Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstruction of transient three-dimensional density distributions using digital speckle tomography

A form of digital speckle tomography has been proposed for calculating transient three-dimensional density distributions. Multiple CCD images have captured movements of speckles due to the variation in density among three angles of view simultaneously and instantaneously so that asymmetrical and transient flows can be measured. Initially, the variations in density of one-nozzle and two-nozzle test sections have been investigated by the use of a cross-correlation method involving the speckle displacements. The results have been compared with measurements made by Mach-Zehnder interferometry to confirm the accuracy. Then, the speckle movements for downward flow of carbon dioxide from a circular small opening with a vertical partition (CO2-air exchange flow) have been obtained by the cross-correlation tracking method so that those distances can be transformed into deflection angles of laser rays for density gradients. The transient three-dimensional density fields have been reconstructed from the deflection angles by tomography.
Academic Governance: Insights and the Way Forward

Academic Governance is a concept of managerial, social, and legal significance. This paper tries to understand the reasons behind the selected malpractices in either the managerial/academic areas or functions of educational institutions. It would help to understand the regulatory mechanisms in academia and their roles in the process and execution of the teaching and learning process. It would help the educationists and activists to come up with areas needing revision and reformation in the academic world. It tries to find the causes/factors leading to certain types of governance issues in academia based on three theories such as governance without government, the new public management/governance, and the network governance theories. It tries to bring out the recent insights as reported articles on the subject from open sources and scholarly publications. Content Analysis using PRISMA Protocol is the research design used in writing the results and discussions. Most of the failure reasons/causes are due to the failures of privatization, and network governance failures. Thus this paper would serve the consolidation of where and why such lapses happen and how to curb them.
PERFORMANCE MODEL AND ANALYSIS OF BLOOD FLOW IN SMALL VESSELS WITH MAGNETIC EFFECTS

In this paper consider a two-fluid model consisting of a core region of suspension of all the erythrocytes (particles) in plasma (fluid) assumed to be a particle-fluid mixture and a peripheral layer of cell-free plasma (Newtonian fluid), has been proposed to represent blood flow in small diameter tubes with effects of magnetic. The analytical results obtained in the proposed model for effective viscosity, velocity profiles and flow rate have been evaluated numerically for various values of the parameters. Quantitative comparison has shown that present model suitability represents blood flow at hematocrit (≤ 40%) and in vessels up to 70μm in diameter. Using experimental values of the parameters, the flow rate for normal and diseased blood has been computed and compared with corresponding values obtained from a well known experimentally tested model in the literature.
A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques

Coronavirus disease, frequently referred to as COVID-19, is a contagious and transmittable disease produced by the SARS-CoV-2 virus. The only solution to tackle this virus and reduce its spread is early diagnosis. Pathogenic laboratory tests such as the polymerase chain reaction (PCR) process take a long time. Also, they regularly produce incorrect results. However, they are still considered the critical standard for detecting the virus. Hence, there is a solid need to evolve computer-assisted diagnosis systems capable of providing quick and low-cost testing in areas where traditional testing procedures are not feasible. This study focuses on COVID-19 detection using X-ray images. The prime objective is to introduce a computer-assisted diagnosis (CAD) system to differentiate COVID-19 from healthy and pneumonia cases using X-ray image sequences. This work utilizes standard transfer-learning techniques for COVID-19 detection. It proposes the master–slave architecture using the most state-of-the-art Densenet201 and Squeezenet1_0 techniques for classifying the COVID-19 virus in chest X-ray image sequences. This paper compares the proposed models with other standard transfer-learning approaches for COVID-19. The performance metrics demonstrate that the proposed approach outperforms standard transfer-learning approaches. This research also fine-tunes hyperparameters and predicts the optimized learning rate to achieve the highest accuracy in the model. After fine-tuning the learning rate, the DenseNet201 model retrieves an accuracy of 83.33%, while the fastest model is SqueezeNet1_0, which retrieves an accuracy of 80%.
Mixed NMF Blind Source Separation Algorithm

The rank one NMF blind source separation algorithm (NMF-R1) was obtained by imposing the sparsity constraint on the fast NMF algorithm based rank one. NMF blind source separation algorithm based on least squares (NMF-LS) was obtained by using pseudo-inverse matrix. NMF-R1 algorithm was superior to the existing blind source separation algorithms based on NMF. NMF-LS algorithm had faster computation speed, but the result of decomposition was not unique. In order to further enhance the signals separated performance, crossover iteration between NMF-R1 and NMF-LS was used to getting the mixing matrix and the signal matrix, and the mixed NMF blind source separation algorithm (NMF-LR) was obtained. Simulation results show that the separation performance of NMF-LR algorithm is better than that of NMF-R1 and NMF-LS, and the computation complexity of NMF-LR algorithm is nearly the same as NMF-R1 algorithm.
On Formation and Intensification of Tropical Cyclones Through Latent Heat Release by Cumulus Convection

