Cloud Dimensions Research Articles

A Comprehensive Comparison of IBM Cloud and Microsoft Azure

The ever-growing demand for scalability, agility, and cost-effectiveness has fuelled cloud computing to the forefront of modern IT infrastructure. According to Precedence Research, the global cloud computing market size was valued at USD 480 billion in 2022 and is expected to hit USD 2297.37 billion by 2032 with a registered CAGR of 17% from 2023 to 2032 [1]. The growth of cloud computing is driven by various factors such as the rapid evolution of technology and the globalization of businesses. Cloud computing is already embraced in various industries like Financial Services, Manufacturing, Healthcare, Retail, Education, and Architecture Engineering & Construction (AEC), where it streamlines operations, enhances collaboration, and revolutionizes processes. Cloud computing holds immense potential, especially in sectors like healthcare, real estate, tourism, and small and medium enterprises, where it will facilitate faster and more informed decision-making processes through data collection and analysis. Additionally, cloud technology is revolutionizing government sectors by driving cost savings, enhancing efficiency, and promoting transparency and citizen engagement. With the increasing number of cloud computing service providers such as IBM, Microsoft, Google, Amazon, and others, it becomes crucial to delve into their technical nuances. Understanding how each provider's offerings align with specific business requirements, technical specifications, and future scalability is paramount for organizations venturing into their cloud journey. The main purpose of this paper is to explore and evaluate the technical dimensions of IBM Cloud and Microsoft Azure and understand its future potential.

Change Detection in Point Clouds Using 3D Fractal Dimension

The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.

Correlation of Escherichia coli inactivation with histotripsy bubble cloud size

Bacterial loads can be effectively reduced with cavitation-mediated focused ultrasound, or histotripsy. Our previous in vitro work with Escherichia coli (E. coli) established strong trends of bactericide with increasing peak negative pressure amplitude, pulse length, PRF, and treatment time. The current study correlates bactericide as a function of the histotripsy bubble cloud size produced for these treatment parameters at several frequencies. Histotripsy was applied to E. coli suspensions in 10-ml sample vials at 810 kHz, 1.2 MHz, or 3.25 MHz for 40 min. Separately, cavitation was recorded using a Photron Fastrax high-speed camera for acoustic parameters equivalent to those used in the E. coli studies. The images were used to quantify the maximum size of each bubble cloud, with the assumption that the cloud was axially symmetric around the propagation direction. A strong linear relationship exists between log kill versus cloud size (R2 = 0.96). Remarkably, across all variables studied, the log-reduction in viable bacteria exhibited a direct proportional relationship with the dimensions of the bubble cloud. This strong correlation between bacterial reduction and cavitation bubble cloud size could have significance for clinical applications where bacteria cannot easily be sampled. [Work supported by NIH R01AR080120 and R01EB023910.]

Reviewing the impact of cloud computing on small and medium enterprises in Africa

The advent of cloud computing has significantly transformed the business landscape globally, offering a plethora of opportunities for organizations to enhance their operational efficiency and competitiveness. This paper aims to review and analyze the impact of cloud computing specifically on Small and Medium Enterprises (SMEs) in the African context. As cloud computing continues to gain traction worldwide, its implications for SMEs in Africa, which constitute a significant portion of the region's economic fabric, become increasingly crucial. The study will adopt a comprehensive approach, combining both qualitative and quantitative methodologies to assess the various dimensions of cloud computing adoption among SMEs in Africa. It will delve into the challenges faced by SMEs in embracing cloud technology, including issues related to infrastructure, data security, and skill gaps. Additionally, the paper will explore the potential benefits derived from the adoption of cloud solutions, such as cost savings, scalability, and improved accessibility to advanced technologies. Furthermore, the research will investigate the role of government policies, industry collaborations, and infrastructure development in shaping the cloud computing landscape for SMEs in Africa. By examining case studies and success stories, the paper will highlight best practices and identify potential strategies for overcoming barriers to adoption. The findings of this review aim to contribute valuable insights for policymakers, business leaders, and technology providers to formulate effective strategies that promote the widespread adoption of cloud computing among SMEs in Africa. Ultimately, the research seeks to enhance our understanding of the dynamics surrounding cloud technology in the African SME sector and provide recommendations for fostering a conducive environment for its sustainable and inclusive growth.

