Memory Mechanisms Research Articles

Shape memory polyimide/carbon nanotube composite aerogels with physical and chemical crosslinking architectures for thermal insulating applications

The development of lightweight smart materials with exceptional shape memory and thermal insulation properties is highly important in the aerospace industry. The reported shape memory polymer aerogels (SMPAs) are restricted to harsh environments because of their poor high-temperature resistance and large volume shrinkage at elevated temperatures. Herein, shape memory polyimide (PI) composite aerogels with superior thermal insulation were fabricated by combining molecularly designed PI chains with amino-functionalized carbon nanotubes (NH2-CNTs) through freezing gelation and subsequent thermal imidization. Because shape memory behavior was thermally triggered, the thermal conductivity associated with the shape memory mechanisms of the aerogels was comprehensively explored. The presence of CNTs promoted heat transfer in the aerogel skeleton, facilitating the shape recovery response. Benefiting from chemical-crosslinked and physical-crosslinked structures, the fabricated PI composite aerogels demonstrated desirable thermo-mechanical properties, exceptional shape memory and superior thermal insulation performance. This study may provide guidelines for designing shape memory PI composite aerogels for harsh environment applications in aerospace engineering.

A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries

In the quest for clean and efficient energy solutions, lithium-ion batteries have emerged at the forefront of technological innovation. Accurate state-of-charge (SOC) estimation across a broad temperature range is essential for extending battery longevity, and enduring effective management of overcharge and over-discharge conditions. However, prevailing challenges persist in achieving precise SOC estimates and generalizing across a wide temperature range, particularly at lower temperatures. Our comparative analysis reveals that, while a single-layer bidirectional LSTM model with a self-attention mechanism achieves remarkable SOC estimation accuracy at room temperature, the intricacies of SOC estimation at lower temperatures necessitate the incorporation of more hidden layers and more complex network architecture to capture intricate features influencing battery dynamics. Hence, we propose a deep learning model, based on convolutional neural networks integrating bidirectional long short-term memory and self-attention mechanism (CNN-Bi-LSTM-AM), specifically designed to tackle the challenges of achieving accurate SOC estimations across a wide temperature range. The proposed model demonstrates proficiency in capturing both spatial and temporal dependencies critical for lithium-ion battery SOC estimation. Furthermore, the integration of a self-attention mechanism enhances the model’s adeptness to discern pertinent features and patterns within the dataset, thereby improving its overall performance and robustness, even in sub-room temperature environments.

GTFE-Net-BiLSTM-AM: An intelligent feature recognition method for natural gas pipelines

The recognition of pipeline features contributes to its safe management by preventing severe consequences such as leakage resulting from bending deformation and denting under external pressure. However, extracting features of such a facility is complex and challenging when machine learning techniques are applied to feature recognition. Hence, this paper proposes a feature recognition technique for gas pipelines based on Gramian Time Frequency Enhancement Net (GTFE-Net), Bi-directional Long Short-Term Memory (BiLSTM) and attention mechanism (AM), namely GTFE-Net-BiLSTM-AM. Specifically, GTFE-Net is applied to enhance the time-frequency input bending strain signal, which is subsequently incorporated with the BiLSTM model to extract spatio-temporal features. The attention mechanism computes the corresponding weight of output features. The results show that the proposed method's recognition accuracy reaches 93.7%. The comparison study with the existing models validates the proposed method's superiority and shows that its accuracy is higher than that of the existing models (more than 0.9%) or their combined models (more than 1.1%). Overall, the proposed method contributes to the safety, reliability, and operation of natural gas pipelines.

Microdose lithium improves behavioral deficits and modulates molecular mechanisms of memory formation in female SAMP-8, a mouse model of accelerated aging.

Alzheimer's disease (AD) is the most common neuronal disorder that leads to the development of dementia. Until nowadays, some therapies may alleviate the symptoms, but there is no pharmacological treatment. Microdosing lithium has been used to modify the pathological characteristics of the disease, with effects in both experimental and clinical conditions. The present work aimed to analyze the effects of this treatment on spatial memory, anxiety, and molecular mechanisms related to long-term memory formation during the aging process of a mouse model of accelerated aging (SAMP-8). Female SAMP-8 showed learning and memory impairments together with disruption of memory mechanisms, neuronal loss, and increased density of senile plaques compared to their natural control strain, the senescence-accelerated mouse resistant (SAMR-1). Chronic treatment with lithium promoted memory maintenance, reduction in anxiety, and maintenance of proteins related to memory formation and neuronal density. The density of senile plaques was also reduced. An increase in the density of gamma-aminobutyric acid A (GABAA) and α7 nicotinic cholinergic receptors was also observed and related to neuroprotection and anxiety reduction. In addition, this microdose of lithium inhibited the activation of glycogen synthase kinase-3beta (GSK-3β), the classical mechanism of lithium cell effects, which could contribute to the preservation of the memory mechanism and reduction in senile plaque formation. This work shows that lithium effects in neuroprotection along the aging process are not related to a unique cellular mechanism but produce multiple effects that slowly protect the brain along the aging process.

