Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological properties of optical fields: description of morphogenesis and bifurcations in focusing regions

Abstract. We analyze the diffraction field when changes in the curvature function of the boundary condition are implemented. The study is performed using differential geometry models with a curvature function displaying local behavior. Depending on the sign of curvature, we classify the diffraction field as elliptic, hyperbolic, or parabolic. In particular, it is shown that the optical field is organized around the parabolic regions, which correspond to focusing regions. The model is experimentally corroborated by applying a coordinate transformation to the transmittance of a zone plate. The reason to use this transmittance comes from the fact that its diffraction field displays multiple foci allowing identification, description, and control of bifurcations and morphogenesis effects, which are studied using the curvature function.
Physically Accurate Large Dynamic Range Pseudo Moments for the MMS Fast Plasma Investigation

The Fast Plasma Investigation onboard the Magnetospheric Multiscale (MMS) mission captures full‐sky particle data over a range of 10 eV to 30 keV every 30 ms for electrons and 150 ms for ions. Only about 5% of the count data at this time resolution are sent to the ground due to telemetry limitations; however, a complete set of onboard trigger terms are sent due to their small data volume. These trigger terms are composed of simplified integrals that scale proportionally to plasma moments. They were initially conceived as a mechanism to rank scientific data for priority downlink; however, they can be adapted to represent pseudo moment quantities that are accurate to within several percent of fully corrected plasma moments. Tuning the dynamic range per plasma region and correcting via a trained neural network allow for far greater accuracy than previous missions. Corrections are performed based on statistical sampling of data from the first MMS day side pass. The corrections are validated by comparing pseudo moments to a published event showcasing detection of kinetic Alfvén waves. Physically accurate pseudo plasma moment quantities are available for the entirety of the MMS prime science regions, and can be a valuable asset for future scientific analysis of the MMS data repository.
Non-blind hardware and software deconvolution for jitter reduction

For many imaging applications installed on moving platforms, especially for smaller platforms without the advantage of large inertial mass, jitter is one the primary drivers in modulation transfer function (MTF) degradation. The use of an inertial measurement unit (IMU) to detect motion for non-blind deconvolution of imagery is not a new concept. However, most systems are focused on still photographs for small optical systems, such as cell phones, and are not focused on real-time implementation for full motion video (FMV) with dynamic systems that have multiple moving parts. Further, no existing system utilizes IMU information to intelligently decide when a camera system should integrate during the typical 33 ms framerate. Having control over when to integrate and for how long allows for greater signal-to-noise ratios (SNRs) at smaller jitters by selecting times when jitter is minimized based on the LOS motion. This work presents the framework of a jitter mitigation approach that both optimally decides on the integration window and implements non-blind deconvolution to produce a system that provides enhanced image resolution under a variety of conditions
Fundamental Quantitative investment research based on Machine learning

In recent years, the status of quantitative investment in China's capital market has been improving, and fundamental quantification has emerged as a promising approach that integratesfundamental analysis and quantitative investment successfully. Hence, this kind of intelligent quantitative investment method has garnered significant attention. In this paper, eight machine learning algorithms, including Lasso regression, ridge regression, partial least squares regression, elastic network regression, decision tree, random forest, support vector machine and K-nearest neighbor method, are used to construct the stock return prediction model. The empirical results show that linear machine learning algorithm outperforms nonlinear machine learning algorithm. The annual return rate of CSI 300 index in the same term is 1.47%, while the investment strategy based on OLS model has an annualized return rate of 35.96%, and the maximum withdrawal rate is only 29.61%, showing its strong return capacity. In this paper, machine learning is introduced in the field of fundamental quantitative investment, which provides investment reference for all kinds of investors and is helpful for the country to promote quantitative investment.
Effect of Lung Compliance-Based Optimum Pressure Versus Fixed Positive End-Expiratory Pressure on Lung Atelectasis Assessed by Modified Lung Ultrasound Score in Laparoscopic Gynecological Surgery: A Prospective Randomized Controlled Trial

