Search Results (442)

New theologies developed in tandem with evolutionary biology during the nineteenth century, which have been called metaphysical evolutionisms and evolutionary theologies. A subset of these theologies analyzed here were developed by thinkers who accepted biological science but rejected both biblical creationism and materialist science. Tools from the cognitive science of religion, including conceptual metaphor theory (CMT) and blending theory, also known as conceptual integration theory (CIT), can help to explain the development of these systems and their transformation between the nineteenth and the twentieth centuries. The analysis focuses on several stable and popular blends of ideas, which have continued with some alteration into the twenty-first century. The three blends evaluated here are Progressive Soul Evolution, Salvation is Evolution, and Evolution is Therapy. Major contributors to these blends are the theosophist and theologian Helena P. Blavatsky and psychologist Frederic W. H. Myers, both influenced by the spiritualist movement, particularly the ideas of the spiritualist and biologist Alfred Russel Wallace. The influence of these blends can be seen in the twentieth-century “Aquarian Frontier,” a group of 145 thinkers and organizations identified in 1975 by counterculture historian Theodore Roszak. Part of the appeal of these blends may be seen in their use of metaphors, including the Great Chain of Being and A Purposeful Life is a Journey. The application of the polysemic term evolution in a sense that does much of the theological work of salvation in Christianity can in part be explained by applying the principles of blending theory, including the vital relation “achieve a human scale,” as well as compressions of time and identity. These blends have been successful because they meet the needs of a population who are friendly towards science but disenchanted with traditional religions. The blends provide a satisfying new theology that extends beyond death for a subset of adherents, particularly in the New Age and spiritual but not religious (SBNR) movements, who combine the agency of self-directed “evolution” with the religious concepts of grace and transcendence. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

The Sustainable Development Goals (SDGs), launched by the United Nations in 2015, outline 17 interconnected objectives designed to promote human well-being and sustainable development worldwide. Education is recognized by the United Nations as a key factor in promoting sustainable development. To cultivate students with both global perspectives and local engagement, it is essential to integrate sustainability education into elementary curricula. Accordingly, this study aimed to enhance elementary school students’ understanding of the SDGs by designing a structured instructional activity and developing a hybrid digital-physical board game. The game was implemented as a supplementary review tool to traditional classroom teaching, leveraging the motivational and knowledge-retention benefits of physical board games while incorporating digital features to support learning process monitoring. To address the limitations of conventional review approaches—such as reduced student engagement and increased cognitive load—the instructional model incorporated the board game during review sessions following formal instruction. This was intended to maintain student attention and reduce unnecessary cognitive effort, thereby supporting learning in sustainability-related content. A quasi-experimental design was employed to evaluate the effectiveness of the instructional intervention and the board game system, focusing on three outcome variables: learning motivation, cognitive load, and learning achievement. The results indicated that students in the game-based Sustainable Development Goals group achieved significantly higher delayed posttest scores (M = 72.91, SD = 15.17) than the traditional review group (M = 61.30, SD = 22.82; p < 0.05). In addition, they reported significantly higher learning motivation (M = 4.40, SD = 0.64) compared to the traditional group (M = 3.99, SD = 0.69; p < 0.05) and lower cognitive load (M = 1.84, SD = 1.39) compared to the traditional group (M = 2.66, SD = 1.30; p < 0.05), suggesting that the proposed approach effectively supported student learning in sustainability education at the elementary level. Full article

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15–19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R², mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R² = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R² = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R² = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

Background/Objectives: Dual task (DT) training consists of practicing exercises while simultaneously performing a concurrent motor or cognitive task. This training modality seems to have beneficial effects on both domains. Various forms of DT training have been implemented for older adults in recent years, but no official guidelines currently exist. This review sought to analyze the studies published on this topic in the last ten years and provide a standard operating procedure (SOP) for healthy older adults in this context. Methods: The review collected articles from PubMed, Web of Science, and Scopus, adopting a designated set of keywords. Selected manuscripts and relevant information were selected, extrapolated, including information related to the training frequency, intensity, time, and type, and secondary tasks adopted. The secondary tasks were grouped according to previously published studies, and the SOP was created based on the frequency of the parameters collected from the included articles. Results: A total of 44 studies were included in the review. Based on the results, the SOP recommends postural balance or resistance training as primary tasks, combined with a mental tracking task as a secondary component. Two 60-min sessions per week for at least 12 weeks are required to achieve measurable results. Conclusions: Despite heterogeneity in the literature reviewed, the findings support the proposal of a SOP to guide future research on DT training in healthy older adults. Given its feasibility and positive effects on both motor and cognitive functions, this type of training can also be implemented in everyday settings. Full article

