V2 Algorithm Research Articles

The first global catalogue of gas flaring sources derived from a multi-temporal time series of OLI and MSI daytime data: the DAFI v2 algorithm

Abstract Gas flaring (GF) is an industrial phenomenon requiring a special attention for the serious impacts on environment, climate and human health. To analyses and map GF sites (GFs), the Daytime Approach for GF Investigation (DAFI), which is based on daytime data from the Operational Land Imager (OLI) aboard the Landsat-8 satellite, has recently been developed. The GFs catalogue from the DAFI system, spanning over the years 2013–2021, represents the first worldwide GF product generated from mid-high spatial resolution data (30 m). The DAFI version 2 (v2), also including information from the Multispectral Instrument (MSI) aboard Sentinel-2 satellites, already showed an improved capacity in the mapping of GFs over Iran and Iraq. In this work, the new catalogue arising from the porting of DAFI v2 at global scale is presented and discussed. By using a parallel workflow, based on a loop tiling scheme running in Google Earth Engine, DAFI v2 allowed us to extend the GF inventory globally up to 2023, by analyzing ∼4 million of OLI/OLI-2 (Collection 2) and MSI imagery. Results of this study show: (i) an increased DAFI v2 capacity, of about 41%, in detecting GFs compared to the previous DAFI version; (ii) a downward trend (∼4.6%) in terms of GF sites detected from 2021 to 2023. These findings demonstrated that DAFI v2, which will also include the estimates of radiative power at different spatial scales, may contribute in assessing and monitoring the GFs activities and in evaluating the effectiveness of the greenhouse gas-emission reduction strategies at global, continental, national and local scales.

Enhanced Area Defense: Magnetic Launcher and Sensor Integration

In this article, the most appropriate design method for a field defense system that operates with magnetic field laws by taking audio and video signals as reference is described. The study is important because it eliminates the operator (soldier) factor in the battlefield. Taking into account the effect of capacitor voltage, capacitance value and accelerator winding inductance value, the main criteria for determining the power supply and armature structure of the electromagnetic launcher are proposed. This proposal is made with MATLAB/Simulink software based mathematical model and the differences are explained by comparing with ANSYS Maxwell simulation. The simulated results show that the speed difference between the models is 7%. Additionally, the design methods of the audio and video-based positioning systems that control the system are explained in the study. In this system where signal receiving microphones are positioned in triangular form, a linear algorithm using the time difference method is utilized. By comparing the theoretical mathematical model with the experimental simulation model, the accuracy of the method and the method function is proved. In this study, a deep learning-based target detection system that operates with the YOLO v2 algorithm is used to increase the system's mission execution capacity. The operator is eliminated by switching the system with the signal received from the positioning systems.

Management of Traditional Business into Modern: from Microsoft Excel to Deep Learning for prototyping classification Swiftlet’s nests

In this article, the transformation of traditional management of Swiftlet’s nests into modern business is proposed. Traditional business means that data management of Swiftlet’s nests is done manually, sorted by recording in Microsoft Excel. This is done by PT Waleta Asia Jaya, a company engaged in processing Swiftlet’s nests. This sorting is done because the number of feathers in the Swiftlet’s nests determines the price and cost of workers in processing feather cleaning. In addition, the shape of the Swiftlet’s nests needs attention. However, because it is complex, sorting is done simpler. Originally, Swiftlet’s nests were sorted into 50 categories. To facilitate sorting, deep learning is used with the SSD Mobile Net V2 algorithm as an algorithm to classify into 7 categories based on feather intensity. The device is still a prototype that shows an 85% accuracy rate but has been quite helpful in the process of purchasing Swiftlet’s nests before processing.

