Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Paraffin–CaCl_(2)·6H_(2)O dosage effects on the strength and heat transfer characteristics of cemented tailings backfill

The challenge of high temperatures in deep mining remains harmful to the health of workers and their production efficiency The addition of phase change materials (PCMs) to filling slurry and the use of the cold storage function of these materials to reduce downhole temperatures is an effective approach to alleviate the aforementioned problem.Paraffin–CaCl_(2)·6H_(2)O composite PCM was prepared in the laboratory.The composition,phase change latent heat,thermal conductivity,and cemented tailing backfill (CTB) compressive strength of the new material were studied.The heat transfer characteristics and endothermic effect of the PCM were simulated using Fluent software.The results showed the following:(1) The new paraffin–CaCl_(2)·6H_(2)O composite PCM improved the thermal conductivity of native paraffin while avoiding the water solubility of CaCl_(2)·6H_(2)O.(2) The calculation formula of the thermal conductivity of CaCl_(2)·6H_(2)O combined with paraffin was deduced,and the reasons were explained in principle.(3) The“enthalpy–mass scale model”was applied to calculate the phase change latent heat of nonreactive composite PCMs.(4)The addition of the paraffin–CaCl_(2)·6H_(2)O composite PCM reduced the CTB strength but increased its heat absorption capacity.This research can give a theoretical foundation for the use of heat storage backfill in green mines.

Evaluation of Interferon-Gamma Release Assay Testing and Tuberculin Skin Test for Early Diagnosis of Tuberculosis in Children and Adolescents

Background: This study aimed to evaluate the diagnostic value of interferon-γ release assay (IGRA), a sensitive microbiological diagnostic method, in children and adolescents with suspected tuberculosis in a country with a high burden of tuberculosis. Method: This study included 581 children and adolescents aged 4 - 19 years who were suspected of having tuberculosis, were latently infected with Mycobacterium tuberculosis, and had received at least one dose of BCG vaccine between April 17, 2019, and February 24, 2021. The study evaluated the TST results of 106 patients who had a positive Quantiferon test and were suspected of having tuberculosis. Results: The study included 581 patients aged between 4 and 19 years. Of these, 106 patients tested positive for the Quantiferon test, while 19 were indeterminate and 456 were negative. The Quantiferon test positivity rate was 18.24%. Among the 106 QFT-Plus-positive cases, 23 patients also tested positive for TST. The difference in distribution was found to be statistically significant. Conclusion: The QFT-Plus test is considered an alternative to TST and other microbiological diagnostic methods for early tuberculosis diagnosis, particularly in children and adolescents.

Stability and Hopf bifurcation of a delayed virus infection model with latently infected cells and Beddington-DeAngelis incidence

In this paper,the dynamical behaviors for a five-dimensional virus infection model with Latently Infected Cells and Beddington-DeAngelis incidence are investigated.In the model,four delays which denote the latently infected delay,the intracel-lular delay,virus production period and CTL response delay are considered.We define the basic reproductive number and the CTL immune reproductive number.By using Lyapunov functionals,LaSalle's invariance principle and linearization method,the threshold conditions on the stability of each equilibrium are established.It is proved that when the basic reproductive number is less than or equal to unity,the infection-free equilibrium is globally asy mptot ically stable;when the CTL immune repro-ductive number is less than or equal to unity and the basic reproductive number is greater than unity,the immune free infection equilibrium is globally asymptotically stable;when the CTL immune reproductive number is greater than unity and immune response delay is equal to zero,the immune infection equilibrium is globally asymptotically stable.The results show that immune response delay may destabilize the steady state of infection and lead to Hopf bifurcation.The existence of the Hopf bifurcation is discussed by using immune response delay as a bifurcation parameter.Numerical simulations are carried out to justify the analytical results.

Prevalence of Latent Tuberculosis Infection [LTBI] in Prison Officers: A Systematic Review and Meta-Analysis

This study aimed to perform a systematic review and meta-analysis to determine the LTBI prevalence in prison officers worldwide. A systematic search was performed in PubMed, WoS, Embase, and BVS, including all articles related to LTBI prevalence and risk factors. After critical evaluation and qualitative synthesis of the identified articles, a meta-analysis was used. Five studies carried out between 2012 and 2022 were included, with a total sample size of 1718 prison officers. The overall LTBI prevalence was 50% [95% confidence interval [CI]: 48% - 52%;n = 816], with high heterogeneity between studies. Smoking [OR = 1.76;CI 95% = 1.26 - 2.46] and males [OR = 2.08;CI 95% = 1.31 - 3.31] were positively related to a higher LTBI prevalence among prison officers. Thus, preventive measures and the rapid and accurate diagnosis of new cases should be emphasized to ensure tuberculosis control, especially among risk groups such as prison officers.

Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.

Current Status of Key Competencies among Medical Students: A Latent Profile Analysis

The purpose of this study was to understand the overall level of key competencies of medical students and explore the potential profile of key competencies, promoting quality education, and improving the quality talent cultivation in medical colleges. A stratified random sampling method selected 734 medical students from four medical colleges in Chongqing Province of China. A general information questionnaire and a key competencies survey questionnaire were used to conduct the survey. The overall score and scores of each dimension of key competencies were analyzed. Latent profile analysis was conducted to classify the key competencies of medical students and compare the distribution differences of demographic variables among different categories. The results showed that 26% of medical students have never heard of the concept of key competencies, and 59% of them are not familiar with the content related to key competencies. The score of key competencies is 3.66 ± 0.60, with the highest score in the dimension of responsibility and the lowest score in the dimension of humanistic accomplishment. The latent profile analysis classified them into three categories: “low key competencies group (14.71%)”, “medium key competencies group (36.79%)”, and “high key competencies group (48.50%)”. The R3STEP regression analysis results showed statistically significant differences in educational level and whether they served as student cadres among different key competencies categories of medical students. This paper discusses three different potential key competencies categories among medical students, and the overall level of key competencies is relatively good. However, medical students lack a comprehensive and systematic understanding of key competencies. Humanistic accomplishment, healthy living, and practical innovation are the three dimensions with lower scores and should be given more attention. Medical colleges should integrate the concept of key competencies into teaching and implement it in medical practice to cultivate more high-quality medical talents for society.

Thermal-water-salt coupling process of unsaturated saline soil under unidirectional freezing

Salinization and desertification are closely related to water-salt migration caused by a temperature gradient.Based on the Darcy Law of unsaturated soils,the law of energy conservation and the law of mass conservation,the thermal-water-salt coupling mathematical model of unsaturated frozen saline soil was established.The model considered the latent heat of phase change,crystallization impedance,crystallization consumption and complete precipitation of solute crystallization in ice.In order to verify the rationality of the model,the unidirectional freezing test of unsaturated saline soil was carried out in an open system with no-pressure water supplement to obtain the spatial distribution of temperature,moisture and salt in the saline soil.Finally,numerical simulations are implemented with the assistance of COMSOL Multiphysics.Validation of the model is illustrated by comparisons between the simulation and experimental data.The results demonstrated that the temperature within saline soil changes with time and can be divided into three stages,namely quick freezing stage,transitional stage and stable stage.The water and salt contents in the freezing zone are layered,with peak values at the freezing front.The coupled model could reveal the heat-mass migration mechanism of unsaturated frozen saline soil and dynamically describe the freezing depth and the movement law of the freezing front,ice and salt crystal formation mechanism,and the change law of thermal conductivity and permeability coefficient.

A semantic and emotion-based dual latent variable generation model for a dialogue system

With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.

Network autoregression model with grouped factor structures

Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.

Mg-3Al-1Zn alloy deformed along different strain paths: Role of latent hardening

The mechanical properties of magnesium alloy AZ31 were investigated experimentally with visco-plastic self-consistent modeling. Tension,compression and plane strain compression(PSC) tests were performed along 3 directions of a hot rolled plate, and the material parameters input in the model were fitted with the uniaxial stress-strain curves. The critical resolved shear stress(CRSS) for tension twinning was modeled with a modified Voce hardening law first decreasing, and then increasing with strain, that could reproduce better the flow stress for twin-predominant deformation. Such CRSS evolution may better model twin nucleation, propagation and growth. Firstly simulations were carried out assuming latent hardening coefficients for slip by other slip systems equal to self-hardening. Then different heterogeneous latent hardening were used, whose values were based on dislocation dynamics simulations from the literature. This study shows that equal self and latent hardening can reproduce the stress strain curves and plastic anisotropy as well as heterogeneous mode on mode latent hardening.Discrepancies between simulations and experimental results from PSC are explained by an under-estimation of twinning for some PSC strain paths.

