Expansion Of Cloud Research Articles

Ice-nucleating particles active below −24 °C in a Finnish boreal forest and their relationship to bioaerosols

Abstract. Cloud properties are strongly influenced by ice formation; hence, we need to understand the sources of ice-nucleating particles (INPs) around the globe. Boreal forests are known as sources of bioaerosol, and recent work indicates that these dominate the INP spectra above −24 °C. To quantify the INP population at temperatures below −24 °C, we deployed a portable cloud expansion chamber (PINE) in a Finnish boreal forest from 13 March 2018 to 11 May 2018. Using the 6 min time resolution PINE data, we present several lines of evidence that INPs below −24 °C in this location are also from biological sources: (i) an INP parameterization developed for a pine forest site in Colorado, where many INPs were shown to be biological, produced a good fit to our measurements; a moderate correlation of INPs with aerosol concentration larger than 0.5 µm and the fluorescent bioaerosol concentration; (ii) a negative correlation with relative humidity that may relate to enhanced release of bioaerosol at low humidity from local sources such as the prolific lichen population in boreal forests; and (iii) the absence of correlation with ultra-fine particles (3.5 to 50 nm), indicating that new particle formation events are not sources of INPs. This study should motivate further work to establish whether the commonality in bioaerosol ice-nucleating properties between spring in Finland and summer in Colorado is more generally applicable to different coniferous forest locations and times and also to determine to what extent these bioaerosols are transported to locations where they may affect clouds.

Hall effects and diamagnetic cavity collapse during a laser plasma cloud expands into a vacuum magnetic field

This paper describes the results of a laboratory experiment on the sub-Alfven expansion of a quasi-spherical laser plasma cloud into a vacuum magnetic field in the regime of nonmagnetized ions. The role of Hall fields and currents in the anomalously fast dynamics of the magnetic field during the collapse phase of a diamagnetic cavity is considered. Detailed spatial measurements of the azimuthal Hall fields configuration are demonstrated and their relationship to diamagnetic cavity collapse is determined. As a result of the experiment, data were obtained confirming the hypothesis about the transfer of the main magnetic field by the movement of electrons associated with Hall currents.

A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions

Cloud computing is a method of providing various computing services, including software, hardware, databases, data storage, and infrastructure, to the public through the Internet. The rapid expansion of cloud computing services has raised significant concerns over their environmental impact. Cloud computing services should be designed in a green manner, efficient in energy consumption, virtualized, consolidated, and eco-friendly. Green Cloud Computing (GCC) is a significant field of study that focuses on minimizing the environmental impact and energy usage of cloud infrastructures. This survey provides a comprehensive overview of the current state of GCC, focusing on the challenges, strategies, and future directions. The review study begins by identifying important challenges in GCC from practical implementations, identifying GCC-introduced environmental protection and prevention initiatives, and expressing the demand for long-term technical progression. It then addresses GCC’s primary concerns, such as energy efficiency, resource management, operational costs, and carbon emissions, and categorizes implementations according to algorithms, architectures, frameworks, general issues, and models and methodologies. Furthermore, enhancements in virtualization, multi-tenancy, and consolidation have been identified, analyzed, and accurately portrayed to address the advancements in GCC. Finally, the survey outlines future research directions and opportunities for advancing the field of GCC, including the development of novel algorithms, technologies for energy harvesting, and energy-efficient and eco-friendly solutions. By providing a comprehensive overview of GCC, this survey aims to serve as documentation for further evolving new emerging technological approaches in the GCC environment.

Electrostatic Modeling of Dust Impact Signals Based on the Expanding Plasma Cloud

Signals generated by dust impacting spacecraft can be detected by electric field instruments. These signals have been simulated by numerous models. However, few models can accurately characterize the expansion of the plasma cloud generated by dust impact. The COMSOL model presented in this paper provides a way to understand the expansion properties of ions and electrons. The model can also be used to analyze the various expected waveforms of dust impact signals as a function of different parameters, such as the spacecraft voltage and the ambient plasma temperature. The results show that close to 50% of ions and electrons in the impact plasma cloud are collected by spacecraft at weak spacecraft potentials and that a fraction of the ions is still collected rather than all of them streaming away from the impact location at V SC > 0 V. The model also confirms that in the expanding plasma cloud, ions are in the form of plumes, while electrons diffuse in an isotropic manner.

