Robust Online Hamiltonian Learning

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Tracking indoor air quality of buildings using BIM

Today, the demand of sustainable buildings is getting higher. The main purpose of buildings is to provide a comfortable living environment to their occupants, considering different aspects including thermal, visual and acoustic comfort as well as Indoor Air Quality. Life cycle assessments are related to many issues such as environmental concerns. Decreasing carbon foot print and energy consumption rates and increasing comfort level for the building users can help to achieve environmental improvements. This comfort level is related highly to Indoor Air Quality (IAQ). This research aims at improving environmental concerns using building information modeling. As-built BIM model is developed to act as a hub to allow transformation of information to an external database, extracted from the BIM Model in COBIE (Construction-Operations Building Information Exchange) format. The database is updated in a dynamic manner to reflect external environmental changes. The environmental changes are captured using sensors that can detect variations in temperature and humidity. Also, carbon emissions and energy consumption rates are reflected back on the model. A case study is presented to demonstrate the use of the proposed framework.Non UBCUnreviewedFacult

Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface

Among the most prominent field in the human-computer interface (HCI) is emotion recognition using facial expressions. Posed variations, facial accessories, and non-uniform illuminations are some of the difficulties in the emotion recognition field. Emotion detection with the help of traditional methods has the shortcoming of mutual optimization of feature extraction and classification. Computer vision (CV) technology improves HCI by visualizing the natural world in a digital platform like the human brain. In CV technique, advances in machine learning and artificial intelligence result in further enhancements and changes, which ensures an improved and more stable visualization. This study develops a new Modified Earthworm Optimization with Deep Learning Assisted Emotion Recognition (MEWODL-ER) for HCI applications. The presented MEWODL-ER technique intends to categorize different kinds of emotions that exist in the HCI applications. To do so, the presented MEWODL-ER technique employs the GoogleNet model to extract feature vectors and the hyperparameter tuning process is performed via the MEWO algorithm. The design of automated hyperparameter adjustment using the MEWO algorithm helps in attaining an improved emotion recognition process. Finally, the quantum autoencoder (QAE) model is implemented for the identification and classification of emotions related to the HCI applications. To exhibit the enhanced recognition results of the MEWODL-ER approach, a wide-ranging simulation analysis is performed. The experimental values indicated that the MEWODL-ER technique accomplishes promising performance over other models with maximum accuracy of 98.91%

Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning

An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models

Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface

Automated facial emotion recognition (FER) is one of the important fields of human-computer interaction (HCI). FER remains challenging due to facial accessories, non-uniform illumination, pose variation, etc. Emotion detection exploiting conventional algorithms has the demerit of mutual optimization of classification and feature extraction. Artificial intelligence (AI) techniques can be employed to identify FER automatically. Deep learning (DL) driven FER models have recently allowed for designing an end-to-end learning process. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning Based FER (HGSO-DLFER) technique for HCI. The HGSO-DLFER technique aims to recognize and identify various kinds of facial emotions. To accomplish this, the HGSO-DLFER technique employs adaptive fuzzy filtering (AFF) for noise removal. In addition, the MobileNet model is used for feature vector generation, and the HGSO algorithm optimally chooses its hyperparameter scan. For the recognition of facial emotions, the HGSO-DLFER technique uses an autoencoder (AE) classifier with a Nadam optimizer. A widespread experimental analysis is made to facilitate a better understanding of the FER results by the HGSO-DLFER technique. The comparative analysis showed the effective performance of the HGSO-DLFER technique over other FER techniques with maximum accuracy of 98.65%

IS711 sequencing of Brucella melitensis and Brucella abortus strains, and use of microchip-based real-time PCR for rapid monitoring

