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My Turn is software used to manage several Covid-19 mass vaccination campaigns in California. The objective of this article is to describe the use of My Turn at two points of dispensing in California and comment on process improvements to reduce manual tasks of six identified processes of vaccination–registration, scheduling, administration, documentation, follow-up, and digital vaccine record–and paper. We reviewed publicly available documents of My Turn and patients vaccinated at George R. Moscone Convention Center in San Francisco and Oakland Coliseum Community Vaccination Clinic. For publicly available documents of My Turn, we examined videos of My Turn on YouTube, and documentation from EZIZ, the website for the California Vaccines for Children Program. For patients, we examined publicly available vaccination record cards on Instagram and Google. At the George R. Moscone Convention Center, 329,608 vaccines doses were given. At the Oakland Coliseum Community Vaccination Clinic, more than 500,000 vaccine doses were administered. The use of My Turn can be used to reduce manual tasks and paper for mass vaccinating patients against Covid-19. [ABSTRACT FROM AUTHOR]

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users' query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions. [ABSTRACT FROM AUTHOR]

Computers and other digital communication media have replaced paper and pencil from the writer's desk. This development has confronted special collections with a problem, as digital papers are difficult to process using established digital preservation strategies, because of their individual and unique nature. According to the proposal suggested in this paper, the creators should instead be involved in the preservation process, and special collections should integrate pre-custodial forms of curation within their range of tasks. The article outlines the use of a cloud architecture as a suitable instrument for accomplishing this task. The benefits and the prospects for such a collection cloud are exposed and discussed. [ABSTRACT FROM AUTHOR]

Important paper-based documents exposed to forgery such as: official certificates, birth, marriage, death certificates, selling and buying documents and other legal documents is more and more serious and sophisticated. With the purposes of fraud, appropriation of property, job application and assignment in order to swindle public authorities, this forgery has led to material loss, belief deterioration as well as social instability. There are many techniques has been proposed to overcome this issue such as: ink stamps, live signatures, documented the transaction in third party like the court or notary. In this paper, it's proposed a feasible solution for forgery prevention for paper-based documents using cloud computing application. With the application of quick response bidirectional barcode and the usage of hash algorithm. The study aims at developing an electronic verification system for official and issued books (documents, endorsements, and other official books) to/from different sections of the Institute using QR technology. [ABSTRACT FROM AUTHOR]

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR's broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh's agriculture areas were affected. [ABSTRACT FROM AUTHOR]

Clouds are crucial in regulating the Earth's energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites. [ABSTRACT FROM AUTHOR]

The technologies of artificial intelligence and cloud computing systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence. [ABSTRACT FROM AUTHOR]

The adoption and popularity of mobile devices by end-users is partially driven by the increasing development and availability of mobile applications that can aid solving different problems and provide access to services in a wide range of domains or categories, namely healthcare, education, e-commerce or entertainment. While these applications use and benefit from the combination of a wide panoply of technologies from the Internet of Things, fog and cloud computing, data security and privacy are typically not fully taken into account before the creation of many mobile applications or during the software development phases. This paper presents an in-depth approach to modeling attacks on the specific cloud and mobile ecosystem, given its importance in the process of secure application development. Moreover, aiming at bridging the knowledge gap between developers and security experts, this paper presents an alpha version of the security by design for cloud and mobile ecosystem (secD4CloudMobile) framework. secD4CloudMobile is a set of tools that covers cloud and mobile security requirement elicitation (CMSRE), cloud and mobile security best practices guidelines (CMSBPG), cloud mobile attack modeling elicitation (CMAME), and cloud mobile security test specification and tools (CM2ST). The purpose of the framework is to provide cloud and mobile application developers useful readily applicable information and guidelines, striving to bring security engineering and software engineering closer, in a more accessible and automated manner, aiming at the incorporation of security by construction. Finally, the paper presents some preliminary results and discussion. [ABSTRACT FROM AUTHOR]

