A medical Chatbot using machine learning and natural language understanding

Multimedia Tools and Applications - For people with busy schedules, travelling to and from a hospital for treatment is considerably time consuming, which leads them to ignore their health problems....     

Software Optimization in Ultrasound Imaging Technique Using Improved Deep Belief Learning Network on the Internet of Medical Things Platform

Wireless Personal Communications - The Internet of Medical Things (IoMT) is the array of medical instruments and related technologies that link Information Technology (IT) systems in...     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Labeling and Characterization of Phenol-Containing Glycopeptides Using Chemoselective Probes with Isotope Tags

Secondary metabolites are structurally diverse natural products (NPs) and have been widely used for medical applications. Developing new tools to enrich NPs can be a promising solution to isolate novel NPs from the native and complex samples. Here, we developed native and deuterated chemoselective labeling probes to target phenol-containing glycopeptides by the ene-type labeling used in proteomic research. The clickable azido-linker was included for further biotin functionalization to facilitate the enrichment of labeled substrates. Afterward, our chemoselective method, in conjunction with LC-MS and MSn analysis, was demonstrated in bacterial cultures. A vancomycin-related phenol-containing glycopeptide was labeled and characterized by our labeling strategy, showing its potential in glycopeptide discovery in complex environments.     

Manpower supply planning for aircraft line maintenance under stochastic incidents

We plan the manpower supply for aircraft line maintenance, taking into account two types of stochastic incidents: manpower demands for a flight and the number of aircraft needing to be serviced at one time. The problem is solved to find the shift and maintenance group combinations best suited for the given airline. The optimal aircraft maintenance certification for a crew is also analyzed to improve the entire manpower structure. In addition, the addition of temporary manpower required for actual daily operations is also considered as a part of understanding the total manpower utilized in actual operations. An integrated method including scenario generation and a stochastic model is developed to deal with the problem. Finally, we perform a case study based on operating data obtained from a major airline in Taiwan. The results and findings are compared with the airline’s current manpower plan in the discussion, and suggestions for improvement are made.     

Mechanistic Insights and Photodegradation of Heterostructure Graphene Oxide/Titanium Dioxide

Topics in Catalysis - In this study, the electrospinning method was used to prepare graphene oxide/titanium dioxide (GO/TiO2) nanofibers with different contents of GO as photocatalysts used to...     

An efficient approach for prediction of Warburg-type resistance under working currents

In the present study, an efficient approach for the prediction of Warburg-type element is proposed via the analysis of the anode-supported solid oxide fuel cell (SOFC) performance under various working conditions. The details of the performance, polarization curves, impedance behavior, and species distribution profiles within the electrode are investigated via the combination of equivalent circuit model (ECM) analysis and multiphysics numerical simulations. The multiphysics simulation is developed and calibrated with experimental results of SOFC button cells under various working currents. With the complete datasets generated from the calibrated simulations, the trends of the element parameters involved in equivalent circuit model are analyzed. Generalized empirical functions are proposed as well as the procedures of prediction of performance under different conditions. The verification cases show good agreement between the predicted results from proposed model and the reference results. This proposed approach can be utilized to quickly predict the properties for desired performance in the manufacturing processes, and it also has the potential of reducing the computational cost in the simulation of large SOFCs.     

Design and analysis of an electrical magnetic holding device

Microsystem Technologies - Electromagnets are widely used as driving devices in industry. In this article, we describe various designs and methods of analysis for a built-in coil electromagnetic...     

Defect Engineering in Ambipolar Layered Materials for Mode-Regulable Nociceptor

Defect engineering represents a significant approach for atomically thick 2D semiconductor material development to explore the unique material properties and functions. Doping-induced conversion of conductive polarity is particularly beneficial for optimizing the integration of layered electronics. Here, controllable doping behavior in palladium diselenide (PdSe₂) transistor is demonstrated by manipulating its adatom-vacancy groups. The underlying mechanisms, which originate from reversible adsorption/desorption of oxygen clusters near selenide vacancy defects, are investigated systematically via their dynamic charge transfer characteristics and scanning tunneling microscope analysis. The modulated doping effect allows the PdSe₂ transistor to emulate the essential characteristics of photo nociceptor on a device level, including firing signal threshold and sensitization. Interestingly, electrostatic gating, acting as a neuromodulator, can regulate the adaptive modes in nociceptor to improve its adaptability and perceptibility to handle different danger levels. An integrated artificial nociceptor array is also designed to execute unique image processing functions, which suggests a new perspective for extension of the promise of defect engineered 2D electronics in simplified sensory systems toward use in advanced humanoid robots and artificial visual sensors.