Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic properties for the loglog laws under positive association

AbstractLet {Xn: n ≥ 1} be a strictly stationary sequence of positively associated random variables with mean zero and finite variance. Set $$S_n = \sum\limits_{k = 1}^n {X_k }$$, $$Mn = \mathop {\max }\limits_{k \leqslant n} \left| {S_k } \right|$$, n ≥ 1. Suppose that $$0 < \sigma ^2 = EX_1^2 + 2\sum\limits_{k = 2}^\infty {EX_1 X_k < \infty }$$. In this paper, we prove that if E|X1|2+δ < for some δ ∈ (0, 1], and $$\sum\limits_{j = n + 1}^\infty {Cov\left( {X_1 ,X_j } \right) = O\left( {n^{ - \alpha } } \right)}$$ for some α > 1, then for any b > −1/2 $$\mathop {\lim }\limits_{\varepsilon \searrow 0} \varepsilon ^{2b + 1} \sum\limits_{n = 1}^\infty {\frac{{(\log \log n)^{b - 1/2} }} {{n^{3/2} \log n}}} E\left\{ {M_n - \sigma \varepsilon \sqrt {2n\log \log n} } \right\}_ + = \frac{{2^{ - 1/2 - b} E\left| N \right|^{2(b + 1)} }} {{(b + 1)(2b + 1)}}\sum\limits_{k = 0}^\infty {\frac{{( - 1)^k }} {{(2k + 1)^{2(b + 1)} }}}$$ and $$\mathop {\lim }\limits_{\varepsilon \nearrow \infty } \varepsilon ^{ - 2(b + 1)} \sum\limits_{n = 1}^\infty {\frac{{(\log \log n)^b }} {{n^{3/2} \log n}}E\left\{ {\sigma \varepsilon \sqrt {\frac{{\pi ^2 n}} {{8\log \log n}}} - M_n } \right\}} _ + = \frac{{\Gamma (b + 1/2)}} {{\sqrt 2 (b + 1)}}\sum\limits_{k = 0}^\infty {\frac{{( - 1)^k }} {{(2k + 1)^{2b + 2} }}} ,$$ where x+ = max{x, 0}, N is a standard normal random variable, and Γ(·) is a Gamma function.
SICAS2: Interactive Tool to Support Programming Learning

In the last decades the area of informatics had an exponential growth. This led to a high number of young students being attracted to higher education courses in the IT area. However, many students face difficulties when they have to learn programming. This often causes frustration and even dropout for many students. This paper presents a tool designed to help to mitigate some student’s difficulties. It is a Moodle plugin that allows students to build programs using flowcharts and see the animation of the execution of those programs. The main objective is to facilitate the comprehension and application of basic programming concepts in programs that may solve common introductory problems.
Red Tape Reduction, More New Firms? Saving Time and Money: Evidence from an Ecuadorian Reform

Abstract Reducing entry costs like bureaucratic procedures might be useful tool for stimulating new firm creation in a country. We exploit an unexpected reform that targets the physical (face-to-face) service channel, offering a decline in these types of costs in the firm creation process in Ecuador. To do this, we rely on a novel and underexplored administrative dataset and apply a difference-in-differences approach which compares physical (in-person) with electronic firm creation schemes before and after the implementation of the policy. We find that the reform increases the number of new firms by approximately 12.05% and the entry rate by approximately 108% in the short term. We also find that the impact varies across provinces and economic sectors. In addition, we find a decrease in the average number of days needed to create a new firm and an increase in the probability of creating a new firm with less initial minimum capital. The main conclusion is that the reform effectively contributes to increasing new firm creation in Ecuador. This matters from a policy perspective in a country with a high share of unregistered business.
A Generic Framework for Prognostics of Complex Systems

In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics.
Packet Classification using Tuple Space with improved Cuckoo Filter

