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This paper presents a novel, sensor-enabled centralised monitoring system for fault detection in streetlights. Unlike traditional methods that depend on citizen reports or manual inspections, this system utilizes Internet of Things (IoT) technology to identify faults automatically and efficiently. The system comprises monitoring devices equipped with sensors and transceivers attached directly to streetlights. These devices collect data on light source functionality and potential wiring faults. This data is then wirelessly transmitted to a central transmitter, which relays it to a cloud platform. A machine learning model, trained with an accuracy of 98.8%, analyzes the data and predicts the streetlight's operational status. The predicted status and detailed fault information are displayed on a dedicated webpage, allowing stakeholders to monitor remotely. This eliminates manual searches, expedites repairs due to precise fault knowledge, and empowers linemen for faster resolution. Overall, this cost-effective system significantly improves streetlight maintenance efficiency, enhancing public safety and contributing to a well-lit urban environment. [ABSTRACT FROM AUTHOR]

Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care. [ABSTRACT FROM AUTHOR]

Vocal learning has found its proliferation in recent years due to the advancement in wireless networking. Vocal art encompasses profound cultural values and aesthetic preferences, making wireless platforms, e.g. Wi-Fi and ZigBee, essential for the enhancement of online vocal learning. ZigBee, as a wireless platform, has more recently been used quite frequently for online vocal learning and music. This paper aims to design an efficient wireless-enabled platform by using the lightweight features of ZigBee to support online vocal learning and expressions. The platform enables data transmission, storage, analysis, and playback of vocal learning sounds through the ZigBee network. It is built on a cloud platform, utilizing Docker virtualization technology to deploy a Hadoop distributed cluster, effectively simulating a distributed environment. The platform incorporates a model for recognizing musical sound and noise signals, empowering the online learning control platform to detect various characteristics of vocal music tones. Vocal music learning is enhanced by extracting endpoint fusion spectrum features and employing a tone adjustment and dynamic detection model. The proposed method improves anti-interference capability, online learning control, vocal tone recognition, and emotional expression accuracy. Simulation results demonstrate its effectiveness in vocal music, online learning control, and tone recognition, leading to more precise vocal music pronunciation and intonation registration. [ABSTRACT FROM AUTHOR]

With the development of multimedia and the Internet, the problem of multimedia copyright infringement has become more and more serious. The combination of digital watermark and security protocol plays a vital role in copyright protection of multimedia in the up-to-date open network environment. Most of the existing schemes involve trusted third parties and arbitration institutions, but they are not usually credible. This paper proposes a new watermarking protocol with multiple parties based on the blockchain. The protocol involves the buyer, the seller, the cloud platform and the blockchain. In the protocol, the certificateless secret key negotiation between the buyer and the seller is utilized to avoid "Man-in-the-middle Attack", and the cloud platform is used to perform homomorphic calculations and watermark embedding operation. By using the characteristics of the blockchain such as non-tamperability and traceability, the security and fairness of the protocol can be ensured. [ABSTRACT FROM AUTHOR]

Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body, thus creating an excessive constraint on the safe construction and sustainable development of cities. The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety. Although the current time-series prediction method can accomplish relatively high accuracy, the predicted settlement points are independent of each other, and the existence of spatial dependence in the data itself is lost. In order to unlock this problem, a spatial convolutional long short-term memory neural network (ConvLSTM) based on the spatio-temporal prediction method for land subsidence is constructed. To this end, a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area. A convolutional structure to extract spatial features is utilized in the proposed model, and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation, thereby rationally predicting the land subsidence progress trend and distribution. The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62% of the understudied area, and the overall mean absolute error (MAE) is reduced by about 7%. The achieved results exhibit better prediction in the subsidence center region, and the spatial distribution characteristics of the subsidence data are effectively captured. The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Data mining algorithms can process target data and extract useful hidden information, which is helpful for decision making. However, current mining algorithms have some shortcomings such as time-consuming processing of big data or inability to process massive data. Since data mining technology cannot be used in the traditional cloud platform environment, it is necessary to improve the algorithm to make it more suitable for the cloud platform environment. By analyzing the actual application process of BP classification algorithm, this paper expounds the practicability of BP classification algorithm, analyzes the data mining process based on Hadoop cloud platform, and expounds the development ideas of BP classification algorithm. The source of data mining algorithm supported by cloud computing is discussed. Finally, Hadoop cloud platform. This paper designed the corresponding system architecture and data interface, and established a suitable test environment for this system, and completed the simulation experiment test through the design. The calculation time of this algorithm is proportional to the amount of data, showing a linear relationship. In data mining, the optimized BP algorithm in this paper can significantly save resources in terms of spatial features. This paper designs an optimized operating system based on Hadoop platform through comprehensive analysis and improvement algorithm. [ABSTRACT FROM AUTHOR]