Memory modeling of counterfactual generation.

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multimodal single-cell analyses reveal mechanisms of perianal fistula in diverse patients with Crohn’s disease

Crohn's disease complicated by perianal fistulae is more prevalent and severe in patients of African ancestry. We profiled single cells from diverse patients with Crohn's disease with perianal fistula from colorectal mucosa and fistulous tracts.Immunofluorescence was performed to validate predicted cell-cell interactions. Unstimulated monocytes were chronically cultured in diverse cohorts. A subset was analyzed by single-nucleus RNA+ ATAC sequencing. Fistulous tract cells from complete proctectomies demonstrated enrichment of myeloid cells compared to paired rectal tissues. Ligand-receptor analysis highlights myeloid-stromal cross-talk and cellular senescence, with cellular co-localization validated by immunofluorescence. Chitinase-3 like-protein-1 (CHI3L1) is a top upregulated gene in stromal cells from fistulae expressing both destructive and fibrotic gene signatures. Monocyte cultures from patients of African ancestry and controls demonstrated differences in CHI3L1 and oncostatin M (OSM) expression upon differentiation compared to individuals of European ancestry. Activating protein-1 footprints are present in ATAC-seq peaks in stress response genes, including CHI3L1 and OSM; genome-wide chromatin accessibility including JUN footprints was observed, consistent with reported mechanisms of inflammatory memory. Regulon analyses confirm known cell-specific transcription factor regulation and implicate novel ones in fibroblast subsets. All pseudo-bulked clusters demonstrate enrichment of genetic loci, establishing multicellular contributions. In the most significant African American Crohn's genetic locus, upstream of prostaglandin E receptor 4, lymphoid-predominant ATAC-seq peaks were observed, with predicted RORC footprints. Population differences in myeloid-stromal cross-talk implicate fibrotic and destructive fibroblasts, senescence, epigenetic memory, and cell-specific enhancers in perianal fistula pathogenesis. The transcriptomic and epigenetic data provided here may guide optimization of promising mesenchymal stem cell therapies for perianal fistula. This work was supported by grants U01DK062422, U24DK062429, and R01DK123758.

Formation of memory assemblies through the DNA-sensing TLR9 pathway

As hippocampal neurons respond to diverse types of information1, a subset assembles into microcircuits representing a memory2. Those neurons typically undergo energy-intensive molecular adaptations, occasionally resulting in transient DNA damage3–5. Here we found discrete clusters of excitatory hippocampal CA1 neurons with persistent double-stranded DNA (dsDNA) breaks, nuclear envelope ruptures and perinuclear release of histone and dsDNA fragments hours after learning. Following these early events, some neurons acquired an inflammatory phenotype involving activation of TLR9 signalling and accumulation of centrosomal DNA damage repair complexes6. Neuron-specific knockdown of Tlr9 impaired memory while blunting contextual fear conditioning-induced changes of gene expression in specific clusters of excitatory CA1 neurons. Notably, TLR9 had an essential role in centrosome function, including DNA damage repair, ciliogenesis and build-up of perineuronal nets. We demonstrate a novel cascade of learning-induced molecular events in discrete neuronal clusters undergoing dsDNA damage and TLR9-mediated repair, resulting in their recruitment to memory circuits. With compromised TLR9 function, this fundamental memory mechanism becomes a gateway to genomic instability and cognitive impairments implicated in accelerated senescence, psychiatric disorders and neurodegenerative disorders. Maintaining the integrity of TLR9 inflammatory signalling thus emerges as a promising preventive strategy for neurocognitive deficits.

Causal functional maps of brain rhythms in working memory

Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.

MemoryBank: Enhancing Large Language Models with Long-Term Memory

Large Language Models (LLMs) have drastically reshaped our interactions with artificial intelligence (AI) systems, showcasing impressive performance across an extensive array of tasks. Despite this, a notable hindrance remains—the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems, psychological counseling, and secretarial assistance. Recognizing the necessity for long-term memory, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user's personality over time by synthesizing information from previous interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory. This mechanism permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a more human-like memory mechanism and enriched user experience. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models such as ChatGLM. To validate MemoryBank's effectiveness, we exemplify its application through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialog data, SiliconFriend displays heightened empathy and discernment in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as multiple users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.

