A Secure Multi-factor Authentication Protocol for Healthcare Services Using Cloud-based SDN

Cloud-based SDN(Software Defined Network)integration offers new kinds of agility,flexibility,automation,and speed in the network.Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement.The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services.It has improved the real-time monitoring of patients by medical practitioners.Patients’data get stored at the central server on the cloud from where it is available to medical practitioners in no time.The centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients’privacy.In recent days,several schemes have been proposed to ensure the safety of patients’data.But most of the techniques still lack the practical implementation and safety of data.In this paper,a secure multi-factor authentication protocol using a hash function has been proposed.BAN(Body Area Network)logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient information.Security Protocol Animator(SPAN)–Automated Validation of Internet Security Protocols and Applications(AVISPA)tool has been used for simulation.The results prove that the proposed scheme ensures secure access to the database in terms of spoofing and identification.Performance comparisons of the proposed scheme with other related historical schemes regarding time complexity,computation cost which accounts to only 423 ms in proposed,and security parameters such as identification and spoofing prove its efficiency.

The multi-factor recombination and processes superimposition model for hydrocarbon accumulation:application to the Silurian in the Tarim Basin

The multi-factor recombination and processes superimposition model for hydrocarbon accumulation is put forward in view of the hydrocarbon geological characteristics of multiple episodes of structural evolution, multiple sets of source-reservoir-seal assemblage, multiple cycles of hydrocarbon accumulation and multiple episodes of readjustment and reconstruction in the complex superimposed basins in China. It is a system including theories and methods that can help to predict favorable exploration regions. According to this model, the basic discipline for hydrocarbon generation, evolution and distribution in the superimposed basins can be summarized in multi-factor recombination, processes superimposition, multiple stages of oil filling and latest stage preservation. With the Silurian of the Tarim basin as an example, based on the reconstruction of the evolution history of the four factors （paleo-anticline, source rock, regional cap rock and kinematic equilibrium belt） controlling hydrocarbon accumulation, this model was adopted to predict favorable hydrocarbon accumulation areas and favorable exploration regions following structural destruction in three stages of oil filling, to provide guidance for further exploration ofoil and gas in the Silurian of the Tarim basin.

Multi-factor analysis of initial poor graft function after orthotopic liver transplantation

BACKGROUND: In the early period of orthotopic liver transplantation (OLT), initial poor graft function (IPGF) is one of the complications which leads to primary graft non-function (PGNF) in serious cases. This study set out to establish the clinical risk factors resulting in IPGF after OLT. METHODS: Eighty cases of OLT were analyzed. The IPGF group consisted of patients with alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) above 1500 IU/L within 72 hours after OLT, while those in the non-IPGF group had values below 1500 IU/L. Recipient-associated factors before OLT analyzed were age, sex, primary liver disease and Child-Pugh classification; factors analyzed within the peri-operative period were non-heart beating time (NHBT), cold ischemia time (CIT), rewarming ischemic time (RWIT), liver biopsy at the end of cold ischemia; and factors analyzed within 72 hours after OLT were ALT and/or AST values. A logistic regression model was applied to filter the possible factors resulting in IPGF. RESULTS: Donor NHBT, CIT and RWIT were significantly longer in the IPGF group than in the non-IPGF group; in the logistic regression model, NHBT was the risk factor leading to IPGF (P < 0.05), while CIT and RWIT were possible risk factors. In one case in the IPGF group, PGNF appeared with moderate hepatic steatosis. CONCLUSIONS: Longer NHBT is an important risk factor leading to IPGF, while serious steatosis in the donor liver, CIT and RWIT are potential risk factors.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Cellular Automata-based Chloride Ion Diffusion Simulation of Concrete Bridges under Multi-factor Coupling Actions

In order to accurately simulate the diffusion of chloride ion in the existing concrete bridge and acquire the precise chloride ion concentration at given time, a cellular automata （CA）-based model is proposed. The process of chloride ion diffusion is analyzed by the CA-based method and a nonlinear solution of the Fick＇s second law is obtained. Considering the impact of various factors such as stress states, temporal and spatial variability of diffusion parameters and water-cement ratio on the process of chloride ion diffusion, the model of chloride ion diffusion under multi-factor coupling actions is presented. A chloride ion penetrating experiment reported in the literature is used to prove the effectiveness and reasonability of the present method, and a T-type beam is taken as an illustrative example to analyze the process of chloride ion diffusion in practical application. The results indicate that CA-based method can simulate the diffusion of chloride ion in the concrete structures with acceptable precision.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

