Search results for "LAB"
showing 10 items of 7932 documents
Effects of environment and sowing time on growth and yield of upland cotton (Gossypium hirsutum L.) cultivars in Sicily (Italy)
2020
Cotton is one of the most important industrial crops in the world. Though widely cultivated in Sicily (Italy) in the past, cotton growth on the island has disappeared today due to a complex variety of agronomic, economic and socio-political reasons. In recent years, increased interest in natural fibers worldwide has led to a revival in cotton plants in the Mediterranean area. The aim of this paper was to assess the response of Gossypium hirsutum L. cultivars to different environments and sowing times. Elsa and Juncal were selected from the most promising cotton cultivars regarding earliness and productivity. Plants were tested with three sowing times and in two Sicilian environments. Cotton…
Hybrid P2P schemes for remote terrain interactive visualization systems
2013
Over the last few years, there has been a lot of development of interactive terrain visualization applications using remote databases. One of the main problems that these applications must face is scalability. These applications usually use a client-server model that cannot support a large number of concurrent requests without using a considerable number of servers. In this paper, we present a full comparative study of new hybrid P2P schemes for terrain interactive visualization systems. The performance evaluation results show that the best strategy consists of avoiding the periodical reporting among peer nodes about the current information contained in each node, while using some servers a…
Massively Parallel ANS Decoding on GPUs
2019
In recent years, graphics processors have enabled significant advances in the fields of big data and streamed deep learning. In order to keep control of rapidly growing amounts of data and to achieve sufficient throughput rates, compression features are a key part of many applications including popular deep learning pipelines. However, as most of the respective APIs rely on CPU-based preprocessing for decoding, data decompression frequently becomes a bottleneck in accelerated compute systems. This establishes the need for efficient GPU-based solutions for decompression. Asymmetric numeral systems (ANS) represent a modern approach to entropy coding, combining superior compression results wit…
Fault-Tolerant Network-on-Chip Design for Mesh-of-Tree Topology Using Particle Swarm Optimization
2018
As the size of the chip is scaling down the density of Intellectual Property (IP) cores integrated on a chip has been increased rapidly. The communication between these IP cores on a chip is highly challenging. To overcome this issue, Network-on-Chip (NoC) has been proposed to provide an efficient and a scalable communication architecture. In the deep sub-micron level NoCs are prone to faults which can occur in any component of NoC. To build a reliable and robust systems, it is necessary to apply efficient fault-tolerant techniques. In this paper, we present a flexible spare core placement in Mesh-of-Tree (MoT) topology using Particle Swarm Optimization (PSO) by considering IP core failures…
Nvidia CUDA parallel processing of large FDTD meshes in a desktop computer
2020
The Finite Difference in Time Domain numerical (FDTD) method is a well know and mature technique in computational electrodynamics. Usually FDTD is used in the analysis of electromagnetic structures, and antennas. However still there is a high computational burden, which is a limitation for use in combination with optimization algorithms. The parallelization of FDTD to calculate in GPU is possible using Matlab and CUDA tools. For instance, the simulation of a planar array, with a three dimensional FDTD mesh 790x276x588, for 6200 time steps, takes one day -elapsed time- using the CPU of an Intel Core i3 at 2.4GHz in a personal computer, 8Gb RAM. This time is reduced 120 times when the calcula…
WarpDrive: Massively Parallel Hashing on Multi-GPU Nodes
2018
Hash maps are among the most versatile data structures in computer science because of their compact data layout and expected constant time complexity for insertion and querying. However, associated memory access patterns during the probing phase are highly irregular resulting in strongly memory-bound implementations. Massively parallel accelerators such as CUDA-enabled GPUs may overcome this limitation by virtue of their fast video memory featuring almost one TB/s bandwidth in comparison to main memory modules of state-of-the-art CPUs with less than 100 GB/s. Unfortunately, the size of hash maps supported by existing single-GPU hashing implementations is restricted by the limited amount of …
SWMapper: Scalable Read Mapper on SunWay TaihuLight
2020
With the rapid development of next-generation sequencing (NGS) technologies, high throughput sequencing platforms continuously produce large amounts of short read DNA data at low cost. Read mapping is a performance-critical task, being one of the first stages required for many different types of NGS analysis pipelines. We present SWMapper — a scalable and efficient read mapper for the Sunway TaihuLight supercomputer. A number of optimization techniques are proposed to achieve high performance on its heterogeneous architecture which are centered around a memory-efficient succinct hash index data structure including seed filtration, duplicate removal, dynamic scheduling, asynchronous data tra…
Language complexity in on-line health information retrieval
2020
The number of people searching for on-line health information has been steadily growing over the years so it is crucial to understand their specific requirements in order to help them finding easily and quickly the specific in-formation they are looking for. Although generic search engines are typically used by health information seekers as the starting point for searching information, they have been shown to be limited and unsatisfactory because they make generic searches, often overloading the user with the provided amount of results. Moreover, they are not able to provide specific information to different types of users. At the same time, specific search engines mostly work on medical li…
ULearn: Personalized Medical Learning on the Web for Patient Empowerment
2019
Abstract. Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents “ULearn” a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call …
Teaching clinical reasoning and decision-making skills to nursing students: Design, development, and usability evaluation of a serious game
2016
Background\ud \ud Serious games (SGs) are a type of simulation technology that may provide nursing students with the opportunity to practice their clinical reasoning and decision-making skills in a safe and authentic environment. Despite the growing number of SGs developed for healthcare professionals, few SGs are video based or address the domain of home health care.\ud \ud Aims\ud \ud This paper aims to describe the design, development, and usability evaluation of a video based SG for teaching clinical reasoning and decision-making skills to nursing students who care for patients with chronic obstructive pulmonary disease (COPD) in home healthcare settings.\ud \ud Methods\ud \ud A prototy…