Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Magnetic Resonance Imaging Evaluation of Traumatic Brachial Plexus Injuries

Brachial plexus injury (BPI) is a severe neurologic injury that causes functional impairment of the affected upper limb. Imaging studies play an essential role in differentiating between preganglionic and postganglionic injuries, a distinction that is crucial for optimal treatment planning. Findings at standard conventional magnetic resonance (MR) imaging help determine the location and severity of injuries. Material and Methods: This is prospective and observational study descriptive study conducted at during January 2013 to June 2013 at Subbaiah Institute of Medical Sciences, Shimoga. Patients who have history of cervical spine or shoulder trauma with suspicious involvement of brachial plexus. Patients of all age with either sex groups were included in the study. Result: MRI examination of brachial plexus was done for 49 patients with clinically suspected traumatic or obstetric brachial plexopathy. Those patients were surgically explored with confirmed intraoperative findings. Thirty-eight patients were presented with traumatic brachial plexus injury: 31 males and 7 females. The most common component involved in brachial plexus injuries was roots 50% of cases followed by trunk is 42.1% of cases. and least were cord injury. Divisions and cords involved in 18.4% and 13.1% of cases respectively. In our study, no patient has involvement of branches. Conclusion: The brachial plexus can be efficiently imaged and effectively interpreted by the general radiologist when approached from a practical standpoint. Practical and useful information that can help the referring physician include, prevs. postganglionic location of lesion, mass vs. non-mass like enhancement, laterality or bilateral nature of disease, location of injury/mass/abnormality in BP segments (eg root, trunk, division, etc.), and anatomical region and surrounding structures involved (eg, interscalene space, costoclavicular triangle, relationship to subclavian/axillary vessels).
Stock Trading Classifier with Multichannel Convolutional Neural Network

Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regression on the prices or classification on trading signal as an operation indication. However, they did not reach a satisfactory financial result. In this work we aim to design a financially profitable stock market method by proposing a novel approach called Multichannel CNN Trading Classifier (MCNN-TC). The model was evaluated using data from the Brazilian stock market. The results indicate a satisfactory financial trading performance compared to the Buy and Hold strategy and good classification metrics.
Compressive strength parallel to grain of earlywood and latewood of yellow pine

The compressive strength parallel to grain of earlywood and latewood from the yellow pine sapwood and heartwood areas was examined in the study. The structure of the basic anatomical elements of wood tracheids, which conduct water and/or perform the mechanical function was also characterized. The compressive strength parallel to grain of latewood in the sapwood area was found to be twice as high as the compressive strength parallel to grain of earlywood. The compressive strength parallel to grain of latewood in the heartwood area, on the other hand, was found to be 2,5 times higher than the compressive strength parallel to grain of earlywood. This was due to the density of particular areas of wood and the dimensions of structural elements tracheids. In the sapwood area, the density of latewood was ca. twice as high as the density of earlywood. Similar relationships were found for heartwood. The thickness of latewood tracheids was found to be 1,5 times greater than the thickness of earlywood tracheids. These relationships were observed in sapwood and heartwood. The diameter of earlywood tracheids in radial direction was twice as large as the diameter of latewood tracheids. These relationships were observed in yellow pine sapwood and heartwood.
Sandwich Enzyme-Linked Immunosorbent Assay (ELISA) Optimization and Validation for Ricin Protein

