Trauma in childhood and adolescence has long-term negative consequences in brain development and behavior and increases the risk for psychiatric disorders. Among them, post-traumatic stress disorder (PTSD) during adolescence illustrates the connection between trauma and substance misuse, as adolescents may utilize substances to cope with PTSD. Drug misuse may in turn lead to neuroadaptations in learning processes that facilitate the consolidation of traumatic memories that perpetuate PTSD. This reflects, apart from common genetic and epigenetic modifications, overlapping neurocircuitry engagement triggered by stress and drug misuse that includes structural and functional changes in limbic brain regions and the salience, default-mode, and frontoparietal networks. Effective strategies to prevent PTSD are needed to limit the negative consequences associated with the later development of a substance use disorder (SUD). In this review, we will examine the link between PTSD and SUDs, along with the resulting effects on memory, focusing on the connection between the development of a SUD in individuals who struggled with PTSD in adolescence. Neuroimaging has emerged as a powerful tool to provide insight into the brain mechanisms underlying the connection of PTSD in adolescence and the development of SUDs.

Shape Memory Alloy (SMA) have emerged as a revolutionary material that holds the potential to change the design and construction methods of buildings and other structures. One of the most promising applications of SMAs in civil engineering is their use as reinforcements for steel structures. This paper presents the latest research progress, opportunities and challenges of SMA in the field of steel structural reinforcement, both in terms of basic components and applications. In terms of components, the construction forms and working mechanisms of Fe-SMA strips, SMA/CFRP composite patches and SMA dampers are introduced. On this basis, the application of SMA in steel structures reinforcement is introduced, and its effect is analyzed from three aspects: crack restoration, seismic retrofitting and structural strengthening. Finally, the results of the current research are summarized and the shortcomings are analyzed, hoping to provide a reference for the research of SMA in the field of steel structures reinforcement.

Brain hemispheres develop rather symmetrically except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated CTh AI in the students, as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, the pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC has a functional lateralization, with the right PHC having a stronger involvement in spatiotemporal context encoding, while the left PHC is involved in semantic encoding. We observed CTh asymmetry toward the left PHC for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home life, and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

Automated lying-posture tracking is important in preventing bed-related disorders such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

The present study examined mechanisms underlying memory deficits in patients with Parkinson disease (PD)and their associations with structural metrics. Nineteen PD and 22 matched controls underwent two memory experiments. In experiment 1 (delayed memory task), subjects were asked to remember an array of colored rectangles with varying memory set sizes [Low-Load (2 items), Low-Load with Distractor, & High-Load (5 items)]. After a 7s delay period, they reported whether the orientation of any relevant figures had changed (test period). In experiment 2 (working memory task), memory arrays were presented in varying set sizes (2 to 6 items) with no distractors that were followed by a 2s delay period and subsequent test period. Brain MRI data were acquired to assess structural differences (volumes and cortical thickness) in brain areas related to attention, working memory storage, and episodic memory. Compared to controls, PD patients had lower memory capacity scores in all memory load conditions for experiment 1 (p <0.021) whereas there were no group differences in any memory load conditions for experiment 2 (p>0.06). In addition, PD patients had lower thickness in the left superior temporal gyrus (p=0.02). Lower thickness values in the left superior temporal gyrus were significant predictors of lower delayed memory performance in Low-Load and Low-Load with Distractor conditions (ps’<0.044) and working memory performance of memory load conditions of 4 and 5 items (p’s<0.012). The present findings suggest that PD patients may have intact working memory storage capacity but impaired attentional filtering and memory consolidation that may lead to lower delayed memory scores. Lower delayed memory in PD may partly be associated with lower cortical thickness in the left superior temporal gyrus.

Dry eye syndrome (DES) has become increasingly prevalent due to the prolonged use of digital devices in modern society. Individual supplementation with lutein or docosahexaenoic acid (DHA) demonstrated potential benefits for eye health. This study aimed to investigate the effects of lutein plus DHA complex supplementation on DES, visual function, and memory in healthy individuals. Thirty-four participants, aged 40~60 years, received lutein/DHA complex supplements (containing 10 mg free-form lutein, 200 mg DHA, 30 mg red algae extract (astaxanthin), phosphatidylserine, and vitamin A) for 12 weeks. Ophthalmological examinations and memory tests were carried out, and a questionnaire was administered at weeks 0, 6, and 12 of the study. After 12 weeks of the intervention, the tear break-up time had significantly increased, and intraocular pressure (IOP) had significantly decreased. This suggests that lutein/DHA complex supplementation may reduce the IOP and offer potential benefits for DES, as indicated by improved tear stability.

The purported cognitive benefits associated with nicotine and its metabolites in the brain are a matter of debate. In this review, the impact of the pharmacologically active metabolite of a nicotine derivative produced by bacteria (6-hydroxy-L-nicotine) on memory, oxidative stress, and the activity of the cholinergic system in the brain was examined. A search in the PubMed, Science Direct, Web of Science, and Google Scholar databases, limiting entries to those no later than July 2023, was conducted. The search focused specifically on articles pertaining to nicotine metabolites, memory, oxidative stress, cholinergic system activity, as well as enzymes or pathways related to nicotine degradation in bacteria. The preliminary search resulted in 696 articles, and following the application of exclusion criteria, 185 articles were deemed eligible for inclusion. This review focuses on experimental studies supporting nicotine catabolism in bacteria, and the chemical and pharmacological activities of nicotine and its metabolite 6-hydroxy-L-nicotine.

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

Abstract: Background: Restless Legs Syndrome /Willis-Ekbom disease (RLS/WED) has occasionally -but not consistently- been associated with cognitive, and most notably, language and executive impairment. The present study was conducted to investigate the cognitive trajectories of older individuals with RLS/WED. Methods: Participants were drawn from the randomly selected, older (>64 years), population-based HELIAD cohort. Individuals without dementia, with available neuropsychological evaluations at baseline and follow-up were considered for potential eligibility. A comprehensive assessment examining five principal components of cognition (memory, visuo-spatial ability, attention, executive function, and language) was administered to the partici-pants. Generalized estimating equations analysis was used to examine the unadjusted and adjusted (for critical factors and covariates) effects of RLS/WED on cognition over time. Results: A total of 1003 predominantly female, older (72.9 ±4.9 years) partici-pants, with follow-up evaluations after a mean of 3.09 ±0.85 years and without demen-tia at baseline and follow-up were included in the present study. Among them, 81 were diagnosed with RLS/WED at baseline. Global cognition, memory, attention, executive and visuo-perceptual skills did not differ between those with and without RLS/WED. However, the RLS/WED group performed worse on language at baseline by 0.249 of a standard deviation, while demonstrating a mitigated language decline over time, by 0.063 of a standard deviation. The unadjusted models yielded similar results. Conclu-sions: Our findings were indicative of a baseline language disadvantage among older individuals with RLS/WED, but the initial discrepancy tends to dissolve over time.