On the relative importance of saltation and suspension for aeolian snow mass transport

AbstractSnow transport affects various hydrological processes significantly through reshaping the glacial landscapes. Snow particles that are transported in a turbulent flow are typically classified under the saltation and suspension states based on the inertia of the particles relative to the strength of the turbulence. In this study, snow transport in the turbulent boundary layer was simulated using the large‐eddy simulation with Lagrangian particle tracking method. Contrary to most previous studies in which saltation and suspension are defined by a specified critical height or particle size, hop time ratio Rt and particle Stokes number St were adopted to judge the state of saltating snow particles, and the contributions of pure saltation, modified saltation, short‐ and long‐term suspension particles were quantified appropriately. A threshold value of St≈5 for pure saltation was proposed through the particle trajectory comparison. The results show that the St criterion measures pure saltation particle appropriately but fails to diagnose long‐term suspension particles. Furthermore, although there are hardly any pure saltation particles in natural snow transport, the contribution of modified saltation cannot be neglected in the lowest centimetres of the atmosphere, which may also dominate the overall mass flux at the near surface under larger friction velocities. Rather, considerable short‐ and long‐term suspended particles spread over the entire vertical dimension of the snow cloud, contributing a major proportion of the overall mass flux, even within the saltation layer. Based on this, the suspension layer should be defined as the entire vertical dimension of the particle cloud. The results can improve our understanding of the motion dynamics of transported snow particles and further provide new ideas in modelling this process.

The cloud sovereignty nexus: How the European Union seeks to reverse strategic dependencies in its digital ecosystem

AbstractHow does the European Union balance the need to migrate data to the cloud with the imperative of reducing its dependence on foreign cloud providers? Cloud computing is a critical technology for the competitiveness of the European Union (EU) in the digital economy. This paper argues that the EU is adopting a host of regulatory requirements and industrial policy tools—which fall under the umbrella term of ‘data sovereignty’—not only to protect the confidentiality of European data, but also to counter the dominance of US vendors in the European cloud market. To demonstrate how data sovereignty principles are woven into current EU policy initiatives, the paper presents two case studies: Gaia‐X and the European Alliance for Industrial Data, Edge, and Cloud. Linking data sovereignty to the lack of a competitive European cloud ecosystem sheds light on the strategic dimension of cloud computing in a way that treating them separately would not do.

Enhancement of Boiling Histotripsy by Steering the Focus Axially During the Pulse Delivery.

Boiling histotripsy (BH) is a pulsed high-intensity focused ultrasound (HIFU) method relying on the generation of high-amplitude shocks at the focus, localized enhanced shock-wave heating, and bubble activity driven by shocks to induce tissue liquefaction. BH uses sequences of 1-20 ms long pulses with shock fronts of over 60 MPa amplitude, initiates boiling at the focus of the HIFU transducer within each pulse, and the remainder shocks of the pulse then interact with the boiling vapor cavities. One effect of this interaction is the creation of a prefocal bubble cloud due to reflection of shocks from the initially generated mm-sized cavities: the shocks are inverted when reflected from a pressure-release cavity wall resulting in sufficient negative pressure to reach intrinsic cavitation threshold in front of the cavity. Secondary clouds then form due to shock-wave scattering from the first one. Formation of such prefocal bubble clouds has been known as one of the mechanisms of tissue liquefaction in BH. Here, a methodology is proposed to enlarge the axial dimension of this bubble cloud by steering the HIFU focus toward the transducer after the initiation of boiling until the end of each BH pulse and thus to accelerate treatment. A BH system comprising a 1.5 MHz 256-element phased array connected to a Verasonics V1 system was used. High-speed photography of BH sonications in transparent gels was performed to observe the extension of the bubble cloud resulting from shock reflections and scattering. Volumetric BH lesions were then generated in ex vivo tissue using the proposed approach. Results showed up to almost threefold increase of the tissue ablation rate with axial focus steering during the BH pulse delivery compared to standard BH.

