Semantic Associations Research Articles

Assessment indexes of cognitive status, anxiety and depression in senior women with diff erent severity of frailty

Frailty is a key geriatric syndrome characterized by an age-associated diminution of the physiological reserve and functions of many body systems leading to increased vulnerability of the elderly people to the eff ects of endo- and exogenous factors. The combination of frailty, cognitive impairment, anxiety and depression is a signifi cant clinical predictor for an unfavorable prognosis.Purpose. Identifi cation and study of the relationship of cognitive impairment, anxiety, depression and frailty assessment indexes in female patients over 60 years old, observed on an outpatient basis.Material and methods. The study included 80 women over 60 years old. All participants were divided into 4 groups: non-frail (10 women), pre-frail (44 women), mildly frail (20 women), more-frail (6 women). A comprehensive clinical examination was carried out: Montreal cognitive assessment (MoCA), Mini-Mental State Examination (MMSE), frontal assessment battery (FAB), phonetic association test, semantic association test, Beck anxiety inventory, geriatric depression scale–15 (GDS-15), questionnaire “Age is no barrier”, Frailty index (FI, Hoover et al., 2013).Results. All women in 4 groups did not signifi cantly diff er in age. There was no statistically signifi cant diff erence in the MMSE, FAB data and mild cognitive impairment (MCI) prevalence among the groups. Lower rates of MoCA scale were associated with higher probability of frailty (odds ratio (OR) = 0.78; 95 confi dence interval (CI) CI: 0.63–0.96, p = 0.016). The cut-off level of 26 and lower according MoCA has sensitivity 69% and specifi city 50% to detect of high frailty risk (according FI). Questionnaire “Age is no barrier” has a sensitivity of 86% and a specifi city of 53% to detect prefrailty (score 1–2) and a sensitivity of 69% and a specifi city of 87% to detect the presence of frailty (score ≥ 3) regarding FI. Combined sensitivity of MoCA (≤ 26 score) and questionnaire “Age is no barrier” (≥ 3 score) of 78%, combined specifi city of 69%. Higher rates of Beck anxiety inventory and GDS-15 were associated with frailty (OR = 1.12; 95 CI: 1.02–1.22, p = 0.0134 and OR = 1.39; 95 CI: 1.05–1.84, p = 0.0201, respectively). A higher index of the Beck anxiety inventory was also associated with mild cognitive impairment according MoCA (OR = 1.12; 95% CI: 1.04–1.2, p = 0.003). Arterial hypertension was signifi cantly more common for women with FS (χ2 = 4.23, p = 0.04).Conclusion. There was no signifi cant association between MMSE, FAB, MCI prevalence and the severity of frailty in senior women due to the insuffi cient study power (< 80%). MoCA cut-off ≤ 26 score is associated with higher burden of frailty. The questionnaire «Age is no barrier» is an acceptable screening method for determining the necessity of additional geriatric examination of independently living women over 60 years old. Higher rates of the Beck anxiety inventory, GDS-15, BMI, lower rates of MoCA scale and arterial hypertension are associated with high rates of FS and may be predictors for the frailty progression.

Supplemental Material for People Constrain Their Semantic Associations When Talking to Both Friends and Strangers

Supplemental Material for People Constrain Their Semantic Associations When Talking to Both Friends and Strangers

Bridging the Cross-Modality Semantic Gap in Visual Question Answering.

The objective of visual question answering (VQA) is to adequately comprehend a question and identify relevant contents in an image that can provide an answer. Existing approaches in VQA often combine visual and question features directly to create a unified cross-modality representation for answer inference. However, this kind of approach fails to bridge the semantic gap between visual and text modalities, resulting in a lack of alignment in cross-modality semantics and the inability to match key visual content accurately. In this article, we propose a model called the caption bridge-based cross-modality alignment and contrastive learning model (CBAC) to address the issue. The CBAC model aims to reduce the semantic gap between different modalities. It consists of a caption-based cross-modality alignment module and a visual-caption (V-C) contrastive learning module. By utilizing an auxiliary caption that shares the same modality as the question and has closer semantic associations with the visual, we are able to effectively reduce the semantic gap by separately matching the caption with both the question and the visual to generate pre-alignment features for each, which are then used in the subsequent fusion process. We also leverage the fact that V-C pairs exhibit stronger semantic connections compared to question-visual (Q-V) pairs to employ a contrastive learning mechanism on visual and caption pairs to further enhance the semantic alignment capabilities of single-modality encoders. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed model outperforms previous state-of-the-art VQA models. Additionally, ablation experiments confirm the effectiveness of each module in our model. Furthermore, we conduct a qualitative analysis by visualizing the attention matrices to assess the reasoning reliability of the proposed model.

