Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF LAMINAR FORCED CONVECTION INSIDE A SQUARE VENTILATED CAVITY USING THE OPENFOAM

Laminar forced convection inside a square cavity with inlet and outlet ports was numerically analyzed. The positions of the inlet and outlet ports were fixed and the ports sizes were equal 25% of the side wall. The influence of the Reynolds and Prandtl numbers on the flow and temperature fields inside the cavity was verified for nine cases, with Re = 50, 100 and 500 and Pr = 0.7, 3 and 5. The heat transfer process in the cavity was analyzed from obtained values for the average Nusselt number and the local Nusselt number on the walls. The open source computer package OpenFOAM® was used for simulations considering a two-dimensional flow. For all tested Prandtl numbers, there is a growth in the rotating vortex regions as Reynolds number is increases. The temperature fields are directly related to the presence of the rotating vortices and the temperature gradient is more noticeable at the interface sections of the throughflow stream with the neighboring vortices and the next to the walls for greater Reynolds and Prandtl numbers. It was verified that the local Nusselt number on the walls varies radically with minimum and maximum points and it is dependent on the flow and temperature fields adjacent to the respective wall. The results for average Nusselt number per wall indicated that the bottom wall is the most susceptible to variations in its average Nusselt number and that the top wall present higher values of this parameter for all tested cases. Finally, the average Nusselt number was increased with increasing the Reynolds and Prandtl numbers indicating the enhanced thermal exchange.
Steganalysis using Regional Correlation and Second-order Markov Features

A blind steganalysis based on regional correlation and second-order Markov transition probability matrix is proposed for JPEG images. By analyzing the region correlation of JPEG image, the Markov transition probability matrix is used to capture the correlation of intra-block and inter-block DCT coefficients. In addition, the calibrated features are extracted from the calibrated images. The difference features between the original and calibrated images are used for training and classifying. Three different image libraries are used to detect the five kinds of typical JPEG steganography schemes. Experimental results show that, in comparison to the several effective steganalysis, the proposed scheme improves the detection accuracy on some JPEG-based steganography schemes, including outguess, Steghide and MB1.
Shared functional specialization in transformer-based language models and the human brain

Humans use complex linguistic structures to transmit ideas to one another. The brain is thought to deploy specialized computations to process these structures. Recently, a new class of artificial neural networks based on the Transformer architecture has revolutionized the field of language modeling, attracting attention from neuroscientists seeking to understand the neurobiology of language in silico. Transformers integrate information across words via multiple layers of structured circuit computations, forming increasingly contextualized representations of linguistic content. Prior work has focused on the internal representations (the “embeddings”) generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into functionally-specialized “transformations” to provide a complementary window onto linguistic computations in the human brain. Using functional MRI data acquired while participants listened to naturalistic spoken stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent syntactic computations performed by individual, functionally-specialized “attention heads” differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers, contextual distances, and syntactic dependencies in a low-dimensional cortical space. Our findings indicate that large language models and the cortical language network may converge on similar trends of functional specialization for processing natural language.
Isolated marginally stable geophysical vortices

Long-term marginal stability of a new family of isolated oceanic vortices is analysed. Sign reversal of the radial gradient of the potential vorticity anomaly, as implied by the isolation requirement, leads to vortex unsteadiness but does not break the coherence of the vortex, which remains marginally stable even for high absolute Rossby numbers $Ro\simeq 0.8$ . The marginally stable vortices are characterized by a zero amount of potential vorticity anomaly on every isopycnal. The marginally stable final state is an unsteady vortex whose inner one-signed potential vorticity anomaly experiences revolution, rotation, precession and nutation.
Structure and Environment of Polar Mesocyclones over the Northeastern Part of the Sea of Japan

AbstractPolar mesocyclones (PMCs) are mesoscale cyclonic vortices that develop poleward of the main polar front. The Sea of Japan is one region where PMCs frequently occur during winter. In this paper, the general characteristics of the structure and environment of PMCs that form over the northeastern part of the Sea of Japan and move southward are examined using composite analysis and numerical simulation. The composite analyses show that the synoptic-scale environment of the PMCs is characterized by a zonal temperature gradient at lower levels and a trough accompanied by cold air at upper levels. These elements of the environmental field form a reverse shear. The mesoscale structure of the PMCs exhibits characteristics of baroclinic development, while it is also accompanied by condensational heating. The numerical simulation in which the composite fields are used for the initial and boundary conditions successfully reproduces a realistic PMC. In this numerical simulation, the mesoscale structures are al.
Enucleation of the C. elegans embryo revealed the mechanism of dynein-dependent spacing between microtubule asters