Intelligence feeder system for stray cats

<span>Recently, <span lang="EN-US">the care of stray cats has become an important matter, given the difficulty for these animals to obtain food or water, whether in wild or remote areas, and this may cause the death of these animals. The aim of this work is to design and implement a low-cost feeding system for feral and indoor cats. The system is controlled by a jetson nano and arduino mega 2,560. The cat detection feeder system is built using the single shot multibox detector (SSD) MobileNet V2 algorithm. The system provides food and water for the cats. The SSD on jetson nano is implemented in real time. Jetson nano takes a picture using a webcam then runs the SSD algorithm. When a cat is detected, the arduino turns on a servo motor and a water pump. The system also includes a sim800l and GPS NEO-6M modules to send an alert message when the food and water tanks are empty. The message also contains the location of the feeder. When testing the system to determine the effectiveness of the functions, the SSD algorithm succeeded in recognizing cats, the system successfully provided food for the cats, and all parts of the device worked in high harmonic.</span></span>

P-186 Combination of proteomics and automatic scoring using artificial neural networks to detect aneuploid embryos

Abstract Study question How powerful is the combination of artificial neural networks that combine embryo's protein profile with its automatic score provided by time-lapse videos to predict ploidy? Summary answer An artificial neural network (ANN) that considers proteomics and automatic score assigned by deep learning achieves 71% accuracy in distinguishing between euploid and aneuploid embryos. What is known already Currently the most widely used technique for detecting chromosomal abnormalities involves biopsy of the developing embryo. However, it has several disadvantages related to invasiveness, technical difficulty, high economic costs, etc. Therefore, different non-invasive techniques are being studied for the detection of aneuploid embryos. The discovery of cell-free DNA (cfDNA) released by the embryo to the culture media during its development marked the beginning of a new era of noninvasive PGT (niPGT) but some factors require adaptation for the analysis. Also, artificial Intelligence (AI) represents a valuable alternative to developing new models for predicting PGT outcomes without disturbing the embryo. Study design, size, duration This study included 294 samples of culture medium from 81 treatments of the PGT-A program. Out of the total, 23 were control samples (medium in which no embryos had been cultured) and 271 were samples where there was a developing embryo. Embryos were cultured until the blastocyst stage in EmbryoScope systems (Vitrolife, Sweden) with single-step medium (Gems, Genea) and automatically scored by iDAScore v2 algorithm from 1 to 9.9. Participants/materials, setting, methods The spent culture medium was collected on day 5/6 of embryo development and chromosome analysis was performed using next-generation sequence technology (Juno Genetics, Valencia). The relative concentrations of 92 proteins were analysed using Proseek Multiplex Assays (Olink Bioscience) using 1 μl of each sample. The final assay readout was presented as Normalized Protein eXpression (NPX) values. Finally, we developed our own ANN algorithms considering protein profile and automatic embryo score. Main results and the role of chance For euploid embryos (n = 101), 35 protein samples analysed had different NPX values between conditioned and control media*. The relative concentration was reduced for 11 proteins (consumption by the embryo) and 24 proteins increased their levels (secretion). For aneuploid embryos (n = 170), 33 protein samples analysed had different NPX values between conditioned and control media*. The relative concentration was reduced for 4 proteins and 29 proteins increased their levels. Out of the total, only 6 proteins had on average different concentrations between normal and abnormal embryos*: MCP_1, IL_17A, CXCL1, IL18, IL_22RA1 and CSF_1. Additionally, the automatic embryo score provided by iDAScore v2 algorithm was higher for euploid embryos than for aneuploid embryos (5.9 ± 2.7 vs. 5.1 ± 2.6)*. For ploidy prediction, three architectures of ANN were developed with different input data ANN1 (six discriminatory proteins), ANN2 (automatic embryo score) and ANN3 (six discriminatory proteins and automatic embryo score). Our dataset was divided into 68% training, 16% validation and 16% test. The accuracy, sensibility and specificity for the test phase were as follows: 63.6%, 76.9% and 44% for ANN1; 61%, 11.8% and 95.8% for ANN2; and 71.1%, 62.5% and 77.3% for ANN3. Limitations, reasons for caution Only one laboratory by using single step culture medium from one brand was involved in this study. Wider implications of the findings Our study showed a new approach to avoid transferring aneuploid embryos in cases where embryo biopsy is not performed. In addition, further studies on this field may result in a new non-invasive methodology for detecting aneuploidy. Trial registration number PI21/00283

P-263 Increased live birth rate, blastocyst formation and quality when embryos were cultured in EmbryoScope time-lapse incubators compared to MINC™ benchtop incubators

