Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Low-Resourced Language in Social Media Platforms Towards Disease Surveillance for Public Health Monitoring using Artificial Intelligence

Early detection of diseases is crucial to control and prevent the emergence of infectious diseases. The disease monitoring and tracking of digital public health refer to social media data. The proposed study pulls data from a Twitter user who posts on various topics. The social media data will be continuously collected and performed computing analysis and interpretation. The results and assessment refer to the trends in emerging infectious diseases disseminated to those who have the right to know to act. This project aims to detect trends in social media posts on emerging infectious diseases in the Philippines, using the low-resourced languages Filipino and Cebuano, to understand the context of a social media post. Machine learning and natural language processing principles shall be used in the study. The insights from this study's dashboard can assist health professionals, officials, and the public have informed decisions and policies for better public health services for all Filipinos. It is well anticipated that policies based on the dashboard analytics will help prevent the surge or emergence of an outbreak or pandemic and, therefore, decrease the risk of economic loss. Thus, this ongoing research can result in fewer social and economic disruptions.
Improving Deeplabv3+ for highland mouse holes segmentation scenarios

The rodent infestation problem is currently one of the important factors in the degradation of grassland in the Sanjiangyuan area. We need to infer the degradation of grassland by the area of grassland being gnawed, and thus provide help for grassland restoration work. To this end we have designed a DeeplabV3+ based mouse infestation scene segmentation method. On the basis of Deeplabv3+, different backbone feature extraction networks are adopted, and attention mechanism is introduced into the backbone to improve the accuracy of feature extraction and solve the problem of sample imbalance in our self-made dataset. For the training and validation of this network, we used a self-developed photographed and produced dataset of the distribution of mouse holes in the grassland pastures of Haibei, Qinghai Province, which contains various features of plateau mouse infestation. The model improvement resulted in a significant reduction in the training time of Deeplabv3+ on this dataset, and a certain degree of improvement in segmentation accuracy.
Intelligent stock recommendation system based on generalized financial knowledge graph

Knowledge graph is widely used in the artificial intelligence field such as intelligent search, intelligent recommendation and intelligent question answering. Financial knowledge graph (FKG) can achieve the purpose of intelligent cognition of investment in financial market, to realize efficient mining and intelligent application of financial data. In this paper, FKG is applied in stock investment analysis and intelligent recommendation, and the design idea of multi-dimensional construction of stock finance knowledge graph (SFKG) is proposed in combination with investment behavior. We conduct research on intelligent recommendation algorithms via knowledge abstraction and generalization. A-share market data of China is used for testing and verification, and an intelligent visual system with auxiliary decision-making function is implemented finally.
A maximum correlation method for image construction of IRAS survey data

An algorithm is presented for the construction of images using linear array data with nonuniform scan coverage of object space and nonuniform detector responses. The algorithm achieves the maximum correlation between adjacent pixels, i.e., the smoothest image, consistent with the data and data uncertainties. For high spatial data density and signal-to-noise ratio, the achievable spatial resolution can exceed the diffraction limit of the optics. The capability of the algorithm is illustrated using 60-micron data from the region centered on the galaxy M101, obtained during the all-sky survey performed by the Infrared Astronomical Satellite. The 60-micron map produced has a resolution of about 36 arcsec and allows the identification of many H II regions by position and aperture photometry for the brighter ones. The achieved resolution is discussed in terms of the a priori estimate of the mean correlation length of the data, the directly measured FWHM in the final image, and the results of aperture photometry of M101 H II regions NGC 5447, 5455, 5461, 5462 and 5471.
A novel mothed for EEG motor imagery classification with graph convolutional network

A motor imagery brain-computer interface system with practical application value should be able to show stable performance when facing new users. The distribution of electrodes on the cerebral cortex is the same for any user. Therefore, in order to solve the subject-independent problem, we propose a novel Graph Convolutional Convolution Transformer Net (GCCTN), which uses a graph convolutional neural network to calculate the relationship between an electrode and other electrodes, uses a convolutional neural network to extract temporal and spatial information and uses a Transformer Encoder for further extraction of time-domain information. Finally, the classification accuracy of our model is optimal.
Skin texture recognition of leg images based on chaotic mapping reorganization feature

