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Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The wide application of sensor networks has brought great challenges to cloud data storage, such as transmission security, data privacy protection and financial risk control. In order to deal with these challenges, the system establishes a comprehensive management early warning system to ensure the data security and financial risk control of the sensor network. The system integrates sensor network technology and cloud data storage technology, and stores data in the cloud through the collection and transmission of environmental data by sensor nodes. In order to ensure the security of the stored process, the system adopts a series of security mechanisms, such as data encryption, access control and identity authentication, and also introduces the financial risk control module, which helps users to warn and manage financial risks through real-time monitoring and analysis of data in the sensor network. The system provides a user-friendly management interface, allowing users to easily configure and monitor the operating state of the sensor network, and supports flexible expansion and customization to adapt to the needs of different scenarios. Through the application of this system, users can effectively manage and control the security and financial risks of sensor networks in cloud data storage, and improve the efficiency and reliability of data storage management.

The Internet of Medical Things (IoMT) bolstered the smart health care industry in present times by enabling quicker patient monitoring and disease diagnosis. However, there have been problems that need to be resolved using Artificial Intelligence (AI) methods. The major goal of this endeavor is to develop an IoMT-based data fusion system for multi-sensor smart healthcare network. To do this, a new optimization and deep learning approaches are being used in this work. In this research work, a unique smart healthcare framework, Tasmanian and Lichtenberg Optimized Attention Deep Convolution (TasLA) is developed for IoMT systems. This system uses an intelligent data fusion algorithms for collecting of medical data and the diagnosis of disorders. Here, data pretreatment and normalization processes are carried out in order to provide a dataset with balanced attribute information. The qualities or characteristics that will aid in classification are then selected using the most modern Tasmanian Devil Optimization (TDO) approach. The Attention Deep Convolution Classification (ADCC) algorithm is also used to classify the medical condition, thereby improving classification precision and reducing false predictions. To optimally compute the loss function during prediction, the Lichtenberg Optimization (LO) technique is employed to enhance classification performance. The effectiveness and results of the proposed TasLA model are validated and contrasted using various benchmark datasets such as Hungarian, Cleveland, Echocardiogram, and Z-Alizadeh.

The Internet of Medical Things (IoMT) bolstered the smart health care industry in present times by enabling quicker patient monitoring and disease diagnosis. However, there have been problems that need to be resolved using Artificial Intelligence (AI) methods. The major goal of this endeavor is to develop an IoMT-based data fusion system for multi-sensor smart healthcare network. To do this, a new optimization and deep learning approaches are being used in this work. In this research work, a unique smart healthcare framework, Tasmanian and Lichtenberg Optimized Attention Deep Convolution (TasLA) is developed for IoMT systems. This system uses an intelligent data fusion algorithms for collecting of medical data and the diagnosis of disorders. Here, data pretreatment and normalization processes are carried out in order to provide a dataset with balanced attribute information. The qualities or characteristics that will aid in classification are then selected using the most modern Tasmanian Devil Optimization (TDO) approach. The Attention Deep Convolution Classification (ADCC) algorithm is also used to classify the medical condition, thereby improving classification precision and reducing false predictions. To optimally compute the loss function during prediction, the Lichtenberg Optimization (LO) technique is employed to enhance classification performance. The effectiveness and results of the proposed TasLA model are validated and contrasted using various benchmark datasets such as Hungarian, Cleveland, Echocardiogram, and Z-Alizadeh. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the clustering ability of grid sparse unbalanced cloud data sets, an adaptive environment density peak clustering algorithm based on cloud computing technology is proposed. Firstly, the storage structure model of grid sparse unbalanced cloud data set is constructed, and the structural reorganization of grid sparse unbalanced cloud data set is carried out by combining the feature space reorganization technology. Meanwhile, the rough feature quantity of the grid sparse unbalanced cloud data set is extracted, and then cloud fusion and peak feature clustering of the data set are carried out according to the grid block distribution of the data set. The peak feature quantity of the grid sparse unbalanced cloud data set is extracted for distributed detection of binary semantic features of the data. Finally, according to semantic fusion and feature clustering results, regression analysis and support vector machine learning are used to optimize clustering of grid sparse unbalanced cloud data sets. Experimental results show that this method has good convergence, low data misclassification rate, and good clustering performance of peak environmental density. [ABSTRACT FROM AUTHOR]

