Research on Information Distribution Optimization of Multi-Federation Interconnected Tactical Communication Simulation Training

2011 ◽  
Vol 460-461 ◽  
pp. 381-387
Author(s):  
Jian Shi Zhang ◽  
Zhi Yi Fang
Keyword(s):  
Communication Network ◽  
Large Scale ◽  
Simulation Training ◽  
High Efficiency ◽  
Information Distribution ◽  
Distribution Problem ◽  
Training Process ◽  
State Data ◽  
Interconnected Structure

Multi-federation interconnected structure is the support structure that adapts to large-scale tactical communication network joint training. It needs to organize the distribution and transmission of various kinds of data effectively to assure high efficiency training. Aiming at the information distribution problem of multi-federation interconnected tactical communication network simulation training process, the single federation structure and multi-federation interconnected structure were brought out. Based on the region discerption of training scale and data classification of data, aiming at the characteristics of network traffic, networking parameters and operation state data, the specific solutions were proposed, so that the information distribution in the simulation training was optimized

The Potential of Photoelectrochemical Carbon Sinks

2018 ◽  
Author(s):  
Matthias May ◽  
Kira Rehfeld
Keyword(s):  
Global Warming ◽  
High Temperature ◽  
Greenhouse Gas ◽  
Large Scale ◽  
High Efficiency ◽  
Carbon Sink ◽  
Solar Fuels ◽  
Negative Emissions ◽  
Viable Approach ◽  
Low Sensitivity

Greenhouse gas emissions must be cut to limit global warming to 1.5-2C above preindustrial levels. Yet the rate of decarbonisation is currently too low to achieve this. Policy-relevant scenarios therefore rely on the permanent removal of CO<sub>2</sub> from the atmosphere. However, none of the envisaged technologies has demonstrated scalability to the decarbonization targets for the year 2050. In this analysis, we show that artificial photosynthesis for CO<sub>2</sub> reduction may deliver an efficient large-scale carbon sink. This technology is mainly developed towards solar fuels and its potential for negative emissions has been largely overlooked. With high efficiency and low sensitivity to high temperature and illumination conditions, it could, if developed towards a mature technology, present a viable approach to fill the gap in the negative emissions budget.<br>

The Potential of Photoelectrochemical Carbon Sinks

2018 ◽  
Author(s):  
Matthias May ◽  
Kira Rehfeld
Keyword(s):  
Global Warming ◽  
High Temperature ◽  
Greenhouse Gas ◽  
Large Scale ◽  
High Efficiency ◽  
Carbon Sink ◽  
Solar Fuels ◽  
Negative Emissions ◽  
Viable Approach ◽  
Low Sensitivity

Greenhouse gas emissions must be cut to limit global warming to 1.5-2C above preindustrial levels. Yet the rate of decarbonisation is currently too low to achieve this. Policy-relevant scenarios therefore rely on the permanent removal of CO<sub>2</sub> from the atmosphere. However, none of the envisaged technologies has demonstrated scalability to the decarbonization targets for the year 2050. In this analysis, we show that artificial photosynthesis for CO<sub>2</sub> reduction may deliver an efficient large-scale carbon sink. This technology is mainly developed towards solar fuels and its potential for negative emissions has been largely overlooked. With high efficiency and low sensitivity to high temperature and illumination conditions, it could, if developed towards a mature technology, present a viable approach to fill the gap in the negative emissions budget.<br>

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

Peculiarities of the phytosanitary state of pome fruits in industrial horticulture

2020 ◽  
Vol 60 (1) ◽  
pp. 159-168
Author(s):  
V. V. Antonenko ◽  
A. V. Zubkov ◽  
S. N. Kruchina
Keyword(s):  
Mechanism Of Action ◽  
Large Scale ◽  
High Efficiency ◽  
Fruit Trees ◽  
Fruit Crops ◽  
Planting Material ◽  
Relative Safety ◽  
Pome Fruits ◽  
Weed Plants ◽  
Spread Of Infection

Data were obtained on the basis of the results of research carried out on the territory of the educational and experimental farm of the Timiryazev State Agrarian University, in Moscow during 2018-2019. As a result of the surveys, the most dangerous diseases and pests of pome crops on the territory of this farm were established. The most resistant apple and pear varieties to major diseases have been identified. Peculiarities of development of alternariosis on pear are described, the harmfulness of the disease on pear and apple seedlings is noted. A possible role in the transfer of alternariosis infection from garden-protective plantations and weed vegetation to fruit trees was noted. A possible role has been established in the transport of septoriosis, powdery dew infection from dicotyledonous weeds plants. The peculiarities of the spread of infection under the influence of wind direction are noted. The results and peculiarities of the application of various methods of scaring birds in the orchard are presented. As a result of route surveys the most harmful weed plants have been identified. The possibility of using herbicides of different mechanism of action in fruit gardens for weed control has been studied. High efficiency and relative safety of application of herbicides of contact action in nursery fields, operational orchards and for control of piglets on fruit trees are shown. Recommendations are given for the use of soil and systemic herbicides of soil in seedlings beds, the first and second fields of the nursery, as well as in the process of production of large-scale planting material and operational orchards of fruit crops. The safety of the herbicides in question is established when used in accordance with the recommended methods of use.