SpFormer: Spatio-Temporal Modeling for Scanpaths with Transformer

Saccadic scanpath, a data representation of human visual behavior, has received broad interest in multiple domains. Scanpath is a complex eye-tracking data modality that includes the sequences of fixation positions and fixation duration, coupled with image information. However, previous methods usually face the spatial misalignment problem of fixation features and loss of critical temporal data (including temporal correlation and fixation duration). In this study, we propose a Transformer-based scanpath model, SpFormer, to alleviate these problems. First, we propose a fixation-centric paradigm to extract the aligned spatial fixation features and tokenize the scanpaths. Then, according to the visual working memory mechanism, we design a local meta attention to reduce the semantic redundancy of fixations and guide the model to focus on the meta scanpath. Finally, we progressively integrate the duration information and fuse it with the fixation features to solve the problem of ambiguous location with the Transformer block increasing. We conduct extensive experiments on four databases under three tasks. The SpFormer establishes new state-of-the-art results in distinct settings, verifying its flexibility and versatility in practical applications. The code can be obtained from https://github.com/wenqizhong/SpFormer.

Serotonin Signaling in Hippocampus during Initial Cocaine Abstinence Drives Persistent Drug Seeking.

The initiation of abstinence after chronic drug self-administration is stressful. Cocaine-seeking behavior on the first day of the absence of the expected drug (Extinction Day 1, ED1) is reduced by blocking 5-HT signaling in dorsal hippocampal cornu ammonis 1 (CA1) in both male and female rats. We hypothesized that the experience of ED1 can substantially influence later relapse behavior and that dorsal raphe (DR) serotonin (5-HT) input to CA1 may be involved. We inhibited 5-HT1A/1B receptors (WAY-100635 plus GR-127935), or DR input (chemogenetics), in CA1 on ED1 to test the role of this pathway on cocaine-seeking persistence 2 weeks later. We also inhibited 5-HT1A or 5-HT1B receptors in CA1 during conditioned place preference (CPP) for cocaine, to examine mechanisms involved in the persistent effects of ED1 manipulations. Inhibition of DR inputs, or 5-HT1A/1B signaling, in CA1 decreased drug seeking on ED1 and decreased cocaine seeking 2 weeks later revealing that 5-HT signaling in CA1 during ED1 contributes to persistent drug seeking during abstinence. In addition, 5-HT1B antagonism alone transiently decreased drug-associated memory performance when given prior to a CPP test, whereas similar antagonism of 5-HT1A alone had no such effect but blocked CPP retrieval on a test 24 h later. These CPP findings are consistent with prior work showing that DR inputs to CA1 augment recall of the drug-associated context and drug seeking via 5-HT1B receptors and prevent consolidation of the updated nondrug context via 5-HT1A receptors. Thus, treatments that modulate 5-HT-dependent memory mechanisms in CA1 during initial abstinence may facilitate later maintenance of abstinence.

Drought priming-induced stress memory improves subsequent drought or heat tolerance via activation of γ-aminobutyric acid-regulated pathways in creeping bentgrass.

Recurrent drought can induce stress memory in plants to induce tolerance to subsequent stress, such as high temperature or drought. Drought priming (DP) is an effective approach to improve tolerance to various stresses; however, the potential mechanism of DP-induced stress memory has not been fully resoved. We examined DP-regulated subsequent drought tolerance or thermotolerance associated with changes in physiological responses, GABA and NO metabolism, heat shock factor(HSF) and dehydrin (DHN) pathways in perennial creeping bentgrass. Plants can recover after two cycle of DP, and DP-treated plants had significantly higher tolerance to subsequent drought or heat stress, with higher leaf RWC, Chl content, photochemical efficiency, and cell membrane stability. DP significantly alleviated oxidative damage through enhancing total antioxidant capacity in response to subsequent drought or heat stress. Endogenous GABA was significantly increased by DP through activating glutamic acid decarboxylase activity and inhibiting GABA transaminase activity. DP also enhanced accumulation of NO, depending on NOS activity, under subsequent drought or heat stress. Transcript levels of multiple transcription factors, heat shock proteins, and DHNs in the HSF and DHN pathways were up-regulated by DP under drought or heat stress, but there were differences between DP-regulated heat tolerance and drought tolerance in these pathways. The findings indicate that under recurrent moderate drought, DP improves subsequent tolerance to drought or heat stress in relation to GABA-regulated pathways, providing new insight into understanding of the role of stress memory in plant adaptation to complex environmental stresses.

