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Damage to the hippocampus produces profound retrograde amnesia, but odour and object discrimination memories can be spared in the retrograde direction. Prior lesion studies testing retrograde amnesia for object/odour discriminations are problematic due to sparing of large parts of the hippocampus, which may support memory recall, and/or the presence of uncontrolled, distinctive odours that may support object discrimination. To address these issues, we used a simple object discrimination test to assess memory in male rats. Two visually distinct objects, paired with distinct odour cues, were presented. One object was associated with a reward. Following training, neurotoxic hippocampal lesions were made using N‐methyl‐D‐aspartate (NMDA). The rats were then tested on the preoperatively learned object discrimination problem, with and without the availability of odour or visual cues during testing. The rats were also postoperatively trained on a new object discrimination problem. Lesion sizes ranged from 67% to 97% of the hippocampus (average of 87%). On the preoperatively learned discrimination problem, the rats with hippocampal lesions showed preserved object discrimination memory when tested in the dark (i.e., without visual cues) but not when the explicit odour cues were removed from the objects. Hippocampal lesions increased the number of trials required to reach criterion but did not prevent rats from solving the postoperatively learned discrimination problem. Our results support the idea that long‐term memories for odours, unlike recall of visual properties of objects, do not depend on the hippocampus in rats, consistent with previous observations that hippocampal damage does not cause retrograde amnesia for odour memories. [ABSTRACT FROM AUTHOR]

Research from several areas suggests that mental representations adapt to the specific tasks we carry out in our environment. In this study, we propose a mechanism of adaptive representational change, task imprinting. Thereby, we introduce a computational model, which portrays task imprinting as an adaptation to specific task goals via selective storage of helpful representations in long-term memory. We test the main qualitative prediction of the model in four behavioral experiments using healthy young adults as participants. In each experiment, we assess participants' baseline representations in the beginning of the experiment, then expose participants to one of two tasks intended to shape representations differently according to our model, and finally assess any potential change in representations. Crucially, the tasks used to measure representations differ in the amount that strategic, judgmental processes play a role. The results of Experiments 1 and 2 allow us to exclude the option that representations used in more perceptual tasks become biased categorically. The results of Experiment 4 make it likely that people strategically decide given the specific task context whether they use categorical information or not. One signature of representational change was however observed: category learning practice increased the perceptual sensitivity over and above mere exposure to the same stimuli., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Many brain diseases lead to a reduction in the number of functional neurons and it would be of value to be able to increase the number of neurons in the affected brain areas. In this study, we examined whether we can promote neural stem cells to produce mature neurons and whether an increase in the mature neurons can affect cognitive performance. We detected that the EphB2 receptor is localized in immature basolateral amygdala (BLA) neurons. We therefore aimed to increase the level of EphB2 activity in neural stem cells (NSCs) in the BLA and examine the effects on the production of mature neurons and cognition. Toward that end, we utilized a photoactivatable EphB2 construct (optoEphB2) to increase EphB2 forward signaling in NSCs in the BLA. We revealed that the activation of optoEphB2 in NSCs in the BLA increased the level of immature and mature neurons in the BLA. We further found that activation of optoEphB2 in BLA NSCs enhanced auditory, but not contextual, long-term fear memory formation. Impairing EphB2 forward signaling did not affect the level of immature and mature neurons in the BLA. This study provides evidence that NSCs can be promoted to produce mature neurons by activating EphB2 to enhance specific brain functions., (© 2024. The Author(s).)