Abstract The effect on large scale motions of latent heat release by deep cumulus convection in a conditionally unstable atmosphere is investigated and a method devised to include this effect directly in the equations for large scale flow. This method is then applied to the hurricane formation problem by incorporating it into time-dependent, circular symmetric dynamic hurricane models, either in gradient-wind balance or unbalanced. Numerical integrations of a two-level approximation of the balanced model have been carried out for two different formulations of the problem (including or not including a frictional radial flow), both starting from a hypothetical initial state characterized by a weak barotropic circular vortex with a maximum tangential velocity of 10 m sec−1 at a distance of 141.2 km from the center. The results obtained without frictional radial flow showed slow intensification of the tangential flow, to about 25 m sec−1, and establishment of a strong radial temperature gradient in the upper .
Meaning Making as a Lifebuoy in Dementia Caregiving: Predicting Depression from a Generation Perspective Using a Fuzzy-Set Qualitative Comparative Analysis

Depressive symptomatology is associated with caregiver burden and poor health outcomes among dementia caregivers. Scholars called for a paradigm shift to focus on positive aspects of caregiving, in particular, meaning making during the caregiving journey. This study draws on the meaning making model and a generation perspective to predict depression among dementia caregivers from two generations, including Baby Boomers who were born between 1946 and 1964 and Generation X who were born between 1965 and 1980, using a configuration approach. Data was collected in a two-wave longitudinal design, from December 2019 to March 2021 in Hong Kong. A fuzzy-set qualitative comparative analysis resulted in six configurations with an overall solution consistency and overall solution coverage of 0.867 and 0.488, respectively. These configurations consist of a different combination of conditions that predict high depressive symptomatology among dementia caregivers in two generations. Specifically, generation is related to five out of six configurations. This study is the first to predict depression among dementia caregivers using a meaning making model from a generation perspective. It advances the understanding of factors contributing to high depressive symptomatology among dementia caregivers from two generations, thus contributing to the future development of generation-responsive assessments, interventions, and policies.
Rate coefficients for charge transfer between hydrogen and the first 30 elements

We present analytic fits to charge exchange rate coefficients over the full range of temperatures which occurs in photoionized or shock-heated plasmas. We consider reactions between neutral hydrogen and all elements with parent ion charge {ital q}=1{minus}4 up to {ital Z}=30. Many rates were obtained from various sources in the literature. For reactions for which no data were available, we calculated rates using the Landau-Zener formalism. For these new reactions, we tabulate both total and state-specific rate coefficients. All are fitted with a consistent, accurate formula. These fits may be incorporated easily into spectral synthesis codes, and we make available an electronic form of our results. We draw attention to the most important reactions without high-quality rate coefficients to encourage further work. {copyright} {ital 1996 The American Astronomical Society. }
Robust Estimation in Heteroscedastic Linear Models.

Abstract : We consider a heteroscedastic linear model in which the variances are a parametric function of the mean responses and a parameter theta. We propose robust estimates for the regression parameter beta and show that, as long as a reasonable starting estimate of theta is available, our estimates of beta are asymptotically equivalent to the natural estimate obtained with known variances. A particular method for estimating theta is proposed and shown by Monte-Carlo to work quite well, especially in power and exponential models for the variances. We also briefly discuss a 'feedback' estimate of beta. (Author)
Vacuum states for compressible flow

In this paper we study the evolutions of the interfaces between gases and the vacuum for both inviscid and viscous one dimensional isentropic gas motions. The local (in time) existence of solutions for both inviscid and viscous models with initial data containing vacuum states is proved and some singular properties on the free surfaces separating the gas and the vacuum are obtained. It is found that the Euler equations are better behaved near the vacuum than the compressible Navier-Stokes equations. The Navier-Stokes equations with viscosity depending on density are introduced, which is shown to be well-posed (at least locally) and yield the desired solutions near vacuum.
A systematic strategy for design of optimum coherent experiments applied to efficient interconversion of double‐ and single‐quantum coherences in nuclear magnetic resonance