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people's awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle. [ABSTRACT FROM AUTHOR]

Worldwide, buildings consume about 40 percent of the overall energy resources and contribute to an average of 30 percent of the global carbon emission. Hence, technologies for improving the energy efficiency of buildings play an essential role in the global fight against climate change. The CELSIUS project aims to improve the energy efficiency and indoor climate of medium to large sized commercial and public buildings by developing an integrated system solution that consists of (1) an IoT-enabled and cloud-based platform for monitoring and diagnostics of building energy performance and indoor climate quality, (2) a middleware software platform for cost-effective large-scale deployment of wireless sensors and gateways, and (3) an IoT network management platform for cost-efficient life-cycle maintenance of sensors and gateways. The integrated system solution will be deployed and demonstrated in a 6000 m2 building in Aarhus, Denmark, and an 18,000 m2 building in Kuala Lumpur, Malaysia. By choosing buildings located in different climate zones on different continents allows the developed system solution to be tested under realistic conditions for the international export market. [ABSTRACT FROM AUTHOR]

The rapid expansion of the remote sensing satellite constellation produced large volumes of Earth's remote sensing data. New approaches are required to store and access the data to enable real-time analytics. As consumers demand not just standard ERS data processing but an integrated virtual environment for ERS data applications, the most promising and sought-after solution for many users is a cloud platform for ERS data processing and analysis. The range of available cloud platforms is extensive which makes it difficult for users to understand the internal structure, interconnections between the platform components, their functions, and properties. There is a need for a scientific systematization of such platforms. This is a review of international and Russian cloud platforms. We identified the standard structure of a cloud platform and its layers with their dedicated functionality, autonomy, standardization, and horizontal scalability. The review indicates that cloud platforms are the most promising tool for ERS data processing and analytics. [ABSTRACT FROM AUTHOR]

With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. However, the past sports training records of students are often accumulated with time and stored in a central cloud platform and therefore, the data volume is too large to be processed with quick response. In addition, the past sports training records of students often contain certain sensitive information, which probably discloses partial user privacy if we cannot protect the data well. Considering these two challenges, a privacy-aware and efficient student clustering approach, named PESC is proposed, which is based on a hash technique and deployed on a central cloud platform connecting multiple local servers. Concretely, in the cloud platform, each student is firstly assigned an index based on the past sports training records stored in a local server, through a uniform hash mapping operation. Then similar students are clustered and registered in the cloud platform based on the students' respective sport indexes. At last, we infer the personalized sport preferences of each student based on their belonged clusters. To prove the feasibility of PESC, we provide a case study and a set of experiments deployed on a time-aware dataset. [ABSTRACT FROM AUTHOR]

To solve the problem of lack of trust in cloud platforms, we propose an image encryption algorithm based on the Lattice Hash function and privacy protection in this paper. A new chaotic system using Tent Map and Sine Map is designed in our proposed scheme, which does not have a period window. Meanwhile, our algorithm uses a new chaos system to provide random matrices F for Lattice Hash functions. The image feature vector and the initial key are also employed to obtain the security key, which can be used as input to the Lattice Hash. Besides, our algorithm utilizes the Paillier cryptosystem to encrypt the feature vector as the ciphertext image index for higher security. In addition, the 2D-Line map is improved and applied as a function for scrambling pixel positions to avoid the problem of storing pixel subscripts. Simulation experiments and security analysis shows that our proposed algorithm can resist differential attacks, statistical attacks, brutal attacks. Therefore, our scheme has good security performance and allows precise search over the ciphertext. [ABSTRACT FROM AUTHOR]