Author Title Number of Pages Date Nina Donnell Sandwich Enzyme-Linked Immunosorbent Assay (ELISA) Optimization and Validation for Ricin Protein 35 pages + 8 appendices 1 December 2017 Degree Bachelor of Laboratory Sciences Degree Programme Laboratory Sciences Instructors Paula Vanninen, PhD, Professor Jukka Niiranen, Lic.Phil., Principal Lecturer Ricin is listed in the Chemical Weapons Convention (CWC) as a schedule 1 toxic chemical with no known antidote and threat of terrorism due to its toxic nature. Because of the potential misuse, a method for precise detection of ricin is crucial. In this study enzymatic ricin detection method, Sandwich Enzyme-Linked Immunosorbent Assay (sandwich ELISA), was optimized and validated for the Finnish Institute for Verification of the Chemical Weapons Convention (VERIFIN). Sandwich ELISA method optimization is focused on antibody biotinylation, multiple buffer solutions, antibody concentrations and conjugate enzyme concentration. Antibody and conjugate enzyme concentrations were determined using titration and concentrations where ideal signal-to-noise ratio were chosen. Validation focused on limit of blank (LOB), limit of detection (LOD), intraand inter-assay precision, recovery and specificity. LOB, LOD and assay precisions were determined from multiple ELISA results. Recovery of the ELISA method was tested using three matrices: milk, red wine and wheat flour. Specificity was tested using two ricin like proteins agglutinin and abrin. Method optimization was successful detecting ricin in 0.03 % BSA 0.1 % TritonX in PBS solution comfortably down to 7.6 pg/ml. Optimized concentrations for antibodies were for coating 2.5 μg/ml, detection 0.8 μg/ml and conjugate enzyme was optimized to 1 ng/ml. LOB was 2.4 pg/ml and LOD was 2.9 pg/ml. Intra-assay precision was lowest at 3.9 % and highest at 105 %, inter-assay precision low was 14 % and a high of 35 %. Sample matrices disturbed the ricin ELISA determination and recovery from three matrices were low. No cross reactivity for abrin was found. However, agglutinin from 25 ng/ml onwards was detected. Sandwich ELISA for ricin works well when using optimized sample matrix, 0.03 % BSA 0.1 % TritonX in PBS, but the other three matrices had substantial effect on the assay and ricin detection. Validation results suggest that the assay has still a lot to be improve upon.
Tracing the electron motion in H2+ using attosecond photoelectron spectroscopy

Charge migration during light–matter interaction is one of the most fundamental processes which plays a key role in chemical and biological processes of molecule. The measurement of this process requires the extreme temporal and subatomic spatial resolutions. Here, we show that a scheme based on infrared pump and short extreme ultraviolet probe technique enables us to trace the laser-controlled electron motion with high spatiotemporal resolution. By numerically solving the time-dependent Schrödinger equation for H2+ combined with a simple double-slit model, we demonstrate that the laser-controlled charge migration can be directly reconstructed from the interference patterns of the photoelectron spectrum.
Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices.
A Survey of Application and Development of Small Disturbance Equation Method in the Aviation Area

All the small disturbance equation (SDE) methods, which are a useful technique to enhance the stability and security of the aircraft, play a significant role in the development of the aviation area. This review paper briefly introduces the development of the SDE and summarizes the two calculation methods combining with the SDE and application of the SDE method in the aviation area. The calculation methods mainly focus on the approximate factorization (AF) algorithm with the SDE and Newton-Orthomin method with the SDE. AF algorithm with the SDE is robust enough, and it can choose the calculation methods according to accuracy rather than stability that significantly saves the calculation cost. The newton-Orthomin method with the SDE can improve computing efficiency by 2.1 to 4.5 times compared with the monotone AF method. The direction of application focus on the flutter analysis, gust analysis and resizing of some component in the aircraft and the disadvantages are explored for each type. In conclusion, the combination of the SDE with the other algorithms can increase calculation efficiency, and the SDE has a wide range of applications in the aviation area. This paper can provide relevant references for the subsequent combination of the SDE and other algorithms development or practical application of SDE.
Recent Advances of Lattice Boltzmann Method in Microfluidic numerical Simulation

The technology of microfluidics is widely adopted in various fields such as biomedicine, microanalysis, and microelectronics. For example, pharmaceutical scientists often use microfluidics as a tool for drug delivery or cell separation. The LBM (Lattice Boltzmann method) is a commonly used numerical simulation in microfluidic researches. LBM is used extensively for simulations containing complicated boundary conditions and multiphase interfaces as it needs relatively low computing power compared to other numerical simulation methods in complex situations. The brilliant capability in parallelism also allows it to have a high multitasking performance, increasing overall efficiency. In this paper, we reviewed several typical applications of LBM in the following three fields: (1) particle regulation; (2) flow control; (3) drug delivery. We concluded defects in current studies and proposed potential improvements to be investigated in the future.
Stellar-mass Black Hole Spin Measurements and its Implementation