About regular expansion of a two-phase ball

The work is devoted to the research of an expansion into a vacuum of a ball, which is filled with a two-phase fluid. In assumptions that the dynamics passes in a regular mode, the velocities of the phases are linear functions by space coordinate and the first phase spreads into the void faster than the second phase there is obtained a solution of equations of two-phase fluid dynamics, which describes an expansion of a ball into a vacuum. This solution generalizes the known Sedov solution, which determines an expansion of a gas cloud into a vacuum, to the case of a ball filled with a gas suspension. In the last part of the work there are derived asymptotic formulas determining a relationship between the velocities of the phases and the radii of two balls.

Analysis of contaminant dispersion in constructed wetland flows with absorptive boundaries

Pollution in wetland flows poses a significant challenge to ecosystem preservation, underscoring the importance of wetland restoration. Constructed wetlands have emerged as a widely embraced solution for contaminant mitigation. In this study, we focus on the contaminant removal process within the wetland flows to contribute to wetland restoration. A constructed wetland with ecological floating and bottom absorptive beds has been employed to enhance the capacity of contaminant removal within wetland flows. However, the environmental dispersion in constructed wetland flows is further complicated by the presence of irreversible first-order absorptive boundaries at the upper and bottom beds. Therefore, our work focuses on comprehending the expansion processes of contaminant clouds under absorptive boundaries through a multiscale analysis to demonstrate the removal effects of constructed wetlands. The findings highlight significant deviations in the spatial distribution of contaminant clouds, particularly in the vertical dimension, attributed to absorptive effects. To analyze the impact of removal mechanisms on the dispersion patterns of contaminant clouds, variations in the vertical concentration peak are examined. A predictive model is developed to estimate the position of the concentration crest in the vertical direction based on changes in absorptive ratios between the upper and bottom boundaries. Furthermore, in order to explore the key factor on removal capacity and efficiency within constructed wetland flows, we look into the process of contaminant depletion by examining variations in removed mass and local absorptive and removal efficiency along the streamwise direction in response to deviations in absorptive strengths at the upper and bottom beds. The ratios of the two boundary removal intensities play a significant role in the absorptive dynamics within wetland flows, particularly during the early stages of cloud expansion. By adjusting the absorption ratio between two absorptive beds to 1:1 with a certain total depletion strength in wetland flows, the enhancement ratio of depletion efficiency can increase by nearly 30%. It has been proven that employing identical absorption strengths at two absorptive beds is an economical and efficient means to amplify the absorption efforts within designed constructed wetland flows. This study provides guidelines for the design of constructed wetlands aimed at enhancing their capacity for sewage treatment.

Combined effects of ignition position and hydrogen ratio on vented CH4/H2/air explosions

Experiments on the effects of ignition position and hydrogen ratio (χ) on the explosions of CH4/H2/air mixtures were experimentally investigated in a vented cylindrical vessel. Relevant experiments were conducted utilizing ignition sources situated at the rear (rear ignition), the center of the vessel (central ignition), or near the vent (front ignition) with χ varying from 0 to 1.0. Two types of cellular structures on the flame surface, owing to the diffusional-thermal instability and acoustically enhanced combustion, respectively, were observed, and the latter resulted in acoustic oscillations of the overpressure within the vessel and a unique overpressure peak p2 with amplitude sensitive to both ignition position and χ. The effects of ignition position and χ on the build-up of the internal pressure were not significant when χ ≤ 0.15. The maximum explosion overpressure in the vessel (pmax) under the explosions of rear and central ignitions (RI and CI) increased monotonically as χ was increased from 0.3 to 1.0, but a nonmonotonic trend was found in the explosions of front ignition (FI). CI could be regarded as the worst-case scenario when 0.45≤χ ≤ 1.0 except for χ = 0.7, because FI resulted in the highest pmax at χ = 0.7. A pressure peak outside the vent (pext) caused by the combustion expansion of the combustible cloud could be distinguished when χ ≥ 0.45 in the explosions of RI and CI. The amplitude of pext increased with an increase in χ. Rear ignition always led to the highest pext when χ > 0.6.

