Performance Evaluation of Novel Eco-Materials Composed of Millet Husks, Rice Husks, and Polystyrene

Managing agricultural waste and expanded polystyrene (EPS) poses significant environmental and economic challenges. This study aims to create composites from millet husks, rice husks, and recycled EPS, using a manufacturing method that involves dissolving the polystyrene in a solvent followed by cold pressing. Various particle sizes and two binder dosages were investigated to assess their influence on the physico-mechanical properties of the composites. The mechanical properties obtained range from 2.54 to 4.47 MPa for the Modulus of Rupture (MOR) and from 686 to 1400 MPa for the Modulus of Elasticity in Bending (MOE). The results indicate that these composites have potential for applications in the construction sector, particularly for wood structures and interior decoration. Moreover, surface treatments could enhance their durability and mechanical properties. This research contributes to the valorization of agricultural and plastic waste as eco-friendly and economical construction materials.

Nonlinear dynamic modeling of planar moving Timoshenko beam considering non-rigid non-elastic axial effects

Due to the importance of vibration effects on the functional accuracy of mechanical systems,this research aims to develop a precise model of a nonlinearly vibrating single-link mobile flexible manipulator.The manipulator consists of an elastic arm,a rotary motor,and a rigid carrier,and undergoes general in-plane rigid body motion along with elastic transverse deformation.To accurately model the elastic behavior,Timoshenko’s beam theory is used to describe the flexible arm,which accounts for rotary inertia and shear deformation effects.By applying Newton’s second law,the nonlinear governing equations of motion for the manipulator are derived as a coupled system of ordinary differential equations(ODEs)and partial differential equations(PDEs).Then,the assumed mode method(AMM)is used to solve this nonlinear system of governing equations with appropriate shape functions.The assumed modes can be obtained after solving the characteristic equation of a Timoshenko beam with clamped boundary conditions at one end and an attached mass/inertia at the other.In addition,the effect of the transverse vibration of the inextensible arm on its axial behavior is investigated.Despite the axial rigidity,the effect makes the rigid body dynamics invalid for the axial behavior of the arm.Finally,numerical simulations are conducted to evaluate the performance of the developed model,and the results are compared with those obtained by the finite element approach.The comparison confirms the validity of the proposed dynamic model for the system.According to the mentioned features,this model can be reliable for investigating the system’s vibrational behavior and implementing vibration control algorithms.

Surviving winter on the Qinghai-Xizang Plateau:Extensive reversible protein phosphorylation plays a dominant role in regulating hypometabolism in hibernating Nanorana parkeri

Changes in protein abundance and reversible protein phosphorylation(RPP)play important roles in regulating hypometabolism but have never been documented in overwintering frogs at high altitudes.To test the hypothesis that protein abundance and phosphorylation change in response to winter hibernation,we conducted a comprehensive and quantitative proteomic and phosphoproteomic analysis of the liver of the Xizang plateau frog,Nanorana parkeri,living on the Qinghai-Xizang Plateau.In total,5170 proteins and 5695 phosphorylation sites in 1938 proteins were quantified.Based on proteomic analysis,674 differentially expressed proteins(438 up-regulated,236 down-regulated)were screened in hibernating N.parkeri versus summer individuals.Functional enrichment analysis revealed that higher expressed proteins in winter were significantly enriched in immune-related signaling pathways,whereas lower expressed proteins were mainly involved in metabolic processes.A total of 4251 modified sites(4147 up-regulated,104 down-regulated)belonging to 1638 phosphoproteins(1555 up-regulated,83 down-regulated)were significantly changed in the liver.During hibernation,RPP regulated a diverse array of proteins involved in multiple functions,including metabolic enzymatic activity,ion transport,protein turnover,signal transduction,and alternative splicing.These changes contribute to enhancing protection,suppressing energy-consuming processes,and inducing metabolic depression.Moreover,the activities of phosphofructokinase,glutamate dehydrogenase,and ATPase were all significantly lower in winter compared to summer.In conclusion,our results support the hypothesis and demonstrate the importance of RPP as a regulatory mechanism when animals transition into a hypometabolic state.