In packet classification, the scheme based on tuple space has low classification performance due to a large number of hash calculations and tuples that need to be accessed. To improve the performance, this paper introduces a filter-assisted approach that improves the cuckoo filter, which makes pre-matches before accessing tuples to reduce the tuple space algorithm's average number of memory accesses. The experiments show that our model has a high pruning rate and spatial performance.
Constrained thin-plate spline reconstruction of 2D deformations from tagged MRI

Efficient constrained thin-plate spline warps are proposed in this paper which can warp an area in the plane such that two embedded snake grids obtained from two SPAMM frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector-field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the myocardium, where no information is available, a C/sup 1/ continuous vector field is interpolated. The formalism proposed in this paper improves on the authors' previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The method has been extensively validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.
Effect of Esculetin on Tert-Butyl Hydroperoxide-Induced Oxidative Injury in Retinal Pigment Epithelial Cells In Vitro

Esculetin is a coumarin-derived compound with antioxidant and anti-inflammatory properties. The current study aims to evaluate the therapeutic implications of esculetin on retinal dysfunction and uncover the underlying mechanisms. Tert-butyl hydroperoxide (t-BHP) at a concentration of 300 μM was used to induce oxidative stress in human retinal pigment epithelial cell line (ARPE-19) cells. Esculetin at concentrations below 250 μM did not cause cytotoxicity to ARPE-19 cells. Cell viability analysis confirmed that t-BHP induced oxidative injury of ARPE-19 cells. However, ARPE-19 cells were protected from t-BHP-induced oxidative injury by esculetin in a concentration-dependent manner. As a result of the TUNEL assay to confirm apoptosis, esculetin treatment reduced the number of TUNEL-positive cells. Esculetin down-regulated the expression levels of Bax, Caspase-3, and PARP and up-regulated the expression level of Bcl2. Collectively, this study demonstrates that esculetin exerts potent antioxidant properties in ARPE-19 cells, inhibiting t-BHP-induced apoptosis under the regulation of apoptotic factors.
Interaction of the Supernova Remnant G18.8+0.3 with the Surrounding Medium

We have carried out a study of the interaction of the supernova remnant (SNR) G18.8+0.3 with the surrounding interstellar medium. Observations of the H I, 12CO, 13CO, and OH (1720 MHz) lines were performed toward a large field around G18.8+0.3 using the Parkes (Australia) 64 m single-dish telescope, the 4 m NANTEN millimetric telescope (Las Campanas Observatory, Chile), and the Very Large Array (NRAO). The present survey has revealed the existence of an elongated molecular cloud (about 21′ × 6′ in size) adjacent to the more flattened borders of the SNR and to the far side of the remnant. The overall CO and H I morphology and kinematics allow us to conclude that the explosion occurred near the border of a preexisting molecular cloud, driving a slow shock into the cloud. The presence of diffuse shock-heated dust with a color temperature of about 30 K was shown in coincidence with the molecular feature using IRAS data. The shocked CO and H I gas was detected between +10 and +27 km s-1 (LSR). The systemic velocity of this complex, about +19 km s-1, yields a kinematic distance of about 1.9 kpc for G18.8+0.3. The shock is presently expanding into the cloud at ∼10 km s-1. Masses of the order of 7300, 1100, and 55 M⊙ are estimated for the associated molecular hydrogen, atomic hydrogen, and heated dust, respectively. The total kinetic energy transferred by the supernova shock to the surrounding interstellar medium is of the order of 1049 ergs. An age of ∼16,000 yr is calculated for G18.8+0.3. The extended molecular feature with a density of ∼600 cm-3 has denser clumps immersed in it, with densities ranging from ∼2500 to ∼6000 cm-3. Three out of five of these clumps were found to contain luminous IRAS pointlike sources compatible with protostellar candidates, suggesting a causal relationship with the supernova explosion that deserves further investigation. The interferometric search for OH (1720 MHz) masers gave negative results. These OH masers can be short-lived occurrences and dissipate quickly in the cooling postshock gas because of the very restrictive conditions under which masers form. Therefore, their absence does not preclude the hypothesis of shock-cloud interaction.
Detection of Begomovirus Disease for Identification of Disease Severity Level in Tomato Leaves Using Convolutional Neural Network (CNN)