In animal production systems around the world, brucellosis is a serious zoonotic disease that creates public health hazards and losses in economic terms. The aim of the study is to genotype and molecularly characterize Brucella melitensis (B. melitensis) and Brucella abortus (B. abortus) collected from different animal species and humans. A total of 50 isolates of Brucella species (16B. melitensis and 34B. abortus) were isolated from 1081 animal and human samples using a culture technique, followed by biochemical identification using the Vitek 2 compact system and proteomic identification using mass spectrometry technology. Molecular genotyping was performed on all isolates using multiplex real-time PCR. Six isolates from each genotype of Brucella species were selected and genetically evaluated by IS711 insertion sequences. Microchips-based real-time PCR for Brucella species identification was performed on twelve genetically characterized isolates as a first attempt. Forty-four (88%) isolates of Brucella species were detected using multiplex real-time PCR. Based on IS711 nucleotide sequencing, twelve isolates were phylogenetically clustered into their specific clusters. The results of the comparative analysis of conventional real time and microchips-based real time indicated that the later is faster and qualitatively more sensitive than conventional real time; however, further studies are needed to ensure that it is capable of serving as a gold standard alternative for Brucella species monitoring

Radioiodination and biodistribution of newly synthesized 3-benzyl-2-([3-methoxybenzyl]thio)benzo[g]quinazolin-4-(3H)-one in tumor bearing mice

3-Benzyl-2-((3-methoxybenzyl)thio)benzo[g]quinazolin-4(3H)-one was previously synthesized and proved by physicochemical analyses (HRMS, 1H and 13C NMR). The target compound was examined for its radioactivity and the results showed that benzo[g]quinazoline was successfully labeled with radioactive iodine using NBS via an electrophilic substitution reaction. The reaction parameters that affected the labeling yield such as concentration, pH and time were studied to optimize the labeling conditions. The radiochemical yield was 91.2 ± 1.22% and the in vitro studies showed that the target compound was stable for up to 24 h. The thyroid was among the other organs in which the uptake of 125I-benzoquinazoline has increased significantly over the time up to 4.1%. The tumor uptake was 6.95%. Radiochemical and metabolic stability of the benzoquinazoline in vivo/in vitro and biodistribution studies provide some insights about the requirements for developing more potent radiopharmaceutical for targeting the tumor cells. Keywords: Benzo[g]quinazoline, Radioiodination, Biodistribution, Tumor cell, NB

Investigation of Structural, Physical, and Attenuation Parameters of Glass: TeO₂-Bi₂O₃-B₂O₃-TiO₂-RE₂O₃ (RE: La, Ce, Sm, Er, and Yb), and Applications Thereof

A novel series of glass, consisting of B2O3, Bi2O3, TeO2, and TiO2 (BBTT) containing rare earth oxide RE2O3, where RE is La, Ce, Sm, Er, and Yb, was prepared. We investigated the structural, optical, and gamma attenuation properties of the resultant glass. The optical energy bands, the linear refractive indices, the molar refractions, the metallization criteria, and the optical basicity were all determined for the prepared glass. Furthermore, physical parameters such as the density, the molar volume, the oxygen molar volume, and the oxygen packing density of the prepared glass, were computed. Both the values of density and optical energy of the prepared glass increased in the order of La2O3, Ce2O3, Sm2O3, Er2O3, and then Yb2O3. In addition, the glass doped with Yb2O3 had the lowest refractive index, electronic polarizability, and optical basicity values compared with the other prepared glass. The structures of the prepared glass were investigated by the deconvolution of infrared spectroscopy, which determined that TeO4, TeO3, BO4, BO3, BiO6, and TiO4 units had formed. Furthermore, the structural changes in glass are related to the ratio of the intensity of TeO4/TeO3, depending on the type of rare earth. It is also clarified that the resultant glass samples are good attenuators against low-energy radiation, especially those that modified by Yb2O3, which exhibited superior shielding efficiency at energies of 622, 1170, and 1330 keV. The optical and gamma ray spectroscopy results of the prepared glass show that it is a good candidate for nonlinear optical fibers, laser solid material, and optical shielding protection