The continuous progress of society has created conditions for the widespread use of information technology. People rely more and more on information technology and the Internet. People can use the Internet to retrieve relevant information to meet their work needs, but the widespread use of the Internet is also accompanied by network security issues. The existence of network security problems will affect people's work to varying degrees. Network security management departments use intrusion detection technology and set firewalls to improve the security of people's personal information on the Internet and prevent viruses and some criminals from stealing people's important information through the Internet, causing adverse effects on the Internet environment. In the daily management of the school, the extensive use of the campus network can not only facilitate students' daily study and life, but also improve the efficiency of school management. Ensuring the network security of campus network is an important work to ensure the campus management and students' study and life. The four parts of network security work include setting firewall, encrypting cloud data, using intrusion detection technology, and recovering data. Intrusion detection technology is extremely important for the development of network security work, which can help people actively find vulnerabilities in networks and systems, identify possible behaviors that may invade systems and networks, and give early warning or automatically close intrusion channels. There are various types of cloud data, so cloud database plays an extremely important role in the development of various fields. This paper uses intrusion detection algorithms to design the campus network security protection system, which provides security for the use of the campus network. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10–3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements. [ABSTRACT FROM AUTHOR]

Aiming at the problem of data classification in enterprise cloud data, this article proposes a data classifier based on Particle Swarm Optimisation and Kernel Function Extreme Learning Machine (PSO-KELM) is proposed. PSO is utilised to get optimal parameters and the parameters of KELM are obtained accurately, which improves the accuracy of data classification. Through the cloud data test and a comparison with Kernel Function Extreme Learning Machine (KELM), Kernel Function Support Vector Machine (KSVM) and Semi-Supervised and Transfer Learning (SSTL), the better classification effect is obtained. Moreover, this article proposes an effective method for data classification. [ABSTRACT FROM AUTHOR]

The introduction of cutting-edge technologies like Internet of Things (IoT) detectors and drones, and farm surveillance, is transforming the farming sector in the Big Data era. Huge quantities of priceless agridata are generated by IoT systems, and cutting-edge application technologies enable the true collection and analysis of this information. This technological combination, referred to as “smart farming,” enables different agriculture-based players to analyze plants in real-time and enhance profitability and efficiency in farm and company activities with the least amount of work. Even though several precision agriculture methods have been developed by academics and businesses, it is sadly never possible to apply those strategies to all farms. A custom, semi-public big data processing infrastructure serves as the foundation for the majority of these applications. Throughout this article, we suggest WALLeSMART, a virtualized precision agriculture management system used in India's Wallonia. The framework presents a broad framework to handle the difficulties associated with collecting, analyzing, storing, and visualizing extremely huge volumes of information on a factual and batch basis. A first version has indeed been created and then pushed to the limit on several fields, with impressive outcomes.

A key challenge was optimizing the speed of the network while maintaining impartiality. Power monitoring solutions have been developed to reduce these load imbalances. These techniques frequently need unique hardware or software on the participants' end. To accomplish the cloud computing system of load balancing, this study created an intelligent virtual machine programming scheme using a machine learning technique. The researchers introduced two methods that identify optimal probabilistic solutions to the issue after conducting an in-depth investigation. A min-max load balancing solution is generated by the second method, while the first approach reduces the load on the network's crowded Access Points (APs). Let's focus on the mapping of links in particular because the mapping of nodes was established a priori. This study provides an innovative and effective Improved Lion Optimization (ILO) with Min-Max Algorithm for enabling VNE in real systems consisting, breaking new ground in the research to employ Genetic Operators for parallelization. Load balancing & power conservation were two crucial goals that must be taken into account, and also the findings reduce the cost of processing time, the proposed system outperforms the sequentially one in terms of both goals. In addition, the adaptive capacity of the proposed algorithm was assessed across various substrate structural configurations. The load balancing goal was achieved; the typical data center usage was higher than that of other methods, reaching nearly 80%; the largest amount of virtual machine migration was reduced by 94.5%; the data center's maximum energy consumption was reduced by 49.13%.