Accurate short-term load forecasting (STLF) is crucial for maintaining reliable and efficient operations within power systems. With the continuous increase in volume and variety of energy data provided by renewables, electric vehicles and other sources, long short-term memory (LSTM) has emerged as an attractive approach for STLF due to its superiorities in extracting the dynamic temporal information. However, traditional LSTM training methods rely on stochastic gradient methods that have several limitations. This paper presents an innovative LSTM optimization framework via the alternating direction method of multipliers (ADMM) for STLF, dubbed ADMM-LSTM. Explicitly, we train the LSTM network distributedly by the ADMM algorithm. More specifically, we introduce a novel approach to update the parameters in the ADMM-LSTM framework, using a backward-forward order, significantly reducing computational time. Additionally, within the proposed framework, the solution to each subproblem is achieved by utilizing either the proximal point algorithm or local linear approximation, preventing the need for supplementary numerical solvers. This approach confers several advantages, including avoiding issues associated with exploding or vanishing gradients, thanks to the inherent gradient-free characteristics of ADMM-LSTM. Furthermore, we offer a comprehensive theoretical analysis that elucidates the convergence properties inherent to the ADMM-LSTM framework. This analysis provides a deeper understanding of the algorithm's convergence behavior. Lastly, the efficacy of our method is substantiated through a series of experiments conducted on two publicly available datasets. The experimental results demonstrate the superior performance of our approach when compared to existing methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Modern industrial processes are characterized by extensive, multiple operation units, and strong coupled correlation of subsystems. Fault detection of large-scale processes is still a challenging problem, especially for tandem plant-wide processes in multiple fields such as water treatment process. In this paper, a novel distributed graph attention network-bidirectional long short-term memory (D-GATBLSTM) fault detection model is proposed for large-scale industrial processes. Firstly, a multi-node knowledge graph (MNKG) is constructed using a joint data and knowledge driven strategy. Secondly, for large-scale industrial process, a global feature extractor of graph attention networks (GATs) is constructed, on the basis of which, sub-blocks are decomposed based on MNKG. Then, local feature extractors of bidirectional long short-term memory (Bi-LSTM) for each sub-block are constructed, in which correlations among multiple sub-blocks are considered. Finally, a multi-subblock fusion collaborative prediction model is constructed and the comprehensive fault detection results are given by the grid search method. The effectiveness of our D-GATBLSTM is exemplified in a secure water treatment process case, where it outperforms baseline models compared, with 27% improvement in precision, 15% increase in recall, and overall F-score enhancement of 0.22., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Hebb repetition effect shows improvement in serial recall of repeated lists compared to random nonrepeated lists. Previous research using simple span tasks found that the Hebb repetition effect is limited to constant uninterrupted lists, suggesting chunking as the mechanism of list learning. However, the Hebb repetition effect has been found in complex span tasks, which challenges the chunking explanation, as successive list items are separated by distractor processing, possibly interfering with the unified representations. We tested the possibility that Hebb repetition learning arises from chunking in simple span, but from position-item associations in complex span. In a series of five experiments, we found evidence that contradicts that hypothesis. Results show that (a) Hebb repetition learning in a complex span task can be transferred to a simple span task; (b) Hebb repetition learning from a complex span task cannot be transferred to a partially repeated simple span task; (c) partial repetition in a complex span task does not lead to learning; (d) Hebb repetition learning from a simple span task can be transferred to a complex span task; and (e) repeating the distractors in complex span has no impact on the Hebb repetition effect. These results suggest that the mechanism underlying the Hebb repetition effect in simple and complex span tasks is the same and points at the creation of chunks while excluding the distractors from the long-term memory representation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

We introduce the Discrete-Temporal Sobolev Network (DTSN), a neural network loss function that assists dynamical system forecasting by minimizing variational differences between the network output and the training data via a temporal Sobolev norm. This approach is entirely data-driven, architecture agnostic, and does not require derivative information from the estimated system. The DTSN is particularly well suited to chaotic dynamical systems as it minimizes noise in the network output which is crucial for such sensitive systems. For our test cases we consider discrete approximations of the Lorenz-63 system and the Chua circuit. For the network architectures we use the Long Short-Term Memory (LSTM) and the Transformer. The performance of the DTSN is compared with the standard MSE loss for both architectures, as well as with the Physics Informed Neural Network (PINN) loss for the LSTM. The DTSN loss is shown to substantially improve accuracy for both architectures, while requiring less information than the PINN and without