Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Fluorine: A new element in protein design

Fluorocarbons are quintessentially man‐made molecules, fluorine being all but absent from biology. Perfluorinated molecules exhibit novel physicochemical properties that include extreme chemical inertness, thermal stability, and an unusual propensity for phase segregation. The question we and others have sought to answer is to what extent can these properties be engineered into proteins? Here, we review recent studies in which proteins have been designed that incorporate highly fluorinated analogs of hydrophobic amino acids with the aim of creating proteins with novel chemical and biological properties. Fluorination seems to be a general and effective strategy to enhance the stability of proteins, both soluble and membrane bound, against chemical and thermal denaturation, although retaining structure and biological activity. Most studies have focused on small proteins that can be produced by peptide synthesis as synthesis of large proteins containing specifically fluorinated residues remains challenging. However, the development of various biosynthetic methods for introducing noncanonical amino acids into proteins promises to expand the utility of fluorinated amino acids in protein design.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90596/1/2030_ftp.pd

An Investigation of Factors Affecting Elementary School Students’ BMI Values Based on the System Dynamics Modeling

This study used system dynamics method to investigate the factors affecting elementary school students’ BMI values. The construction of the dynamic model is divided into the qualitative causal loop and the quantitative system dynamics modeling. According to the system dynamics modeling, this study consisted of research on the four dimensions: student’s personal life style, diet-relevant parenting behaviors, advocacy and implementation of school nutrition education, and students’ peer interaction. The results of this study showed that students with more adequate health concepts usually have better eating behaviors and consequently have less chance of becoming obese. In addition, this study also verified that educational attainment and socioeconomic status of parents have a positive correlation with students’ amounts of physical activity, and nutrition education has a prominent influence on changing students’ high-calorie diets

Neonauclea reticulata

In this study, we investigated whether the protective effects of Neonauclea reticulata water extract against ultraviolet B (UVB) irradiation in human skin fibroblast cell cultures (Hs68) are governed by its ability to protect against oxidative stress and expression of matrix metalloproteinases (MMPs). We found that Neonauclea reticulata extract exhibited DPPH scavenging activity and inhibited AAPH-induced haemolysis of erythrocytes in a dose- and time-dependent manner. We also found that pretreatment of fibroblasts with Neonauclea reticulata water extract resulted in markedly lower levels of MMP-1, -3, and -9 expressions. Furthermore, our results indicate that Neonauclea reticulata extract inhibits the expression of MMPs by inhibiting ERK, JNK, and p38 phosphorylation. Our results also demonstrate that treatment with Neonauclea reticulata extract protects against UVB-induced depletion of collagen. In addition, Neonauclea reticulata extract did not have a cytotoxic effect. These findings indicate that the antioxidant activity of Neonauclea reticulata extract resulted in inhibition of MMP-1, -3, and -9 expressions and in increased levels of collagen activity. Our results suggest that Neonauclea reticulata extract can protect against photoaging

Fabrication and Characterization of Electrospun Semiconductor Nanoparticle—Polyelectrolyte Ultra-Fine Fiber Composites for Sensing Applications

Fluorescent composite fibrous assembles of nanoparticle-polyelectrolyte fibers are useful multifunctional materials, utilized in filtration, sensing and tissue engineering applications, with the added benefits of improved mechanical, electrical or structural characteristics over the individual components. Composite fibrous mats were prepared by electrospinning aqueous solutions of 6 wt% poly(acrylic acid) (PAA) loaded with 0.15 and 0.20% v/v, carboxyl functionalized CdSe/ZnS nanoparticles (SNPs). The resulting fluorescent composite fibrous mats exhibits recoverable quenching when exposed to high humidity. The sensor response is sensitive to water concentration and is attributed to the change in the local charges around the SNPs due to deprotonation of the carboxylic acids on the SNPs and the surrounding polymer matrix

An Obesity Paradox of Asian Body Mass Index after Cardiac Surgery: Arterial Oxygenations in Duration of Mechanic Ventilation

Background. Numerous studies have documented an obesity paradox that overweight of Caucasian patients has better prognosis after cardiac surgery. This study is to examine Asian patients’ BMI to see whether an obesity paradox exists in DMV after cardiac surgery. Methods. A retrospective study consisted of 428 patients after cardiac surgery from January 2006 to December 2010 in the medical center of Taiwan. The Asian BMI was divided into 3 groups: under-normal weight patients (; ), overweight patients (BMI 24 to <27; ), and obese patients (; ). Multivariable analysis and paired were used to compare all variables. Results. Overweight patients were significantly associated with the shortest DMV. Under-normal weight patients had significantly better oxygenations of AaDO2 and P/F ratio in the DMV; however, they correlated with the longest DMV, older age, more female, lower LVSV, higher BUN, more dialysis-dependent, and poorer outcomes, namely, 1-year mortality, HAP, reintubation, tracheotomy, and LOS. Conclusions. Asian overweight patients after cardiac surgery have better prognosis. Under-normal weight patients have higher risk factors, longer DMV, and poorer outcomes; even though they have better arterial oxygenations, they seem to need better arterial oxygenations for successful weaning ventilator

Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or better performance on both in-domain and out-of-domain speech recognition tasks across many languages.Comment: 20 pages, 7 figures, 8 table