In criminal and victim identification, when information such as fingerprint and facial images cannot be obtained, the development of other new bio-metric recognition has become an important task. It is found from investigations that the lower leg is often exposed in an evidence image. If we can use the information to identify the identity of the suspect, it will be a great break-through for criminal investigation. Unlike faces or fingerprints, the image of a lower leg does not have many discriminative features. Therefore, capturing more information from an image has become the key to the success of recognition. Despite the rapid development of deep learning in recent years and the wide application of convolutional neural networks in biometric recognition, they are very dependent on a large amount of training data. In this paper, we propose a recognition method based on the combination of chaotic mapping and local binary pattern (LBP), which captures the nonlinear features of the image. Experimental results show that the method proposed in this paper has a better recognition rate and an encouraging performance.
MRBEE: A novel bias-corrected multivariable Mendelian Randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from measurement error bias, whose scale and direction are influenced by weak instrumental variables and GWAS sample overlap, respectively. We introduce MRBEE (MR using Bias-corrected Estimating Equation), a novel multivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy. In simulations, we showed that MRBEE is capable of effectively removing measurement error bias in the presence of weak instrumental variables and sample overlap. In two independent real data analyses, we discovered that the causal effect of BMI on coronary artery disease risk is entirely mediated by blood pressure, and that existing MR methods may underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. MRBEE possesses significant potential for advancing genetic research by providing a valuable tool to study causality between multiple risk factors and disease outcomes, particularly as a large number of GWAS summary statistics become publicly available.
Insulin determines the effects of TGF-β on HNF4α transcription and epithelial-to-mesenchymal transition in hepatocytes

To date, epithelial-to-mesenchymal transition (EMT) has been observed in cultured hepatocytes, but not in vivo. TGF-β is supposed to initiate EMT in hepatocytes by inhibiting HNF4α through the SMAD2/3 complex. We report that TGF-β does not directly inhibit HNF4α, but contributes to its transcriptional regulation by SMAD2/3 recruiting acetyltransferase CBP/p300 to the HNF4α promoter. The recruitment of CBP/p300 is indispensable for C/EBPa binding, another essential requirement for constitutive HNF4α expression in hepatocytes. In contrast to the observed induction of HNF4α, SMAD2/3 inhibits C/EBPα transcription. Therefore, long-term TGF-β incubation results in C/EBPα depletion, which abrogates HNF4α expression. Intriguingly, SMAD2/3 inhibitory binding to the C/EBPα promoter is abolished by insulin. Thus, maintaining a high insulin concentration in culture medium ensures constitutive HNF4α and thereby prevents TGF-β-induced hepatocyte EMT. Furthermore, insulin inhibits TGF-β-induced SMAD2/3 binding to the promoters of core EMT transcription factors e.g., SNAI1. SNAI1 transcription requires both SMAD2/3 and FOXO1 in nuclei. Insulin inhibits SNAI1 transcription through impeding SMAD2/3 binding to its promoter and inducing FOXO1 phosphorylation. Hence, insulin is the key factor that prevents TGF-β-induced EMT in hepatocytes.
Uncovering specific mechanisms across cell types in dynamical models

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements. Contact adrian.hauber@fdm.uni-freiburg.de Author Summary Mathematical models enable insights into biological systems beyond what is possible in the wet lab alone. However, constructing useful models can be challenging, since they both need a certain amount of complexity to adequately describe real-world observations, and simultaneously enough simplicity to enable understanding of these observations and precise predictions. Regularization techniques were suggested to tackle this challenge, especially when building models that describe two different types of cells, such as healthy and cancer cells. Typically, both cell types have a large portion of biological mechanisms in common, and the task is to identify the relevant differences that need to be included into the model. For more than two types of cells, the existing approaches are not readily applicable, because they require defining one of the cell types as reference, which potentially influences the results. In this work, we present a regularization method that is independent from the choice of a reference. We demonstrate its working principle and compare its performance to existing approaches. Since we implemented this method in a freely available software package, it is accessible to a broad range of researchers and will facilitate the construction of useful mathematical models for multiple types of cells.
Local angiogenic interplay of Vegfc/d and Vegfa drives brain region-specific development of fenestrated capillaries

