Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of knowledge and precautionary practices about nosocomial infection among fourth graded hospital workers in Bangladesh: A mathematical and statistical approach

Abstract The goal of this study was to evaluate the determinants associated with knowledge level and precautionary practices concerning nosocomial infection among the fourth graded hospital workers in Bangladesh. A cross-sectional study based on 343 respondents selected through a convenience sampling method from two hospitals in the Dhaka city was carried out to meet the objective of the study. The outcomes of the chi-square test of association showed that education, types of organization, years of experience and participation in workshop or training on the infection control program had a highly significant association (p-value=0.000) with knowledge level while education, types of organization and participation in workshop or training on the infection control program had a highly significant association (p-value=0.000) with practice level. Besides, employment status had a significant association (p-value=0.033) with practice level as well. Almost analogous outcomes were observed in the binary logistic regression results for knowledge level and multinomial logistic regression results for practice level. The findings of the study also highlight that the fourth graded hospital workers in private hospitals had higher knowledge and performed better precautionary practices than their public hospital counterparts. This translates into the conclusion of paying more attention to the public hospital workers by giving them the opportunity of making themselves trained by engaging them with several workshops or training related to “Hospital Acquired Infection Control”.
MATERNAL SATISFACTION TOWARDS SPINAL ANAESTHESIA FOR CAESAREAN SECTION

ABSTRACT Background There has been an increase in the use of spinal anaesthesia for Caesarean section (CS) in Nigeria in the past decades. There is, therefore, a need to evaluate the level of satisfaction among patients that had spinal anaesthesia, as an index of the quality of reproductive health care. Methodology A prospective observational study of 380 consenting parturients in ASA classes I, II, and III, who underwent CS under spinal anaesthesia between January and December 2019. Satisfaction score was assessed using a three-point Likert scale. Data were analysed using [IBM SPSS software version-22]. The level of statistical significance was set at p-value <0.05. Results A total of 380 consenting patients underwent spinal anaesthesia within the study period with 219 (57.6%) aged ≥30 years. Most were emergency CS 267 (70.3%). Most patients 294 (77.4%) expressed satisfaction for being involved in decision-making about the choice of anaesthesia technique. The majority of the patients 333 (87.6%) were satisfied with pain relief during the operation. The complications experienced by the patients intraoperatively included dizziness, 50 (13.2%), shivering, 139 (36.6%), and intraoperative nausea and vomiting, 48 (12.6%). Overall, 295 (77.6%) of the patients expressed willingness to have spinal anaesthesia again in the future, out of the 295, 293 (99.4%) were satisfied with the current spinal anaesthesia. P = 0.000 Conclusion Maternal satisfaction to spinal anaesthesia in this study was high. This could be attributed to patient’s participation in decision-making, prompt treatment of complications, and overall good anaesthetic care.
Energy management strategy for FCEV considering degradation of fuel cell

ABSTRACT The energy management strategy (EMS) is a key technology to improve the economy and durability of fuel cells. To overcome the shortcomings of most existing strategies, a novel EMS based on game theory is proposed. The power flow modes are mainly divided into four modes, and the power flow distribution problem is modeled as a noncooperative game problem. Different preferences of the power sources are quantified through their individual utility functions. The Nash equilibrium is used to balance the different preferences of the independent power sources. The proposed EMS contributes to better performance in three areas: energy economy, maintaining ultracapacitor state of charge (SOC), and durability of fuel cell. According to the results, compared with the fuzzy control strategy and the power follow strategy, the equivalent hydrogen consumption of the proposed strategy is reduced by 7% and 4.57%, respectively, and the durability of the fuel cell is improved by 125% and 21.8% under the WLTP driving cycle.
Bi-differential calculi, bi-Hamiltonian systems and conformal Killing tensors

The theory of Dimakis and Müller-Hoissen (Dimakis A and Müller-Hoissen F 2000 J. Phys. A: Math. Gen. 33 957-74) concerning bi-differential calculi and completely integrable systems is related to bi-Hamiltonian systems of the Poisson-Nijenhuis type. In the special case where the ambient manifold is a cotangent bundle one is able to recover and elucidate the theory of Ibort et al (Ibort A, Magri F and Marmo G 2000 J. Geom. Phys. 33 210-23), which is in turn a reworking in the bi-Hamitonian context of Benenti's theory of Hamilton-Jacobi separable systems. In particular, it is shown that Benenti's conformal Killing tensor, which is central to his theory, has an even more special form than has hitherto been realized and that when it is converted into a field of endomorphisms by raising an index with the ambient metric, it necessarily has vanishing Nijenhuis torsion.
The FBProphet forecasting model to evaluate the spread of COVID-19 pandemic: A machine learning approach

