucial for students to make decisions of visiting green spaces in a post-pandemic context

Prediction of Tropical Pacific Rain Rates with Over-parameterized Neural Networks

The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces over-parameterized neural networks not only to forecast tropical rain rates, but also to explain their heavy-tailed distribution. The prediction is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the West and East Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although over-parameterized neural networks are well-known for their "double descent phenomenon," little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, over-parameterized neural networks accurately predict the rain rate distributions and outperform other machine learning methods. Spatial maps show that over-parameterized neural networks also successfully describe spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each over-parameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of over-parameterized neural networks in predicting the distribution of the rain rate and explaining extreme values

Purine Metabolism and Pyrimidine Metabolism Alteration Is a Potential Mechanism of BDE-47-Induced Apoptosis in Marine Rotifer <i>Brachionus plicatilis</i>

This present study was conducted to provide evidence and an explanation for the apoptosis that occurs in the marine rotifer Brachionus plicatilis when facing 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) stress. Metabolomics analysis showed that aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, and arginine biosynthesis were the top three sensitive pathways to BDE-47 exposure, which resulted in the reduction in the amino acid pool level. Pyrimidine metabolism and purine metabolism pathways were also significantly influenced, and the purine and pyrimidine content were obviously reduced in the low (0.02 mg/L) and middle (0.1 mg/L) concentration groups while increased in the high (0.5 mg/L) concentration group, evidencing the disorder of nucleotide synthesis and decomposition in B. plicatilis. The biochemical detection of the key enzymes in purine metabolism and pyrimidine metabolism showed the downregulation of Glutamine Synthetase (GS) protein expression and the elevation of Xanthine Oxidase (XOD) activity, which suggested the impaired DNA repair and ROS overproduction. The content of DNA damage biomarker (8-OHdG) increased in treatment groups, and the p53 signaling pathway was found to be activated, as indicated by the elevation of the p53 protein expression and Bax/Bcl-2 ratio. The ROS scavenger (N-acetyl-L-cysteine, NAC) addition effectively alleviated not only ROS overproduction but also DNA damage as well as the activation of apoptosis. The combined results backed up the speculation that purine metabolism and pyrimidine metabolism alteration play a pivotal role in BDE-47-induced ROS overproduction and DNA damage, and the consequent activation of the p53 signaling pathway led to the observed apoptosis in B. plicatilis.</i

Effective Rate Analysis in Weibull Fading Channels

Recently the theory of effective rate has attracted much attention, since it can take the delay aspect into account when performing channel capacity analysis.Weibull fading model is a flexible and practical model for describing fading channels in both indoor and outdoor environments. This letter derives the exact analytical expressions of effective rate over independent but not necessarily identical Weibull fading channels, which can be used in the system analysis in real-time communication scenarios. In addition, the closed-form asymptotic expressions under high signal-to-noise ratio (SNR) and low-SNR regimes are derived to provide useful insights into the effects of system and fading parameters on the effective rate