Determination of Latent Heats of Vaporization and Fusion

Water is the most abundant liquid on the surface of the earth. It is a liquid whose properties are quite surprising, both as a pure liquid and as a solvent. Water is a very cohesive liquid: its melting and vaporization temperatures are very high for a liquid that is neither ionic nor metallic, and whose molar mass is low. Thus, water remains liquid at atmospheric pressure up to 100C while similar molecules such as H2S, H2Se, H2Te for example would give a vaporization temperature close to 80C. This cohesion is in fact ensured by hydrogen bonds between water molecules. This type of bonds between neighboring molecules, hydrogen bonds, is quite often found in chemistry [1] [2]. Any change in the state of aggregation of a substance occurs with the absorption or release of a certain amount of latent heat of transformation. Latent heat of fusion, vaporization or sublimation is the ratio of the energy supplied as heat to the mass of the substance that is melted, vaporized or sublimated. As a result of the reversibility of the processes, the fusion heat is equal to the heat released in the reverse process: crystallization and solidification heat. And likewise the heat of vaporization is equal to the heat of condensation. This equality of heat is often used to determine experimentally either of these quantities. There are two main measurement methods: 1) Direct measurement using the calorimeter, 2) Indirect measure based on the use of the VantHoff relationship. The objective of this work is to measure the latent heat of water vaporization and verify the compatibility of the experimental values with the values given by the tables using the indirect method.

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.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

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.

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.

Multi-Modal Medical Image Fusion Based on Improved Parameter Adaptive PCNN and Latent Low-Rank Representation

Multimodal medical image fusion can help physicians provide more accurate treatment plans for patients,as unimodal images provide limited valid information.To address the insufficient ability of traditional medical image fusion solutions to protect image details and significant information,a new multimodality medical image fusion method(NSST-PAPCNNLatLRR)is proposed in this paper.Firstly,the high and low-frequency sub-band coefficients are obtained by decomposing the source image using NSST.Then,the latent low-rank representation algorithm is used to process the low-frequency sub-band coefficients;An improved PAPCNN algorithm is also proposed for the fusion of high-frequency sub-band coefficients.The improved PAPCNN model was based on the automatic setting of the parameters,and the optimal method was configured for the time decay factor ae.The experimental results show that,in comparison with the five mainstream fusion algorithms,the new algorithm has significantly improved the visual effect over the comparison algorithm,enhanced the ability to characterize important information in images,and further improved the ability to protect the detailed information;the new algorithm has achieved at least four firsts in sixobjectiveindexes.

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.

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.

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.

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.

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.

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.

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.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Effect of Individualized Nursing Intervention on Patients with Tuberculosis Latent Infection and Active Tuberculosis

Objective:This paper aims to explore the effect of individualized nursing intervention on patients with active tuberculosis(ATB)and latent tuberculosis infection(LTBI).Methods:The nursing study started in January 2020 and ended in January 2023.A total of 60 patients with ATB and LTBI were included,and they were divided into two groups according to the intervention schemes selected for control testing,each with 30 cases.The intervention program selected for group A was routine care,and for group B was individualized nursing.The proportion of adverse reactions,changes in the level of lung items,self-management outcomes and satisfaction were evaluated and compared.Results:After evaluating the proportion of adverse reactions,the total proportion of ATB and LTBI in group B was lower than that in group A(P<0.05).Based on the evaluation and testing of the expiratory flow(EF),expiratory volume(EV),and vital capacity(VC)after the intervention,these levels in group B showed higher outcomes than those in group A(P<0.05).The scores in terms of living habits,sleep,diet,and compliance in group B were higher than those in group A(P<0.05).The total proportion of the satisfaction of ATB and LTBI patients in group B was higher than that in group A(P<0.05).Conclusion:After the intervention of individualized nursing measures in patients with ATB and LTBI,it was found that it can not only play a positive role in the prevention and control of adverse reactions,but also improve their lung function,and promote their self-management,with good satisfaction level,thus it has high research and clinical application values.

Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

Experimental Investigation on Prototype Latent Heat Thermal Battery Charging and Discharging Function Integrated with Solar Collector

This paper reports the performance investigation of a newly developed Latent Heat Thermal Battery(LHTB)integrated with a solar collector as the main source of heat.The LHTB is a new solution in the field of thermal storage and developed based on the battery concept in terms of recharge ability,portability and usability as a standalone device.It is fabricated based on the thermal battery storage concept and consists of a plate-fin and tube heat exchanger located inside the battery casing and paraffin wax which is used as a latent heat storage material.Solar thermal energy is absorbed by solar collector and transferred to the LHTB using water as Heat Transfer Fluid(HTF).Charging experiments have been conducted with a HTF at three different temperatures of 68°C,88°C and 108°C and three different flow rates of 30,60 and 120 l/h.It is followed by discharging experiments on fully charged LHTB at three different temperatures of 68°C,88°C and 108°C using HTF at three different flow rates of 30,60 and 120 l/h.It is found that both higher HTF inlet temperature and flow rate have a positive impact on stored thermal energy.However,charging efficiency was decreased by increasing the HTF flow rate.The highest charging efficiency of 29%was achieved using HTF of 108°C at a flow rate of 30 l/h.Most of paraffin melted in this case,while part of the paraffin remained solid in other experiments.On the other hand,the results from discharging experiments revealed that both recovered thermal energy and recovery efficiency increased by either increasing the LHTB temperature or HTF flow rate.Highest recovered thermal energy of 5,825 KJ at 35%recovery efficiency achieved at LHTB of 108°C using 120 l/h of HTF.

WJD 5^(th) Anniversary Special Issues(3): Type 1 diabetes Distinct clinical and laboratory characteristics of latent autoimmune diabetes in adults in relation to type 1 and type 2 diabetes mellitus

Ever since its first appearance among the multiple forms of diabetes,latent autoimmune diabetes in adults(LADA),has been the focus of endless discussions concerning mainly its existence as a special type of diabetes.In this mini-review,through browsing important peer-reviewed publications,(original articles and reviews),we will attempt to refresh our knowledge regarding LADA hoping to enhance our understanding of this controversial diabetes entity.A unique combination of immunological,clinical and metabolic characteristics has been identified in this group of patients,namely persistent islet cell antibodies,high frequency of thyroid and gastric autoimmunity,DR3 and DR4 human leukocyte antigen haplotypes,progressive loss of beta cells,adult disease onset,normal weight,defective glycaemic control,and without tendency to ketoacidosis.Although anthropomorphic measurements are useful as a first line screening,the detection of C-peptide levels and the presence of glutamic acid decarboxylase(GAD)autoantibodies is undoubtedly the sine qua non condi-tion for a confirmatory LADA diagnosis.In point of fact,GAD autoantibodies are far from being solely a biomarker and the specific role of these autoantibodies in disease pathogenesis is still to be thoroughly studied.Nevertheless,the lack of diagnostic criteria and guidelines still puzzle the physicians,who struggle between early diagnosis and correct timing for insulin treatment.

Numerical Study of Impacts of Soil Moisture on the Diurnal and Seasonal Cycles of Sensible/Latent Heat Fluxes over Semi-arid Region

The semi-arid regions, as climatic and ecosystem transitional zones, are the most vulnerable to global environmental change. Earlier researches indicate that the semi-arid regions are characterized by strong landatmosphere coupling in which soil moisture is the crucial variable in land surface processes. In this paper, we investigate the sensitivity of the sensible/latent heat fluxes to soil moisture during the growing season based on the enhanced observations at Tongyu in the Jilin province of China, a reference site of international Coordinated Energy and Water Cycle Observations Project （CEOP） in the semi-arid regions, by using a sophisticated land surface model （NCAR_CLM3.0）. Comparisons between the observed and simulated sensible/latent heat fluxes indicate that the soil moisture has obvious effects on the sensible/latent heat fluxes in terms of diurnal cycle and seasonal evolution. Better representation of the soil moisture could improve the model performance to a large degree. Therefore, for the purpose of simulating the land-atmosphere interaction and predicting the climate and water resource changes in semi-arid regions, it is necessary to enhance the description of the soil moisture distribution both in the way of observation and its treatment in land surface models.