Tomato is one of the horticultural crops that have high demand in the community, which demands continuous increase in production and quality. On the other hand, cultivating and caring for tomato are not simple. The challenge frequently encountered by tomato farmers is the prevalence of diseases on tomato leave caused by bacteria, viruses, and fungus. If the diseases are not treated appropriately, these diseases will cause a decrease in production due to the poor quality of tomatoes. In this study, the optimized Convolutional Neural Network (CNN) model for classification and detection Begomovirus disease in tomato leave is proposed. The optimized model is obtained from the comparison of three types architectures: SSD Mobilenet V1 FPN, EfficientDet D1 and SSD Resnet 50 V1 FPN. For the purpose of this research, a dataset consisted of tomato leaves contracted with various stages of begomovirus has been collected and prepared to be used as training and validation. The dataset is grouped into 4 classes based on the severity level of the disease according to the scale of symptoms that occur in tomato leave. The Experimental results shows that the mAP value of SSD Mobilenet V1 FPN, SSD Resnet 50 V1 FPN and EfficientDet D1 model are 56.17%, 61.58% and 66.03%, respectively. The EfficientDet D1 model resulted in the highest mean Average Precision (mAP) value due to this model produced the lowest loss value of 0.1 compared to the other 2 models. The proposed model uses BiFPN method for feature extraction which resulted in higher accuracy compared to previous models which only used FPN. This performance was able to be achieved with much less number of layers.
Time-varying causality between money supply growth and inflation: new evidence from Turkey

ABSTRACT This research investigates the causal links between money supply growth and inflation in Turkey. Unlike previous literature, In the investigation of causality, time-varying causality tests were applied. The results indicate that there is causality from money supply growth to inflation. However, it emphasizes that when time-varying causality is considered, causality is not homogeneous throughout the sample, but heterogeneous, more sensitive to structural breaks.
Intra- and Interspecies RNA-Seq Based Variants in the Lactation Process of Ruminants

Simple Summary RNA-Seq data provide a new chance to find transcriptome variants. We used RNA-Seq data to detect the variants involved in the three different stages (before peak = BP, peak = P, and after peak = AP) of the lactation process in two sheep and two cow breeds. Furthermore, several KEGG pathways and enriched gene ontologies associated with immune system activation and the metabolic process were demonstrated by analyzing the functional enrichment of the genes that were affected. Findings of the present study also suggest that milk yield and milk composition in sheep and cow breeds at different stages of lactation can be related to known and novel variants of specific genes related to milk fat and protein synthesis. The results pave the way for further studies on determining the genetic basis of milk production. The novel variants discovered here using RNA-seq data may be central and crucial when it comes time to design new SNP chips used as guides for selective breeding programs. Abstract The RNA-Seq data provides new opportunities for the detection of transcriptome variants’ single nucleotide polymorphisms (SNPs) in various species and tissues. Herein, milk samples from two sheep breeds and two cow breeds were utilized to characterize the genetic variation in the coding regions in three stages (before-peak (BP), peak (P), and after-peak (AP)) of the lactation process. In sheep breeds Assaf and Churra, 100,462 and 97,768, 65,996 and 62,161, and 78,656 and 39,245 variants were observed for BP, P, and AP lactation stages, respectively. The number of specific variants was 59,798 and 76,419, 11,483 and 49,210, and 104,033 and 320,817 in cow breeds Jersy and Kashmiri, respectively, for BP, P, and AP stages. Via the transcriptome analysis of variation in regions containing QTL for fat, protein percentages, and milk yield, we detected a number of pathways and genes harboring mutations that could influence milk production attributes. Many SNPs detected here can be regarded as appropriate markers for custom SNP arrays or genotyping platforms to conduct association analyses among commercial populations. The results of this study offer new insights into milk production genetic mechanisms in cow and sheep breeds, which can contribute to designing suitable breeding systems for optimal milk production.
Determining the OIP3 and Bias Network Resonances of Phased-Arrays Using Far-Field Techniques

