Cloud Chamber Research Articles

A synthetic coolant (WS-23) in disposable electronic cigarettes impairs cytoskeletal function in EpiAirway microtissues exposed at the air liquid interface

The design of popular disposable electronic cigarettes (ECs) was analyzed, and the concentrations of WS-23, a synthetic coolant, in EC fluids were determined for 22 devices from 4 different brands. All products contained WS-23 in concentrations that ranged from 1.0 to 40.1 mg/mL (mean = 21.4 ± 9.2 mg/mL). To determine the effects of WS-23 on human bronchial epithelium in isolation of other chemicals, we exposed EpiAirway 3-D microtissues to WS-23 at the air liquid interface (ALI) using a cloud chamber that generated aerosols without heating. Proteomics analysis of exposed tissues revealed that the cytoskeleton was a major target of WS-23. BEAS-2B cells were exposed to WS-23 in submerged culture to validate the main results from proteomics. F-actin, which was visualized with phalloidin, decreased concentration dependently in WS-23 treated BEAS-2B cells, and cells became immotile in concentrations above 1.5 mg/mL. Gap closure, which depends on both cell proliferation and migration, was inhibited by 0.45 mg/mL of WS-23. These data show that WS-23 is being added to popular EC fluids at concentrations that can impair processes dependent on the actin cytoskeleton and disturb homeostasis of the bronchial epithelium. The unregulated use of WS-23 in EC products may harm human health.

In-situ charge reduction of electrospray plumes by unipolar corona ions

Obtaining uniformly-sized microparticles by electrospray drying poses challenges due to the high electric charge of the droplets, which often leads to Coulombic fissions, short lifetimes, and charge buildup on the collection. In this study, we have conducted numerical simulations to investigate the use of unipolar ions for achieving extensive droplet discharging in electrosprays, aiming to prevent Coulombic fissions and attain high efficiency in extracting the electrically discharged aerosol. Our mathematical model incorporates Lagrangian tracking of the droplets, their evaporation till forming a solid residue, Coulomb fission, Eulerian descriptions of ion and progeny droplet transports, and droplet discharge kinetics through ion capture. We have identified an effective system configuration comprising coaxially a capillary electrode supporting the Taylor cone, a nearby ring electrode, and an Earth-grounded plate with a central orifice emitting ions. The electrode potentials are selected to sustain the Taylor cone. Air flows coaxially with the electrospray, passing through the ring hole, and exiting the system through a circular slit in the plate. We have simulated two electrospray systems with identical droplet size distributions but different volatilities of the solvents (acetone and methanol). In these systems, the ion cloud rapidly expands from the plate orifice, driven by strong electrical self-repulsion, subsequently flooding the entire spray while traveling towards the ring. The droplets are electrostatically repelled from the ring and are encouraged by the airflow to pass through the ring hole. Most of the droplets are discharged downstream of the ring, before they undergo their first Coulomb fission. Importantly, when the ring is thin and short, it attracts the majority of excess ions. The remaining ions pass through the ring towards the Taylor cone, but are intercepted by the spray, thus preserving the cone stability. Consequently, our simulations demonstrate that the configuration with axial symmetry comprising a ring and an orifice plate between the spraying and corona tips is a robust strategy to obtain stable and efficient in-situ discharging and extraction of electrospray plumes.

Enhanced website phishing detection based on the cyber kill chain and cloud computing

<span>Crimeware-as-a-service (CaaS) presents a growing cybersecurity threat by facilitating the acquisition of tools for website phishing attacks. Detecting these attacks requires effective techniques to obtain accurate results in real time. Cloud machine learning (CML) emerged as a promising solution with the powerful tools of amazon web services (AWS). This study proposes a novel approach combining cyber kill chain concept with AWS technologies to enhance website phishing detection, using AWS SageMaker to preprocess an 11,430 uniform resource locators (URL) dataset and train 3 algorithms, which are: decision tree (DT), random forest (RF), and support vector machine (SVM), evaluate their performance through batch transform, and deploy them as separate endpoints. Prediction functions are then conducted on each endpoint and compared to batch transform results. Our findings demonstrate that the combination of the cyber kill chain concept and AWS CML significantly enhances website phishing detection by achieving results of 97% for RF in 0.48 seconds, 94% for SVM in 0.94 seconds, and 93% for DT in 0.52 seconds. By leveraging CML algorithms and breaking down attacks into stages, our approach identifies and disrupts attacks earlier, preventing damage. This research highlights the value of our approach in improving cybersecurity and protecting against website phishing attacks.</span>

