Miracle-dz/UniFAR-FTUs huggingface dataset:
IPSC-Derived Sensory Neurons Directing Fate Commitment of Human BMSC-Derived Schwann Cells: Applications in Traumatic Neural Injuries

The in vitro derivation of Schwann cells from human bone marrow stromal cells (hBMSCs) opens avenues for autologous transplantation to achieve remyelination therapy for post-traumatic neural regeneration. Towards this end, we exploited human induced pluripotent stem-cell-derived sensory neurons to direct Schwann-cell-like cells derived from among the hBMSC-neurosphere cells into lineage-committed Schwann cells (hBMSC-dSCs). These cells were seeded into synthetic conduits for bridging critical gaps in a rat model of sciatic nerve injury. With improvement in gait by 12-week post-bridging, evoked signals were also detectable across the bridged nerve. Confocal microscopy revealed axially aligned axons in association with MBP-positive myelin layers across the bridge in contrast to null in non-seeded controls. Myelinating hBMSC-dSCs within the conduit were positive for both MBP and human nucleus marker HuN. We then implanted hBMSC-dSCs into the contused thoracic cord of rats. By 12-week post-implantation, significant improvement in hindlimb motor function was detectable if chondroitinase ABC was co-delivered to the injured site; such cord segments showed axons myelinated by hBMSC-dSCs. Results support translation into a protocol by which lineage-committed hBMSC-dSCs become available for motor function recovery after traumatic injury to both peripheral and central nervous systems.
Interval type-2 trapezoidal fuzzy multi-attribute decision-making method and its application to the corporate investment selection

Interval type-2 trapezoidal fuzzy (IT2TrF) number is a powerful tool to depict fuzzy information. Information measures methods have received more and more attention in recent years as they play an important role in decision-making theory. A new multi-attribute decision-making (MADM) method supported by IT2TrF information measures is investigated in this paper under the IT2TrF information environment. Firstly, three axiomatic definitions of IT2TrF information measures are introduced, which include information entropy, similarity measure and cross-entropy. Secondly, with the help of the exponential function, we formulate some information measure formulas, which are followed by the proofs that the exponential entropy, exponential similarity measure and exponential cross-entropy fit the three axiomatic definitions. Subsequently, a novel IT2TrF MADM method is designed, in which the IT2TrF exponential entropy and cross-entropy are utilized to generate the attribute weights, the IT2TrF exponential similarity measure is employed to obtain the closeness degree of the ideal solution and derive the most satisfying solution. Lastly, we provide a numerical example of corporate investment to demonstrate the applicability and feasibility of the proposed MADM method. The robustness and merits of the developed MADM method are highlighted by the comparative analysis.
Uniqueness of Brownian motion on Sierpinski carpets

We prove that, up to scalar multiples, there exists only one local regular Dirichlet form on a generalized Sierpinski carpet that is invariant with respect to the local symmetries of the carpet. Consequently for each such fractal the law of Brownian motion is uniquely determined and the Laplacian is well defined.
EFFECT OF NONIVASIVE POSITIVE-PRESSURE VENTILATION ON OUTCOME IN PATIENTS WITH ACUTE EXACERBATION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE: A RANDOMIZED CONTROLLED TRIAL