Cognitive biases towards disorder-specific stimuli are suggested to be crucial in the development and maintenance of symptoms in adults with functional gastrointestinal disorders (FGID). Functional abdominal pain disorders (FAPD), a subtype of FGID, are common in children and adolescents, but the influence of cognitive biases is sparsely examined. The aim of this study was to 1) develop a new experimental design for assessing cognitive biases towards gastrointestinal stimuli in children and adolescents (age 8-17 years) and 2) test its feasibility on a healthy sample exploring potential sex- and age-related differences. The online experimental design – BY-FAP (Bias in Youth with Functional Abdominal Pain) includes a word task and a picture task. Stimuli in both tasks are related to general and gastrointestinal symptoms and the design includes three phases: 1) encoding, 2) free recall and 3) recognition. Results from 96 healthy participants (Mage = 12.3, 47.9% female) found no significant difference between sex or age groups in recall and recognition of either words or pictures. The results support that the design is feasible for children and adolescents. In the future, BY-FAP can be used to explore possible cognitive biases in children and adolescents with FAPD.

Children with hearing loss have been found to have significantly more behavioral and emotional challenges than their typically hearing peers, though these outcomes are variable at the individual level. Working memory deficits have been found to relate to executive functioning and overall emotion regulation, leading to behavior challenges. Language development is essential for development of social relationships and communicating one’s needs - this may lead to distress when children cannot communicate effectively. Based on prior findings in children with hearing loss and their typically hearing peers, working memory and language skills were hypothesized to be related to parent and teacher report of socioemotional functioning. Participants were 35 children with hearing loss (66% female, M = 5.17 years old, SD = ±1.97) whose language, working memory, and socioemotional functioning were evaluated during the course of treatment and educational planning. Bivariate analyses indicated that working memory was related to a number of socioemotional domains (e.g., functional communication, atypicality, withdrawal), as were language scores (e.g., social skills, inattention). The direction of these associations was such that stronger working memory and language skills were related to more regulated socioemotional functioning. A call to action of the current study includes more education with regard to profiles and presentations of children with hearing loss, and an early focus on socioemotional learning to foster the development of regulatory skills.

Ultra high molecular weight polyethylene (UHMWPE) is a thermoplastic high-performance polymer in high demand for biomedicine, ship and machine building, anthropomorphic robots and smart prostheses. Highly oriented UHMWPE fibers possess record specific strength and may also be used for the fabrication of self-reinforced PE-PE composites (SRPEC). The temperature evolution study of the small-angle X-ray scattering (SAXS) signature of the supramolecular structure of UHMWPE can help reveal their important role in the mechanism of the shape memory effect in SRPEC. The laboratory SAXS–WAXS beamline XEUSS 3.0 was used for in situ studies of the nanostructure parameters in unidirectional SRPEC. Namely, the radius of gyration and the dimensionality factor were extracted from 2D SAXS patterns using several fitting algorithms. Significant changes of these parameters occurred in temperature ranges that correspond to the initiation of the shape memory effect and melting. The relationship between the material anisotropy and 2D SAXS patterns are discussed in the context of the supramolecular structure evolution.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

This study uses machine learning to predict the convergence results of the Duffing equation with and without damping. The Duffing equation represents a nonlinear second-order differential equation with interesting behavior in undamped free vibration and forced vibration with damping. Convergence alternates randomly between 1 and -1 in undamped free vibration, depending on initial conditions. For forced vibration with damping, multiple factors influence vibration patterns. We utilize the fourth-order Runge-Kutta method to collect convergence results for both conditions. Machine learning techniques, specifically the long short term memory (LSTM) and LSTM-Neural Network (LSTM-NN) method, are employed to predict these convergence values. The LSTM-NN model is a hybrid approach that combines the LSTM method with the addition of hidden layers of neurons. Both the LSTM and LSTM-NN models are thoroughly explored and analyzed in this research. The research process involves three stages: data preprocessing, training, and verification. The results show that the LSTM-NN model becomes more adept at predicting binary datasets, boasting an impressive accuracy of up to 98%. However, when it comes to predicting multiple solutions, the traditional LSTM method outperforms the LSTM-NN approach.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

The pivotal role of the basolateral amygdala (BLA) in the emotional modulation of hippocampal plasticity and memory consolidation is well established. Specifically, multiple studies have demonstrated that the activation of the noradrenergic (NA) system within BLA governs these modulatory effects. However, most of the current evidence has been obtained by direct infusion of synthetic NA or beta-adrenergic agonists. In the present study, we aimed to investigate the effect of endogenous NA release in the BLA, induced by a natural aversive stimulus (coyote urine), on memory consolidation for a neutral hippocampal-dependent task. We combined a weak version of the object location task (OLT) with mild predator odor exposure (POE) for our experiments. To investigate the role of the BLA in memory modulation, a subset of the animals (Wistar rats) were treated with the non-selective beta-blocker propranolol at the end of the behavioral procedures. Hippocampal tissue was collected either 90 minutes after drug infusion or 24 hours after the OLT test. The samples were used to determine the levels of phosphorylated CREB (pCREB) and activity-regulated cytoskeleton-associated protein (Arc) - two molecular markers of experience-dependent changes in neuronal activity. The result suggests that POE is a behavioral paradigm suitable for studying the interaction between BLA and the hippocampus in memory prioritization and selectivity.

The origin and dynamics of a- 2010 pluvial flood in the valley of a large European river are described. In order to study how local people perceive this catastrophic event a small administrative unit (rural municipality) within Holocene floodplain (thus flooded to 90%) was chosen. Using a questionnaire a human-research survey was performed in the field among 287 people living on flood-prone area. Almost half of the interviewees feel safe and do not expect a flood recurrence (interpreted as a levee effect). 17% believe the levee was intentionally breached due to political issues. 6% of interviewees link the breach with small mammals using leeves as a habitat, eg. beavers, moles, foxes. Spatial distribution of the survey results are analyzed. Maps presenting: inundation height, economic loss, attitude to geohazards and perception of possible flood recurrence were drawn. Causes of the flood as viewed by local inhabitants and in the context of the riverine geological setting and its processes are discussed. Particular attention is paid to processes linking the levee breach location with specific geomorpic features of the Holocene floodplain. A wide perspective of fluvial geomorphology where erosive landforms of crevasse channels (and associated depositional crevasse splays) are indicators of geohazards was adopted. This distinct geomorphological imprint left by overbank flow is considered as natural flood marks. Such an approach is completely neglected by interviewees who overestimate a role of hydraulic structures.