Semantic associations restore neural encoding mechanisms.

Lapses in attention can negatively impact later memory of an experience. Attention and encoding resources are thought to decline as more experiences are encountered in succession, accounting for the primacy effect in which memory is better for items encountered early compared to late in a study list. However, accessing prior knowledge during study can facilitate subsequent memory, suggesting a potential avenue to counteract this decline. Here, we investigated the extent to which semantic associations-shared meaning between experiences-can counteract declines in encoding resources. Our hypothesis is that semantic associations restore neural encoding mechanisms, which in turn improves memory. We recorded scalp electroencephalography (EEG) while male and female human participants performed a delayed free recall task. Half of the items from late in each study list were semantically associated with an item presented earlier in the list. We find that semantic associations improve memory specifically for late list items and selectively modulate the neural signals engaged during the study of late list items. Relative to other recalled items, late list items that are subsequently semantically clustered-recalled consecutively with their semantic associate-elicit increased high-frequency activity and decreased low-frequency activity, a hallmark of successful encoding. Our findings demonstrate that semantic associations restore neural encoding mechanisms and improve later memory. More broadly, these findings suggest that prior knowledge modulates the orientation of attention to influence encoding mechanisms.

Navigating the Mental Lexicon: Network Structures, Lexical Search and Lexical Retrieval

This paper examines the implications of the association patterns in our understanding of the mental lexicon. By applying the principles of graph theory to word association data, we intend to explore which measures tap better into lexical knowledge. To that end, we had different groups of English as Foreign language learners complete a lexical fluency task. Based on these empirical data, a study was undertaken on the corresponding lexical availability graph (LAG). It is observed that the aggregation (mentioned through human coding) of all lexical tokens on a given topic allows the emergence of some lexical-semantic patterns. The most important one is the existence of some key terms, featuring both high centrality in the sense of network theory and high availability in the LAG, which define a hub of related terms. These communities of words, each one organized around an anchor term, or most central word, are nicely apprehended by a well-known network metric called modularity. Interestingly enough, each module seems to describe a conceptual class, showing that the collective lexicon, at least as approximated by LA Graphs, is organised and traversed by semantic mechanisms or associations via hyponymy or hiperonymy, for instance. Another empirical observation is that these conceptual hubs can be appended, resulting in high diameters compared to same-sized random graphs; even so it seems that the small-world hypothesis holds in LA Graphs, as in other social and natural networks.

Weaker semantic priming effects with number words in the second language of math learning.

Bilinguals' exact number representations result from associations between language-independent Indo-Arabic digits ("5"), two verbal codes ("fünf" and "cinq") and a common, largely overlapping semantic representation. To compare the lexical and semantic access to number representations between two languages, we recruited a sample of balanced highly proficient German-French adult bilinguals. At school, those bilinguals learned mathematics in German for 6 years (LM1) and then switched to French (LM2) in 7th grade (12 years old) until 13th grade. After the brief presentation of primes (51 ms) consisting of Indo-Arabic digits or number words in German or French, an Indo-Arabic digits target had to be read in either German or French in an online study. Stimuli were numbers from 1 to 9, and we varied the absolute distance between primes and targets from 0 (i.e., 1-1) to 3 (1-4; as in Reynvoet et al., 2002). The priming distance effect (PDE) was used to measure the strength of numerical semantic association. We find comparable PDEs with Indo-Arabic digits and German number word primes, independently from the target naming language. However, we did not find a clear PDE with French number word primes, neither when naming targets in German, nor in French. The weaker PDE from LM2 compared to LM1 primes is interpreted as a weaker lexico-semantic association of LM2 number words. These results indicate a critical role of the LM1 and further emphasize the role of language in processing numbers. They might have important implications for designing bilingual school curricula. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An anagram of the "unknown poet" in K.K. Vaginov's novel "The Goat Song"

