Miracle-dz/UniFAR-FTUs huggingface dataset:
PASSIVE INSTAGRAM USE AND EMERGING INDIAN FEMALE ADULT WELLBEING: A MEDIATION ANALYSIS OF SOCIAL COMPARISON AND FOMO DURING COVID-19

Recent studies in cyberpsychology examined the phenomena of passive Social Networking Sites (SNSs) usage leading to social comparison and thereby leading to FoMO (Fear of Missing Out) George et al. (2019) in general. A review of prior research indicates that for women, but not for males, the relationships between social comparison and FOMO and self-perceived physical beauty were substantial Myers and Crowther (2009). The present study explores the relation between passive Instagram use, social comparison, and FoMO among the Emerging Indian female adults during the covid-19 pandemic.The study focuses on the SNS (Instagram) and its impact on age and gender by undertaking cross-sectional research. It takes into account a diverse sample of college students (N = 302, Mage = 18-25, SD age = between 4 to 5, 100% female). To assess participants' engagement in technology-based social comparisons and feedback-seeking behaviours on Instagram, an 11-item social comparison orientation (INCOM, Iowa-Netherlands comparison orientation scale) English version is used.Social comparison theory Festinger (1954) and Symbolic Interactionism are used to explain the study’s results. The research outcome shows that there is a significant deviation in the mediation of social comparison and FoMO on the passive Instagram usage among the Emerging Indian Female Adults during the covid-19, While the studies conducted before the pandemic show otherwise George et al. (2019).
Gravitational collapse to neutron stars and black holes - Computer generation of spherical spacetimes

A new, general relativistic, hydrodynamical computer code for spherical, stellar colapse to neutron stars and black holes is presented. The code is employed to construct spacetimes for the geometrodynamical evolution of spherical masses which are initially at rest and obey an adiabatic GAMMA-law equation of state. Collapse to a black hole can be followed accurately; the evolution outside the event horizon can be determined far into the future without having the integration terminate because of spacetime singularities inside.We use the code to address several fundamental points of principle regarding spherical, gravitational collapse and requiring a reliable, fully relativistic, numerical computation. We sample a variety of possible collapse and requiring a reliable, fully relativistic, numerical computation. We sample a variety of possible collapse scenarios, characterized by different masses, mass distributions, and equations of state, to examine stellar core collapse and its end fate. We distinguish three possible outcomes for adiabatic core collapse: homologous core, bounce, nonhomologous core bounce, and catastrophic collapse to a black hole. Even a very massive stellar core may undergo a nonhomologous bounce and only later collapse to a black hole on a secular time scale rather than a dynamical one. Accretion-driven neutron star collapse is also treatedmore » numerically. The implications of our findings for realistic core collapse believed to occur in nature are considered. « less
COMPETING RISK ANALYSIS BAGI PASIEN PNEUMONIA DI SUATU RUMAH SAKIT

Analisis survival merupakan salah satu bagian dalam ilmu Statistika yang berkaitan dengan kelangsungan hidup suatu entitas. Analisis survival memiliki penerapan yang luas dalam bidang biostatistika dan membantu banyak praktisi kesehatan dalam memajukan berbagai metode kesehatan. Pada penelitian ini dibahas salah satu topik penting dalam analisis survival, yaitu situasi competing risks. Competing risks adalah suatu kondisi di mana terdapat lebih dari satu kejadian yang mungkin terjadi pada seorang individu. Penelitian ini berfokus pada ilustrasi bahwa penggunaan penduga Kaplan-Meier dalam situasi competing risks tidaklah cukup. Pendekatan dengan menggunakan nilai cumulative incidence function (CIF) dari setiap kejadian adalah salah satu solusi dalam situasi competing risks, baik secara nonparametrik maupun secara parametrik. Berdasarkan hasil uji hipotesis, situasi competing risks benar terjadi pada pasien pneumonia dalam penelitian ini.
Spotting and tracking good biometrics with the human visual system

