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Recurrent Neural Networks are classes of Artificial Neural Networks that establish connections between different nodes form a directed or undirected graph for temporal dynamical analysis. In this research, the laser induced breakdown spectroscopy (LIBS) technique is used for quantitative analysis of aluminum alloys by different Recurrent Neural Network (RNN) architecture. The fundamental harmonic (1064 nm) of a nanosecond Nd:YAG laser pulse is employed to generate the LIBS plasma for the prediction of constituent concentrations of the aluminum standard samples. Here, Recurrent Neural Networks based on different networks, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (Simple RNN), and as well as Recurrent Convolutional Networks comprising of Conv-SimpleRNN, Conv-LSTM and Conv-GRU are utilized for concentration prediction. Then a comparison is performed among prediction by classical machine learning methods of support vector regressor (SVR), the Multi Layer Perceptron (MLP), Decision Tree algorithm, Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Linear Regression, and k-Nearest Neighbor (KNN) algorithm. Results showed that the machine learning tools based on Convolutional Recurrent Networks had the best efficiencies in prediction of the most of the elements among other multivariate methods.

The thermal conductivity and stability of nanofluids have posed the biggest challenges to their adoption as coolants in thermal applications in industries such as electronic equipment, heat exchangers, and solar technologies. In this paper, the thermal conductivity coefficient of an Al$_{12}$Mg$_{17}$ nanofluid is measured by a novel beam displacement method. Besides, the stability, particle size distribution (PSD), TEM micrograph, and electrical conductivity of Al$_{12}$Mg$_{17}$ nanofluids are investigated. For the preparation of nanofluids, three different surfactants are used to disperse Al$_{12}$Mg$_{17}$ nanoparticles in DI water using two-step method. Then, dispersion stability is monitored visually and quantified using a zeta potential test. The thermal conductivity coefficient and particle size distribution are measured using two optical setups. For the purpose of evaluating the outcomes, the thermal conductivity coefficients estimated using the beam displacement method are compared with the KD2 Pro apparatus results, and the PSD findings are examined using TEM micrographs. Results demonstrate that a 1:1 ratio of CTAB and Al$_{12}$Mg$_{17}$ nanoparticles is proper for stabilizing Al$_{12}$Mg$_{17}$ nanofluid. Also, the optimum ultrasonication period is determined to be 2 hours, and the peak of particle size distribution is measured to be 154 nanometers at this time. Thermal conductivity measurements show that the thermal conductivity coefficients improve as the concentration of Al$_{12}$Mg$_{17}$ nanoparticles increases, reaching a maximum enhancement of 40% in comparison to the base fluid at a concentration of 0.05 vol.%.

Additional File 1: Frequency distribution of allocation of a proportion of selected urban and rural centers of Babol city

Numerous investigations have demonstrated that standing acoustic waves can trap particles that range in size from microns to millimeters. Powerful tweezers may trap clusters of particles rather than single ones because their trapping radius is substantially larger than the size of the trapped particle. In this study, clusters of polystyrene particles measuring 450 microns in size that were suspended in an ionic surfactant solution were trapped at the nodes of acoustic standing waves. The correlation between surfactant concentration and threshold radius is examined, and potential mechanisms that could be responsible for the phenomenon are investigated. The findings demonstrated that adding polystyrene made clusters unstable and caused them to spontaneously rupture. Additionally, studies revealed that the cluster began to undergo spontaneous sequential ruptures after its radius above a particular threshold.

In current research, laser Doppler vibrometer (LDV) as a new diagnostic tool is utilized for non-destructive testing of the automotive equipment. LDV technique is working based on measurement of the Doppler shift of a moving object in an interference set-up. The effects of different noises are considered and eliminated from data analysis. Here, the performance of LDV technique is compared with a reference accelerometer device. Furthermore, a simulation by Matlab Simulink is added to the analysis which confirms the results of the experimental data. Results demonstrated that the laser Doppler vibrometer can measure excellently the frequencies of different automotive components for employing in industry. Therefore, it is proposed that LDV technique can be substituted with other traditional non-destructive testing methods.

