Scale Problems Research Articles

Development and validation of a method for deriving MMPI-3 scores from MMPI-2/MMPI-2-RF item responses.

The Minnesota Multiphasic Personality Inventory-3 (MMPI-3; Ben-Porath & Tellegen, 2020a) was developed to expand the content coverage of the MMPI-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008/2011) and to update the test's norms to better represent the U.S. population. Because most MMPI-3 items were carried over from the MMPI-2-RF, most MMPI-3 scales can be either fully scored or prorated from the MMPI-2/MMPI-2-RF item pool. We describe development of a procedure for deriving MMPI-3 scores from MMPI-2/MMPI-2-RF protocols and examine the psychometric properties of prorated MMPI-3 scores across various samples. Following Tarescavage et al. (2016), who developed a method for prorating most MMPI-2-RF scores from the original MMPI, prorated MMPI-3 scales had to meet three criteria: (a) sufficient representation in the MMPI-2-RF item pool, (b) association with the full-length corresponding MMPI-3 scale at r ≥ .90 in multiple samples, and (c) comparable T score means and standard deviations in multiple samples. These criteria resulted in the exclusion of the three MMPI-3 non-content-based Validity Scales, as well as nine Specific Problems Scales, yielding 16 MMPI-3 scales that can be fully scored and 24 for which psychometrically comparable prorated score estimates can be derived from responses to the MMPI-2 or MMPI-2-RF item pools. Evidence of the psychometric functioning of the resulting scale scores and their comparability with the full versions of these scales across samples is presented and discussed. This procedure provides a means to study 40 of the 52 MMPI-3 scales using archival data obtained with older versions of the inventory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Artificial Intelligence in public health: A case study

Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.

Analyzing the Characteristic of Simplex Algorithm

Simplex algorithm is a kind of iterated algorithm which is used to solve linear programming problems. It is raised by George Dantzig in 1974 and is still a valid tool especially when the scale of problem is relatively small or secondary. Simplex algorithm starts at an initializing feasible solution, and constantly approach to the optimal solution until the optimal solution is found or unbounded. Serving as a classical algorithm solving linear programming problem, it plays an important role in both theoretical. With the continuous development of computing technology, the simplex method and its varieties will continue in optimization. This research aims to help people make better decisions in optimization problems by summarizing the formula, characteristics and problem solutions of simplex algorithm. The summary is come from existing literature and including the condition of using simplex algorithm, all kind of different solutions, an example of simplex tableau method and the future of simplex algorithm.

Feasibility of a Home-Based Developmental Behavioral Intervention for Children With Autism Spectrum Disorder in Underserved Taiwanese Families

Purpose: This study aims to investigate the feasibility of the home-based parent- mediated intervention Naturalistic developmental behavioral intervention (HB-NDBI) in underserved Taiwanese families of children with Autism spectrum disorder (ASD) and explore its effects on children’s developmental skills and parents’ parenting stress. Method: 24 underserved Taiwanese families of children with ASD (mean age = 46.5 months) received 12-week HB-NDBI programs. Social Responsiveness Scale, Second Edition (SRS-2), Mullen Scales of Early Learning (MSEL), Child Behavior Checklist (CBCL/1.5-5), and Parenting Stress Index (PSI) were administered before and after the HB-NDBI programs. Results: Following the HB-NDBI programs, significant improvements in social cognition, social communication subscales, receptive language subscale, internalizing, externalizing, and total behavioral problems scales of children, and release of parenting stress were observed. Conclusion: This study demonstrated the feasibility of home-based parent-mediated intervention for underserved families in Taiwan. These promising results might facilitate the development of such interventions for underserved families.

Insomnia treatment effects on negative emotionality among veterans in treatment for alcohol use disorder.

