Constructing Representative Collective Signature Protocols Using The GOST R34.10-1994 Standard

The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.

Modeling Geometrically Nonlinear FG Plates: A Fast and Accurate Alternative to IGA Method Based on Deep Learning

Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.

Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

Dynamics of bioconvection flow of micropolar nanoparticles with Cattaneo-Christov expressions

A numerical analysis is performed to analyze the bioconvective double diffusive micropolar non-Newtonian nanofluid flow caused by stationary porous disks.The consequences of the current flow problem are further extended by incorporating the Brownian and thermophoresis aspects.The energy and mass species equations are developed by utilizing the Cattaneo and Christov model of heat-mass fluxes.The flow equations are converted into an ordinary differential model by employing the appropriate variables.The numerical solution is reported by using the MATLAB builtin bvp4c method.The consequences of engineering parameters on the flow velocity,the concentration,the microorganisms,and the temperature profiles are evaluated graphically.The numerical data for fascinating physical quantities,namely,the motile density number,the local Sherwood number,and the local Nusselt number,are calculated and executed against various parametric values.The microrotation magnitude reduces for increasing magnetic parameters.The intensity of the applied magnetic field may be utilized to reduce the angular rotation which occurs in the lubrication processes,especially in the suspension of flows.On the account of industrial applications,the constituted output can be useful to enhance the energy transport efficacy and microbial fuel cells.

Calibration of a neutron dose rate meter in various neutron standard fields

This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and five simulated workplace fields with241Am-Be moderated sources).The calibration in standard fields with bare sources was conducted by following the recommendations of the ISO 8529 standard.The measured total neutron ambient dose equivalent rates,denoted as H*(10)tot,were analyzed to obtain direct components,denoted as H*(10)dir,using a reduced fitting method.The CF was then calculated as the ratio between the conventional true value of the neutron ambient dose equivalent rate in a free field,denoted as H*(10)FF,and the value of H*(10)dir.In contrast,in the simulated workplace neutron fields,the calibration of the neutron dose rate meter was conducted by following the ISO 12789 standard.The CF was calculated as the ratio between the values of H*(10)totmeasured by a standard instrument(i.e.,Bonner sphere spectrometer)and the neutron dose rate meter.The CF values were obtained in the range of 0.88–1.0.The standard uncertainties(k=1)of the CFs were determined to be in the range of approximately 6.6–13.1%.

Hiding on jagged karst pinnacles:A new microendemic genus and species of a limestone-dwelling agamid lizard(Squamata:Agamidae:Draconinae)from Khammouan Province,Laos

We describe a unique new species and genus of agamid lizard from the karstic massifs of Khammouan Province,central Laos.Laodracon carsticola Gen.et sp.nov.is an elusive medium-sized lizard(maximum snout-vent length101 mm)specifically adapted to life on limestone rocks and pinnacles.To assess the phylogenetic position of the new genus amongst other agamids,we generated DNA sequences from two mitochondrial gene fragments(16S rRNA and ND2)and three nuclear loci(BDNF,RAG1 and c-mos),with a final alignment comprising 7418 base pairs for 64 agamid species.Phylogenetic analyses unambiguously place the new genus in the mainland Asia subfamily Draconinae,where it forms a clade sister to the genus Diploderma from East Asia and the northern part of Southeast Asia.Morphologically,the new genus is distinguished from all other genera in Draconinae by possessing a notably swollen tail base with enlarged scales on its dorsal and ventral surfaces.Our work provides further evidence that limestone regions of Indochina represent unique“arks of biodiversity”and harbor numerous relict lineages.To date,Laodracon carsticola Gen.et sp.nov.is known from only two adult male specimens and its distribution seems to be restricted to a narrow limestone massif on the border of Khammouan and Bolikhamxai provinces of Laos.Additional studies are required to understand its life history,distribution,and conservation status.

Damage in hybrid corrugated core sandwich structures under high velocity hail ice impact:A numerical study

Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact.The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively.The transient processes of sandwich structure under different hail ice impact conditions are analyzed.Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.

Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs

In today’s information technology(IT)world,the multi-hop wireless sensor networks(MHWSNs)are considered the building block for the Internet of Things(IoT)enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service(QoS)in a stipulated time slot to end-user over the Internet.Smart city(SC)is an example of one such application which can automate a group of civil services like automatic control of traffic lights,weather prediction,surveillance,etc.,in our daily life.These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput,energy efficiency,and end-to-end delay,wherein low latency is considered a challenging issue in next-generation networks(NGN).This paper introduces a single and parallels stable server queuing model with amulti-class of packets and native and coded packet flowto illustrate the simple chain topology and complexmultiway relay(MWR)node with specific neighbor topology.Further,for improving data transmission capacity inMHWSNs,an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node.Finally,the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets.The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.

Mountain jade: A new high-elevation microendemic species of the genus Zhangixalus(Amphibia: Anura: Rhacophoridae)from Laos

DEAR EDITOR,We report on a new species, Zhangixalus melanoleucus sp.nov., from Phou Samsoum Mountain(PSM) in Xiengkhouang Province, northeastern Laos, based on an integrative taxonomic approach, including morphological, molecular, and bioacoustic lines of evidence. Morphologically, the new species can be distinguished from its congeners by a combination of the following diagnostic characters: medium body size(SVL 34.4–36.3 mm in males, 53.7 mm in a single female);dorsum smooth and green;chest and belly lacking spots;flanks, axillae, ventral surfaces of forearms, inguinal.