Current backscatter channel estimators employ an inefficient silent pilot transmission protocol, where tags alternate between silent and active states. To enhance performance, we propose a novel approach where tags remain active simultaneously throughout the entire training phase. This enables a one-shot estimation of both the direct and cascaded channels and accommodates various backscatter network configurations. We derive the conditions for optimal pilot sequences and also establish that the minimum variance unbiased (MVU) estimator attains the Cramer-Rao lower bound. Next, we propose new pilot designs to avoid pilot contamination. We then present several linear estimation methods, including least square (LS), scaled LS, and linear minimum mean square error (MMSE), to evaluate the performance of our proposed scheme. We also derive the analytical MMSE estimator using our proposed pilot designs. Furthermore, we adapt our method for cellular-based passive Internet-of-Things (IoT) networks with multiple tags and cellular users. Extensive numerical results and simulations are provided to validate the effectiveness of our approach. Notably, at least 10 dBm and 12 dBm power savings compared to the prior art are achieved when estimating the direct and cascaded channels. These findings underscore the practical benefits and superiority of our proposed technique.

Introduction: COVID-19 is a highly contagious respiratory disease and many factors can affect its severity and mortality. This study aimed to investigate the risk factors associated with the severity and mortality of COVID-19. Methods: In this study, 311 cases with COVID-19 approved by the CORONA database in the Center for Disease Control and Prevention (CDC) of Shiraz University of Medical Sciences were selected using a random systematic sampling method. The study data were collected through interviews and phone calls. Results: The mean age of the participants was 45.82��17.92 years, and the male to female ratio was 1.57:1. In addition, the Case Fatality Rate (CFR) was 4.50%, and the disease was severe in 47.5% of the cases. The most common clinical symptoms were cough (39.22%), fever (31.83%), and dyspnea (24.76%). The severity of COVID-19 was significantly associated with age and history of Cardiovascular Disease (CVD). Besides, the mortality of COVID-19 was significantly related to age, gender, hypertension, CVD, and Chronic Kidney Disease (CKD). The mean interval between the onset of the first symptom and referral to health center was 3.02��2.82 days. Additionally, the mean interval between the referral to health centers and testing was 0.88��2.20 days. Conclusion: Older patients and those who had CVD needed more careful healthcare and early intervention to prevent the exacerbation of COVID-19. Furthermore, the interval between the onset of the first symptom and testing was relatively long. Overall, early diagnosis, isolation, and treatment of patients were found to be essential to control COVID-19., Journal of Preventive Medicine and Hygiene, Vol. 62 No. 4 (2021): 2021624

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead.

OBJECTIVES: This study discussed about challenges and opportunities of institutional accreditation in Iranian medical universities. The lesson learned of the first round of the accreditation would direct initiatives and solutions for future accreditation. METHODOLOGY: This research is a qualitative content analysis research that studied the experience of the survey visit teams. Semistructured interviews with a purposive sampling approach were done to reach data saturation. Constant comparative method was used to analyze the data. RESULTS: The emergent themes were incompatible scheduling with accreditation workload, accreditation sustainability, the weakness of the survey instrument, advantage of the survey instrument, the quality of evaluators' survey, the opportunity of progress for universities, accreditation perspective, defendable cost opportunity, perceived injustice, and the leading strategies. In this study, the relationship between categories and themes was shown with a systematic approach. CONCLUSION: In medical education systems, it is necessary to continue the research and development studies using the findings in each accreditation period to achieve the prospect of international accreditation.

Panel presentation at the Spring 2019 MARAC Conference in Morgantown, WV., In this panel presentation, Fatemeh Rezaei, Archivist; Kristin Conlin, Reference and Instruction Librarian; and Laura Bell, Archivist at the University of Baltimore discuss how to convey the importance of a digital preservation program in a data-supported framework that ensures continuous access to digital assets to library administration. An initial assessment using the Digital Preservation Capability Maturity Model (DPCMM) revealed that our library's storage and retention of records were based on individual department standards which were not uniform or implemented with archival principles for storage and retention in mind. In this panel discussion we share our experience using the DPCMM in the Robert L. Bogomolny library at the University of Baltimore, as well as the impact of the results and the departmental conversations that occurred surrounding digital preservation in our library.

Supplemental material, HKJ760123_online_supplement for Hospitals preparedness using WHO guideline: A systematic review and meta-analysis by Fatemeh Rezaei, Mohammad Reza Maracy, Mohammad H Yarmohammadian and Hojat Sheikhbardsiri in Hong Kong Journal of Emergency Medicine

This paper aims at formulating definitions of topological stability, structural stability, and expansiveness property for an iterated function system( abbrev, IFS). It is going to show that the shadowing property is necessary condition for structural stability in IFSs. Then, it proves the previous converse demonstration with the addition of expansiveness property for IFSs. It asserts that structural stability implies shadowing property in IFSs and presents an example to reject of the converse assertion., Comment: 18 pages