Digitization of industrial enterprises is proposed, permitting the collection, analysis, and visualization of data regarding products, systems, machines, and facilities. The cloud platform of the Industrial Internet of Things permits the connection of any physical devices and sensors to the digital information space. Algorithms and functions included in the platform permit rapid adjustment of the system for equipment monitoring and its adaptation to the needs of the enterprise. By integrating data from physical devices and corporate systems, the enterprise may attain an unprecedented level of transparency and control over all assets and processes. [ABSTRACT FROM AUTHOR]

Cloud internet of things (IoT) is an emerging technology that is already impelling the daily activities of our lives. However, the enormous resources (data and physical features of things) generated from Cloud-enabled IoT sensing devices are lacking suitable managerial approaches. Existing research surveys on Cloud IoT mainly focused on its fundamentals, definitions and layered architecture as well as security challenges. Going by the current literature, none of the existing researches is yet to provide a detailed analysis on the approaches deployed to manage the heterogeneous and dynamic resource data generated by sensor devices in the cloud-enabled IoT paradigm. Hence, to bridge this gap, the existing algorithms designed to manage resource data on various CloudIoT application domains are investigated and analyzed. The emergence of CloudIoT, followed by previous related survey articles in this field, which motivated the current study is presented. Furthermore, the utilization of simulation environment, highlighting the programming languages and a brief description of the simulation packages adopted to design and evaluate the performance of the algorithms are examined. The utilization of diverse network communication protocols and gateways to aid resource dissemination in the cloud-enabled IoT network infrastructure are also discussed. The future work as discussed in previous researches, which pave the way for future research directions in this field is also presented, and ends with concluding remarks. [ABSTRACT FROM AUTHOR]

In a large-scale data center, it is vital to precisely recognize the termination statuses of applications at an early stage. In recent years, many machine learning techniques have been applied to this issue, which is beneficial for optimizing the scheduling policy and improving the efficiency of resource utilization. However, if the application's dynamic information is insufficient at the early stage, the generalization performance of the machine learning model will be lessened, and the prediction accuracy could be low. To overcome this problem, a novel failure prediction method that is based on the association relationships between similar jobs is proposed in this paper to jointly predict task's termination statuses at an earlier stage. The similar jobs whose tasks have similar changing modes of consumed resources, an inherent structural correlation may exist, and the correlation information is significant for improving the prediction model's generalization performance. First, a job clustering algorithm is proposed for identifying the jobs with higher similarity from jobs that have various numbers of tasks. Second, based on the job clustering results, the robust multi-task learning algorithm is introduced to effectively utilize the domain information among jobs (i.e. interactional relationship among jobs on the termination statuses of task). Experiments are conducted on a Google cluster workload traces dataset. The results show that the proposed method can realize higher prediction accuracy, lower misjudgment rate, and higher predictive stability than several state-of-the-art methods at 1/3 the running time of the tasks. [ABSTRACT FROM AUTHOR]

It is the right time to embark upon wireless sensor networks to overcome problems of the structures safety by analyzing them precisely using structural health monitoring techniques. This research lays emphasis on the development of an IOT based smart instrumentation for analyzing the health structure based on simple accelerometer (ADXL320) and MCU node. The accelerometer sensing paves way in determining the health structure by observing the non-linear vibrations in the structure. The real-time data acquired from the sensor is transmitted to a cloud platform (Thingspeak) using a secure API communication that helps client to observe the frequency response of the entire structure remotely. The developed instrumentation is a power efficient, low cost solution having a latency of 15 s, and remarkably efficient. [ABSTRACT FROM AUTHOR]

