Miracle-dz/UniFAR-FTUs huggingface dataset:
Voxel‐Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis

Despite robust postmortem evidence and potential clinical importance of gray matter (GM) pathology in multiple sclerosis (MS), assessing GM damage by conventional magnetic resonance imaging (MRI) remains challenging. This prospective cross‐sectional study aimed at characterizing the topography of GM microstructural and volumetric alteration in MS using, in addition to brain atrophy measures, three quantitative MRI (qMRI) parameters—magnetization transfer (MT) saturation, longitudinal (R1), and effective transverse (R2*) relaxation rates, derived from data acquired during a single scanning session. Our study involved 35 MS patients (14 relapsing–remitting MS; 21 primary or secondary progressive MS) and 36 age‐matched healthy controls (HC). The qMRI maps were computed and segmented in different tissue classes. Voxel‐based quantification (VBQ) and voxel‐based morphometry (VBM) statistical analyses were carried out using multiple linear regression models. In MS patients compared with HC, three configurations of GM microstructural/volumetric alterations were identified. (a) Co‐localization of GM atrophy with significant reduction of MT, R1, and/or R2*, usually observed in primary cortices. (b) Microstructural modifications without significant GM loss: hippocampus and paralimbic cortices, showing reduced MT and/or R1 values without significant atrophy. (c) Atrophy without significant change in microstructure, identified in deep GM nuclei. In conclusion, this quantitative multiparametric voxel‐based approach reveals three different spatially‐segregated combinations of GM microstructural/volumetric alterations in MS that might be associated with different neuropathology.
THE IMPACT OF NETWORKING ON THE SMES’ ABILITY TO ACCESS FINANCIAL GOVERNMENT SUPPORT IN MALAYSIA

The need to strengthen SMEs development in Malaysia is crucial, because SMEs are expected to be an essential element of economic growth, employment creation and transformation towards developed country by year 2020. Consequently, the government has given priority to SMEs and has put in place policies and institutional framework that address their developmental needs. This study investigates the impact of SMEs networking with officers in SME Corp. Malaysia on access to financial government support provided through SME Corp. Malaysia. The study aims also to examine the influence of financial government support on the ability of SMEs to access to different financial resources in Malaysia. The study conducted on manufacturing small and medium enterprises registered under SME Corp. Malaysia. The e-mail survey was used by researchers to collect data from respondents. 500 enterprises were selected, and 182 replied. To analyse the data, theStructural Equations Modelling (SEM) technique was applied by using SmartPLS software. The results of study showed there is a significant and positive effect of networking with officers in SME Corp. Malaysia on the ability of SMEs to gain financial government support. Furthermore, the results confirmed the existence of a significant and positive influence of the ability of SMEs to gain financial government support on the capability of SMEs to access to different financial resources provided by financial institutions in Malaysia.
A Knowledge Organization System for Image Classification and Retrieval in Petroleum Exploration Domain

This paper describes a knowledge organization scheme for types of images in the domain of petroleum exploration based on ontological criteria. The classification separates the characteristics of representation, visualization, and storage from the semantics of the content, where each of these features has its proper organization system. The representation and visualization classes optimize the effort of image annotation by grouping the instances of images according to a set of criteria described in this paper, which are more directly identified by automatic classification methods. These classes keep a relationship with the geological entities depicted in the images. In this way, we can separate training methods for identifying the type of representation and for those that learn about what geological entity this representation is. In this ongoing project, we apply the knowledge organization for information retrieval over a set of 1927 images related to petroleum exploration. Preliminary results show good accuracy in simple classification tasks and indicate the need for improved classifiers in complex tasks, where the proposed ontological system will be fundamental for organizing the image datasets.
Analysis and Optimization of an Electro-Thermally and Laterally Driven Poly-Silicon Micro-Actuator

A Large displacement at low voltage is generally provided by electro-thermal mechanisms. A 3-D MEMS electro-thermal micro-actuator has been designed and simulated using COMSOL Multiphysics 4.3 which is geometrically optimized to explore the effect of dimensional variation on its performance. Metallic electrothermal actuators provide large displacements at low voltages but at the same time, the maximum temperature in the device rises very sharply due to their highly conductive nature. To overcome this problem, a semi-conductor material; poly-silicon which is compatible with IC technology is used. For further improvements in terms of displacement and temperature, the geometry of the actuator is optimized.
The effect of beryllium oxide on retention in JET ITER-like wall tiles