The water depth bias of LiDAR point cloud data has to be corrected. The previous models, which used the fixed parameters for an entire water area, cannot efficiently correct the bias due to the different water environments. Therefore, this paper develops an adaptive model for the correction of the water depth bias. A coordinate system, in which the water depth is taken as the X-axis and the water depth bias is taken as the Y-axis, is defined. All of the sample points are normalized, and then projected into the defined coordinate system according to their water depths and water depth biases. Second, the scatter points are clustered into several different clusters using the developed subdivision algorithm. With the clusters, an entire water area is subdivided into several sub-regions. Finally, each sub-region is fitted using a model, which is used to correct the water depth bias. Experimental verification and comparison analysis are conducted in three different environments: an indoor tank, an outdoor pond and the Beihai sea. The experimental results demonstrate that the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) from the adaptive model are reduced by approximately 61% and 59%, respectively, relative to those from the traditional models. Therefore, it can be concluded that the proposed model can adapt to changes of the different water environments and achieve a higher accuracy for water depth bias correction than traditional methods do.

The management and control of coal mine safety supervision cloud data is very strict, and the design of access authority should satisfy the requirements of classification and security. At present, coal mine safety supervision cloud data has the problems of unclear classification and hierarchy and weak confidentiality in the security management and contrd dimension. And the existing cloud data management and control models are difficult to meet the security requirements of coal mine safety supervision data. In order to solve the above problems, the security access model of coal mine safety supervision cloud data based on blockchain is designed, including access authority model and access control model. Based on the analysis of the access attributes and access objects of coal mine safety supervision cloud data, an access authority model based on user hierarchy and data attributes is designed. The model realizes the classification and hierarchy management and control of cloud data and dynamic generation of authority. Based on the advantages of distributed realization, full transparency and tamper-proof of blockchain, the cloud data access control model is constructed. The model realizes distributed access control, ensures the security of access control by intelligent contract, and enhances the security protection of authority information by encryption technology. The comparative analysis results shows that compared with the common role-based access control(RBAC) model and attribute-based access control(ABAC) model, the access authority model based on user hierarchy and data attributes realizes the fine-grained access authority division for the coal mine safety supervision cloud data. The user authority is intuitive, the authority rules are simple to generate. The access authority model meets the security requirements of the coal mine safety supervision cloud data. Compared with the access control model based on the third party, the access control model based on the blockchain uses the intelligent contract for access control. The model can enhance the security of the coal mine safety supervision cloud data, provide a new solution for the cloud data security problem, and meets the needs of data security access in more scenarios.

Summary: Ensuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate. Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig‐zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state‐of‐the‐art models for proving the efficiency of the proposed technique over the other techniques. [ABSTRACT FROM AUTHOR]

When investigating the forwarding process within the timber supply chain, insufficient data often inhibits long-term studies or make real-time optimisation of the logistics process difficult. Information sources to compensate for this lack of data either depend on other processing steps or they need additional, costly hardware, such as conventional crane scales. An innovative weight-detection concept using information provided by a commonly available hydraulic pressure sensor may make the introduction of a low-cost weight information system possible. In this system, load weight is estimated by an artificial neural network (ANN) based on machine data such as the hydraulic pressure of the inner boom cylinder and the grapple position. In our study, this type of crane scale was set up and tested under real working conditions, implemented as a cloud application. The weight scale ANN algorithm was therefore modified for robustness and executed on data collected with a commonly available telematics module. To evaluate the system, also with regard to larger sample sizes, both direct weight-reference measurements and additional volume-reference measurements were made. For the second, locally valid weight-volume conversion factors for mainly Norway spruce (Picea abies, 906 kg m-3, standard error of means (SEM) of 13.6 kg m-3), including mean density change over the observation time (–0.16% per day), were determined and used as supportive weight-to-volume conversion factor. Although the accuracy of the weight scale was lower than in previous laboratory tests, the system showed acceptable error behaviour for different observation purposes. The twice-observed SEM of 1.5% for the single loading movements (n=95, root-mean-square error (RMSE) of 15.3% for direct weight reference; n=440, RMSE=33.2% for volume reference) enables long-term observations considering the average value, but the high RMSE reveals problems with regard to the single value information...

