Non-equilibrium behaviour in coacervate-based protocells under electric-field-induced excitation

Although numerous strategies are now available to generate rudimentary forms of synthetic cell-like entities, minimal progress has been made in the sustained excitation of artificial protocells under non-equilibrium conditions. Here we demonstrate that the electric field energization of coacervate microdroplets comprising polylysine and short single strands of DNA generates membrane-free protocells with complex, dynamical behaviours. By confining the droplets within a microfluidic channel and applying a range of electric field strengths, we produce protocells that exhibit repetitive cycles of vacuolarization, dynamical fluctuations in size and shape, chaotic growth and fusion, spontaneous ejection and sequestration of matter, directional capture of solute molecules, and pulsed enhancement of enzyme cascade reactions. Our results highlight new opportunities for the study of non-equilibrium phenomena in synthetic protocells, provide a strategy for inducing complex behaviour in electrostatically assembled soft matter microsystems and illustrate how dynamical properties can be activated and sustained in microcompartmentalized media.</p

Central 300 PC of the galaxy probed by the infrared spectra of H3+ and CO: I. Predominance of warm and diffuse gas and high H2 ionization rate

A low-resolution 2.0-2.5 $\mu$m survey of $\sim$500 very red point-like objects in the Central Molecular Zone (CMZ) of our Galaxy, initiated in 2008, has revealed many new bright objects with featureless spectra that are suitable for high resolution absorption spectroscopy of H$_3^+$ and CO.\footnote{Geballe, T. R., Oka, T., Lambridges, E., Yeh, S. C. C., Schlegelmilch, B., Goto, M., Westrick, C. W., WI07 at the 70th ISMS, Urbana, IL, USA,2015} We now have altogether 48 objects mostly close to the Galactic plane located from 142 pc to the west of Sgr A to 120 pc east allowing us to probe dense and diffuse gas by H$_3^+$ and dense gas by CO. Our observations demonstrate that the warm ($\sim$250 K) and diffuse ($\leq$100 cm$^{-3}$) gas with a large column length ($\geq$30 pc) initially observed toward the brightest star in the CMZ, GCS3-2 of the Quintuplet Cluster,\footnote{Oka, T., Geballe, T. R., Goto, M., Usuda, T., McCall, B. J. 2005, ApJ, 632, 882} exists throughout the CMZ with the surface filling factor of $\sim$ 100\% dominating the region. The column densities of CO in the CMZ are found to be much less than those in the three foreground spiral arms except in the directions of Sgr B and Sgr E complexes and indicate that the volume filling factor of dense clouds of 10\% previously estimated is a gross overestimate for the front half of the CMZ. Nevertheless the predominance of the newly found diffuse molecular gas makes the term "Central Molecular Zone" even more appropriate. The ultra-hot X-rays emitting plasma which some thought to dominate the region must be non existent except near the stars and SNRs. Recently the H$_2$ fraction $\mathit{f}$(H$_2$) in diffuse gas of the CMZ has been reported to be $\sim$0.6\footnote{Le Petit, F., Ruaud, M., Bron, E., Godard, B., Roueff, E., Languignon, D., Le Bourlot, J. 2016, A\&A, 585, A105}. If we use this value, the cosmic ray H$_2$ ionization rate $\mathit{\zeta}$ of a few times 10$^{-15}$ s$^{-1}$ reported earlier$^b$ on the assumption of $\mathit{f}$(H$_2$)=1 needs to be increased by a factor of $\sim$3 since the value is approximately inversely proportional to $\mathit{f}$(H$_2$)$^2$

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia

Research on sleep disorders and related risk factors among healthcare workers from Fujian province supporting Hubei province during the COVID-19 pandemic

