Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Custodiol vs warm blood cardioplegia and conditioning of donor hearts during transportation with the organ care system

Cold crystalloid cardioplegia for donor heart harvesting and cold ischemic storage conditions during the transportation is the standard of care during heart transplantation procedure. Organ care system (OCS) was introduced for more prolonged and reliable ex vivo organ management. This study evaluated the two different techniques used for myocardial preservation during the procurement and transportation of the heart using the OCS.
DESIGN DETAILS OF A LOW COST AND HIGH PERFORMANCE ROBOTIC VISION ARCHITECTURE

The implementation of an advanced real-time, low cost video processing platform capable of supporting a variety of demanding robotic applications is presented. The system is designed as an open project, accessible in full detail and has the potential to grow. It is based on a FPGA plus MCU architecture, allowing the implementation of combined fixed-point and 32-bit floating-point applications with optimized resource allocation. The presented platform is optimally integrated with appropriate controllers, like video-input frame grabbers for multiple camera applications, external SDRAM, as well as USB and VGA interfaces. The processing and interfacing capabilities of the proposed system are illustrated by implementing basic feature extraction and preprocessing tasks, achieving the display of processed video frames at a rate of 30 fps with resolution 640x480. The proposed architecture is evaluated in terms of resource usage, power consumption and cost. Potential applications are also discussed.
A Protocol for Extraction of Infective Viromes Suitable for Metagenomics Sequencing from Low Volume Fecal Samples

The human gut microbiome (GM) plays an important role in human health and diseases. However, while substantial progress has been made in understanding the role of bacterial inhabitants of the gut, much less is known regarding the viral component of the GM. Bacteriophages (phages) are viruses attacking specific host bacteria and likely play important roles in shaping the GM. Although metagenomic approaches have led to the discoveries of many new viruses, they remain largely uncultured as their hosts have not been identified, which hampers our understanding of their biological roles. Existing protocols for isolation of viromes generally require relatively high input volumes and are generally more focused on extracting nucleic acids of good quality and purity for down-stream analysis, and less on purifying viruses with infective capacity. In this study, we report the development of an efficient protocol requiring low sample input yielding purified viromes containing phages that are still infective, which also are of sufficient purity for genome sequencing. We validated the method through spiking known phages followed by plaque assays, qPCR, and metagenomic sequencing. The protocol should facilitate the process of culturing novel viruses from the gut as well as large scale studies on gut viromes.
RcAP1, a Homolog of APETALA1, is Associated with Flower Bud Differentiation and Floral Organ Morphogenesis in Rosa chinensis

Rosa chinensis is one of the most popular flower plants worldwide. The recurrent flowering trait greatly enhances the ornamental value of roses, and is the result of the constant formation of new flower buds. Flower bud differentiation has always been a major topic of interest among researchers. The APETALA1 (AP1) MADS-box (Mcm1, Agamous, Deficiens and SRF) transcription factor-encoding gene is important for the formation of the floral meristem and floral organs. However, research on the rose AP1 gene has been limited. Thus, we isolated AP1 from Rosa chinensis ‘Old Blush’. An expression analysis revealed that RcAP1 was not expressed before the floral primordia formation stage in flower buds. The overexpression of RcAP1 in Arabidopsis thaliana resulted in an early-flowering phenotype. Additionally, the virus-induced down-regulation of RcAP1 expression delayed flowering in ‘Old Blush’. Moreover, RcAP1 was specifically expressed in the sepals of floral organs, while its expression was down-regulated in abnormal sepals and leaf-like organs. These observations suggest that RcAP1 may contribute to rose bud differentiation as well as floral organ morphogenesis, especially the sepals. These results may help for further characterization of the regulatory mechanisms of the recurrent flowering trait in rose.
A k-shortest paths based algorithm for multimodal time-dependent networks to compute alternative routes