Corr-Track: Category-Level 6D Pose Tracking with Soft-Correspondence Matrix Estimation.

Category-level pose tracking methods can continuously track the pose of objects without requiring any prior knowledge of the specific shape of the tracked instance. This makes them advantageous in augmented reality and virtual reality applications. The key challenge is how to train neural networks to accurately predict the poses of objects they have never seen before and exhibit strong generalization performance. We propose a novel category-level 6D pose tracking method Corr-Track, which is capable of accurately tracking objects belonging to the same category from depth video streams. Our approach utilizes direct soft correspondence constraints to train a neural network, which estimates bidirectional soft correspondences between sparsely sampled point clouds of objects in two frames. We first introduce a soft correspondence matrix for pose tracking tasks and establish effective constraints through direct spatial point-to-point correspondence representations in the sparse point cloud correspondence matrix. We propose the "point cloud expansion" strategy to address the "point cloud shrinkage" problem resulting from soft correspondences. This strategy ensures that the corresponding point cloud accurately reproduces the shape of the target point cloud, leading to precise pose tracking results. We evaluated our approach on the NOCS-REAL275 and Wild6D dataset and observed superior performance compared to previous methods. Additionally, we conducted cross-category experiments that further demonstrated its generalization capability.

Hall Effects and Diamagnetic Cavity Collapse during a Laser Plasma Cloud Expansion into a Vacuum Magnetic Field

Hall Effects and Diamagnetic Cavity Collapse during a Laser Plasma Cloud Expansion into a Vacuum Magnetic Field

Equivalent notional nozzle model for high nozzle pressure ratio leakage of petrochemical high-pressure facility

Little work has been performed in the petrochemical safety field involving supersonic flows during accidental leakage in petroleum processing. The key technical challenge is on the changing process of gas cloud expansion after the jet. To address this, an equivalent notional nozzle model was developed for high single phase NPR (NPR＞2) gas leakage scenarios in petrochemical high-pressure facilities. This model aimed to determine the essential physical parameters of the equivalent cloud formed after air is drawn in with high NPRs. By utilizing CO2 as the gas phase, the validity of the equivalent notional nozzle model was assessed based on three crucial shock structure properties and the axial and radial velocity fields, the chocked flow intensity was applied as an indicator for the consistent trend of the jet velocity and decompression process of the high NPR gas leakage. The research findings unequivocally demonstrate the efficacy of the equivalent notional nozzle model in facilitating rapid numerical calculations for assessing the impact of high-pressure jets in the petrochemical industry.

MCFP: A multi-target 3D perception method with weak dependence on 2D detectors

3D perception for multi-target in complex traffic environments plays an important role in autonomous driving. Nowadays, some mainstream models use 2D detectors to provide auxiliary information for 3D perception. But it is a challenge that the accuracy of 2D detectors has a sensitive impact on the final results. In this paper, we propose MCFP, a multi-target 3D perception model with weak dependence on 2D detectors, to improve the 3D perception performance. We use the point cloud expansion as the main idea to provide more information about instance objects for model learning. Firstly, the centroid awareness strategy is integrated into the front-end part for downsampling and feature extraction to reduce the loss of object-related information. Secondly, the ball query operation with adaptive radius is designed to obtain more complete object information. Experiments show that our model outperforms baseline models in normal environments, and achieves detection performances close to those of SOTA models in extreme environments, which implies that our model has a strong ability to obtain semantic information and hence enhancing the perception ability. The code and data will be made available at https://github.com/MrSakasky/MCFP.