Evaluation of the field survey procedure and three-dimensional modelling of a building using drone-captured images

ABSTRACT The possibility of acquiring dense point clouds using drones is a topic of growing interest in the architecture, engineering, and construction industry. Aiming to contribute to the dissemination of this technology, this study explored the process of surveying an existing building using drones, processing the collected images, and developing a three-dimensional model of the building based on the point cloud. Compared with the traditional method of built building surveying, the drone-based method provided greater agility in data collection, as well as greater precision and reliability of point cloud dimensions. The studied method holds potential for use in as-built project development. No significant productivity differences were identified between ArchiCAD and Edificius, demonstrating that the advantages of three-dimensional modelling using drone-based point clouds do not depend on the software used.

Mappings, dimensionality and reversing out of deep neural networks

Abstract We consider a large cloud of vectors formed at each layer of a standard neural network, corresponding to a large number of separate inputs which were presented independently to the classifier. Although the embedding dimension (the total possible degrees of freedom) reduces as we pass through successive layers, from input to output, the actual dimensionality of the point clouds that the layers contain does not necessarily reduce. We argue that this phenomenon may result in a vulnerability to (universal) adversarial attacks (which are small specific perturbations). This analysis requires us to estimate the intrinsic dimension of point clouds (with values between 20 and 200) within embedding spaces of dimension 1000 up to 800,000. This needs some care. If the cloud dimension actually increases from one layer to the next it implies there is some ‘volume filling’ over-folding, and thus there exist possible small directional perturbations in the latter space that are equivalent to shifting large distances within the former space, thus inviting possibility of universal and imperceptible attacks.

Сравнительный анализ методик определения максимальных размеров взрывоопасных зон

PURPOSE. Existing regulatory methods for determining the maximum dimensions of explosive zones when flammable gases leak from process equipment do not fully reflect the physical picture of the accident. The purpose of the work in question is to compare existing methods for determining the maximum dimensions of explosive zones. To achieve the goal, the following tasks were solved: an analysis of existing methods for determining the maximum dimensions of explosive zones was carried out; an emergency situation associated with propane leak into open space was simulated in the PyroSim software package. The object of the study is the leakage of flammable gases from process equipment during accidents. The subject of the study is to determine maximum dimensions of explosive zones in case of flammable gases leakage from process equipment during accidents. METHODS. To evaluate existing methods for determining the maximum dimensions of explosive zones, regulatory documents were analyzed. To simulate an emergency situation associated with a propane leak into open space, PyroSim software package was used. FINDINGS. The given study examines accidents related to flammable gases emission into open space. The analysis of emergency situations at the facilities related to the circulation of flammable gases showed that when process equipment depressurizes, in most cases, gas leaks into the environment rather than instantaneously releases. RESEARCH APPLICATION FIELD. The outcomes of the study can be used to improve the methodology for determining maximum dimensions of explosive zones as a result of an emergency situation related to flammable gases emission into open space. CONCLUSIONS. When comparing the calculations outcomes to determine geometric dimensions (radius and height) of an explosive zone using methods from the regulatory documents, it was found out that the values of geometric dimensions of a gas cloud have a significant discrepancy, and the results according to these methods are correct to be used for an instantaneous gas release, and not for gas leakage from process equipment. Numerical modeling will make it possible to determine maximum dimensions of explosive zones of flammable gases in open space during leakage from process equipment more accurately, taking into account physic-chemical properties of flammable gas and environmental parameters.

Automated Extraction of Rail Point Clouds by Multi-Scale Dimensional Features From MLS Data

Existing railway measurement methods are very difficult to adapt to high-density operating conditions in terms of safety and efficiency. A mobile laser scanning (MLS) system using LiDAR begins to be applied in railway measurement in complex scenarios and helps to detect the condition of rails. This paper presents a novel method to extract rails from MLS point cloud data. After the point cloud preprocessing, a spatial distance threshold is first set to obtain the point cloud of the ballast bed part. Then, the multi-scale features of the point cloud dimension are defined, and the dimensional features of each point cloud at different scales are calculated as feature inputs using principal component analysis. Finally, a classifier is constructed using linear discriminant analysis to extract the rail point clouds. The results of several scenarios in existing lines show that our method is highly adaptable and the extracted rail completeness and accuracy are above 94%, which is very promising for automatic extraction of rail point clouds from the point cloud data collected by MLS. The gauges measured on the extracted rail point clouds also validate the method as a promising solution for extracting 3D rails from MLS point clouds.