Exosome-Transmitted miR-224-5p Promotes Colorectal Cancer Cell Proliferation via Targeting ULK2 in p53-Dependent Manner

Objective To investigate the role and molecular mechanism of exosomal miR-224-5p in colorectal cancer(CRC).Methods The miR-224-5p expression in CRC patient tissues and cell-derived exosomes was measured by laser capture microdissection and qRT-PCR,respectively.Dual-luciferase reporter gene assay was used to determine the target gene of miR-224-5p.The protein expressions of p53 and unc-51 like kinase 2(ULK2)in CRC cells were detected by western blot.Flow cytometry was used to detect cell cycle and apoptosis.Cell proliferation was measured by CCK8 and EdU assay.Results The miR-224-5p expression was upregulated in CRC tissues and increased progressively with the rise of CRC stage.CRC cells secreted extracellular miR-224-5p mainly in an exosome-dependent manner,and then miR-224-5p could be transferred to surrounding tumor cells to regulate cell proliferation in the form of autocrine or paracrine.Moreover,ULK2 was characterized as a direct target of miR-224-5p and was downregulated in CRC tissues.Interestingly,ULK2 inhibited CRC cell proliferation in a p53-dependent manner.Furthermore,exosome-derived miR-224-5p partially reversed the proliferation regulation of ULK2 on CRC cells.Conclusion Our findings demonstrate that exosome-transmitted miR-224-5p promotes p53-dependent cell proliferation by targeting ULK2 in CRC,which may offer promising targets for CRC prevention and therapy.

Predictive Ability of Hypertriglyceridemic Waist,Hypertriglyceridemic Waist-to-Height Ratio,and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents

Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indicator is more suitable for children and adolescents.We aimed to investigate the relationship between HW,HWHtR,WHR,and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents.Methods This was a national cross-sectional study.Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6–18 years from seven provinces in China.Demographics,physical activity,dietary intake,and family history of chronic diseases were obtained through questionnaires.ANOVA,χ2 and logistic regression analysis was conducted.Results A significant sex difference was observed for HWHtR and WHR,but not for HW phenotype.The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents(HW:OR=12.22,95%CI:9.54-15.67;HWHtR:OR=9.70,95%CI:6.93-13.58).Compared with the HW and HWHtR phenotypes,the association between risk of cardiometabolic health risk factors(CHRF)clustering and high WHR was much weaker and not significant(WHR:OR=1.14,95%CI:0.97-1.34).Conclusion Compared with HWHtR and WHR,the HW phenotype is a more convenient indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.

Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Rock fracture mechanisms can be inferred from moment tensors(MT)inverted from microseismic events.However,MT can only be inverted for events whose waveforms are acquired across a network of sensors.This is limiting for underground mines where the microseismic stations often lack azimuthal coverage.Thus,there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network.Here,we present a novel,multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform.The framework consists of a deep learning model that is initially trained on 2400000+manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations.Transfer learning is then applied to fine-tune the model on 300000+MT-labelled labscale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts,loading,and rock types in training.The optimal model achieves over 86%F-score on unseen waveforms at both the lab-and field-scale.This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network.This facilitates rapid assessment of,and early warning against,various rock engineering hazard such as induced earthquakes and rock bursts.

Spark Plasma Sintering of Boron Carbide Using Ti_(3)SiC_(2) as a Sintering Additive

Boron carbide has unique properties for wide application possibilities;however,poor sinterability limits its applications.One approach to overcome this limitation is the addition of secondary phases into boron carbide.Boron carbide based composite ceramics are produced by the direct addition of secondary phases into the structure or via reactive sintering using a sintering additive.The present study investigated the effect of Ti_(3)SiC_(2) addition to boron carbide by reactive spark plasma sintering in the range of 1700-1900℃.Ti_(3)SiC_(2) phase decomposed at high temperatures and reacted with B4C to form secondary phases of TiB2 and SiC.The results demonstrated that the increase of Ti_(3)SiC_(2) addition(up to 15 vol%)effectively promoted the densification of B4C and yielded higher hardness.However,as the amount of Ti_(3)SiC_(2) increased further,the formation of microstructural inhomogeneity and agglomeration of secondary phases caused a decrease in hardness.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

