A Stochastic Model to Assess the Epidemiological Impact of Vaccine Booster Doses on COVID-19 and Viral Hepatitis B Co-Dynamics with Real Data

A patient co-infected with COVID-19 and viral hepatitis B can be atmore risk of severe complications than the one infected with a single infection.This study develops a comprehensive stochastic model to assess the epidemiological impact of vaccine booster doses on the co-dynamics of viral hepatitis B and COVID-19.The model is fitted to real COVID-19 data from Pakistan.The proposed model incorporates logistic growth and saturated incidence functions.Rigorous analyses using the tools of stochastic calculus,are performed to study appropriate conditions for the existence of unique global solutions,stationary distribution in the sense of ergodicity and disease extinction.The stochastic threshold estimated from the data fitting is given by:R_(0)^(S)=3.0651.Numerical assessments are implemented to illustrate the impact of double-dose vaccination and saturated incidence functions on the dynamics of both diseases.The effects of stochastic white noise intensities are also highlighted.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

Exploration of the coupled lattice Boltzmann model based on a multiphase field model:A study of the solid-liquid-gas interaction mechanism in the solidification process

A multiphase field model coupled with a lattice Boltzmann(PF-LBM)model is proposed to simulate the distribution mechanism of bubbles and solutes at the solid-liquid interface,the interaction between dendrites and bubbles,and the effects of different temperatures,anisotropic strengths and tilting angles on the solidified organization of the SCN-0.24wt.%butanedinitrile alloy during the solidification process.The model adopts a multiphase field model to simulate the growth of dendrites,calculates the growth motions of dendrites based on the interfacial solute equilibrium;and adopts a lattice Boltzmann model(LBM)based on the Shan-Chen multiphase flow to simulate the growth and motions of bubbles in the liquid phase,which includes the interaction between solid-liquid-gas phases.The simulation results show that during the directional growth of columnar dendrites,bubbles first precipitate out slowly at the very bottom of the dendrites,and then rise up due to the different solid-liquid densities and pressure differences.The bubbles will interact with the dendrite in the process of flow migration,such as extrusion,overflow,fusion and disappearance.In the case of wide gaps in the dendrite channels,bubbles will fuse to form larger irregular bubbles,and in the case of dense channels,bubbles will deform due to the extrusion of dendrites.In the simulated region,as the dendrites converge and diverge,the bubbles precipitate out of the dendrites by compression and diffusion,which also causes physical phenomena such as fusion and spillage of the bubbles.These results reveal the physical mechanisms of bubble nucleation,growth and kinematic evolution during solidification and interaction with dendrite growth.

Considerations for the use of porcine organ donation models in preclinical organ donor intervention research

Use of animal models in preclinical transplant research is essential to the optimization of human allografts for clinical transplantation.Animal models of organ donation and preservation help to advance and improve technical elements of solid organ recovery and facilitate research of ischemia-reperfusion injury,organ preservation strategies,and future donor-based interventions.Important considerations include cost,public opinion regarding the conduct of animal research,translational value,and relevance of the animal model for clinical practice.We present an overview of two porcine models of organ donation:donation following brain death(DBD)and donation following circulatory death(DCD).The cardiovascular anatomy and physiology of pigs closely resembles those of humans,making this species the most appropriate for pre-clinical research.Pigs are also considered a potential source of organs for human heart and kidney xenotransplantation.It is imperative to minimize animal loss during procedures that are surgically complex.We present our experience with these models and describe in detail the use cases,procedural approach,challenges,alternatives,and limitations of each model.

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

An Extensive Study and Review of Privacy Preservation Models for the Multi-Institutional Data

The deep learning models hold considerable potential for clinical applications, but there are many challenges to successfully training deep learning models. Large-scale data collection is required, which is frequently only possible through multi-institutional cooperation. Building large central repositories is one strategy for multi-institution studies. However, this is hampered by issues regarding data sharing, including patient privacy, data de-identification, regulation, intellectual property, and data storage. These difficulties have lessened the impracticality of central data storage. In this survey, we will look at 24 research publications that concentrate on machine learning approaches linked to privacy preservation techniques for multi-institutional data, highlighting the multiple shortcomings of the existing methodologies. Researching different approaches will be made simpler in this case based on a number of factors, such as performance measures, year of publication and journals, achievements of the strategies in numerical assessments, and other factors. A technique analysis that considers the benefits and drawbacks of the strategies is additionally provided. The article also looks at some potential areas for future research as well as the challenges associated with increasing the accuracy of privacy protection techniques. The comparative evaluation of the approaches offers a thorough justification for the research’s purpose.