A systematic strategy for design of optimum experiments in multidimensional coherent spectroscopy is described and applied to nuclear magnetic resonance (NMR). First an appropriate bound on the quantum dynamics is employed to determine a theoretical upper limit on coherence transfer efficiency which translates into the sensitivity of the experiment. Next it is investigated whether the theoretical limit can be achieved by a series of selective rotations. Then the final step serves to convert the selective rotations found into a nonselective pulse experiment which has a more general applicability. The example chosen for illustration is double‐ to single‐quantum coherence transfer in INADEQUATE which is one of the most popular NMR experiments. Using the design strategy presented we show how the sensitivity of INADEQUATE can be doubled.
A Time Series Model Based on Deep Learning and Integrated Indicator Selection Method for Forecasting Stock Prices and Evaluating Trading Profits

A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders.
Investigation on Deformation Behavior of the Crossing Section of Two Municipal Road Tunnels during Construction

With the development of urbanization, the transportation network of underground tunnels has been gradually formed and improved. It is a complicated issue for engineering construction when two municipal road tunnels intersect at one point. Based on a construction site of the crossing point of the Huayuan Road Tunnel and Luzhou Road Tunnel in Hefei City, China, the finite element analysis method is used to calculate and analyze the deformation characteristics of the crossing point of the tunnels during the asymmetric construction of connecting parts. The deformation behaviors of the crossing point of tunnels subjected to symmetrical construction are also studied for comparison. Results show that the deformations of the supporting pile and tunnel frame structures increase rapidly when they are subjected to asymmetric construction, while the lateral movement of the supporting pile and the deformation of the tunnel structure can be greatly limited when the symmetrical construction method is adopted. Some suggestions for engineering construction are put forward to ensure the safety of the structure, such as multi-stage construction and temporary supporting measure.
Pharmacy Student Perceptions of the Roles and Attributes of Pharmacist Preceptors in Australia

The pharmacist preceptor is key to the success of pharmacy student experiential placements with a recent focus being placed on the quality of preceptors. This study sought to investigate student perceptions of the ideal roles and attributes of pharmacist preceptors to inform the development of a preceptor training program. This was a mixed methods study using an online survey of pharmacy students from levels two to four of an undergraduate pharmacy honours program at a regional Australian university followed by qualitative, semi-structured focus group interviews. Surveys were analysed using descriptive statistics and content analysis while interviews were thematically analysed. Thirty-seven complete survey responses were received, and three student focus groups were conducted. Students identified the key role of preceptors in linking theory to practice and felt that the role of the preceptor in student assessment should focus on practical skills, such as communication and professionalism. There was overall strong agreement between the quantitative and qualitative findings on the ideal preceptor attributes with good communication, enthusiasm for the profession and the provision of clear and honest student feedback thought to be most important. Students regarded these attributes as essential for a good preceptor–student relationship to promote learning in the practice environment.
Research on the classification algorithm of aerial image based on the weighted mean-shift

The main problem of aerial image classification is how to classify with robust and high precision at present. Therefore, a new approach of the aerial image classification based on the weighted Mean Shift is proposed in this paper. The kernel density function's local maximum is found by the weighted Mean Shift filter of pixels in feature space using resamplying strategy, and the neighborhood data points are shifted to the local maximum of the region, in which the same course is implemented repeatedly to all the pixels in image. Then the classification image is got by fusing each cluster region.
Receding horizon tracking control for constrained linear continuous time-varying systems

A receding horizon tracking control (RHTC) scheme for input and tracking-error constrained linear continuous time-varying systems is proposed. The control scheme is based on the minimisation of a finite horizon cost function with finite terminal weighting matrices. The proposed RHTC scheme is discussed in terms of the asymptotic property, feasibility and closed-loop stability. The implementation of the proposed RHTC scheme via linear matrix inequality optimisation is also discussed. Through simulation examples it is shown that the resulting RHTC can be an appropriate tracking controller for constrained linear continuous time-varying systems.
Substrate Type and Concentration Differently Affect Colon Cancer Cells Ultrastructural Morphology, EMT Markers, and Matrix Degrading Enzymes