The output 3rd-order intercept point (OIP3) is a figure of merit to evaluate the linearity of an amplifier or a system of amplifier blocks. Also, the OIP3 is a useful term to estimate the 3rd -order intermodulation (IM3) components and the adjacent channel power ratio (ACPR) of a transmitter for complex modulation measurements. A phased array operating in a transmit mode combines the output of several power amplifiers in free space, and the far-field contains an average and a scaled version of the linear and nonlinear components of the individual power amplifiers. In this paper, a method to determine the OIP3 of phased-array amplifiers using a far-field measurement is derived and verified by experiment. The far-field OIP3 is then measured using a 5G 28 GHz 32-element phased array employing 2x2 beamformer chips, and the far-field derived OIP3 agrees well with measurements done on a single amplifier. This technique therefore allows the user to determine the (average) OIP3 of the power amplifiers used in an array even if they do not have access to the individual beamformer chip. An additional benefit is the ability of characterizing the PCB bias network resonances in a phased-array environment.
Development of a Prototype for a Bilingual Patient-Reported Outcome Measure of the Important Health Aspects of Quality of Life in People Living with HIV: The Preference Based HIV Index (PB-HIV)

(1) Background: The aim of this project was to develop a short, HIV-specific, health-related quality of life measure with a scoring system based on patient preferences for the different dimensions of the Preference-Based HIV Index (PB-HIV). (2) Methods: This study is a cross-sectional analysis of data from the Canadian Positive Brain Health Now cohort (n = 854; mean age 53 years). Items from the standardized measures were mapped to the areas from the Patient-Generated Index and formed the domains. A Rasch analysis was used to identify the best performing item to represent each dimension. Each item was then regressed on self-rated health (scored 0 to 100) and the regression parameters were used as scaling weights to form an index score for the prototype measure. (3) Results: Seven independent dimensions with three declarative statements ordered as response options formed the PB-HIV Index (pain, fatigue, memory/concentration, sleep, physical appearance/body image, depression, motivation). Regression parameters from a multivariable model yielded a measure with a scoring range from 0 (worst health) to 100 (perfect health). (4) Conclusions: Preference-based measures are optimal, as the total score reflects gains in some dimensions balanced against losses in others. The PB-HIV Index is the first HIV-specific preference-based measure.
Nanomaterials based sustainable bioenergy production systems: Current trends and future prospects

The global energy crisis affects all of us. With a growing global population and finite fossil fuel supplies, we must find new energy sources. Alternative energy sources must be prioritized. Biofuels like biodiesel, biohydrogen, biomethanol, and bioethanol have come a long way in recent decades. These alternative fuels are from low-cost, renewable sources like algal, microbial, and plant biomass. Several governments, including India, are improving their renewable energy production capabilities. The main obstacles to rapid biofuel adoption are time and cost. For biofuel to truly become a viable alternative to fossil fuels, nanotechnology has recently provided the much-needed impetus. Nanomaterials' unique structural behavior, such as small size (nanoscale size), has increased their use in biofuel production. It improves efficiency and reduces the time required to convert waste into biofuels. This review addresses the latest information on various types of nanoparticles, and challenges faced and the future prospects of emerging applications of nanoparticles in biofuel production.
C-MYC Inhibited Ferroptosis and Promoted Immune Evasion in Ovarian Cancer Cells through NCOA4 Mediated Ferritin Autophagy