In vehicular cloud computing (VCC) systems, the vehicle cloud (VC) consists of computing resources for multiple vehicles to assist the remote cloud (RC) in making real-time decisions on driving vehicles, to improve road safety and driving comfort. The acquisition of additional cloud facilities to increase the capacity of VCC systems when processing intensive requests for cloud resources is costly. A recent approach uses multiple remote microclouds (RMCs) to assist RCs in processing requests because they are smaller and less costly than RCs. Therefore, this paper presents a VCC system with multiple RCs and RMCs, in which an algorithm for allocating request resources among VCs, RMCs, and RCs. Subsequently, this paper proposes an improved simulated annealing algorithm (ImSA) based on density of vehicles in a geographical area to deploy RCs and RMCs with the minimal deployment cost and response cost. For real applications, the accuracy and effectiveness of the proposed algorithm are evaluated on the basis of the real traffic data from three different areas (i.e., urban area, outskirts, and suburban area) in one week. [ABSTRACT FROM AUTHOR]

This research paper is written to provide a hypothetical study on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by Venkatesh et al. (2003); framework on Microsoft OneDrive usage acceptance in one of the established professional college in Malaysia. Questionnaires were distributed to 130 staff and lecturers at one of the branches with 107 returned, an 82.3% return rate. Statistical Package for the Social Sciences (SPSS) version 22 has been used to analyse the data collected. Five variables involved in this study which are Performance Expectancy, Effort Expectancy, Social Norm, Facilitating Conditions and Usage Acceptance. The study recommends future studies should include a bigger scale of the sample that consists of all the professional college branches and add more variables such as trust and security. [ABSTRACT FROM AUTHOR]

The dual-scrambling scheme that combines position transformation and bit-plane transformation is a popular image encryption scheme. However, such schemes need more key information, and the encryption and decryption processes are complicated. In addition, the existing quantum image dual-scrambling schemes mainly deal with square images. In this paper, we propose a hybrid scrambling encryption scheme for multi-mode quantum image representation (MQIR) images based on random permutation, in which the H × W quantum image is represented in MQIR. A random number generator factor s uniquely associates one of the random permutations of integers from 1 to a positive integer, so as to hybrid scramble both the pixel position and the binarized position of each pixel value. Meanwhile, the quantum circuits and some examples of scrambling are given. Furthermore, various analyses of the performance of this scheme were conducted, including effectiveness, key space, and computational complexity. By modifying the random generation factor to construct multiple binary grayscale images, the simulated results on the IBM Quantum Cloud platform demonstrate that the proposed quantum image encryption scheme is effective. In comparison to existing quantum image dual scrambling schemes, it is both simple and effective, offering a large key space, lower computational complexity, and applicability to non-square quantum images. [ABSTRACT FROM AUTHOR]

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users' Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers. [ABSTRACT FROM AUTHOR]

In the last decade, the smart grid has utilized many modern technologies and applications compared with the conventional grid. Cloud computing offers numerous managed services and storage solutions for smart grid data. To balance security, privacy, efficiency, and utility, more efforts should be made to keep up with the rapid evolution of technology. Searchable encryption techniques are widely considered an intelligent solution to ensure data privacy and security while maintaining the functionality to search over encrypted data. In this paper, we propose a more reliable and efficient searchable symmetric encryption scheme for smart grid data. It is a dynamic keyword searchable scheme that uses a symmetric cipher based on a key hashing algorithm (DKS-SCKHA) to generate a secure index. This scheme eliminates the false-positive issue associated with the use of bloom filters and narrows the scope of the retrieved search results. Additionally, it efficiently supports both partial and exact query processing on the encrypted database. Both theoretical and security analyses demonstrate the efficiency and security of the DKS-SCKHA scheme compared to other previous schemes. Comprehensive experiments on a smart grid dataset showed that the DKS-SCKHA scheme is 35–68% more efficient than the schemes compared in this paper. The DKS-SCKHA scheme supports three keyword search scenarios: single, conjunctive, and disjunctive. Furthermore, the DKS-SCKHA scheme is extended to support dynamic fuzzy keyword search on the encrypted database (DEFKS-SCKHA). We evaluated the security and efficiency of the DEFKS-SCKHA scheme through security analysis and experimental evaluation. Theoretical analysis shows that the proposed scheme is secure against known-ciphertext and known-background attacks. [ABSTRACT FROM AUTHOR]