Abstract Study question Does embryo culture using the EmbryoScope or EmbryoScope+ time-lapse incubators affect blastocyst development and birth rate compared to MINC™ benchtop incubators? Summary answer Culture in the EmbryoScope or EmbryoScope+ resulted in higher blastocyst quality and utilisation rates, and birth rates from fresh blastocyst transfers was increased. What is known already Time-lapse incubators enable continuous monitoring of embryos so that morphological changes can be observed, and embryo selection software applied to select the best embryo for transfer. However, there has been considerable debate over the benefits of such systems and a review of 8 randomized controlled trials (Armstrong et al., 2019; Cochrane Database Syst Rev (5)) was inconclusive. Interpretation and comparison of results are complicated by differences among imaging systems and variations in culture protocols between clinics. EmbryoScope time-lapse incubators may also have better temperature and pH stability than benchtop incubators, improving embryo development. Study design, size, duration Retrospective data from 1189 cycles in the EmbryoScope or EmbryoScope+ and 1381 MINC cycles conducted concurrently in the same laboratory over 18 months. All embryos from each patient cycle were cultured in one incubator type. All patients from a treating clinician were allocated to one incubator type. Participants/materials, setting, methods All IVF/ICSI cycles from a single clinic. Cycles with vitrified eggs or embryos were excluded. Embryos were cultured in Vitrolife G-TL media under 5% O2, media was refreshed on Day 5. Embryos were scored using the Gardner grading system. For embryos cultured in the Embryoscope, selection of embryos for transfer was based on a combination of Gardner grading and the KIDScore D5 v2 algorithm. Main results and the role of chance There was an increase in Day 5 top quality blastocysts (AA) from embryos in the Embryoscope compared to the MINC (15.3% vs.9.7%, p < 0.0001), and Day 5 good quality blastocysts (≥BB, 24.4% vs.16.9%, p < 0.0001). Overall, the utilisation rate was significantly higher from embryos in the Embryoscope (51.3% vs.46.0%, p < 0.0001). From fresh single blastocyst transfers, there was a significant increase in detection of fetal heart by ultrasound (34.7% vs 28.0% p < 0.01) and live birth rate (30.9% vs 24.6%, p<0.01) from embryos cultured in the Embryoscope (n = 972) compared to the MINC (n = 633). Interestingly, pronuclei were not observed during fertilisation assessment for 84 embryos that subsequently formed blastocysts cultured in the MINC, compared to only 4 embryos in the Embryoscope, suggesting that continuous monitoring increases accuracy in determining fertilisation. Limitations, reasons for caution Assessment using the EmbryoViewer compared to an inverted microscope may result in discrepancy in blastocyst grades. The use of KIDscore combined with Gardner grading for embryo selection in the Embryoscope but not the MINC may influence transfer outcomes. As data was collected retrospectively, patients were not randomised to incubator types. Wider implications of the findings Culture in the Embryoscope increases the frequency of high-quality usable blastocyst development when compared to a MINC. Importantly, this translates to a higher pregnancy rate and live birth rate from fresh embryo transfers. This may be due to the microwell dishes, incubator performance, embryo selection or all of these. Trial registration number Not applicable

P-199 Ensuring no clinical risk: a cohort analysis on the agreement between the embryo selected by the embryologist and the embryo selected by artificial intelligence