An Anatomical and Radiological Study of Origins of the Arteries Forming the Celiac Trunk: Clinical and Embryological Implications

The blood supply to the most of abdominal organs is provided by the branches of CT. The SMA supply caecum, ascends colon, all of the small bowels except the upper part of duodenum. Knowledge of variable anatomy of celiac axis and SMA may be useful in planning and executing radiological interventions such as celiacography and chemoembolization of hepatic and pancreatic tumors. In this study, the uncommon or low percentage cases of CT and SMA are presented in the light of clinical and embryological information. The celiac axises of a total of 30 adult corpses were examined. Dissections of abdominal region were performed in detail according to Cunningham’s manual. Angiographic images of 100 consecutive adult patients who underwent celiac MDCT angiography were evaluated. During autopsies, an incomplete celiac trunk or bifurcation of celiac trunk associated with the hepatomesenteric and gastrosplenic trunks (0.7%) and a celiacomesenteric trunk associated with high origin superior mesenteric artery and gastrosplenic trunk were detected (0.7%). During MDCT angiography, a case of total absence of celiac trunk associated with a hepatosplenomesenteric trunk (0.7%) and also a case of total absence of celiac trunk alone were observed (0.7%). The persistence or unusual development of ventral splanchnic arteries (VSAs) or ventral longitudinal anastomosis may result in variations or the unusual trunks related to celiac axis and SMA. The anomalous trunks of the CT may be result of either the persistence of some parts of the VSAs or ventral longitudinal anastomose that normally disappear or disappearance of parts that normally persist. The prevalence of unusual trunks of celiac axis and SMA in this study is quite low in literature. These abnormal vessels pose problems for surgeons and radiologists. Such vascular anomalies may cause clinical complications following surgical and radiological procedures such as resection of tumor of the pancreatic head, lymphadenectomy, coeliacography, aortic replacement with reimplantation of the trunk and coembolization of pancreatic and liver tumors.

Pumping-induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

In groundwater hydrology,aquitard heterogeneity is often less considered compared to aquifers,despite its significant impact on groundwater hydraulics and groundwater resources evaluation.A semi-analytical solution is derived for pumping-induced well hydraulics and groundwater budget with consideration of vertical heterogeneity in aquitard hydraulic conductivity(K)and specific storage(S_(s)).The proposed new solution is innovative in its partitioning of the aquitard into multiple homogeneous sub-layers to enable consideration of various forms of vertically heterogeneous K or S_(s).Two scenarios of analytical investigations are explored:one is the presence of aquitard interlayers with distinct K or S_(s) values,a common field-scale occurrence;another is an exponentially depth-decaying aquitard S_(s),a regional-scale phenomenon supported by statistical analysis.Analytical investigations reveal that a low-K interlayer can significantly increase aquifer drawdown and enhance aquifer/aquitard depletion;a high-S_(s) interlayer can noticeably reduce aquifer drawdown and increase aquitard depletion.Locations of low-K or high-S_(s) interlayers also significantly impact well hydraulics and groundwater budget.In the context of an exponentially depth-decaying aquitard S_(s),a larger decay exponent can enhance aquifer drawdown.When using current models with a vertically homogeneous aquitard,half the sum of the geometric and harmonic means of exponentially depth-decaying aquitard S_(s) should be used to calculate aquitard depletion and unconfined aquifer leakage.

Growth, Development and Yield of Kenaf as Affected by Planting Dates and N Fertilization

Kenaf (Hibiscus cannabinus L) consists of various beneficial components like stalks, seeds, leaves, fibers, oils, proteins, allelopathic chemicals, and fiber strands, among other things. Despite the numerous uses of the crop, there is little or no information on optimum agronomic practices such as planting date and N fertilization of the crop in the Upper East Region (UER) of Ghana where the crop is widely cultivated by smallholder farmers. Field experiments were therefore carried out in 2020 and repeated during the 2021 cropping season in the study area. The objective of the study was to determine appropriate planting date and N fertilization for increased kenaf productivity. In each year, the treatments consisted of 3 × 5 factorial combinations of three planting dates (1st July, 7th July and 14th July) and five levels of N (0, 20, 40, 60 and 80 kg/ha) replicated three times. The design of the experiment was a split-plot with the N fertilizer as the main plot and the planting date assigned to sub plot. The results showed that, planting kenaf in early (1st) July or N fertilization at the rate of 60 kg/ha increased plant density, stem height, stem diameter, dry bast and core yields in both cropping seasons.