The spin of the black hole is one of the deterministic qualities for itself, providing us with the knowledge of its birth and evolution. In this review, I described two kinds of the spin measurements, respectively based upon electromagnetic waves and gravitational waves, and discuss the meaning for the black hole spin evolution indicated by the spin results measured by these methods. We find that for X-ray binaries, the spin results are much bigger than those results for natal spin, and for binary black holes the spin result are much smaller than those in X-ray binaries, which may be resulted from their different evolutions. For binary black holes detected through gravitational waves, formation channels of the binary systems are essential for interpreting spin results in these binary systems.
Distribution probability of galaxies in Local Group

The evolution of galaxies is a very important topic for understanding of the evolution of Universe, and this evolution results in that galaxies are not evenly distributed in space. The probability of distribution of galaxies in space can be computed according to probabilistic models. Although the evolution of galaxies has been studied from various angles, the probability of distribution of galaxies in space has yet to be studied with respect to their positions over time. Of galaxies, the positions of galaxies in Local Group have been studied more thoroughly. In this study, we computed the distribution probabilities of galaxies in Local Group, i.e. the distribution probabilities of galaxies at present and previous positions, and the probability for an even distribution of galaxies in space. The results show that galaxies in Local Group have an extremely small chance to distribute evenly, and the distribution probability of galaxies in Local Group increases from the past to the present. The implication is that the accelerating expansion of the universe is increasing the distribution probability of galaxies, i.e. the accelerating expansion of the universe is a probabilistically probable event.
Bayesian Analysis of Two-Parameter Exponentiated Log-logistic Distribution

In this paper, the parameters of the two-parameter exponentiated log-logistic distribution based on a complete sample are estimated using the Markov chain Monte Carlo (MCMC) method. In order to perform full Bayesian analysis of the two-parameter exponentiated log-logistic distribution, the procedures are developed using the MCMC simulation method in OpenBUGS, established software. The researcher has obtained the Bayes estimates of the parameters and their probability intervals are presented. The researcher has also discussed the estimation of the reliability function. For illustration under independent gamma priors, the real data set is considered.
NSS_A_263528 693..704

*These authors contributed equally to this work Purpose: The objective of this study was to investigate whether the serum biomarkers S100 calcium binding protein B (S100B), glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF) change in patients with chronic insomnia disorder (CID), and if this is the case, whether the altered levels of these serum biomarkers are associated with poor sleep quality and cognitive decline in CID. Patients and Methods: Fifty-seven CID outpatients constituted the CID group; thirty healthy controls (HC) were also enrolled. Questionnaires, polysomnography, ChineseBeijing Version of Montreal Cognitive Assessment (MoCA-C) and Nine Box Maze Test (NBMT) were used to assess their sleep and neuropsychological function. Serum S100B, GFAP, BDNF, and GDNF were evaluated using enzyme-linked immunosorbent assay. Results: The CID group had higher levels of S100B and GFAP and lower levels of BDNF and GDNF than the HC group. Spearman correlation analysis revealed that poor sleep quality, assessed by subjective and objective measures, was positively correlated with S100B level and negatively correlated with BDNF level. GFAP level correlated positively with poor subjective sleep quality. Moreover, S100B and GFAP levels correlated negatively with general cognitive function assessed using MoCA-C. GFAP level correlated positively with poor spatial working memory (SWM) in the NBMT; BDNF level was linked to poor SWM and object recognition memory (ORcM) in the NBMT. However, principal component analysis revealed that serum S100B level was positively linked to the errors in object working memories, BDNF and GDNF concentrations were negatively linked with errors in ORcM, and GFAP concentration was positively correlated with the errors in the SWM and spatial reference memories. Conclusion: Serum S100B, GFAP, BDNF, and GDNF levels were altered in patients with CID, indicating astrocyte damage, and were associated with insomnia severity or/and cognitive dysfunction.
Response of some Grain Sorghum Genotypes to Foliar Spray by Humic Acid

