Integrated Model Research Articles

The integrated behavioral model of mental health help seeking (IBM-HS): A health services utilization theory of planned behavior for accessing care.

This article introduces the integrated behavioral model of mental health help seeking (IBM-HS), a theoretical model for understanding the constructs (e.g., systemic, predisposing, and enabling factors; mental health literacy; illness perceptions; perceived need; stigma; shame; perceived benefits, motivation) that influence people's decision making around seeking professional mental health care and their ultimate access to formal treatment. The IBM-HS is a help-seeking-specific adaptation of the empirically supported integrated behavioral model and integrative model, which are themselves evolutions of the theory of planned behavior and theory of reasoned action. The IBM-HS posits that help-seeking determinants (e.g., structural forces; cultural influences; past help-seeking experience; evaluated need; mental health perceptions, knowledge, and skills; social support) influence help-seeking beliefs (i.e., outcome beliefs, experiential beliefs, beliefs about others' expectations, beliefs about others' behavior, logistical beliefs), which in turn determine their respective help-seeking mechanisms (i.e., attitude, perceived norm, personal agency). These mechanisms collectively influence help-seeking intention, which drives prospective help-seeking behavior, subject to the moderating effects of determinants. Finally, prospective behavior has reciprocal feedback loop effects on certain determinants and beliefs. This article describes the need for the IBM-HS, the model's constructs and their interrelations, measurement considerations, and how the model can be used by scholarly and applied users to systematically understand people's intention to seek professional mental health care services and what helps or hinders them from utilizing this care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An Integrative Model of Alcohol-Facilitated Intimate Partner Aggression Perpetration in Sexual and Gender Diverse Couples.

Sexual and gender diverse (SGD) individuals are at heightened risk for intimate partner aggression (IPA) perpetration relative to their heterosexual and cisgender peers. Alcohol is a well-established cause of IPA perpetration in cisgender, heterosexual couples; however, minimal research has investigated the alcohol-IPA perpetration link in SGD couples. The relative lack of work in this area is a major barrier to addressing this health disparity. SGD individuals experience unique stressors related to their and/or their partner's intersecting minoritized identities that are critical to understanding alcohol-IPA etiology and informing culturally affirming intervention programming. We advance prior work by members of the authorship team (see Parrott et al., 2023a; Shorey et al., 2019) to propose an integrative theoretical model that invokes (1) the I3 Model to organize risk and resilience factors at the individual and dyadic level, and (2) Alcohol Myopia Theory to explain the mechanism by which proximal alcohol use facilitates IPA as a function of individual differences in those factors. This integrative model provides a framework to understand how the confluence of stigma, minority stressors, proximal alcohol use, and other factors contribute to IPA perpetration in SGD couples. Application of this integrative model has potential to facilitate more rigorous research (e.g., intensive longitudinal designs, dyadic analysis) focused on putative risk and resilience factors across the social ecology. Further, the model provides guidance for intervention development by identifying how individual (e.g., minority stress), relationship (e.g., relationship functioning), and structural factors (e.g., SGD stigma) interactively contribute to alcohol-facilitated IPA perpetration.

Natural coral recovery despite negative population growth.

Demographic processes that ensure the recovery and resilience of marine populations are critical as climate change sends an increasing proportion on a trajectory of decline. Yet for some populations, recovery potential remains high. We conducted annual monitoring over 9 years (2012-2020) to assess the recovery of coral populations belonging to the genus Pocillopora. These populations experienced a catastrophic collapse following a severe typhoon in 2009. From the start of the monitoring period, high initial recruitment led to the establishment of a juvenile population that rapidly transitioned to sexually mature adults, which dominated the population within 6 years after the disturbance. As a result, coral cover increased from 1.1% to 20.2% during this time. To identify key demographic drivers of recovery and population growth rates (λ), we applied kernel-resampled integral projection models (IPMs), constructing eight successive models to examine annual change. IPMs were able to capture reproductive traits as key demographic drivers over the initial 3 years, while individual growth was a continuous key demographic driver throughout the entire monitoring period. IPMs further detected a pulse of reproductive output subsequent to two further Category 5 typhoon events during the monitoring period, exemplifying key mechanisms of resilience for coral populations impacted by disturbance. Despite rapid recovery, (i.e., increased coral cover, individual colony growth, low mortality), IPMs estimated predominantly negative values of λ, indicating a declining population. Indeed, while λ translates to a change in the number of individuals, the recovery of coral populations can also be driven by an increase in the size of surviving colonies. Our results illustrate that accumulating long-term data on historical dynamics and applying IPMs to extract demographic drivers are crucial for future predictions that are based on comprehensive and robust understandings of ecological change.

ScMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Terroir store brand authenticity

PurposeThe objective of this exploratory study was to investigate the communication themes that retailers should prioritise to convey terroir store brands’ (TSBs) authenticity and the impact of their authenticity on several variables (i.e. value, attitude, trust, intentions and effective purchases). TSBs combine a variety of products that adhere to stringent standards associated with a terroir. Charters et al. (2017) referred to a terroir as “a resource based on unique physical origins and shared cultural personification that shape a product’s benefits into a meaningful value proposition” (p. 755). Three communication themes were studied: the ingredients’ origin, traditional local recipes and the producer’s history in a region (terroir).Design/methodology/approachAn experiment was conducted in a store laboratory. A total of 420 consumers representative of the French population were randomly assigned to four independent groups in a between-subjects study design. They shopped in the store laboratory with a section dedicated to a TSB. Each of the independent samples in this experiment was exposed to posters in the store laboratory related to the three communication themes studied. The control group did not see any posters.FindingsThe study showed that the level of TSB authenticity was highest for the communication theme related to the traditional local recipes of the culinary dishes offered by the TSB. Next came the theme related to the ingredients’ origin, followed by the producer’s history in the terroir. Moreover, for the communication theme related to the traditional local recipes, TSB authenticity had a direct impact on value, trust and intentions. Moreover, the link between intentions and effective purchases of TSB products has only been established for this specific communication theme.Originality/valueFirst, this study adds to the limited research on TSBs’ authenticity and identifies the communication themes retailers should use to promote such brands. It also proposes an integrative model of the consequences of TSBs’ authenticity in the retailing field that highlights the direct and indirect links (through value, attitude, trust and intentions) between authenticity and consumers’ effective purchases. Finally, it indicates the kind of discourse on TSBs that conveys their authenticity.

Meeting the Needs: Description and Evaluation of an Alcohol Related Brain Damage (ARBD) Residential Rehabilitation Service

ABSTRACT To describe and evaluate a cost-effective, integrated model of service provision for people with Alcohol Related Brain Damage (ARBD). Participants identified by three large acute hospitals as having ARBD and suitable for recovery and rehabilitation were offered a 12-week recovery-focussed program at a residential unit. The unit is a collaboration between an NHS board, the local council, and a 3rd sector organization Participants’ pre-admission and post-admission medical data was gathered to analyze the effectiveness of the unit’s program on Emergency Department (ED) attendance and inpatient bed use. Improvement in cognition was measured by comparing the ACE-III results for all participants at admission and just prior to discharge from the program. Thirty-one participants met the inclusion criteria. Paired t-tests revealed the 12-week program overall significantly reduced attendance at ED, use of in-patient beds after discharge, and improved cognitive functioning. This integrated model of service provision provides clear benefits to residents’ cognitive functioning and to the NHS by freeing up inpatient beds and ED capacity. Therefore, it should be considered by commissioners when addressing the needs of people with ARBD.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

Effects of the Pathways for African American Success Program: Reductions in Adolescent Depression Through the Promotion of Racial Equity-Informed Parenting and Racial Pride

African American adolescents face persistent disparities in depression and related behavioral health outcomes, which have been attributed to experiences with discrimination. The integrative model for the study of stress in Black American families provides a comprehensive perspective on how historical and current discrimination has direct and indirect effects on child behavioral health. Culturally based protective mechanisms (e.g., racial socialization) have been demonstrated to buffer adolescents from the negative effects of discrimination. The Pathways for African American Success (PAAS) program was developed as a scalable eHealth preventive intervention to facilitate parents’ use of strategies to protect their children from exposure to racism and the disproportionate consequences of risk behaviors, which we label as racial equity-informed parenting. Using data from a randomized effectiveness trial of PAAS and following stress and resilience approaches, we tested multiple hypotheses about the nature of discrimination as a source of risk, as well as program-driven improvements in adolescent racial pride as a source of protection. Our risk model indicated adolescent depression was associated with their own (direct) and their parents’ (indirect) experience of discrimination. Mediation analyses showed that program-driven improvements in adolescent racial pride served as a risk reducer/compensatory factor. Findings of moderation analyses, however, showed the negative effects of discrimination, even when racial pride was high. Implications for theory and cultural tailoring of evidence-based preventive interventions are presented.