Completion of 6-mo isoniazid preventive treatment among eligible under six children:A cross-sectional study,Lagos,Nigeria

BACKGROUND Nigeria is one of the thirty high burden countries with significant contribution to the global childhood tuberculosis epidemic.Tuberculosis annual risk for children could be as high as 4%particularly in high tuberculosis(TB)prevalent communities.Isoniazid(INH)Preventive Therapy has been shown to prevent TB incidence but data on its implementation among children are scarce.AIM To determine the completion of INH among under six children that were exposed to adults with smear positive pulmonary TB in Lagos,Nigeria.METHODS This was a hospital-based retrospective cross-sectional review of 265 medical records of eligible children<6 years old enrolled for INH across 32 private hospitals in Lagos,Nigeria.The study took place between July and September 2020.Data was collected on independent variables(age,gender,type of facility,TB screening,dose and weight)and outcome variables(INH outcome and proportion lost to follow up across months 1-6 of INH treatment).RESULTS About 53.8%of the participants were female,95.4%were screened for TB and none was diagnosed of having TB.The participants’age ranged from 1 to 72 mo with a mean of 36.01±19.67 mo,and 40.2%were between the ages of 1-24 mo.Only 155(59.2%)of the 262 participants initiated on INH completed the six-month treatment.Cumulatively,107(41.0%)children were lost to follow-up at the end of the sixth month.Of the cumulative 107 loss to follow-up while on INH,largest drop-offs were reported at the end of month 2,52(49%)followed by 20(19%),17(16%),11(10.2%)and 7(6.5%)at months 3,4,5 and 6 respectively.The analysis showed that there was no significant association between age,gender,type of facility and completion of INH treatment(P>0.005).CONCLUSION This study demonstrated suboptimal INH completion rate among children with only 6 out of 10 children initiated on INH who completed a 6-mo treatment in Lagos,Nigeria.The huge drop-offs in the first 2 mo of INH calls for innovative strategies such as the use of 60-d INH calendar that would facilitate reminder and early engagement of children on INH and their caregivers in care and across the entire period of treatment.

The trajectories of physical growth in 4 months postnatal corrected age among preterm infants discharged from neonatal intensive care units and associated factors: A prospective study

Objectives Growth retardation is a risk for premature infants.In addition to demographic and perinatal factors,preterm infants’physical growth may be affected by neonatal intensive care unit(NICU)stress,maternal postpartum depression,and mother-infant interaction.This study aimed to investigate the trajectories of physical growth in 4 months corrected age among preterm infants discharged from the NICU and the impactors on these trajectories.Methods A prospective study was conducted among 318 preterm infants from September 2019 to April 2021 in Shanghai,China.Latent growth modeling was applied to identify the weight,length,and head circumference growth trajectories in 4 months corrected age and explore the effects of demographic and medical characteristics,infant stress during NICU stay,maternal postpartum depression,and mother-infant interaction on each trajectory.Results Unconditional latent growth models showed curve trajectories with increasingly slower growth in weight,length,and head circumference until 4 months of corrected age.Conditional latent growth models showed that a longer length of stay in the NICU and more skin punctures were negatively associated with weight at 40 weeks corrected gestational age(β=−0.43 and−0.19,respectively,P<0.05).The maternal postpartum depression between 40 weeks corrected gestational age and 1 month corrected postnatal age was associated with a lower growth rate of length(β=−0.17,P=0.040),while between 2 and 3 months corrected postnatal age,there were lower growth rates of weight and head circumference(β=−0.15 and−0.19,respectively,P<0.05).The mother-infant interaction scores between 40 weeks corrected gestational age and 1 month corrected postnatal age negatively predicted the growth rate of weight(β=−0.19,P=0.020).Conclusion The physical growth trajectories of preterm infants discharged from the NICU were influenced by infant stress during the NICU stay,maternal postpartum depression and mother-infant interaction.

Reservoir of human immunodeficiency virus in the brain:New insights into the role of T cells

Human immunodeficiency virus(HIV)infection of the central nervous system(CNS)has attracted significant attention because it contributes to severe complications of acquired immunodeficiency syndrome(AIDS)and seriously impairs the life quality of infected patients.In this review,we briefly describe the latent infection of HIV in CNS and focus on the role of the important immune cells,such as T cells,in the formation and maintenance of the HIV reservoir in CNS.This review explores the mechanisms by which T cells enter CNS and establish latent infection of HIV in the CNS.In conclusion,we summarize the role of these cells in the interaction between HIV and CNS.With our better understanding of the underlying mechanisms,we propose future directions for the development of novel strategies to eliminate HIV reservoirs in the CNS based on cellular components.

