The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

In order to investigate the size distributions and seasonal variations of carbonaceous aerosols (OC and EC), the carbonaceous species were collected and then analyzed by using a 9-stage Anderson-type aerosol sampler and DRI Model 2001A Thermal/Optical Carbon Analyzer on the typical industrial city Nanjing in Yangtze River Delta, China in the summer, autumn and winter of 2013 and spring of 2014. OC, EC, SOC and POC exhibited obvious seasonal variations, with the highest level in winter (39.1±14.0, 5.7±2.1, 23.6±11.7 and 14.1±5.7 μg•m^-3) and the lowest level in summer (20.6±6.7, 3.3±2.0, 12.2±3.8 and 8.4±4.1 μg•m^-3), and were mainly centralized in PM_1.1 in four seasons. The concentrations of OC in PM_1.1 varied in the order of winter > autumn > spring > summer, while EC ranked in the order of autumn > winter > summer > spring. In the PM_1.1-2.1 and PM_2.1-10, the concentrations of OC and EC decreased in the sequence of winter > spring > autumn > summer. The size spectra of OC, EC and SOC had bimodal distributions in four seasons, except for EC with four peaks in summer. The size spectra of POC varied greatly with seasons, exhibiting bimodal distribution in winter, trimodal distribution in spring and summer, and four peaks in autumn. The OC/EC ratios were 7.0, 6.3, 7.6 and 6.9 in spring, summer, autumn and winter, respectively, which demonstrated the abundance of secondary organic aerosols in Nanjing. The sources of carbonaceous aerosol varied significantly with seasons, and were dominated by vehicle exhaust, coal and biomass burning in PM_2.1, and dominant by dust, coal and biomass burning in PM_2.1-10.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

The Australian tropical montane cloud forest (TMCF) is home to approximately 18 percent of the nation’s total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and WTWHA is no exception. The current average annual temperature of WTWHA in FNQ is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species endemic to the WTWHA in FNQ, which are already experiencing climate change threats. Few of these plants are used in herbal medicines. This study comprehensively reviewed metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses: i) recent developments in plant metabolomics studies that can be applied to study and understand the interactions of wet tropical plants with climatic stress, ii) medicinal plants and isolated phytochemicals with structural diversity, and iii) reported biological activities of crude extracts and isolated compounds.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters. A higher-order scheme is used to discretize geometry of the structure as well as the relevant physical quantities. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss-Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve computational efficiency, a Chebyshev approximation approach is applied to a fast calculation of the Green function that needs evaluation thousands of times. In addition, OpenMP parallel technique is used to the formation of influence coefficient matrix, which significantly reduces CPU time. Finally, computations are performed on wave exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates good performance of the present method.

Under suitable continuity and uniqueness conditions, solutions of an α order Caputo fractional boundary value problem are differentiated with respect to boundary values and boundary points. This extends well-known results for nth order boundary value problems. The approach used is a standard technique and makes heavy use of recent results for differentiation of solutions of Caputo fractional intial value problems with respect to initial conditions and continuous dependence for Caputo fractional boundary value problems.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

The definitive negative results of various direct searches for dark matter particles and non-observation of them has increasingly indicated that we should consider alternate models like the Modification of Newtonian Dynamics (MOND) and Modification of Newtonian Gravity (MONG). It is puzzling how the Oort cloud, despite its great distance from the Sun, is gravitationally bound to the Sun’s gravity. In this work, we suggest that this could be explained by the modifications to Newtonian dynamics (MOND) and to gravity (MONG).

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

The synchronization of complex networks is believed to be closely related to network topology. How to study synchronization of real network without clear topology and parameters? In this Letter, a synchronized stability boundary equation is derived that is system- and disturbance-independent and applicable to arbitrarily coupled grids. The results imply that spontaneous synchronization in a network may be network independent. These conclusions provide new research paths for network synchronization and analyze the synchronization stability of grids in a unified way.

The diffraction-like process displayed by a spatially localized matter wave is here analyzed in a case where the free evolution is frustrated by the presence of hard-wall-type boundaries (beyond the initial localization region). The phenomenon is investigated in the context of a nonrelativistic, spinless particle with mass m confined in a one-dimensional box, combining the spectral decomposition of the initially localized wave function (treated as a coherent superposition of energy eigenfunctions) with a dynamical analysis based on the hydrodynamic or Bohmian formulation of quantum mechanics. Actually, such a decomposition has been used to devise a simple and efficient analytical algorithm that simplifies the computation of velocity fields (flows) and trajectories. As it is shown, the development of space-time patters inside the cavity depends on three key elements: the shape of the initial wave function, the mass of the particle considered, and the relative extension of the initial state with respect to the total length spanned by the cavity. From the spectral decomposition it is possible to identify how each one of these elements contribute to the localized matter wave and its evolution; the Bohmian analysis, on the other hand, reveals aspects connected to the diffraction dynamics and the subsequent appearance of interference traits, particularly recurrences and full revivals of the initial state, which constitute the source of the characteristic symmetries displayed by these patterns. It is also found that, because of the presence of confining boundaries, even in cases of increasingly large box lengths, no Fraunhofer-like diffraction features can be observed, as happens when the same wave evolves in free space. Although the analysis here is applied to matter waves, its methodology and conclusions are also applicable to confined modes of electromagnetic radiation (e.g., light propagating through optical fibers).

One route to reducing CO2 emissions is to improve the energy efficiency of machines. For example, conventional combustion engines are being downsized (and down-speeded), are now running on lower viscosity engine lubricants (such as SAE 0W-20 or lower viscosity grades) and often also have stop-start systems fitted (to prevent engine idling when the vehicle is stopped). Some of these changes result in higher levels of mixed and boundary friction, and so accurate estimation of mixed/boundary friction losses is becoming of increased importance, both for estimating friction losses, and also for estimating wear volumes. Traditional approaches to estimating mixed/boundary friction, which employ real area of contact modelling such as the Greenwood-Tripp model [1] are widely used, but recent experimental data suggests that these approaches underestimate mixed/boundary friction losses [2-5]. A new model has been developed, based on experimental data, which estimates the proportion of mixed/boundary lubrication, X, as a function of the value (where is the ratio of the oil film thickness separating the surfaces to the combined root mean square surface roughness of the interface surfaces). The precise equation that describes the way in which X varies with takes the form of a “reverse S-curve”, which makes physical sense since S-curves arise naturally in growth processes, and in mixed/boundary lubrication, the real area of contact of rough surfaces grows as the load (and 1/) increases. Numerical estimates of mixed/boundary lubrication losses in internal combustion engines are presented here and compared with published data [2,6]. In addition, the improved method described here is used to estimate both the financial cost of mixed/boundary lubrication for today’s passenger car fleet, and the CO2 emissions associated with these friction losses.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