We mathematically model the mammalian Visual System's (VS) capability of spotting objects. How can a hawk see a tiny running rabbit from miles above ground? How could that rabbit see the approaching hawk? This predatorprey interaction draws parallels with spotting a familiar person in a crowd. We assume that mammal eyes use peripheral vision to perceive unexpected changes from our memory, and then use our central vision (fovea) to pay attention. The difference between an image and our memory of that image is usually small, mathematically known as a 'sparse representation'. The VS communicates with the brain using a finite reservoir of neurotransmittents, which produces an on-center and thus off-surround Hubel/Wiesel Mexican hat receptive field. This is the basis of our model. This change detection mechanism could drive our attention, allowing us to hit a curveball. If we are about to hit a baseball, what information extracted by our HVS tells us where to swing? Physical human features such as faces, irises, and fingerprints have been successfully used for identification (Biometrics) for decades, recently including voice and walking style for identification from further away. Biologically, humans must use a change detection strategy to achieve an ordered sparseness and use a sigmoid threshold for noisy measurements in our Hetero-Associative Memory [HAM] classifier for fault tolerant recall. Human biometrics is dynamic, and therefore involves more than just the surface, requiring a 3 dimensional measurement (i.e. Daugman/Gabor iris features). Such a measurement can be achieved using the partial coherence of a laser's reflection from a 3-D biometric surface, creating more degrees of freedom (d.o.f.) to meet the Army's challenge of distant Biometrics. Thus, one might be able to increase the standoff loss of less distinguished degrees of freedom (DOF).
Phytochemical Profiles, Antioxidant, Antimicrobial and Cytotoxic cell lines activity of Passiflora caerulea L.

Passiflora caerulea L. (blue passion flower) is a member of the genus Passiflora, which contains over 500 species and is popular worldwide for its diverse medicinal properties. Qualitative phytochemical screening was carried out for the methanolic extract of the plant leaves and the tests revealed that the plant contains a wide range of medicinally important secondary metabolites such as alkaloids, cyanogenic compounds, phenolic compounds and glycosyl flavonoids. Among a host of organic solvents which were used for extraction, the methanolic extract was particularly found to possess high concentrations of carbohydrates, glycosides, free amino acids, glycosides, fats and alkaloids. Also, flavonoids, phytosterols, saponins, tannins and other phenolic compounds were present at lower levels in the methanolic extract. We tested the antioxidant potential of the P. caerulea methanolic leaf extract and discovered that it possessed nitric oxide scavenging activity of close to 80% w.r. t at 100 μg/mL against ascorbic acid control (100%). In the ABTS and DPPH radical scavenging assays, the extract (100 μg/mL) possessed antioxidant potential of 72.3% and 70%, respectively, w.r. t ascorbic acid control taken at 100 μg/mL. The plant powder was assessed quantitatively for presence of carbohydrates, proteins, lipids, ascorbic acid, tannins and flavonoids and the concentrations of these compounds were found to be 14.26 mg/g, , respectively. MTT assay results showed that the crude methanolic extract of the plant possessed appreciable activity against breast cancer (MCF-7) cells. Although the IC50 values of the extract against MCF-7 cells were much lesser comparatively w.r. t control doxorubicin, the crude extract possessed considerable activity against MCF-7 cells when compared to the standard drug, doxorubicin. These results necessitate deeper investigations into the pharmacological and therapeutic usefulness of purified compounds from P. caerulea. Though P. incarnata is the most widely studied species in the Passiflora genus, P. caerulea is relatively poorly studied. This paper shows the phytochemical composition and biological activities of P. caerulea.
VOLATILITY SMILE BY MULTILEVEL LEAST SQUARE

The aim of this paper is to propose several algorithms for finding the local volatility from partial observations of the price of an European vanilla option. Dupire's equation is used. The local volatility and the price of the option are discretized by finite elements with highly non uniform meshes and with a coarser mesh for the local volatility. The inverse problem is formulated as a least square problem and the minimization is done by an interior point method. The gradient of the cost function is computed exactly by solving an adjoint problem. A multilevel approach is proposed for accelerating the computations. Also, a suboptimal time-stepping algorithm is considered. For all the proposed algorithms, numerical tests are supplied.
The Nuclear Spectral Energy Distribution of NGC 6251: A BL Lacertae Object in the Center of an FR I Radio Galaxy

We determine the nuclear spectral energy distribution (SED) from the radio to the gamma-ray band for the FR I radio galaxy NGC 6251, by using data from the literature and analyzing Hubble Space Telescope and X-ray archival data. In the log ν- log(νFν) representation, the SED has two broad peaks, and it is remarkably similar to those of blazars. We show that the low-energy peak can be explained in terms of synchrotron radiation, while the high-energy peak is most plausibly produced by inverse Compton scattering. This brings direct support to the FR I-BL Lac unification model. We model the overall emission in the frame of a synchrotron self-Compton scenario, which well describes the SED of BL Lac objects. The model parameters that we obtain also quantitatively confirm the FR I-BL Lac unification model and imply a rather small viewing angle to this source (θ ≲ 20°). NGC 6251 is the second radio galaxy, in addition to Centaurus A, for which a similar analysis has been performed. A significant improvement with respect to the case of Cen A is the absence of obscuration in NGC 6251, which strengthens the overall result.
A dynamical approach to music analysis

