Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach to Animal Flight Mechanics

The mechanics of lift and thrust generation by flying animals are studied by considering the distribution of vorticity in the wake. As wake generation is not continuous, the momentum jet theory, which has previously been used, is not satisfactory, and the vortex theory is a more realistic model. The vorticity shed by the wings in the course of each powered stroke deforms into a small-cored vortex ring; the wake is a chain of such rings. The momentum of each ring sustains and propels the animal; induced power is calculated as the rate of increase of wake kinetic energy. A further advantage of the vortex theory is that lift and induced drag coefficients are not required; estimated instantaneous values of these coefficients are generally too large for steady state aerodynamic theory to be appropriate to natural flapping flight. The vortex theory is applied to hovering of insects and to avian forward flight. A simple expression for induced power in hovering is found. Induced power is always greater than simple momentum jet estimates, and the discrepancy becomes substantial as body mass increases. In hovering the wake is composed of a stack of horizontal, coaxial, circular vortex rings. In forward flight of birds the rings are elliptic; they are neither horizontal nor coaxial because the momentum of each ring balances the vector sum of parasite and profile drag and the bird9s weight. Total power consumption as a function of flight velocity is calculated and compared for several species. Power reduction is one of the major factors influencing the choice of flight style. A large body of data is used to obtain an approximate scaling between stroke period and the body mass for birds. Together with relations between other morphological parameters, this is used to estimate the variation of flight speed and power with body mass for birds, and on this basis deviations from allometric scaling can be related to flight proficiency and to the use of such strategies as the bounding flight of small passerines. Note: Present address: Department of Zoology, University of Bristol, Woodland Road, Bristol BS8 IUG, U.K.
Analysis of Multi-Factor Affine Yield Curve Models

In finance and economics much work has been done on the theoretical modeling and statistical estimation of the yield curve, defined as the relationship between (−1/τ) log pt(τ) and τ, where pt(τ) is the time t price of a zero-coupon bond with payoff 1 at maturity date t+τ. Of considerable current interest are models of the yield curve in which a collection of observed and latent factors determine the market price of factor risks, the stochastic discount factor, and the arbitrage-free bond prices. The model is particularly interesting from a statistical perspective, because the yields are complicated nonlinear functions of the underlying parameters (e.g., those appearing in the evolution dynamics of the factors and those appearing in the model of the factor risks). This nonlinearity tends to produce a likelihood function that is multimodal. In this article we revisit the question of how such models should be fit from the Bayesian viewpoint. Key aspects of the inferential framework include (a) a prior on th.
COMPARISON OF HYDRODISSECTION INJECTION THERAPY USING ULTRASONOGRAPHIC AS GUIDES BETWEEN TRIAMCINOLONE ACETONIDE AND 5% DEXTROSE IN CARPAL TUNNEL SYNDROME

Background: Carpal Tunnel Syndrome (CTS) is a symptomatic compression neuropathy of the median nerve characterized by increased pressure in the carpal tunnel and decreased nerve function due to compression of the median nerve in the carpal tunnel. The purpose of the hydrodissection injection method in CTS is to separate the soft tissue adhesions that cause nerve compression and this method are known for being minimally invasive, fast healing, and easy to apply. Local injection of triamcinolone acetonide (TCA) is often used as therapy for CTS because it stabilizes the sodium channels and reduces abnormal stimulatio, thus it relieved the pain. 5% dextrose injection (D5W) is also widely used as therapy of CTS because it is harmless to nerves and may reduce neurogenic inflammation through inhibition of capsaicin-sensitive receptors. Aim: To compare the effectivity of hydrodissection injection therapy using ultrasound guidance with triamcinolone acetonide and 5% dextrose in CTS. Methods: This study recruited 30 participants who diagnosed with CTS and met the inclusion criteria. Participants were divided into two treatment groups, the first group (n=15) was given 1ml TCA injection and 1 ml lidocaine 2%, while the second group (n=15) was given 5% 5 ml Dextrose injection. The parameters measured in this study were NRS, FSS, and SSS value before injection and 4 weeks after injection of the agent. We compared these parameters at week four after injection between the TCA group and the D5W group. Results: NRS values before and 4 weeks after TCA injection (sig 0.001; p <0.05), FSS values (sig 0.020; p <0.05), and SSS values (sig 0.001; p <0.05). NRS before and 4 weeks after injection of D5W (sig 0.002; p <0.05), FSS (sig 0.001; p <0.05), and SSS (sig 0.000; p <0.05). Comparison between TCA injection and D5W injection at 4 weeks after the injection showed that the results was significantly different on NRS (sig 0.806; p> 0.05) for FSS (sig 0.512; p> 0.05) and SSS (sig 0.293; p> 0.05). Conclusion: There is a significant difference in NRS, FSS and SSS values at 4 weeks after hydrodissection injection, using either TCA or D5W. TCA hydrodissection injection compared to D5W hydrodissection injection was equally effective in improving NRS, FSS and SSS after 4 weeks of injection.
Intelligent credit scoring using deep learning methods

Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.
3′-sulfated LewisA/C: An oncofetal epitope associated with metaplastic and oncogenic plasticity of the gastrointestinal foregut

Metaplasia, dysplasia, and cancer arise from normal epithelia via a plastic cellular transformation, typically in the setting of chronic inflammation. Such transformations are the focus of numerous studies that strive to identify the changes in RNA/Protein expression that drive such plasticity along with the contributions from the mesenchyme and immune cells. However, despite being widely utilized clinically as biomarkers for such transitions, the role of glycosylation epitopes is understudied in this context. Here, we explore 3′-Sulfo-Lewis A/C, a clinically validated biomarker for high-risk metaplasia and cancer throughout the gastrointestinal foregut: esophagus, stomach, and pancreas. We discuss the clinical correlation of sulfomucin expression with metaplastic and oncogenic transformation, as well as its synthesis, intracellular and extracellular receptors and suggest potential roles for 3′-Sulfo-Lewis A/C in contributing to and maintaining these malignant cellular transformations.
Influence of Temperature Chronobiology on Stroke Outcome

The circadian system regulates numerous physiological variables, including body temperature. Additionally, a circadian patter has been described in stroke onset. Considering this, we hypothesised that the chronobiology of temperature may have an impact on stroke onset and functional outcomes. We also studied the variation of blood biomarkers according to stroke onset time. This is a retrospective observational study. Of the patients included, 2763 had a stroke between midnight and 8:00 h; 1571 between 8:00–14:00 h; and 655 between 14:00 h and midnight. Axillary temperature was measured at admission. At this time, blood samples were collected for biomarker analysis (TNF-α, IL-1β, IL-6, IL-10, and glutamate). Temperature was higher in patients admitted from 8:00 h to midnight (p < 0.0001). However, the percentage of poor outcome at 3 months was highest in patients from midnight to 8:00 h (57.7%, p < 0.001). The association between temperature and mortality was highest during night time (OR: 2.79; CI 95%: 2.36–3.28; p < 0.001). These patients exhibited high glutamate (220.2 ± 140.2 µM), IL-6 (32.8 ± 14.3 pg/mL) and low IL-10 (9.7 ± 14.3 pg/mL) levels. Therefore, temperature chronobiology could have a significant impact on stroke onset and functional outcome. Superficial body hyperthermia during sleep seems to be more dangerous than during wakefulness. Further studies will be necessary to confirm our data.
First study on capsular serotypes and virulence factors of Pasteurella multocida isolates from Phan Rang sheep in Vietnam

Background and Aim: Pasteurella multocida is considered as a main factor mediating pneumonic pasteurellosis in ruminants, including sheep. It is also a current threat to Phan Rang sheep in Vietnam. This study aimed to characterize P. multocida isolated from Phan Rang sheep, their antibiotic resistance profile, and the prevalence of some virulence-associated genes of these strains. Materials and Methods: Bacteria were isolated on brain heart infusion, 10% sheep blood agar plates, and screened by biochemical tests. The polymerase chain reaction technique was used with specific primers to identify P. multocida, the presence of virulence-associated genes, and serotypes of isolates. Antimicrobial susceptibility and biofilm formation of isolates were examined using the disk diffusion method and crystal violet-based method, respectively. Results: A total of 41 P. multocida strains were isolated from 485 samples from clinically sick and healthy sheep. Of the isolates, 58.53% were serotype A, 9.75% were serotype B, and 31.71% were serotype D. Healthy animals were infected with serotype D only. All 15 virulence genes were identified in all strains isolated from clinically sick sheep, while strains isolated from healthy sheep carried 11/15 virulence genes tested. Among virulence-associated genes exbB, exbD, tonB, ompA, oma87, fimA, hgbA, and nanB were detected in over 90% of isolates, whereas hgbB, nanH, tbpA and pfhA were less frequent. Interestingly, pmHAS and tadD were highly prevalent in capsular type A strains, whereas the toxA gene was detected in capsular type D strains only. All of the isolated strains were fully susceptible to enrofloxacin, ciprofloxacin, neomycin, and ofloxacin. About 92.68% were susceptible to chloramphenicol and 90.24% to amikacin, but there was high resistance to erythromycin, tetracycline, and amoxicillin. Our results reveal that 53.65% of 41 isolates could produce biofilm, whereas 46.34% could not. Conclusion: Pasteurella multocida from Phan Rang sheep possess many virulence genes and resistance to several common antibiotics such as erythromycin, tetracycline, and amoxicillin. The results are an important warning regarding antibiotic resistance of P. multocida.
IoT Traffic Analyzer Tool with Automated and Holistic Feature Extraction Capability

The Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistically extracts network traffic features from an IoT device in a smart home environment that researchers in various IoT industries can implement to collect information about IoT network behavior. A custom testbed with four IoT devices is created to collect real-time network traffic data based on seventeen comprehensive scenarios of these devices’ possible interactions. The output data is fed into the IoT traffic analyzer tool for both flow and packet levels analysis to extract all possible features. Such features are ultimately classified into five categories: IoT device type, IoT device behavior, Human interaction type, IoT behavior within the network, and Abnormal behavior. The tool is then evaluated by 20 users considering three variables: usefulness, accuracy of information being extracted, performance and usability. Users in three groups were highly satisfied with the interface and ease of use of the tool, with scores ranging from 90.5% to 93.8% and with an average score between 4.52 and 4.69 with a low standard deviation range, indicating that most of the data revolve around the mean
Coherence of fuzzy knowledge bases

Checking the coherence of a set of rules is an important step in the validation process of a knowledge base. Roughly speaking, a knowledge base is "potentially inconsistent", what is called "incoherent" here, if there exists a piece of input data which respects integrity constraints and leads to logical inconsistency when added to the knowledge base. This paper is devoted to the case of bases made of parallel fuzzy rules. It is shown that only implication-based rules (gradual rules and certainty rules) are sensitive to coherence problems. However, unlike conjunction-based rules, with which incoherence may be hidden, they allow one to design coherence checking procedures. Necessary and sufficient conditions that a set of parallel certainty (or gradual) rules has to satisfy in order to avoid inconsistency are given.
Optimizing Soil Moisture Station Networks for Future Climates

Soil moisture is central to local climate on land. In situ soil moisture observations are vital for observing vegetation‐relevant root‐zone soil moisture. However, stations included in the International Soil Moisture Network are sparse in regions with strong land‐atmosphere coupling. We apply a machine‐learning‐based procedure for informing future station placement using virtual soil moisture stations in future CMIP6 projections. Stations are placed where the climate is currently most under‐represented. This strategy outperforms random station placement and station placement according to geographical distance. Doubling the current number of stations using this method alleviates the uneven global distribution of stations, increases the skill in the estimation of inter‐annual variability and trends in dry‐season soil moisture, and reduces its differences across climates in future projections. Stations are predominantly placed in tropical climates, especially when optimizing for drying trends. The results can inform future station placement to support climate change mitigation efforts.
Modulation of polar mesospheric summer echoes (PMSEs) with high-frequency heating during low solar illumination

Abstract. Polar mesospheric summer echo (PMSE) formation is linked to charged dust/ice particles in the mesosphere. We investigate the modulation of PMSEs with radio waves based on measurements with EISCAT VHF radar and EISCAT heating facility during low solar illumination. The measurements were made in August 2018 and 2020 around 20:02 UT. Heating was operated in cycles with intervals of 48 s on and 168 s off. More than half of the observed heating cycles show a PMSE modulation with a decrease in PMSE when the heater is on and an increase when it is switched off again. The PMSE often increases beyond its initial strength. Less than half of the observed modulations have such an overshoot. The overshoots are small or nonexistent at strong PMSE, and they are not observed when the ionosphere is influenced by particle precipitation. We observe instances of very large overshoots at weak PMSE. PMSE modulation varies strongly from one cycle to the next, being highly variable on spatial scales smaller than a kilometer and timescales shorter than the timescales assumed for the variation in dust parameters. Average curves over several heating cycles are similar to the overshoot curves predicted by theory and observed previously. Some of the individual curves show stronger overshoots than reported in previous studies, and they exceed the values predicted by theory. A possible explanation is that the dust-charging conditions are different either because of the reduced solar illumination around midnight or because of long-term changes in ice particles in the mesosphere. We conclude that it is not possible to reliably derive the dust-charging parameters from the observed PMSE modulations.
A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data

ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.
Fully Metallic Luneburg Metalens Antenna in Gap Waveguide Technology at V-Band

This article presents the design of a flat Luneburg metalens antenna at V-band using gap waveguide (GW) technology. The metalens consists of a parallel plate waveguide (PPW) loaded with metallic pins whose height is modulated to get an effective refractive index that follows the Luneburg equation. A Groove GW (GGW) H-plane horn is used to illuminate the metalens, such that the rays are collimated and a planar wavefront is generated in the direction of propagation. Since the structure at hand is planar, it can be efficiently integrated on flat surfaces. Moreover, the fully metallic structure is mechanically robust and presents lower losses than lenses including dielectric substrates. A prototype has been fabricated and tested, simulations and experimental results are in very good agreement. The metalens yields an input reflection coefficient ( $S_{11}$ ) below −10 dB from 45 to 70 GHz, whereas the −3 dB gain fractional bandwidth is 26.2% with respect to a center frequency of 60 GHz, with a peak of 22.5 dB at 61 GHz. These features make this design an interesting solution for millimeter-wave (MMW) applications.
Some remarks on standard gradient models and gradient plasticity

The standard gradient models, originally introduced in the works of Gurtin (Physica D 1996; 92: 178) and Frémond and Nedjar (Int J Solids Struct 1996; 33: 1083) have been studied intensively in the last few decades for various applications in plasticity, damage mechanics and multi-phase analysis. In this paper, these models are revisited and discussed in relation with some classical descriptions of solids such as plasticity and visco-plasticity. The constitutive equations of such a model and the governing equations for a solid have been initially derived by these authors from an extended virtual work principle. Without this starting point, it is shown here that these equations appear as a generalized Biot equation for the solid and can be obtained directly from the global expression of the energy and dissipation potentials. This result gives the possibility to write the governing equations for standard gradient models of any order. These models also appear as a simple generalization of classical descriptions in solid mechanics. As an example, for a time-independent process such as incremental plasticity, our attention is focused on the constitutive modeling and on the governing equations of the response. It is shown that an elastic regularization can be introduced to avoid some theoretical and numerical difficulties which are well known in the classical theory of rigid plasticity.
Socioepidemiological macro-determinants associated with the cumulative incidence of bacterial meningitis: A focus on the African Meningitis Belt

Background Bacterial meningitis (BM) is a public health challenge as it is associated with high lethality and neurological sequelae. Worldwide, most cases are registered in the African Meningitis Belt (AMB). The role of particular socioepidemiological features is essential for understanding disease dynamics and optimizing policy-making. Objective To identify socioepidemiological macro-determinants that contribute to explaining the differences in BM incidence between AMB and the rest of Africa. Methods Country-level ecologic study based on the cumulative incidence estimates of the Global Burden of Disease study and reports of the MenAfriNet Consortium. Data about relevant socioepidemiological features were extracted from international sources. Multivariate regression models were implemented to define variables associated with the classification of African countries within the AMB and the incidence of BM worldwide. Results Cumulative incidences at the AMB sub-regions were 111.93 (west), 87.23 (central), 65.10 (east), and 42.47 (north) per 100,000 population. A pattern of common origin with continuous exposition and seasonality of cases was observed. Socio-epidemiological determinants contributing to differentiating the AMB from the rest of Africa were household occupancy (OR 3.17 CI 95% 1.09–9.22, p = 0.034) and malaria incidence (OR 1.01 CI 95% 1.00–1.02, p = 0.016). BM cumulative incidence worldwide was additionally associated with temperature and gross national income per capita. Conclusion Socioeconomic and climate conditions are macro-determinants associated with BM cumulative incidence. Multilevel designs are required to confirm these findings.
A Review on Phase-type Distributions and their Use in Risk Theory

Phase-type distributions, defined as the distributions of absorption times of certain Markov jump processes, constitute a class of distributions on the positive real axis which seems to strike a balance between generality and tractability. Indeed, any positive distribution may be approximated arbitrarily closely by phase-type distributions whereas exact solutions to many complex problems in stochastic modeling can be obtained either explicitly or numerically. In this paper we introduce phase-type distributions and retrieve some of their basic properties through appealing probabilistic arguments which, indeed, constitute their main feature of being mathematically tractable. This is illustrated in an example where we calculate the ruin probability for a rather general class of surplus processes where the premium rate is allowed to depend on the current reserve and where claims sizes are assumed to be of phase-type. Finally we discuss issues concerning statistical inference for phase-type distributions and related functionals such as e.g. a ruin probability.