A pilot study to evaluate the effect of Taeumjowi-tang on obesity in Korean adults: study protocol for a randomised, double-blind, placebo-controlled, multicentre trial

<p>Abstract</p> <p>Background</p> <p>Obesity, which is described as excessive or abnormal body fat, increases the risk of diet-related diseases. In Korea and around the world, the prevalence of obesity has grown annually from 1998 to 2008. This growth has continued despite various therapeutic efforts. The discovery of new and alternative treatments for obesity should be considered an important priority. Taeumjowi-tang (TJ001), a traditional Korean medicinal extract consisting of eight herbs, is a widely used herbal remedy for obesity in Korea. However, the efficacy and safety of TJ001 have not been fully investigated in a clinical trial. The purpose of this pilot study is to estimate obesity-related parameters and to assess the efficacy and safety of TJ001.</p> <p>Methods</p> <p>Our study is a randomised, double-blind, placebo-controlled, multicentre clinical trial of Taeumjowi-tang (TJ001). For this study, we will recruit obese Korean patients of both sexes, ages 18 to 65 years, from four university hospitals. A total of 104 subjects will be recruited. The participants will receive either 7 g of TJ001 or a placebo three times daily for 12 weeks. The primary end point will be the rate of subjects who lose at least 5% of their baseline body weight. The secondary end points will be changes in body weight, body mass index, waist circumference, hip circumference, waist/hip circumference ratio, lipid profiles, body fat composition, blood pressure, fasting glucose concentration, C-reactive protein and questionnaires related to the quality of life. The outcomes will be measured every 4 weeks. The study period will be 12 weeks and will include a total of five visits with each subject (at screening and at 0, 4, 8 and 12 weeks).</p> <p>Conclusions</p> <p>The results of our study will inform various estimates of TJ001 and will serve as the basis for a larger-scale trial. This study will assess the efficacy and safety of TJ001 as an alternative herbal remedy for obesity.</p> <p>Trial registration</p> <p>Current Controlled Trials <a href="http://www.controlled-trials.com/ISRCTN87153759">ISRCTN87153759</a></p

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author&rsquo;s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long&ndash;Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others

Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems

This paper proposes an effective emotional text-to-speech (TTS) system with a pre-trained language model (LM)-based emotion prediction method. Unlike conventional systems that require auxiliary inputs such as manually defined emotion classes, our system directly estimates emotion-related attributes from the input text. Specifically, we utilize generative pre-trained transformer (GPT)-3 to jointly predict both an emotion class and its strength in representing emotions coarse and fine properties, respectively. Then, these attributes are combined in the emotional embedding space and used as conditional features of the TTS model for generating output speech signals. Consequently, the proposed system can produce emotional speech only from text without any auxiliary inputs. Furthermore, because the GPT-3 enables to capture emotional context among the consecutive sentences, the proposed method can effectively handle the paragraph-level generation of emotional speech.Comment: Accepted by INTERSPEECH202

Thermally Stable Bulk Heterojunction Prepared by Sequential Deposition of Nanostructured Polymer and Fullerene

A morphologically-stable polymer/fullerene heterojunction has been prepared by minimizing the intermixing between polymer and fullerene via sequential deposition (SqD) of a polymer and a fullerene solution. A low crystalline conjugated polymer of PCPDTBT (poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta [2,1-b;3,4-b′]dithiophene)-alt-4,7(2,1,3-benzothiadiazole)]) has been utilized for the polymer layer and PC71BM (phenyl-C71-butyric-acid-methyl ester) for the fullerene layer, respectively. Firstly, a nanostructured PCPDTBT bottom layer was developed by utilizing various additives to increase the surface area of the polymer film. The PC71BM solution was prepared by dissolving it in the 1,2-dichloroethane (DCE), exhibiting a lower vapor pressure and slower diffusion into the polymer layer. The deposition of the PC71BM solution on the nanostructured PCPDTBT layer forms an inter-digitated bulk heterojunction (ID-BHJ) with minimized intermixing. The organic photovoltaic (OPV) device utilizing the ID-BHJ photoactive layer exhibits a highly reproducible solar cell performance. In spite of restricted intermixing between the PC71BM and the PCPDTBT, the efficiency of ID-BHJ OPVs (3.36%) is comparable to that of OPVs (3.87%) prepared by the conventional method (deposition of a blended solution of polymer:fullerene). The thermal stability of the ID-BHJ is superior to the bulk heterojunction (BHJ) prepared by the conventional method. The ID-BHJ OPV maintains 70% of its initial efficiency after thermal stress application for twelve days at 80 °C, whereas the conventional BHJ OPV maintains only 40% of its initial efficiency