AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

The Mechanism of Heating Rate on the Secondary Recrystallization Evolution in Grain Oriented Silicon Steel

Grain-oriented silicon steels were prepared at different heating rates during high temperature annealing,in which the evolution of magnetic properties,grain orientations and precipitates were studied.To illustrate the Zener factor,the diameter and number density of precipitates of interrupted testing samples were statistically calculated.The effect of precipitate ripening on the Goss texture and magnetic property was investigated.Data indicated that the trend of Zener factor was similar under different heating rates,first increasing and then decreasing,and that the precipitate maturing was greatly inhibited as the heating rate increased.Secondary recrystallization was developed at the temperature of 1010℃when a heating rate of 5℃/h was used,resulting in Goss,Brass and{110}<227>oriented grains growing abnormally and a magnetic induction intensity of 1.90T.Furthermore,increasing the heating rate to 20℃/h would inhibit the development of undesirable oriented grains and obtain a sharp Goss texture.However,when the heating rate was extremely fast,such as 40℃/h,poor secondary recrystallization was developed with many island grains,corresponding to a decrease in magnetic induction intensity to 1.87 T.At a suitable heating rate of 20℃/h,the sharpest Goss texture and the highest magnetic induction of 1.94 T with an onset secondary recrystallization temperature of 1020℃were found among the experimental variables in this study.The heating rate affected the initial temperature of secondary recrystallization by controlling the maturation of precipitates,leading to the deviation and dispersion of Goss texture,thereby reducing the magnetic properties.

A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.

A Generative Model-Based Network Framework for Ecological Data Reconstruction

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems.Combining Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data reconstruction.The model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT Analysis.The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data.Reconstructed data is used to retain more semantic information to generate features.The model was applied to species in Southern California,USA,citing SWOT analysis data to train the model.Experiments show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development environments.The model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data domain.This study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

Optimization of an Artificial Intelligence Database and Camera Installation for Recognition of Risky Passenger Behavior in Railway Vehicles

Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation systems.Fog computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end nodes.Consisting of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud computing.In smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system reliability.To address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog environments.This framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation systems.The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud environments.Through simulation-based executions,tasks are allocated to the nearest available nodes with minimum latency.In the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of tasks.Upon successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

基于re3data的中英科学数据仓储平台对比研究

以re3data为数据获取源,选取中英两国406个科学数据仓储为研究对象,从分布特征、责任类型、仓储许可、技术标准及质量标准等5个方面、11个指标对两国科学数据仓储的建设情况进行对比分析,试图为我国数据仓储的可持续发展提出建议:广泛联结国内外异质机构,推进多学科领域的交流与合作,有效扩充仓储许可权限与类型,优化技术标准的应用现况,提高元数据使用的灵活性。

Secondary Data Analysis of Tuberculosis Deaths in Bulawayo Province, Zimbabwe, 2016-2019

Background: Tuberculosis is a leading cause of death globally, and the third leading cause of death in Zimbabwe. Death from any cause following a diag-nosis of tuberculosis is classified as a tuberculosis death. Bulawayo Province reported high tuberculosis death rates from 15.3% in 2016 to 14.2% in 2019 against a threshold of 5%. We analyzed tuberculosis deaths for Bulawayo Province to characterize patients dying and to make recommendations for im-proving treatment outcomes for susceptible tuberculosis cases. Methods: A descriptive cross-sectional study was conducted. We analyzed all (N = 469) records of tuberculosis deaths from 19/19 Bulawayo tuberculosis diagnosing centers from 01 January 2016 to 31 December 2019. Microsoft® Excel 2007 was used to generate graphs and Stata® version 17 was used to conduct chi-square tests for trends. Results: Males accounted for 278/469 (59.3%) of the deaths. The median age of death was 40 years (Q1 = 33: Q3 = 51). The proportion of TB deaths increased from 63/114 (55%) in 2016 to 57/90 (63%) in 2019 for males (p Conclusion: High death rates particularly in the intensive phase, could be attributed to sub-optimal clinical care. Tuberculosis programs should work towards adopting differentiated care models for tuberculosis patients and developing algorithms for patients at high risk of death.