Background: Lung protective ventilation during the intraoperative period is now well established. However, the additional role of positive end-expiratory pressure (PEEP) during the intraoperative period remains uncertain in major laparoscopic gynecological surgery. The authors hypothesized that compliance-based optimum PEEP titration reduces postoperative lung atelectasis and improves outcomes. Methods: Patients undergoing major laparoscopic pelvic gynecological surgeries with healthy lungs were randomized to the fixed PEEP group (PEEP 5 cm H2O and recruitment maneuver {RM}) and optimum PEEP group (compliance-based PEEP and RM). Lung ultrasound and arterial blood gas analysis were performed at four time points. Modified lung ultrasound scoring was done, and the same was used as means of assessing lung aeration and the amount of lung atelectasis. Postoperative supplemental oxygen requirement and duration were also assessed and compared. Results: Lung ultrasound score (LUS) 30 minutes after extubation in fixed (Group F) and optimum (Group O) PEEP groups were median (interquartile range {IQR}) 3 (2-3) versus 1 (1-2), p=0.0001. Ventilatory parameters between Group F and Group O after lung recruitment were tidal volume (mean 357 mL {SD: 35} versus 362 mL {SD: 22}, p=0.46), PEEP (median, 5 cm H2O {IQR: 5-5} versus median 16 cm H2O {IQR: 14-18}), peak airway pressure (median 26 cm H2O {IQR: 24-28} versus median 30 cm H2O {IQR: 28-32} p<0.0001), plateau pressure (median 22 cm H2O {IQR: 20-24} versus median 26 cm H2O {IQR: 24-28} p<0.0001), static compliance (32.07±8.36 mL cm H2O-1 versus 39.58±8.99 mL cm H2O-1, p=0.0002). The number of patients requiring postoperative oxygen therapy to maintain SpO2 >94% after extubation in postanesthesia care unit (PACU) was statistically significantly greater in group F (39/41 {95%} versus 30/41 {73%}, p=0.007). Median (IQR) duration of oxygenation therapy in the first 24 hours of the postoperative period between Group F and Group O differed with statistical significance, with the median (IQR) values being 25 (20-30) minutes versus 10 (0-15) minutes (p<0.0001). Conclusions: The modified lung ultrasound score significantly differed intraoperatively between the two groups, with lower scores in the optimum PEEP group. This has reflected improved postoperative outcomes in optimum PEEP group patients, with fewer patients requiring postoperative oxygen supplementation and reduced supplemental oxygen requirement duration.
The Oscillatory Motion of Oldroyd-B Fluid by Incorporating Some of the Mechanical Factors

The aim of this paper is to present a numerical study of oscillatory motion of Oldroyd-B fluid in a uniform magnetic field through a small circular pipe. First, we derive the orientation stress tensor by considering the Brownian force. Then, the orientation stress tensor is incorporated by taking Hookean dumbbells on Brownian configuration fields in the Oldroyd-B model. The Oldroyd-B model is then reformulated coupled with the momentum equation and the total stress tensor. Finally, we analyze the orientation stress tensor in the pipe by the numerical simulations of the model and showed that the effect of orientation stress tensor is considerable although the Brownian force is sufficiently small.
Applications Of Nanomaterials In Improving The Traditional Diagnostic Approach

The broad art of uses of nanomaterials or nanoparticles and nanodevices that are used in medical healthcare to diagnose and cure a variety of diseases has recently developed as a result of recent advancements in pharmaceutical research.So, in this review article, we will discuss the various art of nanomaterials that are used in various forms to develop various nano-devices and nano technologies that are widely used in medical applications, such as cantilevers, which are highly stable devices that are integrated into highly sensitive disease markers in diagnostic detectors and display reliable performance for a long time. These nanoparticles are also employed in the creation of various dosage forms that are used to either cure or diagnose diseases. These nanotechnologies are frequently used as sophisticated tools or gadgets in the early identification of cancer and atherosclerosis in the human body, where subsequent therapy such as nano-surgery may be used to cure them. These are well-known superior materials that are necessary for many fields due to their nano size.
A Case-Based Reasoning Method with Rank Aggregation

In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.
Improving a primal–dual simplex-type algorithm using interior point methods

ABSTRACT Interior point methods and simplex-type algorithms are the most widely-used algorithms for solving linear programming problems. The simplex algorithm has many important applications. Hence, even small improvements in simplex-type algorithms could result in noticeable practical impact. This paper presents a hybrid algorithm that combines the strengths of interior point methods and exterior point simplex algorithms. It applies an interior point method for a few iterations leading to significant improvement of the objective function value. At this point, the proposed algorithm uses an exterior point simplex algorithm to find an optimal solution. A crucial point is the selection of the interior point that will be used by the exterior point simplex algorithm to calculate a direction to the feasible region. The goal of the proposed implementation is twofold: (i) improve the performance of the exterior point algorithm, and (ii) find an optimal basic solution starting from an interior point (purification process). The latter goal is very important since an optimal basic solution can be used to solve closely related linear programming problems (warm-start) and linear programming relaxations of integer programming problems. Computational results on a set of benchmark problems (Netlib, Kennington, Mészáros) are presented to demonstrate the efficiency of the proposed hybrid algorithm. The results show that the proposed algorithm is faster than the exterior point simplex algorithm.
What Do Selfies Say about Young Kuwaitis