The PRIME-NL study: evaluating a complex healthcare intervention for people with Parkinson’s disease in a dynamic environment

BackgroundAn innovative, integrative care model for people with Parkinson (PRIME Parkinson) has gradually been implemented in a selected region of the Netherlands since 2021. A prospective evaluation of this model (PRIME-NL study) was initiated in parallel, spanning the year prior to implementation (baseline) and the implementation period. Following publication of the original study protocol, the COVID-19 crisis delayed implementation of the full PRIME Parkinson care model by two years and hampered the recruitment of study participants.ObjectiveTo describe which methodological adjustments were made to the study protocol because of these developments.MethodsWe compare various outcomes between a region where PRIME Parkinson care was implemented (innovation region) versus the rest of the Netherlands (usual care region). We use healthcare claims data of virtually all people with Parkinson in the Netherlands and annual questionnaires in a representative subsample of 984 people with Parkinson, 566 caregivers and 192 healthcare professionals. Four major methodological adjustments had to be made since publication of the original protocol. First, we extended the evaluation period by two years. Second, we incorporated annual process measures of the stage of implementation of the new care model. Third, we introduced a real-time iterative feedback loop of interim results to relevant stakeholders. Fourth, we updated the statistical analysis plan.DiscussionThis manuscript provides transparency in how the design and analyses of the evaluation study had to be adapted to control for external influences in a dynamic environment, including eruption of the COVID-19 crisis. Our solutions could serve as a template for evaluating other complex healthcare interventions in a dynamic environment.

Research on the Parametric Generation Method of Model with Multi-Information for the Wooden Architectural Heritage

ABSTRACT There are many traditional wooden buildings with complex structural systems, which leads to tedious modelling processes, and challenges to achieve multidisciplinary collaboration between architecture and structure. This study establish a multi-information model using information and parameterization methods. First, this study identifies typical traditional Chuan-Dou style wood frames in Jingdezhen as an example and uses the parameterization method to quickly generate building information models based on the composition rules of traditional wooden buildings using the Revit-Dynamo platform. Second, by embedding mechanical parameters in the model components, the building and structure information are integrated into a multidimensional integrated model. Finally, a method for applying the generated multi-information model in the project is discussed. This building and structure multi-information model verifies the rationality of the component structure in the building model in real time, thereby optimizing the design scheme and achieving the full-scale recording of the detailed statistics of the building and structural information of the components. These research results have significantly improved the modelling speed of traditional wooden buildings, simplified the tedious process from architectural design to structural calculation, and provided an important research foundation for the intelligent and digital protection of architectural heritage.

Distributed leadership, self-awareness, democracy, and sustainable development: towards an integrative model of school effectiveness

ABSTRACT This study aimed to examine the effects of school principals' distributed leadership behaviors and teachers’ self-awareness on school effectiveness through the mediating role of democratic practices and attitudes towards sustainable development in schools. Accordingly, an integrative model was created. The study data were analyzed using structural equation modeling and bootstrapping tests. The analysis results confirmed that organizational democracy played a prominent mediating role between school principals’ distributed leadership practices and teachers’ perceptions of school effectiveness. We also found evidence for the effects of school principals’ distributed leadership behaviors and teachers’ self-awareness on school effectiveness through the mediating role of teachers’ attitudes towards sustainable development. We concluded that school principals’ distributed leadership practices could enhance school effectiveness through teachers’ perceptions of organizational democracy and attitudes towards sustainable development in schools. Besides, a high-quality educational environment and school effectiveness could be promoted by increasing teachers’ self-awareness and attitudes towards sustainable development.

Stance Detection in Hinglish Data using the BART-large-MNLI Integration Model

Stance Detection in Hinglish Data using the BART-large-MNLI Integration Model

Insecure Attachment, Oxytocinergic System and C-Tactile Fibers: An Integrative and Translational Pathophysiological Model of Fibromyalgia and Central Sensitivity Syndromes

Insecure Attachment, Oxytocinergic System and C-Tactile Fibers: An Integrative and Translational Pathophysiological Model of Fibromyalgia and Central Sensitivity Syndromes

Towards robotic laboratory automation plug & play: Reference architecture model for robot integration

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

Design and Assessment of an Advanced Renewable Energy System with Hydrogen and Monomethylhydrazine Production for Space Shuttles