Cloud has scalable storage and massive computing power, attracting data owners to outsource their data. However, owing to privacy concerns, data is typically encrypted prior to outsourcing, which inevitably presents challenges for querying the data effectively. Although encrypted range query is one of the most prevalent types and has been widely studied, existing schemes still have some problems. They inadvertently disclose the order relationship between the upper/lower bound of a range query and the encrypted index, leading to vulnerability inference attack. Moreover, the cloud server cannot be fully trusted, which may return incorrect and incomplete query results. To deal with these issues, we present a novel verifiable and privacy-preserving range query scheme (VPRQ). The VPRQ scheme utilizes 0/1 technique to transform range comparisons into set intersections. By doing so, it effectively hides the relationship between the upper/lower bound and the encrypted index. On this basis, we design an encrypted garbled bloom filter to securely and effectively achieve range query. This ensures that VPRQ scheme effectively resists inference attack. Additionally, point-value polynomial function technology is integrated into the VPRQ protocol to provide lightweight verification for the query results. Comprehensive security analysis and proof demonstrate its effectiveness in achieving the intended design objectives. Performance evaluations illustrate the feasibility and scalability of the proposed scheme, highlighting its practical applicability. [ABSTRACT FROM AUTHOR]

To evaluate the efficacy of a comprehensive TCM regimen on reducing the 2-year recurrence rate of major depressive disorder(MDD). Methods 　This large-scale multi-centered, prospective cohort study was conducted by recruiting MDD patients from 13 sub-centers nationwide from January 2017 to April 2018 with assistance of the Boshi Medical Cloud Data Management Platform . A comprehensive TCM regimen was used in the intervention of MDD patients. Three cohorts(TCM cohort, integrated Chinese and western medicine cohort, and western medicine cohort) were formed to observe the differences in the recurrence rate of MDD. According to the exposure time of the comprehensive TCM regimen intervention, the TCM cohort and the integrated Chinese and western medicine cohort were further divided into three groups with low, medium and high exposure levels respectively, and difference in the recurrence rate among those groups of different exposure levels was observed, so as to evaluate the long-term efficacy of the comprehensive TCM regimen in reducing the recurrence rate of MDD. Results　A total of 4 440 patients with MDD were included in the TCM cohort(1484 patients), western medicine cohort(1412 patients) and integrated Chinese and Western medicine cohort(1544 patients). The two-year recurrence rate was 25.5% in the TCM cohort, 35. 4% in the western medicine cohort, and 19. 3% in the integrated Chinese and western medicine cohort. Compared with the recurrence risk of the low-level exposure group in the Chinese medicine cohort, it was 41. 6% lower in the medium-level exposure group(HR = 0. 584, 95%CI(0.467 ~ 0.731), P <0. 001), and 71.3% lower in the high-level exposure group(HR =0. 287, 95%CI(0.214~0. 384), P<0. 001). Compared with the recurrence risk of the low-level exposure group in the integrated Chinese and western medicine cohort, it was 54. 3% lower in the medium-level exposure group(HR =0. 457, 95%CI(0. 352~0. 692), P<0. 001) and 75. 2% lower in the high-level exposure group(HR = 0. 248, 95%CI(0. 182 ~0. 337), P<0. 001). Conclusion　The comprehensive TCM regimen seems to reduce the 2-year recurrence rate of MDD. There is probably a close correlation between the exposure level of TCM intervention and the recurrence rate, and a longer exposure duration could possibly lead to a lower recurrence rate. [ABSTRACT FROM AUTHOR]

