Breaking Bread: the Functions of Social Eating

Communal eating, whether in feasts or everyday meals with family or friends, is a human universal, yet it has attracted surprisingly little evolutionary attention. I use data from a UK national stratified survey to test the hypothesis that eating with others provides both social and individual benefits. I show that those who eat socially more often feel happier and are more satisfied with life, are more trusting of others, are more engaged with their local communities, and have more friends they can depend on for support. Evening meals that result in respondents feeling closer to those with whom they eat involve more people, more laughter and reminiscing, as well as alcohol. A path analysis suggests that the causal direction runs from eating together to bondedness rather than the other way around. I suggest that social eating may have evolved as a mechanism for facilitating social bonding

A novel combined DTC method and SFOC system for three-phase induction machine drives with PWM switching method

In this paper, a novel combined Direct Torque Control (DTC) method and Stator-Flux Oriented Control (SFOC) system to increase general performances of Three-Phase Induction Motor (TPIM) drives is proposed. The introduced control scheme includes merits of DTC for instance simple structure, less dependent on PI controller coefficients, fast dynamics, and merits of SFOC such as high precision and constant switching frequency. Specifically, the proposed control scheme includes a table-based variable structure developed on DTC strategy and a PI controller in connection with a Pulse Width Modulation (PWM) algorithm based on SFOC strategy. To confirm the usefulness of the introduced controller, simulation studies are accomplished for a 2.5kW TPIM in different situations. Results under the presented control system approve the good performances of this technique in comparison with classic DTC and classic SFOC. Investigation in TPIM performances under the introduced control system indicates relatively quick dynamic responses with low torque and stator flux ripples

Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa

Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizingtheir binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noiseso that collection of more photons should allow its detection sensitivity to biomolecules of arbitrarily low mass. However, anumber of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit iniSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes themass sensitivity limit by a factor of four to below 10 kDa. We implement this scheme both with a user-defined feature matrixand a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internalreflection mode. Our work opens the door to the optical detection of small traces of disease markers such as alpha-synuclein,chemokines, and cytokines

Corrigendum to “Using GLUE to pull apart the provenance of atmospheric dust” [Aeolian Res. 37 (2019) 1–13] (Aeolian Research (2019) 37 (1–13), (S1875963718301435), (10.1016/j.aeolia.2018.12.001))

The authors regret . The authors would like to apologise for any inconvenience caused

Optimized analysis for sensitive detection and analysis of single proteins via interferometric scattering microscopy

It has been shown that interferometric detection of Rayleigh scattering (iSCAT) can reach an exquisite sensitivity for label-free detection of nano-matter, down to single proteins. The sensitivity of iSCAT detection is intrinsically limited by shot noise, which can be indefinitely improved by employing higher illumination power or longer integration times. In practice, however, a large speckle-like background and technical issues in the experimental setup limit the attainable signal-to-noise ratio. Strategies and algorithms in data analysis are, thus, crucial for extracting quantitative results from weak signals, e.g. regarding the mass (size) of the detected nano-objects or their positions. In this article, we elaborate on some algorithms for processing iSCAT data and identify some key technical as well as conceptual issues that have to be considered when recording and interpreting the data. The discussed methods and analyses are made available in the extensive python-based platform, PiSCAT

Children addiction treatment and rehabilitation residential centers in Iran: report of a pilot study

Background: Until recently, there was no center specialized for the treatment and rehabilitation of children with substance use disorder in Iran. However, recently, a new initiation in the form of Children Addiction Treatment and Rehabilitation Residential Centers (CATRC) has been piloted in Iran. This brief report is an early evaluation of the performance of CATRC in the treatment and rehabilitation of children with drug use/use disorder. Methods: The evaluation on pilot project was done in two CATRCs established in the city of Zahedan. Subjects were 107 children between the ages of 0 and 18 with a history of drug use or drug use disorder sent to CATRCs to fulfill a judicial decision or by the Welfare Organization. Results: Out of 88 children discharged from the two CATRCs during the 1.5 years since their establishment, there were no relapses or criminal activity in 97.7 of children. Conclusion: this study showed early evidence of the positive performance of CATRCs in Iran. © 2019, © 2019 Taylor & Francis Group, LLC

Investigating the Role of Environmental Factors on the Survival, Stability, and Transmission of SARS-CoV-2, and Their Contribution to COVID-19 Outbreak: A Review

Studies conducted in the last four years show conflicting findings on the role of the environment in the survival, stability, and transmission of SARS-CoV-2. Based on the current evidence, the factors that affect the severity of COVID-19 include host interaction, environmental stability, virus volume, stability, transmission, social interactions, and restriction measures. Moreover, the persistence of the virus depends on different environmental conditions, videlicet temperature, humidity, pH, salinity, and solar radiation. The outbreak of respiratory viruses is related mainly to temperature and humidity, and geographical locations (latitude). In SARS-CoV-2, mainly temperature and humidity seem to play a fundamental role. Moreover, studies have indicated that social health factors such as equitable health systems, hygiene, and underlying diseases have played a pivotal role in the incidence and outbreak of COVID-19. Therefore, addressing health issues associated with reducing SARS-CoV-2 outbreaks plays an essential role in global health. In contrast, the environmental stimuli of the COVID-19 outbreak are mainly unknown. Given the ongoing threat of the COVID-19 pandemic, it is important to understand the stimuli to respond quickly to emerging SARS-CoV-2 variants while implementing long-term and sustainable control strategies. This review discusses the role of environmental factors and health conditions in the outbreak of SARS-CoV-2