Native and Exotic Distributions of Siamweed (Chromolaena odorata) Modeled Using the Genetic Algorithm for Rule-Set Production

Weed Science ◽  
2007 ◽  
Vol 55 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Rafael Luís Galdini Raimundo ◽  
Rafael Luís Fonseca ◽  
Ricardo Schachetti-Pereira ◽  
A. Townsend Peterson ◽  
Thomas Michael Lewinsohn
Keyword(s):  
Genetic Algorithm ◽  
Pacific Islands ◽  
Central Africa ◽  
Environmental Parameters ◽  
Native Range ◽  
Natural Habitats ◽  
Data Set ◽  
Western Africa ◽  
Occurrence Data ◽  
Rule Set

Siamweed is an asteraceous shrub native to the Neotropics that ranks among the world's most widespread and troublesome invasive species. It was introduced in several regions of Africa, Southeast Asia, and the Pacific Islands, where it severely infests natural habitats and plantation crops. Although extensive data document the weed's abundance and distribution throughout the invaded continents, the details of its current range are not fully known, especially within its native region. In this study, we used point-occurrence data and digital maps summarizing relevant environmental parameters to generate predictions for the species' geographic distributional potential—specifically, we modeled the native range of siamweed in the Neotropics using the genetic algorithm for rule-set prediction, an evolutionary computing approach. The native range occurrence data set contained 239 published and herbarium records. Models were trained on a random subset of half the points and tested using the other half. The rule sets of the native-range models were projected onto the invaded continents to predict the weed's potential for invasion, blind to its known occurrences in such regions. Native-range models predicted a wide potential distribution of siamweed throughout tropical America, from southern United States to northern Argentina and southern Brazil. The weed's occurrence has been confirmed on the northern Pacific coast, in southeast Brazil, and in other South American areas, where it was supposed to be absent. Independent model projections to Africa, Asia, and Oceania are supported by known occurrence records. Four regions are predicted to be susceptible to siamweed spread: (1) Central Africa, currently being invaded from Western Africa; (2) Infestations spreading northward from South Africa, which have already reached Swaziland and Mozambique and may extend to East Africa and Madagascar; and (3) northern New Zealand and (4) Australia, which are at risk from uncontrolled infestations on several western Pacific islands.

Eragrostis unioloides (Chinese lovegrass).

2021 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
West Africa ◽  
Pacific Islands ◽  
Distribution Range ◽  
Caribbean Region ◽  
Native Range ◽  
Annual Grass ◽  
Natural Habitats ◽  
Tropical Asia ◽  
The Pacific ◽  
The Caribbean

Abstract Eragrostis unioloides is an annual grass or sometimes perennial, erect herb, rooting at nodes. Terrestrial, it grows in many dry as well as moist habitats. It is distributed in temperate and tropical Asia from southern Asia to Malesia and northeast Australia. It has been introduced in the southeast USA, Mesoamerica, the Caribbean, the Pacific Islands and in tropical West Africa. It is a common weedy grass mainly in rice crops in its native range and in some crops in the Caribbean region. It is found principally in disturbed sites, such as roadsides and in damp places in its distribution range. It is cited as invasive in Fiji, the Hawaiian Islands and Mexico, but no further information is available about its impacts in natural habitats or on biodiversity in its non-native range.

Eragrostis amabilis (Japanese lovegrass).

2021 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
Southeast Asia ◽  
South Asia ◽  
Central America ◽  
Pacific Islands ◽  
Upland Rice ◽  
Native Range ◽  
Natural Habitats ◽  
The Pacific

Abstract Eragrostis amabilis is an annual terrestrial grass with pan-tropical distribution, naturalized elsewhere in the neo-tropics, that is used as an ornamental grass and for lawns. It is a common weed in disturbed, open areas, such as those close to forest margins and along roadsides, and often grows as a weed in upland rice crops in South Asia and Southeast Asia. It is listed as invasive in the Pacific Islands, Central America and Cuba, but no further information is available about its impacts or invasiveness in natural or semi-natural habitats in its non-native range.

scholarly journals Eragrostis unioloides (Chinese lovegrass).