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

Vitamin D deficiency and hepatitis viruses-associated liver diseases:a literature review

The secosteroid hormone vitamin D has, in addition to its effects in bone metabolism also functions in the modulation of immune responses against infectious agents and in inhibiting tumorigenesis. Thus, deficiency of vitamin D is associated with several malignancies, but also with a plethora of infectious diseases. Among other communicable diseases, vitamin D deficiency is involved in the pathogenesis of chronic liver diseases caused by hepatitis B and C viruses(HBV, HCV) and high prevalence of vitamin D deficiency with serum levels below 20 mg/mL in patients with HBV and HCV infection are found worldwide. Several studies have assessed the effects of vitamin D supplementation on the sustained virological response(SVR) to interferon(IFN) plus ribavirin(RBV) therapy in HBV and HCV infection. In these studies, inconsistent results were reported. This review addresses general aspects of vitamin D deficiency and, in particular, the significance of vitamin D hypovitaminosis in the outcome of HBVand HCV-related chronic liver diseases. Furthermore,current literature was reviewed in order to understand the effects of vitamin D supplementation in combination with IFN-based therapy on the virological response in HBV and HCV infected patients.

Predicting TBM penetration rate in hard rock condition:A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.

MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

软木塞基碳量子点的水热合成及其生物相融荧光成像应用

以废弃酒瓶软木塞为原料,采用低成本、简单的水热方法合成碳量子点。通过TEM、FTIR、Raman、UV-Vis、PL光谱对碳量子点的结构和光学性能进行分析表征。结果表明,碳量子点的平均直径为6.2±2.7 nm,PL激发谱和碳量子点表面的官能团有关。用硫酸奎宁作为参考,碳量子点的量子效率为1.54%。将获得的碳量子点应用在骨髓间充质干细胞的细胞生物成像上,发现用碳量子点处理后,骨髓间充质干细胞分别在320~380 nm、450~490 nm和450~490 nm范围显示绿色、黄色和红色荧光,表明了碳量子点在荧光成像领域具有潜在的应用价值。

Microalgae:A potential alternative to health supplementation for humans

Microalgae has been consumed in human diet for thousands of years.It is an under-exploited crop for production of dietary foods.Microalgae cultivation does not compete with land and resources required for traditional crops and has a superior yield compared to terrestrial crops.Its high protein content has exhibited a huge potential to meet the dietary requirements of growing population.Apart from being a source of protein,presence of various bio-active components in microalgae provide an added health benefit.This review describes various microalgal sources of proteins and other bio-active components.One of the heavily studied group of bio-active components are pigments due to their anticarcenogenic,antioxidative and antihypertensive properties.Compared to various plant and floral species,microalgae contain higher amounts of pigments.Microalgal derived proteins have complete Essential Amino Acids(EAA)profiles and their protein content is higher than conventional sources such as meat,poultry and dairy products.However,microalgal based functional foods have not flooded the market.The lack of awareness coupled with scarce incentives for producers result in under-exploitation of microalgal potential.Application of microalgal derived components as dietary and nutraceutical supplements is discussed comprehensively.

System Performance of Wireless Sensor Network Using LoRa–Zigbee Hybrid Communication

Wireless sensor network(WSN)is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains.Many sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the gateway.Ensuring communications in heterogeneous WSNs,is a critical issue that needs to be studied.In this research paper,we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid communication.Specifically,two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed by a LoRa gateway.The overall system integrates many different sensors in terms of types,communication protocols,and accuracy,which can be used in many applications in realistic environments such as on land,under water,or in the air.In addition to this,a synchronous management software on ThingSpeak Web server and Blynk app is designed.In the proposed system,the token ring protocol in Zigbee network and polling mechanism in LoRa network is used.The system can operate with a packet loss rate of less than 0.5%when the communication range of the Zigbee network is 630 m,and the communication range of the LoRa network is 3.7 km.On the basis of the digital results collected on the management software,this study proves tremendous improvements in the system performance.

GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).

Optimal treatment of stratified Carreau and Casson nanofluids flows in Darcy-Forchheimer porous space over porous matrix

A comparative three-dimensional(3D)analysis for Casson-nanofluid and Carreau-nanofluid flows due to a flat body in a magnetohydrodynamic(MHD)stratified environment is presented.Flow is estimated to be suspended in a Darcy-Forchheimer medium.Soret and Dufour responses are also accommodated in the flow field.A moving(rotating)coordinate system is exercised to examine the bidirectionally stretched flow fields(flow,heat transfer,and mass transfer).Nanofluid is compounded by taking ethylene glycol/sodium alginate as base fluid and ferric-oxide(Fe3O4)as nanoparticles.Governing equations are handled by the application of optimal homotopy asymptotic method(OHAM),where convergence parameters are optimized through the classical least square procedure.The novel mechanism(hidden physics)due to appearing parameters is explored with the assistance of tabular and graphical expositions.Outcomes reveal the double behavior state for temperature field with thermal stratification/Dufour number,and for concentration field with Soret number due to the presence of turning points.