Non-invasive positive pressure ventilation (NiPPV) is known to be effective in hypercapnic respiratory failure. However, some uncertainty still exists regarding its use in certain subgroups of patients with main consideration that, if ineffective, NiPPV delays time to intubation and may worsen the outcome. 58 subjects with acute exacerbation of COPD, resulting in ICU admission, were included into the randomized, single-blind, controlled study. Study group was treated with NiPPV in PSV or BiLevel mode and increased FiO2, control group – with O2 therapy only. Medical therapy was prescribed to study participants regardless of their group allocation. Rate of tracheal intubation in study group was 7 (25 %) compared to 20 (67 %) in control group (p<0,0001). Relative risk reduction (RRR) was 61,5 % (95 % CI 23-80 %) and absolute risk reduction for study group was 42,0 % (95% CI 17,6-62). Mortality rate in the control group was 36.6 % (11 deaths), while in the study group the mortality rate was 21.4 % (6 deaths). Relative risk of death for the study group in comparison with the control group was found at the level of 0.56 (95 % CI 0.25 – 1.29). Relative risk of death reduction was 43.4 % (95% CI 28.7 – 75.1). Absolute risk of death reduction was 15.2 % (95% CI 5.39 – 38.2). The hospital lengths of stay in the study group was 20.8±11.3 days, in the control group 29.1±12.3 days (p=0.063). Regarding ICU length of stay, no significant difference was found between the groups: 14.7±12.2 days and 10.8±7 days in the control and study groups, respectively (р=0.178). Conclusion: we found evidence in favor of efficacy of NiPPV in COPD patients with acute exacerbation in terms of mortality and tracheal intubation. No difference in hospital and ICU length of stay was found. No evidence of additional risk, related to NiPPV, were found.
Effect of Educational Handouts With Standard Therapy Versus Standard Therapy Alone on Compliance With Oral Iron Supplementation in Antenatal Women With Iron Deficiency Anemia: A Randomized Controlled Trial

Background Iron requirements rise dramatically throughout the second and third trimesters of pregnancy. Pregnant women are more susceptible to anemia because their need for iron increases during pregnancy, which is difficult to achieve through diet alone. Methodology A randomized controlled trial (non-blinded and parallel group) was undertaken with the recruitment of 174 women. However, 35 women were lost to follow-up, and the study was ultimately completed with 139 participants distributing 68 women in Group A (intervention group) and 71 women in Group B (non-interventional group). Educational handouts were explained to the participants with iron supplements in Group A and only supplements were given to Group B, and the participants were followed up till three months before the recruitment period. Compliance with iron supplementation and a rise in hemoglobin were noted. Results In this study, maximum women were in the 22-30 years age group and were almost evenly distributed with respect to parity with no statistically significant difference in the groups. All the participants were started with oral iron therapy. No additional parenteral iron therapy was given. Women in Group A showed good compliance for iron supplementation than those in Group B. It was determined that this difference was statistically insignificant (>0.05). In the majority of women, the reason for poor compliance was frustration to follow oral iron therapy daily (52.3% in Group A and 21.7% in Group B). There were other reasons like forgetfulness, heartburn, vomiting, constipation, and nausea as the reason for poor compliance. The hemoglobin levels were compared at the recruitment and a mean rise in hemoglobin levels was noted in groups A and B at the follow-up period after three months. There was a greater mean rise in hemoglobin concentration in Group A (1.28) than in Group B (0.63), which was statistically insignificant (>0.05). Conclusion The current study found that among pregnant women with iron-deficient anemia, instructional handouts did not promote compliance with oral iron treatment. The main reasons for low compliance were frustration with taking the oral drug, followed by forgetfulness, heartburn, vomiting, constipation, and nausea. In pregnant females with anemia brought on by iron deficiency, educational handouts did not enhance hemoglobin status.
Reducing data sparsity by generalising distributional contexts: application to specialised texts (Réduction de la dispersion des données par généralisation des contextes distributionnels : application aux textes de spécialité) [in French]

Vector space models implement the distributional hypothesis relying on the repetition of information oc- curring in the contexts of words to associate. However, these models suffer from a high number of dimensions and data sparseness in the matrix of contextual vectors. This is a major issue with specialized corpora that are of much smaller size and with much lower context frequencies. We tackle the problem of data sparseness on specialized texts and we propose a method that allows to make the matrix denser, by generalizing of distributional contexts. The evaluation of the method is performed on a French medical corpus, and shows that with a small graphical window and the Jaccard Index, the context generalization with lexico-syntactic patterns improves the results, while with a large window and the cosine measure, it is better to generalize with lexical inclusion. Mots-cles : Analyse distributionnelle, textes de specialite, hyperonymie, dispersion des donnees, modele d'espace vectoriel, methode hybride.
Society 5.0-Inspired Digitalization Framework for Resilient and Sustainable Agriculture

