Scale Cloud Research Articles

Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Land surface albedo (LSA) is an essential component of the surface radiation budget, and has been retrieved extensively as a basic remote sensing product; however, daily LSA products suffer from extensive data gaps primarily caused by cloud cover. Accordingly, several gap-filling methods were developed (e.g., spatiotemporal interpolation and data fusion with albedo climatology), although the traditional methods are limited by cloud scale and surface heterogeneity. Further, as the largest varying surface landscape feature, seasonal snow cover substantially influences LSA and represents a major uncertainty factor of gap recovery because previous studies failed to employ actual surface signals to capture such ephemeral but intense albedo changes under cloud cover. To address this issue, a three-step framework was proposed for estimating 1 km cloudy-sky LSA using passive microwave (PMW) data, albedo climatology, and Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky albedo: (1) All-sky snow albedo was estimated from PMW brightness temperatures using a statistical model, (2) Continuous albedo dynamics were generated by combining the all-sky snow albedo with snow-free climatological albedo, and (3) The 1 km cloudy-sky LSA was predicted after filtering 1 km VIIRS clear-sky LSA by the albedo dynamic series. PMW-derived snow albedo was assessed over the Contiguous US (CONUS), and the final 1 km cloudy-sky LSA was validated across 10 sites from SURFRAD and Core AmeriFlux in 2013. Based on the comparison with high-quality MODIS pixels, the estimated snow albedo yielded an overall RMSE of 0.064 over CONUS, with a bias of −0.010 (R2 = 0.845). The recovered 1 km cloudy-sky LSA produced RMSEs of 0.074 (0.137) for all (snow) samples, a significant improvement over the Global Land Surface Satellite (GLASS) gap-free albedo products especially on snow cases (p-value = 0.027). Corresponding RMSE in calculating surface net radiation was also decreased by 38.91 W·m−2; and anomalous snow samples were corrected as well. The temporal analysis and all-sky LSA mapping suggest that the recovered LSA has satisfactory spatiotemporal continuity, and successfully captured details of spatiotemporal variability, especially for ephemeral snow events. This study provides an innovative solution to recover gaps in LSA data, and considerably improves the LSA accuracy under cloud cover, which can inform snow melting modeling, hazard forecasting, and irrigation management.

Effective shielding of ≲10 GeV cosmic rays from dense molecular clumps

The density of cosmic rays inside molecular clouds determines the ionization rate in the dense cores where stars form. It is also one of the drivers of astrochemistry leading to the creation of complex molecules. Through Fermi Large Area Telescope observations of nearby giant molecular clouds, we observed deficits (holes) in the gamma-ray residual map when modelling with the expected gamma-ray diffuse emission from uniform cosmic rays interacting with the molecular content. We propose that the deficit is due to the lack of penetration of the low-energy (sub-GeV to GeV) cosmic rays into denser regions or clumps. This differs from the prevailing view of fast cosmic ray transport in giant molecular clouds where the magnetic turbulence is suppressed by neutral-ion damping, as our results require a slow diffusion inside dense molecular clumps. Through modelling we find that while the shielding is negligible on the cloud scale, it becomes important in the denser, parsec-sized regions where the gravitational collapse is already at play, changing the initial condition of star formation and astrochemistry.

A novel multi-objective service composition architecture for blockchain-based cloud manufacturing

AbstractIn recent years, many core technologies of Industry 4.0 have advanced significantly, particularly the integration of big data technology and cloud manufacturing (CMfg). The decentralization and traceability features of blockchain technology (BCT) provide an effective solution to provide trusted resource service in CMfg. Service composition is a core issue of CMfg to increase the value of digital assets. However, existing research on service composition based on BCT suffers from both the blockchain proof-of-work (PoW) mechanism and the service composition problem need to consume large computational overheads, as well as the blockchain fork problem affecting the system’s reliability, which reduces the usefulness of these schemes. To solve these problems, this paper proposes a novel multi-objective service composition architecture for blockchain-based CMfg (MOSC-BBCM). In MOSC-BBCM, first, a blockchain-chained storage structure is designed for the actual manufacturing cloud service constraint and scale dynamic changes, which can fully use the historical service information and accelerate the search for high-quality solutions. Second, to reduce the squandered computing resources in the PoW, a mining mechanism based on multi-objective service composition and optimal selection is proposed, where miners competitively solve a nondeterminate polynomial-hard problem to replace the mathematical puzzle. Finally, an incentive mechanism based on the environment selection method is proposed, which can avoid the fork problem while distributing on a labor basis. The effectiveness of the proposed MOSC-BBCM is verified in simulated numerical experiments of CMfg, which shows that the architecture provides a flexible and configurable scheme for blockchain-based CMfg with high availability.