noticeably increasing computational time, thereby demonstrating its potential to improve neural network forecasting of dynamical systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue detection systems. However, many existing methods involves tedious manual parameter tunings, a process that is both time-consuming and results in task-specific models. On the other hand, most of the researches on fatigue recognition are based on class-balanced and sufficient data, and effectively "mine" meaningful information from class-imbalanced and insufficient data for fatigue recognition is still a challenge. In this paper, we proposed two novel models, the attention-based residual adaptive multiscale fully convolutional network-long short term memory network (ARMFCN-LSTM), and the Generative ARMFCN-LSTM (GARMFCN-LSTM) aiming to address this issue. ARMFCN-LSTM excels at automatically extracting multiscale representations through adaptive multiscale temporal convolutions, while capturing temporal dependency features through LSTM. GARMFCN-LSTM integrates Wasserstein GAN with gradient penalty (WGAN-GP) into ARMFCN-LSTM to improve driver fatigue detection performance by alleviating data scarcity and addressing class imbalances. Experimental results show that ARMFCN-LSTM achieves the highest classification accuracy of 95.84% in driver fatigue detection on the class-balanced EEG dataset (binary classification), and GARMFCN-LSTM attained an improved classification accuracy of 84.70% on the class-imbalanced EOG dataset (triple classification), surpassing the competing methods. Therefore, the proposed models are promising for further implementations in online driver fatigue detection systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

While chronic stress induces learning and memory impairments, acute stress may facilitate or prevent memory consolidation depending on whether it occurs during the learning event or before it, respectively. On the other hand, it has been shown that histone acetylation regulates long-term memory formation. This study aimed to evaluate the effect of two inhibitors of class I histone deacetylases (HDACs), 4-phenylbutyrate (PB) and IN14 (100 mg/kg/day, ip for 2 days), on memory performance in mice exposed to a single 15-min forced swimming stress session. Plasma corticosterone levels were determined 30 minutes after acute swim stress in one group of mice. In another experimental series, independent groups of mice were trained in one of three different memory tasks: Object recognition test, Elevated T maze, and Buried food location test. Subsequently, the hippocampi were removed to perform ELISA assays for histone deacetylase 2 (HDAC2) expression. Acute stress induced an increase in plasma corticosterone levels, as well as hippocampal HDAC2 content, along with an impaired performance in memory tests. Moreover, PB and IN14 treatment prevented memory loss in stressed mice. These findings suggest that HDAC2 is involved in acute stress-induced cognitive impairment. None of the drugs improved memory in non-stressed animals, indicating that HDACs inhibitors are not cognitive boosters, but rather potentially useful drugs for mitigating memory deficits., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Martínez-Pacheco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Butt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Accelerated long-term forgetting has been studied and demonstrated in adults with epilepsy. In contrast, the question of long-term consolidation (delays > 1 day) in children with epilepsy shows conflicting results. However, childhood is a period of life in which the encoding and long-term storage of new words is essential for the development of knowledge and learning. The aim of this study was therefore to investigate long-term memory consolidation skills in children with self-limited epilepsy with centro-temporal spikes (SeLECTS), using a paradigm exploring new words encoding skills and their long-term consolidation over one-week delay. As lexical knowledge, working memory skills and executive/attentional skills has been shown to contribute to long-term memory/new word learning, we added standardized measures of oral language and executive/attentional functions to explore the involvement of these cognitive skills in new word encoding and consolidation. The results showed that children with SeLECTS needed more repetitions to encode new words, struggled to encode the phonological forms of words, and when they finally reached the level of the typically developing children, they retained what they had learned, but didn't show improved recall skills after a one-week delay, unlike the control participants. Lexical knowledge, verbal working memory skills and phonological skills contributed to encoding and/or recall abilities, and interference sensitivity appeared to be associated with the number of phonological errors during the pseudoword encoding phase. These results are consistent with the functional model linking working memory, phonology and vocabulary in a fronto-temporo-parietal network. As SeLECTS involves perisylvian dysfunction, the associations between impaired sequence storage (phonological working memory), phonological representation storage and new word learning are not surprising. This dual impairment in both encoding and long-term consolidation may result in large learning gap between children with and without epilepsy. Whether these results indicate differences in the sleep-induced benefits required for long-term consolidation or differences in the benefits of retrieval practice between the epilepsy group and healthy children remains open. As lexical development is associated with academic achievement and comprehension, the impact of such deficits in learning new words is certainly