Individual Models Research Articles

The phobic brain: Morphometric features correctly classify individuals with small animal phobia.

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro-networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies.

Compliance of eating model of individuals with metabolic syndrome with Bulgarian dietary guidelines

Abstract Background The incidence of metabolic syndrome progressively increases among the urban population of Europe. About 1/3 of the Bulgarian adults have metabolic syndrome. Changing the dietary pattern can be a modern approach to control it. The aim of the study is to investigate the eating model of people with metabolic syndrome according to the Bulgarian dietary guidelines. Methods Two hundred individuals with metabolic syndrome aged 54.6±10.5 y. were randomly selected from general practitioners’ practices in Pleven region, Bulgaria. A Food Frequency Questionnaire method was applied to assess the eating pattern of the respondents. The frequency of food consumption of different foods was compared with the dietary guidelines for the Bulgarian population over 18 years of age. Data were processed with IBM SPSS v. 25. Results Grain consumption met the recommendations in almost all respondents. Only 29% consumed fresh fruits and 42% had intake of milk and milk products according to the requirements. Women ate more fresh vegetables and fruits and dairy products than men (p = 0,032). Risk meat consumption, 2-4 times a week, was found: men preferring more red meat (45%) and women - chicken and poultry (56%). Fish consumption met recommendation (once a week) in only 14.7% of the respondents. The food intake of eggs, vegetables, legumes and nuts were low. The preferred oils were sunflower and olive (once a day by 86% and 15% respectively), and butter (21% - 2-4 times a week). The intake of table salt corresponds to the recommendations in two thirds of the participants. One third consumed confectionery every day. Half did not drink alcohol, but 16% used concentrates once a day. Conclusions Foods that increase the nutritional risk for the development of metabolic syndrome and type 2 diabetes - high consumption of confectionery and red meat, low consumption of vegetables, fish and legumes were found. Unbalanced eating model requires new public initiatives to prevent metabolic syndrome. Key messages • Men with metabolic syndrome have a higher consumption of unhealthy foods than women. • Modern Bulgarian dietary guidelines are an important tool for prevention of metabolic syndrome.

Air quality modeling intercomparison and multiscale ensemble chain for Latin America

Abstract. A multiscale modeling ensemble chain has been assembled as a first step towards an air quality analysis and forecasting (AQF) system for Latin America. Two global and three regional models were tested and compared in retrospective mode over a shared domain (120–28° W, 60° S–30° N) for the months of January and July 2015. The objective of this experiment was to understand their performance and characterize their errors. Observations from local air quality monitoring networks in Colombia, Chile, Brazil, Mexico, Ecuador and Peru were used for model evaluation. The models generally agreed with observations in large cities such as Mexico City and São Paulo, whereas representing smaller urban areas, such as Bogotá and Santiago, was more challenging. For instance, in Santiago during wintertime, the simulations showed large discrepancies with observations. No single model demonstrated superior performance over others or among pollutants and sites available. In general, ozone and NO2 exhibited the lowest bias and errors, especially in São Paulo and Mexico City. For SO2, the bias and error were close to 200 %, except for Bogotá. The ensemble, created from the median value of all models, was evaluated as well. In some cases, the ensemble outperformed the individual models and mitigated extreme over- or underestimation. However, more research is needed before concluding that the ensemble is the path for an AQF system in Latin America. This study identified certain limitations in the models and global emission inventories, which should be addressed with the involvement and experience of local researchers.

Integrating experimental and machine learning approaches for predictive analysis of photocatalytic hydrogen evolution using Cu/g-C3N4

This study addresses environmental issues like global warming and wastewater generation by exploring waste-to-energy strategies that produce renewable hydrogen and treat wastewater simultaneously. Cu/g-C3N4 is used to evolve hydrogen from sucrose solution and the impact of reaction parameters such as pH (3, 5, and 7), Cu loading (5, 10, and 15 wt%), catalyst amount (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2) concentration (0, 10, and 20 mM) on the evolved hydrogen amount is examined. Characterization study confirmed successful incorporation of Cu without significantly altering g-C3N4 properties. The highest hydrogen production (1979.25 μmol g−1·h−1) is achieved with 0.3 g/L catalyst, 20 mM H2O2, 5 % Cu loading, and pH 3. The experimental study concludes that Cu/g-C3N4 is an effective photocatalyst for renewable hydrogen production. In addition to the experimental investigations, various machine learning (ML) models, including Random Forest, Decision Tree, XGBoost, among others, are employed to analyze the impact of reaction parameters and forecast the quantities of produced hydrogen. Alongside these individual models, an ensemble approach is proposed and utilized. The R2 values of these ML models ranged from 0.9454 to 0.9955, indicating strong predictive performance across the board. Additionally, these models exhibited low error rates, further confirming their reliability in predicting hydrogen evolution.

