Assessment and Optimization of Autophagy Monitoring Methods in Arabidopsis Roots Indicate Direct Fusion of Autophagosomes with Vacuoles

Designing and conducting standardized microbiological method interlaboratory validation studies is challenging because most methods are manual, rather than instrument-based, and results from the methods are typically subjective. Determinations of method recovery, in particular, are problematic, due to difficulties in assessing the true spike amount. The standardization and validation process used for the seven most recent USEPA 1600-series pathogen monitoring methods has begun to address these challenges. A staged development process was used to ensure that methods were adequately tested and standardized before resources were dedicated to interlaboratory validation. The interlaboratory validation studies for USEPA Method 1622, for Cryptosporidium, USEPA Method 1601 for coliphage, and USEPA Method 1605 for Aeromonas assessed method performance using different approaches, due the differences in the nature of the target analytes and the data quality needs of each study. However, the use of enumerated spikes in all of the studies allowed method recovery and precision to be assessed, and also provided the data needed to establish quantitative quality control criteria for the methods.

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Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09159-y ◽

2021 ◽

Vol 193 (7) ◽

Author(s):

Heini Hyvärinen ◽

Annaliina Skyttä ◽

Susanna Jernberg ◽

Kristian Meissner ◽

Harri Kuosa ◽

...

Keyword(s):

Comparative Studies ◽

Cost Efficiency ◽

Cost Benefit ◽

Marine Ecosystems ◽

Actual Cost ◽

Monitoring Methods ◽

Monitoring Method ◽

Marine Monitoring ◽

The Status ◽

The Cost

AbstractGlobal deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term “cost-efficiency.” We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost–benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost–benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.

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An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽

10.3390/s21020343 ◽

2021 ◽

Vol 21 (2) ◽

pp. 343

Author(s):

Kim Bjerge ◽

Jakob Bonde Nielsen ◽

Martin Videbæk Sepstrup ◽

Flemming Helsing-Nielsen ◽

Toke Thomas Høye

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Vision System ◽

Low Cost ◽

Light Trap ◽

Automatic Monitoring ◽

Light Sources ◽

Monitoring Methods ◽

Computer Vision System ◽

Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

Applied Sciences ◽

10.3390/app11083642 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3642

Author(s):

Oleg Bukin ◽

Dmitry Proschenko ◽

Denis Korovetskiy ◽

Alexey Chekhlenok ◽

Viktoria Yurchik ◽

...

Keyword(s):

Artificial Intelligence ◽

Autonomous Navigation ◽

Oil Pollution ◽

Optical Sensing ◽

Movement Control ◽

Oil Products ◽

Sea Surface ◽

Pollution Monitoring ◽

Monitoring Methods ◽

Marine Fuel

The oil pollution of seas is increasing, especially in local areas, such as ports, roadsteads of the vessels, and bunkering zones. Today, methods of monitoring seawater are costly and applicable only in the case of big ecology disasters. The development of an operative and reasonable project for monitoring the sea surface for oil slick detection is described in this article using drones equipped with optical sensing and artificial intelligence. The monitoring system is implemented in the form of separate hard and soft frameworks (HSFWs) that combine monitoring methods, hardware, and software. Three frameworks are combined to fulfill the entire monitoring mission. HSFW1 performs the function of autonomous monitoring of thin oil slicks on the sea surface, using computer vision with AI elements for detection, segmentation, and classification of thin slicks. HSFW2 is based on the use of laser-induced fluorescence (LIF) to identify types of oil products that form a slick or that are in a dissolved state, as well as measure their concentration in solution. HSFW3 is designed for autonomous navigation and drone movement control. This article describes AI elements and hardware complexes of the three separate frameworks designed to solve the problems with monitoring slicks of oil products on the sea surface and oil products dissolved in seawater. The results of testing the HSFWs for the detection of pollution caused by marine fuel slicks are described.

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Remote Sensing for Plant Water Content Monitoring: A Review

Remote Sensing ◽

10.3390/rs13112088 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2088

Author(s):

Carlos Quemada ◽

José M. Pérez-Escudero ◽

Ramón Gonzalo ◽

Iñigo Ederra ◽

Luis G. Santesteban ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Wave ◽

Technological Development ◽

Water Status ◽

Irrigation Management ◽

Coefficient Of Determination ◽

Plant Water ◽

Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

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Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sensors ◽

10.3390/s21113609 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3609

Author(s):

Mykola Sysyn ◽

Michal Przybylowicz ◽

Olga Nabochenko ◽

Lei Kou

Keyword(s):

High Speed ◽

Experimental Studies ◽

Dynamic Interaction ◽

Theoretical Research ◽

Rolling Stock ◽

Monitoring Methods ◽

Beam Dynamic ◽

Track Geometry ◽

Data Driven Approach ◽

Support Conditions

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

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Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Minerals ◽

10.3390/min11070683 ◽

2021 ◽

Vol 11 (7) ◽

pp. 683

Author(s):

Chris Aldrich ◽

Xiu Liu

Keyword(s):

Image Analysis ◽

Process Monitoring ◽

Chemical Species ◽

Platinum Metal ◽

Local Binary Patterns ◽

Monitoring Methods ◽

Multivariate Statistical ◽

Statistical Process ◽

Froth Stability ◽

Froth Image

Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.

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