Taking a selfie as a social phenomenon is a way that people try to feel better about themselves. Selfies say us something important about the people who take them, and then share them and offer useful insights into the uses of social media in general. It seems that young Kuwaitis are obsessed with taking selfies. Young Kuwaitis, especially females, do not feel good about themselves and how they look. They are very concerned about their online self-image. These young Kuwaitis, especially females, are in danger because their self-esteem is bound to the ‘comments’ and ‘likes’ they receive when they post selfies. It can be said that young Kuwaitis, especially females, are narcissists. It is apparent that narcissistic individuals would be keener than others to take selfies, edit those pictures, post the most attractive ones on social media, keep track of the ‘likes’ and ‘comments’ for those pictures, and get worried when they do not get the desired feedback.
New insights on the rare blind blister beetle genus Meloetyphlus (Coleoptera: Meloidae): occurrence in Colombia, SEM larval description, and molecular phylogenetic placement

Meloetyphlus Waterhouse is a monotypic genus of Meloidae ascribed to the tribe Tetraonycini, subfamily Meloinae. As for most blister beetles, its first-instar larvae (or triungulins) are parasites of bees, but M. fuscatus Waterhouse is the only species known to parasitize members of the tribe Euglossini (Hymenoptera: Apidae: Apinae). Despite being widely distributed in Central and South America, its presence in Colombia has never been confirmed. In this article, we document the occurrence of this genus in Colombia providing the first certain record for this country. In addition, some aspects of its parasitization of Eulaema nigrita Lepeletier are reported and illustrated with macrophotographs and a video. By the means of drawings and SEM micrographs, we provide the most detailed description of the first-instar larva of M. fuscatus to date and a comparison with the triungulin of the closely related Tetraonyx fulvus (LeConte). Finally, we investigated the phylogenetic position of Meloetyphlus using molecular data, to support its attribution to the tribe Tetraonycini.
Association between Lung Microbiota Dysbiosis and Sepsis Induced Acute Respiratory Distress Syndrome

Ye et al. : Microbiota Dysbiosis in Sepsis Induced Acute Respiratory Distress Syndrome Sepsis is associated with acute respiratory distress syndrome, which has a significant impact on the prognosis of patients. Several studies have shown that the microbiota plays a significant role in sepsis-induced acute respiratory distress syndrome, but the relationship between the microbiota and sepsis-induced acute respiratory distress syndrome is not fully understood. We conducted a case-control and single-center study in 19 sepsis-induced acute respiratory distress syndrome patients and 36 sepsis-non-induced acute respiratory distress syndrome patients to investigate the clinical features and microbiota expression. There were 55 subjects enrolled, 19 of whom suffered acute respiratory distress syndrome due to sepsis. A significant increase in the abundance of Pseudomonas aeruginosa , leptospiral virus, Cytomegalovirus , Klebsiella pneumoniae , Streptococcus pneumoniae , Candida albicans , Escherichia coli , Epstein-Barr virus and Staphylococcus aureus . Besides, expressions of peripheral T lymphocytes (cluster of differentiation 3 + , cluster of differentiation 4 + and cluster of differentiation 3 + , cluster of differentiation 8 + ) was much higher in the sepsis-induced acute respiratory distress syndrome group than that in the sepsis non-induced acute respiratory distress syndrome group. The acute physiology and chronic health evaluation II scores, duration of mechanical ventilation and mortality with 28 d and 90 d were much higher in the sepsis-induced acute respiratory distress syndrome group. Patients with sepsis-induced acute respiratory distress syndrome had worse clinical outcomes and a higher expression of peripheral T lymphocytes, as well as the relative abundance of microbiota dysbiosis.
Detecting confounding due to subject identification in clinical machine learning diagnostic applications: a permutation test approach

Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data of each subject randomly assigned to both training and test sets (record-wise data split) can lead to massive underestimation of the cross-validation prediction error, due to the presence of "subject identity confounding" caused by the classifier's ability to identify subjects, instead of recognizing disease. To solve this problem, the authors recommended the random assignment of the data of each subject to either the training or the test set (subject-wise data split). The adoption of subject-wise split has been criticized in Little et al (2017), on the basis that it can violate assumptions required by cross-validation to consistently estimate generalization error. In particular, adopting subject-wise splitting in heterogeneous data-sets might lead to model under-fitting and larger classification errors. Hence, Little et al argue that perhaps the overestimation of prediction errors with subject-wise cross-validation, rather than underestimation with record-wise cross-validation, is the reason for the discrepancies between prediction error estimates generated by the two splitting strategies. In order to shed light on this controversy, we focus on simpler classification performance metrics and develop permutation tests that can detect identity confounding. By focusing on permutation tests, we are able to evaluate the merits of record-wise and subject-wise data splits under more general statistical dependencies and distributional structures of the data, including situations where cross-validation breaks down. We illustrate the application of our tests using synthetic and real data from a Parkinson's disease study.
Accessibility of Institutional Support to Nonfarm Micro Manufacturing Enterprises in Rural Tanzania