Characterization of a 5 mm thick CZT-Timepix3 pixel detector for energy-dispersive γ-ray and particle tracking

The present manuscript describes a comprehensive characterization of a novel highly segmented 5 mm CZT sensor attached to Timepix3. First, the sensor’s IV curve was measured and basic sensor characterization was done with laboratory γ-radiation sources. The sensor resistivity was determined to be (0.155± 0.02) GOhm · cm. The sensor showed decent homogeneity, both for the per-pixel count rate and electron mobility-lifetime product μ e τ e. The latter was measured to be = 1.3 × 10−3 cm2/V with a standard deviation σ = 0.4 × 10−3 cm2/V describing the dispersion of values for different pixels. The basic sensor characterization is complemented by measurements at grazing angle in a 120 GeV/c at the CERN’s Super Proton Synchrotron. The penetrating nature of these particles together with the pixelation of the sensor allows for a determination of the charge collection efficiency (CCE), as well as charge carrier drift properties (drift times, lateral charge cloud expansion) as a function of the interaction depths in the sensor. While CCE drops by 30%–40% towards the cathode side of the sensor, from the drift time dependency on interaction depth, the electron mobility μ e was extracted to be (944.8 ± 1.3) cm2/V/s and τ e = (1.38 ± 0.31) μs. The spectroscopic performance was assessed in photon fields and extracted from energy loss spectra measured at different angles in the pion beam. While at photon energies below 120 keV incomplete charge collection leads to an underestimation of the photon energy when irradiated from the front-side, at higher energies the relative energy resolution was found to be ∼4.5%, while a relative energy resolution of ∼7.5% was found for the particle energy loss spectra. It is shown that the drift time information can be used to reconstruct particle interactions in the sensor in 3D, providing a spatial resolution of σ xyz = 241 μm within the sensor volume and a particle trajectory measurement precision Δxyz = 100 μm, at a distance of 1 m from the sensor. We demonstrate by measurement with a 22Na source, that the energy resolution combined with the 3D reconstruction allows for detection of γ-ray source location and polarity using Compton scattering within the sensor (Compton camera and scatter polarimeter).

Observation of local density increase during pellet homogenization on EAST

By combining the X-mode polarized lower and upper cut-off reflections obtained from the density profile reflectometer, we have successfully attained a comprehensive density profile spanning from the edge to the core region in pellet injection (PI) experiments on EAST. During the homogenization process after PI on EAST, an innovative method was introduced to quantify the local density increase. This approach employed the distinctive ‘dual-reflection’ phenomenon observed in the EAST microwave reflectometer, encompassing measurements of both the X-mode lower and upper cut-off frequencies. Furthermore, experimental investigations were carried out on EAST to comprehensively explore the parallel and poloidal expansion of the high-density pellet cloud. Notably, this study marks the first instance of measuring expansion velocities of pellet materials in both parallel and poloidal directions on EAST. A comparative analysis was performed initially between these experimental measurements and simulation results obtained from the HPI2 code, marking a pivotal stride towards enhancing its applicability in EAST.

Direct creation of interacting quasi-one-dimensional Bose–Einstein condensate through fast evaporative cooling

Preparation of atomic Bose–Einstein condensate (BEC) with tunable interactions in a quasi-one-dimensional (quasi-1D) optical trap is essential for both the observation of bright matter-wave solitons and the quantum simulation based on the discrete atomic momentum states. However, the quasi-1D BEC has been obtained by a complex process, which includes the creation of a three-dimensional BEC and its adiabatic transform into a quasi-1D trap. Here, we report the direct creation of a quasi-1D BEC of 133Cs atoms by the fast evaporative cooling of ultracold atoms prepared by the degenerated Raman sideband cooling. We produce the pure BEC of up to 5.5×104 atoms in a quasi-1D optical trap with an evaporative time of 6 s. We demonstrate the anisotropic expansion of the atomic cloud after the release from the quasi-1D trap and study the dependence of both the phase space density and the temperature on the number of atoms in the trap during the evaporative cooling. Our study facilitates the promising applications of quasi-1D interacting atomic gases.

