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.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection(LTBI)has become a major source of active tuberculosis(ATB).Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI,these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB.Thus,the diagnosis of LTBI faces many challenges,such as the lack of effective biomarkers from Mycobacterium tuberculosis(MTB)for distinguishing LTBI,the low diagnostic efficacy of biomarkers derived from the human host,and the absence of a gold standard to differentiate between LTBI and ATB.Sputum culture,as the gold standard for diagnosing tuberculosis,is time-consuming and cannot distinguish between ATB and LTBI.In this article,we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI,including the innate and adaptive immune responses,multiple immune evasion mechanisms of MTB,and epigenetic regulation.Based on this knowledge,we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning(ML)in LTBI diagnosis,as well as the advantages and limitations of ML in this context.Finally,we discuss the future development directions of ML applied to LTBI diagnosis.

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.

Impact of Assimilating FY-4A Lightning Data with a Latent Heat Nudging Method on Short-Term Forecasts of Severe Convective Events in Eastern China

In this study,a latent heat nudging lightning data assimilation(LDA)method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager(LMI)onboard the Feng-Yun-4A(FY-4A)satellite based on the Weather Research and Forecasting(WRF)model.In this LDA method,the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature.The positive temperature perturbations in the mixed-phase region are then assimilated by a nudging method to adjust the latent heat within the convective system.Meanwhile,the water vapor mixing ratio is adapted to the temperature perturbations accordingly to constrain the relative humidity to remain unchanged.This method considers the physical nature of the convective system,in contrast with other LDA methods that establish an empirical or statistical relationship between the lightning flash rates and model variables.The impact of this LDA method on short-term(≤6 h)forecasts was evaluated using two severe convective events in eastern China:a multi-region heavy rainfall event and a thunderstorm high-wind event.The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period,leading to a more reasonable storm environment.In the forecast fields,the simulations with LDA produced more realistic convective structures,resulting in an improvement in forecasts of precipitation and high winds.

A process monitoring method for autoregressive-dynamic inner total latent structure projection

As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

Network analysis of adolescent non-suicidal self-injury subgroups identified through latent profile analysis

BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents and frequently cooccurs with depression.Understanding the distinct patterns of NSSI behaviors,along with their associated risk and protective factors,is crucial for developing effective interventions.AIM To classify NSSI behaviors and examine interactions between risk and resilience factors in Chinese adolescents.METHODS A cross-sectional study involving 3967 Chinese students(51.7%female,mean age 13.58±2.24 years)who completed questionnaires on parenting styles,bullying,childhood maltreatment,depression,resilience,and NSSI.Latent profile analysis(LPA)was used to identify NSSI subtypes,and network analysis explored interactions between risk and resilience factors.RESULTS Three NSSI subtypes were identified:NSSI with depression(18.8%),NSSI without depression(12.3%),and neither(68.9%).Bullying was the central risk factor across subtypes,while emotional control and family support were key protective factors.Statistical analyses showed significant differences between groups(P<0.001).CONCLUSION This study identified three NSSI subtypes among Chinese adolescents.Bullying emerged as a central risk factor,while emotional control and family support were key protective factors.Targeting these areas may help reduce NSSI behaviors in this population.

Internet Gaming Disorder and Mental Health of Children in China:A Latent Profile Analysis

In recent years,speculation of an increase in Internet Gaming Disorder(IGD)has surfaced with the growing popularity of internet gaming among Chinese children and adolescents.The detrimental impact of IGD on mental health cannot be denied,even though only a small portion of the screen-dependent population exhibits psychopathological and behavioral symptoms.The present study aimed to explore a latent profile analysis(LPA)of Internet Gaming Disorder on the mental health of Chinese school students.The data were collected from a sample of 1005 Chinese school students(49.8%male;age M=13.32,SD=1.34 years)using a paper-pencil survey through convenience sampling.LPA explored three latent profiles of internet gamers:regular gamers(62.4%),moderate gamers(28.1%),and probable disordered gamers(9.4%).Results showed that the probable disordered gamers had significantly higher levels of depression,anxiety,emotional and conduct problems,hyperactivity,and peer problem symptoms as well as lower life satisfaction,and pro-social symptoms compared to regular and moderate gamers(p<0.05).This study would be helpful to mental health professionals in designing interventions for gamers who present IGD symptoms.Future longitudinal studies should also be undertaken to assess whether mental health worsens for probable disordered gamers.