The Communist Party of China's 19th National Congress Report has clearly set the goal of "smart society" and put forward new requirements for the current development of territorial spatial planning. Nowadays, the empowerment of information technology based on the Internet, big data, cloud computing, etc. is the main driving force and development focus of the current practices of smart territorial spatial planning. How to understand and promote the development of smart society is the foundation of the compilation and implementation of smart territorial spatial planning. This paper emphasizes the importance of human-land relationship and the theory of urban life organism to the planning and governance of territory in a smart society. It points out that the overall conceptualization of smart territorial spatial planning should be transformed from informational empowerment to comprehensive empowerment, which includes technological empowerment and innovative empowerment. The paper constructs a smart territorial spatial planning framework-EPTI-based on the ideas of ecological civilization, people- oriented, technology integration application and institutional innovation, and discusses the paths toward smart compilation and implementation of territorial spatial planning. [ABSTRACT FROM AUTHOR]

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications. [ABSTRACT FROM AUTHOR]

Healthcare is a vital part of a healthcare system, which is governed by a set of responsibilities and responsibilities. This paper explores the evolution of healthcare systems through the use of the Internet of Things (IoT) and telemedicine. The paper aims to identify areas for improvement and explore strategies to leverage network technologies to enhance healthcare offerings and outcomes. The data analysis technique used was thematic analysis. Thematic analysis identified and categorized key themes related to challenges and innovations in the collected studies. Telemedicine, supported by cloud computing, offers significant potential for improving healthcare delivery but faces challenges related to high assurance, interoperability, and storage adaptability. The development and use of telecommunications technology in healthcare require careful policy considerations to address the significant challenges in healthcare delivery. The research also explores how technology influences the development of healthcare delivery systems known for its efficiency, accessibility, and improved patient welfare. [ABSTRACT FROM AUTHOR]

Datacenters are the primary driving force behind the energy shift to renewable sources. This shift limits the environmental impact of non-renewable sources and decarbonizes datacenters. However, renewable energy generation fluctuates with time and location, which introduces uncertainty in fulfilling user requests (URs). Thus, non-renewable energy generation continues to power the datacenters to make stability. Recent works direct to the use of renewable energy generation followed by non-renewable generation while assigning the URs to the resources of the datacenters. These works do not model the uncertainty of renewable and non-renewable energy resources and the level of uncertainty. This paper extends the three benchmark algorithms, namely future-aware best-fit (FABEF), highest available renewable energy first (HAREF) and round-robin (RR), by incorporating uncertainty and its level (UNL), and we call them UNL-FABEF, UNL-HAREF and UNL-RR, respectively. The goal of UNL-FABEF is to minimize the overall cost, whereas UNL-HAREF is to maximize the available renewable energy usage. On the contrary, UNL-RR assigns the datacenter to the URs in a roundabout fashion. This paper also introduces the UNL multi-objective scheduling algorithm (UNL-MOSA) to make a trade-off between UNL-FABEF and UNL-HAREF. UNL-MOSA creates a balance between the overall cost and the available renewable energy usage. All four algorithms consider three UNLs of URs, namely low, medium and high, for renewable energy resources. These algorithms are tested using fifty instances of ten datasets with 200 to 2000 URs and 20 to 200 datacenters and compared using five performance metrics: the overall cost, number of used renewable and non-renewable energy resource slots, and uncertainty cost and time. The performance of four algorithms in these performance metrics is extensively examined to know their applicability. [ABSTRACT FROM AUTHOR]