40 years of cloud observations are available globally from satellites allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x and ISCCP-HGM, are analysed. This assessment finds that all three AVHRR based cloud CDRs satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there however remain large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend is stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

The G2 gas cloud motion data and the scarcity of observations on the event horizon-scale distances have challenged the comprehensiveness of the central supermassive black hole model. In addition, the Planck Legacy 2018 release has preferred a positively curved early Universe with a confidence level higher than 99%. This study investigates the impact of the background curvature and its evolution over the conformal time on the formation and morphological evolution of central compact objects and the consequent effect on their host galaxies. The formation of a galaxy from the collapse of a supermassive gas cloud in the early Universe is modeled based on interaction field equations as a 4D relativistic cloud-world that flows and spins through a 4D conformal bulk of a primordial positive curvature considering the preference of the Planck release. Owing to the curved background, this scenario of galaxy formation reveals that the core of the galaxy undergoes a forced vortex formation with a central event horizon leading to opposite vortices (traversable wormholes) that spatially shrink while evolving in the conformal time. It indicates that the galaxy and its core are formed in the same process whereas the surrounding gas clouds form the spiral arms due to the frame-dragging induced by the fast-rotating core. It demonstrates that the accretion flow onto the central supermassive compact object only occurs at the central event horizon of the two opposite vortices while their other ends eject the relativistic jets. This can elucidate the relativistic jet formation and explain the G2 gas cloud motion if its orbit is around one of the vortices but at a distance from the central event horizon. The gravitational potential of the early curved bulk could contribute to galaxy formation while the present spatial flatness deprives the potential of bulk which could lead to galaxy quenching. The formation of a galaxy and its core simultaneously could explain the growth of the supermassive compact galaxy core to a mass of ~10⁹ M_⊙ at just 6% of the current Universe age.

Hands-on type training of Integrated Computational Materials Engineering (ICME) is characterized by assisted application and combination of multiple simulation software tools and data. In this paper, we present recent experiences in establishing a cloud-based infrastructure to enable remote use of dedicated commercial and open access simulation tools during an interactive on-line training event. In the first part, we summarize the hardware and software requirements and illustrate how these have been met using cloud hardware services, a simulation platform environment, a suitable communication channel, common workspaces and more. The second part of the article focuses (i) on the requirements for suitable on-line hands-on training material and (ii) on details of some of the approaches taken. Eventually, the practical experiences made during three consecutive on-line training courses held in September 2020 with 35 nominal participants each, are discussed in detail.

In this paper, we propose an efficient numerical method to solve the diffusive logistic model with a free boundary which is often used to simulate the spreading of the new or invasive species. The boundary movement is tracked by the level-set method, where the Hamilton-Jacobi weighted essentially nonoscillatory (HJ-WENO) scheme is utilized to capture the boundary curve embedded by the Cartesian grids via the embedded boundary method. Then the radial basis function-finite difference (RBF-FD) method is adopted to the spatial discretization and the IMEX scheme is considered for time integration. A variety of numerical examples are carried out to demonstrate the evolution of diffusive logistic model in different initial boundaries.

As technologies such as eco-friendly ships, electric propulsion vessels, and multi-fuel propulsion systems advance, the scope of IoT applications in maritime field is expanding, resulting in increased complexity in control factors. The gradual progression towards Maritime Autonomous Surface Ships (MASS) is further driving the evolution of ship-based IoT applications. These advancements underscore the necessity for a platform capable of ensuring reliable connectivity between ships and onshore, while also being adaptable for deployment and operation to meet the demands of service availability. The limitations of the existing single cloud architecture become evident in this context. In response to these emerging challenges, this paper presents a cloud-based horizontal data platform structure anchored in the architecture of a Multi-Region Fog Cloud. This approach is tailored to fulfill the crucial requirement of maintaining robust and seamless connectivity for vessels navigating globally. Leveraging the capabilities of the Fog Cloud, the platform enhances service responsiveness by facilitating localized data collection and distribution, complemented by efficient data caching from the Cloud Center, serving users within the same geographical region. We outline a strategic framework for enhancing and executing key ship services within the proposed architecture. These services encompass data sharing between ships to elevate collision avoidance, disseminating weather data for optimized route planning, and constructing an operator training simulator (OTS) environment through the integration of digital twin technology. After, we implement an architecture using the AWS Cloud and gauges latency and Packet Loss Rate (PLR) through connections to Seoul, California, and Frankfurt regions. The test results reveal that latency in different regions is about 2-5 times higher than that in the same region for the payload. Furthermore, packet loss occurred at roughly 2-3 Hz packet generation rate (PGR) in different regions, whereas in the same region, it commenced at approximately 10-20 Hz PGR.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of a fault(s) in the system. A subsection with variable boundaries is referred to as island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of islanding boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak busbars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB softwares.

Considerable efforts have been devoted to modifying gravity, which aim to elucidate the possible existence or nature of dark matter and energy, describe observational data more effectively, and formulate quantum gravity. In addition, despite the immense success of the quantum field theory, the framework requires renormalization techniques and breaks down at high energies. Recently, the Planck legacy 2018 release has confirmed the existence of an enhanced lensing amplitude in the cosmic microwave background power spectra, which prefers a positively curved early Universe with a confidence level higher than 99%. This study considers the implied curvature of the early Universe as the curvature of “the background or 4D conformal bulk as a manifestation of vacuum energy” and distinguishes it from the localized curvature that is induced in the bulk by the presence of celestial objects that are regarded as ”4D relativistic cloud-worlds.” Analogously, because gravity appears to emerge owing to spacetime curvature and does not exhibit critical characteristics shared by other fields, it has been incorporated as the local curvature of the bulk affecting the embedded quantum fields that are regarded as propagating “4D relativistic quantum clouds.” To consider the effects of the bulk on embedded clouds, this paper presents interaction field equations in terms of brane-world modified gravity and the perspective of geometrization of the quantum mechanics wherein gravity is manifested by the curvature of 4D conformal bulk as an indicator of the field strength of vacuum energy on the embedded 4D relativistic clouds in addition to the boundary interactions, which could remove the singularities and satisfy a conformal invariance theory. A visualization of the evolution of the 4D relativistic cloud-worlds over the conformal spacetime of the 4D bulk is presented, whereas the standard said theories can be recovered from the interaction field equations.

Quasi-interpolation is a powerful tool for approximating functions using radial basis functions (RBFs) such as Gaussian kernels. This avoids solving large systems of equations as in RBF interpolation. However, quasi-interpolation with Gaussian kernels on compact intervals can have significant errors near the boundaries. This paper proposes a quasi-interpolation method with Gaussian kernels using Chebyshev points and boundary corrections to improve the approximation near the boundaries. The boundary corrections use a linear approximation of the function beyond the interval to estimate the truncation error and add correction terms. Numerical studies on test functions show that the proposed method reduces errors significantly near boundaries compared to quasi-interpolation without corrections, for both equally spaced and Chebyshev points. The convergence and accuracy with the boundary corrections are generally better with Chebyshev points compared to equally spaced points. The proposed method provides an efficient way to perform quasi-interpolation on compact intervals while controlling the boundary errors.