Towards the impact of GMC collisions on the star formation rate

ABSTRACT Collisions between giant molecular clouds (GMCs) are one of the pathways for massive star formation due to the high densities created. However, the enhancement of the star formation rate (SFR) is not well constrained. In this study, we perform a parameter study of cloud–cloud collisions and investigate how the resulting SFR depends on the details of set-up. Our parameter study explores variations in collision speed, magnetic field inclination (with respect to the collisional axis), and resolution, as defined by the number of cells per Jeans length. In all our collision simulations, we find a factor of 2–3 increase in the SFR compared to our no collision simulation, with star formation beginning sooner with (a) high collisional velocities, (b) parallel orientation between the magnetic field and collision axis, (c) and lower resolution. The mean virial parameter of high density (and thus possible star-forming) gas increases with collisional velocity, but has little variation with magnetic field inclination. The alignment of the velocity and magnetic field remains uniform in low-density environments but becomes more perpendicular with increasing density, indicating the compression of the magnetic field by collapsing gas. Comparing the trends in the SFR with other GMC collision studies, we find good agreement with studies that account for the gravitational boundedness of the gas in their star formation algorithm, but not with those that simply form stars above a prescribed density threshold. This suggests that the latter approach should be used with caution when modelling star formation on resolved cloud scales.

Flower trade‐wind clouds are shallow mesoscale convective systems

AbstractFlower clouds are trade‐wind shallow cumuli, with tops reaching 3 km altitude, organised into 100‐km wide clusters. They are widespread over the subtropics and associated with the strongest cloud radiative effect among trade‐wind cumuli mesoscale organisations. In the context of large uncertainty in climate projections due to the representation of shallow clouds, major knowledge gaps remain about the global impact of mesoscale organisations and the local processes driving them. Here, the processes governing the flower organisation are investigated based on the case study of February 2, 2020 from the Elucidate the Couplings Between Clouds, Convection, and Circulation (EURECA) campaign, east of Barbados. One flower cloud is simulated with a large‐eddy simulation (LES), using the Meso‐NH model at 100‐m horizontal grid spacing, and validated extensively with high‐resolution observations from the High Altitude and Long‐range Research Aircraft (HALO), dropsondes, and satellite measurements. The cloud‐top altitudes exhibit a trimodal distribution. The processes shaping flower clouds are wide cold pools and cloudy updrafts organised in one large arc at the western edge. These updrafts are responsible for the highest cloud tops and drive most of the vertical turbulent fluxes of sensible heat, humidity, and momentum. A mesoscale circulation takes place at the scale of the flower clouds and makes them very similar to deep mesoscale convective systems.

FilDReaMS

Context.Both simulations and observations of the interstellar medium show that the study of the relative orientations between filamentary structures and the magnetic field can bring new insight into the role played by magnetic fields in the formation and evolution of filaments and in the process of star formation.Aims.We provide a first application ofFilDReaMS, the new method presented in the companion paper to detect and analyze filaments in a given image. The method relies on a template that has the shape of a rectangular bar with variable width. Our goal is to investigate the relative orientations between the detected filaments and the magnetic field.Methods.We applyFilDReaMSto a small sample of fourHerschelfields (G210, G300, G82, G202) characterized by different Galactic environments and different evolutionary stages. First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred fromPlanckpolarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H2column density.Results.We find preferential relative orientations in the fourHerschelfields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H2column density ≃ 1.1 × 1021cm−2and 1.4 × 1021cm−2, respectively, consistent with the results of previous studies.Conclusions.Our results confirm the existence of a coupling between magnetic fields at cloud scales and filaments at smaller scale. They also illustrate the potential of combiningHerschelandPlanckobservations, and they call for further statistical analyses with our dedicated method.