2020 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
West Africa ◽  
Pacific Islands ◽  
Distribution Range ◽  
Caribbean Region ◽  
Native Range ◽  
Annual Grass ◽  
Natural Habitats ◽  
Tropical Asia ◽  
The Pacific ◽  
The Caribbean

Abstract Eragrostis unioloides is an annual grass or sometimes perennial, erect herb, rooting at nodes. Terrestrial, it grows in many dry as well as moist habitats. It is distributed in temperate and tropical Asia from southern Asia to Malesia and northeast Australia. It has been introduced in the southeast USA, Mesoamerica, the Caribbean, the Pacific Islands and in tropical West Africa. It is a common weedy grass mainly in rice crops in its native range and in some crops in the Caribbean region. It is found principally in disturbed sites, such as roadsides and in damp places in its distribution range. It is cited as invasive in Fiji, the Hawaiian Islands and Mexico, but no further information is available about its impacts in natural habitats or on biodiversity in its non-native range.

scholarly journals Eragrostis pilosa (India lovegrass).

2020 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
North America ◽  
Pacific Islands ◽  
The Philippines ◽  
Native Range ◽  
Annual Grass ◽  
Natural Habitats ◽  
Crop Fields ◽  
Disturbed Areas ◽  
The World

Abstract Eragrostis pilosa is an annual grass native to Eurasia and Africa that has become naturalized in many other tropical and temperate regions of the world. It is a common weed in disturbed areas such as roadsides and crop fields. It is invasive in a number of Pacific Islands, the Philippines, Australia, and North America but no further information is available about its impacts or invasiveness in natural or semi-natural habitats in its non-native range.

Eragrostis pilosa (India lovegrass).

2021 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
North America ◽  
Pacific Islands ◽  
The Philippines ◽  
Native Range ◽  
Annual Grass ◽  
Natural Habitats ◽  
Crop Fields ◽  
Disturbed Areas ◽  
The World

Abstract Eragrostis pilosa is an annual grass native to Eurasia and Africa that has become naturalized in many other tropical and temperate regions of the world. It is a common weed in disturbed areas such as roadsides and crop fields. It is invasive in a number of Pacific Islands, the Philippines, Australia, and North America but no further information is available about its impacts or invasiveness in natural or semi-natural habitats in its non-native range.

scholarly journals Eragrostis amabilis (Japanese lovegrass).

2020 ◽  
Author(s):  
Manuel Angel Duenas-Lopez
Keyword(s):  
Southeast Asia ◽  
South Asia ◽  
Central America ◽  
Pacific Islands ◽  
Upland Rice ◽  
Native Range ◽  
Natural Habitats ◽  
The Pacific

Abstract Eragrostis amabilis is an annual terrestrial grass with pan-tropical distribution, naturalized elsewhere in the neo-tropics, that is used as an ornamental grass and for lawns. It is a common weed in disturbed, open areas, such as those close to forest margins and along roadsides, and often grows as a weed in upland rice crops in South Asia and Southeast Asia. It is listed as invasive in the Pacific Islands, Central America and Cuba, but no further information is available about its impacts or invasiveness in natural or semi-natural habitats in its non-native range.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Colonial Origins of Maoist Insurgency in India

2017 ◽  
Vol 62 (10) ◽  
pp. 2232-2274 ◽  
Author(s):  
Shivaji Mukherjee
Keyword(s):  
Ethnic Inequality ◽  
Long Term Effects ◽  
Security Threat ◽  
Data Set ◽  
Internal Security ◽  
New Instrument ◽  
Maoist Insurgency ◽  
Rule Set ◽  
Set Up ◽  
Indirect Rule

What are the long-term effects of colonial institutions on insurgency? My article shows the historical origins of insurgency by addressing the puzzle of why the persistent Maoist insurgency, considered to be India’s biggest internal security threat, affects some districts along the central eastern corridor of India but not others. Combining archival and interview data from fieldwork in Maoist zones with an original district-level quantitative data set, I demonstrate that different types of British colonial indirect rule set up the structural conditions of ethnic inequality and state weakness that facilitate emergence of Maoist control. I address the issue of selection bias, by developing a new instrument for the British choice of indirect rule through princely states, based on the exogenous effect of wars in Europe on British decisions in India. This article reconceptualizes colonial indirect rule and also presents new data on rebel control and precolonial rebellions.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

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