The ALMA-QUARKS Survey.Ⅰ.Survey Description and Data Reduction

This paper presents an overview of the QUARKS survey,which stands for Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures."The QUARKS survey is observing139 massive clumps covered by 156 pointings at Atacama Large Millimeter/submillimeter Array(ALMA)Band 6(λ~1.3 mm).In conjunction with data obtained from the ALMA-ATOMS survey at Band 3(λ~3 mm),QUARKS aims to carry out an unbiased statistical investigation of massive star formation process within protoclusters down to a scale of 1000 au.This overview paper describes the observations and data reduction of the QUARKS survey,and gives a first look at an exemplar source,the mini-starburst Sgr B2(M).The wide-b and width(7.5 GHz)and high-angular-resolution(~0."3)observations of the QUARKS survey allow for the resolution of much more compact cores than those could be done by the ATOMS survey,and to detect previously unrevealed fainter filamentary structures.The spectral windows cover transitions of species including CO,SO,N_(2)D^(+),SiO,H_(30)α,H_(2)CO,CH_(3)CN,and many other complex organic molecules,tracing gas components with different temperatures and spatial extents.QUARKS aims to deepen our understanding of several scientific topics of massive star formation,such as the mass transport within protoclusters by(hub-)filamentary structures,the existence of massive starless cores,the physical and chemical properties of dense cores within protoclusters,and the feedback from already formed high-mass young protostars.

Evaluation of Water Losses by Evaporation in the Nakanbe Basin

A numerical approach to heat and mass transfer in a large water reservoir is presented. This water reservoir is likened to a parallelepiped reservoir whose vertical and lower walls are adiabatic and impermeable. The equations that govern natural convection in water are solved by the finite volume method and Thomas’salgorithm. The adequacy between the velocity and pressure fields is ensured by the SIMPLE algorithm. We are going to evaluate the water losses by evaporation from three dams in the Nakanbé basin in Burkina Faso for a period of thirty years, that is to say from January 1, 1991, to March 15, 2020. The three dams have a rate of evaporation greater than 40% of the volume of water stored. Indeed the rate of evaporation in each dam increases with the water filling rate in the reservoir: we have observed the following results for each dam in the Nakanbé basin;for the date of 02/27/1988 to 03/13/2020., the Loumbila dam received a total volume of stored water of 22.02 Mm3 and 10.57 Mm3 as the total volume of water evaporated at the same date. At the Ouaga dam (2 + 3), it stored a water volume of 4.06 Mm3 and evaporated 2.03 Mm3 of its storage volume from 01/01/1988 to 05/07/2016. Finally, with regard to the Bagré dam, it stored 745.16 Mm3 of water and 365.13 Mm3 as the volume of water evaporated from 01/01/1993 to 03/31/2020.

A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack

This paper addresses the urgent need to detect network security attacks,which have increased significantly in recent years,with high accuracy and avoid the adverse effects of these attacks.The intrusion detection system should respond seamlessly to attack patterns and approaches.The use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational costs.To achieve better performance of these algorithms and further improve the results,hybridization of algorithms can be used,which leads to more successful results.Nowadays,many studies are conducted on this topic.In this study,a new hybrid approach using Gray Wolf Optimizer(GWO)and Moth-Flame Optimization(MFO)algorithms was developed and applied to widely used data sets such as NSL-KDD,UNSW-NB15,and CIC IDS 2017,as well as various benchmark functions.The ease of hybridization of the GWO algorithm,its simplicity,its ability to perform global optimal search,and the success of the MFO algorithm in obtaining the best solution suggested that an effective solution would be obtained by combining these two algorithms.For these reasons,the developed hybrid algorithm aims to achieve better results by using the good aspects of both the GWO algorithm and the MFO algorithm.In reviewing the results,it was found that a high level of success was achieved in the benchmark functions.It achieved better results in 12 of the 13 benchmark functions compared.In addition,the success rates obtained according to the evaluation criteria in the different data sets are also remarkable.Comparing the 97.4%,98.3%,and 99.2% classification accuracy results obtained in the NSL-KDD,UNSW-NB15,and CIC IDS 2017 data sets with the studies in the literature,they seem to be quite successful.