Deep Learning Based Cloud Cover Parameterization for ICON.

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.

PDFchem: A new fast method to determine ISM properties and infer environmental parameters using probability distributions

ABSTRACT Determining the atomic and molecular content of the interstellar medium (ISM) is of fundamental importance to understand the star-formation process. Although there exist various three-dimensional hydro-chemical codes modelling the ISM, they are computationally expensive and inefficient for studies over a large parameter space. Building on our earlier approach, we present PDFchem, a novel algorithm that models the cold ISM at moderate and large scales using functions connecting the quantities of the local (AV, eff) and the observed (AV, obs) visual extinctions, and the local number density, nH, with probability density functions (PDF) of AV, obs on cloud scales typically tens-to-hundreds of pc as an input. For any given AV, obs-PDF, the algorithm instantly computes the average abundances of the most important species (H i, H2, C ii, C i, CO, OH, OH+, H2O+, CH, HCO+) and performs radiative transfer calculations to estimate the average emission of the most commonly observed lines ([C ii] 158$\mu$m, both [C i] fine-structure lines and the first five rotational transitions of 12CO). We examine two AV, obs-PDFs corresponding to a non-star-forming and a star-forming ISM region, under a variety of environmental parameters combinations. These cover far-ultraviolet intensities in the range of χ/χ0 = 10−1 − 103, cosmic ray ionization rates in the range of $\zeta _{\rm CR}=10^{-17}-10^{-13}\, {\rm s}^{-1}$ and metallicities in the range of $Z=0.1-2\, {\rm Z}_{\odot }$. PDFchem is fast, easy to use, reproduces the PDR quantities of the hydrodynamical models, and can be used directly with observed data of the cold ISM.

Fast color point cloud registration based on virtual viewpoint image

With the increase of point cloud scale, the time required by traditional ICP-related point cloud registration methods increases dramatically, which cannot meet the registration requirements of large-scale point clouds. In this paper, a fast registration technique for large scale point clouds based on virtual viewpoint image generation is studied. Firstly, the projection image of color point cloud is generated by virtual viewpoint. Then, the feature is extracted based on ORB and the rotation and translation matrix is calculated. The experimental results show that the registration time of the proposed method is about 1s when the size of the point cloud is from 300,000 to 2 million, which is improved by 17–258 times compared with the traditional ICP registration method, and the registration error is reduced by 80% from ICP 5.0 to 1.0. This paper provides a new idea and method for large-scale color point cloud registration.

Bird’s eye view of molecular clouds in the Milky Way

Context. The kinematics of molecular gas are crucial for setting the stage for star formation. One key question related to the kinematic properties of gas is how they depend on the spatial scale. Aims. We aim to describe the CO spectra, velocity dispersions, and especially the linewidth-size relation of molecular gas from cloud (parsec) scales to kiloparsec scales in a complete region within the Milky Way disk. Methods. We used the census of molecular clouds within 2 kpc from our earlier work, together with CO emission data for them from the literature. We studied the kinematics and the Larson relations for the sample of individual clouds. We also mimicked a face-on view of the Milky Way and analysed the kinematics of the clouds within apertures of 0.25–2 kpc in size. In this way, we describe the scale-dependence of the CO gas kinematics and Larson’s relations. Results. We describe the spectra of CO gas at cloud scales and in apertures between 0.25 and 2 kpc in our survey area. The spectra within the apertures are relatively symmetric, but show non-Gaussian high-velocity wings. At cloud scales, our sample shows a linewidth-size relation σv = 1.5 · R0.3±0.1 with a large scatter. The mass-size relation in the sample of clouds is MCO = 794 · R1.5±0.5. The relations are also present for the apertures at kiloparsec-scales. The best-fit linewidth-size relation for the apertures is σv = 0.5 · R0.35±0.01, and the best-fit mass-size relation is MCO = 229 · R1.4±0.1. A suggestive dependence on Galactic environment is seen. Apertures closer to the Galactic centre and the Sagittarius spiral arm have slightly higher velocity dispersions. We explore the possible effect of a diffuse component in the survey area and find that such a component would widen the CO spectra and could flatten the linewidth-size relation. Understanding the nature of the possible diffuse CO component and its effects on observations is crucial for connecting Galactic and extragalactic data.