Usual computations of alternative routes and the measure of their similarities and/or dierences do not embrace the variability of networks specics and user preferences. Indeed, the denition and evaluation of the dierence between paths is often embedded into algorithm internals and thus does not take into account that similar or dissimilar paths may vary depending on the user and/or the network. In this article, a generic method to generate alternative routes on FIFO time-dependent multimodal graphs with regular language constraints is presented. It relies on the computation, in a rst stage, of the k-shortest paths before the enforcement of a distinction criteria based on word metrics and comparison procedures in a second stage. We rst present a variant of a k-shortest paths algorithm taking into account both the multimodality and the time-dependency inherent to transportation networks. Then, we propose several methods for evaluating the dierences between routes. Experiments are conducted in realistic cases of transportation networks and associated results show the relative eciency and interest of the approach.
Object Tracking by Using SVM and Trust-Region Method

An SVM (support vector machine) classifier is used to classify the pixels and generate a reliable target probability distribution image. The outstanding trust region numeric optimization algorithm is utilized to locate the target as well as determine its size. The SVM classifier's low error rate of classification offers a better base for the trust region algorithm. Comparison with existing method and present tracking examples show that the proposed method is better in both target detection and precision improvement.
Teaching and Assessing Problem Solving: An Example of an Incremental Approach to Using IRAC in Legal Education

Legal reasoning is a type of problem solving, and is situated within thinking skills, one of the six threshold learning outcomes established under the auspices of the Australian Learning and Teaching Council’s Bachelor of Laws Learning and Teaching Academic Standards Statement. The threshold learning outcomes define what law graduates are ‘expected to know, understand and be able to do as a result of learning’ (Kift et al., 2010, p. 9). The assessment of legal reasoning, and thus problem solving, should receive greater attention in legal education discourse (James, 2011, p. 15, James, 2012, p. 88). The dominant approach for problem-based questions in the discipline of law over the last 40 years is IRAC (issue, rule, application and conclusion). The acronym IRAC is not offensive and potentially instils a positive professional legal identity and is a student-centred approach to problem solving. This journal article documents an incremental approach to IRAC in law where first year students answer a problembased law question using a grid format before preparing a barrister’s advice.
Genome wide characterization, evolution and expression analysis of FBA gene family under salt stress in Gossypium species

Abiotic stress is an important bottleneck in crop yield around the world. The main purpose of this study is to describe the function and expression patterns of Fructose-1, 6-bisphosphate Aldolase (FBA) gene family in Gossypium species under salt stress. FBA genes are implicated in plant growth and development as well as salt stress tolerance. In this study, 19 FBA genes were found in G. hirsutum, while 9 genes each in case of G. arboreum and G. raimondii. Based on protein domains and phylogenetic analysis, FBA genes were categorized into two classes I and II. Gene structure analysis and motifs division of FBA genes in three Gossypium species showed that numerous genes of FBA have unequal distribution of introns, suggesting new functions in the gene family. Combined syntenic blocks of FBA genes revealed 31duplicated genes among the three Gossypium species. Three kinds of duplications (segmental, tandem and dispersed) were observed with most of the FBA genes duplicated in segmental pattern and remaineds conserved, which could be a possible basis of expansion of FBA genes in cotton (Gossypium). Gene ontology results showed that FBA genes were involved in a couple of mechanisms such as vacuole compartmentalization, glycolysis process and zinc ion binding. The expression analysis depicted that most of class I genes were upregulated in salt tolerant species, G. darwinii as compared to salt susceptible species G. hirsutum. These finding shows comprehensive information on structure and promising function of FBA gene family in Gossypium plant under salt stress.
A Strategy of Software Reliability Model Integration Based on Model Measurement

Generally, an integrated model behaves better than a single Software reliability model (SRM). However, there is no clearly guideline to support the component models selection. In this paper, it provides a strategy to component models selection for integration based on two model measurement: robustness and precision. The experiments show that the new model with the selective strategy performs better than the one with non-selective strategy.
ANALYSIS AND CONSTRUCTION OF WORD WEIGHING FUNCTION IN VSM