Insomnia symptoms are pervasive and persistent in alcohol use disorder (AUD), though little is known about the mechanisms that underlie this association. We previously found that cognitive behavioral therapy for insomnia (CBT-I) reduced alcohol-related problems among veterans by improving insomnia severity (NCT03806491). In this planned secondary analysis of the same clinical trial data, we tested negative emotionality as one potential mechanism to explain this effect. Specifically, we tested the change in negative emotionality as a mediator of the association between change in insomnia symptoms and alcohol-related outcomes (craving, heavy drinking frequency, and alcohol-related problems). Participants were 67 veterans in treatment for AUD who also met the criteria for insomnia disorder (91% male, 84% White, average age = 46.3 years). Participants were randomized to five sessions of CBT-I or a single-session sleep hygiene control. Assessments occurred at baseline, immediately posttreatment (~6 weeks after baseline), and at 6-week follow-up. Measures included the Insomnia Severity Index, Penn Alcohol Craving Scale, Timeline Followback, and Short Inventory of Problems. We created a latent negative emotionality indicator based on five validated and reliable measures of negative emotionality. Contrary to hypotheses, CBT-I did not improve negative emotionality relative to sleep hygiene control. However, across both treatment conditions, decreases in insomnia symptoms from baseline to posttreatment were associated with concurrent decreases in negative emotionality, which in turn predicted reductions in alcohol craving and heavy drinking. Negative emotionality may help explain links between insomnia symptoms and alcohol-related outcomes.

Sleep pattern, behavior, duration and incident hypertension: results from two large prospective cohorts

Abstract Background Poor sleep quality pose significant public health challenges globally. The objective of this study is to investigate the association between sleep quality, duration and the risk of hypertension. Methods 284,250 adults from the UK Biobank (UKB) and 6,104 participants from the English Longitudinal Study of Ageing (ELSA) were analyzed. The exposures of interest were sleep quality and duration. Sleep quality was evaluated based on the number of healthy sleep behaviors (UKB cohort) or questionnaire adapted from the Jenkins Sleep Problems Scale (ELSA cohort). Participants were divided into three groups based on the assessment of sleep quality. Long sleep is characterized by a duration exceeding 8 hours, medium sleep ranges from 7 to 8 hours and short sleep denotes less than 7 hours of sleep. The primary endpoint was incident hypertension. Results In UKB cohort, participants with poor sleep quality showed significantly higher risk of hypertension compared those with healthy sleep quality (hazard ratio [HR] 1.276, confidence interval [CI] 1.21, 1.346). Short sleep duration (≤7h) was associated with an elevated hypertension risk in participants regardless of different sleep quality, and both short(≤7h) and prolonged sleep time (≥9h) were associated with an increased hypertension risk in the poor sleep quality group. Several factors were independently associated with a lower risk of hypertension, including appropriate sleep duration (7-8 hours per day), absence of frequent insomnia, no self-reported snoring(P<0.001). Associations between sleep quality and an increased risk of hypertension remained significant regardless of genetic susceptibility to hypertension (P for interaction= 0.067). Conclusions Poor sleep quality is associated with an elevated risk of hypertension. Adjusting sleep behavior to enhance sleep quality within a recommended range could be a promising strategy for mitigating the risk of hypertension.

Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems

We review and present several challenging model classes arising in the context of finding optimized object packings (OP). Except for the smallest and/or simplest general OP model instances, it is not possible to find their exact (closed-form) solution. Most OP problem instances become increasingly difficult to handle even numerically, as the number of packed objects increases. Specifically, here we consider classes of general OP problems that can be formulated in the framework of nonlinear optimization. Research experience demonstrates that—in addition to utilizing general-purpose nonlinear optimization solver engines—the insightful exploitation of problem-specific heuristics can improve the quality of numerical solutions. We discuss scalable OP problem classes aimed at packing general circles, spheres, ellipses, and ovals, with numerical (conjectured) solutions of non-trivial model instances. In addition to their practical relevance, these models and their various extensions can also serve as constrained global optimization test challenges.