To establish a TCM information database for the prevention and treatment of major depressive disorder(MDD) based on cloud data technology, and to evaluate the clinical efficacy of traditional Chinese medicine(TCM) regimen in the prevention and treatment of suicide risk in MDD. Methods　A total of 4 440 patients with MDD who were inpatients or outpatients in 13 hospitals including TCM hospitals, integrated Chinese and Western medicine hospitals and psychiatric hospitals in 7 cities across the country were recruited, among which 2 513 patients without suicidal ideation were included in this cohort study. The data was collected from January 2017 to April 2018. Three groups of cohorts ( TCM, Western medicine, and integrated Chinese & Western medicine) were naturally formed. The follow-up interval was 3 months. Taking the appearance of suicidal ideation as the end event and the Montgomery-Asberg Depression Rating Scale(MARDS) as the evaluation tool in the follow-up, we assessed the patients′ dynamic status of depression and the occurrence of the end event. Results　A TCM cohort(896 patients), a Western medicine cohort(744 patients), and an integrated Chinese and Western medicine cohort(873 patients) were established with MDD patients without suicidal ideation at baseline, and the overall two-year incidence rates of suicidal ideation in the three groups were 34. 5% (309/896), 46. 9% (349/744), and 23. 0% (201/873) respectively. Compared with the Western medicine cohort, the risk of suicidal ideation was reduced in the TCM cohort by 33. 9% (HR = 0. 671, 95% CI(0. 575-0. 784), P <0. 001), and by 59. 4% (HR = 0. 406, 95%CI (0. 341-0. 483), P < 0. 001) in the integrated Chinese and Western medicine cohort. In the TCM cohort, compared with lowlevel TCM exposure time(TCM-ET), the risk of suicidal ideation was reduced by 47. 2%(HR =0. 528, 95%CI(0. 414-0. 674), P<0. 001), with medium-level TCM-ET, and by 81. 6% (HR =0. 184, 95%CI (0. 127-0. 267), P<0. 001), with high-level TCM-ET. In the integrated Chinese and Western medicine cohort, compared with the low-level TCM-ET time, the risk of suicidal ideation was reduced by 39. 9% (HR =0. 601, 95% CI(0. 438-0. 826), P<0. 001) with medium-level exposure time, and by 65. 9% (HR = 0. 341, 95%CI (0. 235-0. 495), P <0. 001) with high-level exposure time, indicating that a higher level of exposure time could reduce the suicidal risk. Conclusion　TCM, Western medicine, or integrated TCM and western medicine could all improve the depressive symptoms and suicidal ideation in MDD patients, and the application of TCM treatment seems to better reduce the probability of suicidal ideation in MDD patients than Western medicine treatment alone. The application of TCM treatment for over 6 months (medium-level or above TCM-ET) indicates a greater effect on the prevention of suicide in patients with MDD. [ABSTRACT FROM AUTHOR]

An application of Cloud Computing (CC) technologies for interconnecting data as well as applications served as of manifold geographic locations is the distributed cloud. For distributed CC, Managing a vast measure of data along with maintaining security are vital aspects. Though lots of techniques were introduced recently to manage and protect data on the distributed cloud, those have not rendered any desirable security. Thus, a secure Data Deduplication (DD) along with Data Portability (DP) for a distributed cloud environment is proposed here. To enhance the user data's security, primarily, the inputted files are bifurcated into blocks and after that, an appropriate Cloud Server (CS) is chosen utilizing Hybrid Forest Genetic Algorithm (HFGA) centred upon split file features together with CS features. In a DD phase, the Whirlpool algorithm converts the split data into Hash Code (HC), and then, the hash chaining technique securely removes the duplicated data. Then, that split data is compressed, encrypted, together with amassed in the selected CS. Next, the Levy Flight-Wind Driven Optimization (LF-WDO) performs the DP to enhance the cloud data's security. Within 3265 ms, the proposed Whirlpool algorithm renders an HC for the 10,000 kb file. The proposed LF-WDO achieves a lower process time of 2987 ms, which is above 5% smaller than the existing techniques. Investigational outcomes exhibit the proposed work's potential. [ABSTRACT FROM AUTHOR]

The label-less characteristics of real vehicle data make engineering modeling and capacity identification of lithium-ion batteries face great challenges. Different from ideal laboratory data, the raw data collected from vehicle driving cycles have a great adverse impact on effective modeling and capacity identification of lithium-ion batteries due to the randomness and unpredictability of vehicle driving conditions, sampling frequency, sampling resolution, data loss, and other factors. Therefore, data cleaning and optimization is processed and the capacity of a battery pack is identified subsequently in combination with the improved two-point method. The current available capacity is obtained by a Fuzzy Kalman filter optimization capacity estimation curve, making use of the charging and discharging data segments. This algorithm is integrated into a new energy big data cloud platform. The results show that the identification algorithm of capacity is applied successfully from academic to engineering fields by charge and discharge mutual verification, and that life expectancy meets the engineering requirements.

The Internet of Things is transforming the world due to which organizations are facing many difficulties. The transformation due to these technologies is affecting several areas and project management is not an exception. The Internet of Things can help project managers to manage their projects as well as their types of equipment in an effective way. The connection between IoT devices and project management can allow businesses to achieve new innovations and services in a more effective manner while reducing the risk factor. In this paper, we proposed a framework for effective project management based on the Internet of Things (IoT). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED $^{+}$ + . By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED $^+$ + surpass the state-of-the-art methods in privacy-preservation level and efficiency. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air. [ABSTRACT FROM AUTHOR]