Analysis of Secured Cloud Data Storage Model for Information

This paper was motivated by the existing problems of Cloud Data storage in Imo State University, Nigeria such as outsourced data causing the loss of data and misuse of customer information by unauthorized users or hackers, thereby making customer/client data visible and unprotected. Also, this led to enormous risk of the clients/customers due to defective equipment, bugs, faulty servers, and specious actions. The aim if this paper therefore is to analyze a secure model using Unicode Transformation Format (UTF) base 64 algorithms for storage of data in cloud securely. The methodology used was Object Orientated Hypermedia Analysis and Design Methodology (OOHADM) was adopted. Python was used to develop the security model;the role-based access control (RBAC) and multi-factor authentication (MFA) to enhance security Algorithm were integrated into the Information System developed with HTML 5, JavaScript, Cascading Style Sheet (CSS) version 3 and PHP7. This paper also discussed some of the following concepts;Development of Computing in Cloud, Characteristics of computing, Cloud deployment Model, Cloud Service Models, etc. The results showed that the proposed enhanced security model for information systems of cooperate platform handled multiple authorization and authentication menace, that only one login page will direct all login requests of the different modules to one Single Sign On Server (SSOS). This will in turn redirect users to their requested resources/module when authenticated, leveraging on the Geo-location integration for physical location validation. The emergence of this newly developed system will solve the shortcomings of the existing systems and reduce time and resources incurred while using the existing system.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Verify the Function of a Potential Growth-Regulating Gene in Marine Bivalve Using a Candidate Model Organism Mulinia lateralis

A better understanding of genetic bases of growth regulation is essential for bivalve breeding,which is helpful to improve the yield of the commercially important bivalves.While previous studies have identified some candidate genes accounting for variation in growth-related traits through genotype-phenotype association analyses,seldom of them have verified the functions of these putative,growth-related genes beyond the genomic level due to the difficulty of culturing commercial bivalves under laboratory conditions.Fortunately,dwarf surf clam Mulinia lateralis can serve as a model organism for studying marine bivalves given its short generation time,the feasibility of being grown under experimental conditions and the availability of genetic and biological information.Using dwarf surf clam as a model bivalve,we characterize E2F3,a gene that has been found to account for variation in growth in scallops by a previous genome-wide association study,and verify its function in growth regulation through RNA interference(RNAi)experiments.For the first time,E2F3 in dwarf surf clam,which is termed as MulE2F3,is characterized.The results reveal that dwarf surf clams with MulE2F3 knocked down exhibit a reduction in both shell size and soft-tissue weight,indicating the functions of MulE2F3 in positively regulating bivalve growth.More importantly,we demonstrate how dwarf surf clam can be used as a model organism to investigate gene functions in commercial bivalves,shedding light on genetic causes for variation in growth to enhance the efficiency of bivalve farming.

The Practice and Exploration of Applying EBM to Bilingual Teaching of Medical Genetics at OSBCM

In the process of teaching medical genetics of undergraduate clinical medicine, the practice and exploration of applying EBM to the bilingual teaching of OSBCM medical genetics are carried out. Using CBL and PBL as the carrier can make up for the shortcomings of a single teaching mode, synthesize the advantages of multiple teaching modes. It starts from integrating the basic theoretical knowledge of medicine and clinical practice knowledge, improving students’ bilingual level of medical genetics, cultivating students’ literature retrieval ability, and promoting early clinical, multi-clinical and repeated clinical consciousness for medical students. Therefore, it is more conducive to cultivate students’ ability to learn independently, accurately analyze and solve problems, improve medical students’ clinical thinking ability and scientific research awareness, improve medical students’ ability of international communication, and lay a solid foundation for improving medical students’ future post competence, innovative spirit and lifelong learning ability.

Can a global mean sea-level rise reduce the Last Interglacial model-data mismatch in East Asia?

末次间冰期有着丰富的重建和模拟资料,为研究未来温暖气候提供了一个理想的参考.然而,关于末次间冰期的东亚气候,模拟与重建的结果间长期存在着不匹配的情况,模拟结果普遍较重建结果更为冷干。本研究利用挪威地球系统模式(NorESM1-F),探讨了在末次间冰期模拟试验中纳入全球平均海平面上升能否减少模式-数据的不匹配.该试验结果表明,海平面上升情况下东亚地区会产生一定的增温增湿效应,但不足以消除模式-数据不匹配.基于这些结果,作者探讨了其它可能造成不匹配的因素以供进一步研究.

The Interdisciplinary Research of Big Data and Wireless Channel: A Cluster-Nuclei Based Channel Model

Recently,internet stimulates the explosive progress of knowledge discovery in big volume data resource,to dig the valuable and hidden rules by computing.Simultaneously,the wireless channel measurement data reveals big volume feature,considering the massive antennas,huge bandwidth and versatile application scenarios.This article firstly presents a comprehensive survey of channel measurement and modeling research for mobile communication,especially for 5th Generation(5G) and beyond.Considering the big data research progress,then a cluster-nuclei based model is proposed,which takes advantages of both the stochastical model and deterministic model.The novel model has low complexity with the limited number of cluster-nuclei while the cluster-nuclei has the physical mapping to real propagation objects.Combining the channel properties variation principles with antenna size,frequency,mobility and scenario dug from the channel data,the proposed model can be expanded in versatile application to support future mobile research.

Predictive model for contrast-enhanced ultrasound of the breast: Is it feasible in malignant risk assessment of breast imaging reporting and data system 4 lesions?

AIM: To build and evaluate predictive models for contrast-enhanced ultrasound(CEUS) of the breast to distinguish between benign and malignant lesions. METHODS: A total of 235 breast imaging reporting and data system(BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve(ROC). RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant(P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively. CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BIRADS classification.

An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.