Driving STEM learning effectiveness: dropout prediction and intervention in MOOCs based on one novel behavioral data analysis approach

AbstractWith the full application of MOOCs online learning, STEM multidisciplinary and knowledge structures have been achieved, but it has also resulted in a massive number of dropouts, seriously affected the learning sustainability of STEM education concepts, and made it difficult to achieve learning effectiveness. Based on the massive STEM learning behavior instances generated by MOOCs, as well as the entire learning periods, this study considers some key explicit and implicit features associated with learning behavior, and achieves the fusion of convolutional neural network and recurrent neural network through data-driven approaches, incorporates long short-term memory mechanism to develop dropout prediction methods and models. Based on the experimental results, we also discuss the relevant problems of dropouts related to STEM learning behavior, explore the key dropout temporal sequences of the learning process, identify related factors that have key impacts on learning behavior, and deduce intervention measures and early warning suggestions. The entire study can provide effective methods and decisions for researching the STEM learning behavior of MOOCs and has strong research feasibility and urgency.

Mechanical memory based on chromatin and metabolism remodeling promotes proliferation and smooth muscle differentiation in mesenchymal stem cells.

Stem cells respond and remember mechanical cues from the microenvironment, which modulates their therapeutic effects. Chromatin organization and energy metabolism regulate the stem cell fate induced by mechanical cues. However, the mechanism of mechanical memory is still unclear. This study aimed to investigate the effects of mechanical amplitude, frequency, duration, and stretch cycle on mechanical memory in mesenchymal stem cells. It showed that the amplitude was the dominant parameter to the persistence of cell alignment. F-actin, paxillin, and nuclear deformation are more prone to be remolded than cell alignment. Stretching induces transcriptional memory, resulting in greater transcription upon subsequent reloading. Cell metabolism displays mechanical memory with sustained mitochondrial fusion and increased ATP production. The mechanical memory of chromatin condensation is mediated by histone H3 lysine 27 trimethylation, leading to much higher smooth muscle differentiation efficiency. Interestingly, mechanical memory can be transmitted based on direct cell-cell interaction, and stretched cells can remodel the metabolic homeostasis of static cells. Our results provide insight into the underlying mechanism of mechanical memory and its potential benefits for stem cell therapy.

Nrf2 pre-recruitment at Enhancer 2 is a hallmark of H2 O2 -induced epigenetic transcriptional memory in the HMOX1 gene in human umbilical artery endothelial cells.

Maternal obesity (MO) is a significant cause of increased cardiometabolic risk in offspring, who present endothelial dysfunction at birth. Alterations in physiologic and cellular redox status are strongly associated with altered gene regulation in arterial endothelium. However, specific mechanisms by which the pro-oxidant fetal environment in MO could modulate the vascular gene expression and function during the offspring's postnatal life are elusive. We tested if oxidative stress could reprogram the antioxidant-coding gene's response to a pro-oxidant challenge through an epigenetic transcriptional memory (ETM) mechanism. A pro-oxidant double-hit protocol was applied to human umbilical artery endothelial cells (HUAECs) and EA.hy 926 endothelial cell lines. The ETM acquisition in the HMOX1 gene was analyzed by RT-qPCR. HMOX1 mRNA decay was evaluated by Actinomycin-D treatment and RT-qPCR. To assess the chromatin accessibility and the enrichment of NRF2, RNAP2, and phosphorylation at serin-5 of RNAP2, at HMOX1 gene regulatory regions, were used DNase HS-qPCR and ChIP-qPCR assays, respectively. The CpG methylation pattern at the HMOX1 core promoter was analyzed by DNA bisulfite conversion and Sanger sequencing. Data were analyzed using two-way ANOVA, and p < 0.05 was statistically significant. Using a pro-oxidant double-hit protocol, we found that the Heme Oxygenase gene (HMOX1) presents an ETM response associated with changes in the chromatin structure at the promoter and gene regulatory regions. The ETM response was characterized by a paused-RNA Polymerase 2 and NRF2 enrichment at the transcription start site and Enhancer 2 of the HMOX1 gene, respectively. Changes in DNA methylation pattern at the HMOX1 promoter were not a hallmark of this oxidative stress-induced ETM. These data suggest that a pro-oxidant milieu could trigger an ETM at the vascular level, indicating a potential epigenetic mechanism involved in the increased cardiovascular risk in the offspring of women with obesity.

