Near Real-time Fashion Research Articles

Online Dynamic Window (ODW) Assisted Two-Stage LSTM Frameworks For Indoor Localization

Ubiquitous presence of smart connected devices within the field of Internet of Things (IoT) have resulted in emergence of innovative ambience awareness concepts such as smart buildings and smart cities. In this context, Inertial Measurement Unit (IMU)-based localization is of particular interest as it provides a scalable solution independent of any proprietary sensors/modules. Existing IMU-based methodologies, however, are mainly developed based on statistical heading and step length estimation techniques that, typically, suffer from cumulative error issues and have extensive computational time requirements.To address the aforementioned issues, we propose the Online Dynamic Window (ODW)-assisted two-stage Long Short Term Memory (LSTM) localization framework. Three ODWs are proposed, where the first model uses a Natural Language Processing (NLP)-inspired Dynamic Window (DW) approach, which significantly reduces the localization computation time. The second framework is developed based on a Signal Processing Dynamic Windowing (SP-DW) approach to further reduce the required processing time. The third ODW, referred to as the SP-NLP, combines the first two windowing mechanisms to further improve the overall achieved accuracy. Compared to the traditional LSTM-based positioning approaches, the proposed ODW-assisted models can perform indoor localization in a near-real time fashion with high accuracy. Performances of the proposed ODW-assisted models are evaluated based on a real Pedestrian Dead Reckoning (PDR) dataset. The results illustrate potentials of the proposed ODW-assisted techniques in achieving high classification accuracy with significantly reduced computational time.

Whole-body Imaging of Cell Death Provides a Systemic, Minimally Invasive, Dynamic, and Near-real Time Indicator for Chemotherapeutic Drug Toxicity.

Response to toxicity in chemotherapies varies considerably from tissue to tissue and from patient to patient. An ability to monitor the tissue damage done by chemotherapy may have a profound impact on treatment and prognosis allowing for a proactive management in understanding and mitigating such events. For the first time, we investigated the feasibility of using whole-body imaging to map chemotherapeutic drug-induced toxicity on an individual basis. In a preclinical proof-of-concept, rats were treated with a single clinical dose of cyclophosphamide, methotrexate, or cisplatin. In vivo whole-body imaging data were acquired using 99mTc-duramycin, which identifies dead and dying cells as an unambiguous marker for tissue injury in susceptible organs. Imaging results were cross-validated using quantitative ex vivo measurements and histopathology and compared with standard blood and serum panels for toxicology. The in vivo whole-body imaging data detected widespread changes, where spatially heterogeneous toxic effects were identified across different tissues, within substructures of organs, as well as among different individuals. The signal changes were consistent with established toxicity profiles of these chemotherapeutic drugs. Apart from generating a map of susceptible tissues, this in vivo imaging approach was more sensitive compared with conventional blood and serum markers used in toxicology. Also, repeated imaging during the acute period after drug treatment captured different kinetics of tissue injury among susceptible organs in males and females. This novel and highly translational imaging approach shows promise in optimizing therapeutic decisions by detecting and managing drug toxicity on a personalized basis.Toxicity to normal tissues is a significant limitation in chemotherapies. This work demonstrated an in vivo imaging-based approach for characterizing toxicity-induced tissue injury in a systemic, dynamic, and near-real time fashion. This novel approach shows promise in optimizing therapeutic decisions by monitoring drug toxicity on a personalized basis.

Emissions of Trace Products of Incomplete Combustion from a Pilot-Scale Incinerator Secondary Combustion Chamber.

Experiments were performed on a 73 kW rotary kiln incinerator simulator equipped with a 73 kW secondary combustion chamber (SCC) to examine emissions of products of incomplete combustion (PICs) resulting from incineration of carbon tetrachloride (CC14) and dichloromethane (CH2C12). Species were measured using an on-line gas chromatograph (GC) system capable of measuring concentrations of eight species of volatile organic compounds (VOCs) in a near-realtime fashion. Samples were taken at several points within the SCC, to generate species profiles with respect to system residence time. For the experiments, the afterburner on the SCC was operated at conditions ranging from fuel-rich to fuellean, while the kiln was operated at a constant set of conditions. Results indicate that combustion of CH2C12 produces higher levels of measured PICs than combustion of CC14, particularly 1, 2 dichlorobenzene, and to a lesser extent, monochlorobenzene. Benzene emissions were predominantly affected by the afterburner air/fuel ratio regardless of whether or not a surrogate waste was being fed.