ObjectiveTo explore the impact of COVID-19 on the sleep of healthcare workers from Fujian Province supporting Hubei Province and its related risk factors.MethodsA cross-sectional, anonymous, self-reported online questionnaire survey was conducted among all participants. The questionnaire consisted of five parts: sociodemographic characteristics and COVID-19 epidemic-related factors, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Morningness-Eveningness Questionnaire-5 (MEQ-5), and 12-item General Health Questionnaire (GHQ-12).ResultsAmong 552 participants, 203 (36.8%) had a PSQI score &gt; 7, indicating the presence of sleep disorders. Logistic regression analysis revealed that sleep disorders were independently associated with a history of previously diagnosed sleep disorders (OR 6.074, 95% CI 2.626–14.049, P &lt; 0.001), rotating night shifts &gt; 3 times per week (OR 3.089, 95% CI 1.650–5.781, P &lt; 0.001), using electronic devices before sleep &gt;1 h (OR 1.685, 95% CI 1.131–2.511, P = 0.010), concern about contracting COVID-19 (OR 1.116, 95% CI 1.034–1.204, P = 0.005), perception of societal support for supporting healthcare workers in Hubei (OR 0.861,95% CI 0.744–0.998, P = 0.047) (OR 0.861, 95% CI 0.744–0.998, P = 0.047), non-medical staff (OR 0.257, 95% CI 0.067–0.987, P = 0.048), ESS score (OR 1.068, 95% CI 1.018–1.121, P = 0.007), and GHQ-12 score (OR 1.511, 95% CI 1.281–1.782, P &lt; 0.001).ConclusionSleep disorders were highly prevalent among healthcare workers from Fujian Province supporting Hubei Province during the COVID-19 pandemic. Risk factors for sleep disorders included a history of previously diagnosed sleep disorders, rotating night shifts &gt; 3 times per week, using electronic devices before sleep &gt;1 h, excessive concern about contracting COVID-19, and poorer psychological health. Higher perceived societal support and understanding of support for healthcare workers supporting Hubei were associated with a reduced risk of sleep disorders, as was being non-medical staff. Providing more sleep hygiene education and psychological health services for frontline healthcare workers is necessary

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.Comment: WWW 202

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: a perspective from long‐term data assimilation

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback

Indentation of fibre-metal sandwich panels

This report discusses the static indentation response of fibre-metal honeycomb sandwich panels through the study of load-displacement curve, failure modes occurred as well as effects of facesheet composition and core thickness. 10cm x 10cm sandwich panels with four facesheet types and two core thicknesses are designed and fabricated. The four types of facesheet have variation in Metal Volume Fraction (MVF) from 0 to 1 through combinations of aluminium sheet and Glass Fibre Reinforced Polymer (GFRP) prepreg. The two types of core thicknesses used in the project are 15mm and 25mm. Fabrication process of honeycomb composite sandwich panels from design, hand lay-up construction to autoclave curing is presented before experimental setup and procedure are introduced. Failure mode occurring sequences is then investigated by close examination of the indentation location of specimens at various stages of the indentation tests. In the next chapter, effects of facesheet MVF and core thickness are discussed from the experimental data. It is found that facesheet MVF mainly affect the peak loads and the energy absorption ability, A higher facesheet MVF generally leads to higher peak load and better energy absorption ability. It is also suggested that displacement required to reach second peak load and energy absorption ability are major factors dominated by core thickness. However, core thickness only improves mass specific energy for sandwich panels with very low MVF.Bachelor of Engineering (Aerospace Engineering

NONLINEAR OPTICAL AND EXCITONIC PROPERTIES OF TWO-DIMENSIONAL TRANSITION METAL DICHALCOGENIDES AND ANTIFERROMAGNETIC MATERIALS

Ph.DDOCTOR OF PHILOSOPHY (FOS

Design of Monitoring System for Rural Drinking Water Source Based on WSN

In order to solve the existing traditional rural drinking water monitoring in a lot of manpower, material resources, real-time, this paper introduces a WSN based on the rural drinking water source monitoring system design, the system consists of five parts: water quality monitoring, soil monitoring node node, node, routing node and gateway server. Water quality monitoring node, soil monitoring nodes send the collected data to the gateway node through the wireless module sent directly, or through the routing gateway node to the gateway node, each node of the data collection, unified by the GPRS module to upload server. The system can periodically detect the water quality and the important indicators of the soil in the rural water sources, and combine the water pollution with the soil non-point source pollution to realize on-line monitoring and provide guidance for pollution control. Network test shows that the designed system can realize data acquisition and remote transmission, stability, range of dissolved oxygen system for 1.09%~1.86% acquisition error, pH error is in the range of 0.64%~1.68%, Cu concentration in the range of error is 1.98%~2.22%, Cu concentration in the range of error is 1.58%~ 2.01%