The application of STEM (Science, Technology, Engineering, and Math) education in solving real-world problems is challenging. To tackle this challenge, a project-based learning approach that integrates robots with cost-effective self-made toys to problem-solving was adopted to assist rural primary school students to apply STEM skills. The purpose of this study was to evaluate the effects of the project-based learning approach which includes lecturing and hands-on activities on primary school students' STEM learning outcomes and attitudes. An experiment was conducted with 25 primary school students who voluntarily participated in a STEM summer-camp programme. Students were asked to assemble a paper house equipped with an IoT control module and LED light switches, and write and edit robot scripts to produce a robot-based storytelling narrative using the paper house they made as a context. The results show that the project-based learning approach was an effective approach for cultivating primary school students' STEM knowledge and skills as evidenced from the post-written test. The relationships between the students' STEM learning outcomes and attitudes were also confirmed by a clustering analysis. Students who had higher learning attitudes also achieved higher STEM learning outcomes; the findings are also supported by the feedback from the open-ended questionnaire items. This study suggests that integrating low-cost self-made toys and robots in project-based learning activities is an effective and practical approach to enhance primary school students' learning outcomes and learning attitudes in STEM education. [ABSTRACT FROM AUTHOR]

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively. [ABSTRACT FROM AUTHOR]

Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

To investigate how the petroleum industry evaluates and selects powerful cloud service providers, first, an evaluation index system including 25 indices such as scalability and private data protection is built. This index system can systematically examine the comprehensive strengths of cloud service providers. Aiming to solve the problems that the traditional expert evaluation method has high requirements on expert experience and is easily affected by subjective factors, a novel artificial intelligence evaluation model named TentISSA-BPNN is proposed. The objective evaluation ability of this model can be effectively used in evaluation research on cloud service providers for petroleum enterprises. In this model, the SSA algorithm is optimized by Tent chaotic mapping and adaptive inertia weight; an algorithm, TentISSA, that has good stability and fast convergence speed is designed and proposed; and the BPNN is improved with TentISSA to obtain more accurate evaluation results. To evaluate the performance of the TentISSA algorithm, nine unimodal and multimodal functions are selected in this paper to test the convergence accuracy. Then, seven models are selected as the control groups to validate the effectiveness and performance of the TentISSA-BPNN evaluation model proposed in this paper. Finally, the preprocessed data of the candidate cloud service providers are input into the trained neural network model proposed in this paper for evaluation. Based on the ranking of the evaluation scores, the comprehensive strengths of the cloud service providers are obtained to provide a decision-making reference for managers of petroleum enterprises in the process of choosing cloud service providers. [ABSTRACT FROM AUTHOR]

The ICTs industry is witnessing the rise of a groundbreaking technology known as cloud computing. This innovation is built upon the Internet, offering a virtual infrastructure for computers. This virtual setup encompasses databases, servers, storage, software, networking, processing power, office applications, and artificial intelligence. What sets cloud computing apart is its capacity to deliver services without the need for physical proximity to the underlying computer hardware. This approach brings with it numerous advantages over traditional computing methods, including grid computing. Also Cloud Computing introducing new challenges for environmental protection. This paper offers an extensive survey, overview and analysis of the fundamental concepts, historical evolution, components of cloud computing, diverse cloud services and categories, cloud integration, the merits of this technology, its focal areas, security considerations, and the implications of adopting cloud computing. This comprehensive analysis is based on the examination of more than 26 research papers and articles. The findings of this survey shed light on the transformation that the IT industry undergoes before and after embracing cloud computing. [ABSTRACT FROM AUTHOR]

Recently, several private and political cloud initiatives emerged in Europe. This paper demonstrates how the sociotechnical imaginaries of three European cloud projects reveal a performative coupling of innovation and political ideas of control, territoriality and sovereignty. I ascertain three elements of the concept of sociotechnical imaginaries (innovation, boundary making and material properties) guiding the empirical analysis. Taking technology in the making and its role in (geo)politics seriously, this paper shows how imaginaries shape and interact with current geostrategic and political developments in Europe. The analysis of Microsoft's cloud, Bundescloud and GAIA-X reveals that rising privacy and data security issues have been integrated into cloud imaginaries that traditionally highlight progress and innovation. More specifically, state actors and cloud providers link and sometimes merge allegedly opposing technological aspects of innovation and politicised ideas of control such as digital sovereignty. This shift constitutes a move towards erecting political borders and localising IT within a global infrastructure. [ABSTRACT FROM AUTHOR]