An analysis of decipherable red blood cell abnormality detection under federated environment leveraging XAI incorporated deep learning

In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging. These privacy concerns can be handled by Federation Learning (FL), which builds an unbiased global model from local models trained with client data while maintaining the confidentiality of local data. Using FL, this study solves the problem of centralized data collection by detecting deformations in images of Red Blood Cells (RBC) in a decentralized way. To achieve this, RBC data is used to train multiple Deep Learning (DL) models, and among the various DL models, the most efficient one is considered to be used as the global model inside the FL framework. The FL framework works by copying the global model’s weight to the client’s local models and then training the local models in client-specific devices to average the weights of the local model back to the global model. In the averaging process, direct averaging is performed and alongside, weighted averaging is also done to weigh the individual local model’s contribution according to their performance, keeping the FL framework immune to the effects of bad clients and attacks. In the process, the data of the client remains confidential during training, while the global model learns necessary information. The results of the experiments indicate that there is no significant difference in the performance of the FL method and the best-performing DL model, as the best-performing DL model reaches an accuracy of 96% and the FL environment reaches 94%-95%. This study shows that the FL technique, in comparison to the classic DL methodology, can accomplish confidentiality secured RBC deformation classification from RBC images without substantially diminishing the accuracy of the categorization. Finally, the overall validity of the classification results has been verified by employing GradCam driven Explainable AI techniques.

Going Insane: Battered Muslim Women Reclaim a Positive Identity.

This study examines the process of identity negotiation of 15 Muslim women who resisted severe abuse by their husbands and extended family by becoming mentally ill and thereafter, divorcing. Content analysis of the interview narratives shows that these women were poor, married young, and endured years of battering, isolation, and silencing for the sake of family honor and children's well-being. Entrapped within a web of sociocultural norms legitimizing wife beating, and abusive extended family relationships that annihilate their voice by branding them as maj'nuna/insane, these women explained that they were terrorized helpless victims fearing the stigma of being labeled insane and the resultant harm to their children. With the deterioration of their health, threat of annihilation, and imminent danger to themselves and their children, these women broke through the normative oppressive framework by becoming maj'nuna/mentally ill. Detached from the extended family and no longer caring to endorse a label that discredited what they said or did, these women overtly resisted by escaping to the family of origin and/or mental health clinic to reveal the abuse, divorce, and seek treatment. Severing all family ties, and now residing in public housing, these women felt safe to renegotiate a favorable identity and reclaim the right to live with dignity. Implications/recommendations: (1) The criminalization of battering and prosecution of batterers is not enough to deter when cultural norms sanction battering, (2) additional diagnostic categories are needed to identify the precursors of battering within the strategies of overt and covert resistance battered women adopt in collectivistic cultures such as mental and neurophysiological dysfunctions, and (3) it is necessary to transcend the individualistic model titling battered women within the false dichotomy of victimization or agency as it fails to reflect battered women's experience in collectivistic cultures and their resistant strategies to abuse in the extended family.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.

This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction. Data from ICU patients (December 2016 to December 2021), including serum levels of trace elements, vitamins, cholesterol, and prealbumin, were retrospectively analyzed using AI models. Models employed included category boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and multilayer perceptron (MLP). Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1-score. The performance was evaluated using 10-fold crossvalidation. The SHapley Additive exPlanations (SHAP) method provided interpretability. CatBoost emerged as the top-performing individual AI model with an AUROC of 0.756, closely followed by LGBM, MLP, and XGBoost. Furthermore, the ensemble model combining these four models achieved the highest AUROC of 0.776 and more balanced metrics, outperforming all models. SHAP analysis indicated significant influences of prealbumin, Acute Physiology and Chronic Health Evaluation II score, and age on predictions. Notably, the ratios of selenium to age and low-density lipoprotein to total cholesterol also had a notable impact on the models' output. The study underscores the critical role of nutrition-related parameters in ICU patient outcomes. Advanced AI models, particularly in an ensemble approach, demonstrated improved predictive accuracy. SHAP analysis offered insights into specific factors influencing patient survival, highlighting the need for broader consideration of these biomarkers in critical care management.