The identification of solitonlike coherent structure (SCS) in boundary layer flows is crucial for understanding turbulence origins and in particular for laminar-turbulence transition. However, the task of finding solutions for solitonlike coherent structure from the Navier-Stokes equations poses a significant challenge. In this paper, based on the author's previous work [Sun (2023) in Ref.31], we are able to study convergent flow boundary layers, whose solution encompasses both shock wave and solitary wave solutions, and their superposition gives rise to solitary-like waves, namely solitonlike coherent structure. It is found that the solitonlike coherent structure can only be obtained by combining the Navier-Stokes equations and mass conservation, since the combined equations will have the third order derivatives respect to coordinate $x$, in other words, without the mass conservation condition, the Navier-Stokes equations does not contain solitonlike coherent structure by itself. Finally proved solitonlike coherent structure do exit in all kind of flows.

In order to reveal the impact of boundary-spanning activities of cooperative innovation teams on team innovation performance, this paper takes the panel data of 71 cooperative innovation teams from January to November 2022 as the research sample. It introduces intermediary variables (teamwork crafting and individual work crafting) to analyze the impact mechanism of boundary-spanning activities of teams on innovation performance, 71 teams were divided into 41 experimental groups and 30 control groups, and a quasi-natural experiment was conducted on the innovation performance of team boundary-spanning activities using the Double Difference Model (DID).Research has shown that boundary-spanning activities of collaborative innovation teams can promote team innovation performance. Team job crafting has a mediating effect on team innovation performance in boundary-spanning activities of collaborative innovation teams. Team job crafting and individual job crafting mediate between the boundary-spanning activities of collaborative innovation teams and team innovation performance. Further analysis using the double difference model found that compared to teams without boundary-spanning activities, teams with boundary-spanning activities can directly improve team innovation performance. When team reflection is vital, and task interdependence is high, it will promote team innovation performance. This research enriches the research on the effects of boundary-spanning activities of collaborative innovation teams, explores solutions based on quasi-nature, and provides a reference for improving the team innovation performance of collaborative innovation teams.

This paper proposes a data-driven approach to the calculation of ship overtaking boundary conditions in narrow channels based on ship overtaking characteristics by the Automatic Identification System (AIS) data. First, we collect the ship tracking process data in a narrow channel by processing the original AIS data, followed by a normality test and correlation analysis, and finally, the boundary conditions are obtained using a weighted K-means clustering algorithm. to identify the relevant data. We applied the proposed approach to estimate the overtaking boundary conditions of ships in the straight channel section of Shanghai South Passage. The method yields boundary conditions that significantly reduce collision risk when a ship engages in pursuit behavior in narrow waterways, providing a scientific basis for the overtaking of Marine Autonomous Surface Ships (MASS) in autonomous navigation within such environments, further promoting the automation level of USVs and autonomous vessels.

The exact similarity solutions (also called as special exact solutions) of two dimensional laminar boundary layer were obtained by Blasius in 1908, however, no similarity solutions for two dimensional turbulent boundary layers have been reported in the literature. With the help of dimensional analysis and invariance principle, Prandtl mixing length $\ell=\kappa y$ for one dimensional turbulent boundary layer is extended to $\ell(x,y)=\kappa y (1-\frac{y}{\delta})\sqrt{\frac{\nu}{U\delta}} $ for the two dimensional turbulent boundary layers, furthermore, with a similarity transformation, we successfully transform the two dimensional turbulent boundary layers partial differential equations into a single ordinary differential equation $f'''+ ff''+\beta(1-f'^2)+\kappa^2[\eta^2(1-\eta)^2f''|f''|]'=0$. As an application, similarity solutions of the two dimensional turbulent boundary layer on a flat plate at zero incidence have been studied in detail. To solve the ordinary differential equation numerically, a complete Maple code is provided.

The paper describes the Field Boundary Element Method applied to the fracture analysis of a 2D rectangular plate made of Functionally Graded Material to calculate Mode I Stress Intensity Factor. The object of the Field Boundary Element Method is the transversely isotropic plane plate. Its material presents an exponential variation of the elasticity tensor depending on a scalar function of position, i.e., the elastic tensor results from multiplying a scalar function by a constant taken as a reference. Several examples using a parametric representation of the structural response show the suitability of the method that constitutes a sight of Stress Intensity Factor evaluation of Functionally Graded Materials plane plates even in the case of more complex geometries.

A solution for the Prandtl-Blasius equation is essential to all kinds of boundary layer problems. This paper revisits this classic problem and presents a general Maple code as its numerical solution. The solutions were obtained from the Maple code, using the Runge-Kutta method. The study also considers convergence radius expanding and an approximate analytic solution is proposed by curve fitting. Similarly, the study resolves some boundary layer related problems and provide relevant Maple codes for these.

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center ops solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data − network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and native constructs of VMware Aria Operations for Logs in terms of Event Types and their distributions to group those entities, potentially automatically enrich with indicative tags, and recommend for further management tasks and policies. Our methods discriminate not only diverse kinds of applications, but also their specific deployments thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in the cloud management scenarios.

The reliance on cloud computing services and related technologies has increased due to their many benefits, including resource sharing and the need for organizations to maintain their competitive advantage in the digital market. Also, the pace of cloud growth has significantly increased because of COVID-19. Many companies and businesses have shifted their core processes to the cloud for seamless operations during the pandemic. This migration of legacy systems to the cloud creates many opportunities for businesses. However, on the other hand, it may also bring substantial challenges. Hence, a careful decision is needed to align with company goals. In this paper, we demonstrate the need and advantages of cloud computing, followed by highlighting AWS's characteristics as one of the leading cloud service providers. We also discuss the merger of AWS with SDN to improve cloud data security and performance. The outcomes of this study can provide a data migration strategy and guide decision-makers toward a secure and sustainable migration to a cloud computing environment.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

Starting from the analysis of the lack of positivity of the Cattaneo heat equation, the article addresses the thermodynamic relevance of the positivity constraint in irreversible thermodynamics, than is at least as important as the entropic constraints. The fulfillment of this condition in hyperbolic models leads to the parametrization of the concentration fields with respect to internal variables associated with the microscopic dynamics. Using Brownian motion theory as a landmarking example for deriving macroscopic transport equations from the equations of motion at the particle/molecular level, we discuss two typical problems involving hydrodynamic interactions at microscale: surface chemical reactions at a solid interface of a diffusing reactant, and mass-balance equations in a complex viscoelastic fluid, in which the physics of the interaction leads either to the overcome the parabolic diffusion model or to consider the parametrization of the concentration with respect to the degrees of freedom associated with the relaxation dynamics of the solvent fluid.

