Miracle-dz/UniFAR-FTUs huggingface dataset:
Hepatic Functional Status of the Tea Garden Workers of West Tripura District , Tripura , India

This cross-sectional study was conducted to examine hepatic function via liver enzymes/proteins assessments, along with the nutritional status of the male and female tea garden workers of West Tripura district, Tripura, India.Nutritional status wasanalyzed by using body mass index (BMI) and other anthropometric indices. Hepatic function was studied by the analysis of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase(ALP), serum proteins, total bilirubin, and direct bilirubin. Results revealed that except total protein and globulin, all other parameters of liver function test were significantly higher in males than the female workers.About 60.73% of maleswere having elevated levels of liver function test measures than the females(49.53%). Analysis of the severity of the liver function damage by albumin-bilirubin score (ABLI) revealed that that 72.63% of the male and 70.70% of the female workers werehaving less good liver function(Grade 2), 27.37% of the male and 29.30% of the female population were having good liver function(Grade 1). However, no workers was having worst liver function damage(Grade 3). This study revealed that the hepatic functional status of the tea garden workers of the West Tripura district of Tripura is in alarming condition and males are more affected than females.
Network Structure using Hybrid Optoelectronic Routers

Large data centers hosting hundreds of thousands of servers have been built to handle huge amounts of data. In a large data center, servers cooperate with each other, and thus the data center network must accommodate a large amount of traffic between servers. However, the energy consumption of large data center networks is a major problem because several largecapacity switches or many conventional switches are required to handle the traffic. A low-energy hybrid optoelectronic router has been proposed to provide high bandwidth between data center servers. The hybrid optoelectronic router has an optical packet switching functionality and packet buffering in the electronic domain. The optical packet switching provides large bandwidth communication between the optical ports. In addition, the router can be directly connected to server racks by its electronic port. The packets from the server racks are stored in an electronic buffer in the router. Then, the packets are converted into optical packets and sent to another router. Finally, when the packets arrive at the router connected to the destination server rack, they are stored in the buffer, converted to electronic packets, and sent to the destination server rack. In this paper, we discuss a data center network structure that contains hybrid optoelectronic routers. We propose a method for constructing a data center network that uses hybrid optoelectronic routers efficiently. Furthermore, we discuss the effect of the network structure on the number of routers required to accommodate a large amount of traffic in a mega data center. Keywords—Data Center Network; Topology; Optical Packet Switch
ENHANCED PREDICTION OF HEART DISEASE WITH OPTIMAL FEATURE SELECTION

As the data is growing very rapidly in all domains, it is challenging task for data processing and analysis techniques to handle this data. So the traditional record maintenance strategy in the healthcare system is replaced with e-healthcare data efficiently. In the e-healthcare system, it is vital to develop a model that is able to handle the huge amount of data efficiently. This paper focuses on the challenging tasks of selecting critical features from the enormous set of available features and predicting heart disease. In classification problems, feature selection is one of the most widely used preprocessing steps. The existing system consists of a modified Differential Evolution (DE) algorithm with feed-forward neural network. This algorithm performs feature selection and optimization for cardiovascular disease. In proposed system, backpropagation model is used in which error minimization will be carried out in the prediction of heart disease to improve the performance in terms of accuracy, specificity and sensitivity. The performance of the system will be tested on dataset available in an online repository of the University of California, Irvine (UCI) which contains 303 data samples of individuals.
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A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process. In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole information aggregation as stochastic sequential processes. The algorithm works on a much larger search space, utilizes reasoning history to perform inference, and is robust to noisy edges. We apply our algorithm to multiple complex graph reasoning and prediction tasks and show that our algorithm consistently outperforms state-of-the-art graph-structured models by a significant margin.
Massively Multiplayer Operas : Interactive Systems for Collaborative Musical Narrative

Music, narrative, and social interaction have long been intertwined. The objective of this thesis is to create a platform, designed for interactive multiplayer operas, that explores the potential for technology-enabled systems to facilitate creativity through expression, the emotional affordances of musical storytelling, and the spatiotemporal boundaries of copresence. A variety of design experiments for collaborative musical narrative are implemented and evaluated. The work also introduces a real-time lyrical conversation system, with user interfaces that allow for simultaneous musical and narrative expression with a high degree of granularity. These experiences are encapsulated by an overarching lyrical multiplayer narrative opera platform. This project seeks to provide a novel means of creating and understanding multi-user, interactive music systems in which users participate in active and collaborative music-making in conjunction with narrative engagement. Thesis Supervisor: Tod Machover Muriel R. Cooper Professor of Music and Media Program in Media Arts and Sciences Massively Multiplayer Operas: Interactive Systems for Collaborative Musical Narrative
Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in Danish

Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.
On some characterizations of nets in frames

This paper deals with some specific characterizations of nets in frames. We start by exploring certain vital facts about regular and prime nets in frames and using this, an analysis of their behavior in different kinds of frames has been done. We prove here that in a Boolean frame, primeness and maximality of nets are equivalent concepts whereas in a compact regular frame, completely prime nets and maximal regular nets coincide. Finally we introduce the notion of Universal nets in frames and conclude the paper by giving a necessary and sufficient condition for a net to be universal using the concept of prime and balanced filters.
FULL PAPER – A HYBRID ELASTIC METAMATERIAL WITH TUNABLE BENDING STIFFNESS

Achieving vibration and/or wave attenuation with locally resonant metamaterials has attracted a great deal of attention due to their frequency dependent negative effective mass density. Moreover, adaptive phononic crystals with shunted piezoelectric patches have also demonstrated a tunable wave attenuation mechanism by controlling electric circuits to achieve a negative effective stiffness. In this paper, we propose an adaptive hybrid metamaterial that possesses both a negative mass density as well as an extremely tunable stiffness by properly utilizing both the mechanical and electric elements. The tunable wave manipulation abilities are investigated and revealed in terms of both the effective negative mass density and/or bending stiffness of the hybrid metamaterial. The programmed flexural wave manipulations and broadband negative refraction are then illustrated through three-dimensional (3D) multi-physical numerical simulations in hybrid metamaterials. Our numerical results demonstrate that the flexural wave propagation can essentially be switched between “ON/OFF” states by connecting different shunting circuits.
Farmers' Engagement with Community Food Insecurity: Approaches, Perspectives, and Implications for Extension

Hunger is an issue of growing concern nationwide, and farmers can play a critical role in helping individuals and families gain access to healthy, fresh, locally produced food. In 2011, we conducted interviews with 12 Vermont farmers who provide local food to low-income Vermonters through a wide array of activities including sale, donation, or other means. By better understanding how and why farmers work to address hunger in communities, Extension professionals can better support them to achieve the dual goals of food security and farm viability.
SEQUENTIAL EQUALIZATION AND DECODING

We consider receiver design for coded transmission over InterSymbol Interference (ISI) channels. We restrict ourselves to the class of lattice codes and formulate the joint equalization and decoding problem as a closest lattice point search (CLPS). Here, the lattice is formed by considering the combined effect of channel distortion and the lattice of the channel code used at the transmitter. We adopt a tree search framework for solving the CLPS. In our framework, the CLPS algorithm is decomposed into the preprocessing and tree search stages. We argue that the forward and feedback (matrix) filters of the minimum mean square error decision feedback equalizer (MMSE-DFE) are instrumental for solving the joint equalization and decoding problem in a single search stage. For the search stage, we propose an efficient sub-optimal algorithm that resembles the well known Fano sequential decoder. Using simulation results, we demonstrate the excellent performancecomplexity tradeoff achieved by the proposed MMSE-DFE Fano decoder.
Evidence-based review of efficacy and adverse effects of joint medication and evaluation of synovial fluid and serum markers for osteoarthritis in the horse

The objective of this study was to investigate the concentrations of interleukin-1 receptor antagonist (IL-1Ra) and interleukin-1ß (IL-1ß) in synovial fluid (SF) and serum (SE) of horses with different grades of osteoarthritis (OA) and septic arthritis. Based on SF analysis, radiographic, and arthroscopic scores, 40 horses were classified into three groups as follows: mild OA, advanced OA, and septic arthritis. Horses without orthopedic problems served as a control group. Equine-specific antibody enzyme-linked immunosorbent assays were used to determine the concentration of IL-1Ra and the catabolic cytokine IL-1ß in SF and SE. Results were further compared with levels of the previously described biomarkers C-telopeptide fragments of type II collagen (CTX-II) and myeloperoxidase. In the present study, the SF of healthy joints, those with nonseptic OA, and those with septic arthritis contained significantly different levels of IL-1Ra. Serum concentrations of IL-1Ra were only significantly elevated in horses with septic arthritis when compared with the control group. Different levels of IL1ß were detected in SE of control horses compared with those with various degrees of joint disease. Synovial fluid concentrations of IL-1ß were only moderately elevated in the groups of horses with joint disease. In addition to lameness examination and standard diagnostic procedures, the determination of IL-1Ra concentration in SF, in combination with further biomarkers, might be useful to assess the extent of intrasynovial inflammation.
Performance of aLow-PowerCylindrical Hall Thruster