Risk uncertainty analysis in shield tunnel projects

Subway shield tunnels usually pass through the complex geological environment of a city. There are many uncertain factors in shield tunnel construction risk, undoubtedly increasing the difficulty of construction safety control. Risk evaluation prior to project construction is necessary for the implementation of tunnel projects and reduces risk levels during construction. However, tunnel risks are uncertain and are characterized by random and fuzzy uncertainty. To account for these two uncertainties in the quantitative transformation of risk qualitative concepts and to improve the evaluation accuracy, in this study a new risk evaluation method based on a two-dimensional cloud model (TDCM) is proposed for shield tunnel projects. First, the risk evaluation criteria are quantified with the harmfulness and the probability of risk as the evaluation dimensions of the two-dimensional cloud, and the conversion relationship between the risk level and the quantitative domain is improved. A risk evaluation index system based on construction accidents and risk types for shield tunnel projects is established. Then, the Delphi method is used to evaluate the probability and harmfulness of the risk, the index weight and numerical characteristics of the two-dimensional cloud model are determined, and a shaped two-dimensional comprehensive cloud of thousands of cloud droplets is generated by a positive cloud generator. Finally, the domain is defined as the overlap between the comprehensive evaluation cloud and the standard cloud at different risk levels, and the contribution of comprehensive evaluation cloud drops to the comprehensive evaluation cloud in each domain is analysed. The risk level of the project is predicted based on the principle of maximum membership degree. The risk evaluation method proposed in this study is applied to two subway shield sections, and the risk level of these projects is accurately predicted. The TDCM evaluation method is compared with the one-dimensional cloud model (ODCM) evaluation method and the fuzzy comprehensive evaluation method (FCEM), and the advantages and applicability of the TDCM evaluation method are discussed.

Nanoparticle-mediated histotripsy using dual-frequency histotripsy pulsing: Comparison of bubble-cloud characteristics and ablation efficiency

Nanoparticle-mediated histotripsy (NMH) is a targeted ablation method using perfluorocarbon-filled nanoparticles to generate bubble-clouds at pressure levels (9–12 MPa) significantly below the histotripsy intrinsic threshold (>25 MPa). Prior studies have also shown a significant reduction in ablation efficiency compared to conventional histotripsy, likely from reduced bubble expansion and bubble-cloud density. Here, we investigate the bubble-cloud characteristics and ablation efficiency for NMH using dual-frequency pulsing. We hypothesize this method will increase ablation efficiency by increasing the bubble-cloud density and individual bubble expansion. High-speed optical imaging was used to characterize the cavitation threshold, cloud dimensions, and bubble-density of bubble clouds generated in agarose tissue phantoms, with and without perfluorohexane-filled nanocones, exposed to single-cycle dual-frequency pulses using a 500 kHz–3 MHz array transducer (1:1 pressure ratio). Ablation efficiency was investigated using red blood cell phantoms. Results showed dual-frequency NMH predictably produced smaller, denser, and more well-confined bubble-clouds and increased ablation efficiency compared to previous single-frequency studies, with complete ablation of the focal volume observed within 2000 pulses. This study demonstrates the potential of enhancing NMH ablation efficiency with dual-frequency pulsing and highlights the need for further studies to optimize NMH pulsing parameters for future clinical therapy development.

Gait Recognition Using Spatio-Temporal Information of 3D Point Cloud via Millimeter Wave Radar

Gait recognition is one of the crucial methods in identity recognition, which has a wide range of applications in many fields, such as smart home, smart office, and health monitoring. The camera is the most mainstream traditional solution. But the camera is difficult to maintain stable performance in the dark, low light, and bad weather conditions. In addition, privacy leakage is also one of the important issues that people worry about. In contrast, as the latest research progress in gait recognition, millimeter wave radar can not only protect people’s privacy, but also maintain normal perception performance in dark conditions. In this paper, we propose a system for gait recognition named MTPGait using spatio-temporal information via millimeter wave radar. We specially design a neural network that can extract multiscale spatio-temporal features along space and time dimensions of 3D point cloud concisely and efficiently. We use LSTM to design the context flow of local and global time and space, fusing local and global spatio-temporal features. In addition, we construct and release a millimeter wave radar 3D point cloud data set, which consists of 960-minute gait data of 40 volunteers. Using the data set, we evaluate the system and compare it with four state-of-the-art algorithms. The experimental results show that MTPGait is able to achieve 96.7% recognition accuracy in a single-person scene on fixed route and 90.2% recognition accuracy when two people coexist, while none of the existing methods is more than 90% recognition accuracy in either scenario.