Abstract The pandemic due to the COVID-19 virus causes financial disruption almost in all the countries in the world. It breaks the supply chain management system, especially in the food, energy and industrial sectors. This COVID-19 pandemic hits the world over different periods of time in a year and continues from the year 2020. It is important to predict the severity of the infection rate and death rate to prepare and take necessary actions for the future. But it is still difficult to forecast the outbreaks for the long term with higher accuracy. Here, a machine learning (ML) algorithm, FBProphet, is employed to forecast COVID-19 outbreaks in Bangladesh as a time series forecasting. This model predicts the daily and cumulative infection and death rates with a high and low error rate. Due to the seasonality feature of the FBProphet model, it can predict the different waves of outbreaks with higher accuracy. Furthermore, cross-validation of the predicted results has been performed to ensure the accuracy of the results. The effort and the outcomes of this computational study will help the decision makers of this country to take necessary actions for the future which can save lives and prevent economic disruption.
Formation of bright solitary matter-waves

This thesis presents the development of an experimental apparatus to produce Bose-Einstein condensates (BECs) with tunable interparticle interactions. The ability to precisely control the strength of these interactions, and even to switch them from repulsive to attractive, allows one to probe novel regimes of condensate physics, from the collapse of attractively interacting BECs and the formation of solitary matter-waves to the observation of beyond mean-field effects in strongly repulsive condensates. The construction and characterisation of both a single and crossed beam optical dipole trap is presented. In the single beam case we develop a technique allowing the guided transport of atoms along the beam and up to a room-temperature surface; a technique which can be used to evaporatively cool the trapped atomic cloud. We produce Bose-Einstein condensates of 87Rb in the F=1, mF=-1 state in this trap, comparing the effect of beam waist on the evaporation trajectory. In the crossed beam trap Bose-Einstein condensation of 87Rb is realised in three distinct trapping configurations, along with a 1D optical lattice formed by changing the polarisation of the beams. A method of direct cooling of 85Rb atoms in the crossed trap is developed using a magnetic Feshbach resonance to precisely tune both the elastic and inelastic scattering properties of the atoms. The resonance used for this work occurs at 155G in collisions between atoms in the F=2, mF=-2 state of 85Rb. Bose-Einstein condensates of up to 40,000 85Rb atoms are formed in this trap and we demonstrate the presence of tunable interatomic interactions, exploring the collapse phenomenon associated with attractive condensates. By loading the 85Rb condensate into a quasi-1D waveguide we show that stable attractive condensates can be created, taking the form of bright solitary matter-waves. We observe a solitary wave of ~2,000 atoms which propagates, without dispersion, along the waveguide over a distance of ~1.1mm. The particle-like nature of the solitary wave is demonstrated by classical reflection of the wavepacket from a repulsive Gaussian barrier.
Detecting crack orientation using patch antenna sensors

Crack orientation is a very important parameter for structural integrity analysis. Even though many sensors have been developed to detect crack presence and length, very few sensors can detect crack orientation. Recently, we have demonstrated a patch antenna sensor that can detect crack propagation with sub-millimeter resolution. In this paper, the capability of the antenna sensors for crack orientation detection is studied. An antenna sensor with a rectangular antenna patch radiates at two fundamental modes. The effect of a ground-plane crack on these two resonant frequencies depends on its orientation. Hence, the crack orientations can be monitored by analyzing the crack-induced shifts of both antenna resonant frequencies. The principle of operation will be discussed first, followed by detailed descriptions on the numerical simulation, sensor fabrication, experimental procedure, results and analysis. Simulation as well as experimental results confirmed that the antenna sensor is capable of providing quantified information about the crack orientation.
The Einstein objective grating spectrometer survey of galactic binary X-ray sources

The results of observations of 22 bright Galactic X-ray point sources are presented, and the most reliable measurements to date of X-ray column densities to these sources are derived. The results are consistent with the idea that some of the objects have a component of column density intrinsic to the source in addition to an interstellar component. The K-edge absorption due to oxygen is clearly detected in 10 of the sources and the Fe L and Ne K edges are detected in a few. The spectra probably reflect emission originating in a collisionally excited region combined with emission from a photoionized region excited directly by the central source. 64 refs.
Mid-infrared integrated optics: versatile hot embossing of mid-infrared glasses for on-chip planar waveguides for molecular sensing