The article analyzes the hidden semantic associations, allusions and reminiscences associated with the artistic technique of anagramming the surname of the main character of K.K. Vaginov's novel "The Goat Song" (1928) — the "unknown poet" Agafonov. The subject of our research attention was the linguistic and cultural complex of associations as part of an incomplete anagram of the writer's surname (Vaginov) in the family "mask" of the autobiographical hero (Agafonov). Special attention is paid to the analysis of the phenomenological structure of this image, based on elementary semantic connections with the formally absent linguistic and cultural complex of the reconstructed context: the "Dionysian" component (the motif of the "unknown god" in the Pradionysian cults); the cultural and semantic stereotype of the Silver Age (in particular, the features of the symbolist "poet-theurgist" are obvious); the Nietzschean image "the artist of intoxication and sleep"; and finally, with the ancient Greek etymology of the word-concept ἀγάπη ( love, Christian meals of love) in the structure of the surname of the character in question – Agafonov. The anagramming technique is presented from the perspective of the artistic teleology of Vagin's work and is considered by us within the framework of the methodological guidelines of historical poetics (O.M. Freudenberg) and selectively applied methods of deconstructivist analysis. As part of the analysis of the phenomenological structure of the image of the "unknown poet", we sought, using a wide range of interpretative possibilities of the Vagin text, to demonstrate the hidden semantic structures associated with the anagram of the surname of the main character of the novel – the "unknown poet" Agafonov. The main result of the conducted research is the conclusion that the anagramming technique is presented by K. K. Vaginov in the perspective of the artistic teleology of the novel narrative, which is based on the principle of multifunctional interaction between the author and the character. A special research contribution to the development of the presented topic is an indication of specific structural and semantic complexes linking the surname of the hero of the Vaginovsky novel with a wide linguistic and cultural tradition – from antiquity (Plato's "Feast" dialogue) to the modern era ("The Birth of Tragedy from the Spirit of Music" by F. Nietzsche).

A multi-type vulnerability detection framework with parallel perspective fusion and hierarchical feature enhancement

A core problem of vulnerability detection is to detect multi-type vulnerabilities simultaneously by characterizing vulnerabilities of high diversity and complexity in real program source code. Current methods mainly adjust and compromise multiple code representations such as code sequence and code graph based on composite graph. However, sequential features extracted by graph are hardly sufficient to model the contextual semantic associations of the token sequence. Meanwhile, structural features of the code graph extracted by models based on Euclidean Graph Neural Network are difficult to fit the tree-like calling relationships between code lines. These limitations make it difficult to detect diverse vulnerabilities. In addition, most of the existing models ignore the type of code statement, which is closely associated with some specific vulnerability types. In this paper, we propose a Parallelism Framework with Hierarchical feature Enhancement for Multi-type Vulnerability Detection (PFHE-MVD). PFHE-MVD models program code from three parallel perspectives, containing sequence, code graph, and Abstract Syntax Tree statistic. Hyperbolic Graph Convolutional Neural Network is integrated to model the top-down hierarchical calling structure in program code graph through hyperbolic space mapping. Besides, the statement type of code is embedded along with the code text to strengthen the identification ability for different types of vulnerabilities. Experimental results show that PFHE-MVD achieves new state-of-the-art results in multi-type vulnerability detection. PFHE-MVD captures tree-like hierarchical code structure feature and enhances the distinguishing ability for vulnerabilities by code statement type embedding.

Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the sentiment polarity of specific aspects. Recent studies have employed graph attention networks and graph convolutional networks to model dependency trees, effectively establishing explicit associations between aspects and opinions, yielding promising performance. However, these methods have limitations in capturing complex linguistic features and the intricate dependencies between aspects and their contexts, resulting in suboptimal performance. In this paper, we propose a dual graph neural network that incorporates syntax and semantics, called IDGNN. Specifically, we utilize the relational graph attention network (RGAT) to encode the syntactic dependency tree and obtain syntactic information, while incorporating dependency labels to enhance aspect representation. Additionally, the semantic graph convolutional network (SemGCN) is employed to encode the self-attention matrix and capture semantic information, with the inclusion of orthogonal regularization to enhance semantic association. Furthermore, we introduce two fusion strategies based on gate mechanisms: the syntax fusion module (SYF) and the semantic fusion module (SEF). SYF combines contextual and syntactic representations to obtain global syntactic features, while SEF fuses semantic information with global syntactic features to obtain the final feature representation. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on several benchmark datasets.