The Swiss Prison Study (SWIPS): Protocol for Establishing a Public Health Registry of Prisoners in Switzerland (Preprint)

2020 ◽  
Author(s):  
Thomas Gaisl ◽  
Naser Musli ◽  
Patrick Baumgartner ◽  
Marc Meier ◽  
Silvana K Rampini ◽  
...  
Keyword(s):  
Public Health ◽  
Medical History ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Disease Prevalence ◽  
Vulnerable Population ◽  
International Classification ◽  
Classification Of Diseases ◽  
Health Registry

BACKGROUND The health aspects, disease frequencies, and specific health interests of prisoners and refugees are poorly understood. Importantly, access to the health care system is limited for this vulnerable population. There has been no systematic investigation to understand the health issues of inmates in Switzerland. Furthermore, little is known on how recent migration flows in Europe may have affected the health conditions of inmates. OBJECTIVE The Swiss Prison Study (SWIPS) is a large-scale observational study with the aim of establishing a public health registry in northern-central Switzerland. The primary objective is to establish a central database to assess disease prevalence (ie, International Classification of Diseases-10 codes [German modification]) among prisoners. The secondary objectives include the following: (1) to compare the 2015 versus 2020 disease prevalence among inmates against a representative sample from the local resident population, (2) to assess longitudinal changes in disease prevalence from 2015 to 2020 by using cross-sectional medical records from all inmates at the Police Prison Zurich, Switzerland, and (3) to identify unrecognized health problems to prepare successful public health strategies. METHODS Demographic and health-related data such as age, sex, country of origin, duration of imprisonment, medication (including the drug name, brand, dosage, and release), and medical history (including the International Classification of Diseases-10 codes [German modification] for all diagnoses and external results that are part of the medical history in the prison) have been deposited in a central register over a span of 5 years (January 2015 to August 2020). The final cohort is expected to comprise approximately 50,000 to 60,000 prisoners from the Police Prison Zurich, Switzerland. RESULTS This study was approved on August 5, 2019 by the ethical committee of the Canton of Zurich with the registration code KEK-ZH No. 2019-01055 and funded in August 2020 by the “Walter and Gertrud Siegenthaler” foundation and the “Theodor and Ida Herzog-Egli” foundation. This study is registered with the International Standard Randomized Controlled Trial Number registry. Data collection started in August 2019 and results are expected to be published in 2021. Findings will be disseminated through scientific papers as well as presentations and public events. CONCLUSIONS This study will construct a valuable database of information regarding the health of inmates and refugees in Swiss prisons and will act as groundwork for future interventions in this vulnerable population. CLINICALTRIAL ISRCTN registry ISRCTN11714665; http://www.isrctn.com/ISRCTN11714665 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23973

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals Functionalization of Congo red dye as a light harvester on solar cell

2020 ◽  
Vol 18 (1) ◽  
pp. 287-294
Author(s):  
Harsasi Setyawati ◽  
Handoko Darmokoesoemo ◽  
Irmina Kris Murwani ◽  
Ahmadi Jaya Permana ◽  
Faidur Rochman
Keyword(s):  
Solar Cell ◽  
Congo Red ◽  
Large Scale ◽  
High Efficiency ◽  
Solar Cell Device ◽  
Dye Sensitized ◽  
Congo Red Dye ◽  
Elemental Analyses ◽  
Red Dye ◽  
Solar Cell Technology

AbstractThe demands of ecofriendly technologies to produce a reliable supply of renewable energy on a large scale remains a challenge. A solar cell based on DSSC (Dye-Sensitized Solar Cell) technology is environmentally friendly and holds the promise of a high efficiency in converting sunlight into electricity. This manuscript describes the development of a light harvester system as a main part of a DSSC. Congo red dye has been functionalized with metals (Fe, Co, Ni), forming a series of complexes that serve as a novel light harvester on the solar cell. Metal-congo red complexes have been characterized by UV-VIS and FTIR spectroscopy, and elemental analyses. The performance of metal complexes in capturing photons from sunlight has been investigated in a solar cell device. The incorporation of metals to congo red successfully improved of the congo red efficiency as follows: Fe(II)-congo red, Co(II)-congo red and Ni(II)-congo red had efficiencies of 8.17%, 6.13% and 2.65%, respectively. This research also discusses the effect of metal ions on the ability of congo red to capture energy from sunlight.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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