Text classification is the basis and core of text mining, and plays an important rule in traditional information retrieval, construction of website architecture, and search for web information. It has become a hot research project in recent years. In this paper, the hypostasis of VSM (vector space model), a kind of frequently-used classical text classification model, is analyzed to find the reason for its low classification precision, and a weight adjustment method is put forward in which the IDF function is replaced by evaluation function used in feature selection. Also made are theoretic analysis and experimental comparison with the performance of weight adjustment using various evaluation functions. And a novel approach to construct a new high-powered evaluation function is presented.
History of Substance in Philosophy

A lot of words investigated by philosophers get their inception for conventional or extra-philosophical dialect. Yet the idea of substance is basically a philosophical term of art. Its employments in normal dialect tend to derive, often in a twisted way, different from its philosophical usage. Despite this, the idea of substance differs from philosophers, reliant upon the school of thought in which it is been expressed. There is an ordinary concept in play when philosophers discuss “substance”, and this is seen in the concept of object, or thing when this is contrasted with properties, attributes or events. There is also a difference in view when in the sense that while the realists would develop a materialistic theory of substance, the idealist would develop a metaphysical theory of substance. The problem surrounding substance spans through the history of philosophy. The queries have often been what is substance of? And can there be substance without its attributes? This paper tends to expose the historical problems surrounding substance. This paper criticizes the thinking which presupposes that there could be a substance without its attributes or substance existing alone. This paper adopts complimentary ontology principles which state that for anything to exist, it must serve as a missing connection to reality. This suggests that everything interconnects to each other and substance cannot exist in isolation.
Sierpinski triangle based data-center network architecture in cloud computing / Qi Han

This thesis reports on the research to develop of a data center network (DCN) architecture to solve the problem of network performance in cloud-oriented data centers. Computational clouds are increasingly becoming popular for the provisioning of computing resources and service on demand basis. A DCN is an important component of data centers that consists of a large number of hosted servers and switches connected with high speed communication links. As a backbone in data centers, a DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-oriented services and applications configured over virtual machines (VMs), and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN tree-based architecture lacks aggregate bandwidth, scalability and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud-oriented data centers. To solve this problem, the method developed in this research is used to mitigate the aggregation throughput and improve the network performance of DCN by using a novel DCN architecture. The proposed method, called Sierpinski Triangle Based (STB) DCN architecture, is developed on the basis of the well-know Sierpinski triangle fractal to mitigate throughput bottleneck in aggregate layers as accumulated in tree-based structure. STB is a fault-tolerant architecture which provides at least two parallel paths for each pair of servers. It also supports various bandwidth-intensive applications by providing high network throughput for allto- all traffic. STB architecture was implemented in a real cloud data center environment and evaluated in Network Simulator 2 (NS2) simulation. The performance of STB architecture is validated by comparing the results with the traditional tree-based, and DCell DCN architectures. Theoretical analysis and implementation experiences verify that the iii proportion of server to entire nodes in STB is same with DCell but higher than that of tree-based architecture. The average shortest path length is restricted between 5.0 and 6.7, when node failure proportion remains between 0.02 and 0.2, shorter than DCell in a 4-level architecture. The results of the experiment also show that the STB architecture has higher throughout than both traditional tree-based and DCell architectures from the scale of 12 to 363 servers with/without link failure happens. From the results of both simulation and experiment in actual devices, we speculate that STB still can achieve better network performance in throughput, server utilization, average shortest path length than DCell and tree-based architectures in real large-scale cloud-oriented DCN.
Design and Implementation for SVG Components of Mobile GIS Based on .NET

According to limited capacity(low CPU frequency,small memory and narrow bandwidth of network) of Mobile GIS client devices,the spatial data were organized and encoded in this paper based on the Mobile SVG which has small files and high transmission efficiency,can infinitely zoom in or out and can be run in multi-platform.Spatial data and attribute data were separately managed by using.NET Compact Framework,thus,both the used memory and the time on creating data structure were reduced,and the functions of analyzing,rendering,querying and editing for the SVG document were implemented.Furthermore,a new kind of Mobile GIS component was designed based on.NET and SVG,which made GIS escape the limitations of the complex spatial data structure,large data quantity and poor ability of mobile devices.
The Elements Of Java Style