The Attentional Control Theory (ACT) suggests that trait anxiety does not directly affect performance but may alter processing efficiency through the generation of compensatory strategies. However, the ACT does not describe the nature of these strategies, which could be reflective in nature. One hundred ten students (M = 20.12; SD = 2.10) completed questionnaires on trait anxiety, metacognitive beliefs, and emotion regulation strategies (ERS). They performed two working memory tasks: the digit span task from the WAIS-IV and an emotional n-back task. Anxiety, metacognitive beliefs, and maladaptive ERS were not linked to performance but were associated with longer response times. Additionally, participants using maladaptive ERS, such catastrophizing and rumination, made fewer negative commission errors and employed more strategies during the digit span. These findings support the role of the reflective level in influencing trait anxiety's impact on performance and emphasize considering metacognitive beliefs and maladaptive ERS for a comprehensive understanding of the ACT. Identifying these variables provides insights to optimize cognitive abilities and promote academic success.

This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytics policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, Conventional Machine Learning techniques such as Support Vector Machines (SVM) yield an accuracy rate of 52.57%, Naive Bayes at 57.44 %, and Logistic Regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional se-rial program can not meet the requirements of engineering。Therefore, reducing the calcula-tion time of temperature control simulation program has important engineering significance for real-time simulation of temperature field and stress field, and then adopting more reasona-ble temperature control and crack prevention measures. GPU parallel computing is introduced into the temperature control simulation program of massive concrete to solve this problem and the optimization is carried out. Considering factors such as GPU clock rate, number of cores, parallel overhead and Parallel Region, The improved GPU parallel algorithm analysis indicator formula is proposed. It makes up for the shortcomings of traditional formula that focus only on time. According to this formula, when there are enough threads, the parallel effect is limited by the size of the parallel domain, and when the parallel domain is large enough, the efficiency is limited by the parallel overhead and the clock rate. This paper studies the optimal Kernel execu-tion configuration. Shared Memory is utilized to improve memory access efficiency by 155%. After solving the problem of bank conflicts, an accelerate rate of 437.5x was realized in the sub-routine of the matrix transpose of the solver. The asynchronous parallel of data access and logi-cal operation is realized on GPU by using CUDA Stream , which can overlap part of the data access time. On the basis of GPU parallelism, asynchronous parallelism can double the compu-ting efficiency. Compared with the serial program, the accelerate rate of inner product matrix multiplication of the GPU asynchronous parallel program is 61.42x. This study further proposed a theoretical formula of data access overlap rate to guide the selection of the number of CUDA streams to achieve the optimal computing conditions. The GPU parallel program compiled and optimized by CUDA Fortran platform can effectively improve the computational efficiency of the simulation program for concrete temperature control, and better serve for engineering computing.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction networks identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed, and relevant evidence was summarized. Gene information will be useful in the discovery of biomarkers and the identification of therapeutic targets for AD.

The acetylcholinesterase-inhibitory potential of the oleanane-type triterpenes and their glycosides from the Terminalia arjuna (Combreatceae) bark, i.e. arjunic acid, arjunolic acid, arjungenin, arjunglucoside I, sericic acid, and arjunetin, is presented. The studies are based on the in silico pharmacokinetic and biomimetic studies, the acetylcholinesterase (AChE) inhibitory activity tests, and the molecular docking research. Based on the calculated pharmacokinetic parameters, arjunetin and arjunglucoside I are indicated as able to cross the blood-brain barrier. The compounds of interest exhibit marked acetylcholinesterase inhibitory potential, which was tested in the TLC bioautography test. The longest time to reach brain equilibrium is observed for both the arjunic and arjunolic acids and the shortest one for arjunetin. All compounds exhibit high and relatively similar magnitude of binding energies, varying from ca. -15 to -13 kcal/mol. The superposition of the most favorable positions of all ligands interacting with AChE is analyzed. The correlation between the experimentally determined IC50 values and the steric parameters of the molecules is investigated. The inhibition of the enzyme by the analyzed compounds shows their potential to be used as cognition enhancing agents. For the most potent compound (arjunglucoside I; ARG), the kinetics of AChE inhibition is tested. The Michaelis–Menten constant (Km) for the hydrolysis of the acetylthiocholine iodide substrate was calculated to be 0.011 mM.

Immune responses to influenza (flu) antigens reflect memory to prior infections or vaccinations, which might influence immunity to new flu antigens. Memory of past antigens has been termed “original antigenic sin” or more recently “immune imprinting” and “seniority”. We have researched a comparison between the immune response to live flu infections and inactivated flu vaccina-tions. A brief history of antibody generation theories is presented culminating in new findings about the immune-network theory suggesting that a network of clones exists between an-ti-idiotypic antibodies and T cell receptors. Findings regarding the 2009 pandemic flu strain and immune responses to it are presented, including memory B cells and conserved regions within the hemagglutinin protein. The importance of CD4+ memory T cells and cytotoxic CD8+ T cells responding to both infections and vaccinations are discussed and compared. Innate immune cells like natural killer (NK) cells and macrophages are discussed regarding their roles in adaptive immune responses. Antigen presentation via macroautophagy processes are described. New vaccines in development are mentioned along with the results of some clinical trials. The manu-script concludes with how repeated vaccinations are impacting the immune system and a sketch of what might be behind the imprinting phenomenon, including future research directions.

Astragaloside IV (AIV) – triterpenoid saponin from the Astragalus mongholicus roots with the proved acetylcholinesterase (AChE)-inhibitory capability was used as the substance with the potential to attenuate memory impairment. Scopolamine (SCOP), antagonist of muscarinic cholinergic receptors and lipopolysaccharide (LPS), neuroinflammation inducer were used to impair memory processes in the passive avoidance (PA) test in mice. This memory impairment in the SCOP-treated mice was attenuated by the earlier intraperitoneal (ip) administration of AIV in the dose of 25 mg/kg. The reduction of memory impairment by LPS was not observed, therefore AIV does not reverse memory impairments through the anti-inflammatory mechanisms. All the studied doses of AIV did not affect basic locomotor activity of mice. As a result of the post-mortem analysis of mice body tissues, the largest content of AIV was determined in the kidneys, then in the spleen, liver and the smallest one in brain by mass spectrometry.

