Miracle-dz/UniFAR-FTUs huggingface dataset:
The calculation by finite differences of steady two-dimensional laminar flow in a T-junction

This thesis describes the development of a computer program to study the steady two-dimensional laminar incompressible flow in a T-junction, at which one fully-developed channel flow impinges at right angles upon another. The calculation is made by finite differences on a non-uniform mesh. Solutions are presented for varying inlet velocity ratios, and for Reynolds numbers in the range 10[2] to 10[4]. Two further flow problems are also considered briefly using the same program. In the first the side channel is transformed to a square cavity. This allows a direct comparison with the work of previous authors, and good agreement is found. In the second the side channel is transformed to an outlet. The computational problem proves unusually awkward because it is surprisingly difficult to find a method of iteration which converges with a viable expenditure of computer time, and, more importantly, because the flow pattern proves very sensitive to the numerical treatment of the immediate neighbourhood of the 270-degree corners. The convergence difficulty is substantially overcome after a great deal of experimentation. The corner-flow difficulty is treated by a novelmesh configuration. Nevertheless the separated flow proves sensitive to the mesh size at the corner. This sensitivity is investigated, with the aid of analytical solutions, and it is concluded that the mesh size at the corner needs to be of the order of the Stokes radius, which in turn is inversely proportional to the Reynolds number. Considerable attention is given to the accuracy of the finite difference solutions, both theoretically, and practically by duplicating solutions using alternative finite difference equations.
Implementation of Black Box Models for Internal Ballistics Optimization Using an Artificial Neural Network

The process of UUV delivery is a typical nonlinear transient dynamic phenomenon, which is generally described by the internal ballistic model. Evaluation of optimal internal ballistics parameters is a key step for promoting ballistic weapon performance under given launch constraints. Hence, accurate and efficient optimization techniques are required in ballistics technology. In this study, an artificial neural network (ANN) is used to simplify the process of regression analysis. To this end, an internal ballistics model is built in this study as a black box for a classic underwater launching system, such as a torpedo launcher, based on ANN parameter identification. The established black box models are mainly employed to calculate the velocity of a ballistic body and the torque of a launching pump. Typical internal ballistics test data are adopted as samples for training the ANN. Comparative results demonstrate that the developed black box models can accurately reflect changes in internal ballistics parameters according to rotational speed variations. Therefore, the proposed approach can be fruitfully applied to the task of internal ballistics optimization. The optimization of internal ballistics precision control, optimal control of the launching pump, and optimal low-energy launch control were, respectively, realized in conjunction with the established model using the SHERPA search algorithm. The results demonstrate that the optimized internal ballistics rotational speed curve can achieve the optimization objectives of low-energy launch and peak power while meeting the requirements of optimization constraints.
Rational Design and Multicomponent Synthesis of Lipid–Peptoid Nanocomposites towards a Customized Drug Delivery System Assembly

Nanotechnology has assumed a significant role over the last decade in the development of various technologies applied to health sciences. This becomes even more evident with its application in controlled drug delivery systems. In this context, peptoids are a promising class of compounds for application as nanocarriers in drug delivery systems. These compounds can be obtained efficiently and with highly functionalized structural diversity via the Ugi 4-component reaction (U-4CR). Herein, we report the design of the process control strategy for the future development of lipid–peptoid-based customized drug delivery system assemblies. Over 20 lipid–peptoid nanocomposites were synthesized via the U-4CR in good to excellent yields. These products were successfully submitted to the nanoparticle formation by the emulsification–evaporation process from lipophilic solution and analyzed via Dynamic Light Scattering (DLS). Several molecules generated nanoparticles with a size ≤200 nm, making them good candidates for drug delivery systems, such as in cancer treatment.
A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators

This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.
Application New Iterative Method For Solving Nonlinear Burger'S Equation And Coupled Burger'S Equations

In the recent research , the numerical solution of Nonlinear Burger’s equation and coupled Burger’s equation is obtained Nonlinear using a New Iterative Method (NIM) is being proposed to obtain. We have shown that the NIM solution is more accurate as compared to the techniques like, Burger’s equation and coupled Burger’s equation method and HPM method. more, results also demonstrate that NIM solution is more reliable, easy to compute and computationally fast as compared to HPM method.
Design based inference on spatial totals with Elastic-Net regularization