Firstly, this paper introduces the types of clustering algorithm, and introduces the classical K-means algorithm and canopy algorithm in detail. Then, combining the map reduce computing model and spark cloud computing framework, this paper introduces the parallel Canopy-K-means algorithm after using Canopy algorithm to optimize the initial value of K-means algorithm. However, because Canopy algorithm needs to introduce a new distance threshold parameter T2, and the parameter needs to be set by human experience, it is difficult to determine the parameter artificially for large data, so this paper proposes a parallel adaptive Canopy-K-means algorithm, which can be used in cloud computing framework to determine the distance threshold parameter T2 adaptively based on statistical method. Using the parallelism of Map-Reduce computing model, the parallel Canopy-K-means algorithm is optimized by adaptive parameter estimation, which solves the problem that parameters depend on manual experience selection in Canopy process. After introducing the relevant theories and derivation process of this algorithm, cloud computing experiment platform is built based on the Spark framework, and the contrast experiments were performed using the Stanford Large Network Dataset Collection (SNAP) dataset and self-built Dimension Networks dataset. The experimental results show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper focuses on the core problem of database resource integration mode. The theme of this paper is the construction and implementation of database resource integration model, and some legal and commercial constraints related to it, so the content of the research involves the integration of technology, integration system, the integration of the status analysis and other aspects. The basic concept, classification, structure system and basic principles of Oracle RAC are discussed. Combined with business needs, a solution based on Oracle RAC is proposed. We implemented the planning and deployment of shared storage, file storage and network setup based on Oracle RAC database. The automatic failover without user intervention is realized, which greatly improves the reliability of the database, and has a certain guiding significance for the application and construction of the enterprise database. First, the research status of database resources integration at home and abroad is expounded through literature analysis. Secondly, the model of database resource integration is put forward, and its realization is discussed. Thirdly, the status of database resource integration is analyzed. Finally, the problems to be solved are put forward in the end. Through the research of this topic, it provides some reference and basis for the database resource integration model. At the same time, it also makes a paving for the maximum elimination of information isolated island and promoting the sharing of database resources. [ABSTRACT FROM AUTHOR]

In view of the dynamic demand of the business flow for the intelligent transportation cloud service platform, the relevant technologies such as platform workflow business process are researched, the workflow system architecture based on the cloud service platform is designed and implemented, and the process customization solution for the cloud service platform is proposed Program. Based on the design and implementation of a workflow-based process customization model, this program establishes a workflow-based protocol with a platform as its core. The workflow engine manages the entire process state and implements the intelligent scheduling of platform workflow. Finally, through the concrete application of customer service call system in cloud platform, the feasibility and effectiveness of the proposed technology are verified. [ABSTRACT FROM AUTHOR]

In order to explore the data sharing in the cloud platform, which is easy to manage and use data, but also ensures the safety of the data, on the basis of data sharing, the study of cloud platform wireless sensor network monitoring system for environmental monitoring methods is carried out. The prototype system of environmental monitoring cloud platform is built, which achieves the support and management of the sensor data and file data. The sensor data, in the system, adopts the OGCSWE standard, and provides the corresponding interface for other systems. In the platform, it is capable of querying the data needed by users, and making the paid or unpaid data sharing, which is convenient for users to use more and more useful data. The test results show that, the system runs stably and runs normally at the same time of multi users landing, and the data acquisition speed is fast. Based on the above findings, it is conclude that, under the condition of relying on distributed computing and parallel computing in cloud platform, the system can effectively monitor in a wide range and process the data quickly to achieve the hardware and software services required by users. [ABSTRACT FROM AUTHOR]

Large amounts of data at various temporal and spatial scales require terabyte (TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine (GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges. [ABSTRACT FROM AUTHOR]