Fenestrated and blood-brain barrier (BBB)-forming endothelial cells constitute major brain capillaries, and this vascular heterogeneity is crucial for region-specific neural function and brain homeostasis. How these capillary types emerge in a brain region-specific manner and subsequently establish intrabrain vascular heterogeneity remains unclear. Here, we show a core angiogenic mechanism critical for fenestrated brain capillary development via a comparative analysis of the zebrafish choroid plexuses (CPs) and circumventricular organs (CVOs), demonstrating capillary-type-selective vascularization mechanisms. We found that zebrafish deficient for Gpr124, Reck, or Wnt7aa exhibit severely-impaired BBB angiogenesis without any apparent defect in fenestrated capillary formation in the CPs and CVOs. Conversely, simultaneous genetic loss of various Vegf combinations revealed remarkable heterogeneity of endothelial requirements for Vegfs-dependent angiogenesis within and across these organs, identifying unexpected interplay of Vegfc/d and Vegfa in fenestrated brain capillary formation. Expression analysis and paracrine activity-deficient vegfc mutant characterization suggest that endothelial cells and non-neuronal specialized cell types present in the CPs and CVOs are major sources of Vegfs responsible for regionally-restricted angiogenic interplay. Thus, local presentations and interplay of Vegfc/d and Vegfa control brain region-specific emergence of fenestrated capillaries, providing insight into fenestrated capillary formation in other organs and also how intra-organ vascular heterogeneity arises.
RoboEarth: connecting robots worldwide

In this paper, we present the core concept and the benefits of an approach called RoboEarth which will be highly beneficial for future robotic applications in science and industry. RoboEarth is a world-wide platform which robots can use to exchange position and map information as well as task-related, hardware-independent action recipes. This will enable manufacturers worldwide to break down their costs and efforts for reproducing software algorithms for robot behavior over and over again. The RoboEarth framework can store all relevant data from algorithms to complex behavior descriptions what allows robots to act autonomously in an unknown, unspecified environment. Especially in the field of interaction with humans RoboEarth can bring forward the behavior of robots and simplify the software design for developers in that field.
Influence of selected social factors on career decisionmaking of grade 12 learners in township secondary schools in South Africa

Career decision-making is a challenge to most learners in secondary schools in South Africa. In this study, we examined the influence of selected social factors (influence of older siblings, peer influence, teachers' influence, and career information services) on career decision-making of Grade 12 learners in township secondary schools in South Africa. We adopted a correlational survey research design. The sample size of 260 learners was obtained using the stratified random sampling technique. The questionnaire that included reference to peer influence, teachers' influence, career information service, influence of older siblings, and career decisionmaking was used to collect data. We used Cronbach's alpha coefficient analysis to measure the internal consistency of the questionnaire; all sub-scales had excellent internal consistent reliability. We used the Kaiser-Meyer-Oklin measure of sample adequacy (KMO Index) and the Bartlett's Test of Sphericity to confirm internal validity. We analysed quantitative data using inferential statistics such as Pearson Product Moment Correlation, linear, and multiple regression analysis. According to the findings, the strongest correlation was between teacher influence and career decision-making (r=.643, n=204, p.01), followed by the relationship between career information services and career decision-making (r =.607, n=204, p.01) while peer influence had the least relationship to career decision-making (r (204) =. 514, p.01. The influence of older siblings also had a significant direct relationship with career decision-making among the Grade 12 learners (r=.566, n=204, p<.01). We recommend that teacher counsellors adopt a multifaceted approach in developing career decision making programmes for learners in secondary schools.
What precisely are E∞ring spaces and E∞ring spectra?

E∞ ring spectra were defined in 1972, but the term has since acquired several alternative meanings. The same is true of several related terms. The new formulations are not always known to be equivalent to the old ones and even when they are, the notion of “equivalence” needs discussion: Quillen equivalent categories can be quite seriously inequivalent. Part of the confusion stems from a gap in the modern resurgence of interest in E∞ structures. E∞ ring spaces were also defined in 1972 and have never been redefined. They were central to the early applications and they tie in implicitly to modern applications. We summarize the relationships between the old notions and various new ones, explaining what is and is not known. We take the opportunity to rework and modernize many of the early results. New proofs and perspectives are sprinkled throughout.
The effect of hydrogen on the network disorder in hydrogenated amorphous silicon