Introduction: The concept of hypersexuality belongs to modern parlance, according to a predominantly clinical meaning, understood as a psychological and behavioural alteration as a result of which sexually motivated stimuli are sought in inappropriate ways, often experienced in a way that is not completely satisfactory. Methods: Literature up to February 2023 was reviewed. Results: Forty-two articles were included in the review. Conclusion: Hypersexuality is a potentially clinically relevant condition, consisting of one or more dysfunctional and pathological behaviours of one's sexual sphere and graded according to the severity of impairment of subjective acting out; for this reason, the Perrotta Hypersexuality Global Scale (PH-GS) is suggested, which distinguishes high-functioning forms (pro-active and dynamic hypersexuality) from those of attenuated and corrupted functioning (dysfunctional and pathological hypersexuality of grade I and II). Future research is hoped to address the practical needs of this condition, such as the exact etiopathology, the role of oxytocin in dopaminergic hypotheses (and its ability to attenuate the symptomatology suffered by the patient in terms of manic drive), the best structural and functional personality framing of the subject, and the appropriate therapy to pursue.

Fibreglass composite plates are used for applications ranging from aerospace and automotive industries to construction industry. Vibroacoustic properties of the fibreglass plate has been investigated for different boundary conditions. The theory of the plate vibration and sound radiation has been used to determine the deflection of the composite plate at different locations on its surface. 3-Dimentional (3D) deformations and their corresponding contour velocities of plate have been computed for a range of frequencies. It has been shown that clamping the plate at four edges delayed first resonance of the plate by 15 Hz and second resonance of the plate by 30 Hz. Vibroacoustic indicators and the radiation impedance matrix of the plate have also been calculated. The results of this study show that fibreglass plate could be used in building industry to dampen the vibration of machine and structural vibration of buildings, and to sandwich building structures to form panels in order to attenuate airborne sound and to lower noise transmission of structural borne sound

In this work, meshless methods are applied for the solution of two-dimensional steady-state heat conduction problems with nonlocal multi-point boundary conditions (NMBC). These meshless procedures are based on multiquadric radial basis function (MQ RBF) and its modified version (i.e. integrated MQ RBF). The proposed meshless methods which were recently published in \cite{Reutskiy2016} is compared with standard collocation method (i.e. Kansa's method). Three different sorts of solution domain are considered in which Dirichlet boundary condition is specified on some part of the boundary and is related to the unknown function values at a discrete set of interior points. The influence of NMBC on the accuracy and condition number of the system matrix associated to the proposed methods is investigated. The sensitivity of the shape parameter is also analyzed in the proposed methods. Performance of the proposed approaches in terms of accuracy and efficiency is confirmed on the benchmark problems.

Recently, the conformable derivative and its properties have been introduced. In this paper, we propose and prove some new results on conformable Boundary Value Problems. First, we introduce a conformable version of classical Sturm´s separation, and comparison theorems. For a conformable Sturm-Liouville problem, Green's function is constructed, and its properties are also studied. In addition, we propose the applicability of the Green´s Function in solving conformable inhomogeneous linear differential equations with homogeneous boundary conditions, whose associated homogeneous boundary value problem has only trivial solution. Finally, we prove the generalized Hyers-Ulam stability of the conformable inhomogeneous boundary value problem.

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

This venture targets giving a correspondence framework that can improve the legal framework execution and cooperation and furthermore make it simple to lead execution audits. This examination depicts restricted admittance to the right legitimate data just as admittance to courts and court administrations as the essential factors that limit admittance to equity. The basic target fundamental for accomplishing the point incorporates planning an online easy to understand data framework that would improve correspondence and association between legal professionals like legal advisors and disputants. The undertaking uses a subjective methodology that zeroed in on the substance investigation of essential information. Key members were court laborers like attorneys and agents, just as court clients like disputants. Pre-plan discoveries uncovered that current legitimate data frameworks – basically court sites – neglect to recognize their crowd by treating all court clients similarly comparative with the correspondence of lawful data. Likewise, the issue of admittance to equity includes the significant expense of equity also, restricted legal data that elevates admittance to equity. Post-plan discoveries from the online overview directed after the turn of events and execution of the IT curio uncovered a powerful and productive legitimate data framework. Exploration discoveries supported the advancement of an IT antiquity that records for huge shortcomings in current legal data frameworks, for example, their failure to pass on fitting legitimate data intelligently and productively. In outline, the investigation prescribes the reception of saw control to work fair and square of certainty of court clients and the differentiation between court clients to convey the right data to the right crowd.

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

In the summertime, convections occur frequently over the Tibetan Plateau (TP) because of the large dynamic and thermal effects of the landmass. Measurements of vertical air velocity in convective cloud are useful for advancing our understanding of the dynamic and microphysical mechanisms of clouds and can be used to improve the parameterization of current numerical models. This paper presents a technique for retrieving high-resolution vertical air velocity from convective cloud over the TP, by using Doppler spectra from a vertically pointing Ka-band cloud radar. The method is based on the development of a “small-particle-traced” idea and the necessary data processing and uses three modes of radar. Spectral broadening corrections, uncertainty estimations, and result merging are used to ensure accurate results. Qualitative analysis of two typical convective cases shows that the retrievals are reliable and agree with the expectant results inferred from other radar measurements. A quantitative retrieval of vertical air motion from a ground-based optical disdrometer is used to preliminarily validate our radar-derived results. The comparison illustrates that while the data trends from the two methods of retrieval are similar, with the updrafts and downdrafts coinciding, cloud radar has a much higher resolution and can reveal the small-scale variation of vertical air motion.

The studies of laminar unsteady boundary layer flows is crucial for understanding laminar-turbulence transition and origins of turbulence. However, the task of finding its solutions poses a significant challenge. In this paper, we propose a novel approach by introducing a similar transformation to convert the 2D unsteady laminar boundary layer equations into a single partial differential equation. By applying this transformation, we are able to obtain the "exact" similarity solutions for the 2D unsteady laminar boundary layer equations, specifically for the case of flat plate boundary flow. Notably, this is the first time that such an exact solution has been obtained. Application of this exact solution is being used to solve Stokes' first problem in 2D boundary layer. Perspectives on the transition fron Rayleigh solution to Blasius solution is provided.

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally, leads to the experimental application of Artificial Intelligence (AI) in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from Very high resolution (VHR) World View-2 image, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for machine, as against 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data, that could neither be geometrically compared with human digitised, nor actual cadastral data from the field. These results provide an updated snapshot with regards to the performance of contemporary machine-drive feature extraction techniques compared to conventional manual digitising.