Preliminary results investigating the microstructure, bonding and effect of beryllium oxide formation on retention in the JET ITER-like wall beryllium tiles, are presented. The tiles have been investigated by several techniques: Scanning Electron Microscopy (SEM) equipped with Energy Dispersive X-ray (EDX), Transmission Electron microscopy (TEM) equipped with EDX and Electron Energy Loss Spectroscopy (EELS), Raman Spectroscopy and Thermal Desorption Spectroscopy (TDS). This paper focuses on results from melted materials of the dump plate tiles in JET. From our results and the literature, it is concluded, beryllium can form micron deep oxide islands contrary to the nanometric oxides predicted under vacuum conditions. The deepest oxides analyzed were up to 2-micron thicknesses. The beryllium Deuteroxide (BeO x D y ) bond was found with Raman Spectroscopy. Application of EELS confirmed the oxide presence and stoichiometry. Literature suggests these oxides form at temperatures greater than 700°C where self-diffusion of beryllium ions through the surface oxide layer can occur. Further oxidation is made possible between oxygen plasma impurities and the beryllium ions now present at the wall surface. Under Ultra High Vacuum (UHV) nanometric Beryllium oxide layers are formed and passivate at room temperature. After continual cyclic heating (to the point of melt formation) in the presence of oxygen impurities from the plasma, oxide growth to the levels seen experimentally (approximately two microns) is proposed. This retention mechanism is not considered to contribute dramatically to overall retention in JET, due to low levels of melt formation. However, this mechanism, thought the result of operation environment and melt formation, could be of wider concern to ITER, dependent on wall temperatures.
Integrated Scientific Cloud Computing Workflows SC2IT: A Toolkit to Bring Novel Computational Methods to Non-specialists

Scientific Cloud Computing (SCC) applies the advantages of Clouds to computational science. We present the SC2IT Scientific Cloud Computing tools that abstract complex computational workflows and allow state-of-the-art scientific high-performance computing software to be distributed in a user-friendly manner. We present Linux and Java “SC2IT” toolsets to manage virtual cloud clusters and implement scientific workflows. We demonstrate several GUIs that implement the “SC2IT” interface for materials science applications. In particular, the ClusterManager is a generalpurpose SCC GUI to which new workflows can be added easily. Keywords-Cloud Computing; High-Performance Computing; Scientific Computing; Workflows; User Interface Design
Measurement Task Ontology

Measurement is a key process in several domains. Although it has particularities according to the application domain, part of the knowledge related to the measurement process is common to all of them. This paper presents the Measurement Task Ontology (MTO), which establishes a common conceptualization regarding core aspects of measurement. MTO addresses behavioral aspects of the measurement process and extends the Core Ontology on Measurement, combining both domain and task-related aspects of measurement. As evaluation of MTO, we described a real-world case scenario regarding a laboratory test, showing that MTO is capable of representing real world situations.
The Designated Project Committee Approves the Master’s Project Titled Image-Based Localization of User-Interfaces By

Image localization corresponds to translating the text present in the images from one language to other language. The aim of the project is to develop a methodology to translate the text in image captions from English to Hindi by taking context of the images into account. A lot of work has been done in this field [22], but our aim was to explore if the accuracy can be further improved by consideration of the additional information imparted by the images apart from the text. We have explored Deep Learning using neural networks for this project. In particular, Recurrent Neural Networks (RNN) have been used which are ideal for sequence translations and would meet the needs of this project which involves text sequences. This technique of image localization would be beneficial in a lot of fields. For example, in order to make the text data accessible to everyone, text data should be translated in multiple languages spoken by people across the world. This will help in the growth at the rural areas and countries where English is not spoken by giving them access to data in their local languages. This could also benefit tourists who would then be able to understand the sign boards and posters in a foreign country. With accurate data translation, the old manuscripts can also be translated to English upon which further research can be carried out.
Effects of loading rate and temperature on strength and deformability of Maha Sarakham salt

Uniaxial and triaxial compression tests are performed to assess the effects of temperature and loading rate on strength and deformability of Maha Sarakham salt. Specimens are compressed to failure under constant confining pressures between 0 and 12 MPa while the axial stress is increased at constant rates of 0.001, 0.01, and 0.1 MPa/s. Elasticity and strength at dilation and at failure increase with loading rates. Sets of empirical equations are derived for the strength, temperature, and applied stress rate in forms of power, logarithmic, and exponential functions. The distortional strain energy at dilation and at failure varies linearly with the mean normal stress. The proposed strength criteria are applied to determine the safe maximum withdrawal rate of a compressed-air energy storage cavern in the Maha Sarakham formation. The strain energy criterion that considers both distortional and mean stress-strain tends to give the most conservative result.
A bifurcation theorem for nonlinear matrix models of population dynamics