It is possible to identify significant units of a piece of music, like salient motifs or tonally prominent pitch classes, by looking at the stable states of suitably defined dynamic systems associated with the piece and analytical paradigms assigned to the piece. The Lyapunov functions of these systems represent a paradigmatic energy around stable states and derive from eigenfunctions that reflect their centrality within a network of states. The theory presented here is illustrated through an experiment in paradigmatic motivic analysis. Furthermore, it is shown that the approach subsumes the usual repetition paradigm as a limiting case and that it can also be applied to the construction of tonally characteristic pitch class profiles from distance functions.
On the Representation of High-Latitude Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model

AbstractSupercooled liquid water (SLW) layers in boundary layer clouds are abundantly observed in the atmosphere at high latitudes, but remain a challenge to represent in numerical weather prediction (NWP) and climate models. Unresolved processes such as small-scale turbulence and mixed-phase microphysics act to maintain the liquid layer at cloud top, directly affecting cloud radiative properties and prolonging cloud lifetimes. This paper describes the representation of supercooled liquid water in boundary layer clouds in the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP model and in particular the change from a diagnostic temperature-dependent mixed phase to a prognostic representation with separate cloud liquid and ice variables. Data from the Atmospheric Radiation Measurement site in Alaska and from the CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite missions are used to evaluate the model parameterizations. The prognostic scheme s.
Improving out of vocabulary words recognition accuracy for an end-to-end Russian speech recognition system

Automatic Speech Recognition (ASR) systems are experiencing an active introduction into our daily lives, simplifying the way we interact with electronic devices. The advent of end-to-end approaches has only accelerated this process. However, the constant evolution and a high degree of inflection of the Russian language lead to the problem of recognizing new words outside the vocabulary (Out Of Vocabulary, OOV) because they did not take part in the training process of the ASR system. In such a case, the ASR model tends to predict the most similar word from the training data which leads to a recognition error. This is especially true for ASR models that use decoding based on a Weighted Finite State Transducer (WFST), since they are obviously limited by the list of vocabulary words that may appear as a result of recognition. In this paper, this problem is investigated on the basis of an open data set of the Russian language (common voice) and an integrated ASR system using the WFST decoder. A method for retraining an integral ASR system based on the discriminative loss function MMI (maximum mutual information) and a method for decoding the integral model using a TG graph are proposed. Discriminative learning allows smoothing the probability distribution of acoustic class prediction, thus adding more variability in the recognition results. Decoding using the TG graph, in turn, is not limited to recognizing only vocabulary words and allows the use of a language model trained on a large amount of external text data. An eight-hour subset from the common voice base is used as a test set. The total number of OOV words in this test sample is 18.1 %. The results show that the use of the proposed methods allows to reduce the word recognition error (Word Error Rate, WER) by 3 % in absolute value relative to the standard method of decoding integral models (beam search), while maintaining the ability to recognize OOV words at a comparable level. The use of the proposed methods should improve the overall quality of recognition of ASR systems and make such systems more resistant to the recognition of new words that were not involved in the learning process.
Exact calculation of n-d scattering at 14. 1 MeV with a local realistic interaction: Elastic-channel results. [Cross sections, polarizations, and spin correlation coefficients]

The Faddeev differential equations in configuration space are solved numerically for the nucleon-nucleon interaction of de Tourreil and Sprung effective in the states $^{1}S_{0}$, $^{3}D_{1}$, $^{3}D_{1}$, $^{1}P_{1}$, and $^{3}P_{0,1,2}$. Differential cross section, polarizations ${P}_{n}$, $i{T}_{11}$, $Q$, and $R$, and spin correlation coefficients ${C}_{\mathrm{xx}}$ and ${C}_{\mathrm{yy}}$ are computed here. Contributions from other interaction terms are evaluated perturbatively. Agreement with experimental data is found, except for the vector polarization $i{T}_{11}$, and ${C}_{\mathrm{xx}}$.
Digital Media Influencers: A Systematic Literature Review