R ECENT mission studies [1,2] have shown that a Hall thruster that operates at relatively constant thrust efficiency (45–55%) over a broad power range (300 W–3 kW) is enabling for deep space science missions when compared with state-of-the-art ion thrusters. Although conventional (annular) Hall thrusters can operate at high thrust efficiency at kilowatt power levels, it is difficult to construct one that operates over a broad power envelope down to O 100 W while maintaining relatively high efficiency [3]. Scaling to low power requires a decrease in the thruster channel size and an increase in the magnetic field strength while holding the dimensionless performance scaling parameters constant [4,5]. Increasing the magnetic field becomes technically challenging because the field can more easily saturate the miniaturized inner components of the magnetic circuit, and scaling down the magnetic circuit leaves very little room for magnetic pole pieces and heat shields. This makes it difficult to arrive at an optimal magnetic field configuration. Nonoptimal fields lead to enhanced power and ion losses that lower efficiency and result in increased heating and erosion of thruster components, particularly the critical inner components comprising the coaxial channel and magnetic circuit. Erosion of the thruster channel is one of the main life-limiting factors in conventional Hall thrusters [4]. An alternative approach to the miniaturization problem that has been demonstrated is embodied in the cylindrical-geometry Hall thruster (CHT) [6], which has recently been further developed especially for low-power applications [7]. Figure 1a shows that the CHT departs from the conventional, purely annular, Hall thruster geometry [4]. In contrast to the conventional annular geometry, in the cylindrical geometry the axial potential distribution is critical for electron confinement. This is because there is a large axial gradient in the magnetic field over the cylindrical part of the channel. The electrons drift both azimuthally around the thruster axis and outwards through the erB force. In the absence of an axial potential, the electrons would mirror out of the region of high magnetic field. In the CHT, the axial potential that accelerates ions outwards also plays an important role in confining the electrons within the thruster by counteracting the mirroring effect. In addition to being unlike an annular Hall thruster, the CHT differs from an end Hall thruster [8], which has a purely cylindrical geometry, biased channel walls, and a mostly axial applied magnetic field. This is in contrast to the cylindrical ceramic channel with a short annular region to sustain ionization and produce a magnetic field with a strong radial component in the CHT. The detailed physics in the discharge channel of the CHT has been extensively studied [6,7,9]. In this Note, we report the measured performance (Isp, thrust, and efficiency) of a cylindrical Hall thruster operating at O 100 W input power.
R O ] 1 O ct 2 01 9 Composing Diverse Policies for Temporally Extended Tasks

Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics. These change-points are often exploited in hierarchical motion planning to build approximate models and to facilitate the design of local, regionspecific controllers. However, it becomes combinatorially challenging to implement such a pipeline for complex temporally extended tasks, especially when the sub-controllers work on different information streams, time scales and action spaces. In this paper, we introduce a method that can compose diverse policies comprising motion planning trajectories, dynamic motion primitives and neural network controllers. We introduce a global goal scoring estimator that uses local, per-motion primitive dynamics models and corresponding activation statespace sets to sequence diverse policies in a locally optimal fashion. We use expert demonstrations to convert what is typically viewed as a gradient-based learning process into a planning process without explicitly specifying preand postconditions. We first illustrate the proposed framework using an MDP benchmark to showcase robustness to action and model dynamics mismatch, and then with a particularly complex physical gear assembly task, solved on a PR2 robot. We show that the proposed approach successfully discovers the optimal sequence of controllers and solves both tasks efficiently.
AN EFFICIENT AND RELIABLE METHOD FOR EXTRACTION OF GENOMIC DNA FROM STINK BUG SPECIES ( HETEROPTERA : PENTATOMIDAE )

The author represents a very simple, rapid, an extremely efficient and modified method of DNA extraction from different stink bug species belonging to order Heteroptera. These bugs pose huge financial problems to the farmers because of their resistance to commonly used pesticides in the agricultural fields, their tendency to form large populations within short period of time, causing remarkable damage to food crops and fruit trees, thereby, reducing the global yield and quality of food. They are considered one of the major agricultural pests of economically important crops throughout the world that includes legumes (soyabeans), cereals (rice, wheat) and tree crops (coconut, palms, citrus, cocoa, coffee). Most of the species are plant specific and thus depending on the type of plant they infest, their economic importance varies greatly from species to species. Molecular techniques have entered successfully in the field of taxonomy in order to supplement the taxonomic data of the worker. However, due to varied habitats, habits, feeding behavior, breeding behaviors, different life cycles patterns and very large populations of insects, a single method of DNA extraction cannot be used for all the insects belonging to different orders. So it becomes necessary to develop a suitable protocol for a particular group of insects in order to yield reproducible products. Thus a simple, time savior and a cost efficient method is recommended for the extraction of genomic DNA when a large number of bug samples are to be processed in a short period of time or even if a single specimen is available, this method should act as a best and convenient method to evaluate the result of that small amount of DNA in a sample.
Estimating Catchment-Scale Sediment Yield from Bank Erosion using Simple Distributed Variables : An Example from Victoria , Australia

