scholarly journals Low-cost bilayered structure for improving the performance of solar stills: Performance/cost analysis and water yield prediction using machine learning

2022 ◽  
Vol 49 ◽  
pp. 101783
Author(s):  
Ammar H. Elsheikh ◽  
S. Shanmugan ◽  
Ravishankar Sathyamurthy ◽  
Amrit Kumar Thakur ◽  
Mohamed Issa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cost Analysis ◽  
Low Cost ◽  
Water Yield ◽  
Yield Prediction ◽  
Bilayered Structure

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

2019 ◽  
Author(s):  
Qiannan Duan ◽  
Jianchao Lee ◽  
Jinhong Gao ◽  
Jiayuan Chen ◽  
Yachao Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Toxicity ◽  
Model Building ◽  
Low Cost ◽  
Enzyme Reaction ◽  
Chemical Effect ◽  
Technological Innovations ◽  
Acute Toxicity Test ◽  
Toxicity Prediction ◽  
Traditional Understanding

<p>Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p>

What’s in, what’s out? Towards a rigorous definition of the boundaries of benefit-cost analysis

2021 ◽  
pp. 1-17
Author(s):  
Daniel Acland
Keyword(s):  
Cost Analysis ◽  
Willingness To Pay ◽  
Low Cost ◽  
Consumer Sovereignty ◽  
Benefit Cost Analysis ◽  
Rigorous Definition ◽  
Pareto Criterion ◽  
Definition Of ◽  
Benefit Cost

Abstract Benefit-cost analysis (BCA) is typically defined as an implementation of the potential Pareto criterion, which requires inclusion of any impact for which individuals have willingness to pay (WTP). This definition is incompatible with the exclusion of impacts such as rights and distributional concerns, for which individuals do have WTP. I propose a new definition: BCA should include only impacts for which consumer sovereignty should govern. This is because WTP implicitly preserves consumer sovereignty, and is thus only appropriate for ‘sovereignty-warranting’ impacts. I compare the high cost of including non-sovereignty-warranting impacts to the relatively low cost of excluding sovereignty-warranting impacts.

scholarly journals Removal of Toxic Elements and Microbial Contaminants from Groundwater Using Low-Cost Treatment Options

2021 ◽  
Author(s):  
Dana A. Da’ana ◽  
Nabil Zouari ◽  
Mohammad Y. Ashfaq ◽  
Mohammed Abu-Dieyeh ◽  
Majeda Khraisheh ◽  
...  
Keyword(s):  
Cost Analysis ◽  
Toxic Elements ◽  
Low Cost ◽  
Treatment Technique ◽  
Groundwater Treatment ◽  
Efficient Treatment ◽  
Treatment Techniques ◽  
Cost Treatment ◽  
Treatment Technologies ◽  
Microbial Contaminants

Abstract Purpose of Review This paper reviews various low-cost treatment techniques such as adsorption, permeable reactive barrier, and biological techniques for the simultaneous removal of chemical and microbial contaminants from groundwater and discusses treatment mechanisms of different treatment techniques. This paper also discusses the challenges of groundwater treatment, how to choose the appropriate treatment technique, and cost analysis of groundwater treatment. Recent Findings Various treatment technologies have been used for the treatment of groundwater: physical, chemical, and biological technologies with different success rates. In the literature, various adsorbents have been successfully synthesized from low-cost and environmentally friendly materials. Adsorption is considered an efficient treatment technique for the removal of both toxic elements and pathogens by utilizing different adsorbents. For example, the nanostructures of MgO with a BET surface area of up to 171 m2/g obtained a very high adsorption capacity of 29,131 mg/g for fluoride ions in water, while the incorporation of iron in activated carbon has improved its adsorption capacity to 51.3 mg/g for arsenic. Moreover, certain adsorbents have shown the capability to remove 99% of the rotavirus and adenovirus from groundwater. Summary Groundwater resources are contaminated with toxic metals and pathogens. Therefore, water treatment technologies should be evaluated for their efficiency to remove such contaminants. Determination of the most cost-effective and efficient treatment technique is not an easy task and requires the understanding of various aspects such as the contaminants present in water, the reuse options considered, and cost analysis of the treatment technique.

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Digital Smoke Taint Detection in Pinot Grigio Wines Using an E-Nose and Machine Learning Algorithms Following Treatment with Activated Carbon and a Cleaving Enzyme

Fermentation ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 119
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Activated Carbon ◽  
Low Cost ◽  
Aroma Compounds ◽  
Machine Learning Algorithms ◽  
Enzyme Treatment ◽  
Mean Values ◽  
Volatile Aroma ◽  
Carbon Treatment ◽  
Volatile Aroma Compounds

The incidence and intensity of bushfires is increasing due to climate change, resulting in a greater risk of smoke taint development in wine. In this study, smoke-tainted and non-smoke-tainted wines were subjected to treatments using activated carbon with/without the addition of a cleaving enzyme treatment to hydrolyze glycoconjugates. Chemical measurements and volatile aroma compounds were assessed for each treatment, with the two smoke taint amelioration treatments exhibiting lower mean values for volatile aroma compounds exhibiting positive ‘fruit’ aromas. Furthermore, a low-cost electronic nose (e-nose) was used to assess the wines. A machine learning model based on artificial neural networks (ANN) was developed using the e-nose outputs from the unsmoked control wine, unsmoked wine with activated carbon treatment, unsmoked wine with a cleaving enzyme plus activated carbon treatment, and smoke-tainted control wine samples as inputs to classify the wines according to the smoke taint amelioration treatment. The model displayed a high overall accuracy of 98% in classifying the e-nose readings, illustrating it may be a rapid, cost-effective tool for winemakers to assess the effectiveness of smoke taint amelioration treatment by activated carbon with/without the use of a cleaving enzyme. Furthermore, the use of a cleaving enzyme coupled with activated carbon was found to be effective in ameliorating smoke taint in wine and may help delay the resurgence of smoke aromas in wine following the aging and hydrolysis of glycoconjugates.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Low-cost Analysis Methods of Automotive Electric Machines for Aviation Applications

2021 ◽  
Author(s):  
Kevin J. Yost ◽  
Luke Chen ◽  
Zachary Adamson ◽  
Tommy Baudendistel ◽  
William Perdikakis ◽  
...  
Keyword(s):  
Cost Analysis ◽  
Low Cost ◽  
Electric Machines ◽  
Analysis Methods

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

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