Aim of the study was to understand the behavior of colon cancer LoVo-R cells (doxorubicin-resistant) vs. LoVo-S (doxorubicin sensitive) in the initial steps of extracellular matrix (ECM) invasion. We investigated how the matrix substrates Matrigel and type I collagen-mimicking the basement membrane (BM) and the normal or desmoplastic lamina propria, respectively-could affect the expression of epithelial-to-mesenchymal transition (EMT) markers, matrix-degrading enzymes, and phenotypes. Gene expression with RT-qPCR, E-cadherin protein expression using Western blot, and phenotypes using scanning electron microscopy (SEM) were analyzed. The type and different concentrations of matrix substrates differently affected colon cancer cells. In LoVo-S cells, the higher concentrated collagen, mimicking the desmoplastic lamina propria, strongly induced EMT, as also confirmed by the expression of Snail, metalloproteases (MMPs)-2, -9, -14 and heparanase (HPSE), as well as mesenchymal phenotypes. Stimulation in E-cadherin expression in LoVo-S groups suggests that these cells develop a hybrid EMT phenotype. Differently, LoVo-R cells did not increase their aggressiveness: no changes in EMT markers, matrix effectors, and phenotypes were evident. The low influence of ECM components in LoVo-R cells might be related to their intrinsic aggressiveness related to chemoresistance. These results improve understanding of the critical role of tumor microenvironment in colon cancer cell invasion, driving the development of new therapeutic approaches.
Effects of Electromagnetic Inductive Attack on the Performance of a Boost DC-DC Converter

The performance of electric drives used in many applications is based on the correct integration of sensors and informatic systems, which can be affected by intentional cyberphysical attacks. In particular, voltage and current sensors are essential for the correct implementation of the control of a power converter. Usually in a sensor the measured physical quantity is transformed into an analog signal which is acquired by the ADC of a microcontroller. During this stage, some intentional electromagnetic interference can add a disturbance to the signal, resulting in the possibility of introducing a permanent fault in the drives. Usually, the attack is much more likely to introduce only a deterioration in the performance of the converter. This paper presents a study of the effect of an EMI attack on the control of a boost DC-DC converter, and points out some relations able to describe the deterioration of converter performance. By means of numerical simulations, two case studies are analyzed.
Finding the best combination of numerical schemes for 2-D SPH simulation of wedge water entry for a wide range of deadrise angles

Abstract Main aim of this paper is to find the best combination of numerical schemes for 2-D SPH simulation of wedge water entry. Diffusion term is considered as laminar, turbulent, and artificial viscosity. Density filter that seriously affects the pressure distribution is investigated by adopting no filter, first order filter, and second order filter. Validation of the results indicates that turbulent model and first order density filter can lead to more reasonable solutions. This simulation was then conducted for wedge water entry with wide range of deadrise angles including 10 degrees, 20 degrees, 30 degrees, 45 degrees, 60 degrees and 81 degrees, with extreme deadrise angles of 10 degrees, 60 degrees and 81 degrees being considered. Comparison of SPH results with BEM solutions has displayed favorable agreement. In two particular cases where experimental data are available, the SPH results are shown to be closer to the experiments than BEM solution. While, accuracy of the obtained results for moderate deadrise angles is desirable, numerical findings for very small or very large deadrise angles are also very reasonable
Potent and Selective Inhibition of CYP1A2 Enzyme by Obtusifolin and Its Chemopreventive Effects

Obtusifolin, a major anthraquinone component present in the seeds of Cassia tora, exhibits several biological activities, including the amelioration of memory impairment, prevention of breast cancer metastasis, and reduction of cartilage damage in osteoarthritis. We aimed to evaluate the inhibitory effects of obtusifolin and its analogs on CYP1A enzymes, which are responsible for activating procarcinogens, and investigate its inhibitory mechanism and chemopreventive effects. P450-selective substrates were incubated with human liver microsomes (HLMs) or recombinant CYP1A1 and CYP1A2 in the presence of obtusifolin and its four analogs. After incubation, the samples were analyzed using liquid chromatography-tandem mass spectrometry. Molecular docking simulations were performed using the crystal structure of CYP1A2 to identify the critical interactions between anthraquinones and human CYP1A2. Obtusifolin potently and selectively inhibited CYP1A2-mediated phenacetin O-deethylation (POD) with a Ki value of 0.031 µM in a competitive inhibitory manner in HLMs, whereas it exhibited negligible inhibitory effect against other P450s (IC50 > 28.6 µM). Obtusifolin also inhibited CYP1A1- and CYP1A2-mediated POD and ethoxyresorufin O-deethylation with IC50 values of <0.57 µM when using recombinant enzymes. Our molecular docking models suggested that the high CYP1A2 inhibitory activity of obtusifolin may be attributed to the combination of hydrophobic interactions and hydrogen bonding. This is the first report of selective and potent inhibitory effects of obtusifolin against CYP1A, indicating their potential chemopreventive effects.