Riverbanks can be a dominant sources of sediment in catchments. Several attempts have been made to predict rates of bank retreat from channel, catchment, and flow characteristics. Many of these are meander models that are not appropriate for whole of catchment analyses. The SedNet model (Prosser et al, 2001) is a general model of catchment scale erosion, including a streambank component and was used to provide continental estimates of erosion as part of the Australian National Land and Water Resources Audit. Several limitations have been recognised in the streambank erosion component of SedNet. This project is aimed at developing a more accurate model for predicting sediment yield from bank erosion at the catchment scale. In this paper we report a framework for a generic bank erosion model that accounts for fluvial entrainment and mass failure processes. The rate of bank erosion along a stream link is considered a function of stream power, bank height, and bank strength. The model will be developed based on information contained in widely available data sets. For Victoria, these data sets include the statewide assessment of physical stream conditions (SOS), the Index of Stream Condition (ISC), and stream gauging records, while the Atlas of Australian soils and Ausrivas databases provide national coverage. Bank height is one of the model components and is used as an example of the derivation of inputs from widely available data sets for six basins using the SOS database. In most basins there is a statistically significant relationship between easily derivable surrogates of channel forming characteristics and an average bank height, although these were different for different basins. The model predicts to some extent using the example of the Goulburn River basin (20,000 km). Examining the model using the actual parameters of some of the surrogates may better capture the variability in stream bank height. When all of the variables in the model have been compiled, the output of the model will be tested by comparing the results with published information e.g. sediment yields, and lateral migration rates of bank erosion
ADAPTIVE RDF GRAPH REPLICATION FOR MOBILE SEMANTIC WEB APPLICATIONS

An increasing number of applications are based on Semantic Web technologies and the amount of information available on the Web in the form of RDF is continuously growing. The adaption of the Semantic Web for Personal Information Management and the increasing desire for mobility is often accompanied by situations where no network connectivity is available and hence access to remote data is limited. Such situations could be obviated when mobile devices are able to operate on offline data replicas and synchronize changes when connectivity is reestablished. In this paper we present a framework allowing for adaptive RDF graph replication and synchronization on mobile devices. It extends the architecture of typical Semantic Web applications with components that analyze various information sources of semantic applications (including ontologies, queries, and expressed user interest) and use them for selecting parts of RDF data bases, which are then made available offline using a proxy SPARQL endpoint on a mobile device. Thus, we provide access to Semantic Web data without the need for permanent network connectivity.
Influence of nutrient utilization and remineralization stoichiometry on phytoplankton species and carbon export : A modeling study at BATS

The primary objective of this research is to understand the underlying mechanisms of the time-varying flux of carbon in the Sargasso Sea. To address this objective, a one-dimensional multi-component lower trophic level ecosystem model that includes detailed algal physiology as well as nutrient cycles is used at the Bermuda Atlantic Time-series Study (BATS, 31 400N, 64 100W) site. In this model autotrophic growth is represented by three algal groups and the cell quota approach is used to estimate algal growth and nutrient uptake. This model is tested and evaluated for year 1998 using the bimonthly BATS cruise data. Results show that phosphorus and dissolved organic matter (DOM) are necessary compartments to correctly simulate organic elemental cycles at the BATS site. Model results show that autotrophic eukaryotes and cyanobacteria (i.e. Prochlorococcus and Synechococcus) are the most abundant algal groups and are responsible for 63% and 33% of carbon production in the region, respectively. Sensitivity analyses show that the annual contribution of nitrogen fixation and atmospheric nitrogen deposition to new production is approximately 9% and 3%, respectively. However, the recycled nitrogen and phosphorus are important components of the ecosystem dynamics because sustained growth of algal groups depends on remineralized nutrients which accounts for 75% of the annual carbon production. Nutrient uptake and remineralization stoichiometry can play an important role in determining the surface ocean nutrient distribution. Model results suggest phosphate limitation even during the spring bloom. Phosphate may thus limit the growth of all algal groups throughout the year. r 2007 Elsevier Ltd. All rights reserved.