DTC: A Dynamic Transaction Chopping Technique for Geo-Replicated Storage Services

Replicating data across geo-distributed datacenters is usually necessary for large scale cloud services to achieve high locality, durability and availability. One of the major challenges in such geo-replicated data services lies in consistency maintenance, which usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to address this challenge. However, existing chopping is conducted statically during programming, which is stubborn and complex for developers. In this article, we propose Dynamic Transaction Chopping (DTC), a novel technique that does transaction chopping and determines piecewise execution in a dynamic and automatic way. DTC mainly consists of two parts: a dynamic chopper to dynamically divide transactions into pieces according to the data partition scheme, and a conflict detection algorithm to check the safety of the dynamic chopping. Compared with existing techniques, DTC has several advantages: transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to data partition schemes. A prototype of DTC is implemented to verify the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much better performance than similar work.

Analysis of the Three-Dimensional Structure of the Misocyclones Generating Waterspouts Observed by Phased Array Weather Radar: Case Study on 15 May 2017 in Okinawa Prefecture, Japan

Tornadoes are one of the most severe meteorological phenomena on the earth and their high winds cause serious damage to society. It is well known that vortices (mesocyclone or misocyclone, depending on their scale) in convective clouds contribute to tornadogenesis. High temporal resolution radar observations are necessary to elucidate the mechanism of tornadogenesis because convective clouds change drastically over time. This study focused on waterspouts that occurred on 15 May 2017 near Okinawa, Japan. Using Phased Array Weather Radar (PAWR) data, which provide three-dimensional data with high temporal resolution (every 30 s), radar reflectivity factors and Doppler velocity data were used to detect the three-dimensional structure of vortices. Using PAWR data, vorticities and diameters of these misocyclones were detected every 30 s and their potential vorticities, which are only possible because of the three-dimensional observation by PAWR, were calculated to understand the vortex generation and advection. The structures of two misocyclones (MC1 and MC2) were detected from Doppler velocity patterns. Combined with the radar reflectivity analysis, MC2 can be divided into two misocyclones (MC2a and MC2b). Potential vorticity of MC1 increased with time, probably because an outflow from the strong echo enhanced the lower horizontal shear. Potential vorticities in MC2a and MC2b were conserved in each period, with MC2b being nearly twice as large as MC2a.

GEOMETRIC FEATURES INTERPRETATION OF PHOTOGRAMMETRIC POINT CLOUD FROM UNMANNED AERIAL VEHICLE

Abstract. Recent days point clouds have become one of the most common 3D sources of information which is provides accurate geometry features of the object. 3D point clouds can be derived from either photogrammetry, Lidar or SAR in some cases depending upon the application. These point clouds consisting of 3D geospatial location of an object in form of XYZ coordinates which can be used in various ways to deduct information related to that object either based on visualisation or geometrical interpretation. Quality assessment standards for these point clouds are still very much in nascent stage with optimum accuracy in relative terms only. In this paper, multiple scale of point cloud has been used to understand the level of information these clouds consist on these multiple scales. Based on the 3D spatial information of these point cloud in local neighbourhood, some of invariant geometric properties can be computed for each 3D point with respective covariance matrix. These can be used to describe the local 3D structure using eigenvalues for these matrices. Using these Geometric features an approach is developed to understand the point cloud quality assessment. The proposed methodology exploits these special geometric properties to evaluate the 3D scene structure. Further, the point cloud is classified using shape detection algorithm which evaluates the geometric features to detect the mathematical shapes in the point cloud. This paper also enlightens on different geometric features that can be extracted from a point cloud and the importance of it.

PVT: Point‐voxel transformer for point cloud learning

The recently developed pure transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks. However, existing point cloud Transformers are computationally expensive because they waste a significant amount of time on structuring irregular data. To solve this shortcoming, we present the Sparse Window Attention module to gather coarse-grained local features from nonempty voxels. The module not only bypasses the expensive irregular data structuring and invalid empty voxel computation, but also obtains linear computational complexity with respect to voxel resolution. Meanwhile, we leverage two different self-attention variants to gather fine-grained features about the global shape according to different scale of point clouds. Finally, we construct our neural architecture called point-voxel transformer (PVT), which integrates these modules into a joint framework for point cloud learning. Compared with previous transformer-based and attention-based models, our method attains a top accuracy of 94.1% on the classification benchmark and 10 × $10\times $ inference speedup on average. Extensive experiments also validate the effectiveness of PVT on semantic segmentation benchmarks. Our code and pretrained model are avaliable at https://github.com/HaochengWan/PVT.