Macrophages undergo different cellular states upon activation that can be hyporesponsive (tolerated) or hyperresponsive (primed or trained) to subsequent stimuli. Epigenetic modifications are known to play a key role in determining these cellular states. However, little is known about the role of signaling pathways that lead to these epigenetic modifications. Here, we examined the effects of various inhibitors targeting key signaling pathways induced by lipopolysaccharide (LPS) on tolerance and priming in the murine macrophages. We found that a prolonged inhibition (>18 h) of the MEK1/2-ERK signaling axis reversed tolerance and primed cells in expressing IL-1β and other inflammatory cytokines such as IL-6, TNFα, and CXCL10. Ectopic expression of catalytically active and inactive MEK1 mutants suppressed and enhanced IL-1β expression, respectively. Primed cells by the MEK1/2 inhibitor U0126 expressed higher levels of gene sets associated with immune responses and cytokine/chemokine production but lower levels of genes with cell cycle progression, chromosome organization, and heterochromatin formation than non-primed cells. Of interest, expression of the histone 3 lysine 9 (H3K9) methyltransferase Suv39h1 and the H3K9 methylation reader Cbx5 was substantially suppressed, whereas the H3K9 demethylase Kdm7a was enhanced, suggesting a role of the MEK1/2-ERK signaling axis in H3K9 demethylation. The H3K9 trimethylation levels in the genomic regions of IL-1β, TNFα, and CXCL10 were decreased by U0126. Also, the H3K9 methyltransferase inhibitor BIX01294 mimicked and overexpression of Cbx5 prevented the U0126 training effects in both RAW264.7 cells and bone marrow-derived macrophages. Collectively, these data suggest that prolonged inhibition of the MEK1/2-ERK signaling axis reverses tolerance and primed macrophages likely through decreasing H3K9 methylation levels.

Cu-Sn shape-memory microwires were fabricated by a glass-coated melt spinning method. Effects of Sn content on the microstructure and mechanical property of microwires were investigated. The phase transforms from martensite to austenite with Sn content increasing from 14.0 atomic percent (at.%) to 16.5 at.%, and highly ordered intermetallic phase, δ, formed with higher Sn content. The fracture stress, σf, and the critical stress for martensitic transformation, σMs, increases with Sn content increasing. The mechanical property as well as superelasticity were improved greatly by high cooling rate of the glass-coated melt spinning method.

We study the short−term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and modern languages. To model the short–term capacity, we have considered the number of words per interpunctions, the “word interval” , because this parameter can model how the human mind memorizes “chunks” of information. Since can be calculated for any alphabetical text, we can perform experiments − otherwise impossible − with ancient readers by studying the literary works they used to read. The “experiments” compare the of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of their similar short–-term memory capacity) and by defining an “overlap index”. We also define a population of universal readers who can read any the New Testament written in alphabetical language. More than 50% of the readers of specific languages overlap with the universal readers with probability . Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.

In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian Coherent Structures (LCS) in large-scale Direct Numerical Simulation (DNS) datasets across incompressible, supersonic, and hypersonic flow regimes. LCS play a significant role in turbulent boundary layer analysis, and our proposed methodology offers valuable insights into their behavior in various flow conditions. Our novel owning-cell locator method enables efficient, constant-time cell search, and the algorithm draws inspiration from classical search algorithms and modern multi-level approaches in numerical linear algebra. The proposed method is implemented for both multi-core CPUs and Nvidia GPUs, demonstrating strong scaling up to 32,768 CPU cores and up to 62 Nvidia V100 GPUs. By decoupling particle advection from other problems, we achieve modularity and extensibility, resulting in consistent parallel efficiency across different architectures. Our methodology was applied to calculate and visualize the FTLE on four turbulent boundary layers at different Reynolds and Mach numbers, revealing that coherent structures grow more isotropic proportional to the Mach number, and their inclination angle varies along the streamwise direction. We also observed increased anisotropy and FTLE organization at lower Reynolds numbers, with structures retaining coherency along both spanwise and streamwise directions. Additionally, we demonstrated the impact of lower temporal frequency sampling by upscaling with an efficient linear upsampler, preserving general trends with only 10% of the required storage. In summary, we present a particle search scheme for particle advection workloads in the context of visualizing LCS via FTLE that exhibits strong scaling performance and efficiency at scale. Our proposed algorithm is applicable across various domains requiring efficient search algorithms in large structured domains. While this manuscript focuses on the methodology and its application to LCS, an in-depth study of the physics and compressibility effects in LCS candidates will be explored in a future publication.

This paper presents a concise exploration of the synthetic biology currently to artificially 1 create life, juxtaposed against natural life’s emergence over Earth’s 4.5-billion-year history. It explores 2 the complexities of life’s origins, sustaining factors, and the consequences of synthetic life creation, 3 addressing key scientific milestones such as the Urey-Miller experiment and Jacques Monod’s pro- 4 posals. The narrative navigates through the roles of energy, Maxwell’s demons, bifurcations, and 5 genetic information in the emergence and evolution of complex biological systems. Despite significant 6 strides in genetic engineering, the challenge of creating life from scratch persists, necessitating a deep 7 understanding of diverse scientific disciplines. While synthetic biology holds promise for medicine 8 and biotechnology, the creation of truly autonomous synthetic life forms raises ethical concerns. The 9 paper also stresses the imperative for careful ethical evaluation of the societal and environmental 10 implications arising from these advancements.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

Cultural Tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists the tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for Riyadh for peak visitor time. Primarily, the map data and cultural event dataset are processed for location, such as grouping with Kriging Interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes are processed with word embedding. Meanwhile, the Social Network Sites (SNS) data like reviews and news are extracted with an external Application Programming Interface (API). The review data is processed with keyword extraction and word embedding, whereas the news data is processed with score value estimation. Lastly, the data are fused corresponding to a historical site and given to the Multi-Quadratic-Long Short Term Memory (MQ-LSTM) Recommendation System (RS); also, the recommended result with the map is stored in a database. Lastly, the database security is maintained with Locality Sensitive Hashing (LSH); moreover, by attaining higher performance values, the proposed model is experimentally verified.

This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility: the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, a characterization of the SMA wire at constant and variable loads was carried out: the characteristics detected are elongation vs. electric current and elongation vs. electrical resistance. The control system is PC-based with a data acquisition card (DAQ). A drive board has been designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.

Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.