Fast Verification of Network Configuration Updates

With the expansion of network services,large-scale networks have progressively become common.The network status changes rapidly in response to customer needs and configuration changes,so network configuration changes are also very frequent.However,no matter what changes,the network must ensure the correct conditions,such as isolating tenants from each other or guaranteeing essential services.Once changes occur,it is necessary to verify the after-changed network.Whereas,for the verification of large-scale network configuration changes,many current verifiers show poor efficiency.In order to solve the problem ofmultiple global verifications caused by frequent updates of local configurations in large networks,we present a fast configuration updates verification tool,FastCUV,for distributed control planes.FastCUV aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring correctness.This paper presents a method to determine the network range affected by the configuration change.We present a flow model and graph structure to facilitate the design of verification algorithms and speed up verification.Our scheme verifies the network area affected by obtaining the change of the Forwarding Information Base(FIB)before and after.FastCUV supports rich network attributes,meanwhile,has high efficiency and correctness performance.After experimental verification and result analysis,our method outperforms the state-of-the-art method to a certain extent.

Deep Neural Network for Detecting Fake Profiles in Social Networks

This paper proposes a deep neural network(DNN)approach for detecting fake profiles in social networks.The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles.In addition,the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks,which has been developed using 16 features based on content-based and profilebased features.The results demonstrated that the proposed method could detect fake profiles with an accuracy of 99.4%,equivalent to the achieved findings based on bigger data sets and more extensive profile information.The results were obtained with the minimum available profile data.In addition,in comparison with the other methods that use the same amount and kind of data,the proposed deep neural network gives an increase in accuracy of roughly 14%.The proposed model outperforms existing methods,achieving high accuracy and F1 score in identifying fake profiles.The associated findings indicate that the proposed model attained an average accuracy of 99%while considering two distinct scenarios:one with a single theme and another with a miscellaneous one.The results demonstrate the potential of DNNs in addressing the challenging problem of detecting fake profiles,which has significant implications for maintaining the authenticity and trustworthiness of online social networks.

Optimization of Cognitive Femtocell Network via Oppositional Beetle Swarm Optimization Algorithm

In past decades,cellular networks have raised the usage of spectrum resources due to the victory of mobile broadband services.Mobile devices create massive data than ever before,facing the way cellular networks are installed pre-sently for satisfying the increased traffic requirements.The development of a new exclusive spectrum offered to meet up the traffic requirements is challenging as spectrum resources are limited,hence costly.Cognitive radio technology is pre-sented to increase the pool of existing spectrum resources for mobile users via Femtocells,placed on the top of the available macrocell network for sharing the same spectrum.Nevertheless,the concurrent reuse of spectrum resources from Femto networks poses destructive interference on macro networks.To resolve this issue,this paper introduces an optimal channel allocation model using the Oppo-sitional Beetle Swarm Optimization Algorithm(OBSOA)to allocate the channel with interference avoidance.A new OBSOA is derived in this paper by the inclu-sion of opposition-based learning(OBL)in BSOA.This algorithm allocates the channels used by PUs(PUs)to the secondary users(SUs)in such a way that inter-ference is minimized.This proposed approach is implemented in the MATrix LABoratory(MATLAB)platform.The performance of this proposed approach is evaluated in terms of several measures and the experimental outcome verified the superior nature of the OBSOA-based channel allocation model.OBSOA mod-el has resulted in a maximum signal-to-interference-plus-noise ratio value of 86.42 dB.

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.

Medi-Block Record Secure Data Sharing in Healthcare System:Issues,Solutions and Challenges

With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.