The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand-drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.

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NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System

https://cyberleninka.org/article/n/1276528.pdf

Review : Untangling the influence of air-mass history in interpreting observed atmospheric composition

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid–protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid–protein interactions, and to link lipid–protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid–protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid–prot...

Emerging Diversity in Lipid–Protein Interactions

The increasing interest in the Mediterranean diet hinges on its healthy and anti-ageing properties. The composition of fatty acids, vitamins and polyphenols in olive oil, a key component of this diet, is considered a key feature of its healthy properties. Therefore, it is of significance that the Rod of Asclepius lying on a world map surrounded by olive tree branches has been chosen by the World Health Organization as a symbol of both peace and well-being. This review travels through most of the current and past research, recapitulating the biochemical and physiological correlations of the beneficial properties of olive tree (Olea europaea) polyphenols and their derivatives found in olive oil. The factors influencing the content and beneficial properties of olive oil polyphenols will also be taken into account together with their bioavailability. Finally, the data on the clinical and epidemiological relevance of olive oil and its polyphenols for longevity and against age- and lifestyle-associated pathologies such as cancer, cardiovascular, metabolic and neurodegenerative diseases are reviewed.

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Nutraceutical Properties of Olive Oil Polyphenols. An Itinerary from Cultured Cells through Animal Models to Humans.

Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm

The proportion of video traffic on the internet is expected to reach 82% by 2022, mainly due to the increasing number of consumers and the emergence of new video formats with more demanding features (depth, resolution, multiview, 360, etc.). Efforts are therefore being made to constantly improve video compression standards to minimize the necessary bandwidth while retaining high video quality levels. In this context, the Joint Collaborative Team on Video Coding has been analyzing new video coding technologies to improve the compression efficiency with respect to the HEVC video coding standard. A software package known as the Joint Exploration Test Model has been proposed to implement and evaluate new video coding tools. In this work, we present parallel versions of the JEM encoder that are particularly suited for shared memory platforms, and can significantly reduce its huge computational complexity. The proposed parallel algorithms are shown to achieve high levels of parallel efficiency. In particular, in the All Intra coding mode, the best of our proposed parallel versions achieves an average efficiency value of 93.4%. They also had high levels of scalability, as shown by the inclusion of an automatic load balancing mechanism.

Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cta.3175

Comment on “Parameter extraction of single‐diode photovoltaic module using experimental current–voltage data”

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Correction to: On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

Abstract It is well-known that each new video coding standard significantly increases in computational complexity with respect to previous standards, and this is particularly true for the HEVC and VVC video coding standards. The development of techniques for reducing the required complexity without affecting the rate/distortion (R/D) performance is therefore always a topic of intense research interest. In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel strategies based on frame partitioning is able to achieve speedups of up to 26 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> when 16 threads are used. The R/D penalty in terms of the BD-BR metric depends on the video content, the compression rate and the number of OpenMP threads, and was consistently between 0.35 and 10% for the video sequence test set used in our experiments 

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On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

Three-dimensional wavelet transform (3D-DWT) encoders are good candidates for applications like professional video editing, video surveillance, multi-spectral satellite imaging, etc. where a frame must be reconstructed as quickly as possible. In this paper, we present a new 3D-DWT video encoder based on a fast run-length coding engine. Furthermore, we present several multicore optimizations to speed-up the 3D-DWT computation. An exhaustive evaluation of the proposed encoder (3D-GOP-RL) has been performed, and we have compared the evaluation results with other video encoders in terms of rate/distortion (R/D), coding/decoding delay, and memory consumption. Results show that the proposed encoder obtains good R/D results for high-resolution video sequences with nearly in-place computation using only the memory needed to store a group of pictures. After applying the multicore optimization strategies over the 3D DWT, the proposed encoder is able to compress a full high-definition video sequence in real-time.

/pdf/multicore-based-3d-dwt-video-encoder-2qp9xai4i9.pdf

Vicente Galiano

Papers

Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder

Comment on “Parameter extraction of single‐diode photovoltaic module using experimental current–voltage data”

Correction to: On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

On the use of deep learning and parallelism techniques to significantly reduce the HEVC intra-coding time

Multicore-based 3D-DWT video encoder