detrimental., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Although reactive oxygen species (ROS) are known to have harmful effects in organisms, recent studies have demonstrated expression of ROS synthases at various parts of the organisms and the controlled ROS generation, suggesting possible involvement of ROS signaling in physiological events of individuals. However, physiological roles of ROS in the CNS, including functional roles in higher brain functions or neuronal activity-dependent ROS production, remain to be elucidated. Here, we demonstrated involvement of ROS - 8-NO 2 -cGMP signaling in motor learning and synaptic plasticity in the cerebellum. In the presence of inhibitors of ROS signal or ROS synthases, cerebellar motor learning was impaired, and the stimulus inducing long-term depression (LTD), cellular basis for the motor learning, failed to induce LTD but induced long-term potentiation (LTP)-like change at cerebellar synapses. Furthermore, ROS was produced by LTD-inducing stimulus in enzyme-dependent manner, and excess administration of the antioxidant vitamin E impaired cerebellar motor learning, suggesting beneficial roles of endogenous ROS in the learning. As a downstream signal, involvement of 8-NO 2 -cGMP in motor learning and cerebellar LTD were also revealed. These findings indicate that ROS - 8-NO 2 -cGMP signal is activated by neuronal activity and is essential for cerebellum-dependent motor learning and synaptic plasticity, demonstrating involvement of the signal in physiological function of brain systems., Competing Interests: Declaration of competing interest All authors declare no potential conflict of interest in the above paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The capacity of visual working and visual long-term memory plays a critical role in theories of cognitive architecture and the relationship between memory and other cognitive systems. Here, we argue that before asking the question of how capacity varies across different stimuli or what the upper bound of capacity is for a given memory system, it is necessary to establish a methodology that allows a fair comparison between distinct stimulus sets and conditions. One of the most important factors determining performance in a memory task is target/foil dissimilarity. We argue that only by maximizing the dissimilarity of the target and foil in each stimulus set can we provide a fair basis for memory comparisons between stimuli. In the current work we focus on a way to pick such foils objectively for complex, meaningful real-world objects by using deep convolutional neural networks, and we validate this using both memory tests and similarity metrics. Using this method, we then provide evidence that there is a greater capacity for real-world objects relative to simple colors in visual working memory; critically, we also show that this difference can be reduced or eliminated when non-comparable foils are used, potentially explaining why previous work has not always found such a difference. Our study thus demonstrates that working memory capacity depends on the type of information that is remembered and that assessing capacity depends critically on foil dissimilarity, especially when comparing memory performance and other cognitive systems across different stimulus sets., (© 2023. The Psychonomic Society, Inc.)

To (i) determine whether accelerated long-term forgetting (ALF) can be found using standardized verbal memory test materials in children with genetic generalized epilepsy (GGE) and temporal lobe epilepsy (TLE), and (ii) to establish whether ALF is impacted by executive skills and repeat testing over long delays. One hundred and twenty-three children aged 8 to 16, (28 with GGE, 23 with TLE, and 72 typically developing; TD) completed a battery of standardized tests assessing executive functioning and memory for two stories. Stories were recalled immediately and after a 30-min delay. To examine whether repeat testing impacts long-term forgetting, one story was tested via free recall at 1-day and 2-weeks, and the other at 2-weeks only. Recognition was then tested for both stories at 2-weeks. Children with epilepsy recalled fewer story details, both immediately and after 30-min relative to TD children. Compared to TD children, the GGE group, but not the TLE group, showed ALF, having significantly poorer recall of the story tested only at the longest delay. Poor executive skills were significantly correlated with ALF for children with epilepsy. Standard story memory materials can detect ALF in children with epilepsy when administered over long delays. Our findings suggest that (i) ALF is related to poor executive skills in children with epilepsy, and (ii) repeated testing may ameliorate ALF in some children.

In recognition memory, the variance of the target distribution is almost universally found to be greater than that of the lure distribution. However, these estimates commonly come from long-term memory paradigms where words are used as stimuli. Two exceptions to this rule have found evidence for greater lure variability: a short-term memory task (Yotsumoto et al., Memory & Cognition, 36, 282-294 2008) and in an eyewitness memory paradigm (Wixted et al., Cognitive Psychology, 105, 81-114 2018). In the present work, we conducted a series of recognition memory experiments using different stimulus (faces vs. words) along with different paradigms (long-term vs. short-term paradigms) to evaluate whether either of these conditions would result in greater variability in lure items. Greater target variability was observed across stimulus types and memory paradigms. This suggests that factors other than stimuli and retention interval might be responsible for cases where variability is less for targets than lures., (© 2023. The Author(s).)