Fuzzy correlational analysis for dynamic consolidation of virtual machines in cloud computing environment

This theoretical research in fuzzy correlation analysis integrates data uncertainty analysis by measuring the strength of the linear relationship restricted to two fuzzy sets. As the relevant contribution to this research area, this paper presents the axiomatic definition of the n-dimensional generalized fuzzy correlation coefficient (nGCC), assigning to n input fuzzy sets an output value in the interval [−1,1]. Thus, the properties of general overlap functions and fuzzy negations are studied, discussing binary non-normed restricted dissimilarity functions, and the n-dimensional non-normed conjunctive functions. This study provides new methods for the n-dimensional generalized fuzzy correlation coefficient analysis, regarding their applications in solving multi-criteria and decision-making problems founded on fuzzy logic extensions. The n-dimensional non-normed conjunctive aggregation functions are also introduced, as range domain extensions from [0,1] to [−1.1], covering the interpretation of negative to positive variable associations. The proposal correlation analysis promotes a better evaluation and data selection even when more than one algorithm is applied to evaluate the reduction methods in the defuzzification process based on Interval-valued Fuzzy Logic. We also investigate the relevance to determine a reliable result from the fuzzy inference system based on the nGCC methodology. This correlation methodology applied to the Interval Fuzzy Load Balancing for Cloud Computing (Int-FLBCC) model contributes as a flexible approach for virtual machines dynamic consolidation enabling improvements in resource usage and power efficiency, improving the computational system’s energy efficiency. So, the nGCC methodology extends the Int-FLBCC model by adding other degrees of reliability to the results obtained with diverse evaluations through n-dimensional generalized fuzzy correlation coefficient expressions, exploring average aggregations as arithmetic and exponential means and the median operator.

Nonlinear evolution of space synthetic electric field in positive DC corona mode transition

Positive corona discharge in the air exists under several distinctive forms depending on DC voltage amplitude, including pulses or pulseless glow. Previous studies obtained the current and optical image characteristics of corona discharge through experiments. However, the synthesized electric field characteristics of the different corona modes have not been deeply studied by experiments, and lack of sufficient understanding of the effect of space charges. In this paper, electro-optical modulation technology is used to investigate the characteristics of the space synthetic electric field (SSE) for positive DC corona modes transition. The experimental results show the significant nonlinear evolution characteristics of the SSE and discharge modes transition with the increase of applied voltage. In the pulsed mode, the SSE first changes slowly and then increases sharply. As the applied voltage increases, the discharge mode transition from pulse mode to pulseless glow mode, while the SSE changes slowly. The electric field change rate in the sharp increase is 2–3 times that in the slow change. Dynamic accumulation behavior of space charges is the main reason for the nonlinear evolution of the SSE. When the pulse current starts, the discharge amplitude and frequency are low, and the ions are far away from the ionization region, which has little effect on the migration process of the high field strength region and subsequent ions. With the increase of the applied voltage, the continuous accumulation of positive ions enhances the migration of subsequent positive ions, thereby rapidly increasing the SSE. During the glow discharge mode, the generation and dissipation of space charges are close to dynamic equilibrium, and the fusion of multiple positive ion clouds will increase the SSE.

Enhancements in Cloud Condensation Nuclei Activity From Turbulent Fluctuations in Supersaturation

AbstractThe effect of aerosols on the properties of clouds is a large source of uncertainty in predictions of weather and climate. These aerosol‐cloud interactions depend critically on the ability of aerosol particles to form cloud droplets. A challenge in modeling aerosol‐cloud interactions is the representation of interactions between turbulence and cloud microphysics. Turbulent mixing leads to small‐scale fluctuations in water vapor and temperature that are unresolved in large‐scale atmospheric models. To quantify the impact of turbulent fluctuations on cloud condensation nuclei (CCN) activation, we used a high‐resolution Large Eddy Simulation of a convective cloud chamber to drive particle‐based cloud microphysics simulations. We show small‐scale fluctuations strongly impact CCN activity. Once activated, the relatively long timescales of evaporation compared to fluctuations causes droplets to persist in subsaturated regions, which further increases droplet concentrations.

Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an Improved Coati Optimization Algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multi-objective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.