The Fractal Nature of Clouds in Global Storm‐Resolving Models

AbstractClouds in observations are fractals: they show self‐similarity across scales ranging from 1 to 1,000 km. This includes individual storms and large‐scale cloud structures typical of organized convection. It is not known whether global storm‐resolving models reproduce the observed fractal scaling laws for clouds and organized convection. We compute the fractal dimension of clouds using Himawari satellite data and compare this to global storm‐resolving model simulations completed as part of the DYAMOND intercomparison project. We find cloud fields in these simulations are indeed fractal, and reproduce the observed fractal dimension to within 10%. We find the fractal dimension is sensitive to the choice of boundary layer parametrization scheme used in each model simulation, and not to the convection parametrization as might have been expected.

Fractal dimension of optical cirrus in Stripe82

ABSTRACT The geometric characteristics of dust clouds provide important information on the physical processes that structure such clouds. One of such characteristics is the 2D fractal dimension D of a cloud projected on to the sky plane. In previous studies, which were mostly based on infrared (IR) data, the fractal dimension of individual clouds was found to be in a range from 1.1 to 1.7 with a preferred value of 1.2–1.4. In this work, we use data from Stripe82 of the Sloan Digital Sky Survey to measure the fractal dimension of the cirrus clouds. This is done here for the first time for optical data with significantly better resolution as compared to IR data. To determine the fractal dimension, the perimeter-area method is employed. We also consider IR (IRAS and Herschel) counterparts of the corresponding optical fields to compare the results between the optical and IR. We find that the averaged fractal dimension across all clouds in the optical is $\langle D \rangle =1.69^{+0.05}_{-0.05}$ which is significantly larger than the fractal dimension of its IR counterparts $\langle D\rangle =1.38^{+0.07}_{-0.06}$. We examine several reasons for this discrepancy (choice of masking and minimal contour level, image and angular resolution, etc.) and find that for approximately half of our fields the different angular resolution (point spread function) of the optical and IR data can explain the difference between the corresponding fractal dimensions. For the other half of the fields, the fractal dimensions of the IR and visual data remain inconsistent, which can be associated with physical properties of the clouds, but further physical simulations are required to prove it.

Simulation of Aerosol Indirect Effects on Cloud Streets Over the Northwestern Pacific Ocean

AbstractWe simulated aerosol effects on cloud streets formed during continental cold‐air outbreaks over the Northwestern Pacific using an aerosol‐sensitive cloud microphysical scheme coupled with the Weather Research and Forecast model. The micro‐ and macro‐scale responses were examined under a wide range of aerosol concentrations. The cloud streets generally remain in a transient state, indicating that the aerosol particles may significantly affect the cloud system's development. The simulation results revealed a clear Twomey effect, but they were contrary to some of the Albrecht effect, especially under relatively clean conditions. Such reversed Albrecht effects stem mainly from dynamic response to changes in atmospheric stability associated with drizzle evaporation in the sub‐cloud layer. The dynamic response works against thermal forcing from the large air‐sea temperature gradient in a cold‐air outbreak. As the aerosol increases, the drizzle mechanisms weaken, and the boundary layer becomes more convective, leading to stronger dry‐air entrainment from the free troposphere. In individual clouds, the effect of entrainment on cloud water content may be compensated by more substantial vertical vapor flux. These mechanisms lead to a reduction in the cloud amount. Furthermore, the reduced moisture flux from drizzle evaporation in the sub‐cloud layer, as well as the reduced sedimentation speed of the smaller cloud drops, tend to lessen cloud thickness. The overall reduction in cloud dimensions may offset the Twomey effect by up to 20% in cloud albedo.