The centrosome is a major microtubule-organizing center in animal cells. The intracellular positioning of the centrosomes is important for proper cellular function. One of the features of centrosome positioning is the spacing between centrosomes. The spacing activity is mediated by microtubules extending from the centrosomes; however, the underlying mechanisms are not fully understood. To characterize the spacing activity in the Caenorhabditis elegans embryo, a genetic setup was developed to produce enucleated embryos. The centrosome duplicated multiple times in the enucleated embryo, which enabled us to characterize the chromosome-independent spacing activity between sister and non-sister centrosome pairs. We knocked down genes in the enucleated embryo and found that the timely spacing was dependent on cytoplasmic dynein. Based on these results, we propose a stoichiometric model of cortical and cytoplasmic pulling forces for the spacing between centrosomes. We also found a dynein-independent but non-muscle myosin II-dependent movement of the centrosomes in a later cell cycle phase. The dynein-dependent spacing mechanisms for positioning the centrosomes revealed in this study is likely functioning in the cell with nucleus and chromosomes, including the processes of centrosome separation and spindle elongation.
X-ray and optical properties of X-ray luminous active galactic nuclei

Giacconi et al. (1962) discovered a diffuse cosmic X-ray background with rocket experiments when they searched for lunar X-ray emission. Later satellite missions found a spectral peak in the cosmic X-ray background at ∼30 keV. Imaging X-ray satellites such as ROSAT (1990-1999) were able to resolve up to 80% of the background below 2 keV into single point sources, mainly active galaxies. The cosmic X-ray background is the integration of all accreting super-massive (several million solar masses) black holes in the centre of active galaxies over cosmic time. Synthesis models need further populations of X-ray absorbed active galaxy nuclei (AGN) in order to explain the cosmic X-ray background peak at ∼30 keV. Current X-ray missions such as XMM-Newton and Chandra offer the possibility of studying these additional populations. This Ph.D. thesis studies the populations that dominate the X-ray sky. For this purpose the 120 ksec XMM-Newton Marano field survey, named for an earlier optical quasar survey in the southern hemisphere, is analysed. Based on the optical follow-up observations the X-ray sources are spectroscopically classified. Optical and X-ray properties of the different X-ray source populations are studied and differences are derived. The amount of absorption in the X-ray spectra of type II AGN, which are considered as a main contributor to the X-ray background at ∼30 keV, is determined. In order to extend the sample size of the rare type II AGN, this study also includes objects from another survey, the XMM-Newton Serendipitous Medium Sample. In addition, the dependence of the absorption in type II AGN with redshift and X-ray luminosity is analysed. We detected 328 X-ray sources in the Marano field. 140 sources were spectroscopically classified. We found 89 type I AGN, 36 type II AGN, 6 galaxies, and 9 stars. AGN, galaxies, and stars are clearly distinguishable by their optical and X-ray properties. Type I and II AGN do not separate clearly. They have a significant overlap in all studied properties. In a few cases the X-ray properties are in contradiction to the observed optical properties for type I and type II AGN. For example we find type II AGN that show evidence for optical absorption but are not absorbed in X-rays. Based on the additional use of near infra-red imaging (K-band), we were able to identify several of the rare type II AGN. The X-ray spectra of type II AGN from the XMM-Newton Marano field survey and the XMM-Newton Serendipitous Medium Sample were analysed. Since most of the sources have only ∼40 X-ray counts in the XMM-Newton PN-detector, I carefully studied the fit results of simulated X-ray spectra as a function of fit statistic and binning method. The objects revealed only moderate absorption. In particular, I do not find any Compton-thick sources (absorbed by column densities of NH > 1.5 × 10 24 cm). This gives evidence that type II AGN are not the main contributor of the X-ray background around 30 keV. Although bias effects may occur, type II AGN show no noticeable trend of the amount of absorption with redshift or X-ray luminosity.
Electromyographic Correlates of Effortful Listening in the Vestigial Auriculomotor System