Lagrangian and Eulerian Supersaturation Statistics in Turbulent Cloudy Rayleigh–Bénard Convection: Applications for LES Subgrid Modeling

Abstract Turbulent fluctuations of scalar and velocity fields are critical for cloud microphysical processes, e.g., droplet activation and size distribution evolution, and can therefore influence cloud radiative forcing and precipitation formation. Lagrangian and Eulerian water vapor, temperature, and supersaturation statistics are investigated in direct numerical simulations (DNS) of turbulent Rayleigh–Bénard convection in the Pi Convection Cloud Chamber to provide a foundation for parameterizing subgrid-scale fluctuations in atmospheric models. A subgrid model for water vapor and temperature variances and covariance and supersaturation variance is proposed, valid for both clear and cloudy conditions. Evaluation of phase change contributions through an a priori test using DNS data shows good performance of the model. Supersaturation is a nonlinear function of temperature and water vapor, and relative external fluxes of water vapor and heat (e.g., during entrainment-mixing and phase change) influence turbulent supersaturation fluctuations. Although supersaturation has autocorrelation and structure functions similar to the independent scalars (temperature and water vapor), the autocorrelation time scale of supersaturation differs. Relative scalar fluxes in DNS without cloud make supersaturation PDFs less skewed than the adiabatic case, where they are highly negatively skewed. However, droplet condensation changes the PDF shape response: it becomes positively skewed for the adiabatic case and negatively skewed when the sidewall relative fluxes are large. Condensation also increases correlations between water vapor and temperature in the presence of relative scalar fluxes but decreases correlations for the adiabatic case. These changes in correlation suppress supersaturation variability for the nonadiabatic cases and increase it for the adiabatic case. Implications of this work for subgrid microphysics modeling using a Lagrangian stochastic scheme are also discussed.

Using and Testing Camera Sensors with Different Devices at Cosmic Ray Detection

Cosmic Ray (CR) is high-energy charged particles that reach the earth from space. CR detection methods and studies have been progressing rapidly since the beginning of the 20th century. One of these methods is the use of digital cameras with Charge Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) sensors. Mobile phone cameras or webcams offer an easily using and economical measurement system for CR measurement. The sensors are exposed to CR during a long exposure. CRs leave traces in the background. Cosmic particle tracks are then separated from the background noise and can be classified. Making the traces of the particles visible is important for understanding the subject. In this context, traces of particles such as electrons, muons, and alphas can be seen with the cloud chamber experiments. Help of sensor technology and cameras have developed in recent years, CR traces can be easily detected so that it can be seen. There are many software and international projects that detect CR using CMOS sensors in cell phone cameras. In this study, related projects, programs and studies were researched; CR traces that we captured with the help of Cosmic-Ray Extremely Distributed Observatory (CREDO) and Cosmic Ray Finder (CRF) software with web cam and a mobile phone cam CMOS sensor are presented. Links have been made about astrophysical events coinciding with previously detected particle images.

Development and characterization of the Portable Ice Nucleation Chamber 2 (PINCii)

Abstract. The Portable Ice Nucleation Chamber 2 (PINCii) is a newly developed continuous flow diffusion chamber (CFDC) for measuring ice nucleating particles (INPs). PINCii is a vertically oriented parallel-plate CFDC that has been engineered to improve upon the limitations of previous generations of CFDCs. This work presents a detailed description of the PINCii instrument and the upgrades that make it unique compared with other operational CFDCs. The PINCii design offers several possibilities for improved INP measurements. Notably, a specific icing procedure results in low background particle counts, which demonstrates the potential for PINCii to measure INPs at low concentrations (<10 L−1). High-spatial-resolution wall-temperature mapping enables the identification of temperature inhomogeneities on the chamber walls. This feature is used to introduce and discuss a new method for analyzing CFDC data based on the most extreme lamina conditions present within the chamber, which represent conditions most likely to trigger ice nucleation. A temperature gradient can be maintained throughout the evaporation section in addition to the main chamber, which enables PINCii to be used to study droplet activation processes or to extend ice crystal growth. A series of both liquid droplet activation and ice nucleation experiments were conducted at temperature and saturation conditions that span the spectrum of PINCii's operational conditions (-50≤ temperature ≤-15 ∘C and 100 ≤ relative humidity with respect to ice ≤160 %) to demonstrate the instrument's capabilities. In addition, typical sources of uncertainty in CFDCs, including particle background, particle loss, and variations in aerosol lamina temperature and relative humidity, are quantified and discussed for PINCii.