Be prepared for interruptions: EEG correlates of anticipation when dealing with task interruptions and the role of aging

Dealing with task interruptions requires the flexible use of working memory and attentional control mechanisms, which are prone to age-related changes. We investigated effects of age on dealing with task interruptions and potential advantages of anticipating an interruption using EEG and a retrospective cueing (retro-cue) paradigm. Thirty-two young (18–30 years) and 28 older (55–70 years) participants performed a visual working memory task, where they had to report the orientation of a target following a retro-cue. Within blocks of 10 trials, they were always, never, or randomly interrupted with an arithmetic task before the onset of the retro-cue. The interruption-induced decline in primary task performance was more pronounced in older participants, while only these benefited from anticipation. The EEG analysis revealed reduced theta and alpha/beta response to the retro-cue following interruptions, especially for the older participants. In both groups, anticipated interruptions were associated with increased theta and alpha/beta power prior and during the interruption, and stronger beta suppression to the retro-cue. The results indicate that interruptions impede the refocusing of attention on the task-relevant representation of the primary task, especially in older people, while anticipation facilitates preparation for the interruption task and resumption of the primary task.

Cross-frequency coupling in cortico-hippocampal networks supports the maintenance of sequential auditory information in short-term memory.

It has been suggested that cross-frequency coupling in cortico-hippocampal networks enables the maintenance of multiple visuo-spatial items in working memory. However, whether this mechanism acts as a global neural code for memory retention across sensory modalities remains to be demonstrated. Intracranial EEG data were recorded while drug-resistant patients with epilepsy performed a delayed matched-to-sample task with tone sequences. We manipulated task difficulty by varying the memory load and the duration of the silent retention period between the to-be-compared sequences. We show that the strength of theta-gamma phase amplitude coupling in the superior temporal sulcus, the inferior frontal gyrus, the inferior temporal gyrus, and the hippocampus (i) supports the short-term retention of auditory sequences; (ii) decodes correct and incorrect memory trials as revealed by machine learning analysis; and (iii) is positively correlated with individual short-term memory performance. Specifically, we show that successful task performance is associated with consistent phase coupling in these regions across participants, with gamma bursts restricted to specific theta phase ranges corresponding to higher levels of neural excitability. These findings highlight the role of cortico-hippocampal activity in auditory short-term memory and expand our knowledge about the role of cross-frequency coupling as a global biological mechanism for information processing, integration, and memory in the human brain.

Dissecting Mechanisms of Epigenetic Memory Through Computational Modeling.

Understanding the mechanistic basis of epigenetic memory has proven to be a difficult task due to the underlying complexity of the systems involved in its establishment and maintenance. Here, we review the role of computational modeling in helping to unlock this complexity, allowing the dissection of intricate feedback dynamics. We focus on three forms of epigenetic memory encoded in gene regulatory networks, DNA methylation, and histone modifications and discuss the important advantages offered by plant systems in their dissection. We summarize the main modeling approaches involved and highlight the principal conceptual advances that the modeling has enabled through iterative cycles of predictive modeling and experiments. Lastly, we discuss remaining gaps in our understanding and how intertwined theory and experimental approaches might help in their resolution.

AI-Powered Online Symptom Checkers: Enhancing Accuracy and Guiding Users to Appropriate Medical Care

Online symptom checkers have emerged as valuable tools for individuals seeking health advice and guidance. Powered by artificial intelligence (AI) algorithms, these symptom checkers aim to provide more accurate and personalized health assessments, directing users to the appropriate level of medical care. This study presents an innovative architecture that combines Vector Database (VDB), Faiss similarity search algorithm, and GPT (Generative Pre-trained Transformer) API to analyze user inputs, identify intents, and generate tailored responses. The proposed system leverages the power of VDB to store symptom and test data as embeddings, enabling efficient similarity searches. The GPT model is employed to analyze user inputs, determining whether the user is seeking symptom analysis, test suggestions, booking details, booking help, or general health queries. Based on the identified intent, the Faiss algorithm searches for relevant tests and returns appropriate recommendations. The GPT model then generates personalized responses by considering the user's input, suggested tests, intent, and a summary of the chat history. The chatbot also incorporates a memory mechanism to store conversation summaries, providing context for subsequent interactions. Experimental results demonstrate the effectiveness of the proposed architecture in delivering accurate and context-aware health advice, guiding users to the most suitable medical care options. This study highlights the potential of AI-powered online symptom checkers in improving healthcare accessibility, reducing unnecessary medical visits, and empowering individuals to make informed decisions about their health.

Delayed feedback could either help or hurt test-enhanced learning; it depends.

Nelson uses mechanisms of memory, Cognitive Load Theory, and Desirable Difficulty Theory to propose a nuanced understanding of Ryan et al’s observations of workplace‐based assessment