The Cygnus Allscale Survey of Chemistry and Dynamical Environments: CASCADE

Context. While star formation on large molecular cloud scales and on small core and disk scales has been investigated intensely over the past decades, the connection of the large-scale interstellar material with the densest small-scale cores has been a largely neglected field. Aims. We wish to understand how the gas is fed from clouds down to cores. This covers dynamical accretion flows as well as the physical and chemical gas properties over a broad range of spatial scales. Methods. Using the IRAM facilities NOEMA and the IRAM 30 m telescope, we mapped large areas (640 arcmin2) of the archetypical star formation complex Cygnus X at 3.6 mm wavelengths in line and continuum emission. The data were combined and imaged together to cover all accessible spatial scales. Results. The scope and outline of The Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE) as part of the Max Planck IRAM Observatory Program (MIOP) is presented. We then focus on the first observed subregion in Cygnus X, namely the DR20 star formation site, which comprises sources in a range of evolutionary stages from cold pristine gas clumps to more evolved ultracompact Hii regions. The data covering cloud to cores scales at a linear spatial resolution of <5000 au reveal several kinematic cloud components that may be part of several large-scale flows around the central cores. The temperature structure of the region is investigated by means of the HCN/HNC intensity ratio and compared to dust-derived temperatures. We find that the deuterated DCO+ emission is almost exclusively located toward regions at low temperatures below 20 K. Investigating the slopes of spatial power spectra of dense gas tracer intensity distributions (HCO+, H13CO+, and N2H+), we find comparatively flat slopes between −2.9 and −2.6, consistent with high Mach numbers and/or active star formation in DR20. Conclusions. This MIOP large program on star formation in Cygnus X provides unique new data connecting cloud with core scales. The analysis of the DR20 data presented here highlights the potential of this program to investigate in detail the different physical and chemical aspects and their interrelations from the scale of the natal molecular cloud down to the scale of accretion onto the individual protostellar cores.

Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition

An exacerbated precipitation–temperature relationship can lead to compound extremes. The role of clouds in such a relationship is relatively uncertain. Here, we investigate the cloud–precipitation–temperature relationships over the Indochina Peninsula during the summer monsoon transition. The negative correlation between cloudiness/precipitation and surface maximum temperature is valid on seasonal and interannual timescales. The near-surface temperature exhibits interdecadal variability and a long-term warming trend. The warming trend has accelerated in the past two decades. In the anomalous warm years, the remarkably strong western Pacific subtropical high inhibits the development of clouds, especially the middle and high cloud-top regimes, leading to the suppression of deep convection and precipitation. There are more optically thin (moderate to thick) clouds with smaller (larger) effective radii in the high cloud-top regime for the warm (cold) years. The dominance of shallow cumulus is a distinct feature in the warm years. The daytime heating of enhanced surface insolation due to decreased cloudiness is worsened by the dry condition of the precipitation deficit. The water vapor warming effect can prevent an efficient drop in nighttime temperature, thereby exacerbating the warm condition under the warming trend. The cloud–precipitation–temperature relationships coupling with the monsoon development can be used to diagnose the regional scale cloud–climate interactions in climate models.