Reliability and Validity of the Diagnostic Interview Trauma and Stressors- Intellectual Disability in Adults with Mild Intellectual Disabilities or Borderline Intellectual Functioning

ABSTRACT Objective To assess the reliability and construct validity of the Diagnostic Interview Trauma and Stressors- Intellectual Disability – Adult version (DITS-ID-adults) in adults with mild intellectual disabilities or borderline intellectual functioning (MID-BIF). Method The DITS-ID-adults, Brief Symptom Inventory–18 (BSI–18), and Impact of Event Scale-Intellectual Disability (IES – ID) were administered to 97 participants with MID-BIF who lived in supported housing. The Anxiety, Depression, and Mood Scale (ADESS) and Behavior Problems Inventory (BPI) were administered to their relatives. Results The interrater reliability of the DITS-ID-adults was good to excellent. The construct validity of the DITS-ID-adults was good, based on positive correlations between the BSI–18, IES-ID, ADESS and DITS-ID-adults, and mainly positive correlations between the BPI and DITS-ID-adults (r = .21 to r = .75). Reporting potentially traumatic events listed under the A criterium for PTSD was associated with fulfilling PTSD symptom criteria. In this sample, 58% were classified with post-traumatic stress disorder (PTSD) according to the DITS-ID-adults, while PTSD diagnosis on file was low (7%). Conclusion The present findings support the DITS-ID-adults as a reliable and valid basis for classifying PTSD in individuals with MID-BIF.

Sequential Memetic Algorithm Optimization for Allocation Planning in Hostelry Establishments

Hostelry establishments face the challenge of devising a table and chair allocation for accommodating their customers on a daily basis. This problem scales significantly with the introduction of constraints, such as scenario obstacles or the requirement of a minimum distance separation. The TLP (Table Location Problem) and the CLP (Chair Location Problem) are NP-Hard complexity problems that aim to attain the optimal table and chair distribution for certain applications. Existing approaches to this problem fail to address both the TLP and CLP simultaneously, thus resulting in suboptimal solutions achieved by imposing optimization constraints. Therefore, in this paper, a sequential optimization methodology based on a GBLS MA (Gradient-Based Local Search Memetic Algorithm) optimizations is proposed for optimizing the table and chair disposition simultaneously while also considering scenario and distancing restrictions. The proposed methodology is then implemented into a realistic establishment, where different optimization strategies within the CLP are compared. Results prove the viability and flexibility of the proposed sequential optimization for complex hostelry scenarios.

Functional network disruptions in youth with concussion using the Adolescent Brain Cognitive Development study

ABSTRACT Objective This study aimed to compare psychosocial outcomes and functional neuroimaging among youth with concussion, youth with anxiety, and age- and sex-matched controls. Methods Using archival data from the Adolescent Brain Cognitive DevelopmentSM Study, we analyzed between-group differences in psychosocial outcomes measured by the Child Behavior Checklist’s internalizing and externalizing problem scales, and assessed brain function using resting-state fMRI network-region connectivity (specifically frontoparietal network (FPN) and default mode network (DMN) connectivity with the amygdala). Results Significant differences in psychosocial outcomes were found across all groups, with the anxiety group reporting the most internalizing problems, followed by the concussion group which significantly differed from controls. Additionally, FPN-amygdala connectivity was significantly reduced in the concussion group only; this reduced connectivity did not predict psychosocial outcomes across groups. Conclusion This study provided preliminary findings that brain connectivity is reduced exclusively in individuals with concussion. Although disruptions were observed in the concussion group, further investigation is warranted to understand how disruptions may be associated with concussion symptoms. Studies that utilize well-defined control and study groups, and comprehensive cognitive and mental health measures will offer a deeper understanding of the relationship between brain function and psychosocial outcomes.

Beyond the Bite: Exploring Mental Health of Dental Faculty in Pakistan– a multi-institutional study