A novel dual-nozzle ejector for enhancement of hydrogen recirculation applied to proton exchange membrane fuel cell system

The ejector enables hydrogen recycling, offering advantages of non-parasitic power and low noise. However, traditional ejector falls short in operating the proton exchange membrane fuel cell(PEMFC) system across various operating points efficiently. To overcome this limitation, a novel dual-nozzle ejector is proposed for optimal hydrogen recirculation at different power. The computational fluid dynamics(CFD) method is utilized to simulate and verified by experimental results. Firstly, nozzle structure is optimized to address unstable flow phenomena such as vortex generated at nozzle outlet. Subsequently, analysis of flow field is conducted by different structure under idle and full-load conditions. Results show that initial optimization resulted in an 18.8% increase of recirculation ratio under idle condition. Overall performance of constant-section mixing chamber diameter (Dm) of 6.5 is optimal within the output current range from 31.1A to 559.8A. Under idle condition, highest recirculation ratio is achieved when constant-section mixing chamber length is 4Dm, nozzle exit position is at 1.1Dm, diffuser chamber angle is 3°. While under full-load condition, the energy loss caused by wall flow resistance is more significant. By exploring the mechanism of different structure changes under two conditions, particularly under idle condition, this study provides a reference for optimization of ejectors in the future.

Investigation of Facilitating English Speaking of Elementary School Students in Authentic Contexts with UEnglish

Despite the importance of English in various aspects of life, students learning English as a foreign language often lack sufficient opportunities to practice speaking in their daily lives. To address these issues, this study proposes an innovative mobile app called Ubiquitous English (UEnglish) that facilitates English speaking learning in authentic contexts anytime and anywhere. Leveraging advanced technologies, UEnglish incorporates automatic speech recognition (ASR), cloud machine translation (MT), and text-to-speech (TTS) functionalities. The primary aim of this study is to assess the impact of UEnglish on students’ English speaking ability, specifically focusing on the quality and quantity of their spoken English when describing comic pictures and authentic pictures. To investigate the learning behaviors and the influence on English speaking learning achievement in this study, 56 fifth-grade students were employed, including 28 students in an experimental group (EG) using UEnglish and 28 students in a control group (CG) with a paper-based teaching method. The results indicated that, after the experiment, EG outperformed CG and EG had significant differences in the quality and quantity of English speaking in the context of describing comic pictures and authentic pictures. Students’ learning portfolios revealed that EG students not only could use more English words and more complex sentences to express their thoughts but also easily applied what they learned when encountering similar contexts. Our findings suggested that authentic contextual English learning with UEnglish was helpful for students to practice speaking English related to their surroundings.

Adsorption and permeability of heavy metals (Fe, Cu, Pb, Zn, Cr, and Cd) onto the adaxial cuticle of Ficus elastica leaf

Air pollution represents a serious worldwide problem as atmospheric deposition of pollutants widely affects human and wildlife health. Heavy metals are significant pollutants with high toxicity, persistence, and bioaccumulation. In an environment polluted by industry and transportation, deposits of heavy metals on the leaves and their subsequent uptake by the plant occur. The leaf cuticle is the most important barrier between plant tissue and the atmosphere. To understand the processes of adsorption of heavy metals and their diffusion into the leaf, adaxial cuticles of Ficus elastica were enzymatically isolated and used in batch adsorption experiments and permeability measurements using diffusion chambers. A demonstration experiment with a whole leaf was also carried out, which pointed to the real uptake of metals by the leaf. Cuticles were subjected to characterization by methods of elemental analysis, Fourier transform infrared spectroscopy, and scanning electron microscopy. In the adsorption experiment, significant differences in the maximum adsorption capacity (Cd>Pb>Cr>Fe>Cu>Zn), and mechanism of adsorption (Langmuir model for Fe and Cr, Freundlich model for Zn, Cu, Pb, and Cd) were found. The permeability of intact adaxial cuticle for studied heavy metals did not differ significantly. The extent of foliar uptake differed between metals, with the relatively highest uptake of Fe and Zn. The transport of metals in the leaf was also observed. It seems that adsorption to the leaf surface more than the permeability of the cuticle is a limiting factor for foliar uptake of heavy metals from atmospheric deposits.

Designing Cloud Chamber for Simulate the Microphysics Processes in the Formation of Ice Crystals

The simulate microphysical processes in the atmosphere, a cloud chamber, which behaves as the atmosphere, has been designed and fabricated in order to be used to execute several atmospheric experiments. These experiments allow a better understanding of clear visual cloud formation. The executed temperature and saturated pressure experiments clarify the basic mechanisms of the ice crystals nucleation, which in contrast, represent the core of the cloud microphysics. The cloud chamber can represent an isolated environment that deals with a certain volume of pure air and contains certain nucleation particles or ice nuclei. The temperature range in such a chamber is similar to that found at the mid-latitudes between the surface of the earth and the top of the troposphere. Thus, it is possible to simulate clouds of the type of Cirrostratus at the bottom of the chamber. It has been designed with dimensions of 22×22×59 cm3 and made of 3mm in thickness copper with a purity of 99.9%, and thermal conductivity of 401 W/m.°K.