CDI-E

We live in the gilded age of data-driven computing. With public clouds offering virtually unlimited amounts of compute and storage, enterprises collecting data about every aspect of their businesses, and advances in analytics and machine learning technologies, data driven decision making is now timely, cost-effective, and therefore, pervasive. Alas, only a handful of power users can wield today's powerful data engineering tools. For one thing, most solutions require knowledge of specific programming interfaces or libraries. Furthermore, running them requires complex configurations and knowledge of the underlying cloud for cost-effectiveness. We decided that a fundamental redesign is in order to democratize data engineering for the masses at cloud scale. The result is Informatica Cloud Data Integration - Elastic (CDI-E). Since the early 1990s, Informatica has been a pioneer and industry leader in building no-code data engineering tools. Non-experts can express complex data engineering tasks using a graphical user interface (GUI). Informatica CDI-E is built to incorporate the simplicity of GUI in the design layer with an elastic and highly scalable run time to handle data in any format without little to no user input using automated optimizations. Users upload their data to the cloud in any format and can immediately use them in conjunction with their data management and analytic tools of choice using CDI-E GUI. Implementation began in the Spring of 2017, and Informatica CDI-E has been generally available since the Summer of 2019. Today, CDI-E is used in production by a growing number of small and large enterprises to make sense of data in arbitrary formats. In this paper, we describe the architecture of Informatica CDI-E and its novel no-code data engineering interface. The paper highlights some of the key features of CDI-E: simplicity without loss in productivity and extreme elasticity. It concludes with lessons we learned and an outlook of the future.

On the scale height of the molecular gas disc in Milky Way-like galaxies

ABSTRACT We study the relationship between the scale height of the molecular gas disc and the turbulent velocity dispersion of the molecular interstellar medium within a simulation of a Milky Way-like galaxy in the moving-mesh code arepo. We find that the vertical distribution of molecular gas can be described by a Gaussian function with a uniform scale height of ∼50 pc. We investigate whether this scale height is consistent with a state of hydrostatic balance between gravity and turbulent pressure. We find that the hydrostatic prediction using the total turbulent velocity dispersion (as one would measure from kiloparsec-scale observations) gives an overestimate of the true molecular disc scale height. The hydrostatic prediction using the velocity dispersion between the centroids of discrete giant molecular clouds (cloud–cloud velocity dispersion) leads to more accurate estimates. The velocity dispersion internal to molecular clouds is elevated by the locally enhanced gravitational field. Our results suggest that observations of molecular gas need to reach the scale of individual molecular clouds in order to accurately determine the molecular disc scale height.

Spinning nano-carbon grains: Viable origin for anomalous microwave emission

Context. Excess microwave emission, commonly known as anomalous microwave emission (AME), is now routinely detected in the Milky Way. Although its link with the rotation of interstellar (carbonaceous) nano-grains seems to be relatively well established at cloud scales, large-scale observations show a lack of correlation between the different tracers of nano-carbons and AME, which has led the community to question the viability of this link. Aims. Using ancillary data and spinning dust models for nano-carbons and nano-silicates, we explore the extent to which the AME that come out of the Galactic Plane might originate with one or another carrier. Methods. In contrast to previous large-scale studies, our method is not built on comparing the correlations of the different dust tracers with each other, but rather on comparing the poor correlations predicted by the models with observed correlations. This is based on estimates that are as realistic as possible of the gas ionisation state and grain charge as a function of the local radiation field and gas density. Results. First, nano-carbon dust can explain all the observations for medium properties, in agreement with the latest findings about the separation of cold and warm neutral medium in the diffuse interstellar medium. The dispersion in the observations can be accounted for with little variations in the dust size distribution, abundance, or electric dipole moment. Second, regardless of the properties and abundance of the nano-silicate dust we considered, spinning nano-silicates are excluded as the sole source of the AME. Third, the best agreement with the observations is obtained when the emission of spinning nano-carbons alone is taken into account. However, a marginal participation of nano-silicates in AME production cannot be excluded as long as their abundance does not exceed YSi ∼ 1%.

Analysis of the Development of IT Outsourcing Companies in The Era of Cloud Computing--Take Infosys as an Example

With the development of information technology and mobile Internet, especially with the impact of the COVID-19 pandemic, cloud computing, which emphasizes distributed patterns rather than the centralized pattern is gradually known and applied. More and more enterprises have to adopt remote work, which greatly promotes the process of enterprise digital transformation, the digitization is greatly changing the world. The emergence of cloud computing is a huge change in traditional enterprises’ concept and thinking of IT resources. The characteristics of flexibility, innovation, diversity and scale of cloud computing stimulate the innovation vitality of enterprises and promote the development of enterprises. At the same time, the enterprises in the process of digital transformation are faced with various difficulties, therefore, how to choose high quality and efficient enterprise digital transformation method has become an important task for the current enterprise development. This paper takes India’s digital service provider Infosys as an example and analyzes the future development direction of traditional IT outsourcing companies in the current digital environment.