Abstract Study question Is there any clinical risk in selecting the embryo to be transferred using a deep learning-based model? Summary answer Most embryos selected by senior embryologists match the automatically best scoring embryo, and clinical outcomes are better when this coincidence occurs. What is known already In vitro fertilization (IVF) techniques have changed over time with the aim of improving clinical results. Today, embryology is facing a change common to most areas of medicine, the introduction of automation. The use of automated systems in the IVF laboratory is already happening, for example, with electronic witnessing and the ranking of embryos according to their implantation potential. Relying on this type of system is not easy for operators, as they must ensure that the treatments will not be damaged. Study design, size, duration This is a single-center cohort study including 5,411 patients who underwent IVF treatments. Their embryos were cultured in EmbryoScope® time-lapse systems (Vitrolife, Denmark) and routinely evaluated by senior embryologists according to ASEBIR morphological criteria. Then, embryos were automatically scored using the iDAScore v2 algorithm. Participants/materials, setting, methods A total of 7,178 embryo transfers were analyzed. The transferred embryo was selected by the embryologists and retrospectively scored with the deep learning algorithm from 1 to 9.9. Finally, we performed a cohort analysis on the agreement between the embryo selected by the embryologist and the embryo selected by iDAScore v2. The relative risk (or incidence of implantation) (RR), relative risk reduction (RRR) and absolute risk reduction (ARR) were calculated. Main results and the role of chance In general, considering all transfers (fresh and frozen) with known clinical outcome (n = 7,178), the implantation rate was higher when the transferred embryo matched the highest-scoring embryo (58.37% vs. 55.49% p = 0.014). Embryos selected by senior embryologists for single embryo transfer in the fresh cycle (n = 2,783) matched the top-scoring embryo 63.50% of the time. An ongoing pregnancy was achieved in 57.96% of the patients. Out of the 36.54% of patients who had fresh transfer of an embryo that did not have the maximum iDAScore, 553 patients underwent transfer of a single frozen embryo. In this group, the first devitrified embryo coincided with the highest scoring embryo 44.67% of the time (n = 247). An ongoing pregnancy was achieved in 62.34% the patients. However, when the devitrified embryo did not correspond to the highest scoring embryo, the implantation rate was 55.27%. The RR showed that each patient was 1.13 times more likely to become pregnant if the transferred embryo matched the embryo with the highest score. The RRR stated that if there was this coincidence, the probability of implantation was increased by 12.79%. The ARR (0.07) suggested that for every 100 transfers with this match, 7 more embryos would implant than without this match. Limitations, reasons for caution This study is limited by its retrospective nature. Furthermore, the single-center design should be considered when generalizing the results, although our clinic was not involved in the development of this specific model. Wider implications of the findings The high coincidence between the embryologist's decision and the artificial intelligence's decision should comfort assisted reproduction professionals and patients. This coincidence also justifies that realistic models based on artificial intelligence perform the embryo selection procedure as good as the most experienced embryologist. Trial registration number Not applicable

CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies

Abstract. Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture and ecosystems to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present CHELSA-W5E5 (https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air temperatures, precipitation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5∘ W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near-surface air temperatures in regions that are prone to cold-air pooling or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the Weather Research and Forecasting (WRF) regional climate model, as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide higher-resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level and for applications that cover more than one region and benefit from using a consistent dataset across these regions.

Evaluation of the updated SOCcer v2 algorithm for coding free-text job descriptions in three epidemiologic studies.

Computer-assisted coding of job descriptions to standardized occupational classification codes facilitates evaluating occupational risk factors in epidemiologic studies by reducing the number of jobs needing expert coding. We evaluated the performance of the 2nd version of SOCcer, a computerized algorithm designed to code free-text job descriptions to US SOC-2010 system based on free-text job titles and work tasks, to evaluate its accuracy. SOCcer v2 was updated by expanding the training data to include jobs from several epidemiologic studies and revising the algorithm to account for nonlinearity and incorporate interactions. We evaluated the agreement between codes assigned by experts and the highest scoring code (a measure of confidence in the algorithm-predicted assignment) from SOCcer v1 and v2 in 14,714 jobs from three epidemiology studies. We also linked exposure estimates for 258 agents in the job-exposure matrix CANJEM to the expert and SOCcer v2-assigned codes and compared those estimates using kappa and intraclass correlation coefficients. Analyses were stratified by SOCcer score, score distance between the top two scoring codes from SOCcer, and features from CANJEM. SOCcer's v2 agreement at the 6-digit level was 50%, compared to 44% in v1, and was similar for the three studies (38%-45%). Overall agreement for v2 at the 2-, 3-, and 5-digit was 73%, 63%, and 56%, respectively. For v2, median ICCs for the probability and intensity metrics were 0.67 (IQR 0.59-0.74) and 0.56 (IQR 0.50-0.60), respectively. The agreement between the expert and SOCcer assigned codes linearly increased with SOCcer score. The agreement also improved when the top two scoring codes had larger differences in score. Overall agreement with SOCcer v2 applied to job descriptions from North American epidemiologic studies was similar to the agreement usually observed between two experts. SOCcer's score predicted agreement with experts and can be used to prioritize jobs for expert review.