This paper constituted an extension of two previous studies concerning the mathematical development of the grain boundary grooving in polycrystalline thins films in the cases of the evaporation/condensation and diffusion taken separately. The thermal grooving processes are deeply controlled by the various mass transfer mechanisms of evaporation–condensation, surface diffusion, lattice diffusion, and grain boundary diffusion. This study proposed a new original analytical solution to the mathematical problem governing the grain groove profile in the case of simultaneous effects of evaporation-condensation and diffusion in polycrystalline thin films submitted to thermal and mechanical stress, and fatigue effects; by resolving the corresponding fourth-order partial differential equation ∂y∂t=C∂2y∂x2-B∂4y∂x4 obtained from the approximation y'2≪1. The comparison of the new solution to that of diffusion alone proved an important effect of the coupling of evaporation and diffusion on the geometric characteristics of the groove profile. A second analytical solution based on the series development was also proposed. It was proved that change of the boundary conditions of the grain grooving profile largely affected the different geometric characteristics of the groove profile.

The study of the change in entropy during the formation of stars in collapsing molecular clouds is an important area of research in astrophysics. As the cloud decreases in size, the presence of gases leads to an increase in entropy. However, the extent of this change in entropy is influenced by the amount of space available for the gases. In this scientific investigation, I conduct a detailed analysis of entropy on a small scale within the contracting cloud, following the principles of the second law of thermodynamics. Initially, my focus is on a turbulent cloud composed of N particles with a mass of M, which collapses under the force of gravity. It becomes clear that in order for the collapse to occur, gravitational energy must overcome other pressure forces that oppose it. This process results in the rapid movement of particles within the cloud, ultimately leading to a change in entropy

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

The Smartphone industry has expanded significantly over the last few years. According to the available data, each year, a marked increase in the number of devices in use is observed. Most consumers opt for Smartphones due to the extensive number of software applications that can be downloaded on their devices, thus increasing their functionality. However, this growing trend of application installation brings an issue of user protection, as most applications seek permission to access data on a user’s device. The risks this poses to sensitive data is real to both corporate and individual users. While Android has grown in popularity, this trend has not been followed by the efforts to increase the security of its users. This is a well-known set of problems, and prior solutions have approached it from the ground up; that is, they have focused on implementing reasonable security policies within the Android’s open-source kernel. While these solutions have achieved the goals of improving Android with such security policies, they are severely hampered by the way in which they have implemented them. In this work, a framework referred to as CenterYou is proposed to overcome these issues. It applies a pseudo data technique and a cloud-based decision-making system to scan and protect Smartphone devices from unnecessarily requested permissions by installed applications and identifies potential privacy leakages. The current paper demonstrated all aspects of the CenterYou application technical design. The work presented here provides a significant contribution to the field, as the technique based on pseudo data is used in the actual permissions administration of Android applications. Moreover, this system is user and cloud-driven, rather than being governed by over-privileged applications.

Quality of Service (QoS) has a significant role in the provision of resources within service oriented distributed systems. In Quality of Service, cloud computing creates new challenges for improvements using the concept of virtualization. Currently, Cloud Computing is very emerging technology in every field of data storage and resource distribution over the network. Considering this new emerging technology, for the ease of data accessibility, price, resource use, restoration, response time and number of constraints the quality performance measures need to be upgraded. The paper highlights the research gap in providing a solution to achieve a Quality of Services in Cloud Computing. We also review the issues and challenges arising in cloud computing to guarantee quality.

In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitude during summer. We study noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employ linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a Convolutional Neural Networks (CNN) based method for the detection of noctilucent clouds. The results clearly indicate that the CNN based approach outperforms LDA based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.

Mobile robots need an environmental perception ability in order to interact with the surrounding environment. In this paper we present the G-SLAM method, where the map is consisted of a cloud of scattered points in the continuous space and each point is accompanied by an obstacle existence probability. On the other hand the robot's pose (and trajectory) is estimated by an particle filters while the cloud of points is estimated by an adaptive recursive algorithm which is presented. Main feature of the presented method is the adaptive repositioning of the scattered points and their convergence around obstacles. In addition to our previous publications, In this paper a mathematical derivation of the method is presented and real case scenarios are discussed, presented and compared with other methods.

The Lorentz boost and rapidity for a radially falling frame are derived for the Schwarzschild metric in Kruskal-Szekeres coordinates. It is found through this analysis that the spacetime is radially length contracted toward the metric source in the falling frame as a result of the Lorentz boosting relative to the rest frame (which is shown to be consistent with the rest frame). When the falling frame reaches the event horizon, the Lorentz boost goes to infinity, causing the falling frame to become light-like, but trapped at the horizon. It is shown that in the falling frame at the horizon, the density of the source goes to infinity as a result of the length contraction.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

The Chebyshev collocation method (ChC) implemented as Chebfun is used in order to solve a class of second order one-dimensional singular and genuinely nonlinear boundary value problems. Efforts to solve these problems with conventional ChC have generally failed, and the outcomes obtained by finite differences or finite elements are seldom satisfactory. We try to fix this situation using the new Chebfun programming environment. However, for toughest problems we have to loosen the default Chebfun tolerance in Newton's solver as the ChC runs into trouble with ill-conditioning of the spectral differentiation matrices. Although in such cases the convergence is not quadratic the Newton updates decrease monotonically. This fact, along with the decreasing behaviour of Chebyshev coefficients of solutions, suggests that the outcomes are trustworthy, i.e., the collocation method has exponential (geometric) rate of convergence or at least an algebraic rate. We consider first a set of problems that have exact solutions or prime integrals and then another set of benchmark problems that do not possess these properties. Actually, for each test problem carried out we have determined how Chebfun solution converges, its length, the accuracy of the Newton method and especially how well the numerical results overlap with the analytical ones (existence and uniqueness).

Active flow control of canonical laminar separation bubbles by steady and harmonic vortex generator jets (VGJs) was investigated using direct numerical simulations. Both control strategies were found to be effective in controlling the laminar boundary-layer separation. However, the present results indicate that using the same blowing amplitude, harmonic VGJs were more effective and efficient in reducing the separated region than the steady VGJs considering the fact that the harmonic VGJs use less momentum than the steady case. For steady VGJs, longitudinal structures formed immediately downstream of injection location led to formation of hairpin-type vortices causing an earlier transition to turbulence. Symmetric hairpin vortices were shown to develop downstream of the forcing location for the harmonic VGJs as well. However, the increased control effectiveness for harmonic VGJs flow control strategy is attributed to the fact that shear-layer instability mechanism was exploited. As a result, disturbances introduced by VGJs were strongly amplified leading to development of large-scale coherent structures, which are very effective in increasing the momentum exchange, thus, limiting the separated region.