A new renewable energy-driven advanced plant is designed and developed to produce multiple useful outputs, namely electricity, heat, cooling, hydrogen and monomethylhydrazine (as space shuttle fuel) using solar and wind energies locally available. A specific location needed for this kind of unique work is identified as the Kennedy Space Center (KSC), which is known as located on Merritt Island, Florida, is one of the National Aeronautics and Space Administration's (NASA) ten field centers. Since December 1968, KSC has been NASA's primary launch center of American spaceflight, research, and technology. This paper presents a comprehensive analysis of the subsystems that constitute the newly proposed system for KSC and highlights their complex relationships and synergies. Leveraging renewable energy sources, such as Concentrated Solar Power (CSP) and wind energy, coupled with innovative technologies like the Regenerative Rankine Cycle, MSF desalination, hydrogen production and Li-Br Absorption Chiller, the KSC's system maximizes energy conversion efficiency while minimizing environmental impact. Noteworthy outcomes include daily water production of 177.63 kg/s and hydrogen production of 169.61 kg/day, alongside annual energy contributions of 212,692,688 kWh for CSP and 66,158,552 kWh for wind turbines. More than that, it has a production of 23.04 kg/s MMH as fuel. Additionally, the overall energy and exergy efficiencies of the system are 30.6% and 33% respectively. Despite challenges, such as lower efficiencies in certain subsystems like MMH production, ongoing research and development efforts aim to optimize processes and enhance sustainability. This study underscores the KSC's commitment to sustainability, innovation, and progress, serving as a model for sustainable energy integration in space exploration and terrestrial applications.

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78–0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65–0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients’ early risk stratification.

Advanced multi-image segmentation-based machine learning modeling strategy for corrosion prediction and rust layer performance evaluation of weathering steel

Corrosion images are the most accessible type of data to collect. To utilize these data to monitor and evaluate the corrosion of weathering steel (WS) efficiently. This study established a dataset of corrosion images of WS by accelerated corrosion experiments with dry and wet cycles. Image segmentation algorithms and machine learning were used for training, modeling, and data mining of the image data. The results showed that 22 features in the corrosion images were highly correlated with the corrosion thickness loss, with the highest correlation coefficient exceeding 0.7. The rust layer texture and color features showed a synergistic evolution during the corrosion process. On this basis, a classification model of the rust layer was developed to quantify the evolution of the protective properties of the rust layer during the corrosion process. In addition, the established integrated learning model achieves accurate prediction (R2 > 0.94) for WS corrosion thickness loss.

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

Abstract The proposed article focuses on developing a methodology for constructing typical operational scenarios in power systems considering the spatiotemporal characteristics of new energy output. The integration of new energy sources on a large scale into power systems can lead to issues such as line congestion and supply-demand imbalances. To support the planning and scheduling of future high-capacity power systems, this paper introduces a method that accounts for the spatiotemporal characteristics of new energy outputs. The method begins by proposing a spatiotemporal fusion-based multi-head attention model for generating new energy output scenarios. This model employs multi-head attention mechanisms to extract the spatiotemporal characteristics of new energy outputs, eliminating redundancy in the feature data. The processed spatiotemporal feature information is then transferred to a gated feature fusion model for integration, using an encoder structure based on Long Short-Term Memory (LSTM) to generate new energy output scenarios. The paper, by means of an advanced Fuzzy C-Means (FCM) clustering algorithm, identifies typical scenarios of net energy output. Subsequently, the proposed method’s efficacy and precision are confirmed through simulation analysis with real data from a certain area.

Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in TNBC cells. Analyzing the effects of co-targeting specific network nodes by systematically simulating the model predicted synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, co-targeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that while AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven HCC cell line, where there is a synergistic effect of co-targeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment.

Economic/Sustainability Optimization/Analysis of an Environmentally Friendly Trigeneration Biomass Gasification System Using Advanced Machine Learning

The present research presents an innovative thermal integration model for a biomass-based power plant to generate power, coolant, and liquefied hydrogen. This integrated arrangement comprises biomass gasification, gas turbine cycle, organic flash-bi-evaporator refrigeration cycle, multi-effect desalination, solid oxide electrolyzer, and Claude cycle. The selection of an eco-friendly fluid for the electricity-cooling production cycle is conduced relying on a comparative analysis. The subsequent examination evaluates the economic-sustainability performance criteria through sensitivity and contour analyses. The comparative assessment identifies R1234ze(Z) as the most suitable working fluid. Four objective functions are scrutinized, and two optimization scenarios are devised based on a machine learning approach using artificial neural networks and a multi-objective grey wolf optimization technique. In Scenario A, the focused objectives are the total destructed exergy and the cost of producing liquefied hydrogen. Meanwhile, Scenario B integrates the sustainability index and the overall system cost rate as primary criteria. Scenario A exhibits more favorable operational conditions and outcomes than scenario B, showing the objectives mentioned as 9,955 kW and 3.25 $/kg, respectively. In this scenario, the sustainability index, the overall investment cost rate, and the net present value are obtained to be 1.74, 236 $/h, and 205 M$, correspondingly.