Latent Categories of Mental Health Literacy in Patients with Coronary Artery Disease and Their Relationship with Quality of Life

Objectives: This study aims to explore the latent categories of mental health literacy among patients with coronary artery disease and examine their associations with quality of life. Design: A cross-sectional quantitative design was used. Methods: The study sample consisted of 208 patients with coronary artery disease from five wards in the Department of Cardiology at a tertiary hospital. Data were collected using a general information questionnaire, the Chinese version of the Multiple Mental Health Literacy Scale and the Chinese Cardiovascular Patient Quality of Life Assessment Questionnaire. The data were analysed with Mplus (v.8.3) and SPSS (v.25.0). Results: The mental health literacy of the 208 patients was categorised into four latent categories: low literacy (n = 28, 13.5%), high knowledge-low resources (n = 53, 25.5%), low knowledge-high resources (n = 63, 30.2%) and high literacy (n = 64, 30.8%). A significant difference in quality of life was observed according to mental health literacy category (P Conclusion: The quality of life of patients with coronary artery disease is significantly influenced by their levels of mental health literacy. Targeted interventions addressing the various profiles of mental health literacy should be implemented to improve the quality of life for patients with coronary artery disease.

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.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Latent profile analysis of perceived stress and influencing factors in colorectal cancer patients

Objective:To understand the latent categories of perceived stress in colorectal cancer patients and analyze the characteristics of different categories of patients.Methods:A total of 255 colorectal cancer patients receiving treatment in the gastrointestinal surgery and oncology depar tments of a ter tiary Grade A hospital in Sichuan Province,from January 2023 to June 2023,were selected as the study subjects.General information questionnaire,Chinese version of the Perceived Stress Scale(CPSS),and Comprehensive Score Table for Patient-Repor ted Outcome Measures of Economic Toxicity(COST-PROM)were used for data collection.Results:Perceived stress in colorectal cancer patients was classified into 3 latent categories:C1“Low stress-stable type”(19.2%),C2“Moderate stress-uncontrolled type”(23.9%),and C3“High stress-anxious type”(56.9%).The average score of perceived stress was(34.07±5.08).Compared with C1 type,patients with a monthly household income of≤3000 RMB were more likely to belong to the C2 and C3 types(P<0.05),and patients without a stoma were less likely to belong to C3 type(P<0.05).Compared with C2 type,male patients were more likely to belong to C3 type(P<0.05),and patients without a stoma were less likely to belong to C3 type(P<0.05).Compared with C3 type,patients with higher economic toxicity scores were more likely to be classified into C1 and C2 types(P<0.05).Conclusions:Perceived stress in colorectal cancer patients exhibits distinct categorical features.Male gender,lower income,presence of a stoma,and higher economic toxicity are associated with higher levels of perceived stress in colorectal cancer patients.

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 αe. 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 six objective indexes.

Feasibility analysis of urban sewage source heat pump system with freezing latent heat collection

Aiming to resolve the problem that conventional sewage source heat pump systems cannot satisfy heat peak loads of buildings,a new idea that the freezing latent heat is exacted as the auxiliary heat source at the peak heat load is proposed.First,on the basis of sewage characteristics,a freezing latent heat exchanger is developed to safely eliminate ice,continuously extract heat and remove sewage soft-dirt.A reasonable form of the urban sewage source heat pump system with freezing latent heat collection is presented.Then,the feasibility of the system is theoretically analyzed.The calculation results under typical operating conditions show that the heating ability of the new system is higher than that of the conventional one and the ratio of these two highest heating rates is between 4.5 and 8.7,which proves that the new system has great application potential in cold regions.

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.

Effect of condensation latent heat release on the relative vorticity tendency in extratropical cyclones:a case study

Taking an extratropical cyclone that produced extreme precipitation as the research object,this paper calculates the contribution of condensation latent heat release(LHR)to relative vorticity tendency based on the complete-form vertical vorticity tendency equation.The results show that the heating rate of convectional condensation LHR can reach up to about 40 times that of stable condensation LHR.Both the stable and convectional heating centers are higher than 700 hPa,which would cause∂Q/∂z>0 and a positive vorticity source in the lower troposphere.The vertical gradient of stable condensation LHR contributes little to the growth of relative vorticity,while the relative vorticity tendency associated with the vertical gradient of convectional condensation LHR can be an order of magnitude higher than the former.The positive vorticity source is always located right below the latent heating center,and its maximum value can always be found in the lower troposphere.Convectional LHR is the primary factor for cyclone development from the perspective of diabatic heating.The horizontal gradient of total condensation LHR can contribute about 65%of the actual vorticity growth,but the effect of the vertical gradient of convectional condensation(LHR)can reach twice as much.The adiabatic heating from LHR can cause vorticity tendency directly.However,it can also change the vertical and horizontal gradient of potential temperature,which can further induce vorticity tendency.

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.

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.