This paper represents a groundbreaking paradigm shift in network optimization. Departing from traditional static methodologies, this innovative approach harnesses the power of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) to optimize cloud networks dynamically. By integrating advanced AI algorithms, this framework continuously adapts and evolves, ensuring optimal real-time performance. This dynamic optimization enhances efficiency and resilience, allowing cloud networks to adjust seamlessly to changing demands and conditions. Through the fusion of cutting-edge technology and adaptive intelligence, this approach heralds a new era in network optimization, empowering organizations to achieve unprecedent-ed levels of agility and scalability in their cloud infrastructures.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

Mobile Cloud Computing(MCC) is a recent technology used by various users worldwide. In 2015, more than 240 million users used mobile cloud computing which gives a profit of $5.2 billion to service providers. MCC is a combination of Mobile computing and cloud computing. By the combination of these two, it gives various challenges like network access, elasticity, management, availability, security, privacy, etc. Here security issues are considered because both the security issues of mobile computing and cloud computing are considered as important like data security, virtualization security, partitioning security, Mobile cloud application security, Mobile device security. This paper gives a detailed study of security issues in mobile cloud computing and its prevention measures.

This article will provide an overview on internet of things (IoT), 5G communication System and Distributed Clouds. The basic concepts and benefits will be briefly presented, along with current standardization activities. In a nutshell, but the research will focus on relating internet of things, 5G and Distributed Cloud Computing.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

It is strongly desirable to accurately detect the clouds in hyperspectral images onboard before compression. However, conventional onboard cloud detection methods are not appropriate to all situation such as shadowed cloud or darken snow covered surfaces which are not identified properly in the NDSI test. In this paper, we propose a new spectral–spatial classification strategy to enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is performed to classify the cloud pixels coarsely based on spectral information. Then aMRF is performed to do optimal process by using spatial information, which improved the classification performance significantly. Some misclassification points still exist after aMRF processing because of the noisy data in the onboard environment. DSR is used to eliminate misclassification points in binary labeling image after aMRF. Taking level 0.5 data from hyperion as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method can provide cloud mask for the on-going EO-1 images and related satellites with the same spectral settings without manual intervention. The experiment indicate that the proposed method reveals better performance than the classical onboard cloud detection or current state-of-the-art hyperspectral classification methods.

We primarily investigate the existence of solutions for fractional neutral integro-differential equations subjected to Neumann-type boundary conditions, which is crucial for understanding natural phenomena. Taking into account factors such as neutral type, fractional-order integrals, and fractional-order derivatives, we employ probability density functions, Laplace transforms, and resolvent operators to formulate a well-defined concept of a mild solution for the specified equation. Following this, by integrating fixed point theorems, we establish the existence of mild solutions under more relaxed conditions.

The magnetic polarization tensor has a promising capability of determining the geometry and material properties of metallic samples. In this paper, a novel computation method is proposed to estimate the magnetic polarization tensors for the metallic samples using the boundary element method. In this method, the metallic sample is placed in a uniformly distributed magnetic field. Based on assumptions that the excitation frequency and/or the conductivity of the sample is very high, the metallic sample is regarded as a perfect electrical conductor (PEC). Therefore, the scattered field at a certain distance can be simulated. By utilising the boundary element method, the magnetic polarization tensor can be derived from the simulated scattered field. The theoretical calculation is presented and simulations and experiments have been carried out to validate the proposed method. The results from the simulation are matched with the analytical solution for the case of sphere samples. Moreover, there is a good agreement between the simulation results and the experimental results for the copper cylindrical samples.

It is shown that, for a de Sitter Universe, the Hartle-Hawking (HH) wave function can be obtained in a simple way starting from the Friedmann- Lemaitre-Robertson-Walker (FLRW) line element of cosmological equations. An oscillator having imaginary time is indeed derived starting from the Hamiltonian obtaining the HH condition. This proposes again some crucial matter on the meaning of complex time in cosmology. In order to overcome such difficulties, we propose an interpretation of the HH framework based on de Sitter Projective Holography.

In this article, we establish the existence of and then compare smallest eigenvalues for nabla fractional boundary value problems involving a fractional difference boundary condition, using the theory of u0-positive operators with respect to a cone in a Banach space.

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

Background: Advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Methods: Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies are used for developing a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the whole genome sequencing dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). Results: The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Conclusions: Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to high performance computing clusters and cloud platforms for different production scales and requirements.

Localized turbulence is common in geophysical flows, where the roles of rotation and stratification are paramount. In this study, we investigate the evolution of a stratified turbulent cloud under rotation. Recognizing that a turbulent cloud is composed of vortices of varying scales and shapes, we start our investigation with a single eddy. Compared to an eddy under pure rotation, the stratified eddy shows the physical manifestation of a known potential vorticity mode, appearing as a static stable vortex. In addition, the expected shift from inertial waves to inertial-gravity waves is observed. In our numerical simulations of the turbulent cloud, carried out at a constant Rossby number over a range of Froude numbers, stratification causes columnar structures to deviate from vertical alignment. This deviation increases with increasing stratification, slowing the expansion rate of the cloud. The observed characteristics of these columnar structures are consistent with the predictions of linear theory, particularly in their tilt angles and vertical growth rates, suggesting a significant influence of inertial-gravity waves. Using Lagrangian particle tracking, we have identified regions where wave activity dominates over turbulence. In scenarios of milder stratification, these inertial-gravity waves are responsible for a significant energy transfer away from the turbulent cloud, a phenomenon that attenuates with increasing stratification.

Whether it’s cellphones, personal computers, or gaming consoles, technology is a part of everyone’s everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services—AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services’ shared features and unique aspects that they provide to consumers.

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality calling for sustainable measures to reduce the overall consumption and peak load. The dawn of the smart grid concept, embedded systems and ICTs have paved the way to novel HEMS design. In this regard, the Internet of Things (IoT), an enabler for smart and efficient energy management systems is seeing increasing attention for optimizing HEMS design and mitigate its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low-cost, technology availability and scalability. We focus on the backend complexities of IoT Home Area Networks (HAN) using the OCF IoTivity-Lite middleware. To augment the quality, servicing and reduce cost and complexities, this work leverages open-source Cloud technologies from Back4App as BaaS to provide consumer and Utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) and appliance operation control via a HEM App via an Android smartphone.

Abstract—as the complexity in the cloud services increases day by day the role of brokers used in the cloud gain more importance. Here we basically resolve this issue by discussing preference based cloud service recommender that support MCDM approach.^[24] The implementation and specifications details are properly discussed in a unified way to deal with the problem. SC3 is a tool that compromises capabilities of the quality assurance dimension of CSB and also it strengthen the flexibility of cloud services. The anxieties between definition procedure and implementation procedure are separated by service completeness compliance checker (SC3). Now a days companies use cloud computing for their economic benefits and market competitions that increase the demand of “cloud computing”. So for the calculation of critical success factor of “cloud computing” we focus on plan do check act strategy.

This research proposed in this paper focuses on gathering evidence from devices with Windows 10 operating systems in order to discover and collect artifacts left by cloud storage applications that suggest their use even after the deletion of the Google client application. We show where and what type of data remnants can be found using our analysis which can be used as evidence in a digital forensic investigations.