ABSTRACT We prove a general theorem for nonlinear matrix models of the type used in structured population dynamics that describes the bifurcation that occurs when the extinction equilibrium destabilizes as a model parameter is varied. The existence of a bifurcating continuum of positive equilibria is established, and their local stability is related to the direction of bifurcation. Our theorem generalizes existing theorems found in the literature in two ways. First, it allows for a general appearance of the bifurcation parameter (existing theorems require the parameter to appear linearly). This significantly widens the applicability of the theorem to population models. Second, our theorem describes circumstances in which a backward bifurcation can produce stable positive equilibria (existing theorems allow for stability only when the bifurcation is forward). The signs of two diagnostic quantities determine the stability of the bifurcating equilibrium and the direction of bifurcation. We give examples that illustrate these features.
Improving the relationship between reference frames by using the Thin-Plate Spline modelling

The adoption of new geodetic reference systems and/or the availability of new reference frames occur, mainly, by the availability of new data sets. For the use of products in different reference frames, methods are necessary to transform the coordinates and modelling the distortions that compromise the transformation. In this paper a method is presented for three-dimensional coordinates transformation with modeling of distortions based on Thin-Plate Splines (TPS) mapping functions. Experiments were performed with real data of SAD 69 (South American Datum – 69) stations in the realization of 1996 (SAD69/96) and its homologous coordinates in SIRGAS2000. In the control points, the values of the root mean square error (RMSE) of the discrepancies in latitude and longitude, respectively, were 0.9 mm and 0.6 mm, and in the check points of 7.8 cm and 6.7 cm. By using distinct points of the network, the comparison of direct and inverse transformation results in the standard deviation were of 0.5 mm in latitude and 0.3 mm in longitude. In the comparison between the TPS model and the ProGriD, the statistical indicators were reduced by 97% (average), indicating that the proposed method is promising.
Are athletes addicted to their identity? The development and validation of the Athletic Identity Addiction (AIA) scale

The purpose of this study was to develop a reliable and valid instrument for assessing the extent to which athletic identity is related to the tenets of addiction. Specifically, it was investigated whether athletes experience a behavioral addiction regarding their participation and involvement in sport. A total of 576 athletes (118 student-athletes, 458 former student-athletes) were included within a two-phase study. The first phase focused on crafting and testing the instrument, while the second phase refining the instrument for validity and reliability. Results indicated a four-factor solution comprising the Athletic Identity Addiction (AIA) scale: (1) mood alteration, (2) withdrawal, (3) life conflict, and (4) relapse. Concerns within the EFA led us to remove salience and tolerance before reaching the final model. Each of the four factors established within AIA is substantiated by previous studies outlining challenges faced by athletes. This research provides insight into deep seeded issues of athletic identity, indicating the importance of sport managers’ developing appropriate means for aiding athletes in transitioning from elite sport participation. Findings also provide future, alternative perspectives for researchers to examine athletic identity.
On Embeddings in Relational Databases

We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information. Embeddings across entities in a relational database have been less explored due to the intricate data relations and representation complexity involved. Relational databases are an inter-weaved collection of relations that not only model relationships between entities but also record complex domain-specific quantitative and temporal attributes of data defining complex relationships among entities. Recent methods for learning an embedding constitute of a naive approach to consider complete denormalization of the database by materializing the full join of all tables and representing as a knowledge graph. This popular approach has certain limitations as it fails to capture the inter-row relationships and additional semantics encoded in the relational databases. In this paper we demonstrate; a better methodology for learning representations by exploiting the underlying semantics of columns in a table while using the relation joins and the latent inter-row relationships. Empirical results over a real-world database with evaluations on similarity join and table completion tasks support our proposition.
Transthyretin Intron 1 based Interordinal Relationships of Laurasiatheria