Experimental study and simulation of a solid desiccant cooling system installed in northern Algeria

An experimental investigation of a solid desiccant cooling system for a research laboratory on the Algerian coast has been carried out. The system utilized flat-plate solar collectors for regeneration and was evaluated through a combination of individual component characterization, system modeling (using TRNSYS), and experimental validation. The results obtained have been validated and show that the efficiency of the rotary heat exchanger reached 65 %, the evaporative humidifier efficiency reached 94 %, and the desiccant wheel achieved efficiencies of 73 % on the process side and 66 % on the regeneration side. This translates to a significant dehumidification effect, reducing absolute humidity in the treated air by 9 g/kg, for the supply air temperature effectively maintained at a comfortable 24 °C. The close agreement between the simulated and experimental COP values (1.13 and 1.08, respectively) validates the chosen approach and confirms the potential of this technology for energy-efficient building cooling in the hot and humid Mediterranean climate of northern Algeria.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

Improved Ensemble Model for Insecticide Recognition by Incorporating Insect Toxicity Data.

Pesticide molecules, such as insecticides, play a critical role in modern agricultural production. Traditional pesticide development methods are often inefficient and expensive, while data-driven artificial intelligence (AI) techniques have emerged as a useful tool to facilitate drug discovery. However, currently available commercial pesticide data is limited, which makes the trained models unsatisfactory in terms of performance and generalization. From a domain knowledge perspective, insect toxicity data were incorporated to improve the insecticide recognition of AI models. Compared to the models trained with the original data set, the new models performed better in the external validation, and their generalization was more desirable. In addition, by integrating different types of individual models, we obtained an ensemble model with better performance. Based on this, an online platform was developed to provide researchers with free access to insecticide screening (https://dpai.ccnu.edu.cn/InsectiVS/). Finally, two potential insecticide molecules with insecticidal activity against Plutella xylostella were successfully identified in a real-world scenario. In conclusion, this idea connects the fields of AI and agricultural chemistry and is expected to have wide application in pesticide research.

Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Cervical cancer remains a significant global health concern, demanding accurate and efficient diagnostic approaches for early detection and treatment. However, there are situations where a single deep learning model might not be sufficient to adequately capture the pertinent information required for precise disease prediction from intricate datasets. In this paper, we introduce an innovative ensemble learning framework that leverages the Differential Evolution (DE) algorithm to enhance the detection of cervical cancer. Our innovative approach combines the collective strength of a diverse set of base models using feature fusion techniques to achieve precise and robust classification. We have developed three base models, denoted as Fusion 1, Fusion 2, and Fusion 3, where each fusion model incorporates two pre-trained models using a feature fusion technique. To capture sequential dependencies and improve feature attention, we have integrated ConvLSTM layers and Squeeze-and-Excitation (SE) blocks into these base models. Subsequently, we've amalgamated the predictions of each base model using a weighted aggregation scheme, with the weights optimized using the DE algorithm. Our ensemble framework introduces a novel method for dynamic weight adjustment, utilizing advanced fusion techniques to improve cervical cancer detection accuracy. This approach ensures robust diagnostic performance across cancer cells datasets, enhancing patient outcomes and community health. Our evaluation covers two datasets, SIPaKMeD and Mendeley LBC, demonstrating the remarkable performance and robustness of our ensemble model. It consistently outperforms individual base models and even surpasses state-of-the-art methods, achieving an impressive accuracy rate of 98.96 % on the Mendeley LBC dataset and 95.68 % on the SIPaKMeD dataset. The incorporation of ConvLSTM layers, SE blocks, and DE optimization significantly contributes to the model's exceptional accuracy, its ability to generalize effectively, and its adaptability to diverse scenarios. Additionally, we employ Grad-CAM to visualize how the base models focus on crucial regions for accurate predictions, providing insights into the model's decision-making process. Our ensemble model exhibits outstanding Area under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis, further confirming its effectiveness. This research represents a significant advancement in cervical cancer detection, underscoring the potential of ensemble learning techniques optimized through DE for enhancing diagnostic accuracy and advancing patient care.