Abstract. The versatility of hot embossing for shaping photonic components on-chip for mid-infrared (IR) integrated optics, using a hard mold, is demonstrated. Hot embossing via fiber-on-glass (FOG), thermally evaporated films, and radio frequency (RF)-sputtered films on glass are described. Mixed approaches of combined plasma etching and hot embossing increase the versatility still further for engineering optical circuits on a single platform. Application of these methodologies for fabricating molecular-sensing devices on-chip is discussed with a view to biomedical sensing. Future prospects for using photonic integration for the new field of mid-IR molecular sensing are appraised. Also, common methods of measuring waveguide optical loss are critically compared, regarding their susceptibility to artifacts which tend artificially to depress, or enhance, the waveguide optical loss.
A numerical study of chorus generation and the related variation of wave intensity using the DAWN code

Chorus waves play an important role in energetic electron dynamics in the inner magnetosphere. In this work, we present a new hybrid code, DAWN, to simulate the generation of chorus waves. The DAWN code is unique in that it models cold electrons using linearized fluid equations and hot electrons using particle‐in‐cell techniques. The simplified fluid equations can be solved with robust and simple algorithms. We demonstrate that discrete chorus elements can be generated using the code. Waveforms of the generated element show amplitude modulation or “subpackets,” and the frequency sweep rate of the generated element is found to be consistent with that of observed chorus waves. Using the DAWN code, we then investigate the variation of wave intensity ( ∝Bw2 ) with respect to linear growth rates on the equatorial plane. Previous observations showed that the change in linear growth rates of whistler waves modulated by external processes such as density modulations is usually small ( O(10−1) ), while the variation of the wave intensity is large ( O(101−102) ). Using a chosen set of background plasma parameters, we demonstrate that a small change ( O(10−1) ) in linear growth rates can lead to significant variation ( O(101) ) of wave intensity only in the transition from the broadband whistler wave generation regime to the chorus wave generation regime. Our results demonstrate the importance of including nonlinear dynamics of chorus generation in understanding the whistler wave intensity modulation process in the inner magnetosphere.
A hybrid numerical asymptotic method for scattering problems

We develop a hybrid numerical asymptotic method for the Helmholtz equation. The method is a Galerkin finite element method in which the space of trial solutions is spanned by asymptotically derived basis functions. The basis functions are very “efficient” in representing the solution because each is the product of a smooth amplitude and an oscillatory phase factor, like the asymptotic solution. The phase is determined a priori by solving the eiconal equation using the ray method, while the smooth amplitude is represented by piecewise polynomials. The number of unknowns necessary to achieve a given accuracy with this new basis is dramatically smaller than the number necessary with a standard method, and it is virtually independent of the wavenumber k. We apply the method to the problems of scattering from a parabola and from a circle and compare the results with those of a standard finite element method.
The Naïve Bayes Algorithm for the Stride Length Classification

Stride length is one of the elements that will be examined for stroke patients' walking training. The stride length reveals the distance in stepping between the right and left feet or vice versa which can be used to track the development of a patient's training. This study proposes to use an inertial measurement unit (IMU) sensor to classify the step imbalance of a stroke patient. To monitor the position of the patient's leg, an IMU sensor is connected in the user thigh. The data from the IMU sensor is collected at a sample frequency of 100 Hz. The signal from the IMU sensor is then refined from the noise and exported to acquire the signal feature. In the investigation, the signal characteristic containing the positive and negative peaks of the accelerometer and gyroscope is employed. These four features are utilized as inputs for the Naïve Bayes model. The accuracy of the Nave Bayes model is 90.35 percent. The accuracy, recall, and F-measure of the Nave Bayes model are 0.906, 0.904, and 0.902, respectively. As a result, the Naïve Bayes method may be utilized to categorize the stride length for walking training.
Motor Imagery Brain Activity Recognition through Data Augmentation using DC-GANs and Mu-Sigma

The brain-computer interface is a technology that allows a machine to connect with the human brain and work based on the commands released by thoughts and activities of the brain. Electrodes are placed on the scalp and the changes in electric waves released by the brain are recorded as Electroencephalography (EEG) signals. In this work, we propose the use of generative adversarial networks and musigma methods to augment the EEG signals. Some of the existing deep learning methods such as convolutional neural network and recurrent neural network for classification of the EEG signals are implemented and their classification performance is examined with and without data augmentation. It is shown that the use of data augmentation can improve the performance of the EEG signal classification with deep learning models to a considerable extend.
A Survey of Segmentation Techniques for Medical Images