This study contributes to understanding of how the rural nonfarm micro manufacturing enterprises in Tanzania access institutional support for their growth and survival. It aimed at exploring the extent to which rural nonfarm micro manufacturing enterprises access support and approaches that the responsible public institutions are using to provide support for them. A total of 62 micro manufacturing enterprises were sampled for this study. Primary data were collected using questionnaire, semi-structured interview and observation. Secondary data were collected through review of relevant documents and policies. Qualitative data were analysed through content while quantitative data were analysed through SPSS. Results indicate low level of accessibility of institutional support to rural micro manufacturing enterprises from responsible institutions. Furthermore, the study learnt that the responsible institutions are insufficiently providing support which do not reach the rural nonfarm micro manufacturing enterprises. It appears that unintended enterprises are likely receiving support that were primarily targeting rural nonfarm micro manufacturing enterprises. This study established that the responsible support institutions do not use the appropriate that can help them reach a voluminous number of targeted rural nonfarm micro manufacturing enterprises. It is recommended that the responsible support institutions change the approach and make use of participatory that engage more closely the Local Government Authorities.
Approximate analytical solution for the Black-Scholes equation by method of lines

The Stochastic Partial Differential Equations are part of a set of non-linear partial differential equations (PDE), which by their random behavior are difficult to solve analytically and numerically; One of them has been known as the Black-Scholes PDE since 1973, which determines the valuation of goods and/or assets called financial options. The development of the present work is to find numerical approximations to the solution of Black-Scholes PDE by the Method of Lines (MOL). The previous was achieved by means of a methodology based on an analytical study of the classic solution of the BlackScholes PDE. Finally, numerical methods and algorithms were applied to the Black Scholes PDE.
Implementation of a web application to optimize the financial management of an automotive company using Extreme Programming

The need for companies to have systems that can automate the different processes that are carried out is increasingly high, even more so, from the COVID-19 pandemic that evidenced great automation shortcomings in a large part of the commercial sector. The implementation of a web application focused on automating financial processes in the automotive company R.A. L.E will allow payments, collections, order requests and control of the different administrative and operational processes to be carried out in a controlled and efficient manner. For the development of the application, the XP methodology is used, Power Designer for data modeling, Laravel as a development framework and MySQL as the database engine. To evaluate the response time in the different processes, the ISO/IEC 25010 standard was applied, taking time for both the current process and the automated process. The results show that payment, collections and orders processes significantly decreased execution times. Ingeneral terms, based on terms of performance efficiency, the web application is rated as VERY GOOD, resulting in a weighted rating of 75% which is the result of the temporary behavior and use of system resources.
Fractional Effects on the Light Scattering Properties of a Simple Binary Mixture

Abstract A fractional generalized hydrodynamic (GH) model of the concentration fluctuations correlation function is analyzed. Our analysis is based on a previously proposed irreversible thermodynamics non-fractional model that describes the first GH deviations in a finite frequency and wavelength regime where the local equilibrium assumption is not valid. Our basic purpose is to generalize this theory to investigate the time fractional effects on the intermediate scattering function (ISF) of a model of a binary mixture. We discuss two different forms of introducing the fractional derivatives into the hydrodynamic time evolution equation of the ISF and examine their consistency in the non-fractional limit. We calculate analytically the fractional intermediate scattering function (FISF) and compare it with the non-fractional one. We find that although the FISFs are in general less than ISF, the FISF may be a significant fraction of the ISF (∼36%) and might be measurable by light or neutron scattering techniques. We show that fractional time derivatives provide a consistent description of this correlation function in the GH regime and reduce to its well-known behavior in the Navier–Stokes limit (NS) where local equilibrium is restored. We also suggest an experimental verification of our results for near equilibrium states. Finally, we summarize the main results of our work and make some further physical remarks.
New Bounds for Assortment Optimization Under the Nested Logit Model

We consider the assortment optimization problem under the nested logit model and obtain new bounds on the gap between the optimal expected revenue and an upper bound based on a certain continuous relaxation of the assortment problem. Our bounds can be tighter than the existing bounds in the literature and provide more insight into the key drivers of tractability for the assortment optimization problem under the nested logit model. Moreover, our bounds scale with the nest dissimilarity parameters and we recover the well-known tractability results for the assortment optimization problem under the multinomial logit model when all the nest dissimilarity parameters are equal to one. We extend our results to the cardinality constrained assortment problem where there are constraints that limit the number of products that can be offered within each nest.