Green AI in the finance industry: Exploring the impact of feature engineering on the accuracy and computational time of Machine Learning models

As research and practice in Artificial Intelligence (AI) applications rapidly expand, the support for AI deployment is also increasing. While the abundance of data allows for sophisticated feature engineering techniques that can enhance accuracy, it is crucial to highlight both the computational costs and the efficiency with which these models operate. This paper compares the processing time and accuracy of individual and hybrid Machine Learning (ML) models in predicting customer loyalty within financial contexts. Frameworks that incorporate feature engineering and green AI principles are used separately in both individual and hybrid approaches. The individual models are the commonly used regressor-based algorithms applied to business problems. The hybrid models first use k-Means to cluster customers, followed by the application of individual regressor-based models (e.g., decision trees, gradient boosting, and LightGBM). The present results show that using fewer features results in only a marginally lower accuracy compared to models with more features (a difference of ≈0.01 in MAE when comparing the use of 18 versus 85 features). Additionally, this article clearly demonstrate the trade-off between higher accuracy and longer computational time in hybrid ML models versus lower accuracy and shorter computational time in individual models when predicting customer loyalty. Hybrid models exhibit a lower MSE (≈0.88) compared to individual models (≈0.91). These findings provide managers with insights on selecting the most appropriate model based on their organization’s specific needs.

MFB: A Generalized Multimodal Fusion Approach for Bitcoin Price Prediction Using Time-Lagged Sentiment and Indicator Features

Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.

A Novel Approach to the Evaluation of Groups: Type of Group and Facet of Evaluation Matter

In three studies, we examined a novel approach to the social evaluation of groups whereby we combined a typology of groups with a recent model of the two fundamental dimensions and four facets of social evaluation. We investigated two main questions. First, whereas previous research has proven the usefulness of the two-dimension/four-facet model for individuals, we here tested it for groups. Second, whereas previous research has not systematically distinguished between different types of groups, we here applied a typology of groups that distinguishes between intimacy groups, task groups, and social categories. Furthermore, we hypothesized that these dimensions and facets would manifest differently across various group types. We conducted studies in three countries and analyzed judgments of the three main types of groups, resulting in a standard list of 20 traits. The data confirmed our two-dimension/four-facet model while showing variations of the structure across different types of groups. These results, as well as the facets ratings, highlight the relevance of distinguishing the types of groups in social evaluation.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Clinical Effects of Asynchronous Provider-Guided Practice Sessions During Blended Care Therapy for Anxiety and Depression: Pragmatic Retrospective Cohort Study.

Blended care therapy models are intended to increase the efficiency and effectiveness of evidence-based psychotherapy by combining synchronous and asynchronous components of care. This retrospective cohort study evaluated the clinical effects of synchronous video therapy sessions and asynchronous guided practice session elements on anxiety and depression in a blended care therapy program, with a novel focus on asynchronous provider feedback messages. Participants were adults (N=33,492) with clinical symptoms of anxiety (Generalized Anxiety Disorder 7-item scale [GAD-7] score of ≥8) and depression (Patient Health Questionnaire 9-item scale [PHQ-9] score of ≥10) at intake. Symptom trajectories were evaluated via individual growth curve models. Time-varying covariates evaluated effects of synchronous video session attendance and the presence or absence of each asynchronous guided practice session element occurring within 7 days and 8-14 days prior to each clinical outcome assessment. Guided practice session elements included client digital lesson completion, client digital exercise completion, and feedback messages sent by providers. Approximately 86.6% (29,012/33,492) of clients met criteria for clinical improvement by end of care (median 6, IQR 4-8 synchronous sessions). Synchronous video session attendance and client digital lesson completion in the past 7 days and in the past 8-14 days were each uniquely and significantly associated with lower GAD-7 scores (video session effects: bsession7=-0.82, bsession8-14=-0.58, P values<.001; digital lesson effects: blesson7=-0.18, blesson8-14=-0.26, P values <.001) and PHQ-9 scores (video session effects: bsession7=-0.89, bsession8-14=-0.67, P values <.001; digital lesson effects: blesson7=-0.12, blesson8-14=-0.30, P values <.001). Client digital exercise completion in the past 8-14 days was significantly associated with lower GAD-7 scores (bexercise8-14=-0.10; P<.001) but exercise completion in the 7 days prior to clinical outcome assessment was not (bexercise7=0.00; P=.89). Exercise completion in the past 7 days was significantly associated with lower PHQ-9 scores (bexercise7=-0.16; P<.001) but exercise completion in the past 8-14 days was not (bexercise8-14=-0.05; P=.09). Provider feedback messaging in the past 7 days and in the past 8-14 days was significantly associated with lower GAD-7 and PHQ-9 scores, respectively (GAD-7: bfeedback7=-0.12, P<.001; bfeedback8-14=-0.07, P=.004; PHQ-9: bfeedback7=-0.15, P<.001; bfeedback8-14=-0.08, P=.01). Provider feedback between synchronous therapy sessions provided significant benefit for symptom reduction, beyond the effects of client digital engagement and synchronous video sessions. When guided practice sessions are well integrated into care, blended care therapy provides meaningful improvements upon the traditional, synchronous session-only therapy model. Provider guidance and feedback for clients between synchronous sessions support more efficient and effective mental health care overall.

