Images of green infrastructure (gardens, green corridor, green roofs and grasslands) large area can be captured and processed to provide spatial and temporal variation in colours of plant leaves. This may indicate ave...
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Images of green infrastructure (gardens, green corridor, green roofs and grasslands) large area can be captured and processed to provide spatial and temporal variation in colours of plant leaves. This may indicate average variation in plant growth over large urban landscape (community gardens, green corridor etc). Towards this direction, this short technical note explores development of a simple automated machine learning program that can accurately segregate colors from plant leaves. In this newly developed program, a machine learning algorithm has been modified and adapted to give the proportion of different colors present in a leaf. python script is developed for an image processing. For validation, experiments are conducted in green house to grow Axonopus compressus. script first extracts different RGB (Red Green and Blue) colors present in the leaf using the K-means clustering algorithm. Appropriate centroids required for the clusters of leaf colors are formed by the K-means algorithm. The new program provides saves computation time and gives output in form of different colors proportion as a CSV (Comma-Separated Values) file. This study is the first step towards the demonstration of using automated programs for the segregation of colors from the leaf in order to access the growth of the plant in an urban landscape.
This work presents a novel general tool for modeling the process of evaporation without the need for modifying existing software using python. The tool was developed based on the MDAnalysis package, which is used to i...
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This work presents a novel general tool for modeling the process of evaporation without the need for modifying existing software using python. The tool was developed based on the MDAnalysis package, which is used to import a Molecular Dynamics trajectory. The tool then removes solvent molecules and outputs a new structure file to be used for further simulation and analysis. This process is designed to be iterated by using the resulting dynamic simulation trajectory as the input file. The evaporation is designed to randomly delete solvent molecules while preserving solvation shells around solutes. The evaporation rate can be controlled by the length of the MD simulations and the number of particles removed between dynamic simulations. Validity of the tool was tested extensively using the Gromacs suite. Advantages of this tool include its genericness, simplicity and user friendliness, as no significant modification of existing software platform or Gromacs specific tools are needed. Program summary Program title: GenEvaPa CPC Library link to program files: https://doi .org /10 .17632 /y5c3jnbjvs .1 Developer's repository link: github .com /bradsharris /GenEvaPa Licensing provisions: GNU General Public License 3 Programming language: python >3 Nature of problem: Approximate evaporation/drying processes in atomistic and coarse-grained molecular dynamics simulations while maintaining solvation shells around solute of interest. Forced drying in this manner allows for the study of a range of concentrations and self-assembly interactions. Solution method: A python wrapper for existing molecular dynamics codes that randomly selects solvent for removal relative to a distance criteria around a solute to maintain solvation shells. Removal on final structure files maintains generic applicability for MD source codes and enables incorporation into automated loops to study longer drying. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-
Most of the previous researchers used manual image processing approach through a public domain tool (ImageJ) to interpret soil surface moisture content. However, the manual processing could not be possible, when the n...
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Most of the previous researchers used manual image processing approach through a public domain tool (ImageJ) to interpret soil surface moisture content. However, the manual processing could not be possible, when the number of images is significantly large. In addition, results could not be reproduced with conventional manual image processing. This technical note introduces a novel technique to automate the quantification process of soil surface moisture content. A stepwise strategy was demonstrated to remove user dependency for soil colour analysis using an autonomous python script. The images of the compacted soil were captured using a commercially available camera model. The image analysis was conducted using conventional manual image processing approach and newly developed technique. The difference between the mean gray values obtained from the above mentioned two approaches was very low (< 3%). Hence, the newly developed technique is cost-effective and feasible for programming with drones to monitor soil surface moisture content in large areas.
This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Lik...
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This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Likelihood. Satellite data from Landsat and Sentinel 2 was classified using a developed python model, providing an economical and time-saving approach. The accuracy of the classification was evaluated through a confusion matrix and area computation. The findings indicate a negative trend in the overall decadal change, with significant tree loss attributed to jhum cultivation, mining, and quarry activities. However, positive changes were observed in recent years due to the ban on illegal mining. The study highlights the dynamic nature of tree cover and emphasizes the need for biennial assessments using at least five time-series data. Micro-level analysis in Shallang, West Khasi hills, revealed a concerning trend of shortening jhum cycles. Automation in canopy change analysis is crucial for effective forest monitoring, providing timely information for law enforcement proposals and involving forest managers, stakeholders, and watchdog organizations.
The production of designer-length tobacco mosaic virus (TMV) nanorods in plants has been problematic in terms of yields, particularly when modified coat protein subunits are incorporated. To address this, we have inve...