This research paper proposes a digitalization framework based on Society 5.0 principles for promoting resilient and sustainable agricultural value chains in the context of climate change. Climate change is affecting the productivity and sustainability of agricultural systems and threatening food security in many parts of the world. Digitalization has the potential to enhance the resilience of agricultural value chains to climate change by improving efficiency, promoting sustainability, and reducing vulnerability to climate risks. This study reviews the literature to investigate the potential benefits and challenges of Society 5.0-inspired digitalization for agricultural value chains in the context of resilience and sustainability. Further, this study establishes critical design requirements for digitalization, which inform the development of a theoretical framework for Society 5.0-inspired digitalization framework in realizing resilient and sustainable agricultural value chains.
Brain Metastasis in HER2-Positive Breast Cancer: Case Report and Review of Literature

Introduction: Breast cancer (BC) is one of the most common cancers in women in developed countries. Approximately 15 to 20% of patients with BC have tumors with elevated levels of Human Epidermal Growth Factor Receptor 2 (HER2), which are associated with an aggressive clinical phenotype and poor prognosis. Brain metastases (BM) appears in more than 15% in the HER2-positive breast cancer population, which cause serious decrease in survival.
An Iris Recognition Algorithm Based on Cross Power Spectrum

An encoding algorithm based on cross power spectrum is proposed. A two-step locating approach combining coarse location with fine location is used to locate the iris,which can effectively decrease the blindness of searching computation.To extract the feature of the iris,the iris image is firstly divided into many blocks,and then the cross power spectra between each block and 2D Gabor wavelet family are computed and the blocks are encoded.Finally,the pattern match is executed by using Hamming distance.The results show that this approach has very high computational speed and good recognition rate and is superior to Daugman's method in coding performance.
Measuring semantic similarity: representations and methods

This dissertation investigates and proposes ways to quantify and measure semantic similarity between texts. The general approach is to rely on linguistic information at various levels, including lexical, lexico-semantic, and syntactic. The approach starts by mapping texts onto structured representations that include lexical, lexico-semantic, and syntactic information. The representation is then used as input to methods designed to measure the semantic similarity between texts based on the available linguistic information. While world knowledge is needed to properly assess semantic similarity of texts, in our approach world knowledge is not used, which is a weakness of it. We limit ourselves to answering the question of how successfully one can measure the semantic similarity of texts using just linguistic information. The lexical information in the original texts is retained by using the words in the corresponding representations of the texts. Syntactic information is encoded using dependency relations trees, which represent explicitly the syntactic relations between words. Word-level semantic information is relatively encoded through the use of semantic similarity measures like WordNet Similarity or explicitly encoded using vectorial representations such as Latent Semantic Analysis (LSA). Several methods are being studied to compare the representations, ranging from simple lexical overlap, to more complex methods such as comparing semantic representations in vector spaces as well as syntactic structures. Furthermore, a few powerful kernel models are proposed to use in combination with Support Vector Machine (SVM) classifiers for the case in which the semantic similarity problem is modeled as a classification task.
The 30–50 Day Mode at 850 mb During MONEX

Abstract The analyzed wind field at 850 mb during the summer monsoon experiment (MONEX) is subjected to a time series analysis to confirm the existence of a peak in the time range of 30–50 days. Having established that, this study presents a mapping of the motion field for this time scale. In this short paper we illustrate the steady meridional propagation of a train of troughs and ridges that seem to form near the equator and dissipate near the Himalayas. The meridional scale of this mode is around 300 km, and its meridional speed of propagation is ∼0.75° latitude per day. The amplitude of the wind for this mode is around 3–6 m s−1. The salient contributions here are the mapping and the demonstration of a very regular behavior of this mode; its existence is here noted from a period well before the onset of monsoon, i.e., from early May to late July. Three major storms during MONEX were noted to form within the trough line of this system, and the period of major cessation of rains over the Indian subconti.
Nonlinear Normal Mode Initialization of a Limited Area Model