The popularity of social media influencers (SMIs) has been growing exponentially in recent years, making influencer marketing (IM) a predominant business strategy. Even though there is growing interest from researchers and practitioners, most academic work continues to be divergent, partial, and fragmented. In light of the pivotal role SMIs play in consumer decisions and the fact that this field of research is still in its developmental stages, a comprehensive and critical view of existing research on this topic is greatly needed. This paper is a response to this and is the first to consolidate the current state of IM research within social media settings. More precisely, this paper presents a systematic literature review of relevant studies published in academic journals and magazines in order to identify key themes and dominant concepts in the field. Our analysis of 31 articles from 14 different journals helped give a broader understanding of this phenomenon. Through our analysis we identified a number of research gaps. We then proposed directions for future research to reflect on important emerging areas and unexplored domains with reference to theory, context, and methodology. Lastly, we discuss the implications of this study in theory and practice. The main limitations of the study come from the fact that it only considers articles written in the English language, it is only published in peer-reviewed academic journals, and it is limited to just three online platforms. This study is a first in that it provides a systematic overview of existing literature on digital media influencers.
Seasonal Abundance and Molecular Identification of Aquatic Larvae of Culex pipiens L. and Culex antennatus Becker in Fayoum Governorate, Egypt

The present study was designed to investigate the seasonal abundance of Culex pipiens and Culex antennatus aquatic larvae in Fayoum Governorate, Egypt. The study was carried out in the En Nazla region, Ibshway during winter, spring, summer and autumn of 2021. In addition, the physico-chemical parameters of breeding water habitat were evaluated during different seasons, and relationships between measured parameters and mosquito abundance were addressed. PCR and sequencing confirmed the genetic identification of both Culex species. Generally, C. pipiens was more abundant than C. antennatus in the study area. Summer recorded the highest abundance for both C. pipiens and C. antennatus (about 683 and 400 larvae), superior to what has been recorded in other seasons with a significant margin ( P < 0.05), while winter recorded the lowest abundance of both mosquito species. The highest correlation between seasons for C. pipiens calculated an abundance between winter and autumn (0.83), while the lowest was recorded between winter and summer (0.22). Data assessed revealed that, the TDS is the most reliable factor that mosquito abundance depends on, followed by the pH value for C. pipiens and temperature for C. antennatus. On the other hand, C. pipiens showed a clear relationship with species identified from Portugal and Kenya, with an evolutionary distance of approx. 0.001164. Moreover, Cx antennatus displayed a close relationship to a species identified from Madagascar and Kenya, with an evolutionary distance of approx. 0.00734, respectively.
Liquid metal diffusion experiments in microgravity—vibrational effects

Diffusion and convection in melts play an important role in most technological processes involving the liquid state. Many diffusion experiments in liquids suffer from additional transport induced by gravity-driven flow. Therefore, different research groups performed experiments under microgravity conditions aboard a space vehicle. But even there, diffusion measurements can be disturbed by residual accelerations and vibrations. To analyse this additional transport, model experiments using water as liquid were performed inside an airplane performing zero gravity flight manoeuvres. Because of the short duration of low gravity, the analogy between diffusive mass transport and the faster heat transport was used. The angle between the capillary and the direction of acceleration ranged between 0° and 90°. For an oscillation amplitude of 100 mm, the increase of the frequency up to 1 Hz led to a slight increase of the additional transport. Experiments with 1.7 Hz showed an additional transport of a 50% maximum. The effect of the angle between the capillary and the acceleration vector was within the experimental error limits.
Performance Analysis of State-of-the-Art Deep Learning Models in the Visual-Based Apparent Personality Detection

This paper analyses the performances of pre-trained deep learning models as feature extractors forapparent personality trait detection (APD) by utilising different statistical methods to find the bestperforming pre-trained model. Accuracy and computational cost were used to measure the modelperformance. Personality is measured using the Big Five Personality Schema. CNN-RNN networks weredesigned using VGG19, ResNet152, and VGGFace pre-trained models to measure the personality withscene data. The models were compared using the mean accuracy attained and the average time is taken fortraining and testing. Descriptive statistics, graphs, and inferential statistics were applied in modelcomparisons. Results convey that, ResNet152 model reported the highest mean accuracy in the test dataset(0.9077), followed by VGG19 with 0.9036; VGGFace recorded the lowest (0.8962). ResNet152 consumedmore time than other architectures in model training and testing since the number of parameters iscomparably higher than the other two architectures involved. Statistical test results prove no significantevidence to conclude that VGG19 and ResNet152 based CNN-RNN models performed differently. Thisleads to the conclusion that even with a comparably lower number of parameters VGG19 model performedwell. The findings reveal that satisfactory accuracy is obtained with a limited number of frames extractedfrom videos since models achieved more than 90% accuracy.
Exploring Hybrid Reality Environments for Overview+detail Tasks in Immersive Data Visualisation

There have been a lot of advances in the field of Augmented Reality (AR), but there is limited research on the combined usage of AR and physical displays (known as Hybrid Reality Environments). We explore hybrid reality environments using a combination of Augmented Reality and high-density displays for large graph visualisation. We present the design of a system and early observation from a pilot study involving navigation in such a system.