Transthyretin (TTR), a major thyroid hormone distributor protein (THDP) in the blood, is coded by a gene which contains four exons and three introns. Nowadays, several nuclear gene introns have been successfully utilized to elucidate the relationships of organisms, including TTR intron 1 nucleotide sequence. In this study, the utilization of TTR intron 1 to resolve the interrelatedness of mammals in Laurasiatheria was explored. The complete TTR intron 1 sequences from 36 animal species including human (Homo sapiens) were extracted from the genome sequences retrieved from the GenBank. In addition, the sequences from two microbats e.g. intermediat horseshoe bat (Rhinolophus affinis) and lesser Asiatic yellow house bat (Scotophilus kuhlii) were amplified by PCR and sequenced. The phylogenetic relationship of the studied animals was analyzed using MEGA 6 program. Based on the TTR intron 1, the phylogenetic tree with most of major nodes was strongly supported by bootstrap >75% was successfully produced. The monophyletic relationships within each order were strongly supported, ranging from 67% (within Eulipotyphla) to 100% (within Cetratiodactyla and Carnivora) bootstrap. However, the placement of Perissodactyla as the sister group to Carnivora was supported at low bootstrap. The paraphyletic relationships of the members within Chiroptera was highly supported with >85% bootstrap. In addition, more closely relatedness of the microbat R. affinis to the megabats than to the other microbats was supported with 97% bootstrap. The results presented here indicated to the usefulness of TTR intron 1 in resolving the relationships of the animals in Laurasiatheria. คําสําคัญ: วิวัฒนาการ อินทรอน ลอราเซียเทอเรีย วิวัฒนาการของส่ิงมีชีวิต ทรานส์ไทรีติน
Formation of communicative competence of future specialists by means of group work

In this article the authors report on the research conducted to study the general and the specific features attributed to the formation of the communicative competence of the future specialists, when this type of competence is developed by means of group work under conditions of a contemporary institution of higher education. Communicative competence is expressed in the ability to carry out the selfassessment and in the level the interpersonal experience formed for the effective performance of their professional activities. The model engaged in the development of communicative competence by means of group work has been checked for the effectiveness of its influence on the development of communicative competence of the above mentioned professional group and it has been experimentally verified that the student’s general competence of high level increased.
AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive Content

Social media is a globally open place for online users to express their thoughts and opinions. There are numerous advantages of social media but some severe challenges are also associated with it. Anti-social and abusive conduct has become more common due to the emergence of social media. Identiﬁcation of Hate Speech, Cyber-aggression, and Oﬀensive language is a very challenging task. The nature of structures of the natural language makes this task even more tedious. Being a challenging task, we are fascinated to propose a deep learning system based on Convolutional Neural Networks to identify Hate Speech, Oﬀen-sive language, and Profanity. We have done experiments with three diﬀer-ent embeddings. These experiments have been associated with comments of code-mixed Hindi-English and multi-domain social media text. We have found that One-hot embedding performed better than pre-trained fastText embedding for the code-mixed Hindi dataset.
Dissection of Phenotypic and Genetic Variation of Drought‐Related Traits in Diverse Chinese Wheat Landraces

Variations in 16 seedling traits under normal and drought conditions were investigated. Extremely resistant and sensitive accessions were identified for future analyses. Under normal and drought conditions, 57 and 29 QTL were identified, respectively. A total of 77 candidate genes were identified, and four were validated by qRT‐PCR.
Putting “human crowds” in th a collaborative working envi

The current impact of financial crisis on societal and scien volumes of data in a socially-mediated interaction contex scale. Existing mechanisms are inefficient for a single hu a large number of publications. This time-consuming and effort grounded on scientific metrics and theoretical foun representations. This paper reports on a work in progress semantic analytics focused on what scientific research da of intellectual labor among social, computer, and citizen to traditional bibliometric approaches, attempting to unco connections can be examined to demonstrate how a scient © 2013 Published by Elsevier Ltd. Selectio
Spatial Variation in Residential Land Value Determinants in Lagos Nigeria

Seven hundred and fifty five household heads selected through stratified random sampling technique are interviewed using already prepared questionnaires. The information that is collected from the field are subjected to factor and principal component analysis. This is done to show the spatial variation that existed between factors of land values such as accessibility, rent, and transport improvement, quality of neighbourhoods, infrastructural facilities and government regulations.
Posture Recognition of Standing Human Bodies in Static Images

Recognition of human bodies in images is a challenging task that can facilitate numerous applications, like scene understanding and activity recognition. In order to cope with the highly dimensional pose space, scene complexity, and various human appearances, the majority of existing works require computationally complex training and template matching processes. We propose a bottomup methodology for automatic extraction of human bodies from single images, in the case of almost upright poses in cluttered environments. The position, dimensions, and color of the face are used for the localization of the human body, construction of the models for the upper and lower body according to anthropometric constraints, and estimation of the skin color. Different levels of segmentation granularity are combined to extract the pose with highest potential. The segments that belong to the human body arise through the joint estimation of the foreground and background during the body part search phases, which alleviates the need for exact