Pooled testing efficiency increases with test frequency

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around x. However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

CPS-SPWM Implementation Based on Multi - Controller Collaboration

In order to improve the modularization degree of cascaded H-bridge converter and reduce the development cost, a modularized carrier phase shifted sine pulse width modulation (CPS-SPWM) based on multi-controller is proposed in this paper, which can easily increase or decrease the number of submodules in cascaded H-bridge. In order to solve the problem of coordination in multi-controller structure, a two-stage control structure is proposed, which uses the master controller to carry out closed-loop control for multiple slave controllers, and uses the approximate natural sampling method to realize digital CPS-SPWM modulation, which reduces computation and makes full use of controller resources. The experimental result shows that the stepped voltage waveform output by the proposed method at the AC side is of high quality and H bridge submodule is easy to be increased and decreased.

easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it can be used with other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile application

Large-scale ecological sampling can be difficult and costly, especially for organisms that are too small to be easily identified in a natural environment by eye. Typically, these microscopic floral and fauna are sampled by collecting substrates from nature and then separating organisms from substrates in the laboratory. In many cases, diverse organisms can be identified to the species-level using molecular barcodes. To facilitate large-scale ecological sampling of microscopic organisms, we used a geographic data-collection platform for mobile devices called Fulcrum that streamlines the organization of geospatial sampling data, substrate photographs, and environmental data at natural sampling sites. These sampling data are then linked to organism isolation data from the laboratory. Here, we describe the easyFulcrum R package, which can be used to clean, process, and visualize ecological field sampling and isolation data exported from the Fulcrum mobile application. We developed this package for wild nematode sampling, but it is extensible to other organisms. The advantages of using Fulcrum combined with easyFulcrum are (1) the elimination of transcription errors by replacing manual data entry and/or spreadsheets with a mobile application, (2) the ability to clean, process, and visualize sampling data using a standardized set of functions in the R software environment, and (3) the ability to join disparate data to each other, including environmental data from the field and the molecularly defined identities of individual specimens isolated from samples.

Deep Neural Network Analysis for Environmental Study of Coral Reefs in the Gulf of Eilat (Aqaba)

Coral reefs are undergoing a severe decline due to ocean acidification, seawater warming and anthropogenic eutrophication. We demonstrate the applicability of Deep Learning (DL) for following these changes. We examined the distribution and frequency appearance of the eleven most common coral species at four sites in the Gulf of Eilat. We compared deep learning with conventional census methods. The methods used in this research were natural sampling units via photographing the coral reef, line transects for estimating the cover percentage at the four test sites and deep convolutional neural networks, which proved to be an efficient sparse classification for coral species using the supervised deep learning method. The main research goal was to identify the common coral species at four test sites in the Gulf of Eilat, using DL to detect differences in coral cover and species composition among the sites, and relate these to ecological characteristics, such as depth and anthropogenic disturbance. The use of this method will produce a vital database to follow changes over time in coral reefs, identify trend lines and recommend remediation measures accordingly. We outline future monitoring needs and the corresponding system developments required to meet these.

TEKNIK NATURAL SAMPLING PWM INVERTER TIGA LEVEL

Tulisan ini menampilkan bahasan tentang teknik natural sampling PWM untuk inverter diode clamped tiga-fasa. Teknik natural sampling PWM dengan komposisi sinyal carrier berbeda yaitu disposisi sinyal carrier (level-shifted) dan disposisi sinyal carrier dengan pergeseran fasa (phase-shifted) masing - masing untuk sinyal referensi sinusoidal dan sinyal referensi sinusoidal yang diinjeksi dengan gelombang harmonisa ketiga dibahas dan dilihat pengaruhnya terhadap unjuk kerja inverter diode clamped tiga-level tiga-fasa ditinjau dari sisi harmonisa gelombang atau riak arus keluarannya melalui simulasi. Hasil simulasi menunjukkan bahwa teknik natural sampling PWM level-shifted menghasilkan unjuk kerja inverter yang lebih baik jika dibandingkan dengan teknik natural sampling PWM phase-shifted.

An Approach to Natural Sampling Using a Digital Sampling Technique for SPWM Multilevel Inverter Modulation

This paper introduces an approach that applies a digital sampling technique for a sinusoidal pulse width modulation (SPWM) multilevel inverter modulation that reduces the total harmonic contents in the output voltage compared to that of classical regular sampling techniques. This new modulation emulates with a high degree of fidelity a natural sampling pulse width modulation (PWM). The theoretical analysis of this new digital technique compared with natural sampling has been validated by simulations and through experiments with a built prototype that performed five–level inverter modulations with vertically displaced carriers in phase disposition. Both simulation and experimental results generate a SPWM output voltage with higher fidelity than classic regular sampling techniques, allowing a reduction of the filtering demands on the inverter output, which in turn can decrease the converter size and its manufacturing costs. As the presented technique is digital, the resulting modulation is more robust against switching noise, jitter, and other system perturbations and the modulation parameters can be changed easily, even in an automated way. For this reason, the modulation introduced here can be a useful tool to perform spectral analysis for different multilevel modulations and systems.