A field experiment was carried out at Agronomy Department Research Farm, Faculty of Agriculture, Assiut University, Egypt, during 2016 and 2017 seasons to evaluation some sorghum genotypes under different concentrations of humic acid. The experiment was laid out in randomized complete block design (RCBD) using strip-plot arrangement with three replications. The genotypes {Horus, Hybrid 2, Hybrid 305 and Hybrid 306} were assigned vertically while, humic acid concentrations (control, 0.5 ml L, 1.0 ml L,1.5 ml L,2.0 ml L and 2.5 ml L) were allocated horizontally. The obtained results showed that the studied grain sorghum genotypes had a significant effect on plant height, panicle length, grain weight plant, grain and fodder yields traits in the both seasons. , furthermore the interaction between grain sorghum genotypes and humic acid concentrations had a significant effect on the plant height in the second season, seed weight plant in the first season as well as grains and fodder yields/fed in the two growing seasons. In addition, the maximum average of grain yield fed. (26.7 and 25.2 ardab fed. in the first and second seasons, respectively) were gained from Hybrid 2 plants which were sprayed by 2.5 ml l of humic acid in the first season and Hybrid 306 plants which were sprayed by 2.0 ml l in the second one.
A Versatile Double-Cross-Linking Approach to Transparent, Machinable, Super-Compressible, Highly Bendable Aerogel and Xerogel Thermal Superinsulators

Right This document is the unedited author's version of a Submitted Work that was subsequently accepted for publication in Chemistry of Materials, copyright © American Chemical Society after peer review. To access the final edited and published work, see https://doi.org/10.1021/acs.chemmater. 8b00563. ; この論文は 出版社版でありません。 引用の際には出版社版をご確認 ご利用ください。 ; This is not the published version. Please cite only the published version.
A Generic Model for Swarm Intelligence and Its Validations

The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.
Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance

This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable; and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.
Biomedical Question Answering: A Survey of Approaches and Challenges

Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access, and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into five distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base, and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and we discuss some potential future directions to explore.
A Second-Order Nonlocal Approximation for Manifold Poisson Model with Dirichlet Boundary

Recently, we constructed a class of nonlocal Poisson model on manifold under Dirichlet boundary with global $\mathcal{O}(\delta^2)$ truncation error to its local counterpart, where $\delta$ denotes the nonlocal horizon parameter. In this paper, the well-posedness of such manifold model is studied. We utilize Poincare inequality to control the lower order terms along the $2\delta$-boundary layer in the weak formulation of model. The second order localization rate of model is attained by combining the well-posedness argument and the truncation error analysis. Such rate is currently optimal among all nonlocal models. Besides, we implement the point integral method(PIM) to our nonlocal model through 2 specific numerical examples to illustrate the quadratic rate of convergence on the other side.
Optimal Embedding Calibration for Symbolic Music Similarity

In natural language processing (NLP), the semantic similarity task requires large-scale, high-quality human-annotated labels for fine-tuning or evaluation. By contrast, in cases of music similarity, such labels are expensive to collect and largely dependent on the annotator's artistic preferences. Recent research has demonstrated that embedding calibration technique can greatly increase semantic similarity performance of the pre-trained language model without fine-tuning. However, it is yet unknown which calibration method is the best and how much performance improvement can be achieved. To address these issues, we propose using composer information to construct labels for automatically evaluating music similarity. Under this paradigm, we discover the optimal combination of embedding calibration which achieves superior metrics than the baseline methods.
Multilingual Search Engine in Commercial websites

: In this modern internet world has developed with many cloud the cloud with more advanced services. The internet world is used by well-educated persons and people aware of internet. The main object of this project is to make the illiterate make use of resources available in the internet and make them to involve in online shopping site by providing the speech to text service for performing search operations and further more operations and item identification is also there for the so far the Google, Bing…and many other search engines are powered with speech based searches. But in case for Commercial websites textual based searching medium is only supported for searching products. In this project the online websites are made search through voice based search operations and Multilingual based search using virtual keyboard for the Indian languages. For the language identification performed through the encoding processing and then the search is made in the selected language. Thus the users can search the product through the voice based input. Thus in country like India with more diversity there will be more number of language used among various regions then among the various languages there will be different dialect. So for different dialect machine learning is implemented for keyword identification in different dialects through displaying the pictures of random products pictures in the database. Thus the main objective is to bring the illiterates to the internet world for the initiation the multilingual and the speech recognition support is to be given in commercial website with NLP support and Machine learning to fetching the appropriate product from the queries given in multiple languages and displaying the product.