Akin to the impact that digital microelectronics had on electronic devices for information technology, digital microfluidics (DMF) was anticipated to transform fluidic devices for lab-on-a-chip (LoC) applications. However, despite a wealth of research and publications, electrowetting-on-dielectric (EWOD) DMF has not achieved the anticipated wide adoption, and commercialization has been painfully slow. By identifying the technological and resource hurdles in developing DMF chip and control systems as the culprit, we envision democratizing DMF by building a standardized design and manufacturing platform. To achieve this vision, we introduce a proof-of-concept cloud platform that empowers any user to design, obtain, and operate DMF chips (https://edroplets.org). For chip design, we establish a web-based EWOD chip design platform with layout rules and automated wire routing. For chip manufacturing, we build a web-based EWOD chip manufacturing platform and fabricate four types of EWOD chips (i.e., glass, paper, PCB, and TFT) to demonstrate the foundry service workflow. For chip control, we introduce a compact EWOD control system along with web-based operating software. Although industrial fabrication services are beyond the scope of this work, we hope this perspective will inspire academic and commercial stakeholders to join the initiative toward a DMF ecosystem for the masses. [ABSTRACT FROM AUTHOR]

Virtualisation is a major part of the cloud as it permits the deployment of several virtual servers over the same physical layer. Due to the adaption of cloud services, the count of the application running on repositories increases, resulting in overload. However, the application migration in the cloud with optimal resource allocation is still a challenging task. The application migration is employed to reduce the dilemma of resource allocation. Hence, this paper proposes a technique for portability and interoperability-based application migration in the cloud platform. The cloud simulation is done with the Physical Machine (PM), Virtual Machine (VM), and container. The interoperable application migration is provided using the newly devised Lion-based shuffled shepherd (Lion-SS) optimisation algorithm. The Lion-SS algorithm combines the shuffled shepherd optimisation algorithm (SSOA) and the Lion optimisation algorithm (LOA). The new objective function is devised based on predicted load, demand, transmission cost, and resource capacity. Besides, the prediction of the load is performed using Deep long short-term memory (Deep LSTM). The proposed technique obtained the minimal load of 0.007 and resource capacity of 0.342. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices' energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network's objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object's movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding. [ABSTRACT FROM AUTHOR]

Cloud computing has several benefits over traditional systems, such as scalability and high availability. However, these benefits, to be eventuated, require efforts in the area of resource provisioning and scaling, to match resources to current and future demand, and this is not always trivial to achieve. Since workload may fluctuate substantially in cloud environments, over-provisioning is a common practice to avoid abrupt quality of service (QoS) drops that may result in service level agreement (SLA) violations, but at the price of increased provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a clouds can be improved. Therefore, in this paper, we show the potential benefits of a proactive resource provisioning scheme augmented by three of the most promising machine learning prediction techniques in this context, namely ARIMA, MLP, and GRU, that are known to be able to cope with the dynamic behavior of our target applications. We analyze the trade-off between resource consumption and quality of service using SLA violations in web workloads, considering real case provisioning requirements and constraints, extensively simulating and analyzing the impact of prediction and scaling intervals, and publishing all used tools and datasets to allow reproducibility. Simulation experiments with a proactive approach are executed using real traces from NASA and Wikipedia workloads and achieved a reduction of 40% in SLA violations on average when compared to a reactive approach, while reducing the provisioned resources by almost 3%. [ABSTRACT FROM AUTHOR]