Background/Objectives: Daily caloric restriction (DCR) is a common dietary weight loss strategy, but leads to metabolic and behavioral adaptations, including maladaptive eating behaviors and dysregulated appetite. Intermittent fasting (IMF) may mitigate these effects by offering diet flexibility during energy restriction. This secondary analysis compared changes in eating behaviors and appetite-related hormones between 4:3 intermittent fasting (4:3 IMF) and DCR and examined their association with weight loss over 12 months. Methods: Adults with overweight or obesity were randomized to 4:3 IMF or DCR for 12 months. Both randomized groups received a matched targeted weekly dietary energy deficit (34%), comprehensive group-based behavioral support, and a prescription to increase moderate-intensity aerobic activity to 300 min/week. Eating behaviors were assessed using validated questionnaires at baseline and months 3, 6, and 12. Fasting levels of leptin, ghrelin, peptide YY, brain-derived neurotrophic factor, and adiponectin were measured at baseline and months 6 and 12. Linear mixed models and Pearson correlations were used to evaluate outcomes. Results: Included in this analysis were 165 adults (mean ± SD; age 42 ± 9 years, BMI 34.2 ± 4.3 kg/m², 74% female) randomized to 4:3 IMF (n = 84) or DCR (n = 81). At 12 months, binge eating and uncontrolled eating scores decreased in 4:3 IMF but increased in DCR (p < 0.01 for between-group differences). Among 4:3 IMF, greater weight loss was associated with decreased uncontrolled eating (r = −0.27, p = 0.03), emotional eating (r = −0.37, p < 0.01), and increased cognitive restraint (r = 0.35, p < 0.01) at 12 months. There were no between-group differences in changes in fasting appetite-related hormones at any time point. Conclusions: Compared to DCR, 4:3 IMF exhibited improved binge eating and uncontrolled eating behaviors at 12 months. This may, in part, explain the greater weight loss achieved by 4:3 IMF versus DCR. Future studies should examine mechanisms underlying eating behavior changes with 4:3 IMF and their long-term sustainability. Full article

The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening. Full article

The transfer of learning (TL) is the process of applying knowledge and skills learned in one context to a new and different context. Efficient use of memory is essential in achieving successful TL and good learning outcomes. This study uses a cognitive computing approach to identify and explore brain activity patterns related to memory efficiency in the context of learning a new programming language. This study hypothesizes that prior programming knowledge reduces cognitive load, leading to improved memory efficiency. Spatio-temporal brain data (STBD) were collected from a sample of participants (n = 26) using an electroencephalogram (EEG) device and analyzed by applying a spiking neural network (SNN) approach and the SNN-based NeuCube architecture. The findings revealed the neural patterns demonstrating the effect of prior knowledge on memory efficiency. They showed that programming learning outcomes were aligned with specific theta and alpha waveband spike activities concerning prior knowledge and cognitive load, indicating that cognitive load was a feasible metric for measuring memory efficiency. Building on these findings, this study proposes that the methodology developed for examining the relationship between prior knowledge and TL in the context of learning a programming language can be extended to other educational domains. Full article

Prediction of individual differences in academic achievement is one of the most prominent longstanding goals of differential psychology. Historically, the main source of prediction has been measures of intelligence and related cognitive abilities. Researchers have suggested that non-ability traits, such as personality, may also provide useful information in predicting academic achievement. Meta-analyses have indicated that there are significant correlations between such variables, but most of the existing studies have been conducted with cross-sectional designs, or with a limited inclusion of intelligence/cognitive ability variables, making it difficult to determine whether the non-ability measures provide incremental predictive validity for academic achievement. In this longitudinal study, both extensive cognitive ability and non-ability trait measures (personality, interests, self-concept/self-estimates of abilities, and motivational traits) were administered at the beginning of secondary school, and criterion measures of ability and academic achievement were obtained after four years of secondary school. The results indicate that although non-ability trait measures have significant and meaningful correlations with the criterion measures, their incremental predictive validity over cognitive abilities alone is somewhat diminished. Nonetheless, there is potential utility for including assessments of non-ability traits for predicting future academic performance and elective course enrollments. Full article