Multi-Factor Password-Authenticated Key Exchange via Pythia PRF Service

Multi-factor authentication(MFA)was proposed by Pointcheval et al.[Pointcheval and Zimmer(2008)]to improve the security of single-factor(and two-factor)authentication.As the backbone of multi-factor authentication,biometric data are widely observed.Especially,how to keep the privacy of biometric at the password database without impairing efficiency is still an open question.Using the vulnerability of encryption(or hash)algorithms,the attacker can still launch offline brute-force attacks on encrypted(or hashed)biometric data.To address the potential risk of biometric disclosure at the password database,in this paper,we propose a novel efficient and secure MFA key exchange(later denoted as MFAKE)protocol leveraging the Pythia PRF service and password-to-random(or PTR)protocol.Armed with the PTR protocol,a master password pwd can be translated by the user into independent pseudorandom passwords(or rwd)for each user account with the help of device(e.g.,smart phone).Meanwhile,using the Pythia PRF service,the password database can avoid leakage of the local user’s password and biometric data.This is the first paper to achieve the password and biometric harden service simultaneously using the PTR protocol and Pythia PRF.

A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.

Multi-factor high-order intuitionistic fuzzy timeseries forecasting model

Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.

Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer's disease and evaluate drug effect based on a rat model

Alzheimer's disease(AD)represents the main form of dementia;however,valid diagnosis and treatment measures are lacking.The discovery of valuable biomarkers through omics technologies can help solve this problem.For this reason,metabolomic analysis using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS)was carried out on plasma,hippocampus,and cortex samples of an AD rat model.Based on the metabolomic data,we report a multi-factor combined biomarker screening strategy to rapidly and accurately identify potential biomarkers.Compared with the usual procedure,our strategy can identify fewer biomarkers with higher diagnostic specificity and sensitivity.In addition to diagnosis,the potential biomarkers identified using our strategy were also beneficial for drug evaluation.Multi-factor combined biomarker screening strategy was used to identify differential metabolites from a rat model of amyloid beta peptide 1e40(Aβ_(1-40))plus ibotenic acid-induced AD(compared with the controls)for the first time;lysophosphatidylcholine(LysoPC)and intermediates of sphingolipid metabolism were screened as potential biomarkers.Subsequently,the effects of donepezil and pine nut were successfully reflected by regulating the levels of the abovementioned biomarkers and metabolic profile distribution in partial least squaresdiscriminant analysis(PLS-DA).This novel biomarker screening strategy can be used to analyze other metabolomic data to simultaneously enable disease diagnosis and drug evaluation.

Multi-factor principle for electrolyte additive molecule design for facilitating the development of electrolyte chemistry

When I read the paper“Electrolytes enriched by potassium perfluorinated sulfonates for lithium metal batteries”from Prof.Jianmin Ma’s group,which was published in Science Bulletin(doi.org/10.1016/j.scib.2020.09.018),I felt excited as presented a multi-factor principle for applying potassium perfluorinated sulfonates to suppress the dendrite growth and protect the cathode from the viewpoint of electrolyte additives.The effects of these additives are revealed through experimental results,molecular dynamics simulations and first-principle calculations.Specifically,it involves the influence of additives on Li^(+)solvation structure,solid electrolyte interphase(SEI),Li growth and nucleation.Following the guidance of the multi-factor principle,every part of the additive molecule should be utilized to regulate electrolytes.This multifactor principle for electrolyte additive molecule design(EAMD)offers a unique insight on understanding the electrochemical behavior of iontype electrolyte additives on both the Li metal anode and high-voltage cathode.In these regards,I would be delighted to write a highlight for this innovative work and,hopefully,it may raise more interest in the areas of electrolyte additives.

Multi-Factor Authentication for Secured Financial Transactions in Cloud Environment

The rise of the digital economy and the comfort of accessing by way of user mobile devices expedite human endeavors in financial transactions over the Virtual Private Network(VPN)backbone.This prominent application of VPN evades the hurdles involved in physical money exchange.The VPN acts as a gateway for the authorized user in accessing the banking server to provide mutual authentication between the user and the server.The security in the cloud authentication server remains vulnerable to the results of threat in JP Morgan Data breach in 2014,Capital One Data Breach in 2019,and manymore cloud server attacks over and over again.These attacks necessitate the demand for a strong framework for authentication to secure from any class of threat.This research paper,propose a framework with a base of EllipticalCurve Cryptography(ECC)to performsecure financial transactions throughVirtual PrivateNetwork(VPN)by implementing strongMulti-Factor Authentication(MFA)using authentication credentials and biometric identity.The research results prove that the proposed model is to be an ideal scheme for real-time implementation.The security analysis reports that the proposed model exhibits high level of security with a minimal response time of 12 s on an average of 1000 users.