Measurement of the collision rate coefficients between atmospheric ions and multiply charged aerosol particles in the CERN CLOUD chamber

Abstract. Aerosol particles have an important role in Earth's radiation balance and climate, both directly and indirectly through aerosol–cloud interactions. Most aerosol particles in the atmosphere are weakly charged, affecting both their collision rates with ions and neutral molecules, as well as the rates by which they are scavenged by other aerosol particles and cloud droplets. The rate coefficients between ions and aerosol particles are important since they determine the growth rates and lifetimes of ions and charged aerosol particles, and so they may influence cloud microphysics, dynamics, and aerosol processing. However, despite their importance, very few experimental measurements exist of charged aerosol collision rates under atmospheric conditions, where galactic cosmic rays in the lower troposphere give rise to ion pair concentrations of around 1000 cm−3. Here we present measurements in the CERN CLOUD chamber of the rate coefficients between ions and small (<10 nm) aerosol particles containing up to 9 elementary charges, e. We find the rate coefficient of a singly charged ion with an oppositely charged particle increases from 2.0 (0.4–4.4) × 10−6 cm3 s−1 to 30.6 (24.9–45.1) × 10−6 cm3 s−1 for particles with charges of 1 to 9 e, respectively, where the parentheses indicate the ±1σ uncertainty interval. Our measurements are compatible with theoretical predictions and show excellent agreement with the model of Gatti and Kortshagen (2008).

Re-evaluating cloud chamber constraints on depositional ice growth in cirrus clouds – Part 1: Model description and sensitivity tests

Abstract. Ice growth from vapor deposition is an important process for the evolution of cirrus clouds, but the physics of depositional ice growth at the low temperatures (<235 K) characteristic of the upper troposphere and lower stratosphere is not well understood. Surface attachment kinetics, generally parameterized as a deposition coefficient αD, control ice crystal habit and also may limit growth rates in certain cases, but significant discrepancies between experimental measurements have not been satisfactorily explained. Experiments on single ice crystals have previously indicated the deposition coefficient is a function of temperature and supersaturation, consistent with growth mechanisms controlled by the crystal's surface characteristics. Here we use observations from cloud chamber experiments in the Aerosol Interactions and Dynamics in the Atmosphere (AIDA) aerosol and cloud chamber to evaluate surface kinetic models in realistic cirrus conditions. These experiments have rapidly changing temperature, pressure, and ice supersaturation such that depositional ice growth may evolve from diffusion limited to surface kinetics limited over the course of a single experiment. In Part 1, we describe the adaptation of a Lagrangian parcel model with the Diffusion Surface Kinetics Ice Crystal Evolution (DiSKICE) model (Zhang and Harrington, 2014) to the AIDA chamber experiments. We compare the observed ice water content and saturation ratios to that derived under varying assumptions for ice surface growth mechanisms for experiments simulating ice clouds between 180 and 235 K and pressures between 150 and 300 hPa. We found that both heterogeneous and homogeneous nucleation experiments at higher temperatures (>205 K) could generally be modeled consistently with either a constant deposition coefficient or the DiSKICE model assuming growth on isometric crystals via abundant surface dislocations. Lower-temperature experiments showed more significant deviations from any depositional growth model, with different ice growth rates for heterogeneous and homogeneous nucleation experiments.

Corneal Permeability and Uptake of Twenty-Five Drugs: Species Comparison and Quantitative Structure-Permeability Relationships.