Recently, [1] and [3] have proposed a penalized spline regression estimator to estimate a spatial total. This estimator is capable of properly taking into account the spatial autocorrelation, which typically characterizes spatial populations. It can be implemented for finite spatial populations (the sample is drawn from a finite number of plots) as well as for continuous populations (the sample is made of a points selected from a compact and connected subset of the plane). Such estimator is approximately design unbiased and consistent. Furthermore, under certain conditions it can be superefficient, that is, the sampling variance is of order greater than n−1 (see [3]). In this work we explore the use of glmnet, implemented in the R programming system for Elastic-Net regularization proposed by [2]. This algorithm is based on the coordinate descendent technique and is quite efficient. It enables faster computation of the regression coefficients required by the penalized spline estimator of the spatial total. Furthermore, this function fits generalized linear models with Elastic-Net as penalties. Using this tool, we can quickly extend the penalized spline regression technique to estimate spatial total of dichotomous data, multinomial data, and count data. Finally, through a simulation study, we compare ridge and Lasso based penalized spline regression estimators for different types of data to highlight the potentiality of this approach.
Age, growth and sex ratio of the Catfish Malapterurus electricus (Gmelin, 1789) from the Large Reservoir, Lake Nasser, Egypt

The Electric Catfish, Malapterurus electricus, a species from order Siluriformes is widely distributed in Africa and occurs in all freshwater systems. It is a commercial species in Nasser Lake fisheries, with an average annual catch of about 130 MT. Limited biological data are available on this species. In the present study, random samples of M. electricus were
Sensitivity of Subsurface Ocean Temperature Variability to Specification of Surface Observations in the Context of ENSO

AbstractTo estimate the state of the ocean in the context of monitoring and prediction, ocean analysis products combine observed information from various sources that include both in situ ocean measurements and estimates of atmospheric forcings derived either from numerical models or from objective analysis methods. In the context of El Nino–Southern Oscillation (ENSO) variability in the equatorial tropical Pacific, this study discusses two questions: 1) the role of surface forcings in resolving the observed variability of subsurface ocean temperatures, and 2) which component of surface forcings plays a more important role.The analysis approach is based on ocean model simulations where specification of surface forcings is controlled and the resulting ocean state is either compared among various simulations or is compared with an independent ocean analysis (where information from in situ ocean temperature measurements is included). The results highlight the importance of the contribution of observed sea su.
Population structure and genetic connectivity of Plasmodium falciparum in pre-elimination settings of Southern Africa

To accelerate malaria elimination in the Southern African region by 2030, it is essential to prevent cross-border malaria transmission. However, countries within the region are highly interconnected due to human migration that aids in the movement of the parasite across geographical borders. It is therefore important to better understand Plasmodium falciparum transmission dynamics in the region, and identify major parasite source and sink populations, as well as cross-border blocks of high parasite connectivity. We performed a meta-analysis using collated parasite allelic data generated by microsatellite genotyping of malaria parasites from Namibia, Eswatini, South Africa, and Mozambique (N = 5,314). The overall number of unique alleles was significantly higher (P ≤ 0.01) in Namibia (mean A = 17.3 ± 1.46) compared to South Africa (mean A = 12.2 ± 1.22) and Eswatini (mean A = 13.3 ± 1.27, P ≤ 0.05), whilst the level of heterozygosity was not significantly different between countries. The proportion of polyclonal infections was highest for Namibia (77%), and lowest for Mozambique (64%). A was significant population structure was detected between parasites from the four countries, and patterns of gene flow showed that Mozambique was the major source area and Eswatini the major sink area of parasites between the countries. This study showed strong signals of parasite population structure and genetic connectivity between malaria parasite populations across national borders. This calls for strengthening the harmonization of malaria control and elimination efforts between countries in the southern African region. This data also proves its potential utility as an additional surveillance tool for malaria surveillance on both a national and regional level for the identification of imported cases and/or outbreaks, as well as monitoring for the potential spread of anti-malarial drug resistance as countries work towards malaria elimination.
Loss of normal Alzheimer's disease-associated Presenilin 2 function alters antiseizure medicine potency and tolerability in the 6-Hz focal seizure model