IntroductionMental health issues are being reported increasingly amongst healthcare staff and students globally. The aim of this study was to investigate the frequency of common mental health issues amongst dental faculty members at multiple institutions in Pakistan.MethodsFollowing approval from the institutional ethics review board, dental faculty members at 14 dental institutions were invited to participate in an online survey based on globally validated scales for mental health problems including the Patient Health Questionnaire (PHQ-9), and the Depression, Anxiety, and Stress Scale (DASS-21). Two open-ended questions were included in the survey to identify perceived factors contributing to poor mental health and recommendations for improving institutional support.ResultsA total of 200 faculty members out of provided their responses to the survey questionnaire but complete responses were provided by 183 participants which included 120 (65.57%) females, and 63 (34.43%) males. The total number of faculty members at the participating institutions was 426 and 183 responses translated into an overall response rate of 43%. Most participants were in the 31–40 years age-group (n = 81, 44.26%) followed by 25–30 year (n = 51, 22.87%) and 41–50 years (n = 40, 21.86%). The mean score on PHQ-9 was 6.51 (SD ± 5.4) while the mean DASS-21 score was 13.04 (SD ± 10.95). PHQ-9 Depression, and DASS-21 Depression, Anxiety, and Stress scores were all significantly positively correlated for the whole sample, and within each subgroup of each demographic factor. Job-related workload, lack of institutional support, financial limitations, and poor work life balance were identified as the main factors contributing adversely to the mental health of the participants.DiscussionThis study provides useful insights into the scale of mental health status amongst dental faculty members at 14 institutions in Pakistan. Underlying factors affecting the mental health of faculty members adversely were identified and recommendations are provided to address these challenges.

Eating Problems in Young Adults: The Role of Unbalanced Family Cohesion and Difficulties in Emotion Regulation

Strong emotional bonds with the family often result in many positive consequences for human functioning, including emotional regulation and eating patterns. The study aimed to determine the relationship between unbalanced family cohesion, emotion regulation, and eating problems in young adults. A correlation research design was used with a non-probability purposive sampling technique to recruit a sample consisting of 300 university students, 50% men and 50% women, with an age range from 18 to 25 years ( M = 21.60, SD = 1.95) from the four universities of Lahore. Demographic performa, Family Cohesion Scale (FCS), Difficulties in Emotion Regulation Scale (DERS), and Eating Problem Scale (EPS) were used to explore the study variables. Data was analyzed using SPSS 21. Pearson product-moment correlation indicated a significant positive relationship between unbalanced family cohesion, difficulties in emotion regulation, and eating problems. It also revealed that a positive relationship was found among unbalanced family cohesion, difficulties in emotion regulation, and eating problems in young adults. Mediation analysis shows that emotion regulation fully mediates the relationship between unbalanced family cohesion and eating problems in young adults. The findings will help students and family counselors identify risk factors of eating problems and its impact on their transition into marriage life. They will contribute to a deeper understanding of unbalanced family dynamics, difficulties in emotion regulation, and eating problems.

학교폭력 가해자에 대한 경찰다이버전 정책 기반 다중체계치료 사례연구

Objectives This purpose of the study was conducted to explore the effectiveness of Multisystemic Therapy for school violence perpetrators as part of the police diversion policy. Methods The subject of the study was a 14-year-old male student who was reported to have school violence, which is extortion and intimidation, and was released on the condition of participating MST. In accordance with the multisystemic therapy manual, five key goals were derived by analyzing risk factors for adolescents' problematic behaviors through evaluation of various systems surrounding him. An intervention strategies were established to achieve the core goals(i.e., stop smoking and alcohol, controlled use of SNS, making pro-social friends, stay away from delinquents, promoting positive parenting skills of parents). The essential parts of MST as like the therapy team, the visiting service, and weekly supervision were implemented. In order to measure pre-post changes, Children Behavior Checklist 6-18, the Parenting Stress Index, the Parenting Attitude Test, and the MMPI-A were conducted. The package of scales was implemented at 3 points as pre-time, post-time, and 8 months follow-up. Results First of all, the level of the externalization problem scale of CBCL 6-18, which was at the clinical range at the pre-test, recovered to the level of the normal range in the post-test, and these results were maintained even at the 8 months follow-up. Regarding MMPI-A, the score was lowered by nearly 2 standard deviations in the scale of Pd compared to pre-test. Then, parenting stress decreased and positive parenting attitudes of parents increased. In addition, all the five core goals were achieved. Conclusions The results of this study showed that multisystemic therapy for school violence perpetrator can help decrease adolescents’ problematic behaviors including school violence.