The purpose of this study was to determine corneal permeability and uptake in rabbit, porcine, and bovine corneas for twenty-five drugs using an N-in-1 (cassette) approach and relate these parameters to drug physicochemical properties and tissue thickness through quantitative structure permeability relationships (QSPRs). A twenty-five-drug cassette containing β-blockers, NSAIDs, and corticosteroids in solution at a micro-dose was exposed to the epithelial side of rabbit, porcine, or bovine corneas mounted in a diffusion chamber, and the corneal drug permeability and tissue uptake were monitored using an LC-MS/MS method. Data obtained were used to construct and evaluate over 46,000 quantitative structure-permeability (QSPR) models using multiple linear regression, and the best-fit models were cross-validated by Y-randomization. Drug permeability was generally higher in rabbit cornea and comparable between bovine and porcine corneas. Permeability differences between species could be explained in part by differences in corneal thickness. Corneal uptake between species correlated with a slope close to 1, indicating generally similar drug uptake per unit weight of tissue. A high correlation was observed between bovine, porcine, and rabbit corneas for permeability and between bovine and porcine corneas for uptake (R2 ≥ 0.94). MLR models indicated that drug characteristics such as lipophilicity (LogD), heteroatom ratio (HR), nitrogen ratio (NR), hydrogen bond acceptors (HBA), rotatable bonds (RB), index of refraction (IR), and tissue thickness (TT) are of great influence on drug permeability and uptake. When data for all species along with thickness as a parameter was used in MLR, the best fit equation for permeability was Log (% transport/cm2·s) = 0.441 LogD - 8.29 IR + 8.357 NR - 0.279 HBA - 3.833 TT + 10.432 (R2 = 0.826), and the best-fit equation for uptake was Log (%/g) = 0.387 LogD + 4.442 HR + 0.105 RB - 0.303 HBA - 2.235 TT + 1.422 (R2 = 0.750). Thus, it is feasible to explain corneal drug delivery in three species using a single equation.

Accumulation and Diffusion of PM from Outdoor to Indoor by Clothing under Haze Conditions: A Case Study of Zhengzhou, China

Clothing can absorb fine particulate pollutants from the atmosphere outdoors and then release them after entering the room in a form similar to three-handed smoke. In this study, the experimental site in Zhengzhou, a typical city with heavy pollution in central China, was used to investigate the adsorption and diffusion patterns of three different fabrics of polyester, polyester-cotton, and cotton clothing for fine particle pollutants, to guide people’s behavior patterns and indoor air distribution, and to reduce indoor pollution exposure. The results showed that when garments of three different fabrics were exposed to moderate and heavy PM2.5 pollution for 1 h, 2 h, and 3 h, respectively, and then transferred to the diffusion chamber, polyester always released the highest PM2.5 concentration to indoor air during the same release cycle, followed by cotton and polyester-cotton. In addition, the concentration of indoor air pollutants will be periodically affected by the diffusion of fine particulate adsorbed by clothing fabrics. With the increase in outdoor pollution and exposure duration, the indoor PM2.5 concentration takes longer to stabilize at certain levels after the clothing is transferred to the indoor side. Finally, the comparison of natural ventilation experiments proved that it is not feasible to rely solely on natural ventilation to improve the influence of clothing pollution sources on indoor air quality.

Numerical simulation on the behavior of a negative streamer encountered with a cloud of positive ions in atmospheric pressure plasma jet

Plasma jet normally operates in a regime of positive or negative streamers. Different propagating behaviors have been investigated for positive streamers. However, there are few studies on negative streamers. In this paper, a two-dimensional fluid model is used to simulate the behavior of a negative streamer encountered with a cloud of positive ions. Simulation results indicate that the negative streamer behaves like a free streamer when the ion cloud has a low charge density. After initiation near the needle tip, the streamer propagates along the axis, passes through the ion cloud, and finally reaches the anode vicinity along a straight line. When the streamer reaches the ion cloud, its head radius (R0) is investigated as a function of parameters, including the voltage amplitude and the gap width, as well as the charge density, axial position, and spatial size of the ion cloud. At a given moment, electric field strength and electron density near the streamer head increase with increasing charge density of the ion cloud. In the case of a high charge-density ion cloud, after a primary streamer propagates some distance along the axis, a positive secondary streamer, and a negative secondary streamer start to propagate from the cloud to the cathode and the anode, respectively.

Neuro-explicit semantic segmentation of the diffusion cloud chamber.

For decades, in diffusion cloud chambers, different types of subatomic particle tracks from radioactive sources or cosmic radiation had to be identified with the naked eye which limited the amount of data that could be processed. In order to allow these classical particle detectors to enter the digital era, we successfully developed a neuro-explicit artificial intelligence model that, given an image from the cloud chamber, automatically annotates most of the particle tracks visible in the image according to the type of particle or process that created it. To achieve this goal, we combined the attention U-Net neural network architecture with methods that model the shape of the detected particle tracks. Our experiments show that the model effectively detects particle tracks and that the neuro-explicit approach decreases the misclassification rate of rare particles by 73% compared with solely using the attention U-Net.