Introduction Patients with early-onset Alzheimer's disease (EOAD) experience seizures and subclinical epileptiform activity, which may accelerate cognitive and functional decline. Antiseizure medicines (ASMs) may be a tractable disease-modifying strategy; numerous ASMs are marketed with well-established safety. However, little information is available to guide ASM selection as few studies have rigorously quantified ASM potency and tolerability in traditional seizure models in rodents with EOAD-associated risk factors. Presenilin 2 (PSEN2) variants evoke EOAD, and these patients experience seizures. This study thus established the anticonvulsant profile of mechanistically distinct ASMs in the frontline 6-Hz limbic seizure test evoked in PSEN2-knockout (KO) mice to better inform seizure management in EOAD. Methods The median effective dose (ED50) of prototype ASMs was quantified in the 6-Hz test in male and female PSEN2-KO and wild-type (WT) C57BL/6J mice (3–4 months old). Minimal motor impairment (MMI) was assessed to estimate a protective index (PI). Immunohistological detection of cFos established the extent to which 6-Hz stimulation activates discrete brain regions in KO vs. WT mice. Results There were significant genotype-related differences in the potency and tolerability of several ASMs. Valproic acid and levetiracetam were significantly more potent in male KO than in WT mice. Additionally, high doses of valproic acid significantly worsened MMI in KO mice. Conversely, carbamazepine was significantly less potent in female KO vs. WT mice. In both male and female KO mice vs. WTs, perampanel and lamotrigine were equally potent. However, there were marked genotype-related shifts in PI of both carbamazepine and perampanel, with KO mice exhibiting less MMI at the highest doses tested. Gabapentin was ineffective against 6-Hz seizures in KO mice vs. WTs without MMI changes. Neuronal activation 90 min following 6-Hz stimulation was significantly increased in the posterior parietal association cortex overlying CA1 and in the piriform cortex of WT mice, while stimulation-induced increases in cFos immunoreactivity were absent in KO mice. Discussion Acute ASM potency and tolerability in the high-throughput 6-Hz test may be significantly altered with loss of normal PSEN2 function. Seizures in discrete EOAD populations may benefit from precisely selected medicines optimized for primary ASM pharmacological mechanisms.
On the “Anharmonic Effects” on the Collective Oscillations in Spherical Even Nuclei. I

A new method which enables us to transcribe the dynamics of the system of even num­ ber of fermions 'into that of the boson system is developed, with the purpose of analysing the " anharmonic effects " on the collective oscillations in spherical even nuclei from the standpoint of the microscopic theory of the collective excitations. In the "harmonic ap­ proximation", a pair of bound quasi-pa:I;ticles is replaced by the "phonon" as an ideal boson. This replacement inevitably leads to neglecting two main effects; the dynamical effect due to the residual interactions omitted under the "harmonic approximation" and the effect of the Pauli-principle among the quasi-particles composing the different pairs. Ac­ cording to our theory, the two effects which cause "anharmonic effects" can be evaluated correctly, in principle. It is not our purpose to go into detailed quantitative calculations, but rather to develop the basic idea.
High-resolution Observations of a White-light Flare with NST

Using high-resolution data from the New Solar Telescope, we studied fine spatial and temporal details of an M1.3 white-light (WL) flare, which was one of three homologous solar flares (C6.8, M1.3, and M2.3) observed in close proximity to the west solar limb on 2014 October 29 in NOAA active region 12192. We report that the TiO WL flare consists of compact and intense cores surrounded by less intense spatial halos. The strong and compact WL cores were measured to be Mm across, with an area of about 1014 cm2. Several TiO features were not cospatial with Hα flare ribbons and were displaced toward the disk center by about 500 km, which suggests that the TiO and Hα radiation probably did not originate in the same chromospheric volume. The observed TiO intensity enhancements are not normally distributed and are structured by the magnetic field of the penumbra.
Dense fog simulation in southern Brazil using the WRF model with high spatial resolution

Fog is an atmospheric phenomenon that reduces horizontal visibility to 1000 m or less. These phenomena affect different human activities, mainly those associated with air, maritime, and road transport. Rio Grande city, located in southern Brazil, has one of the main Brazilian ports, and fog occurrences at this location cause several inconveniences, such as the interruption of port activities and increases in operating costs. Thus, a better understanding of fog formation and dissipation, and the consequent improvement in its forecasts, can bring significant help to the port activities in that region. Therefore, this work seeks to evaluate the WRF model’s ability to simulate fog events in Rio Grande city. Four study cases of dense fogs were performed for this goal, as well as a sensitivity test to select the best vertical resolution configuration of the model. Meteorological data from the Rio Grande pilotage station and the Brazilian Coastal Monitoring System buoys were analyzed. Satellite images and synoptic charts were also analyzed for the study cases. The numerical simulations were performed using the WRF model with three nested domains, where 1 km was the highest horizontal resolution. The Fog Stability Index (FSI) and two estimates of horizontal visibility were evaluated. Simulations show promising results, highlighting the ability of the model to identify the fog occurrence in the area of interest and represent aspects of its dissipation process. Furthermore, it is noteworthy that the FSI and the evaluated visibility estimates were sensitive to the occurrence of the events.
Topological properties of bound states in the continuum in geometries with broken anisotropy symmetry