This study aimed to compare procedural learning skills between Spanish-speaking preschool children (ages 4 years to 4 years, 11 months) with Developmental Language Disorder (DLD) and their typically developing (TD) peers. Using the Serial Reaction Time task, participants (30 DLD and 30 TD children) respond to visual stimuli in a sequenced manner over four blocks, followed by a random order block. The task assessed reaction time (RT) and accuracy. Results showed a significant interaction between group and block for reaction time (RT) and accuracy, with children with DLD exhibiting longer RTs and accuracy deficits across blocks. In contrast, the TD group showed higher RT efficiency and accuracy in the sequential blocks and as expected, decreased performance in the random block according to the experimental manipulation. Overall, the results of this investigation suggest that there was no implicit learning in the DLD group, as indicated by the SRT task paradigms of procedural memory. These findings align with the Procedural Deficit Hypothesis (PDH), suggesting that linguistic deficits in the TD population may derive from a general dysfunction of the procedural memory system domain in the Spanish context.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

In this study, additive manufacturing of reinforced parts using metallic wire as both a reinforcement component and a shape memory stimulus through Fused Deposition Modeling (FDM) was investigated. A Shape Memory Polymer (SMP) restores its original shape and recovers it upon specific stimuli. This research employed chromium-nickel metal wire as a reinforcing component to enhance mechanical properties and introduce the capability for thermal stimulation of polylactic acid (PLA) via electrical current using the "in-situ impregnation" method within FDM process. Reinforced specimens were fabricated with wire with two diameters of 0.1 and 0.15 mm, along with two volume percentages of 5 and 10. Comprehensive evaluations encompassing mechanical (tensile and flexural) and thermal properties of the printed specimens were conducted. The outcomes revealed a significant enhancement in both tensile and flexural properties of the polymer matrix due to the embedding metallic wire, even under elevated temperatures during bending test. Furthermore, the thermal properties of the reinforced specimens were examined by subjecting them to various voltages, resulting in temperature ranging from 36.4 to 150.1°C. These findings highlight the ability to tailor a wide range of mechanical properties and shape recovery in the reinforced specimens by carefully selecting the wire volume fraction, voltage, and wire diameter, thus regulating the materials properties with specific application requirements.

Generally, when cognitive radio users are in Underlay mode, through the analysis of the electromagnetic spectrum in time, frequency, space, and field of multiple dimensional data accounts for judging the inherent correlation of the radio spectrum used/idle state, this is the realization of cognitive radio users (secondary users, SUs) efficient access to the foundation of the limited spectrum resources. Therefore, how to efficiently use of spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper addressing the problem. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results show that our model achieved better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirmed the feasibility of the model.

Brain physiology and morphology are vulnerable to chronic stress, affecting cognitive performance and behavior. However, functional compounds found in food may alleviate these alterations. White quinoa (Chenopodium quinoa, Wild) seeds have high content of n-3 fatty acids, including alpha-linolenic acid. This work aimed to evaluate the possible neuroprotective role of a quinoa-based functional food (QFF) in rats. Prepubertal male Sprague-Dawley rats were fed with rat chow or QFF (50% rat chow + 50% dehydrated quinoa seeds) and exposed or not to restraint stress protocol (2 hours/day; 15 days). Four experimental groups were used: Non-stressed (rat chow), Non-stressed + QFF, Stressed (rat chow) and Stressed + QFF. Weight gain, locomotor activity (open field), anxiety (elevated plus maze, light-dark box), spatial memory (Y-maze), and dendritic length in the hippocampus were measured in all animals. QFF intake did not affect anxiety-like behaviors, while the memory of stressed rats fed with QFF improved compared to those fed with rat chow. In addition, QFF intake countered the stress-induced dendritic atrophy in pyramidal neurons located in CA3 area of the hippocampus. The results suggest that a quinoa-supplemented diet could have a protective role on the memory of chronically stressed rats.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Data-driven models (DDMs) are extensively used in environmental modeling but face challenges due to limited training data and potential results not adhering to physical laws. To address this challenge, this study developed a process-guided deep learning (PGDL) model, integrating a long short-term memory (LSTM) neural network and a process-based model (PBM), CE-QUAL-W2 (W2), to predict water temperature in a stratified reservoir. The PGDL included an energy constraint term from W2's thermal energy equilibrium into the cost function of the LSTM, besides the mean square error term. In PGDL, parameters were optimized by penalizing deviations from the energy law, ensuring adherence to physical constraints. Compared to LSTM, PGDL demonstrated enhanced satisfaction with the energy balance and superior performance in water temperature prediction. Even with less field data for training, PGDL outperformed both LSTM and calibrated W2 after pre-training with data generated using the uncalibrated W2. Therefore, integration of DDM with a PBM ensured physical consistency in water temperature prediction for complex stratified reservoirs with limited data. Moreover, pre-training the PGDL with PBM proved highly effective in mitigating bias and variance due to insufficient field measurement data.

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the motor's weight. This can result in decreased both the ride comfort and driving stability. To resolve this issue, this study aims to apply an optimal switching controller with a semi-active n actuator; magnetorheological (MR) damper. For the implementation of the optimal switching controller, a road type classification is also carried out. An acceleration sensor is used for road type classification, and control logics include a ride comfort controller; the linear quadratic regulator (LQR _Paved Road) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both LQR_ Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are undertaken to evaluate vibration control performance, including the ride comfort and driving stability at various road profiles.

This study examines the prospective association of egg consumption with multiple domains of cognitive function in older, community-dwelling men and women followed 16.3 years. Participants were 617 men and 898 women from the Rancho Bernardo Cohort aged 60 and older who were surveyed about egg intake/week in 1972-74, and attended a 1988-91 research visit when cognitive function was assessed with 12 tests. Analyses showed that egg intake ranged from 0-24/week (means: men=4.23.2, women=3.52.7, p<0.0001). In men, covariate-adjusted regressions showed egg intake was associated with better performance on Buschke total (p=0.04), long-term (p=0.02) and short-term (p=0.05) recall. No significant associations were observed in women (p’s>0.05). Analyses in those <60y at in 1972-74 showed egg intake was positively associated with scores on Heaton copying(p<.04) and the Mini-Mental Status Exam(MMSE;p<.02) in men, and category fluency(p<0.05) in women. Egg intake was not significantly associated with odds of poor performance on MMSE, Trails B, or category fluency in either sex. These reassuring findings suggest there are no long-term detrimental effects of egg consumption on multiple cognitive function domains, and for men, there may be beneficial effects for verbal episodic memory. Egg consumption in middle-age may also be related to better cognitive performance later in life.

Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building's earthquake response accurately. The training dataset consists of 1,197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (Machine Learning based Prediction of building structures' Earthquake Response), several metrics are employed. These include mean absolute percentage error (MAPE) and mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of MLPER as a reliable tool for predicting building earthquake response, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

Shape-shifting polymers usually require not only reversible stimuli-responsive ability, but also strong mechanical properties. A novel shape-shifting photochromic hydrogel system was designed and fabricated through embedding hydrophobic spiropyran (SP) into double polymeric network (DN) by micellar copolymerization. Here, sodium alginate (Alg) and poly acrylate-co-methyl acrylate-co-spiropyran (P(SA-co-MA-co-SPMA)) were employed as the first network and the second network respectively to realize high mechanical strength. After soaked in the CaCl2 solution, the carboxyl groups in the system undergo metal complexation with Ca2+ to enhance the hydrogel. Moreover, after the hydrogel is exposed to UV-light, the closed isomer of spiropyran in the hydrogel network can be converted into an open zwitterionic isomer MC, which is considered to interact with Ca2+ ions. Interestingly, Ca2+ and UV-light responsive programmable shape of the copolymer hydrogel can recover to its original form by immersing in pure water. Since its excellent metal ion and UV-light stimuli-responsive and mechanical properties, the hydrogel has potential applications in the field of soft actuators.

The microalgae Phaeodactylum tricornutum (PT) is distinguished by its rich nutrient profile, characterized by well-documented neuroprotective activities, including fucoxanthin (FX), a major carotenoid, and polyunsaturated omega-3 fatty acids (n-3 PUFA). The current study aims to evaluate the protective effects of a standardized extract of PT (Mi136) containing 2% FX on cognitive function, oxidative stress, and inflammation parameters in a mouse model of accelerated aging. Seventy-two (72) male mice were randomly assigned to the blank control group (BC), negative control group (NC), and four similar microalgae extract of PT groups (branded as BrainPhytTM) with different human equivalent doses. From day 01 to day 51, mice in the BC group were injected with a 0.9% normal saline solution, while mice in all other groups were subcutaneously injected with D-galactose (D-Gal) at a dose of 150 mg/kg once per day, five days per week. Results indicated that, for the three higher microalgae extract of PT dose groups, spatial cognitive function, swim latency, and step-through latency impairments induced by chronic D-Gal intoxication were significantly and fully inhibited, with mean values similar to those in the BC group during each day of testing. Similar benefits were observed in biochemical analysis, specifically regarding brain and plasma levels of lipid peroxidation, TNF-α, and IL-6 markers. These data underscore the positive effects of a standardized extract of PT containing 2% FX on cognitive function parameters such as spatial working memory, long-term memory, and short-term memory through the regulation of oxidative stress and inflammation pathways.

Deep reinforcement learning is one of the research hotspots in artificial intelligence and has been successfully applied in many research areas, however, the low training efficiency and high demand for samples are problems that limit the application To address these problems, a hierarchical episodic control model extending episodic memory to the domain of hierarchical reinforcement learning is proposed in this paper. The model is theoretically justified and employs a hierarchical implicit memory planning approach for counterfactual trajectory value estimation. Starting from the final step and recursively moving back along the trajectory, a hidden plan is formed within the episodic memory. Experience is aggregated both along trajectories and across trajectories, and the model is updated using a multi-headed backpropagation similar to bootstrapped neural networks. This model extends the parameterized episodic memory framework to the realm of hierarchical reinforcement learning and is theoretically analyzed to demonstrate its convergence and effectiveness. Experiments conducted in Four Room, Mujoco, and UE4-based active tracking , highlight that the hierarchical episodic control model effectively enhances training efficiency. It demonstrates notable improvements in both low-dimensional and high-dimensional environments, even in cases of sparse rewards.

The interconnection between the objectives of territorial development and the agro-ecological transition highlights the value of past knowledge in the sustainable management of resources and agro-ecological systems. However, retrospection in rural areas presents difficulties due to a lack of data. This paper contributes to the search for such knowledge from the past by developing an interactive methodology capable of combining heterogeneous sources of information and the activation of local collective memory. Its effectiveness is based on ensuring the interoperability of information and communication in an environment simultaneously shaped by geoinformatics and 3D geo-visualizations. This virtual environment fosters participation and interactivity, supported by representations of the paleo-landscape. Furthermore, synergies were achieved between information sources and their integration into local spatial systems. The application example involved identifying a rice field that existed from the 15th to the 19th century in Thessaly, Greece. It is an interesting case because the location and organization of cultivation in combination with its spatio-temporal coordination ensures the sustainable use of natural resources. It appears that the interplay between information and communication facilitates community participation and the activation of its collective memory as a source of information that enriches the search and local intelligence.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

In the field of adaptive building technologies, this research introduces the development of a self-regulating solar shading actuator, that utilizes the thermal shape memory effect. The study places its focus on addressing the actuator’s performance under diverse environmental conditions. Thermal simulations were carried out during the development of the individual components of the actuator and for the prediction of specific switching temperatures. The investigation includes an analysis of the sunshade’s response to varying environmental conditions, emphasizing its effectiveness on clear summer days and identifying challenges during overcast periods. The critical coordination between the solar collector and the shape memory alloy (SMA) wire is examined, shedding light on the impact of SMA temperature dynamics on the actuation performance. Through the integration of simulation data and real-world measurements, the study validates the thermal model for the solar collector, establishing the robustness of the system’s operation. This research work contributes significantly to the development of intelligent actuators and outlines the importance of validation of SMA-based applications under real conditions.

This paper describes a memory model with 2 levels of information. The lower-level stores source data, is Markov-like and unweighted. Then an upper-level ontology is created from a further 3 phases of aggregating source information, by transposing from an ensemble to a hierarchy at each level. The ontology is useful for search processes and the aggregating process transposes the information from horizontal set-based sequences to more vertical typed-based clusters. The base memory is essentially neutral, where any weighted constraints or preferences should be sent by the calling module. This therefore allows different weight sets to be imposed on the same linking structure. The success of the ontology typing is open to interpretation, but the author would suggest that when clustering text, the result was types based more on use and context, for example, 'linking' with 'structure' or 'provide' with 'web,' for a document describing distributed service-based networks. This allows the system to economise over symbol use, where links to related symbols will be clustered together. The author then conjectures that a third level would be more neural in nature and would include functions or operations to be performed on the data, along with related memory information.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

This paper explores indigenous perspectives on the understanding and management of cultural heritage within the Mapungubwe and Makgabeng Cultural landscapes in South Africa. State-based management system paid little attention to indigeneity resulting in contested interpretation of the archaeological past. This by-passing of indigenous input is a product of colonial and apartheid policies that glorified Western practices at the expense of black African heritage stakeholders. World heritage mechanisms are also skewed in favor of modern practice which is overly designed to be sensitive to tangible aspects of the past than the intangible elements that make up much of indigenous African heritage. We used the archaeology of Mapungubwe and Makgabeng Cultural landscapes as a test case for the impact of indigenous voices on heritage, but the results have wider applicability. We employed semi-structured interviews and focus group discussions to gather qualitative data that we calibrated with our lived experiences as products of Tsonga-speakers from this broader landscape. The results expose that indigenous people use physical manifestation of sites, ethnographic inquiry, names, oral history, spirit mediums, landscape memory, genealogy, and praise poetry to identify, interpret, evaluate, and manage their cultural heritage. Additionally, indigenous perspectives which rely on collective memory, ritual practices, and indigenous knowledge systems have huge potential for enriching our understanding of the archaeological heritage. We propose a more inclusive archaeology and heritage management practice that involve indigenous groups in the identification, interpretation, grading, and presentation of the African past.