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Efficient and reliable task scheduling, network reprogramming, and data storage for wireless sensor networks

Wireless sensor networks (WSNs) typically consist of a large number of resource-constrained nodes. The limited computational resources afforded by these nodes present unique development challenges. In this dissertation, we consider three such challenges. The first challenge focuses on minimizing energy usage in WSNs through intelligent duty cycling. Limited energy resources dictate the design of many embedded applications, causing such systems to be composed of small, modular tasks, scheduled periodically. In this model, each embedded device wakes, executes a task-set, and returns to sleep. These systems spend most of their time in a state of deep sleep to minimize power consumption. We refer to these systems as almostalways-sleeping (AAS) systems. We describe a series of task schedulers for AAS systems designed to maximize sleep time. We consider four scheduler designs, model their performance, and present detailed performance analysis results under varying load conditions. The second challenge focuses on a fast and reliable network reprogramming solution for WSNs based on incremental code updates. We first present VSPIN, a framework for developing incremental code update mechanisms to support efficient reprogramming of WSNs. VSPIN provides a modular testing platform on the host system to “plug-in” and evaluate various incremental code update algorithms. The framework supports Avrdude, among the most popular Linux-based programming tools for AVR microcontrollers. Using VSPIN, we next present an incremental code update strategy to efficiently reprogram wireless sensor nodes. We adapt a linear space and quadratic time algorithm (Hirschberg’s Algorithm) for computing maximal common subsequences to build an edit map specifying an edit sequence required to transform the code running in a sensor network to a new code image. We then present a heuristic-based optimization strategy for efficient edit script encoding to reduce the edit map size. Finally, we present experimental results exploring the reduction in data size that it enables. The approach achieves reductions of 99.987% for simple changes,
Proposal of a zoom and focus control method using an ultrasonic distance-meter for video-based eye-gaze detection under free-head conditions

To develop noncontact free-head video-based eye-gaze detection devices, there is a need for a zoom and focus control system which controls a zoom lens, so that the sharp eye's image appears in the same size irrespective of back and forth head movements against the camera. In this study, such a zoom and focus control system using an ultrasonic distance meter was developed. On the front of the zoom lens, a 45-deg tilt transparent glass plate was installed to reflect ultrasound. Thus, the sound traveling axis and the camera axis were made coaxial. This made it possible to stably measure the distance from the camera to its subject, i.e., the eye. Also, a method using a striped panel and a custom-made image processor was proposed to calibrate the relationship between the distance and the focus and zoom control voltages. In the experiment, the head was moved back and forth in a range of 50-70 cm from a camera rotation center, and the zoom and focus was controlled in real time by using the information from the distance meter and the calibrated relationships. The result showed quick and precise control concerning both the zoom and focus.
An Approach to Implement Map Reduce with NoSQL Databases

MapReduce is a well-known programming model and an implementation method for executing, processing and generating massive data sets. MapReduce algorithm consists of a map function that processes a key/value pair to produce a set of intermediate key/value pairs, and a reduce function which combine all these values related with the same intermediate key. MapReduce executes in parallel itself without implementing any parallel programming model and it is the most efficient way to process unstructured data. In this research MapReduce algorithm is implemented on a cluster based machine using Hadoop distributed file system (HDFS) in order to perform a Pattern matching algorithm for different volumes of datasets. The quantitative performance analysis of MapReduce algorithm is done for the different volumes of data on the basis of execution time and number of patterns searched. So far relational databases are used for storing the data for the applications but now there is need to store huge amount of data to store and manage which cannot stored by relational databases. NoSQL technology over comes this problem. This research paper provides a brief introduction to NoSQL database working and comparative study between MongodB and CouchDB, Which are mostly used for big data application. The operations are performed to explore the results as distinguish between both NoSql databases. This paper shows the performance of Mongodb and CouchDB. Results proves that CouchDB is more powerful than Mongodb to load and process on big data and processing very fast as compare to Mongodb. This paper describes the functionality of Mongodb and CouchDB over the large dataset.
Frog Where Are You

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Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.