Previous Raman scattering studies of the effect of hydrogen on the atomic network disorder in various hydrogenated amorphous silicon (a-Si:H) materials resulted in contradicting conclusions. We resolve these contradictions by showing that the surface and the bulk of a-Si:H films can behave differently due to their different hydrogen contents. In particular, we establish that hydrogen has a relatively moderate effect in improving the short-range order but a profound effect in improving the intermediate-range order of the atomic network.
Multilayer shallow water equations with complete Coriolis force. Part 2. Linear plane waves

Abstract We investigate the behaviour of linear plane waves in multilayer shallow water equations that include a complete treatment of the Coriolis force. These equations improve upon the conventional shallow water equations, based on the traditional approximation, that include only the part of the Coriolis force due to the locally vertical component of the rotation vector. Including the complete Coriolis force leads to dramatic changes in the structure of long linear plane waves. It allows subinertial waves to exist with frequencies below the inertial frequency, the minimum frequency for which waves exist under the traditional approximation. These subinertial waves are characterized by a distinguished limit in which the horizontal pressure gradient becomes comparable to the upwellings and downwellings driven by the non-traditional Coriolis term in the vertical momentum equation. The subinertial waves connect wave modes that remain separate in the conventional multilayer shallow water equations, such as the surface and internal waves in a two-layer system. Eastward-propagating surface waves in a two-layer system connect with westward-propagating internal waves, and vice versa, via the long subinertial waves. The long subinertial waves cannot be classified as either surface or internal waves, due to the phase difference between the disturbances to the interfaces in these waves.
Dually slender modules and steady rings

A module M over a ring R is called dually slender if Hom (M, —) commutes with direct sums of -modules. For example, any finitely generated module is dually slender. A ring R is called right steady if each dually slender right -module is finitely generated. We provide a model theoretic necessary and sufficient condition for a countable ring to be right steady. Also, we prove that any right semiartinian ring of countable Loewy length is right steady. For each uncountable ordinal σ, we construct examples of commutative semiartinian rings Γσ, and Qa, of Loewy length σ +1 such that Τσ is, but Q0 is not, steady. Finally, we study relations among dually slender, reducing, and almost free modules. 1991 Mathematics Subject Classification: 16D40, 16D90, 16E50, 03C60.
Experimental validation of a Richtmyer–Meshkov scaling law over large density ratio and shock strength ranges

A universal scaling law for the Richtmyer–Meshkov instability is validated with experimental results covering a wide range of density ratios and shock strengths. These results include the first membraneless, gas-phase, interface experiments for A>0.5 and M>1.5. The shock-accelerated, sinusoidal interface experiments are conducted in a vertical shock tube with a large square cross section and cover the experimental parameter space: 0.29<A<0.95, 1.1<M<3, and 3.1×104<Re<1.4×107. Results provide growth-rate data for comparison with computational fluid dynamics simulation codes and verify the nondimensional time and amplitude parameters chosen for scaling are the correct ones. Correct scaling is obtained by including a growth-reduction factor that accounts for diffusion at the interface. Planar imaging techniques are used to diagnose the instability development for a nearly single-mode interface, and results are reported for eight scenarios (including three distinct gas pairs) that span the linear and nonlinea.
Delayed plastic relaxation on patterned Si substrates: Coherent SiGe pyramids with dominant {111} facets

Zhenyang Zhong, W. Schwinger, F. Schaffler, G. Bauer, G. Vastola, F. Montalenti, and L. Miglio Institute for Semiconductor and Solid State Physics, Johannes Kepler University Linz, A-4040 Linz, Austria L-NESS and Department of Materials Science, University of Milano Bicocca, Via R. Cozzi 53, 20125 Milano, Italy Surface Physics Laboratory, Fudan University, Shanghai 200433, China (Received 7 August 2006; published 23 April 2007) Unimodal SiGe islands with dominant f111g facets were grown coherently on pit-patterned Si (001) substrates by molecular beam epitaxy. With increasing Ge deposition, the f111g pyramids evolve from dome-shaped islands, reaching significantly larger volumes than are coherently possible on flat substrates. Finite element calculations and molecular dynamics simulations show that SiGe islands in pits can have less misfit strain with respect to islands of the same shape on flat substrates. The injection of dislocations is thus delayed, allowing for the observed development of coherent islands with a very high aspect ratio.