The role of users impulsiveness in detecting mobile phone excessive dependence: A feature selection analysis

With the advancement of information and communications technologies, and the booming of mobile apps, mobile phone addiction is on the rise. The current approach to detect mobile phone excessive dependence is through the amount of phone usage, including duration, times picking up the phone and etc. However, literature on addiction suggests that impulsive action is also a key indicator of addictive behavior. Thus, this study proposes that the impulsive behavior rather than the amount of usage can be a better predictor of mobile phone dependence. With longitudinal phone usage data collected from 60 users, this study has identified that the minimum time interval between two pickups describes mobile phone dependence better than the amount of usage. Planned future analysis and potential contributions are discussed

On the design evolution of hip implants: A review

This manuscript reviews the development of femoral stem prostheses in the biomedical field. After a brief introduction on the development of these prostheses and the associated problems, we describe the standard design of these systems. We review the different materials, constructions, and surfaces used in the development of femoral stems, in order to solve and avoid various problems associated with their use. Femoral stem prostheses have undergone substantial changes and design optimizations since their introduction. Common materials include stainless steel, cobalt–chromium alloy, titanium alloy, and composites. The structural development of femoral stem prostheses, including their length, shape, porosity, and functional gradient construction, is also reviewed. The performance of these prostheses is affected not only by individual factors, but also by the synergistic combination of multiple effects; therefore, several aspects need to be optimized. The main purpose of this study is to summarize various strategies for the material and construction optimization of femoral stem prostheses, and to provide a reference for the combined optimization of their performance. Substantial research is still needed to develop prostheses emulating the behavior of a real human femoral stem

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Real-time face recognition

Face recognition has been one of the most popular and important research topics in computer vision, and was applied to many other fields such as security and law enforcement. Over the past two decades, face recognition are still challenging, due to the technology limit, wide range of input, and complexity of the algorithms. Researchers and engineers have devoted in a lot of efforts trying to improve the algorithms so that faster, better or more reliable performance can be achieved. There are different types of face recognition algorithms. Each has its pros and cons and there is no robust method for all kinds of situations. Modern embedded system is a computer system that based on microcontrollers which often come with integrated memory. Embedded system is normally applied in portable devices such as digital watches, music players, smart devices etc. Due to its hardware resource limitations, the software system that built in such devices needs to pay special attention to the efficiency measure and hardware costs. This report will introduce and implement one of the idea based on ARENA and Nearest Neighbour (NN) to build a real-time based face recognition system, which aims to find out whether the captured face is within the current face database. With the consideration of hardware limit, the performance and time cost will be evaluated. The input query image can be taken from either video or static image, and used to compare with the predefined face database through the application. Also, the flows, functionalities, and testing results of the application will be presented in order to show the full picture of the whole process.Bachelor of Engineering (Computer Science

Quinclorac resistance induced by the suppression of the expression of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase and ACC oxidase genes in Echinochloa crus-galli var. zelayensis

We previously reported that the mechanism of quinclorac resistance in Echinochloa crus-galli var. zelayensis may be closely related to ethylene biosynthesis and the detoxification of cyanide. Differences in EcCAS gene sequences and expression levels may result in higher capacity to detoxify cyanide in resistant biotypes, which may avoid cyanide accumulation and avoid more ethylene and cyanide production and then avoid damage. In the present study, we focused on the mechanism of resistance related to ethylene biosynthesis in E. crus-galli var. zelayensis. The fresh weight of susceptible and moderately resistant biotypes were significantly reduced after treatment with quinclorac. However, AOA, an ethylene biosynthesis inhibitor, reduced the impact of quinclorac. On pretreatment with AOA, ethylene production was significantly reduced in the three biotypes. The highly resistant biotype produced less ethylene compared to the other two biotypes. Three ACS and seven ACO genes, which are the key genes in ethylene biosynthesis, were obtained. The expression levels of EcACS-like, EcACS7, and EcACO1 varied in the three biotypes upon treatment with quinclorac, which could be manipulated by AOA. In summary, it is inferred that the expression of EcACS-like, EcACS7, and EcACO1 can be stimulated to varying extent after quinclorac treatment in three E. crus-galli var. zelayensis biotypes, which consequently results in varying levels of ethylene production. Lower expression of these three genes results in more resistance to quinclorac, which may also be related to quinclorac resistance in E. crus-galli var. zelayensis

Phospholipid Complexation for Bioavailability Improvement of Albendazole: Preparation, Characterization and In Vivo Evaluation

The current study aimed to improve the poor solubility of albendazole (ABZ) by means of phospholipid complexation, hence to improve its oral bioavailability. The solvent-evaporation method for ABZ-phospholipid complex (ABZ-PC) preparation was established for the first time. And a systematic optimization of preparation conditions of ABZ-PC was performed. Physicochemical studies of ABZ-PC were performed with FTIR, DSC, and XRD measurements to confirm the formation of the ABZ-PC and reveal the interaction mechanism between ABZ and phospholipid molecules. Solubility determination and morphological characterization were applied to verify the solubility improvement of prepared ABZ-PC. Moreover, the pharmacokinetic performance of ABZ-PC was further evaluated in vivo compared with raw materials of ABZ. Under optimal preparation conditions, the AE of ABZ-PC could be approximately 100%. Physicochemical studies indicated that the P = O group in the phospholipid molecule would interact with the N-H group in the ABZ molecule through hydrogen bonds and ABZ was dispersed in an amorphous state after being prepared into ABZ-PC. The aqueous solubility of ABZ-PC in deionized water (pH7.0) improved by 30-folds than free ABZ, and the AUC(0-t) of ABZ-PC was significantly increased by 2.32 times in comparison with raw materials of ABZ through oral administration. The current study developed an effective method for the phospholipid complexation of ABZ. With significantly improved solubility in an aqueous environment, the prepared ABZ-PC exhibited improved oral bioavailability and pharmacokinetic characteristics indicating it could be potentially applied in the oral drug delivery of ABZ