Search

Laser induced breakdown spectroscopy technique is employed for quantitative analysis of aluminum samples by different classical machine learning approaches. A Q-switch Nd:YAG laser at fundamental harmonic of 1064 nm is utilized for creation of LIBS plasma for prediction of constituent concentrations of the aluminum standard alloys. In current research, concentration prediction is performed by linear approaches of support vector regression, multiple linear regression, principal component analysis integrated with MLR and SVR, and as well as nonlinear algorithms of artificial neural network, kernelized support vector regression , and the integration of traditional principal component analysis with KSVR, and ANN. Furthermore, dimension reduction is applied on various methodologies by PCA algorithm for improving the quantitative analysis. The results presented that the combination of PCA with KSVR algorithm model had the best efficiency in predictions of the most of elements among other classical machine learning algorithms.

In the last decades, to improve the CAP treatment efficiency, its biological effects in combination with other physical modalities have widely investigated. However, the physical insight into most of supposed synergistic effects remained elusive. In this regard, the synergetic effect of cold plasma and magnetic field has been used for different applications, especially due to considerable synergistic in biological media reactivity. In the present paper, using a 420 mT N42 magnet, the effect of the perpendicular external static magnetic field (SMF) on the cold atmospheric pressure plasma (CAP) characteristics, such as electron temperature and density, are investigated based on the optical emission spectroscopy, utilizing the Boltzmann plot method, Saha-Boltzmann equation and Specair software simulation. Results showed that the rotational and electronic excitational temperature experienced 100 K and 550 K increases in the presence of SMF, respectively. While the vibrational and translational temperatures remained constant. Moreover, electron temperature estimated as 1.04 eV in the absence of SMF and increased up to 1.24 eV in the presence of SMF. In addition, the Saha-Boltzmann equation illustrated that the electron density increased in presence of the additional SMF. The results of the present study indicated that the magnetic field could be an assistant to the cold plasma effect, beneficial in medical applications due to modifications in plasma temperature and electron density.

Optimal beamforming designs under imperfect successive interference cancellation (SIC) decoding for a symbiotic network of non-orthogonal multiple access (NOMA) primary users and a secondary ambient tag have been lacking. We address that issue here. The primary base station (BS) serves NOMA users and a passive tag simultaneously in this network. We develop two transmit beamforming designs to meet the user and tag requirements while mitigating the effect of imperfect SIC. Specifically, we design optimal BS transmit beamforming and power allocation to either maximize the weighted sum rate of NOMA users and the tag or minimize the BS transmit power under the minimum rate requirements while satisfying the tag minimum energy requirement. Because both these problems are non-convex, we propose algorithms using alternative optimization, fractional programming, and semi-definite relaxation techniques. We also analyze their computational complexity. Finally, we present extensive numerical results to validate the proposed schemes and to show significant performance gains while keeping the tag design intact. For example, the proposed digital beamforming increases the harvested power and data rate by 2.16e3 % and 314.5 % compared to random beamforming., This paper was submitted for publication in IEEE on Feb. 4, 2023

Sustainability is a multidisciplinary developing science, and sustainable urban development focuses on socioeconomic, cultural, and environmental dimensions. Sustainable urban development is considered as a form of development covering urban spaces. Therefore, sustainability is regarded as one of the most important issues in urban planning. The present study aims to evaluate urban sustainability in nine regions of Tehran’s District 4 using the barometer of sustainability. The study area, covering twenty populous areas with various socioeconomic, cultural, and environmental problems, is located in the northeast of Tehran. The results obtained from overall sustainability and Prescott-Allen ranking indicate that regions 7, 6, 2, 3, and 8 are in medium sustainability level with the scores of 0.492, 0.484, 0.471, 0.411, and 0.457 respectively. However, other regions including regions 9, 1, 4 and 5 with the scores of 0.370, 0.330, 0.281 and 0.274, respectively, were found to be potentially unsustainable. In terms of human and ecosystem well-being, regions 2 and 3 gained the highest score, and regions 9 and 5 had the lowest scores. Based on the results, some practical solutions were provided to improve the sustainability in the area.

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.

This study aimed to characterize the surface quality of beech wood (Fagus sylvatica L.) cut by a CO2-laser. Boards were conditioned to a low (about 8% moisture content), 12% and a high, (about 18% moisture content). Laser cutting was performed at varying processing parameters, i.e. cutting speed, gas pressure and focal-point position. A confocal microscope was used to determine the average surface roughness perpendicular to the grain. The anatomical structures of the laser-cut surfaces were examined with scanning electron microscope. The result showed that smoother surfaces were obtained at the low moisture content when processed at a gas pressure of 21 bar. Focal-point positioning did only have an effect on the surface roughness at 12% moisture content whereas the value was substantially lower for focal-point positioned on the surface. The surfaces cut at 18% moisture content, and at a cutting speed of 3.5 m/min generated a rougher surface than cut at low moisture content and at a lower speed. Laser cutting produced a rougher surface as compared to sawn surface (circular saw). The structural integrity of the laser-cut surface was more intact when the wood was having high moisture content and processed at a high cutting speed., Wood Material Science & Engineering, 17 (6), ISSN:1748-0272, ISSN:1748-0280

The Digital Lifecycle Management Interest Group (DLMIG) of the University System of Maryland and Associated Institutions (USMAI) formed in 2020 and provides a space for multi-institutional collaboration. The group is working toward a USMAI-wide digital lifecycle management strategy that will provide sustainable and scalable digital preservation and practices at consortium institutions of all sizes. This poster will share the group’s experiences and challenges thus far as a model for other consortia and collaborations.