In recent years, with the increase in users in social network, the social network has had the feature of big data. The large-scale social network has become an indispensable part in people's life. However, the traditional data mining technology cannot suit the large-scale social network. Thus, it is urgent to develop a more suitable mining technology for the large-scale social network. In this section, a crawler model based on semantic analysis and spatial clustering is proposed firstly. Then, the content extraction model based on document object model tree is built to extract the target text information from the links fetched by the proposed crawler model. The similarities between textual information in different regions are computed to choose the important information. Moreover, a two-stage topic clustering model based on time information is presented. The time information is introduced into the similarity computation between two posts or clusters. The single-pass algorithm is improved and applied in different clustering stage to improve the clustering accuracy. Finally, the proposed algorithms are evaluated on Hadoop platform. The Hadoop platform can effectively reduce the computing time and improve the server quality of users in large-scale social network. Meanwhile, the experiments demonstrate that the proposed algorithms are suitable for the data processing in large-scale social network. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing is used widely due to the vital growth of cloud resources along with its great performance, effective use, and on-demand resources availability. On the other hand, the cloud is uncertain due to various intrusion attacks such as malicious software and backdoors creation. The virtualization concept is popular in the cloud due to its virtual concept for hardware and software. In the cloud, the employment of an intrusion detection system (IDS) needs a scalable and virtualized infrastructure for reducing the traffic that is often carried by the virtualized server. In this work, the cloud platform-based virtual machine anomaly detection system is designed. The Globality-aware Locality Preserving Projection and Incremental Virtual Machine Workload Clustering Algorithm are designed for important modules including data dimension reduction and anomaly detection in the system. From the experimental analysis, it is inferred that the design not only improves accurateness of the online anomaly detecting mechanism, but also the average operation time is less than 0.25 s consistent with the timeliness requirements, shortens the average calculating time to a greater extent, improves the timeliness, and achieves the objective of anomaly detection. [ABSTRACT FROM AUTHOR]

There are more and more application systems running on the cloud platform, which will produce large amounts of effective data everyday. In order to preserve them and make fun use of the storage space, those effective data must be compressed and those compressed data, if necessary, should be recovered correctly. Meanwhile, there are a lot of equivalent data item values (or equivalent data item values within the system error) in the original data. So, it is not right to compress those effective data directly. In order to make fun use of the storage space and correctly recover the original data, a new method occurs. When compressed, those effective data must be processed firstly and then the handled data should be compressed with Huffman coding; when the compressed data need recover, the process is against with that of data compression.The experiment shows that this method has the advantages of fast compression speed, high compression ratio and lossless recovery. [ABSTRACT FROM AUTHOR]

Effective resource distribution to regulate load uniformly in heterogeneous cloud environments is crucial. Resource allotment which is taken after capable task scheduling is a critical worry in cloud environment. The incoming job requests are assigned to resources equally by load balancer in such a way that resources are utilized effectively. Number of cloud clients is very great in number, degree of approaching job requests is uninformed and information is tremendous in cloud application. Resources in cloud environment are constrained. Hence, it is not easy to deploy different applications with unpredictable limits and functionalities in heterogeneous cloud environment. The proposed method has two phases such as allocation of resources and scheduling of tasks. Effective resource allocation is proposed using social group optimization algorithm and scheduling of tasks using shortest-job-first scheduling algorithm for minimizing the makespan time and maximizing throughput. Experimentations are performed for accurate simulations on artificial data for heterogeneous cloud environment. Experimental results are compared with first-in, first-out and genetic algorithm-based shortest-job-first scheduling. Validity of the proposed method noticeably gives improved performance of the system in provisions of makespan time and throughput. [ABSTRACT FROM AUTHOR]

The development of information, materials science, and technology has led to profound changes in manufacturing. Many new ideas and technologies have emerged in recent years, such as cloud manufacturing and 3D printing. Cloud manufacturing (CM) is a new type of networked manufacturing mode proposed in 2010, and 3D printing is an example of new manufacturing methods. CM and 3D printing both have significant influence in the manufacturing mode and manufacturing method and are already research hotspots in the field of advanced manufacturing technology. What kind of innovations will be produced when they integrate? To explore this issue, this paper first analyses the characteristics of cloud manufacturing and 3D printing. Second, the combination research of cloud manufacturing and 3D printing in recent years is studied, including 3D printing cloud platform (3D-PCP) service architectures, 3D printing resource access technology, 3D printing service optimized configuration technology, 3D printing service control and monitoring technology, and 3D printing service evaluation technology. There are several research achievements and commercial application cases of 3D-PCP, but a true sense of 3D printing cloud ecology from the perspective of a manufacturing system has not been formed to date. On the basis of the survey and analysis, this paper highlights the shortcomings in current research and proposes development suggestions of the combination of 3D printing and cloud manufacturing, including the potential research direction of 3D-PCP system modes, the Internet of Things in 3D printing, and the 3D printing service process control and evaluation. [ABSTRACT FROM AUTHOR]