Non-uniform grain boundary sliding can induce strain and rotation incompatibilities at perfectly planar interfaces. Explicit analytic expressions of stress and lattice rotation jumps are thus derived at a planar interface in the general framework of heterogeneous anisotropic thermo-elasticity with plasticity and grain boundary sliding. Both elastic fields are directly dependent on in-plane gradients of grain boundary sliding. It is also shown that grain boundary sliding is a mechanism that may relax incompatibility stresses of elastic, plastic and thermal origin although the latter are not resolved on the grain boundary plane. This relaxation may be a driving force for grain boundary sliding in addition to the traditionally considered local shears on the grain boundary plane. Moreover, the obtained analytic expressions are checked by different kinds of bicrystal shearing finite element simulations allowing grain boundary sliding and where a pinned line in the interface plane aims at representing the effect of a triple junction. A very good agreement is found between the analytic solutions and the finite element results. The performed simulations particularly emphasize the role of grain boundary sliding as a possible strong stress generator around the grain boundary close to the triple line because of the presence of pronounced gradients of sliding.

Warm cloud-precipitation plays a vital role in the hydrological cycle, weather, and climate. Comprehensive observation and study of warm cloud-precipitation can advance our understanding of the internal physical processes and provide valuable information for developing the numerical models. This paper mainly focused on a study of characteristics of warm cloud-precipitation in South China during the pre-flood season using datasets observed from a Ka-band cloud radar, laser ceilometer and disdrometer. Eighteen kinds of quantities from these three instruments were used to precisely elucidate the distribution, diurnal variation, vertical structure, and physical property of warm cloud-precipitation. The results showed that the occurrence of aloft cloud-precipitation decreased with the increase of height, and most of the hydrometeors were distributed below 2 km. During the observation period, the ground rainfall mainly came from light precipitation; however, short-time and sharp showers contributed to the majority of rain amounts. Most of the cloud layers were single-layer, with base heights below 2.2 km, thickness thinner than 2.1 km, and top heights within 0.6-4.2 km. Warm cloud-precipitation owned certain diurnal variations, with a rising trend of cloud base heights in the afternoon and midnight. During 0230-1100, 1200-1800, and 2100-2300, the convections were relatively active with higher cloud tops, thicker cloud thickness, and higher rainfall occurrences. Separation and statistical results of cloud and precipitation indicated that they owned different vertical structures and physical properties, exhibiting different value ranges and changes of radar reflectivity, vertical air motion, particle size, number concentration, liquid water, and rain rate at different height levels. The particle size distributions of cloud and precipitation both were exponential. Radar-derived raindrop size distribution was very coherent with the ground measurement when the reflectivity of precipitation was within 10-20 dBZ. However, for other reflectivity regimes, instrument sensitivity, sampling height, attenuation, and non-precipitating weak targets can affect the comparison.

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of the data itself. In the latest decades, in the fields of bioengineering data management, among others, eye-tracking data has become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which led to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system is developed by using Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. Results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios. Even if preliminary, results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

Nowadays, information security is a challenge especially when transmitted or shared in public clouds. Many of researchers have been proposed technique which fails to provide data integrity, security, authentication and another issue related to sensitivity data. The most common techniques were used to protect data during transmission on public cloud are cryptography, steganography, and compression. The proposed scheme suggests an entirely new approach for data security on public cloud. Authors have suggested an entirely new approach that completely makes secret data invisible behind carrier object and it is not been detected with the image performance parameters like PSNR, MSE, entropy and others. The details of results are explain in result section of paper. Proposed technique have better outcome than any other existing technique as a security mechanism on a public cloud. Primary focus of suggested approach is to minimize integrity loss of public storage data due to unrestricted access rights by uses. To improve reusability of carrier even after data concealed is really a challenging task and achieved through suggested approach.

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we~propose a novel pragmatic approach for the process analysis, implementation and execution. This~is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models' implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

We consider boundary value problems for a nonlinear mass transfer model, which generalizes the classical Boussinesq approximation, under inhomogeneous Dirichlet boundary conditions for the velocity and the substance’s concentration. It is assumed that the leading coefficients of viscosity and diffusion and the buoyancy force in the model equations depend on concentration. We develop mathematical apparatus of studying inhomogeneous boundary value problems under consideration. It is based on using of a weak solution of the boundary value problem and construction of liftings of the inhomogeneous boundary data. They remove the inhomogeneity of the data and reduce original problems to equivalent homogeneous boundary value problems. Based on this apparatus we will prove the theorem of the global existence of a weak solution to boundary value problem under study and establish important properties of the solution. In particular, we will prove the validity of the maximum principle for the substance’s concentration. We will also establish sufficient conditions for the problem data, ensuring the local uniqueness of weak solutions.

Recently, Li et al [1] investigated the nonlinear Langevin equations existence including two fractional orders with infinite-point boundary conditions. It has shown that the outcomes given it does count on the solution form for which it has boundary values. It means that their solution needs more to be in the final step. In current work, we will have discussion about the existence and uniqueness for the same boundary conditions by investigating the closely explicit solution form that has no boundary values. Numerical example is supported to have more vision to compare the between new and previous results. It turns out that our results are superior.

In the laboratory practice, several standards for testing the bending stiffness of corrugated board are used. There are often cases of tests where the results depend on the way the sample is placed on the supports. The problem arises when the board is five-ply (with two corrugated layers with different corrugation heights) or when the board has asymmetrically selected papers on the flat layers. In this article, we focus our attention on the problem related to boundary conditions, with particular attention to the local effects of the support of the sample. Since the cardboard layers, both flat and corrugated, have a small thickness, a slight deformation of the papers can always be observed at the point of contact between the sample and the support, which affects the readings of the measured stiffness. The paper presents theoretical and numerical analyzes showing how much the method of supporting the sample affects the measured bending stiffness of various samples. Numerical observations were also compared with the results of analyzes presented by other scientists as well as with experimental results.

Modeling fluid flow in three-dimensional (3D) Discrete Fracture Networks (DFNs) is of relevance in many engineering applications, such as hydraulic fracturing, oil/gas production, geothermal energy extraction, nuclear waste disposal and CO₂ sequestration. A new Boundary Element Method (BEM) technique with discontinuous quadratic elements and a parallel Domain Decomposition Method (DDM) is presented herein for the simulation of the steady-state fluid flow in 3D DFN systems with wellbores, consisting of planar fractures having arbitrary properties and wellbore trajectories. Numerical examples characterized by DFNs of increasing complexity are investigated to evaluate the accuracy and efficiency of the presented technique. The results show that accurate solutions can be obtained with less nodes than with mesh-based methods (e.g. Finite Element Method). In addition, the DDM algorithm used provides a quite fast convergence. The simulation results of the fluid flow around intersections among traces (linear intersections between fractures), intersections between traces and a fracture boundaries, and wellbore intersections is accurate. Source code is available at : https://github.com/BinWang0213/PyDFN3D.