Individual Differences in Leveraging Regularity in Emergent L2 Readers in Rural Côte d’Ivoire

ABSTRACT Purpose Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of the rich statistical regularities in the writing system. Reading skills in developing first- and second-language readers in English have been shown to be predicted by individual differences in sensitivity to regularities in mappings from orthography to phonology (O-P) and semantics (O-S), with good readers relying more on O-P consistency, and less on O-S associations. However, SL and its relation to reading has been primarily studied in English readers in high-income Western countries. Method We examine individual differences in sensitivity to regularities in emergent French readers in rural agricultural communities in Côte d’Ivoire (N = 134). Results We show that, in contrast to previous studies, in this cohort better readers are leveraging semantic associations more strongly, while individual differences in sensitivity to orthographic consistency did not predict reading skill. Relatively little variance in reading skill was explained by sensitivity to regularities, and we discuss these findings in terms of literacy acquisition in low-literacy and low-exposure contexts. This showcases the importance of cross-linguistic and cross-cultural research to back up universal theories of literacy, and suggests that current SL accounts of reading must be updated to account for this variance in reading skills.

The effects of forewarning and divided attention on context retrieval in false recognition

ABSTRACT After studying a list of words that are semantically associated to a critical lure, participants are more likely to attribute a falsely recognised critical lure to the context of its strong than weak semantic associates. This is known as the source-strength effect. The current study investigated the roles of automatic and controlled processing in context retrieval in false recognition that is demonstrated by the source-strength effect. The results revealed that the source-strength effect was impervious to forewarning (Experiment 1) and remained intact when attentional resources at encoding were reduced (Experiment 2), suggesting that context retrieval in false recognition is based on automatic processes that are not amenable to conscious control and do not require many attentional resources. This interpretation is consistent with the associative activation theory, which proposes that context retrieval in false recognition is based on memory associations between contexts and critical lures that are automatically created when critical lures become automatically activated via spreading activation process.

A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.

Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings

A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.

Music, Memory, and Imagination

This article argues that the capacity of music to reliably cue both autobiographical memories and fictional imaginings can be leveraged to better understand the relationship and interdependence between memory and imagination more generally. The multiple levels involved in musical engagement provide a rich forum for investigating how emotional, semantic, and contextual associations with musical cues influence both memories and imaginings. Moreover, musical excerpts are extended in time and can influence the trajectory of a memory or imagining dynamically as it develops, allowing for a more precise manipulation of the implied semantic space. Because music’s uses and contextual associations are culturally constrained, and culture can be shared, autobiographical memories and fictional imaginings cued by music can show surprising similarities among individuals from the same culture. This article surveys the research on music-evoked autobiographical memories and music-evoked fictional imaginings, proposing a framework for bringing these separate strands of work together to shed light on larger questions about shared underlying mechanisms.

Thangka Image—Text Matching Based on Adaptive Pooling Layer and Improved Transformer

Image–text matching is a research hotspot in the multimodal task of integrating image and text processing. In order to solve the difficult problem of associating image and text data in the multimodal knowledge graph of Thangka, we propose an image and text matching method based on the Visual Semantic Embedding (VSE) model. The method introduces an adaptive pooling layer to improve the feature extraction capability of semantic associations between Thangka images and texts. We also improved the traditional Transformer architecture by combining bidirectional residual concatenation and mask attention mechanisms to improve the stability of the matching process and the ability to extract semantic information. In addition, we designed a multi-granularity tag alignment module that maps global and local features of images and text into a common coding space, leveraging inter- and intra-modal semantic associations to improve image and text accuracy. Comparative experiments on the Thangka dataset show that our method achieves significant improvements compared to the VSE baseline method. Specifically, our method improves the recall by 9.4% and 10.5% for image-matching text and text-matching images, respectively. Furthermore, without any large-scale corpus pre-training, our method outperforms all models without pre-training and outperforms two out of four pre-trained models on the Flickr30k public dataset. Also, the execution efficiency of our model is an order of magnitude higher than that of the pre-trained models, which highlights the superior performance and efficiency of our model in the image–text matching task.