Objective: We aimed to construct the ferritin autophagy regulatory network and illustrate its mechanism in ferroptosis, TME immunity and malignant phenotypes of ovarian cancer. Methods: First, we used Western blot assays and immunohistochemistry to detect the pathway expression in ovarian cancer samples (C-MYC, NCOA4). Then, we performed RIP and FISH analysis to verify the targeted binding of these factors after which we constructed ovarian cancer cell models and detected pathway regulator expression (NCOA4). Co-localization and Western blot assays were used to detect ferritin autophagy in different experimental groups. We selected corresponding kits to assess ROS contents in ovarian cancer cells. MMP was measured using flow cytometry and mitochondrial morphology was observed through TEM. Then, we chose Clone, EdU and Transwell to evaluate the proliferation and invasion abilities of ovarian cancer cells. We used Western blot assays to measure the DAMP content in ovarian cancer cell supernatants. Finally, we constructed tumor bearing models to study the effect of the C-MYC pathway on ovarian cancer tumorigenesis and TME immune infiltration in in vivo conditions. Results: Through pathway expression detection, we confirmed that C-MYC was obviously up-regulated and NCOA4 was obviously down-regulated in ovarian cancer samples, while their expression levels were closely related to the malignancy degree of ovarian cancer. RIP, FISH and cell model detection revealed that C-MYC could down-regulate NCOA4 expression through directly targeted binding with its mRNA. Ferritin autophagy and ferroptosis detection showed that C-MYC could inhibit ferroptosis through NCOA4-mediated ferritin autophagy, thus reducing ROS and inhibiting mitophagy in ovarian cancer cells. Cell function tests showed that C-MYC could promote the proliferation and invasion of ovarian cancer cells through the NCOA4 axis. The Western blot assay revealed that C-MYC could reduce HMGB1 release in ovarian cancer cells through the NCOA4 axis. In vivo experiments showed that C-MYC could promote tumorigenesis and immune evasion in ovarian cancer cells through inhibiting HMGB1 release induced by NCOA4-mediated ferroptosis. Conclusion: According to these results, we concluded that C-MYC could down-regulate NCOA4 expression through directly targeted binding, thus inhibiting ferroptosis and promoting malignant phenotype/immune evasion in ovarian cancer cells through inhibiting ferritin autophagy.
Developments in unsteady pipe flow friction modelling

This paper reviews a number of unsteady friction models for transient pipe flow. Two distinct unsteady friction models, the Zielke and the Brunone models, are investigated in detail. The Zielke model, originally developed for transient laminar flow, has been selected to verify its effectiveness for "low Reynolds number" transient turbulent flow. The Brunone model combines local inertia and wall friction unsteadiness. This model is verified using the Vardy's analytically deduced shear decay coefficient C * to predict the Brunone's friction coefficient k rather than use the traditional trial and error method for estimating k. The two unsteady friction models have been incorporated into the method of characteristics water hammer algorithm. Numerical results from the quasi-steady friction model and the Zielke and the Brunone unsteady friction models are compared with results of laboratory measurements for water hammer cases with laminar and low Reynolds number turbulent flows. Conclusions about the range of validity for the three friction models are drawn. In addition, the convergence and stability of these models are addressed.
Fractal dimension based corneal fungal infection diagnosis

We present a fractal measure based pattern classification algorithm for automatic feature extraction and identification of fungus associated with an infection of the cornea of the eye. A white-light confocal microscope image of suspected fungus exhibited locally linear and branching structures. The pixel intensity variation across the width of a fungal element was gaussian. Linear features were extracted using a set of 2D directional matched gaussian-filters. Portions of fungus profiles that were not in the same focal plane appeared relatively blurred. We use gaussian filters of standard deviation slightly larger than the width of a fungus to reduce discontinuities. Cell nuclei of cornea and nerves also exhibited locally linear structure. Cell nuclei were excluded by their relatively shorter lengths. Nerves in the cornea exhibited less branching compared with the fungus. Fractal dimensions of the locally linear features were computed using a box-counting method. A set of corneal images with fungal infection was used to generate class-conditional fractal measure distributions of fungus and nerves. The a priori class-conditional densities were built using an adaptive-mixtures method to reflect the true nature of the feature distributions and improve the classification accuracy. A maximum-likelihood classifier was used to classify the linear features extracted from test corneal images as 'normal' or 'with fungal infiltrates', using the a priori fractal measure distributions. We demonstrate the algorithm on the corneal images with culture-positive fungal infiltrates. The algorithm is fully automatic and will help diagnose fungal keratitis by generating a diagnostic mask of locations of the fungal infiltrates.