This paper demonstrates the application of cognitive modeling methods to study problems relating to the functioning and advancement of a strategy for the development of rural areas. Rural areas play a vital role in human life; they have enormous economic, natural, demographic, historical, and cultural potential, and their revival ensures the achievement of the United Nations Sustainable Development Goals in the field of SDG 2: “End hunger, achieve food security and improve nutrition and promote sustainable agriculture”. The relevance of the study lies in the need to develop approaches in order to improve the efficiency of agriculture and to ensure the sustainable development of rural areas. The goal was to use cognitive modeling tools to understand the cause-and-effect mechanism of ensuring sustainable development of rural areas and analyze their possible development under the influence of internal and external factors to select the best strategy for sustainable development. Based on 24 selected quantitative and qualitative indicators, a cognitive map “Sustainable Development of Rural Areas” was constructed, and an analysis of 351 cycles of the cognitive model was carried out, among which 286 positive and 65 negative cycles were observed, indicating the structural stability of the model. Computational experiments were carried out using pulse and scenario modeling; the results are presented and visualized in the form of five scenarios for complex systems development. The results of this study can be used as decision support tools for substantiating strategies and developing policies for the balanced development of rural areas. Full article

Spatial scenes, as fundamental units of geospatial cognition, encompass rich objects and spatial relationships, and their generation techniques hold significant application value in disaster simulation and emergency drills, delayed spatial reconstruction and analysis, and other fields. However, existing studies still face limitations in modeling complex spatial relationships during scene generation, leading to insufficient semantic consistency and geographical accuracy. The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. Specifically, SceneDiffusion employs a spatial scene representation framework to uniformly characterize objects and their topological, directional, and distance relationships, enhances the interactive modeling of objects and relationships through a Spatial relationship Attention-aware Graph (SAG) module, and finally generates high-quality scene images conforming to geographic semantics using a Layout information-guided Conditional Diffusion (LCD) module. Both qualitative and quantitative experiments demonstrate the superiority of SceneDiffusion, achieving a 56.6% reduction in FID and a 35.3% improvement in SSIM compared to baseline methods. Ablation studies confirm the importance of multi-relational modeling with attention mechanisms. By generating scenes that satisfy spatial distribution constraints, this work provides technical support for applications such as emergency scene simulation and virtual scene construction, while also offering insights for theoretical research and methodological innovation in GeoAI. Full article

Background/Objectives: Sleep disorders are prevalent among aging populations and are often linked to cognitive decline, chronic conditions, and reduced quality of life. Traditional diagnostic methods, such as polysomnography, are resource-intensive and limited in accessibility. Meanwhile, individuals frequently describe their sleep experiences through unstructured narratives in clinical notes, online forums, and telehealth platforms. This study proposes a machine learning pipeline (sleepCare) that classifies sleep-related narratives into clinically meaningful categories, including stress-related, neurodegenerative, and breathing-related disorders. The proposed framework employs natural language processing (NLP) and machine learning techniques to support remote applications and real-time patient monitoring, offering a scalable solution for the early identification of sleep disturbances. Methods: The sleepCare consists of a three-tiered classification pipeline to analyze narrative sleep reports. First, a baseline model used a Multinomial Naïve Bayes classifier with n-gram features from a Bag-of-Words representation. Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. Finally, a transformer-based model (BERT) was fine-tuned to extract contextual embeddings, using the [CLS] token as input for SVM classification. Each model was evaluated using stratified train-test splits and 10-fold cross-validation. Hyperparameter tuning via GridSearchCV optimized performance. The dataset contained 475 labeled sleep narratives, classified into five etiological categories relevant for clinical interpretation. Results: The transformer-based model utilizing BERT embeddings and an optimized Support Vector Machine classifier achieved an overall accuracy of 81% on the test set. Class-wise F1-scores ranged from 0.72 to 0.91, with the highest performance observed in classifying normal or improved sleep (F1 = 0.91). The macro average F1-score was 0.78, indicating balanced performance across all categories. GridSearchCV identified the optimal SVM parameters (C = 4, kernel = ‘rbf’, gamma = 0.01, degree = 2, class_weight = ‘balanced’). The confusion matrix revealed robust classification with limited misclassifications, particularly between overlapping symptom categories such as stress-related and neurodegenerative sleep disturbances. Conclusions: Unlike generic large language model applications, our approach emphasizes the personalized identification of sleep symptomatology through targeted classification of the narrative input. By integrating structured learning with contextual embeddings, the framework offers a clinically meaningful, scalable solution for early detection and differentiation of sleep disorders in diverse, real-world, and remote settings. Full article