A TMA-Seq2seq Network for Multi-Factor Time Series Sea Surface Temperature Prediction

Sea surface temperature (SST) is closely related to global climatechange, ocean ecosystem, and ocean disaster. Accurate prediction of SST isan urgent and challenging task. With a vast amount of ocean monitoring dataare continually collected, data-driven methods for SST time-series predictionshow promising results. However, they are limited by neglecting complexinteractions between SST and other ocean environmental factors, such as airtemperature and wind speed. This paper uses multi-factor time series SSTdata to propose a sequence-to-sequence network with two-module attention(TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMASeq2seq is an LSTM-based encoder-decoder architecture facilitated by factorand temporal-attention modules and the input of multi-factor time series. Ittakes six-factor time series as the input, namely air temperature, air pressure,wind speed, wind direction, SST, and SST anomaly (SSTA). A factor attentionmodule is first designed to adaptively learn the effect of different factors onSST, followed by an encoder to extract factor-attention weighted features asfeature representations. And then, a temporal attention module is designedto adaptively select the hidden states of the encoder across all time steps tolearn more robust temporal relationships. The decoder follows the temporalattention module to decode the feature vector concatenated from the weightedfeatures and original input feature. Finally, we use a fully-connect layer tomap the feature into prediction results. With the two attention modules, ourmodel effectively improves the prediction accuracy of SST since it can notonly extract relevant factor features but also boost the long-term dependency.Extensive experiments on the datasets of China Coastal Sites (CCS) demonstrate that our proposed model outperforms other methods, reaching 98.29%in prediction accuracy (PACC) and 0.34 in root mean square error (RMSE).Moreover, SST prediction experiments in China’s East, South, and Yellow Seasite data show that the proposed model has strong robustness and multi-siteapplicability.

Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.

Enhancing Multi-Factor Authentication in Modern Computing

Most network service providers like MTN Nigeria, currently use two-factor authentication for their 4G wireless networks. This exposes the network subscribers to identify theft and users data to security threats like snooping, sniffing, spoofing and phishing. There is need to curb these problems with the use of an enhanced multi-factor authentication approach. The objective of this work is to create a multi-factor authentication software for a 4G wireless network. Multi-factor authentication involves user’s knowledge factor, user’s possession factor and user’s inherence factor;that is who the user is to be presented before system access can be granted. The research methodologies used for this work include Structured System Analysis and Design Methodology, SSADM and Prototyping. The result of this work will be a Multi-factor authentications software. This software was designed with programming languages like ASP. NET, C# and Microsoft SQL Server for the database.

Productivity Testing Design Method of Multi-Factor Control for Unconsolidated Sandstone Gas Reservoir

Reservoir safety, testing-string safety, and flow control are key factors that should be considered in deep-water unconsolidated sandstone gas well testing work system. Combined with the feature of testing reservoir, pipe string type and sea area, the required minimum testing flow rate during cleaning up process, as well as minimum test flow rate without hydrate generation, pipe string erosion critical production, the maximum testing flow rate without destroying sand formation and the minimum output of meeting the demand of development was analyzed;based on the above critical test flow rates, testing working system is designed. Field application showed that the designed work system effectively provided good guidance for field test operations;no sand production or hydrate generation happened during the test process;the test parameter evaluated the reservoir accurately;the safe and efficient test operation was achieved.

The Role of Multi-factor Authentication for Modern Day Security

Multi-factor Authentication(MFA)often referred to as Two-factor Authen­tication(2FA),which is a subset of MFA,is the practice of implementing additional security methods on top of a standard username and password to help authenticate the identity of a user and increase the security of data.This chapter will investigate the problem with username and password log­ins,the different types of authentication,current best practice for multi-fac­tor authentication and interpretations about how the technology will grow in the upcoming years.

Long-term efficacy of microvascular decompression on trigeminal neuralgia and multi-factor research of recurrence

Objective To research the factors that affects the efficacy of 2 826 cases of of trigeminal neuralgia after microvascular decompression during follow-up more than 1 year. Methods The patients with trigeminal meuralgia were followed-up for 1-25 years ( mean 9. 8 years) after microvascular decompression from January 1984 to

Spatial Variability of PM_(2.5) Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China

Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may be overlooked. Meanwhile, the inconsistent spatial distribution of the natural and socioeconomic conditions brings unique implications for the cities within a region, which may lead to an uncertain understanding of the relationship between pollution and environmental factors. This study focused on the Beijing-TianjinHebei(BTH) Region, China, which presents complex and varied background conditions. Potential impact factors on PM_(2.5) were firstly screened by combining systematic cluster analysis with a random forest recursive feature elimination algorithm. Then, the representative multi-factor responsible for PM_(2.5) pollution in the region during the key period of 2014–2018(when the strict national air pollution control policy was implemented). The results showed that the key driving factors of PM_(2.5) pollution in the BTH cities are different, indicating that the uniqueness of a city will have an impact on the leading causes of pollution. Further discussion shows that air control policy provides an effective way to improve air quality. This study aims to deepen the understanding of the risk drivers of air pollution within the BTH Region. In the future, it is recommended that more attention should be paid to the specific differences between the cities when formulating PM_(2.5) concentration control measures.