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The production of designer-length tobacco mosaic virus (TMV) nanorods in plants has been problematic in terms of yields, particularly when modified coat protein subunits are incorporated. To address this, we have investigated the use of a replicating potato virus X-based vector (pEff) to express defined length nanorods containing either wild-type or modified versions of the TMV coat protein. This system has previously been shown to be an efficient method for producing virus-like particles of filamentous plant viruses. The length of the resulting TMV nanorods can be controlled by varying the length of the encapsidated RNA. Nanorod lengths were analyzed with a custom-written python computer script coupled with the Nanorod UI user interface script, thereby generating histograms of particle length. In addition, nanorod variants were produced by incorporating coat protein subunits presenting metal-binding peptides at their C-termini. We demonstrate the utility of this approach by generating nanorods that bind colloidal gold nanoparticles.
Basis weight uniformity can affect various properties of fibrous porous networks, including their porosity and mechanical behaviour. In this paper, a novel numerical technique based on the well-known quadrant analysis...
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Basis weight uniformity can affect various properties of fibrous porous networks, including their porosity and mechanical behaviour. In this paper, a novel numerical technique based on the well-known quadrant analysis method is introduced to statistically quantify the uniformity of fibrous porous networks in the computer environment. First, four fibrous porous networks with different uniformity levels were generated with the in-house script. Then, uniformity was computed numerically based on basis weight analysis for various quadrant sizes for these fibrous porous networks. Uniformity parameter commonly used for quality control purposes turns into design parameter that can be used to optimise the microstructure of fibrous porous networks before manufacturing them with the developed algorithm.
Linear regression is one of the oldest statistical modeling approaches. Still, it is a valuable tool, particularly when it is necessary to create forecast models with low sample sizes. When researchers use this method...
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Linear regression is one of the oldest statistical modeling approaches. Still, it is a valuable tool, particularly when it is necessary to create forecast models with low sample sizes. When researchers use this method and have numerous potential regressors, choosing the group of regressors for a model that fulfills all the required assumptions can be challenging. In this sense, the authors developed an open-source python script that automatically tests all the combinations of regressors under a brute-force approach. The output displays the best linear regression models, regarding the thresholds set by users for the required assumptions: statistical significance of the estimations, multicollinearity, error normality, and homoscedasticity. Further, the script allows the selection of linear regressions with regression coefficients according to the user's expectations. This script was tested with an environmental dataset to predict surface water quality parameters based on landscape metrics and contaminant loads. Among millions of possible combinations, less than 0.1 % of the regressor combinations fulfilled the requirements. The resulting combinations were also tested in geographically weighted regression, with similar results to linear regression. The model's performance was higher for pH and total nitrate and lower for total alkalinity and electrical conductivity. center dot A python script was developed to find the best linear regressions within a dataset. center dot Output regressions are automatically selected based on regression coefficient expectations set by the user and the linear regression assumptions. center dot The algorithm was successfully validated through an environmental dataset.
Human errors within a manufacturing industry are an essential factor that needs to be pre vented to continue their operation. However, human errors can be a major obstacle within the production process and its safety....
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ISBN:
(纸本)9781665483285;9781665483292
Human errors within a manufacturing industry are an essential factor that needs to be pre vented to continue their operation. However, human errors can be a major obstacle within the production process and its safety. Companies need to focus on their human error detection process to pre vent this type of disruption. Human errors within a manufacturing industry need to be eliminated and identified within a less time and that can only be possible after the engagement of machine learning techniques. Focus of this research article will be on different types of machine learning tools used within the Indian manufacturing Industry to eliminate human errors and the challenge they might have faced during its post and pre implementation process. In the introduction part, the background of this research area based on global context and aim, and objectives of this research has been presented. In the LR section, different findings from journal articles have been presented which focuses on the python script used by construction sites, to improve the process of manufacturing Geopolymer. Moreover, various other tools such as different regression models in the food production industry and other manufacturing industries will be discussed in this research paper with challenges. A mixed method has been used in this research paper to identify primary and secondary both data. It has been found that, effect of ML models to eliminate human errors in the manufacturing industry is mainly positive, apart from some specific technical challenges and poor algorithms.
The prediction of the mechanical strength of composites must be known before use or fabrication. The computerized modeling and analysis helps in prediction of the realistic performance of the composite products. The c...
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In this paper, we use python script to read the function detailed description documents of CAN-FD IP, extract the required information according to the main keywords and other information, and automatically generate t...
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ISBN:
(纸本)9781728189789
In this paper, we use python script to read the function detailed description documents of CAN-FD IP, extract the required information according to the main keywords and other information, and automatically generate the qualified System Verilog constrained randomized register configuration stimulator. The simulation results show that the stimulator can meet the design requirements for the configuration of excitation, and can effectively improve the design efficiency of the verification platform.
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