Derivative dynamic time warping algorithm with introduced correction for varying load fault diagnosis of nuclear power system steam turbine units

The Derivative Dynamic Time Warping (DDTW) algorithm is improved to address the multi-parameters time series classification problem faced by nuclear power system steam turbine units varying load fault diagnosis. Firstly, the entire load changing process is treated as a single sample rather than multiple time-step-samples. This ensures the complete information on the load changing processes, while avoiding interference from normal data fluctuations during faults. Secondly, Time Series Position Coefficient and Time Series Length Coefficient are proposed to correct the DDTW algorithm from two perspectives: the sequences lengths and the data positions in the sequences. This solves the singularities and timeline scaling problems, thereby preventing interference introduced by data sequences' lengths differences and ''similar data appearing at different times'' problem. The nuclear power system steam turbine unit simulation model was built to obtain load changing processes data under normal and faults statuses. In the varying load fault diagnosis test based on these data, the improved DDTW algorithm achieved an accuracy of 1.38%–12.06% higher than other methods, reaching 87.50%. Finally, The Deep Convolutional Generative Adversarial Networks (DCGAN) model was used to generate data to supplement the limited samples of complete load changing processes, and the accuracy of the novel method increased to 95.51% with the increase of data used to support the comparison.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Obsessive-compulsive symptoms relating to psychosocial functioning for people with schizophrenia, schizoaffective disorder, or bipolar disorder.

To assess the psychosocial functioning concerning obsessive-compulsive symptoms (OCS) and/or obsessive-compulsive disorder (OCD) comorbidity in people with schizophrenia, schizoaffective disorder, or bipolar disorder diagnosed in a large case register database in Southeast London. Data were retrieved from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) register using Clinical Record Interactive Search (CRIS) system, a platform allowing research on full but de-identified electronic health records for secondary and tertiary mental healthcare services. Information of schizophrenia, schizoaffective disorder, bipolar disorder diagnosis and OCS/OCD status was ascertained from structural or free-text fields through natural language processing (NLP) algorithms based on artificial intelligence techniques during the observation window of January 2007 to December 2016. Associations between comorbid OCS/OCD and recorded Health of the Nation Outcome Scales (HoNOS) for problems with activities of daily living (ADLs), living conditions, occupational and recreational activities, and relationships were estimated by logistic regression with socio-demographic confounders controlled. Of 15,412 subjects diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder, 2,358 (15.3%) experienced OCS without OCD, and 2,586 (16.8%) had OCD recorded. The presence of OCS/OCD was associated with more problems with relationships (adj.OR = 1.34, 95% CI: 1.25-1.44), ADLs (adj.OR = 1.31, 95%CI: 1.22-1.41), and living conditions (adj.OR = 1.31, 95% CI: 1.22-1.41). Sensitivity analysis revealed similar outcomes. Comorbid OCS/OCD was associated with poorer psychosocial functioning in people with schizophrenia, schizoaffective disorder, or bipolar disorder. This finding highlights the importance of identification and treatment of comorbid OCS among this vulnerable patient group.

Association of Cannabis Use Reduction With Improved Functional Outcomes: An Exploratory Aggregated Analysis From Seven Cannabis Use Disorder Treatment Trials to Extract Data-Driven Cannabis Reduction Metrics.

This exploratory analysis sought to determine whether decreases in cannabis use are associated with improvements in cannabis-related problems and functional outcomes, and if so, what percentage decrease is associated with improvement. Data were aggregated from seven cannabis use disorder treatment trials conducted in the United States (N=920; ages 13 years and older; mean age, 25 years; 30% female, 7% Black, 11% Hispanic/Latinx). Outcome measures included the patient-reported Marijuana Problems Scale (MPS), Health-Related Quality of Life scale (HRQOL), and Pittsburgh Sleep Quality Index and the clinician-rated Clinical Global Impressions (CGI) severity and improvement scales (CGI-S and CGI-I). Generalized estimating equations tested the association between changes in 4-week cannabis use and improvements in functional outcomes. Classification and regression tree (CART) models were developed to determine what reductions in cannabis use could be used as classifiers of improvement. Decreases in the amount and frequency of cannabis use were significantly associated with improvements in MPS severity and total scores as well as improvements on the CGI-I and in sleep quality, but not improvements on the HRQOL. CART models performed best for CGI-I scores (72%-75% correct classification), while other outcome measures did not perform as well (40%-62% correct classification). CART models showed improvements on the CGI at 74% reduction in use amounts and 47% reduction in use days. Reductions in cannabis use (∼50% reduction in use days and ∼75% reduction in use amounts) were associated with clinician-assessed improvement, which suggests that cannabis use reduction may yield benefit among individuals with cannabis use disorder. These exploratory results extract a data-driven metric to inform future studies, clinicians, patients, and policy recommendations.