Maturity-onset diabetes of the young type 9 or latent autoimmune diabetes in adults:A case report and review of literature

BACKGROUND Maturity-onset diabetes of the young(MODY)is a monogenic genetic disease often clinically misdiagnosed as type 1 or type 2 diabetes.MODY type 9(MODY9)is a rare subtype caused by mutations in the PAX4 gene.Currently,there are limited reports on PAX4-MODY,and its clinical characteristics and treatments are still unclear.In this report,we described a Chinese patient with high autoimmune antibodies,hyperglycemia and a site mutation in the PAX4 gene.CASE SUMMARY A 42-year-old obese woman suffered diabetes ketoacidosis after consuming substantial amounts of beverages.She had never had diabetes before,and no one in her family had it.However,her autoantibody tested positive,and she managed her blood glucose within the normal range for 6 mo through lifestyle interventions.Later,her blood glucose gradually increased.Next-generation sequencing and Sanger sequencing were performed on her family.The results revealed that she and her mother had a heterozygous mutation in the PAX4 gene(c.314G>A,p.R105H),but her daughter did not.The patient is currently taking liraglutide(1.8 mg/d),and her blood glucose levels are under control.Previous cases were retrieved from PubMed to investigate the relationship between PAX4 gene mutations and diabetes.CONCLUSION We reported the first case of a PAX4 gene heterozygous mutation site(c.314G>A,p.R105H),which does not appear pathogenic to MODY9 but may facilitate the progression of latent autoimmune diabetes in adults.

Latent tuberculosis infection in health-care workers in the government sector in Brunei Darussalam:A cross-sectional study

Objective:Health-care workers(HCWs)are known to be at high risk for occupational biological hazards,and this includes exposure to mycobacterium tuberculosis(TB)which can result in either active or latent TB infection(LTBI).This study aims to provide an overview of the incidence of LTBI among HCWs in Brunei Darussalam,to examine associated risk factors,and to evaluate LTBI treatment compliance.Materials and Methods:This is a retrospective cross-sectional study which was conducted using data from January 2018 to December 2021,on notified cases of LTBI in HCWs which identified 115 cases.Demographic data,underlying medical conditions,and compliance to treatment were assessed through reviews of their electronic health records.Results:The incidence of LBTI was 14.6/year/1000 HCWs.The incidence rate reached a high of 24.6/1000 in 2020,and majority of cases were in the older age groups.There was good treatment acceptance and compliance(82.6%),and this was observed to be significantly higher in females than males(P=0.02).Conclusion:This study showed an average incidence of LTBI of 14.6/1000 HCWs over 4 years and high LTBI treatment acceptance(82.6%)and compliance.Emphasis on infection prevention and control measures in health-care settings and actions to increase awareness of LTBI are crucial interventions toward reducing the burden of LTBI.

Chronic Spontaneous Urticaria on Omalizumab Therapy and Latent Tuberculosis Infection: A New Case Report

Background: Chronic Spontaneous urticarial (CSU) is a common dermatological problem characterized by recurrent pruritic or burning wheals last less than 24 hours and treated by many modalities of therapy including systemic antihistamines and in refractory cases with Omalizumab anti-IgE antibody biological injection. Latent tuberculosis infection (LTBI) is diagnosed based on a positive tuberculin skin test or QuantiFERON-TB test without evidence of active tuberculosis. Aim: To document a new case report of a patient with a history of CSU and latent tuberculosis on Omalizumab therapy during Isoniazid (INH) prophylaxis. Case Report: A-53-year-old woman with a history of CSU and newly identified LTBI who have been treated with INH monotherapy before starting Omalizumab injection followed up over 24 weeks course of therapy for any sign of tuberculosis reinfection. Conclusion: Omalizumab injection was used effectively for the treatment of CSU in a patient with latent tuberculosis infection with minimal risk of tuberculosis reactivation.

Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used to analyze the relationships between mode choice and three classes of attributes;Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models

Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.

Latent Growth Mixture Modeling to Estimate Differential PTSD Trajectories and Associated Risk Factors in Psychiatric Staff Following Workplace Violence

Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.