To solve large scale linear equations involved in the Fast Multipole Boundary Element Method (FM-BEM) efficiently, an iterative method named the generalized minimal residual method (GMRES)(m)algorithm with Variable Restart Parameter (VRP-GMRES(m) algorithm) is proposed. By properly changing a variable restart parameter for the GMRES(m) algorithm, the iteration stagnation problem resulting from improper selection of the parameter is resolved efficiently. Based on the framework of the VRP-GMRES(m) algorithm and the relevant properties of generalized inverse matrix, the projection of the error vector r_m+1 on r_m is deduced. The result proves that the proposed algorithm is not only rapidly convergent but also highly accurate. Numerical experiments further show that the new algorithm can significantly improve the computational efficiency and accuracy. Its superiorities will be much more remarkable when it is used to solve larger scale problems. Therefore, it has extensive prospects in the FM-BEM field and other scientific and engineering computing.

Eastern boundary upwelling systems feature strong zonal gradients of physical and biological ocean properties between cool, productive coastal oceans and warm, oligotrophic subtropical gyres. Zonal currents and jets (striations) are therefore likely to contribute to the transport of water properties between coastal and open oceanic regions. Multi-sensor satellite data are used to characterize the signatures of striations in sea surface temperature (SST), salinity (SSS), and chlorophyll-a (Chl-a) in subtropical eastern North/South Pacific (ENP/ESP) upwelling systems. In the ENP, tracers exhibit striated patterns extending up to ~2500 km offshore. Striations in SST and SSS are highly correlated with quasi-zonal jets, suggesting that these jets contribute to SST/SSS mesoscale patterns via zonal advection. Chl-a striations are collocated with sea surface height (SSH) bands, a possible result of mesoscale eddy trains trapping nutrients and forming striated signals. In the ESP, striations are only found in SST and coincide with SSH bands, consistently with quasi-zonal jets located outside major zonal tracer gradients. An interplay between large-scale SST/SSS advection by the quasi-zonal jets, mesoscale SST/SSS advection by the large-scale meridional flow and eddy advection may explain the persistent ENP hydrographic striations. These results underline the importance of quasi-zonal jets for surface tracer structuring at the mesoscale.

Aerosol-cloud interactions play a crucial role in shaping Earth’s climate and hydrological 1 cycle. Observing these interactions with high precision and accuracy is of the utmost importance 2 for improving climate models and predicting Earth’s climate. Over the past few decades, lidar 3 techniques have emerged as powerful tools for investigating aerosol-cloud interactions due to their 4 ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and 5 temporal resolutions. This review paper provides an overview of recent advancements in the study 6 of aerosol-cloud interactions using lidar techniques. The paper begins with a description of the 7 different cloud microphysical processes that are affected by the presence of the aerosol, and with 8 an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The 9 subsequent sections delve into the key findings and insights gained from lidar-based studies of 10 aerosol-cloud interactions. This includes investigations into the role of aerosol particles in cloud 11 formation, evolution, and microphysical properties. Finally, the review concludes with an outlook 12 on future research. By reporting the latest findings and methodologies, this review aims to provide 13 valuable insights for researchers engaged in climate science and atmospheric research.

: In today’s rapidly expanding era of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the emphasis on robust data security and insightful data interpreta- tion is more pronounced than ever. This research introduces a comprehensive approach to both data protection and predictive analytics, leveraging the diverse dataset TON IoT.csv, sourced from a myriad of IoT and IIoT environments. For data security, a dual-encryption technique incorporating both AES and RSA algorithms is established. Its efficacy is evidenced by a perfect match between the original and decrypted datasets, underscoring the integrity of our encryption process. Concurrently, the study ventures into predictive modeling using a modified Snake Optimization Algorithm (SOA) to streamline hyperparameter selection. This subsequently aids in the development and fine-tuning of an LSTM network, which exhibits remarkable predictive accuracy. Additionally, the paper provides an in-depth examination of various encryption methodologies like elliptic curve cryptography (ECC), Lightweight Cryptography for Cloud computing, and homomorphic encryption, while also emphasizing the nuances of encryption in cloud setups, particularly contrasting server-side with client-side encryption and efficient key management. The insights presented serve as a cornerstone for ensuing research, promising a bright future for advancements in IoT and IIoT data protection and analysis

In recent years, Cloud Computing and Big Data have been considered the most attractive areas that are revolutionizing the IT world. Cloud Computing paradigm has recently appeared that allows running proprietary or difficult portable applications outside their original software environment on one or more virtual hardware platforms. Therefore, we are to developing such techniques which make it possible to secure communication between the communicating Cloud entities. These techniques must take into account several factors due to the data transmitted in this type of environment is proprietary and of significant size. Conventional data security techniques are not suitable for today's cloud usage. Hence, the main research of this thesis is to define an adaptable architecture with the aim to propose a scalable system that supports cloud services. We will define feasible security solutions dedicated to the Cloud computing context in order to robustly protect data stored in the Cloud. We are more precisely looking for working on NoSQL databases. We also intend to propose a secure solution based on the blockchain that has powerful features like decentralization, autonomy, security, reliability, and transparency.

Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration so it becomes possible for application managers to deploy their applications in a unified manner across different cloud providers. A practical problem is however feature incompatibility between different K8s vendors, who offer commercial K8s products based on the open-source K8s distribution. A large number of documented features in this open-source distribution are optional features that are turned off by default, but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper we present a unified, vendor-agnostic feature management approach that bypasses the proprietary customization interface of K8s vendors in order to consistently activate optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration; this disruption impact does not appear using the proprietary customization methods of the K8s vendors. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.

Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way how the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.

The web ecosystem is rapidly evolving with changing business and functional models. Cloud platforms are available in a SaaS, PaaS and IaaS model designed around commoditized Linux based servers. 10 billion users will be online and accessing the web and its various content. The industry has seen a convergence around IP based technology. Additionally, Linux based designs allow for a system wide profiling of application characteristics. The customer is an OEM who provides Linux based servers for telecom solutions. The end customer will develop business applications on the server. Customers are interested in a latency profiling mechanism which helps them to understand how the application behaves at run time. The latency profiler is supposed to find the code path which makes an application block on I/O, and other synchronization primitives. This will allow the customer to understand the performance bottleneck and tune the system and application parameters.

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

The processing of cloud free geo-referenced imagery is one of the preliminary processing step of any land application. This letter describe the methodology developed to obtain a seamless cloud free composite of Africa for 2016 using Sentinel-2A data at 10 meters resolution freely available from the European Space Agency. The method is based on an hybrid method resulting from the merging of the two most robust time series methods namely the "darkest pixel" and the "maximum NDVI" previously developed with AVHRR time series.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.