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and cloud computing are emerging technologies whose use has been proven in various fields of medicine and healthcare. The positive impact of these technologies on improving the performance of medical centers is also evident. Therefore, the purpose of the present study is to investigate the technological requirements of the Cloud Internet of Things (CIoT) in the healthcare industry. This was a quantitative study conducted in two rounds using the Delphi method. A non-random, purposeful, and criterion-based method was used for sampling. A minimum of one paper or research project in the IoTs, cloud computing, or CIoT fields should be completed by participants. In addition to having a master's degree or specialized doctorate and at least 5 years of work experience, they should be faculty members. Fifteen participants in the first round of Delphi and 12 participants in the second round who completed all of the questionnaire's questions made up the research sample. A questionnaire was used to gather information. This questionnaire was designed in four main parts with 14 axes and 48 questions. After collecting the questionnaires in the first round of Delphi, each axis was scored on a five-point Likert scale. Finally, the requirements for the implementation of CIoT in healthcare were determined by the collective agreement of experts. Functional requirements of the network layer and cloud computing, platform layer requirements, and components of quality requirements were extracted. The architectures of the application layer, cloud layer, server layer, network layer, and user layer are explored thoroughly. Furthermore, communication technologies, equipment types, analysis and management, wireless networks and sensors, secure data exchange and management, deployment details, and approaches for security management and control are the main infrastructural keys that are extracted during the Delphi rounds. The findings showed that, with a very high level of importance, the use of wireless local networks was approved more than other network layer components. Compliance with security principles emerged as the most crucial component of the platform layer. We also found the use of hybrid clouds and the necessity of software as a service to be important. All the experts emphasized the need to use application development tools, cloud services, development, and deployment on the platform as a service, data analysis, and application programming interface related to the requirements of the platform layer. Scientifically speaking, they found that the components of secure information exchange, establishing secure protocol communication, key exchange mechanisms, and maintaining information confidentiality are crucial in the field of secure data exchange. Based on the findings, it can be concluded that the implementation of this technology can improve the quality of clinical services and increase the speed of responses and patient satisfaction. It is expected that by identifying the technological requirements of CIoT, a suitable model can be designed for healthcare. [ABSTRACT FROM AUTHOR]

As Global Navigation Satellite Systems (GNSS) infrastructure expands and more satellites are launched, the number of signals and measurements available to users increases, resulting in a corresponding rise in data generation rates. GNSS receivers with the ability to process triple frequency measurements and multi-constellation reception, have been complemented with the massive deployment of connected, GNSS-enabled devices such as smartphones, wearables, and IoT sensors, triggering the growth of a highly dispersed cloud of GNSS data. This trend observed in other domains presents both challenges and opportunities for applications of GNSS technology. The scientific exploitation of GNSS data involves developing new concepts through the analysis of the aforementioned sources of information. Traditionally, the extraction of insights from GNSS data has relied on searching and downloading datasets from multiple repositories. These datasets provided by numerous organisations are scattered worldwide, containing gems of information difficult to track and discover. This approach hinders research productivity, as scientists are forced to shift their focus from data exploration, analysis, modelling, visualization, and realisation of conclusions to a long list of repetitive and manual tasks of little added value. The GNSS Science Support Centre (GSSC) hosts ESA's innovative Digital Platform for GNSS, GSSC Now, which integrates a large GNSS repository with cutting-edge features for data exploration and analysis. The paper presents key functionalities of GSSC Now and highlights its value through case studies of real-world GNSS applications. This platform expands established computational limits, placing the data source side-by-side with cloud computing capabilities. The change of paradigm provides scientists with seamless execution of Machine Learning models, interactive algorithms and visualisation tools on a vast repository of GNSS data, removing barriers that hinder research productivity. [ABSTRACT FROM AUTHOR]

