scholarly journals Technologies for recovery and reuse of plant nutrients from human excreta and domestic wastewater: a protocol for a systematic map and living evidence platform

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Biljana Macura ◽  
James Thomas ◽  
Geneviève S. Metson ◽  
Jennifer R. McConville ◽  
Solveig L. Johannesdottir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Municipal Wastewater ◽  
Data Extraction ◽  
Domestic Wastewater ◽  
Research Impact ◽  
Plant Nutrients ◽  
Systematic Map ◽  
Human Excreta ◽  
Machine Learning Model ◽  
Recovery And Reuse

Abstract Background Research and development on the recovery and reuse of nutrients found in human excreta and domestic wastewater has intensified over the past years, continuously producing new knowledge and technologies. However, research impact and knowledge transfer are limited. In particular, uptake and upscaling of new and innovative solutions in practice remain a key challenge. Achieving a more circular use of nutrients thus goes beyond technological innovation and will benefit from a synthesis of existing research being readily available to various stakeholders in the field. The aim of the systematic map and online evidence platform described in this protocol is threefold. First, to collate and summarise scientific research on technologies that facilitate the recovery and reuse of plant nutrients and organic matter found in human excreta and domestic and municipal wastewater. Second, to present this evidence in a way that can be easily navigated by stakeholders. Third, to report on new relevant research evidence to stakeholders as it becomes available. Methods Firstly, we will produce a baseline systematic map, which will consist of an extension of two previous related syntheses. In a next stage, with help of machine learning and other automation technologies, the baseline systematic map will be transformed into ‘living mode’ that allows for a continually updated evidence platform. The baseline systematic map searches will be performed in 4 bibliographic sources and Google Scholar. All searches will be performed in English. Coding and meta-data extraction will include bibliographic information, locations as well as the recovery and reuse pathways. The living mode will mostly rely on automation technologies in EPPI-Reviewer and the Microsoft Academic database. The new records will be automatically identified and ranked in terms of eligibility. Records above a certain ‘cut-off’ threshold will be manually screened for eligibility. The threshold will be devised based on the empirically informed machine learning model. The evidence from the baseline systematic map and living mode will be embedded in an online evidence platform that in an interactive manner allows stakeholders to visualise and explore the systematic map findings, including knowledge gaps and clusters.

scholarly journals A Study of Factors Influencing Plant Growth by WSN Approach and Plant Nutrient Deficiency Classification in Tomato Using SVM

2021 ◽  
Vol 23 (06) ◽  
pp. 36-46
Author(s):  
Vrunda Kusanur ◽  
◽  
Veena S Chakravarthi ◽  
Keyword(s):  
Machine Learning ◽  
Plant Growth ◽  
Nutrient Deficiency ◽  
Learning Model ◽  
Plant Nutrients ◽  
Polynomial Kernel ◽  
Second Phase ◽  
Nutrient Deficiencies ◽  
Deficiency Symptoms ◽  
Machine Learning Model

Soil temperature and humidity straight away influence plant growth and the availability of plant nutrients. In this work, we carried out experiments to identify the relationship between climatic parameters and plant nutrients. When the relative humidity was very high, deficiency symptoms were shown on plant leaves and fruits. But, recognizing and managing these plant nutrients manually would become difficult. However, no much research has been done in this field. The main objective of this research was to propose a machine learning model to manage nutrient deficiencies in the plant. There were two main phases in the proposed research. In the first phase, the humidity, temperature, and soil moisture in the greenhouse environment were collected using WSN and the influence of these parameters on the growth of plants was studied. During experimentation, it was investigated that the transpiration rate decreased significantly and the macronutrient contents in the plant leave decreased when the humidity was 95%. In the second phase, a machine learning model was developed to identify and classify nutrient deficiency symptoms in a tomato plant. A total of 880 images were collected from Bingo images to form a dataset. Among all these images, 80% (704 images) of the dataset were used to train the machine learning model and 20% (176 images) of the dataset were used for testing the model performance. In this study, we selected K-means Clustering for key points detection and SVM for classification and prediction of nutrient stress in the plant. SVM using linear kernel performed better with the accuracy rates of 89.77 % as compared to SVM using a polynomial kernel.