In the domain of image processing, Image Segmentation plays an essential role. Digital images are divided into segments, which are composed of sets of pixels, using a segmentation algorithm in order to study significant data and information from the digital images. Segmentation makes information retrieval easier from the region of interest. It assists in conversion of the digital images into more meaningful set of information that is relatively easy to examine. In recent years, segmentation algorithms have steadily gained popularity in medical field as well as it has witnessed successful applications of Neutrosophy. This paper outlines a comparative study and a comprehensive literature review of existing and novel segmentation methods proposed recently. The literature reviews have revealed a strong preference for hybrid models and deep learning techniques as diagnostic and treatment tools.
Analyzing Relative Humidity and BER Relation in Terahertz VANET Using BCH Coding

Data traffic is increasing dramatically as a result of the quick development of electronic gadgets and different new applications. To fulfill the demand of high data rate, higher frequency bands are being examined for communications. Future communication will be led by the Terahertz (THz). The THz waves are susceptible to propagation and atmospheric absorption characteristics. Achieving ultra-reliability to support critical safety applications in Vehicular Ad-hoc Network (VANET) is challenging. So new coding schemes or modifying conventional coding techniques need to be introduced. This article aims to provide a channel model for Terahertz (THz) VANET communication in which the performance of Bose-Chaudhuri-Hocquenghem (BCH) coding is investigated and the relation between Relative Humidity (RH) and BER is analyzed. The closed-form equations BCH coding and Alamouti Space-Time Block Coding (BCH-ASTBC) BER derived in our previous study are used for analysis. Our results interpret that there is a strong dependence of BER on RH. The results are analyzed at different code rates. Moreover, we have compared our results with the performance of orthodox ASTBC expression in THz channel. The results depict that the proposed model surpasses the behavior of the ASTBC equation in THz. This research provides the guidelines for estimating the BER in error control coding and a road map to design various VANET architectures in THz channel.
Analysis of Effectiveness of Iptables on Web Server from Slowloris Attack

In the application of network technology, a Web Server is needed that can run well, by providing data services that have the function to accept HTTP or HTTPS requests by maintaining the security of the Web Server from various disturbances or attacks. Along with the development of information technology, it can cause types of crime in technology, especially on web servers. One type of crime that can occur is the Slow loris Distribute Denial of Service (DDoS) attack, which type of attack is focused on disrupting service availability. To overcome these types of attacks in a simple and fast way, it can be applied using the firewall installation method with IP tables filtering tools by conducting research on testing and comparative analysis from before and after the implementation of the IP tables method against Slow loris attacks. Finally, this paper discusses about future solution of DDoS attack and recommendation for secure environment, using IP tables configuration is very effective for secure data, because a simple configuration of IP tables can also limit and block IP directly or completely to attackers who can overcome the attack.
Accuracy Guarantees for ℓ1-Recovery

We discuss two new methods of recovery of sparse signals from noisy observation based on $\ell_1$- minimization. They are closely related to the well-known techniques such as Lasso and Dantzig Selector. However, these estimators come with efficiently verifiable guaranties of performance. By optimizing these bounds with respect to the method parameters we are able to construct the estimators which possess better statistical properties than the commonly used ones. We also show how these techniques allow to provide efficiently computable accuracy bounds for Lasso and Dantzig Selector. We link our performance estimations to the well known results of Compressive Sensing and justify our proposed approach with an oracle inequality which links the properties of the recovery algorithms and the best estimation performance when the signal support is known. We demonstrate how the estimates can be computed using the Non-Euclidean Basis Pursuit algorithm.
Clutch Control Timing Optimization for Adjacent Gearshift Process with Multiple Clutches Based on Adaptive Kriging Surrogate Model

Controlling more than two active clutches within one shift event in mass-produced automatic transmission is challenging. Determination of the control timings and the control trajectories for the actively controlled clutches are two essential issues in the gearshift process with multiple clutches. In this study, an efficient multi-objective optimization framework for clutch control timing is established. A multi-objective optimization problem is formulated to determine the optimal clutch control timings by considering the shift jerk, shift duration, and clutch friction work. To mitigate the excessive computation required by the iterative simulation of the physical model in the multi-objective optimization process, we construct a surrogate model to substitute for physical model based on Kriging model and an adaptive sampling method. The efficient surrogate-assisted multi-objective optimization framework based on NSGA-II is proposed. A Pareto set containing various optimal solutions is obtained through the proposed optimization framework. The improved radar method is employed to determine an optimal trade-off solution in the Pareto set. The shift transitions of the optimal trade-off solution are simulated and analyzed. Results validate the effectiveness of the proposed optimization framework in improving shift performance and optimization efficiency.