In human microbiome studies, mediation analysis has recently been spotlighted as a practical and powerful analytic tool to survey the causal roles of the microbiome as a mediator to explain the observed relationships between a medical treatment/environmental exposure and a human disease. We also note that, in a clinical research, investigators often trace disease progression sequentially in time; as such, time-to-event (e.g., time-to-disease, time-to-cure) responses, known as survival responses, are prevalent as a surrogate variable for human health or disease. In this paper, we introduce a web cloud computing platform, named as microbiome mediation analysis with survival responses (MiMedSurv), for comprehensive microbiome mediation analysis with survival responses on user-friendly web environments. MiMedSurv is an extension of our prior web cloud computing platform, named as microbiome mediation analysis (MiMed), for survival responses. The two main features that are well-distinguished are as follows. First, MiMedSurv conducts some baseline exploratory non-mediational survival analysis, not involving microbiome, to survey the disparity in survival response between medical treatments/environmental exposures. Then, MiMedSurv identifies the mediating roles of the microbiome in various aspects: (i) as a microbial ecosystem using ecological indices (e.g., alpha and beta diversity indices) and (ii) as individual microbial taxa in various hierarchies (e.g., phyla, classes, orders, families, genera, species). To illustrate its use, we survey the mediating roles of the gut microbiome between antibiotic treatment and time-to-type 1 diabetes. MiMedSurv is freely available on our web server (http://mimedsurv.micloud.kr). [ABSTRACT FROM AUTHOR]

With the emergence of the Industrial Revolution 4.0, modern technologies such as cloud computing, artificial intelligence, and big data are profoundly transforming the education ecosystem. The development of education is not only faced with huge challenges but also contains rare opportunities. New concepts such as deep learning, adaptive learning, and blended learning do not break through the inherent barriers of learning theory, and they also expedite the restoration of the educational technology ecosystem. Based on the ASSURE model, this paper systematically discusses the new development concept of educational technology. Heinich, Russell, and Molenda proposed the ASSURE model in 1989. The "A" stands for Analyze Learner, the first "S" is assumed as Objectives and State Standards; the second "S" for Selecting Media, Strategies, Materials, and Technology; the "U" for Utilizing Materials, Media, and Technology; the "R" stands for Required Learner Participation; and the "E" is assigned for Revise and Evaluate. It is widely used in classroom instruction, online learning, and organizational training. Educational technology must embrace new technology, renew the concept of educational development, shape the new pattern of educational ecology, and better serve the needs of cultivating innovative talents. [ABSTRACT FROM AUTHOR]

With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called n − b r o o m transposition trees. A single broom or simply a b r o o m is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an n − b r o o m is created by joining the ends of n brooms to one vertex, known as the n − b r o o m center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an n − b r o o m for n > 2 that reduces to a novel 2 − b r o o m algorithm and that further reduces to two instances of a 1 − b r o o m algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler. [ABSTRACT FROM AUTHOR]

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions. [ABSTRACT FROM AUTHOR]

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. [ABSTRACT FROM AUTHOR]

Applying blockchain technology to the cloud manufacturing platform (CMfg) endowed it with features such as decentralized resource management, distributed data storage, and platform robustness. However, during the implementation of service optimization composition, challenges are encountered, including the need for centralized resource scheduling, data entry dependence on the moment of block generation, and a lack of dynamic adaptability in the execution scheme. To address this, this paper proposed a solution in the form of cloud dual-chain oriented to service optimization composition. The cloud dualchain comprised a manufacturing service chain (MSC) and a process data chain (PDC). It segregated the management of service resources and composition scheme into distinct chains, allowing the CMfg to reconcile blockchain characteristics with real-world service collaboration needs. The manufacturing service within the cloud dual-chain was self-managed by the enterprise, and the MSC chain was formed by smart contracts based on task orders, balancing distributed and centralized management. The PDC, generated based on service composition, recorded process data in parallel. In case of service failure, a new service was invoked in the corresponding MSC node, updating the information in the PDC node to ensure dynamic adaptability of the execution plan. Experimental results demonstrate that cloud dual-chain facilitates flexible service management, distributed parallel entry of process data, and provides dynamic adaptability to execution scheme. Cloud dual-chain can effectively support the development of CMfg. [ABSTRACT FROM AUTHOR]