Abstract Limited area models with pronounced topographic features need to be carefully initialized. We have attempted to adapt the nonlinear normal mode initialization technique developed for large-scale forecast models. A baroclinic model with fixed boundary conditions is considered. The horizontal structure of the normal modes is sinusoidal. Geostrophic and gravity modes are easily separated on the basis of their frequency. The standard procedure proposed by Machenhauer (1977) is found to be useful and leads to significantly better results than a linear balance equation.
GTD solution with higher order terms to the diffraction by an edge : towards a uniform solution

A UTD solution for an edge of a perfectly conducting wedge is presented which includes terms of order higher than the ordinary UTD. The problem is studied for three special cases: (i) plane and (ii) spherical wave incidence on a straight wedge with planar surfaces, and (iii) cylindrical wave incidence on a wedge with surfaces curved in the direction normal to the edge. This solution not only compensates the jump discontinuities in the GO field but also the discontinuities in the derivative. The solution found is exact for the special case of a plane wave incident on a halfplane. The solution with the higher order terms is found to be accurate when the large parameter is reduced by a factor of two as compared with the ordinary UTD solution.
Comparison of Direct Regularization Methods of Ill-conditioned Problems Solution

In order to resolve ill-conditioned problems,suitable regularization methods are needed to choose correctly.The characteristics of truncated singular value decomposition(TSVD) method and Tikhonov regularization method are discussed respectively.On the basis,L-curve method and generalized cross validation(GCV) method are both employed to attain the optimal regularization parameters.Numerical results show that TSVD method and Tikhonov method can eliminate ill-condition of observation equation effectively.Through applying to L-curve method and GCV method,continuous regularization parameter for Tikhonov method can be confirmed reasonably.Furthermore,discrete regularization parameter for TSVD method can be determined accurately.Finally,the accuracy and robustness of regularization solution for four combined methods are investigated.
Some Properties of Anosov Flows

Anosov flows are a generalization of geodesic flows on the unit tangent bundles of compact manifolds of negative sectional curvature. They were introduced and are dealt with at length by Anosov in [2]. Moreover they form an important class of examples of flows satisfying Smale's axioms A and B (see [15]). In that paper Smale poses the problem of determining which manifolds admit Anosov flows. In this paper we obtain information about the fundamental groups of such manifolds. These generalize results which have been obtained for the fundamental groups of manifolds of negative curvature (see Preissmann [13], Byers [6]).
Particle Filter Face Tracking Using Color and Shape Histogram as Clues

Face tracking problem comprises two key factors: one is the design of tracker;the other is the selection and representation of tracking cues.To relieve the shortcoming that the general face tracer is frequently disturbed by background when a simple ellipse model is used to describe the facial shape,and in accordance with the fact that the dynamic model and observation model of video object tracking problem are always non-Gaussian and non-linear,a robust particle filter face tracking algorithm which uses shape and color represented in the form of histogram as clues is proposed.The tracking algorithm is based on particle filter,in which a new shape representation method using histogram is introduced and modified to be as one of tracking clues.At the same time,an experiential edge detection algorithm is proposed in order to alleviate background disturbance.Experimental results show that the proposed algorithm is robust to the rotation of face,background skin color distraction and partial occlusion.In addition,the proposed tracker can recover the lost terget due to the severe occlusion.
Fractals Generated by Various Iterative Procedures - A Survey

These days fractals and the study of their dynamics is one of the emerging and interesting area for mathematicians. New fractals for various equations have been created using one-step iterative procedure, two-step iterative procedure, three-step iterative procedure and four-step iterative procedure in the literature. Fractals are geometric shapes that have symmetry of scale. In this paper, a detailed survey of fractals existing in the literature such as Julia sets, Mandelbrot sets, Cantor sets etc have been given.
When owl: sameas isn't the same redux: a preliminary theory of identity and inference on the semantic web

The Web changes knowledge representation in a number of surprising ways, and decentralized knowledge representation systems such as the Semantic Web will require a theory of identity for the Web beyond current use of sameAs links between various data-sets. We propose that one problem, as posed by the semantics of sameAs in OWL, is enshrining a metaphysical notion of individual identity into the formal semantics of the logic that prevents universal identity linking across semantic roles. However, as shown by languages such as RDF and KIF, a metaphysics-free semantics is possible that can handle both classes and properties as first-class citizens of the universe of discourse that can have identity statements and so produce valid inferences. We empirically analyze the behavior of identity and inference on the Semantic Web currently in order to analyze the size of the problem, and show the non-normal distribution of equivalence classes inference produces. We also show via a human experiment how inference leads to more difficulty in judgments about identity by individual experts, yet adding experts leads to fairly reasonable results, even over inferred triples.
Travelling wave solutions of Burgers' equation for Gee-Lyon fluid flows

In this work we present some analytic and semi-analytic traveling wave solutions of a generalized Burger’ equation for isothermal unidirectional ‡ow of viscous non-Newtonian ‡uids obeying the Gee-Lyon nonlinear rheological equation. The solutions include the corresponding well-known traveling wave solution of the Burgers’equation for Newtonian ‡ow as a special case. We also derive estimates of shock thickness for the non-Newtonian ‡ows.
Mixed Seabed Sediment Classification Based on Transferred Convolutional Neural Network: A Case Study in the Ancient River Valley

The coastal zone is connected to the hinterland of the basin and the wide sea, which is not only affected by complex natural factors, but also by human activities. The study of sediment classification in this area will help to further explain topographic evolution and dynamic mechanism. Aiming at the complex mixed sediment environment in the ancient valley area of shallow seas, an advanced deep convolutional neural network (DCNN) classification model based on transfer learning—selective kernel hybrid dilated ResNet-50 (SKHD-ResNet-50) is thus proposed and applied to the coastal area in the firth of forth, Scotland, U.K. The model overcomes problems of low accuracy of sediment classification with small samples, while effectively improving the efficiency of mixed sediment classification. Five transferred convolutional neural network models, including CNN, AlexNet, VGG-19, GoogleNet, and SKHD-ResNet-50, were used to conduct seabed sediment classification experiments on multibeam backscatter mosaics in the study area. An ablation study about the effect of selective kernel convolution (SKC) and hybrid dilated convolution (HDC) was also conducted to further prove the usability of the proposed method. Finally, the optimized convolutional neural network was used to build a multilayer deep learning model for sediment classification, combining the transfer learning method with the neural network to obtain the optimal network weights through deep global optimization, thus accurately mining statistical characteristics and distribution rules of backscatter intensity data. The experimental results show that the advanced model obtains better classification accuracy than that of state-of-art methods, with the overall accuracy and Kappa coefficient of 96.87% and 0.9482, respectively. Findings highlight the proposed sediment classification model, which can effectively distinguish mixed sediments, and greatly improve the accuracy and efficiency of sediment classification.
Detection of 35 new 51–60 A+ -methanol masers towards IRAS sources

We present the results of a search for 6.6-GHz A + -methanol masers towards 235 colour-selected IRAS sources which were not known to have associated 1.6-GHz hydroxyl or 12.2-GHz methanol maser emission. 35 new 6.6-GHz maser sources were detected above a 3σ level of 3 Jy. The majority of the newly detected sources lie in the inner spiral arms of the Galaxy, with only one source in the Carina arm region