Research on the Current Status of Pension Models for Individuals with Mental Disorders in China

Objective: By analyzing the existing literature, this paper aims to explore the current situation of the pension model for patients with mental disorders in China and put forward corresponding suggestions for improvement. How: This study analyzes the current situation of the pension model for patients with mental disorders in China by referring to the views of experts and scholars, relevant cases and statistical data through the literature and review of CNKI. Focusing on the existing elderly care service system, policy support and social cognition, this paper reveals the main characteristics and existing problems of the current Chinese elderly care model for people with mental disabilities. By comparing the advanced pension models at home and abroad, this paper puts forward some suggestions on strengthening policy support and improving service system. Results: The research shows that China's existing pension models mainly include family care, institutional pension and community service. The main problems facing: insufficient service supply, shortage of professional talents, social cognition and discrimination. Home care is still the main way, more than 70% of people with mental disorders rely on home care, but face great pressure and challenges; The construction of institutional elderly care and community service systems is gradually developing, but there are still insufficient resources, low service quality, and uneven distribution between urban and rural areas. Policy suggestions: Strengthen the construction and improvement of community support system; Improve professional training to ensure service quality; Use scientific and technological means to assist the elderly and ease the shortage of human resources. It is suggested that we should strengthen the construction of community support system and improve the level of professional service in the future. Conclusion: In China, the mode of nursing care for patients with mental disorders is in the transition period, and family nursing is still the mainstream, but it also faces great challenges. Institutional care and community services have developed, but they are not yet sufficient to replace the role of the family. The future development direction should be multi-dimensional, forming a diversified and three-dimensional elderly care service network.

Enhancing artificial permafrost table predictions using integrated climate and ground temperature data: A case study from the Qinghai-Xizang highway

The complexities of permafrost changes, driven by climate warming and engineering activities, coupled with challenges in data acquisition, make it crucial and challenging to accurately predict the artificial permafrost table, particularly for subgrades in high-temperature unstable permafrost regions. To address this, this study developed a hybrid machine learning model (RF-LSTM-XGBoost) for permafrost table prediction. By analyzing climate change and ground temperature data from various positions and depths along the subgrade in the Tuotuo River section of the Qinghai-Xizang Highway, the Spearman correlation coefficient method was initially used to determine the important influencing factors. Random Forest (RF), Long Short-Term Memory Neural Network (LSTM), and Extreme Gradient Boosting (XGBoost) models were used to predict the artificial permafrost table, and grid search and cross-validation methods were employed to optimize the hyperparameters of each model. A linear weighted combination method based on the minimum cumulative absolute error was utilized to merge the models, and its performance was compared with the individual RF, LSTM, and XGBoost models. Subsequently, the feature importance of the variables in the machine learning model was analyzed. The results indicated a strong correlation between artificial permafrost table changes and factors such as daily average atmospheric temperature, subgrade surface ground temperature, and subgrade surface ground heat flux during the freezing-thawing cycle. The combined model highlighted daily atmospheric temperature as the most influential predictor, followed by ground heat flux, with the surface ground temperature being less impactful. The combined model demonstrated improved predictive accuracy, with MSE, MAPE, RMSE, MAE, and R2 values of 0.003, 0.052, 0.0085, 0.029, and 0.989, respectively, surpassing those of individual models. This model offers a rapid, accurate, and reliable approach for permafrost table prediction, advancing subgrade stability research in challenging permafrost environments.

Action-based explanations as a basis for the analysis and design of the social world.

In sociology, the question of what it means to explain social phenomena and how this relates to the purpose of the social sciences is important but nowadays rarely asked. This article elaborates on this question and provides an answer by outlining the program of "explanatory sociology" as a branch of the modern social science approach. It is shown that, in this framework, to explain means to uncover cause-effect relationships based on models of individuals who are assumed the central force in social life. This idea is taken further to uncover specific challenges that individuals face in social life and how and why they establish and manage (or do not) social forms that help to organize the world from the viewpoint of their abilities and needs. Such action-oriented explanations have been presented and developed in sociology since its very beginning. Two main forms or logics to construct action-based explanations are outlined and developed due to the form and function of the used action theory or model. The article contributes to the discussion about the form and task of sociological theorizing by presenting action-based explanations as a form of sociological theorizing that defines a clear task in exploring challenges in social life and assessing possible forms of coping with them from the perspective of individuals. By doing so, two main ways of broadening explanations are considered and compared in light of what the purpose of sociology might be.