Evaluating User Acceptance of Mobile Payment Systems in Uganda

Purpose: This research evaluates user acceptance of mobile payment systems. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Combining machine learning with DW systems provides significant advantages in different fields. This synergy boosts analytical aptitudes, allowing the organization to go a notch higher than descriptive analytics in predictive and prescriptive analytics. However, such a decision is not simple as it has implementation matters such as data quality problems, scalability, and real-time processing problems. Integration best practices include in-database machine learning processing, a feature store, and proper MLOps practices. Real-life examples from the healthcare industry, banking and financial services, retail, and manufacturing industries show that this integration brings operational enhancements for the business and positive effects on customers and overall organizational performance. Recommendations: Mobile payment providers should prioritize user-centric design principles, focusing on simplicity, usability, and intuitive interfaces. Policymakers should develop regulatory frameworks that establish clear standards for mobile payment systems, focusing on data protection, privacy, and security. Keywords: User Acceptance, Mobile Payment Systems

Deep Reinforcement Learning with Local Attention for Single Agile Optical Satellite Scheduling Problem

This paper investigates the single agile optical satellite scheduling problem, which has received increasing attention due to the rapid growth in earth observation requirements. Owing to the complicated constraints and considerable solution space of this problem, the conventional exact methods and heuristic methods, which are sensitive to the problem scale, demand high computational expenses. Thus, an efficient approach is demanded to solve this problem, and this paper proposes a deep reinforcement learning algorithm with a local attention mechanism. A mathematical model is first established to describe this problem, which considers a series of complex constraints and takes the profit ratio of completed tasks as the optimization objective. Then, a neural network framework with an encoder–decoder structure is adopted to generate high-quality solutions, and a local attention mechanism is designed to improve the generation of solutions. In addition, an adaptive learning rate strategy is proposed to guide the actor–critic training algorithm to dynamically adjust the learning rate in the training process to enhance the training effectiveness of the proposed network. Finally, extensive experiments verify that the proposed algorithm outperforms the comparison algorithms in terms of solution quality, generalization performance, and computation efficiency.

Thin-film evaporation in microchannels: A combined analytical and computational fluid dynamics approach to assessing meniscus curvature impact

Computational fluid dynamics (CFD) is employed to investigate the effects of variation in bulk meniscus curvature on evaporative heat and mass transfer in microchannels. Evaporation in microchannels presents a classic multiscale problem, encompassing both microscopic and macroscopic length scales in the thin-film and bulk meniscus regions, respectively. Therefore, the liquid-vapor interface or meniscus shape is first determined through thin-film evaporation analysis using numerical approaches found in the literature. However, a new approach for establishing boundary conditions is also proposed, allowing for the representation of varying curvatures within the bulk meniscus region. Then, the obtained meniscus thickness profiles are exported to a two-dimensional CFD framework. The primary advantage of this developed CFD framework lies in its capability to simultaneously analyze evaporative heat and mass transfer from both microscopic and macroscopic regions, a feature that enhances research endeavors concerning micro and miniature heat pipes. The developed model is applied to various rectangular microchannel sizes, ranging from 10 to 100 μm in width, with wall superheats varying from 0.1 to 3.0 K. The aim is to quantify the effects of variations in bulk meniscus curvature on the evaporative characteristics of the extended meniscus (comprising both thin-film and bulk meniscus regions), using the concept of thermal resistance. Consequently, based on the CFD simulation results, multiple regression analysis is employed to formulate the total thermal resistance in terms of independent parameters, namely the microchannel width, wall superheat, and the radius of curvature (Rc). It is observed that the radius of curvature has a marginal impact on the total thermal resistance, allowing its influence to be expressed by a power-law function of (Rc/Rm)0.102, where Rm represents the minimum radius of curvature.