The high prevalence of concomitant chronic illnesses and the resulting higher number of medications in the elderly population increase the risk of adverse drug reactions due to drug-drug interactions (DDIs) and potentially inappropriate medications (PIMs). Therefore, the aim of this study was to investigate the prevalence and factors associated with DDIs and PIMs in outpatient geriatrics. In this cross-sectional study, 1512 prescriptions belonging to patients aged ≥65 years from five public pharmacies in Tehran were evaluated. Clinically relevant (C, D, and X) and significant DDIs (D and X) were documented according to the Lexicomp®. Additionally, Zhan criteria were used to detect PIMs. At least one clinically relevant DDI was detected in 61.7% of the prescriptions containing ≥2 medications. The largest percentage of prescriptions with DDIs was prescribed by cardiologists (74.3%). The number of medications in prescriptions and the specialty of the prescriber significantly affected both clinically relevant and significant DDIs in a logistic regression model. At least one PIM was identified in 16.3% of the prescriptions. General practitioners (GPs) were the largest prescribers of PIMs. The mean number of medications was significantly higher in prescriptions with PIMs. In conclusion, clinically relevant DDIs are frequent in the elderly. In terms of PIMs, more attention should be paid to the education of GPs.

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

In this paper, for iterated function systems, we define the classic concept of the dynamical systems: topological conjugacy of diffeomorphisms. We generalize the Hartman-Grobman theorem for one dimensional iterated function systems on R. Also, we introduce the basic concept of structural stability for an iterated function system and so we investigate the necessary condition for structural stability of an iterated function system on R., 18 pages

INTRODUCTION: The occurrence of natural disasters such as floods, earthquakes, and storms often has devastating effects on human societies and causes heavy casualties on their inhabitants. By destroying the buildings and infrastructure of society, these events impose a wide range of economic and social burdens on societies and countries. Research in the management of incidental accidents improves the ability to respond correctly in the event of accidents. Therefore, this study was conducted to evaluate the readiness of hospitals in unexpected events in 2017. METHODS: This cross-sectional study was conducted in 2017 on 10 selected hospitals affiliated to Isfahan University of Medical Sciences. Data collection with the standard checklist included 137 yes/no questions in 10 areas of emergency, admission, transmission, traffic, communication, security, human resources, management, education, and support. At the end of the study, the data were encoded and entered into the SPSS 22 software and were examined. RESULTS: The results of this study showed that the readiness of hospitals in each of the areas studied was as follows: emergency (79% = good), admission (72.5% = good), transmission (70.4% = good), traffic (58% = moderate), communication (79.6% = Good), security (59.7% = average), human resources (83.6% = very good), management (58% = moderate), education (81% = very good), support (73% = good), and command and management (81.8% = very good). CONCLUSION: Since hospital conditions are completely changed during a crisis, hospital adaptation to new conditions should be based on the projected plans. As a result, everyone should be prepared to take responsibility according to their responsibilities.

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

A 46-year-old male patient referred to Department of Oral Medicine, with the primary chief complaint of a painless swelling in the right side of mandibular. A panoramic radiograph revealed a well-defined, multilocular radiolucent bony lesion with thin and straight septa in the right side of mandible extending from distal of canine to mesial of third molar. Histological examination showed a solid proliferation of atypical plasmacytoid cells, which was indicative of plasmacytoma. A systemic workup for the final diagnosis was performed to rule out multiple myeloma.

Objectives: Identifying the underlying factors contributing to smoking among teenagers is important in establishing smoking control programs. The present study was designed to identify and compare factors revealed in a preceding qualitative study conducted on 13-15 year-old boys living in two different socio-economic districts in the Northern and Southern parts of Tehran. Methods: Two completely similar case-control studies, each with 200 subjects, were conducted using a snowball sampling. The case and control subjects were matched based on the intimacy relations, i.e. six smokers were recruited in each of the Northern and Southern districts; they were then asked to introduce one of their smoker friends as a case and a non-smoker one as a control. Results: Multi-variable conditional logistic regression revealed that having a smoker father is the single effective factor in the two districts. As for boys living in the Northern (wealthier) part of the city, social capital (OR: 0.59, 95% CI: 0.43-0.81) played a protective role against smoking, whereas quitting home after a quarrel (OR: 15.07, 95% CI: 1.54-147.25), monthly allowance (OR: 2.22, 95% CI: 1.29-3.82) and hyperactivity (OR: 1.69, 95% CI: 2.64-240.45) were associated with a higher risk of becoming a smoker. Conclusions: The studied variables can be classified as personal, familial, and school-level factors. Familial intervention is effective for all the factors which revealed to be influential on the risk of becoming a smoker. It could be concluded that interventions on the family level should be used to prevent the cigarette use in Tehran.

Purpose Methods and Materials Results Conclusion References Personal Information, Purpose: One of the best ways of stopping the breast cancer is diagnosis in primary stages of its creation. According to global statistics, one of every 8 women will suffer the breast cancer, and one of every 35 patients who are suffering this disease will...