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

Pet markets involve in great commercial possibilities, which boost thriving development of veterinary hospital businesses. The service tends to intensive competition and diversified channel environment. Information technology is integrated for developing the veterinary hospital cloud service platform. The platform contains not only pet medical services but veterinary hospital management and services. In the study, QR Code andcloud technology are applied to establish the veterinary hospital cloud service platform for pet search by labeling a pet's identification with QR Code. This technology can break the restriction on veterinary hospital inspection in different areas and allows veterinary hospitals receiving the medical records and information through the exclusive QR Code for more effective inspection. As an interactive platform, the veterinary hospital cloud service platform allows pet owners gaining the knowledge of pet diseases and healthcare. Moreover, pet owners can enquire and communicate with veterinarians through the platform. Also, veterinary hospitals can periodically send reminders of relevant points and introduce exclusive marketing information with the platform for promoting the service items and establishing individualized marketing. Consequently, veterinary hospitals can increase the profits by information share and create the best solution in such a competitive veterinary market with industry alliance. [ABSTRACT FROM AUTHOR]

The KNOWLEDGE-CARE vision is to enable new e-health services that are personalized, preventive, predictive, pre-emptive, participatory, pervasive and precise. This vision aims to make a move from reactive medicine to predictive medicine, developing dynamic and responsive models through knowledge extraction and using such models for personalized clinical healthcare. The proposed healthcare ecosystem can learn over time and can adapt to the variation seen in the actual real-world population. [ABSTRACT FROM AUTHOR]

There are many datastore systems to choose from that differ in many ways including public versus private cloud support, data management interfaces, programming languages, supported feature sets, fault tolerance, consistency guarantees, configuration, and their deployment processes. In this paper, we focus on technologies for structured data access (database/datastore systems) in cloud systems. Our goal is to simplify the use of datastore systems through automation and to facilitate their empirical evaluation using real world applications. To enable this, we provide a cloud platform abstraction layer that decouples a data access API from its implementation. Applications that use this API can use any datastore that 'plugs into' our abstraction layer, thus enabling application portability. We use this layer to extend the functionality of multiple datastores without modifying the datastores directly. Specifically, we provide support for ACID transaction semantics for popular key-value stores (none of which provide this feature). We integrate this layer into the AppScale cloud platform-an open-source cloud platform that executes cloud applications written in Python, Java, and Go, over virtualized cluster resources and infrastructures-as-a-service (Eucalyptus, OpenStack, and Amazon EC2). We use this system to investigate the impact of extending disparate datastores via the application portability layer with distributed transaction support. [ABSTRACT FROM AUTHOR]

In this paper, we present the design and implementation of Neptune, a simple, domain-specific language based on the Ruby programming language. Neptune automates the configuration and deployment of scientific software frameworks over disparate cloud computing systems. Neptune integrates support for MPI, MapReduce, UPC, X10, StochKit, and others. We implement Neptune as a software overlay for the AppScale cloud platform and extend AppScale with support for elasticity and hybrid execution for scientific computing applications. Neptune imposes no overhead on application execution, yet significantly simplifies the application deployment process, enables portability across cloud systems, and promotes lock-in avoidance by specific cloud vendors. [ABSTRACT FROM AUTHOR]

With the commencement of the COVID-19 pandemic, social distancing and quarantine are becoming essential practices in the world. IoT health monitoring systems prevent frequent visits to doctors and meetings between patients and medical professionals. However, many individuals require regular health monitoring and observation through medical staff. In this proposed work, we have taken advantage of the technology to make patients life easier for earlier diagnosis and treatment. A smart health monitoring system is being developed using Internet of Things (IoT) technology which is capable of monitoring blood pressure, heart rate, oxygen level, and temperature of a person. This system is helpful for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions. However, if any changes occur in a patient's health based on standard values, then the IoT system will alert the physician or doctor accordingly. The maximum relative error (% ϵ r was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients.2 was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients., Competing Interests: Conflict of interestAll the authors declare that he/she has no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.)