Perirhinal cortex is a brain area that has been considered crucial for the object recognition memory (ORM). However, with the use of an ORM enhancer named RGS14 414 as gain-in-function tool, we show here that frontal association cortex and not the Perirhinal cortex is essential for the ORM of objects with complex features that consisted of detailed drawing on the object surface (complex ORM). An expression of RGS14 414 , in rat brain frontal association cortex, induced the formation of long-term complex ORM, whereas the expression of the same memory enhancer in Perirhinal cortex failed to produce this effect. Instead, RGS14 414 expression in Perirhinal cortex caused the formation of ORM of objects with simple features that consisted of the shape of object (simple ORM). Further, a selective elimination of frontal association cortex neurons by treatment with an immunotoxin Ox7-SAP completely abrogated the formation of complex ORM. Thus, our results suggest that frontal association cortex plays a key role in processing of a high-order recognition memory information in brain., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Engagement in cognitive activity in adulthood is one of the factors that enable successful cognitive aging, both in humans and rodents. However, some studies emphasize that the beneficial effect on cognition of such an activity may reflect carry over from one test situation to another, including memory for procedural aspects of the behavioral tasks, and thus question whether this effect can be limited to the trained cognitive domain or whether it can be transferred to an untrained ones. In the current study, we assessed whether adulthood intermittent working memory training has beneficial effect on long-term memory of aged rats using two very different test situations. To this aim, rats trained in a delayed non-matching to position task in operant box at 3 and 15 months of age were tested in a place learning task in water maze when they were 24 months. The two tasks differ with regard to the cognitive domain but also in their spatial ability requirement and the nature of the reinforcer used. During the memory tests, accuracy of the platform search indicated age-related impairment only in the aged-untrained group. Thus, intermittent training during adult life in a task involving working memory protects aged animals from the deleterious effects of aging on spatial reference memory. This result highlights the long-term beneficial effects of training on a working memory task on an untrained cognitive domain., (© 2023. The Author(s), under exclusive licence to American Aging Association.)

Despite considerable cognitive neuroscience research demonstrating that emotions can influence the encoding and consolidation of memory, research has failed to demonstrate a relationship between self-reported ratings of emotions collected soon after a traumatic event and memory for the event over time. This secondary analysis of data from a multisite longitudinal study of memories of the September 11, 2001 terrorist attacks, asked the question of whether emotional language use could predict memory over time. In the 2 weeks following the 9/11 attacks, participants ( N = 691; M age = 36.8; 72% identifying as male; 76% identifying as white) wrote narratives about how they learned of the attacks and the impact of the attacks on them. Language features of these narratives were extracted using the Linguistic Inquiry Word Count program and used to predict three types of memory: (a) event memory accuracy, (b) flashbulb memory consistency, and (c) emotion memory consistency. These outcomes were assessed at the time of writing, 1, 3, and 10 years after the 9/11 attacks. Results of linear mixed-effects models indicate that greater use of negative emotion words in narratives predicts better event memory accuracy 3 and 10 years after the attacks and worse flashbulb memory consistency 10 years after the attacks. However, emotion word use does not predict emotion memory consistency across time. We also examine whether other exploratory linguistic predictors are associated with memory over time. These findings suggest that written language may serve as a potential early indicator of memory over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The memory benefit that arises from distributing learning over time rather than in consecutive sessions is one of the most robust effects in cognitive psychology. While prior work has mainly focused on repeated exposures to the same information, in the real world, mnemonic content is dynamic, with some pieces of information staying stable while others vary. Thus, open questions remain about the efficacy of the spacing effect in the face of variability in the mnemonic content. Here, in two experiments, we investigated the contributions of mnemonic variability and the timescale of spacing intervals, ranging from seconds to days, to long-term memory. For item memory, both mnemonic variability and spacing intervals were beneficial for memory; however, mnemonic variability was greater at shorter spacing intervals. In contrast, for associative memory, repetition rather than mnemonic variability was beneficial for memory, and spacing benefits only emerged in the absence of mnemonic variability. These results highlight a critical role for mnemonic variability and the timescale of spacing intervals in the spacing effect, bringing this classic memory paradigm into more ecologically valid contexts., Competing Interests: Competing interests statement:The authors declare no competing interest.