scholarly journals What evidence exists on ecotechnologies for recycling carbon and nutrients from domestic wastewater? A systematic map

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Solveig L. Johannesdottir ◽  
Biljana Macura ◽  
Jennifer McConville ◽  
Dag Lorick ◽  
Neal R. Haddaway ◽  
...  
Keyword(s):  
Domestic Wastewater ◽  
Evidence Base ◽  
Common Type ◽  
Geographical Information ◽  
Treated Wastewater ◽  
Bibliographic Databases ◽  
Nitrogen And Phosphorus ◽  
Systematic Map ◽  
Screening Process ◽  
Recovery And Reuse

Abstract Background Eutrophication of the Baltic Sea, and many other water bodies, is partly the result of point-source emissions of nutrients and carbon from wastewater. At the same time, nitrogen and phosphorus planetary boundaries have been breached. There is a need for more efficient resource management, including the recovery and reuse of nutrients and carbon in waste. The aim of this paper is to collate evidence on ecotechnologies intended for use in the wastewater sector globally to facilitate the recovery or reuse of carbon and/or nutrients. Methods Searches were performed on literature published between 2013 and 2017 and in 5 bibliographic databases, 1 search engine, and 38 specialist websites. Database searches were performed in English. Searches in specialist websites were also performed in Finnish, Polish and Swedish. There was no geographical limitation. Screening was conducted at title and abstract level, and on full texts. Apart from bibliographical information, we extracted information on ecotechnology type, intervention, details of the recovery or reuse, the type of wastewater stream to which the ecotechnology is applied, the study location, type and design. Prior to screening and coding, we conducted consistency checks amongst reviewers. We generated a searchable database of coded studies. Findings were synthesised narratively and visualised in a geographical information system (i.e. an evidence atlas). We identified a series of knowledge gaps and clusters that warrant further research. Results The search resulted in 4024 records, out of which 413 articles were retained after the screening process. In addition, 35 pre-screened studies from the specialist website searches were added. Together, these 448 articles contained 474 individual studies of 28 types of ecotechnologies. A combination of ecotechnologies (16.7%), followed by microalgae cultivation (14.1%) were the most frequent ecotechnologies in the evidence base. Ecotechnologies for recovery composed 72.6% of the evidence base. The most common wastewater streams for recovery were mixed wastewater and sludge (73.8%). There was a relative lack of studies on recovery from source-separated wastewater. The most common type of recovery was energy (27.3%), followed by simultaneous recovery of nitrogen and phosphorus (22.1%). Reuse of recovered substances was described in 22.8% of the studies. The most common type of reuse was of nitrogen and phosphorus (57.4%), followed by joint reuse of organic carbon, nitrogen and phosphorus (35.2%). Reuse ecotechnologies were mostly focused on the use of wastewater for irrigation or reuse of biosolids, and not on the nutrients that had been extracted through e.g. precipitation of struvite. In 22 studies both recovery and reuse were described. In total, 60 different study countries were reported in the evidence base, and the most common study location was China. Conclusions We found substantial evidence for the recovery and reuse of nutrients and carbon from wastewater sources. The relative abundance of studies where substances are recovered compared to studies where they are reused, suggests a knowledge gap on reuse of recovered nutrients and carbon. The majority of studies on reuse were on irrigation with treated wastewater or reuse of biosolids, and not on reuse of extracted nutrients such as struvite.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests

2020 ◽  
Author(s):  
Thomas Tschoellitsch ◽  
Martin Dünser ◽  
Carl Böck ◽  
Karin Schwarzbauer ◽  
Jens Meier
Keyword(s):  
Machine Learning ◽  
Polymerase Chain Reaction ◽  
Characteristic Curve ◽  
Cohort Analysis ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Blood Tests ◽  
Routine Blood ◽  
Machine Learning Model ◽  
Polymerase Chain

Abstract Objective The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2. Methods In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 1353 unique features was trained to predict the RT-PCR results. Results Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1528 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.90. Conclusion Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.

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