Waveguiding structures made of anisotropic media support bound states in the continuum (BICs) that arise when the radiation channel of otherwise semileaky modes is suppressed. Hitherto, only structures with optical axes aligned in symmetric orientations inside the waveguide plane, where BICs appear as lines in the momentum-frequency dispersion diagram, have been considered. Here we address settings where such symmetry is broken and unveil a number of fundamental different features. Weak and strong symmetry-breaking regimes are identified, corresponding to azimuthal and polar optical axes orientation asymmetries, respectively. The azimuthal symmetry breaking is found to still preserve the existence loci of BICs in the momentum-frequency dispersion diagram as lines. However, all possible BICs become interferometric, while the polarization separable states that occur in symmetric settings cease to exist. The polar symmetry breaking has stronger effects and transforms the BICs’ existence loci from lines to points, which correspond to full-vector states that exist at discrete values of the optical axis orientation for a given wavelength. Such transformation results in fundamental changes in the topological properties of the radiated field around the BICs.
PhaCIA-TCNs: Short-Term Load Forecasting Using Temporal Convolutional Networks With Parallel Hybrid Activated Convolution and Input Attention

Temporal convolution networks (TCNs) are recently proposed to be used in the short-term load forecasting (STLF) tasks in modern smart grids, however, TCNs have two shortcomings, i.e., redundant convolutional operation and equal input importance problems. Therefore, we propose a novel TCN-based backbone model, called PhaCIA-TCNs, to achieve a more accurate short-term load forecasting, where parallel hybrid activated convolution (PhaC) and input attention (IA) are proposed to resolve the above problems, respectively. Specifically, IA is proposed to highlight important input elements while depressing irrelevant ones, which thus rises the model's forecasting accuracies but also brings additional time-cost; then PhaC is further proposed to remedy the efficiency problem and to further enhance the forecasting accuracies by shortening the convolutional learning path to overcome the redundant convolutional operation problem. Extensive experimental results show that i) PhaCIA-TCNs significantly outperform all state-of-the-art RNN-based and TCNs-based baselines in forecasting-error-based evaluation metrics on all datasets; ii) ablation studies show that PhaC and IA are both effective and essential for PhaCIA-TCN to achieve the superior forecasting accuracies in STLF tasks, and by integrating IA and PhaC with TCN, the proposed PhaCIA-TCN not only greatly outperforms TCN in forecasting accuracies but also keeps similar (sometimes even better) learning efficiency.
Progress and Prospects of Gene Editing in Pluripotent Stem Cells

Applying programmable nucleases in gene editing has greatly shaped current research in basic biology and clinical translation. Gene editing in human pluripotent stem cells (PSCs), including embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), is highly relevant to clinical cell therapy and thus should be examined with particular caution. First, since all mutations in PSCs will be carried to all their progenies, off-target edits of editors will be amplified. Second, due to the hypersensitivity of PSCs to DNA damage, double-strand breaks (DSBs) made by gene editing could lead to low editing efficiency and the enrichment of cell populations with defective genomic safeguards. In this regard, DSB-independent gene editing tools, such as base editors and prime editors, are favored due to their nature to avoid these consequences. With more understanding of the microbial world, new systems, such as Cas-related nucleases, transposons, and recombinases, are also expanding the toolbox for gene editing. In this review, we discuss current applications of programmable nucleases in PSCs for gene editing, the efforts researchers have made to optimize these systems, as well as new tools that can be potentially employed for differentiation modeling and therapeutic applications.
Efficient DRL-Based Selection Strategy in Hybrid Vehicular Networks

Emerging V2X applications, like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) require Ultra-Reliable Low Latency Communications (URLLC). Unfortunately, none of the existing V2X communication technologies, such as ETSI ITS-G5 or C-V2X (Cellular V2X including 5G NR), can satisfy these requirements independently. In this paper, we propose a scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs). To this purpose, we propose a novel ITS station protocol stack and a decentralized RAT selection strategy that uses Deep Reinforcement Learning (DRL). The proposed approach employs a double deep Q-learning (DDQN) algorithm that allows each vehicle to determine the optimal RAT combination to meet the specific needs of the V2X application while limiting resource consumption and channel load. Furthermore, we assess the ability of our architecture to offer reliable and high throughput communication in two different scenarios with varying traffic flow densities. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, the selection strategy exhibits about a 20% improvement in throughput and a 10% reduction in the channel busy ratio (CBR).