Bubble cloud characteristics and ablation efficiency in dual-frequency intrinsic threshold histotripsy

Histotripsy is a non-thermal focused ultrasound ablation method that destroys tissue through the generation and activity of acoustic cavitation bubble clouds. Intrinsic threshold histotripsy uses single-cycle pulses to generate bubble clouds when the dominant negative pressure phase exceeds an intrinsic threshold of ∼25–30 MPa. The ablation efficiency is dependent upon the size and density of bubbles within the bubble cloud. This work investigates the effects of dual-frequency pulsing schemes on the bubble cloud behavior and ablation efficiency in intrinsic threshold histotripsy. A modular 500 kHz:3 MHz histotripsy transducer treated agarose phantoms using dual-frequency histotripsy pulses with a 1:1 pressure ratio from 500 kHz and 3 MHz frequency elements and varying arrival times for the 3 MHz pulse relative to the arrival of the 500 kHz pulse (−100 ns, 0 ns, and +100 ns). High-speed optical imaging captured cavitation effects to characterize bubble cloud and individual bubble dynamics. The effects of dual-frequency pulsing on lesion formation and ablation efficiency were also investigated in red blood cell (RBC) phantoms. Results showed that the single bubble and bubble cloud size for dual-frequency cases were intermediate to published results for the component single-frequencies of 500 kHz and 3 MHz. Additionally, bubble cloud size and dynamics were shown to be altered by the arrival time of the 3 MHz pulse with respect to the 500 kHz pulse, with more uniform cloud expansion and collapse observed for early (−100 ns) arrival. Finally, RBC phantom experiments showed that dual-frequency exposures were capable of generating precise lesions with smaller areas and higher ablation efficiencies than previously published results for 500 kHz or 3 MHz. Overall, results demonstrate dual-frequency histotripsy’s ability to modulate bubble cloud size and dynamics can be leveraged to produce precise lesions at higher ablation efficiencies than previously observed for single-frequency pulsing.

금융권의 제로 트러스트 보안모델 적용방안에 관한 연구

The changed hybrid work environment such as the expansion of cloud computing technology and the increase in telecommuting due to the coronavirus infection (COVID-19), has brought about new security problems. The traditional perimeter security model has limitations in protecting various devices and environments because it is difficult to clearly distinguish network boundaries. The Zero Trust model, which has been proposed as an alternative security strategy to overcome this, is based on the premise that there is no trust in the assets and actors that are connected components when configuring corporate infrastructure and workflow. The financial sector is actively seeking to introduce the zero trust security model as a means of strengthening security. Demand for responding to advanced cyber-security threats, strengthening security policies, and applicability and necessity of a zero trust security model in the financial sector has expanded. This study proposed the components of a zero trust security model to be applied to the financial world based on financial governance and zero trust principles, components of the maturity model, and the results of expert interviews. The zero trust security model in the financial sector consists of four layers and eight detailed steps: perimeter security layer, authority definition layer, behavior detection layer, and internal control layer. This zero trust security model for the financial sector is expected to provide guidelines for key elements and construction steps to be considered when introducing zero trust in the financial sector.

Strong Dipolar Repulsion of One-Dimensional Interfacial Excitons in Monolayer Lateral Heterojunctions.