Multi-granularity retrieval of mineral resource geological reports based on multi-feature association

Massive geologic report contains all kinds of multimodal geologic data information (geologic text, geologic maps, geologic tables, etc.), which contain a lot of rich geologic basic knowledge and expert experience knowledge about rocks and minerals, stratigraphic structure, geologic age, geographic location, and so on. Accurate retrieval of specific information from massive geologic data has become an important need for geologic information retrieval. However, the majority of existing research primarily revolves around extracting and associating information at a single granularity to facilitate geological semantic retrieval, which ignores many potential semantic associations, leading to ambiguity and fuzziness in semantic retrieval. To solve these problems, this paper proposes a multi-granularity (document-chapter-paragraph) geological information retrieval framework for accurate semantic retrieval. The framework firstly extracts topic feature information, spatiotemporal feature information, figure and table feature information based on the multi-granularity of geological reports. Then, an improved apriori algorithm is used to mine and visualize the associations among the feature information to discover the semantic associations of the geological reports at multiple levels of granularity. Finally, experiments are designed to validate the application of the proposed multi-granularity information retrieval framework on the accurate retrieval of geological reports. The experimental results show that the proposed multi-granularity information retrieval framework in this paper can dig deeper into underlying geo-semantic information and realize accurate retrieval.

Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero- and Few-shot Learning with Domain Distribution Alignment

Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework ( TTGCIF ) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that none of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in TTGCIF is trained to learn data semantic association among different domains via semantic prototype transduction, so that the “ unlabeled ” crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in TTGCIF can be sensitive to any alteration on training data, ensuring that even if only few annotated resource in target domain are available for “Fine-tune,” TTGCIF can still react promptly to achieve effective target domain adaptation. According to the experimental results, TTGCIF can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on no more than 10% annotated resource in target domain. More importantly, even if TTGCIF has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by TTGCIF on the target domain are still superior to those generated by models already fine-tuned on the target domain.

Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis

The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments.

Integrative Development of Modern and Contemporary Literary Works and Traditional Culture Combined with Semantic Association Network Modeling

Abstract In recent years, the development of network novels has pushed traditional cultures such as Taoism, Buddhism, and Confucianism to a peak of attention, making traditional cultures constantly emphasized and excavated, forming an important social subject. The evaluation of textual entities with respect to the integration of modern and contemporary literary works and traditional culture is supported by a semantic association network model proposed in this paper. The model fully exploits the heterogeneity of semantic associations between modern and contemporary literary works and traditional culture and utilizes the RSS model to extract the emotional words of traditional cultural elements in the text of literary works in order to decide the global importance of traditional cultural elements in literary works. Finally, the effectiveness of the method was verified in the dataset. The results of the study show that in the dataset of modern and contemporary literary works, the traditional cultures with the highest semantic association strengths are Taoist culture (0.657), Confucian culture (0.583), and folk culture (0.651), respectively. The incorporation of traditional culture by writers in their literary works can result in a mutual achievement and development of literary works and traditional culture.

Shorter sleep duration and lesser sleep efficiency are associated with poorer memory functions among non-demented, middle-aged, and older rural Indians.

Sleep is known to be involved in cognitive processes, such as memory encoding and consolidation, and poor sleep is a potential risk factor for dementia. This study aims to investigate the effect of sleep quality on memory functions among middle-aged and older adults from a rural Indian population. Participants were non-demented, rural Indians (≥45 years) from an ongoing, prospective, aging cohort study, namely Srinivaspura Aging, NeuroSenescence, and COGnition (SANSCOG) study. Cross-sectional (baseline) data on seven sleep dimensions was obtained using the Pittsburgh Sleep Quality Index (PSQI). Memory functions were assessed using immediate recall, delayed recall, name-face association, and semantic association from a culturally validated, computerized, neurocognitive test battery. Linear regression models, unadjusted and adjusted for cognitive status, age, sex, and depression were used to analyze the association between each sleep dimension and the memory tests. A total of 1195 participants, with a mean age of 57.10 years, were included. Out of the seven sleep dimensions of the PSQI, only two dimensions, namely sleep duration and sleep efficiency, were significantly associated with memory functions. In the fully adjusted model, shorter sleep duration was significantly associated with poorer performance in delayed recall, and lesser sleep efficiency was significantly associated with poorer delayed recall and semantic association performance. Specific sleep characteristics appear to influence memory functions in aging Indians well before the onset of dementia. In the backdrop of the non-availability of a definitive treatment for dementia, promptly identifying and addressing these problems could be an effective, community-level strategy for preventing dementia.