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

This study aimed to determine whether the EQ-5D-5L tool captures the most common persistent symptoms, such as fatigue, memory/concentration problems and dyspnea in patients with post COVID-19 conditions, and if adding these symptoms improves the explained variance of the health-related quality of life (HRQoL). In this exploratory cross-sectional study, two cohorts of Swedish patients (n=177) with a history of COVID-19 infection answeared a questionnaire covering sociodemographic characteristics, clinical factors and HRQoL assessed using EQ-5D-5L with the Visual Analogue Scale (EQ-VAS). Spearman rank correlation and multiple regression analyses were employed to investigate the extent to which the most common persistent symptoms such as fatigue, memory/concentration problems and dyspnea were explained by the EQ-5D-5L. The explanatory power of EQ-5D-5L for EQ-VAS was also analysed, both with and without inclusion of symptom(s). We found that the EQ-5D-5L dimensions partly captured fatigue and memory/concentration problems but performed poorly in capturing dyspnea. Specifically, the EQ-5D-5L explained 55% of the variance in memory/concentration problems, 47% in fatigue and only 14% in dyspnea. Adding fatigue to the EQ-5D-5L increased the explained variance of the EQ-VAS by 5.7%, while the addition of memory/concentration problems and dyspnea had a comparatively smaller impact on the explained variance. Our study highlights the EQ-5D-5L’s strength in capturing fatigue and memory/concentration problems in post COVID-19 patients. However, it also underscores the challenges in assessing dyspnea in this group. Fatigue emerged as a notably influential symptom, significantly enhancing the EQ-5D-5L's predictive ability to for the EQ-VAS score in these patients.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

This study explores the multifaceted influence of litter size, maternal care, exercise, and aging on rats' neurobehavioral plasticity and dentate gyrus microglia dynamics. Body weight evolution revealed a progressive increase until maturity, followed by a decline during aging, with larger litters exhibiting lower weights initially. Notably, exercised rats from smaller litters displayed higher body weights during mature and aged stages. Dentate gyrus volumes showed no significant differences among groups, except for aged sedentary rats from smaller litters, which exhibited a reduction. Maternal care varied significantly based on litter size, with large litter dams showing lower frequencies of caregiving behaviors. Behavioral assays highlighted the detrimental impact of a sedentary lifestyle and reduced maternal care/large litters on spatial memory, mitigated by exercise in aged rats from smaller litters. Microglial dynamics in the layers of dentate gyrus revealed age-related changes modulated by litter size and exercise. Exercise interventions mitigated microgliosis associated with aging, particularly in aged rats. These findings underscore the complex interplay between early-life experiences, exercise, microglial dynamics, and neurobehavioral outcomes during aging.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

We study epidemic spreading in complex networks by a multiple random walker approach. Each walker performs an independent simple Markovian random walk on a complex undirected (ergodic) random graph where we focus on Barabási-Albert (BA), Erdös-Rényi (ER) and Watts-Strogatz (WS) types. Both, walkers and nodes can be either susceptible (S) or infected and infectious (I) representing their states of health. Susceptible nodes may be infected by visits of infected walkers, and susceptible walkers may be infected by visiting infected nodes. No direct transmission of the disease among walkers (or among nodes) is possible. This model mimics a large class of diseases such as Dengue and Malaria with transmission of the disease via vectors (mosquitos). Infected walkers may die during the time span of their infection introducing an additional compartment D of dead walkers. Infected nodes never die and always recover from their infection after a random finite time. This assumption is based on the observation that infectious vectors (mosquitos) are not ill and do not die from the infection. The infectious time spans of nodes and walkers, and the survival times of infected walkers, are represented by independent random variables. We derive stochastic evolution equations for the mean-field compartmental populations with mortality of walkers and delayed transitions among the compartments. From linear stability analysis, we derive the basic reproduction numbers R M , R 0 with and without mortality, respectively, and prove that R M < R 0 . For R M , R 0 > 1 the healthy state is unstable whereas for zero mortality a stable endemic equilibrium exists (independent of the initial conditions) which we obtained explicitly. We observe that the solutions of the random walk simulations in the considered networks agree well with the mean-field solutions for strongly connected graph topologies, whereas less well for weakly connected structures and for diseases with high mortality. Our model has applications beyond epidemic dynamics, for instance in the kinetics of chemical reactions, the propagation of contaminants, wood fires, among many others.

In this paper, a new type of self-excited thermomechanical oscillator containing an oscillating shape memory alloy (SMA) filament with two symmetrically arranged spheres is investigated. The self-excitation of the oscillations is due to a heater of constant temperature, which causes periodic contractions of the filament when it approaches it. The contracted filament moves away from the heater a distance sufficient to cool it. Under the action of the weight of the spheres, the cooled filament re-approaches the heater, causing the above processes to repeat periodically. On the basis of experimental studies, approximating functions of the heater's heat field distribution are derived. A dynamic model of the oscillator has been created, in which the minor and major hysteresis in the SMA alloy and the distribution of the heat field around the heater have been taken into account. By numerical solutions of the differential equations, the laws of motion of the spheres are obtained. The displacements of the spheres in two perpendicular directions were measured using an experimental system. The obtained experimental results validate the proposed dynamic model and its assumptions with a high degree of confidence. Conclusions are drawn about the stochastic nature of the oscillations due to the hysteresis properties of the SMA and the temperature variation of the natural frequency of the oscillating system.