Repulsive and long-range exciton-exciton interactions are crucial for the exploration of one-dimensional (1D) correlated quantum phases in the solid state. However, the experimental realization of nanoscale confinement of a 1D dipolar exciton has thus far been limited. Here, we demonstrate atomically precise lateral heterojunctions based at transitional-metal dichalcogenides (TMDCs) as a platform for 1D dipolar excitons. The dynamics and transport of the interfacial charge transfer excitons in a type II WSe2-WS1.16Se0.84 lateral heterostructure were spatially and temporally imaged using ultrafast transient reflection microscopy. The expansion of the exciton cloud driven by dipolar repulsion was found to be strongly density dependent and highly anisotropic. The interaction strength between the 1D excitons was determined to be ∼3.9 × 10-14 eV cm-2, corresponding to a dipolar length of 310 nm, which is a factor of 2-3 larger than the interlayer excitons at two-dimensional van der Waals vertical interfaces. These results suggest 1D dipolar excitons with large static in-plane dipole moments in lateral TMDC heterojunctions as an exciting system for investigating quantum many-body physics.

Robust atom optics for Bragg atom interferometry

Multi-photon Bragg diffraction is a powerful method for fast, coherent momentum transfer of atom waves. However, laser noise, Doppler detunings, and cloud expansion limit its efficiency in large momentum transfer (LMT) pulse sequences. We present simulation studies of robust Bragg pulses developed through numerical quantum optimal control. Optimized pulse performance under noise and cloud inhomogeneities is analyzed and compared to analogous Gaussian and adiabatic rapid passage pulses in simulated LMT Mach–Zehnder interferometry sequences. The optimized pulses maintain robust population transfer and phase response over a broader range of noise, resulting in superior contrast in LMT sequences with thermal atom clouds and intensity inhomogeneities. Large optimized LMT sequences use lower pulse area than Gaussian pulses, making them less susceptible to spontaneous emission loss. The optimized sequences maintain over five times better contrast with tens of momentum separation and offer more improvement with greater LMT. Such pulses could allow operation of Bragg atom interferometers with unprecedented sensitivity, improved contrast, and hotter atom sources.

APUNet: Attention-guided upsampling network for sparse and non-uniform point cloud

Point cloud upsampling is a basic low-level task, that is important for improving the quality of a point cloud. However, existing point cloud upsampling methods perform poorly on sparse and non-uniform point clouds, due to that they fail to fully model the relationship between points. To address this issue, in this paper, we propose an attention-guided network called APUNet to exploit the correlation between points, which can perform unsampling for sparse and non-uniform point cloud. In particular, we first propose a feature extraction unit, DisTransformer, which can effectively model the relationship between points by introducing a distance prior to the attention mechanism. We also design a point cloud feature extraction network based on DisTransformer. By computing the correlation between patches and the correlation between points, we fuse the global and local features to better model the correlation of the whole object. Furthermore, we propose a feature prediction module based on attention mechanisms that avoids generating clustered points by transforming the point cloud expansion task into a point cloud prediction task. Qualitative and quantitative experiments reveal the superiority of our method compared to the current state-of-the-art methods. Compared with other point cloud upsampling methods, APUNet can much better upsample non-uniform and extremely sparse point clouds.

Introducing an adaptive model forauto‐scalingcloud computing based on workload classification

SummaryWith the increasing expansion of cloud computing services, one of the main goals of researchers is to solve its major challenges. Cloud service providers must satisfy the service level agreement for customers and prevent resource wastage as much as possible. Without a precise, optimal, and dynamic policy, this is unattainable. The key idea is the ability to acquire resources as you need them and release resources when you no longer need them, named “Cloud Elasticity.” Elasticity is a trade‐off between resource acquisition and release, and if this optimization is done best, the service level agreement will be fully achieved and the cloud provider will have the least waste of resources. The researchers used machine learning techniques to predict user workload and decide to scale up/out the resources. A challenging issue is the different characteristics of the users' workloads. The results show that each prediction algorithm works well on a class of users' workloads not all. Hence, in this study, a new architecture has been suggested to automatically classify the workloads based on their sequential statistical characteristics. First, the sequential statistical characteristics of the users' workload are extracted and then a trained neural network classifies the user's workload. The developed adaptive model chooses the best suitable algorithm among LR, SVM, and ARIMA to predict the workload. The results indicate a 10% improvement in forecast error.