HIV Self-Testing Kits Uptake in Mashonaland West Province, Zimbabwe, 2019-2020: A Secondary Data Analysis

Background: Human Immuno-Deficiency Virus Self-Testing (HIVST) is a process where an individual who wants to know their HIV status collects a specimen, performs a test and interprets the result by themselves. HIVST data from the Zimbabwe AIDS and TB Program (ATP) directorate showed that between 2019-2020, only 31% of the target HIVST kits were distributed in the country. Mashonaland West Province was one of the least performing provinces in meeting targets for HIVST kits distribution. Gaps in the implementation of the HIVST in the province ultimately affect the nationwide scaleup of targeted testing, a key enabler in achieving HIV epidemic control. We analyzed HIVST trends in Mashonaland West Province to inform HIV testing services programming. Methods: We conducted a cross-sectional study using HIVST secondary data obtained from the District Health Information Software 2 (DHIS2) electronic database. We conducted regression analysis for trends using Epi Info 7.2 and tables, bar graphs, pie charts and linear graphs were used for data presentation. Results: A total of 31,070 clients accessed HIVST kits in Mashonaland West Province from 2019-2020. A slightly higher proportion (50.4% and 51.7%) of females as compared to males accessed HIVST kits in 2019 and 2020 respectively. Overall, an increase in the trend of HIVST kits uptake was recorded (males R2 = 0.3945, p-value = 0.003 and females R2 = 0.4739, p-value = 0.001). There was generally a decline in the trend of community-based distribution of HIVST kits from the third quarter of 2019 throughout 2020 (R2 = 0.2441, p-value = 0.006). Primary distribution of HIVST kits remained the dominant method of distribution, constituting more than half of the kits distributed in both 2019 (67%) and 2020 (86%). Conclusion: Mashonaland West Province was mainly utilising facility-based distribution model for HIVST over the community-based distribution model. We recommended training more community-based distribution agents to increase community distribution of HIVST kits.

Strigolactones modulate cotton fiber elongation and secondary cell wall thickening

Cotton is one of the most important economic crops in the world,and it is a major source of fiber in the textile industry.Strigolactones(SLs)are a class of carotenoid-derived plant hormones involved in many processes of plant growth and development,although the functions of SL in fiber development remain largely unknown.Here,we found that the endogenous SLs were significantly higher in fibers at 20 days post-anthesis(DPA).Exogenous SLs significantly increased fiber length and cell wall thickness.Furthermore,we cloned three key SL biosynthetic genes,namely GhD27,GhMAX3,and GhMAX4,which were highly expressed in fibers,and subcellular localization analyses revealed that GhD27,GhMAX3,and GhMAX4 were localized in the chloroplast.The exogenous expression of GhD27,GhMAX3,and GhMAX4 complemented the physiological phenotypes of d27,max3,and max4 mutations in Arabidopsis,respectively.Knockdown of GhD27,GhMAX3,and GhMAX4 in cotton resulted in increased numbers of axillary buds and leaves,reduced fiber length,and significantly reduced fiber thickness.These findings revealed that SLs participate in plant growth,fiber elongation,and secondary cell wall formation in cotton.These results provide new and effective genetic resources for improving cotton fiber yield and plant architecture.

The secondary injury cascade after spinal cord injury:an analysis of local cytokine/chemokine regulation