Application of Fractal Dimension of Terrestrial Laser Point Cloud in Classification of Independent Trees

Tree precise classification and identification of forest species is a core issue of forestry resource monitoring and ecological effect assessment. In this paper, an independent tree species classification method based on fractal features of terrestrial laser point cloud is proposed. Firstly, the terrestrial laser point cloud data of an independent tree is preprocessed to obtain terrestrial point clouds of independent tree canopy. Secondly, the multi-scale box-counting dimension calculation algorithm of independent tree canopy dense terrestrial laser point cloud is proposed. Furthermore, a robust box-counting algorithm is proposed to improve the stability and accuracy of fractal dimension expression of independent tree point cloud, which implementing gross error elimination based on Random Sample Consensus. Finally, the fractal dimension of a dense terrestrial laser point cloud of independent trees is used to classify different types of independent tree species. Experiments on nine independent trees of three types show that the fractal dimension can be stabilized under large density variations, proving that the fractal features of terrestrial laser point cloud can stably express tree species characteristics, and can be used for accurate classification and recognition of forest species.

Electronic records management systems and the competency of educational institutions: Evidence from Yemen

In the modern world, the amount of information stored in modern technology has been exponentially increasing. Access to vast amounts of information has changed how governments, institutions, organizations, and individuals conduct their business and record keeping. The increased use of cloud computing in conjunction with information and communication technologies (ICT), office automation, and digitalization has altered how electronic records are generated. Organizations should embrace this emerging environment to ensure competent operations and regulatory compliance well into the future. The absence of framework makes it difficult to implement the Electronic Records Management System (ERMS). Thus, this study proposed a framework for ERMS implementation and identified the most critical factors that are related to the ERMS characteristics and cloud characteristics. The ERMS implementation will improve Yemeni public sector educational institution competency and such implementation will be facilitated by the proposed framework. The study uses the Technology Acceptance Model 3 (TAM3) to implement a cloud Electronic Records Management System (ERMS). In addition to that, the study used a quantitative approach method and distributed questionnaires to 350 academicians and managers in the Yemeni public education sector. Variable relationships were identified using Partial Least Square-Structural Equation Modelling (PLS-SEM) through a second-order analysis method. All the identified factors were found to be essential and have a significant relationship with the behavioral intention to implement ERMS. The findings also revealed that ERMS plays a substantial and vital role in the competency of educational organizations. In other words, the study results demonstrated the importance of ERMS and Cloud dimension to ERMS implementation as well as the significant effect of ERMS implementation on public sector educational competency. These findings of the study have the potential to assist in shaping the direction of theory and empirical studies concerning the ERMS, particularly in its implementation to support the competency of educational institutions.

Affecting factors of cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences: A cross-sectional study in the Southeast of Iran.

ObjectiveHealth care organizations require cloud computing to remain efficient and cost-effective, and provide high-quality health care services. Adoption of this technology by users plays a critical role in the success of its application. This study aimed to determine factors affecting cloud computing adoption in public hospitals affiliated with Zahedan University of Medical Sciences.MethodsA cross-sectional descriptive and analytic study was performed in 2017. The study population comprised information technology and hospital information system authorities and hospital information system users. The sample consisted of 573 participants. The data were collected using a questionnaire and analyzed with the Statistical Package for Social Sciences software using descriptive and analytical statistics.ResultsThe mean score of environmental, human, organizational, technological, and intention dimensions of cloud computing adoption was 3.39 ± 0.81, 3.27 ± 0.63, 3.19 ± 0.71, 3 ± 0.43, and 3.55 ± 1.10, respectively. Furthermore, a significant positive relationship was found between intention of cloud computing adoption and environmental (R = 0.521, p = 0.000), organizational (R = 0.426, p = 0.000), human (R = 0.492, p = 0.000), and technological dimensions (R = 0.157, p = 0.000).ConclusionsBenefits of cloud computing adoption, relative advantage, and competitive pressure were identified as the most influential factors in accepting cloud computing. Simplifying the users’ understanding of this technology and its application, improving the staff's technical capabilities, promoting executive managers’ understanding of the nature and functions of cloud computing, and fully supporting and increasing governmental mandates for adoption of new technologies are necessary for facilitating the adoption of cloud computing in given hospitals.