Recently, electromyographic (EMG) signals of auricular muscles have been shown to be an indicator of spatial auditory attention in humans, based on a vestigial pinna-orienting system. Because spatial auditory attention in a competing speaker task is closely related to the more gen-eralized concept of attentional effort in listening, the current study inves-tigated the possibility that the EMG activity of auricular muscles could also reflect correlates of effortful listening in general. Twenty subjects were recruited. EMG signals from the left and right superior and poste-rior auricular muscles (SAM, PAM) were recorded while subjects attended a target podcast in a competing speaker paradigm. Three different lev-els of listening effort, low, medium, and high, were generated by varying the number and pitch of distractor streams, as well as the signal-to-noise ratio. All audio streams were either presented from a loudspeaker placed in front of the participants (0°), or in the back (180°). Averaged PAM activity was not affected by different levels of listening effort, but was sig-nificantly larger when stimuli were presented from the back, as opposed to the front. Averaged SAM activity, however, was significantly larger in the high listening effort condition, compared to low and medium, but was not affected by stimulus direction. We hypothesize that the increased SAM activity is a response of the vestigial pinna–orienting system to effortful stream segregation task.
Evidence for gene transfer between mycoviruses and their host: Curvulaviridae as a case study

Gene transfer between distinct evolutionary lineages has been recognized as a frequent event occurring between viruses and their hosts. This phenomenon has been studied to some extent in animal and plant viruses, not so much in the case of mycoviruses, for which the evolutionary origins of their proteins remain poorly understood. In this study, we have tested the hypothesis of a mosaic origin for mycoviruses’ genomes, with the RNA-dependent RNA-polymerase (RdRp) being of viral origin and the coat protein (CP) resulting from one or more transfer events from the host genome. Firstly, phylogenetic trees were constructed for the RdRps and the CPs from a selection of viruses to address for possible incongruent evolutionary histories. Moreover, a PSI-BLAST search using the CP sequences from the different mycovirus groups retrieved hypothetical proteins (HP) with many orthologues in fungal genomes showing significant sequence homology with the CP from the members within the Curvulaviridae family. The structures of these HPs, predicted in silico using AlphaFold, tend to show high similarity with viral CPs suggesting the occurrence of gene transfer between viruses and fungi, although no clear function has been yet attributed to these genes in the host. Phylogenetic analyses suggest that this gene transfer could have occurred in multiple independent events. Additional selection analysis supports the notion that the most parsimonious explanation is the transfer of the HP from the host to an ancestral viral genome followed by fast evolution to accommodate the newly acquired protein to function as a CP.
Predicting Fixed Effects in Panel Probit Models

We present a method to estimate and predict fixed effects in a panel probit model when N is large and T is small, and when there is a high proportion of individual units without variation in the binary response. Our approach builds on a bias-reduction method originally developed by Kosmidis and Firth (2009) for cross-section data. In contrast to other estimators, our approach ensures that predicted fixed effects are finite in all cases. Results from a simulation study document favorable properties in terms of bias and mean squared error. The estimator is applied to predict period-specific fixed effects for the extensive margin of health care utilization (any visit to a doctor during the previous three months), using German data for 2000-2014. We find a negative correlation between fixed effects and observed characteristics. Although there is some within-individual variation in fixed effects over sub-periods, the between-variation is four times as large.
Using citizen science to protect threatened amphibian populations in urban spaces: ecology, life history, and conservation of green toads and fire salamanders in Jerusalem and Haifa