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Si et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Amini, Kalantari. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The detection of Activities of Daily Living (ADL) holds significant importance in a range of applications, including elderly care and health monitoring. Our research focuses on the relevance of ADL detection in elderly care, highlighting the importance of accurate and unobtrusive monitoring. In this paper, we present a novel approach that that leverages smartphone data as the primary source for detecting ADLs. Additionally, we investigate the possibilities offered by ambient sensors installed in smart home environments to complement the smartphone data and optimize the ADL detection. Our approach uses a Long Short-Term Memory (LSTM) model. One of the key contributions of our work is defining ADL detection as a multilabeling problem, allowing us to detect different activities that occur simultaneously. This is particularly valuable since in real-world scenarios, individuals can perform multiple activities concurrently, such as cooking while watching TV. We also made use of unlabeled data to further enhance the accuracy of our model. Performance is evaluated on a real-world collected dataset, strengthening reliability of our findings. We also made the dataset openly available for further research and analysis. Results show that utilizing smartphone data alone already yields satisfactory results, above 50% true positive rate and balanced accuracy for all activities, providing a convenient and non-intrusive method for ADL detection. However, by incorporating ambient sensors, as an additional data source, one can improve the balanced accuracy of the ADL detection by 7% and 8% of balanced accuracy and true positive rate respectively, on average., (© 2024. The Author(s).)

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems. Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic patterns of traffic flow with a single temporal learning approach, which relies heavily on constructing graphs and has low training efficiency. Different from existing studies, this paper proposes a spatio-temporal ensemble network that aims to leverage the strengths of different sequential capturing approaches to obtain the intrinsic dependencies of traffic flow. Specifically, we propose a novel model named graph temporal convolutional long short-term memory network (GT-LSTM), which mainly consists of features splicing and patterns capturing. In features splicing, the spatial dependencies of traffic flow are captured by employing self-adaptive graph convolutional network (GCN), and a non-inter-embedded approach is designed to integrate the spatial and temporal states. Further, the aggregated spatio-temporal states are fed into patterns capturing, which can effectively exploit the advantages of temporal convolutional network (TCN) and bidirectional long short-term memory network (Bi-LSTM) to extract the intrinsic patterns of traffic flow. Extensive experiments conducted on four real-world datasets demonstrate that the proposed network obtains excellent performance in both forecasting accuracy and training efficiency., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

We often encounter more information than we can remember, making it critical that we are selective in what we remember. Being selective about which information we consolidate into our long-term memory becomes even more important when there is insufficient time to encode and retrieve information. We investigated whether older and younger adults differ in how time constraints, whether at encoding (Experiment 1) or retrieval (Experiment 2), affect their ability to be selective when remembering important information that they need to recall later. In Experiment 1, we found that younger and older adults exhibited similar selectivity, and the participants remained selective when rushed at encoding. In Experiment 2, older adults maintained their selectivity when given insufficient time at retrieval, but younger adults' selectivity was increased when given limited recall time. Altogether, the present experiments provide new support for negligible, and in some cases, even beneficial, effects of time constraints on older and younger adults' ability to selectively encode and retrieve the most valuable information. These findings may provide insight into a mechanism that allows older adults to use their long-term memory efficiently, despite age-related cognitive declines, even when faced with constraining encoding and retrieval situations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measuring behavior in the form of numerical data is difficult, especially for studies involving complex actions. DanioVision is a closed-chamber system that utilizes subject tracking to comprehensively record behavior, while also mitigating the influence of environmental conditions. We used DanioVision to record activity of juvenile dwarf cuttlefish (Sepia bandensis) during the inaccessible prey (IP) procedure, a memory experiment in which cuttlefish learn to inhibit capture attempts towards inaccessible prey. By quantifying total movement and orientation of the body, we found that cuttlefish show memory by selectively inhibiting tentacle strikes without reducing total movement, or orientation towards the prey. We show that DanioVision can be used to assess multiple components of dynamic responses that are not measurable by direct observation alone and provide new evidence that strike inhibition is the product of learning, and not motor fatigue. [ABSTRACT FROM AUTHOR]