After spinal cord injury,there is an extensive infiltration of immune cells,which exacerbates the injury and leads to further neural degeneration.Therefore,a major aim of current research involves targeting the immune response as a treatment for spinal cord injury.Although much research has been performed analyzing the complex inflammatory process following spinal cord injury,there remain major discrepancies within previous literature regarding the timeline of local cytokine regulation.The objectives of this study were to establish an overview of the timeline of cytokine regulation for 2 weeks after spinal cord injury,identify sexual dimorphisms in terms of cytokine levels,and determine local cytokines that significantly change based on the severity of spinal cord injury.Rats were inflicted with either a mild contusion,moderate contusion,severe contusion,or complete transection,7 mm of spinal cord centered on the injury was harvested at varying times post-injury,and tissue homogenates were analyzed with a Cytokine/Chemokine 27-Plex assay.Results demonstrated pro-inflammatory cytokines including tumor necrosis factorα,interleukin-1β,and interleukin-6 were all upregulated after spinal cord injury,but returned to uninjured levels within approximately 24 hours post-injury,while chemokines including monocyte chemoattractant protein-1 remained upregulated for days post-injury.In contrast,several anti-inflammatory cytokines and growth factors including interleukin-10 and vascular endothelial growth factor were downregulated by 7 days post-injury.After spinal cord injury,tissue inhibitor of metalloproteinase-1,which specifically affects astrocytes involved in glial scar development,increased more than all other cytokines tested,reaching 26.9-fold higher than uninjured rats.After a mild injury,11 cytokines demonstrated sexual dimorphisms;however,after a severe contusion only leptin levels were different between female and male rats.In conclusion,pro-inflammatory cytokines initiate the inflammatory process and return to baseline within hours post-injury,chemokines continue to recruit immune cells for days post-injury,while anti-inflammatory cytokines are downregulated by a week post-injury,and sexual dimorphisms observed after mild injury subsided with more severe injuries.Results from this work define critical chemokines that influence immune cell infiltration and important cytokines involved in glial scar development after spinal cord injury,which are essential for researchers developing treatments targeting secondary damage after spinal cord injury.

Trend Analysis of HIV Testing Services in Zimbabwe, 2007-2016: A Secondary Dataset Analysis

Background: HIV Testing Services (HTS) is a full range of services (e.g. counselling and post-test referrals) that are offered together with HIV testing. It is an important prevention strategy and the gateway to treatment. The national targets in 2016 were to test 1.1 million people of which 54% was achieved. We determined trends of HTS in Zimbabwe from 2007 to 2016. Methods: A secondary dataset analysis was conducted using National Aids Council Core-Output Indicators dataset. Variables captured include total and repeat tests, counselling and referrals. Microsoft excel and Epi Info was used to generate frequencies, percentages and conduct chi square test for trends. Panda-Class Libraries was to attain estimates of HTS uptake till 2020. We used χ2 and p-values for statistical significance. Results: All (10,847.223) records were analysed. HIV tests per year increased from 340,705 in 2007, to 1,974,795 in 2015 (χ2 0.10492, p-value 0.74615). In 2007, 31% (n = 106,884) clients tested positive whilst in 2016 only 7% (n = 121,196) were positive (χ2 0.01166, p-value 0.91402). The 25 - 49 year age-group tested consistently highest throughout the 10year period (χ2 0.0558 p-value 0.813). The 15 - 24 year age-group had the highest yield (11% in 2015). Females (χ2 0.1074, p-value 0.743) consistently tested higher than males (χ2 0.0614, p-value 0.804). From 2007 to 2013 women had higher yields but by September 2016 males had a higher positivity of 8% (p-value χ2 0.658 p-value = 0.417). We estimate that 179,935 people living with HIV will know their status by 2020. Conclusion: HIV tests in Zimbabwe have increased but yield has decreased. Increase in repeat tests may be an indication of exhaustion of particular HTS strategies. Following this analysis it was recommended that HTS utilize various models such as HIV self-test to cater for populations with high yields.

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Experimental study on secondary air mixing along the bed height in a circulating fluidized bed with a multitracer-gas method

A multitracer-gas method was proposed to study the secondary air(SA)mixing along the bed height in a circulating fluidized bed(CFB)using carbon monoxide(CO),oxygen(O_(2)),and carbon dioxide(CO_(2))as tracer gases.Experiments were carried out on a cold CFB test rig with a cross-section of 0.42 m×0.73 m and a height of 5.50 m.The effects of superficial velocity,SA ratio,bed inventory,and particle diameter on the SA mixing were investigated.The results indicate that there are some differences in the measurement results obtained using different tracer gases,wherein the deviation between CO and CO_(2) ranges from 42%to 66%and that between O_(2) and CO_(2) ranges from 45%to 71%in the lower part of the fluidized bed.However,these differences became less pronounced as the bed height increased.Besides,the high solid concentration and fine particle diameter in the CFB may weaken the difference.The measurement results of different tracer gases show the same trends under the variation of operating parameters.Increasing superficial velocity and SA ratio and decreasing particle diameter result in better mixing of the SA.The effect of bed inventory on SA mixing is not monotonic.