Introduction and Clinical Validation of an Updated Biomechanically Corrected Intraocular Pressure bIOP (v2)

Purpose To improve the stability of the Corvis ST biomechanically-corrected intraocular pressure measurements (bIOP) after refractive surgery and its independence of corneal biomechanics. Methods A parametric study was carried out using numerical models simulating the behavior of the eye globe under the effects of IOP and Corvis ST external air pressure and used to develop a new algorithm for bIOP; bIOP(v2). It was tested on 528 healthy participants to evaluate correlations with CCT and age. Its ability to compensate for the geometrical changes was tested in 60 LASIK and 80 SMILE patients with six months follow up. The uncorrected Corvis ST IOP (CVS-IOP) and the two versions of biomechanically corrected IOP; bIOP(v1) and bIOP(v2), were compared. Results In the healthy dataset, bIOP(v2) had weak and non-significant correlation with both CCT (R = −0.048, p = .266) and age (R = 0.085, p = .052). For bIOP(v1), the correlation was non-significant with CCT (R = −0.064, p = .139) but significant with age (R = −0.124, p < .05). In both LASIK and SMILE groups, the median change in bIOP(v2) following surgery was below 1 mmHg at follow-up stages and the interquartile range was smaller than both bIOP(v1) and CVS-IOP. Conclusion The bIOP(v2) algorithm performs better than bIOP(v1) and CVS-IOP in terms of correlation with CCT and age. The bIOP(v2) also demonstrated the smallest variation after LASIK and SMILE refractive surgeries indicating improved ability to compensate for geometrical changes.

Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios.

Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements’ specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person’s face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to for 3DDFA_V2 depending on the plane of movement, while the mean error reaches and for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.

A YOLOv3-based computer vision system for identification of tea buds and the picking point

Famous tea industry which need to harvest tea buds has great economic benefits. However, the harvesting is time-consuming and labor-intensive, especially with the shortage of labor currently, an intelligent tea bud picking robot is urgently needed. The vision system is a precursor to the development of a tea bud picking robot. To resolve such issues, we applied robotics and deep learning technologies to develop a computer vision system for intelligent picking of tea buds. The system was designed to recognize tea buds and extract their picking points. A method for locating the picking points was proposed based on a combination of YOLO-v3 algorithm, semantic segmentation algorithm, skeleton extraction and minimum bounding rectangle. An intelligent tea end-effector based on Personal Computer and microcontroller collaborative control was designed to solve the picking problem like complex shading and easy breakage. Thus, the picking rate of the overall system was improved. Based on Openmv smart camera embedded mobilenet_v2 algorithm as the visual model of the classification device, so that the quality of tea buds was preliminatively classified. Finally, the effects of different shooting angles and shooting methods as well as the accuracy of target detection and semantic segmentation algorithms on the extraction of tea bud picking points were investigated. The results show that the average accuracy of YOLO-v3 for identification of tea buds is 71.96% and the average horizontal positioning error of the robotic arm is 2.4 mm. Also, the average depth positioning error is 4.2 mm and the accuracy of tea bud picking point extraction is 83%. After the test, the successful picking rate of tea buds is 80% by this computer vision system of robot. The results of this study is potential to develop a machine-based tea picking system for industry and would contribute to the development of precision agriculture.

Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN

Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.

DiODe v2: Unambiguous and Fully-Automated Detection of Directional DBS Lead Orientation

Directional deep brain stimulation (DBS) leads are now widely used, but the orientation of directional leads needs to be taken into account when relating DBS to neuroanatomy. Methods that can reliably and unambiguously determine the orientation of directional DBS leads are needed. In this study, we provide an enhanced algorithm that determines the orientation of directional DBS leads from postoperative CT scans. To resolve the ambiguity of symmetric CT artifacts, which in the past, limited the orientation detection to two possible solutions, we retrospectively evaluated four different methods in 150 Cartesia™ directional leads, for which the true solution was known from additional X-ray images. The method based on shifts of the center of mass (COM) of the directional marker compared to its expected geometric center correctly resolved the ambiguity in 100% of cases. In conclusion, the DiODe v2 algorithm provides an open-source, fully automated solution for determining the orientation of directional DBS leads.

Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy

Thin section microscopy has been historically used for modal mineralogy in exploration and for monitoring plant performance. Despite this, the technique relies on visual detection from expert mineralogists which is error prone and slow. Consequently, mineralogy characterisation has been largely replaced by automated mineralogy solutions like Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) which rely on chemical composition differences from electron-sample or x-ray spectrums respectively. However, these techniques are limited when minerals of interest have similar chemical compositions but different optical reflectance properties.This study aims to utilise deep learning algorithms to overcome the limitations of thin section microscopy by automating the process for quicker identification. This is done through Convolutional Neural Networks (CNNs), which are deep learning-based instance segmentation algorithms capable of detecting grain boundaries and classify minerals. Through this methodology, the proposed algorithm can evaluate grade by mineralogy compared to elemental grade in other commonly used methods.In this study, two instance segmentation algorithms were compared, namely Mask R-CNN and SOLO v2 in their ability to identify, and segment minerals to estimate surface modal mineralogy and particle size distribution and speed. The SOLO v2 algorithm achieved superior performance (APCOCO = 50.4% vs APCOCO = 44.3%) and can segment 640 × 480 thin section microscopy images at a speed of 20 frame/second which is four times faster than Mask-RCNN. This is equivalent to 120,000 grains/minute which is 2,000 times and 180 times faster than a human expert and SEM/EDS respectively.

Classification of caries in third molars on panoramic radiographs using deep learning

The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

Retrievals of Aerosol Optical Depth and Spectral Absorption From DSCOVR EPIC

A new algorithm is described for joint retrievals of the aerosol optical depth and spectral absorption from EPIC observations in the UV—Vis spectral range. The retrievals are illustrated on examples of the wildfire smoke events over North America, and dust storms over greater Sahara region in 2018. An initial evaluation of single scattering albedo (SSA) at 443 nm over these regions shows a good agreement with AERONET data, generally within the uncertainty of AERONET SSA of ± 0.03. A particularly good agreement is achieved for dust with R~0.62, rmse~0.02, negligible bias, and 85% points within the expected error. This new capability is part of version 2 MAIAC EPIC algorithm. The v2 algorithm has recently completed reprocessing of the EPIC record covering the period of 2015–2020.

Computational prediction of microRNA targets of ion channels and aquaporins in Aedes aegypti

Background & objectives: Ion channels and water channel aquaporins are ubiquitous in most life forms and responsible for several physiological functions. Sanguivorous insects, like the mosquitoes also have ion channels which regulate many pivotal cellular processes. Alterations of ion channel/AQPs can have profound impact on mosquito physiology. The miRNAs, a newly discovered class of regulators in the gene network, can fine tune the expression of ion channel genes and this is of growing interest in research for the treatment of various channelopathies. In the present study, we tried to predict the miRNA targets of different types of ion channels and aquaporins of Aedes aegypti mosquito using in silico approaches. Methods: The list of Ae. aegypti ion channels and aquaporin genes and their sequences were retrieved from TransportDB and FlyBase databases. The miRNA sequences were retrieved from the miRBase. The miRNA targets of each channel gene were predicted using RNA22 v2 algorithm. Results: The miRNA targets of 198 ion channel genes (including potassium channels, sodium channels, calcium channels, chloride channels and several non-selective cation channels) and six aquaporin genes were predicted. Interpretation & conclusion: The results of the present study showed that each ion channel has many miRNA targets and a single miRNA can target multiple ion channel genes. Few miRNAs are specific for channel types, e.g. mir-31 targets most of the calcium ion channels; miR31 and mir-276-2 target multiple sodium channels. The present study can be considered as a leading step in the initiation of a miRNA database construction, exclusively for mosquito ion channels.

A Study on Fruit Quality Identification Using YOLO V2 Algorithm

A Study on Fruit Quality Identification Using YOLO V2 Algorithm

Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

Abstract. Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 LT) SIF estimates on the global scale from the GOME-2 sensor on the MetOp-A satellite (GOME-2A) for the period 2007–2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and being less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007–2012) of atmospheric spectra over the Sahara and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterised the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compare well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations and is best used for mostly clear-sky scenes and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data being used as an independent constraint on photosynthetic activity on regional to global scales.