The rapid urbanization processes occurring worldwide are amongst the main factors driving the current biodiversity crisis. In particular, a third of known amphibian species are directly threatened by urbanization. The negation of this threat will require conservation efforts aimed at sustaining viable amphibian populations within the urban landscape, which must be informed by a deep understanding of the way amphibian populations are affected by the unique risk factors of the urban environment. To address this need for four populations of amphibians in Israel, we performed a capture-recapture analysis on two datasets. The larger of the two datasets is the result of a multi-year citizen science program focused on two Salamandra infraimmaculata populations within the city of Haifa, Israel. The second dataset is the result of one year of survey following a similar protocol that we performed on two Bufotes variabilis populations within the city of Jerusalem and at a nature reserve near it. Individuals of both species have unique and recognizable dorsal spot patterns, which allowed for noninvasive recapture identification. The results of our analysis provide insights that can guide future conservation of the specific studied population, but our conclusions have wider implications, regarding both the ecology of the studied species and applied conservation science: using the salamander dataset, we developed a method of length-based age estimations for this species and found that the studied salamanders have a prolonged period of increased vulnerability throughout their first years of life, even after reaching sexual maturity. Additionally, the shared conclusions from the two case studies indicate that the creation of fish-containing artificial water bodies in Mediterranean habitats can have detrimental impacts on the resident amphibian populations. Synthesis and implications: The significance and extent of our results demonstrate the effectiveness of citizen science as a tool for research and conservation in the urban environment. Our findings call for the implementation of management practices that prioritize the protection of urban amphibians and their habitats. By identifying the vulnerability of amphibians during critical life stages and highlighting the negative impacts of fish-containing water bodies, our study contributes to the development of informed conservation policies with implications for urban planning, habitat management, and biodiversity conservation strategies.
Flow in fetoplacental microvessels in vitro enhances perfusion, barrier function, and matrix stability

Proper placental vascularization is vital for pregnancy outcomes, but assessing it with animal models and human explants has limitations. Here, we present a 3D in vitro model of human placenta terminal villi that includes fetal mesenchyme and vascular endothelium. By co-culturing HUVEC, placental fibroblasts, and pericytes in a macro-fluidic chip with a flow reservoir, we generate fully perfusable fetal microvessels. Pressure-driven flow is crucial for the growth and remodeling of these microvessels, resulting in early formation of interconnected placental vascular networks and maintained viability. Computational fluid dynamics simulations predict shear forces, which increase microtissue stiffness, decrease diffusivity and enhance barrier function as shear stress rises. Mass-spec analysis reveals the deposition of numerous extracellular proteins, with flow notably enhancing the expression of matrix stability regulators, proteins associated with actin dynamics, and cytoskeleton organization. Our model provides a powerful tool for deducing complex in vivo parameters, such as shear stress on developing vascularized placental tissue, and holds promise for unraveling gestational disorders related to the vasculature.
Quantum Mechanics in the general quantum systems (VI): the eigenvalue problem of Hamiltonian

We obtain a complete series solution of stationary Schrodinger's equation in the general quantum systems. It is exact in the sense that any approximation means is not used, or that the whole corrections or contributions from all order perturbations are involved if the perturbation concept is introduced. Furthermore, the useful forms of our exact solution and a new expression the complete Green operator are given out. As a universal and analytical solution, it is helpful for the theoretical derivations and practical calculations in quantum theory.
Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.
Cost-Based Abduction and Linear Constraint Satisfaction

Abduction is the problem of finding the best explanation for a given set of observations. Cost-based abduction approaches attempt to prove the observation by assuming some hypotheses. Hypotheses have associated ``costs'''' and the best proof is the one which assumes the least costly set. Previous approaches have formalized minimum cost-based abduction as a search problem. Heuristics were then sought to aid in the search for the best proof. However, efficient admissible heuristics seem to be difficult to find. In this paper, we present an approach to the problem from the standpoint of optimization. By using linear constraints to represent causal relationships, we provide a new framework capable of modeling cost-based abduction. Moreover, we are able to utilize the highly efficient optimization tools of operations research, thus yielding a computationally efficient method for solving cost-based abduction problems.
The timing of cellular events: a stochastic vs deterministic perspective

Changes in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by numerical integration of deterministic models, these ignore intrinsic noise and hence their predictions may be inaccurate. Here we study the differences between deterministic and stochastic model predictions for the mean trigger times using simple models of gene expression, post-transcriptional feedback control, and enzyme-mediated catalysis. By comparison of the two predictions, we show that when promoter switching is present there exists a transition from a parameter regime where deterministic models predict a longer trigger time than stochastic models to a regime where the opposite occurs. Furthermore, the ratio of the trigger times of the two models can be large, particularly for auto-regulatory genetic feedback loops. Our theory provides intuitive insight into the origin of these effects and shows that deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells.