Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-person 2D Pose Estimation via CNN (Computer Vision)

We re-implemented a box-free bottom up approach to efﬁciently detect 2D human pose in multi-person images. The proposed model should be able to maintain high accuracy while achieving real-time performance, irrespective of the number of people in the image. We propose to use convolutional neural network and Part Afﬁnity Fields (PAFs), inspired by [1, 2]. Trained on COCO 2017 dataset, we expect our model to show high accuracy in performance and efﬁciency in both training and testing stages.
The Influence of Inorganic Carbon on Anaerobic Ammonia Oxidation

Through running anaerobic ammonia oxidation reactors for a few days, we found that inorganic carbon source has great influence on anaerobic ammonia oxidation reactions. The inorganic carbon source increasing, the effect of anaerobic ammonia oxidation bacteria reaction is much better and the processing rate is high. And if there are no inorganic carbon source, anaerobic ammonia oxidation reactions are running anomaly. The processing rate of nitrate nitrogen is about 80%, and the processing rate of ammonia nitrogen is only 30% or so. The anaerobic ammonia oxidation bacteria metabolic is getting worse and worse.
Determinants of complications in ﬁrst ever acute stroke patients: a prospective observational study from India

Background and Aims: Post-stroke complications can lead to frequent and major causes of death in the early phases after an acute stroke. Aims of the present study were to determine the association of several risk factors with the occurrence of poststroke complications, the predictors for the development of complications, and to evaluate how these complications affected the overall prognosis among subjects with the first ever acute stroke. This study is the pioneer study of this kind from Eastern India. Previous national and international studies on this lack uniformity in their results probably because of the following reasons: no standard definitions for complications following the stroke, varied follow-up periods ranging from 1 week to 30 months, type and severity of stroke affecting the incidence, and finally, the demographic variables.Methods: This was an observational, prospective study, in which a total of 521 adult male and female patients who had a first-ever acute stroke, aged between 12 years to 75 years were enrolled over a period of 2 years (September 2016 to September 2018), admitted in Department of Neuromedicine, Bangur Institute of Neurosciences (BIN), Kolkata, India. Patients with first ever acute stroke were studied for post-stroke complications (predefined with standard criteria) during the hospital stay at Bangur Institute of Neurosciences (BIN), Kolkata, India, and their association with risk factors, severity of stroke (National Institutes of Health Stroke Scale Score, NIHSS), length of hospital stay (in days) and outcome (Modified Rankin Scale, MRS) were assessed. Results: A total of screened 498 patients who were admitted at Bangur Institute of Neurosciences (BIN), Kolkata, India, with first ever acute stroke were recruited. The mean age was 61.02 years (range 23-75 years), the majority being male (n = 307; 61.6%). Post-stroke complication during hospital stay was documented in 270 patients (54.2%)(males and females combined). In logistic regression analysis, multiple risk factors (OR, 1.571; 95% CI, 1.084-2.278; P-0.017), severity of stroke (National Institutes of Health Stroke Scale Score, NIHSS) (OR, 1.425; 95% CI, 1.027-1.976; P-0.034) and length of hospital stay (in days) (OR, 3.565; 95% CI, 2.029-6.264; P < 0.0001) were the most robust predictors of occurrence of complications. The independent predictors of poor outcome in subjects with complications were: chest infection (OR, 2.07; 95% CI, 1.006-4.26; P = 0.048), bedsores (OR, 2.26; 95% CI, 1.03-4.94; P-0.042), and seizures (OR, 5.072; 95% CI, 1.08-23.79; P-0.039). Conclusion: In our study, the most independent predictors of complications were severity of stroke and length of hospital stay. This observation might help clinicians in taking appropriate measures towards preventing post-stroke complications and thereby improving outcome following a stroke.
Organization bankruptcy risk forecasting methodology based on analytic hierarchy process

In this article, the authors propose a methodology for assessing the bankruptcy risk of organizations operating in the framework of one industry. The authors identify six criteria qualitative factors influencing the bankruptcy of an organization: industry risk (IR), management risk (MR), financial flexibility (FF), creditability (CR), competitiveness (CO) and operating risk (OR). The authors use the hierarchy analysis method to obtain a vector of aggregated default risk estimates. key words: corporate bankruptcy, analytic hierarchy process, expert evaluation Resumen Los autores proponen una metodología para evaluar el inicio del riesgo de quiebra de las organizaciones que operan en el marco de una industria. La técnica conlleva pasos específicos e incluye cinco etapas. Se identifican seis criterios como factores que influyen en la quiebra: riesgo industrial (IR, industry risk), riesgo de gestión (MR, management risk), flexibilidad financiera (FF, financial flexibility), сredibilidad (CR, creditability), competitividad (CO, competitiveness) y riesgo operativo (OR, operating risk). Se utiliza el método de análisis de jerarquía para obtener un vector de estimaciones de riesgo de incumplimiento agregadas. Palabras clave: quiebra de una organización, método de análisis de jerarquía, evaluaciones de expertos
Heuristic Learning for Look-Ahead Manipulation Planning in Continuous Action Space

Consider the example shown in Fig. 1 where a robot is tasked with manipulating, using prehensile and nonprehensile planar actions, an object (the orange fruit) to a target location. The robot end-effector has to navigate its way through the clutter whilst not dropping any off the objects of the edges of the surface. Fundamental to the successful execution of such a manipulation task is the ability to reason over the effect of an action on achieving the task goal. Further, it is essential for the actions to be generated from a closed-loop control scheme in real-time for the robot to adapt its motion to the evolution of the interaction sequence with the real world. These two requirements are challenged by the complex interaction dynamics, edge constraints, and variation in object shapes and numbers. Receding Horizon Planning (RHP) presents itself as an attractive solution that can be run in near real-time when a suitable heuristic is available. RHP works by running multiple stochastic physics-based roll-outs up to a certain horizon in the physics simulator as illustrated by the simulation rendered images in Fig. 1. Each roll-out is evaluated based on the collected rewards during the roll-out and the expected rewards beyond the horizon state: R0:H = r1 + γr2 + . + γrH+γ v(sH). RHP returns the first action of the best roll-out. In this process, the heuristic is required for guiding the roll-outs towards parts of the state space with higher expected rewards, and for estimating the expected rewards beyond the horizon state. The goal of this work is to acquire, without prior knowledge that might bias the solution, efficient (in terms of number of actions) manipulation skills that can seamlessly generalize over an arbitrary number of novel objects on a cluttered planar surface in the real world. To achieve this goal, we proposed in [1], [2] interleaving real-world execution with physics-based RHP that is guided by a learned heuristic. This approach is based on dynamically mapping the state of the real world to the simulator, where RHP is engaged. The returned action is then executed by the real robot. To this end, most learning based approaches for manipulation in clutter relies on operating in a discrete action space [3], [2] or well defined action primitives [4]. The achievements of these implementation in challenging high dimensional state spaces was made possible by the relative stability of off-policy RL algorithms (ex: deep Qlearning) in discrete actions spaces. However, this often
Light: A Scalable and Efficient Wavelength-Routed Optical Networks-On-Chip Topology

Wavelength-routed optical networks-on-chip (WRONoCs) are known for delivering collision- and arbitration-free on-chip communication in many-cores systems. While appealing for low latency and high predictability, WRONoCs are challenged by scalability concerns due to two reasons: (1) State-of-the-art WRONoC topologies use a large number of microring resonators (MRRs) which result in much MRR tuning power and crosstalk noise. (2) The positions of master and slave nodes in current topologies do not match realistic layout constraints. Thus, many additional waveguide crossings will be introduced during physical implementation, which degrades the network performance. In this work, we propose an N x (N - 1) WRONoC topology: Light with a 4 x 3 router Hash as the basic building block, and a simple but efficient approach to configure the resonant wavelength for each MRR. Experimental results show that Light outperforms state-of-the-art topologies in terms of enhancing signal-to-noise ratio (SNR) and reducing insertion loss, especially for large-scale networks. Furthermore, Light can be easily implemented onto a physical plane without causing external waveguide crossings.
Category Specific Impairments in Dementia of the Alzheimer’s Type: A Study with a Probe Question Task

This study examined the processing and conceptualizing impairments in dementia of the Alzheimer’s type. Fifteen mildly demented (age range 62-72 years, Mean age=68.40, SD=3.68 years) and ten moderately demented patients (age range 65-80 years, Mean=69.50, SD=4.28 years) screened on the basis of Hindi version of mini mental status examination, Mattis dementia scale and modified version of Wechsler memory scale and fifteen matched control subject answered the probe questions task which was classified in to living and man -made categories. In this probe task three questions were concerned with the perceptual attributes while other three questions were regarding functional aspects of the items. An analysis for living and man-made categories based on six probe questions task demonstrated man-made categories to be yielding more correct answer than living categories. Patients with DAT were more impaired on probe question task concern with living categories. The ANOVA for these categories also showed significant main effect of category (F1, 37 =24.04). The results about less impaired response for the objects belonging to man-made categories than object of living categories suggested that the perceptual attribute are more impaired than functional attributes. These results extend the theory that the perception of demented patients remains intact but the organization effort leading to articulate the attributes is severely affected.
Electrical Conduction Properties of Sb & Se Co-Doped PbTe Thin Films

Thenanocrystalline Sb & Se co-doped PbTe thin films were prepared by using an integrated physical-chemical approach by evaporating chemically synthesized Sb& Se co-doped PbTenanopowders on glass substrates. The prepared films were analysed by XRD, SEM, EDAX and their results were discussed in detail. The DC electrical conduction mechanism in Al/SbPbTeSe/Al thin film sandwich system at different temperatures (298–423 K) was studied and was found to be Poole-Frenkel type.
East African Scholars Journal of Education, Humanities and Literature

Nida's functional equivalence represents not just that the equivalence is between the function of the original text in the original culture and the function of the target text (translation) in the target culture, but that function can be thought of as a property of the text. In this view, this paper makes a comparative analysis of the two English versions of the ancient Chinese poem Gui Yuan from the two aspects of form and meaning. It aims to verify the feasibility of functional equivalence theory in English translation of Chinese poem. Meanwhile, it provides some new perspectives for the appreciation and exploration of English translation of Chinese poem.
Knowledge, attitude and practice of self care among diabetic patients at Jimma University Medical Centre Diabetic Clinic, Jimma, Oromia region, South West Ethiopia: A survey study

Objective: To assess knowledge, attitude and practice of self-care among diabetic patients having followed up at Jimma University Medical Centre, Oromia region, southwest Ethiopia. Methods: Institutional study based design was conducted from March to May, 2019 in Jimma university medical centre diabetic follow up clinic. The study populations were all diabetic patients who came to JUMC diabetic follow up clinic during data collection time. Convenient sampling method was used using standardized questionnaires; information was gathered by face-to-face interview. Result: From total of 365 participants, 330 were given full information which is 90.41% respondent rate. From this 130(39.4%) were Type I DM patients and 200(60.6%) were type II DM patients, from the total respondents house wives were most affected groups 71(21.5%) and followed by farmers 73(22.1%). From total or 330 patients with the age groups of 50-59, 79(23.93%) were developed DM and mostly affected from other age groups. Regarding DM knowledge related to education about the diet (diabetic food), store insulin in buried pot with water and insulin injection in the abdomen, 300(90.9%), 90(26.47%) and 44(12.9%) respectively were obtained, from the total patients 300 (90.9%) of DM patients are interested in diabetic diets and 310 (93.9%) are interested in exercises. Conclusion: The commonest age group affected by diabetes mellitus was the age group 50-59 years which indicates most of them are type II DM patients. Significant number of patients learns about diabetic diet. There is gap between diabetic patients who know about diabetic diet and who eat ANALYSIS 24(106), November December, 2020 Medical Science ISSN 2321–7359 EISSN 2321–7367
Predicting lung cancer survival time (Healthcare & Computer Vision)

This project is part of a challenge [6] proposed by the company Owkin. The goal of this challenge is to develop methods to predict survival time of patients diagnosed with lung cancer. In order to complete this challenge, Owkin provides a dataset containing segmented radiology images (Computed Tomography scanner or CT scan) and numerical features. Predicting the survival time is essential in order to predict a patient’s risk and adapt treatment strategies. We can list several challenges in this project. The most obvious ones are the following:
Synergistic Influence of Environmental Factors for Increased Energy Production by Microalga

Microalgal like C. vulgaris, being a unicellular green algae, is been widely studied for nitrogen and phosphorus removal and as potential feedstock for biodiesel production. This not only saves water from eutrophication but also becomes a good source of biodiesel. Hence, if two approaches are combined we can overcome the major challenge of both eutrophication and energy crisis. In the present study we have examined the effect of decreasing Nitrogen and phosphorus concentration in medium on growth, biomass and lipid content of C. vulgaris. Significant decrease in growth and biomass was observed with the decrease of nitrogen and phosphorus concentration in the medium from (1.5g/l to 0.0g/l) and (0.04g/l to 0.0g/l) respectively. Whereas the lipid accumulation showed reverse trend of increase when the
MRNGAN: Reconstructing 3D MRI Scans Using A Recurrent Generative Model

Oftentimes, physicians ﬁnd themselves with only a partial magnetic resonance imaging (MRI) scan of a brain, and not the full volume. That is, it is difﬁcult for physicians to obtain an MRI scan of the full volume of a patient’s brain. Instead, physicians may only have a few 2D slices of the full volume, making diagnostics extremely challenging, as it becomes hard to align the scans, and determine where in the brain each 2D slice is. Thus, we present a method for generating the entire volume of the brain given as little as one 2D slice. Another application for this 2D to 3D reconstruction task is to augment and supplement incomplete datasets, which could potentially be very beneﬁcial to deep learning based brain imaging research, as most MRI datasets are quite small. To tackle this 3D reconstruction challenge, we used numerous previously researched topics, such as generative adversarial networks (GANs) and pix2pix, before amalgamating them into our ﬁnal RNN based model. In this paper we present our novel end-to-end model for re-constructing an MRI scan of the entire volume of a brain from one (or a few) 2D slices and show both quantitatively and qualitatively on a sample dataset that our model creates high ﬁdelity reconstructions.
Synthesis and X-ray Crystal Structure of a Urana[1]ferrocenophane, the First Tris(1,1‘-ferrocenylene) Metal Compound

Reaction of UCl4 with Li2fc·tmeda (fc = 1,1‘-ferrocenylene, tmeda = tetramethylethylenediamine) gave the tris(1,1‘-ferrocenylene) uranium complex [Li2(py)3U(fc)3] (1) (py = pyridine). X-ray analysis of a pyridine solvate of 1 revealed the propeller type structure of the U(fc)3 fragment, with the planar cyclopentadienyl rings of each fc group being parallel and with U−C and U−Fe bond distances of 2.52(7) and 3.14(2) A, respectively.
The Enantioselective Michael Addition of Thiols to Cycloalkenones by Using (2S, 4S)-2-Anilinomethyl-1-ethyl-4-hydroxypyrrolidine as Chiral Catalyst

Catalytic asymmetric addition of thiols to 2-cycloalkenone was studied by using the chiral amino alcohols, derived from L-hydroxyproline or (S)-proline, as base catalysts. Detailed investigation was carried out on the effects of the structure of the catalyst, the reaction medium, the temperature, and the concentration on the enantioselectivity. Good optical yields (47–88%) were achieved by the reaction of arenethiols and 2-cyclohexen-1-one in toluene at −5 °C, by using the catalyst (2S, 4S)-2-anilinomethyl-1-ethyl-4-hydroxypyrrolidine.
Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles

Engineered nanoparticles (ENPs) are being extensively used in a great variety of applications with a pace that is increasingly growing. The evaluation of the biological effects of ENPs is of utmost importance and for that experimental and most recently computational methods have been suggested. In an effort to computationally explore available datasets that will lead to ready-to-use applications we have developed and validated a QNAR model for the prediction of the cellular uptake of nanoparticles in pancreatic cancer cells. Our insilico workflow was made available online through the Enalos InSilicoNano platform (http://enalos.insilicotox.com/QNAR_PaCa2/), a web service based solely on open source and freely available software that was developed with the purpose of making our model available to the interested user wishing to generate evidence on potential biological effects in the decision making framework. This web service will facilitate the computer aided nanoparticle design as it can serve as a source of activity prediction for novel nano-structures. To demonstrate the usefulness of the web service we have exploited the whole PubChem database within a virtual screening framework and then used the Enalos InSilicoNano platform to identify novel potent nanoparticles from a prioritized list of compounds.
A homogenised model for flow, transport and sorption in a heterogeneous porous medium

Abstract A major challenge in flow through porous media is to better understand the link between microstructure and macroscale flow and transport. For idealised microstructures, the mathematical framework of homogenisation theory can be used for this purpose. Here, we consider a two-dimensional microstructure comprising an array of obstacles of smooth but arbitrary shape, the size and spacing of which can vary along the length of the porous medium. We use homogenisation via the method of multiple scales to systematically upscale a novel problem involving cells of varying area to obtain effective continuum equations for macroscale flow and transport. The equations are characterised by the local porosity, a local anisotropic flow permeability, an effective local anisotropic solute diffusivity and an effective local adsorption rate. These macroscale properties depend non-trivially on the two degrees of microstructural geometric freedom in our problem: obstacle size and obstacle spacing. We exploit this dependence to construct and compare scenarios where the same porosity profile results from different combinations of obstacle size and spacing. We focus on a simple example geometry comprising circular obstacles on a rectangular lattice, for which we numerically determine the macroscale permeability and effective diffusivity. We investigate scenarios where the porosity is spatially uniform but the permeability and diffusivity are not. Our results may be useful in the design of filters or for studying the impact of deformation on transport in soft porous media.
SDSS-IV MaNGA: The physical origin of off-galaxy Hα blobs in the local Universe

Hα blobs are off-galaxy emission-line regions with weak or no optical counterparts. They are mostly visible in Hα line, appearing as concentrated blobs. Such unusual objects have been rarely observed and studied, and their physical origin is still unclear. We have identified 13 Hα blobs in the public data of MaNGA survey, by visually inspecting both the optical images and the spatially resolved maps of Hα line for ∼4600 galaxy systems. Among the 13 Hα blobs, 2 were reported in previously MaNGA-based studies and 11 are newly discovered. This sample, though still small in size, is by far the largest sample with both deep imaging and integral field spectroscopy. Therefore, for the first time we are able to perform statistical studies to investigate the physical origin of Hα blobs. We examine the physical properties of these Hα blobs and their associated galaxies, including their morphology, environments, gas-phase metallicities, kinematics of ionized gas, and ionizing sources. We find that the Hα blobs in our sample can be broadly divided into two groups. One is associated with interacting/merging galaxy systems, of which the ionization is dominated by shocks or diffuse ionized gas. It is likely that these Hα blobs used to be part of their nearby galaxies, but were stripped away at some point due to tidal interactions. The other group is found in gas-rich systems, appearing as low-metallicity star-forming regions that are visually detached from the main galaxy. These Hα blobs could be associated with faint disks, spiral arms, or dwarf galaxies.