Objective: To comprehensively evaluate the interaction between diet quality and multivitamin intake during pregnancy on offspring neurodevelopment. Methods: This analysis was grounded in mother-child dyads from the 3D Cohort Study in Quebec, Canada. Among the 2366 participants initially enrolled in the 3D study, 1535 women successfully completed the 3-day food record during 20–24 weeks of gestation. A Canadian adaptation of the Healthy Eating Index (HEI-C) 2010 was used to quantify diet quality. The total HEI-C score was dichotomized into low and high diet quality by median split. Cognitive and motor development in childhood were assessed using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Language abilities were measured using the toddler short-form version of the MacArthur–Bates Communicative Development Inventories (MCDI) questionnaire, administered in either English or French. After excluding participants with missing covariate data, cognitive, motor, and language development scores at 2 years of age were available for 1066, 1040, and 981 children, respectively. Multiple linear regression models were employed to calculate adjusted effect estimates. The interaction on an additive scale was assessed by incorporating a product term into the linear regression model. Results: Statistically significant interactions were detected between diet quality and multivitamin intake in relation to the cognitive and language development outcomes of the offspring (interaction p-values were 0.018 and 0.023, respectively). The lowest cognitive and language scores were observed in the group of women who neither took multivitamins nor maintained a high-quality diet. Among women not taking multivitamins, a high-quality diet was associated with improved offspring cognitive and language scores (mean difference [95% CI] = 4.2 [0.1, 8.2], p = 0.04; and 11.3 [3.1, 19.5], p = 0.01, respectively). However, among women taking multivitamins, no such associations were identified. Conversely, in participants with a low-quality diet, multivitamin intake was associated with a 3.0-point increase in cognitive composite scores (95% CI: 0.3, 5.8, p = 0.03), but this was not the case for those with a high-quality diet. No statistically significant interactions were observed between maternal diet quality and multivitamin intake for motor development outcomes. Conclusions: Adequate nutritional supply during pregnancy, achieved either through a high-quality diet or multivitamin supplementation, is fundamental for the neurodevelopment of children. Full article

The cognitive core of human beings is closely connected to the concept of animacy, which significantly influences their memory, vision, and complex language comprehension. While animacy is reflected in language through subtle constraints on verbs and adjectives, it is also acquired and honed through non-linguistic experiences. In the same vein, we suggest that the limited capacity of LLMs to grasp natural language, particularly in relation to animacy, stems from the fact that these models are trained solely on textual data. Hence, the question this paper aims to answer arises: Can LLMs, in their digital wisdom, process animacy in a similar way to what humans would do? We then propose a systematic analysis via prompting approaches. In particular, we probe different LLMs using controlled lexical contrasts (animate vs. inanimate nouns) and narrative contexts in which typically inanimate entities behave as animate. Results reveal that, although LLMs have been trained predominantly on textual data, they exhibit human-like behavior when faced with typical animate and inanimate entities in alignment with earlier studies, specifically on seven LLMs selected from three major families—OpenAI (GPT-3.5, GPT-4), Meta (Llama2 7B, 13B, 70B), and Mistral (Mistral-7B, Mixtral). GPT models generally achieve the most consistent and human-like performance, and in some tasks, such as sentence plausibility and acceptability judgments, even surpass human baselines. Moreover, although to a lesser degree, the other models also assume comparable results. Hence, LLMs can adapt to understand unconventional situations by recognising oddities as animated without needing to interface with unspoken cognitive triggers humans rely on to break down animations. Full article