The ongoing SARS-CoV-2 pandemic has raised concerns surrounding immunological protection against the virus, particularly for people with inborn errors of immunity (IEI). While COVID-19 vaccination induces robust antibody, memory B-cell, and T-cell responses in healthy individuals, how well vaccination protects people with IEI against infection and severe disease remains unclear – especially in the context of new viral variants and vaccine formulations – leading to anxiety, ongoing self-isolation, and repeated vaccination with limited evidence of increased efficacy. Whilst most people with IEI generate some level of cellular and/or humoral immunity to COVID-19 vaccination, the level of protection and durability of the response are unclear. Alongside vaccination, antibody-based therapeutics are aimed at limiting infection and severe disease in this cohort. Immunoglobulin replacement therapy (IgRT) provides passive immunity in antibody-deficient individuals. However, to successfully prevent SARS-CoV-2 infection, a sufficient number of neutralizing antibodies must be present in product. While antibodies against circulating variants are eventually present in IgRT products (both from natural infection and vaccination of the donors), evidence of their capacity to recognize new variants is limited. Furthermore, while antibody-based pre- and post-exposure prophylaxis can be effective, they are susceptible to decreased efficacy in the context of variant evolution. This review provides an in-depth overview of current knowledge about COVID-19 vaccine efficacy in IEIs, the efficacy of SARS-CoV-2-specific antibody products, and knowledge and technological advances required for continued protection of people with IEI.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

This work introduces a neuromorphic sensor based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of spiking neuron in order to make the sensor to generate spikes with frequency that depends on the applied force. The performance of the proposed sensor is evaluated for the control of a SMA-actuated ro-botic finger by monitoring the force during steady state when the finger pushes on a tweezers. For comparison purposes, we performed similar evaluation when the SNN receives input from a widely used compression load cell (CLC). The results show that the proposed FSR based neuro-morphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous with good variation range. However, when compared to the CLC, the proposed sensor has lower linearity of the response, but it benefits from two orders of magnitude lower power consumption. These aspects imply that the neuromorphic sensor based on FSR are useful for bioinspired control of humanoid robotics representing a low power and low cost alternative the widely used sensors.

Ecosystemic services are those that provide material or immaterial benefits to the inhabitants of a landscape from its water, food, medicines and raw materials. They are classified into four types: provisioning, regulating, supporting and cultural. The latter can be defined as those non-material benefits that humans obtain through ecosystems. They include educational value, cultural diversity, sources of inspiration, spirituality and religious values, aesthetic value, social relations, rootedness or belonging, cultural heritage, recreational and ecotourism services, and scientific knowledge, among others. Characterising such diverse factors requires a systematisation in study facets for their management. We propose three: memory, image and socio-system, as differentiated spheres of approach to the urban landscape. The memory facet would include the ecosystemic services of belonging, cultural heritage and, in certain themes, cultural diversity, sources of inspiration and religious values. The image facet includes the aesthetic value and perceptual aspects of ecotourism. In the socio-system we would study the educational value, scientific knowledge and recreational or spirituality services, reflected in the endowments; but also social relations, rootedness and another vision of ecotourism.

Background: Dietary quality and the consumption of antioxidant-rich foods has been shown to protect against memory decline. Therefore, this double-blind, randomized, placebo-controlled study aimed to investigate the effects of a nutritional supplement on changes in cognitive performance. Methods: In adults aged 40 to 70 years with subjective memory complaints, participants were randomly allocated to take a supplement containing vitamin E, astaxanthin, and grape juice extract daily for 12 weeks or a matching placebo. The primary outcomes comprised changes in cognitive tasks assessing episodic memory, working memory, and verbal memory. Secondary and exploratory measures included changes in the speed of information processing, attention, and self-report measures of memory, stress, and eye and skin health. Moreover, changes in plasma concentrations of brain-derived neurotrophic factor, malondialdehyde, tumor-necrosis factor-α, and interleukin-6 were measured, along with changes in skin carotenoid concentrations. Results: compared to the placebo, nutritional supplementation was associated with larger improvements in episodic memory, a self-report measure of everyday memory, increased plasma brain-derived neurotrophic factor, decreased plasma malondialdehyde, and increased skin carotenoid concentrations. However, there were no group differences in changes in the remaining outcome measures. Conclusions: Twelve weeks of supplementation with a nutritional supplement was associated with improvements in episodic memory and several biological markers associated with cognitive health. Future research will be essential to extend and validate the current findings.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

In this paper thermo-mechanical behavior of 4D printed Polylactic Acid (PLA) was investigated, focusing on its response to varying strain rates and temperatures below the glass transition temperature. Dynamic mechanical analysis and uniaxial tensile tests confirmed PLA's dependency on strain rate, showcasing its sensitivity to external stimuli. Stress-strain curves exhibited typical thermoplastic behavior, with yield stresses varying with strain rates, underscoring PLA's responsiveness to different deformation speeds. Clear strain rate dependence was observed, particularly at quasi-static rates, with temperature and strain rate fluctuations significantly influencing PLA's mechanical properties, including yield stress and deformation behavior. Isothermal compression tests demonstrated predictable stress-strain curves with distinct yield points, while adiabatic tests reveal additional complexities such as heat accumulation leading to further softening. Thermal imaging revealed temperature increase during deformation, highlighting the material's thermo-sensitive nature. These findings provide the basis for future research with the focus on advanced modeling techniques and mitigation strategies for self-heating effects, aiming to enhance PLA-based product reliability and performance in applications with deformations at higher strain rates, while also developing models to simulate shape recovery in 4D printed PLA structures at both cold and hot programming temperatures.

This article explores the intricate dynamics of complexity and emergence through the lens of a graphical model representing hippocampal functions. Utilizing a series of overlapping curves that intersect at varying degrees and frequencies, we create a visual metaphor for the sophisticated processes to store information observed in the hippocampus. This model not only depicts the spatial and frequency-related interactions of these curves but also illustrates the emergent patterns that arise from these interactions. These patterns exemplify the principles of complexity and emergence, where the collective behavior of simple elements results in intricate and unexpected outcomes. This graphical representation serves as a bridge between the abstract mathematical concepts of complexity theory and the tangible biological processes observed in neuroscience. By drawing parallels to the hippocampus, known for its role in memory formation and spatial navigation, the article sheds light on how complex systems, such as neural networks, exhibit properties that are more than just the sum of their parts. The model thus offers a unique perspective on understanding complex systems, not only in neuroscience but in broader scientific inquiries where complexity and emergent phenomena are pivotal.

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (&lt;50%), compared to those presented in the related literature where the whole set of channels is used.

The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

Preterm birth accounts for about 10% of births worldwide. Studying risk factors for perinatal brain damage is essential, as findings suggest that almost 20% of disabilities are linked to risks in the early stages of development. This research aimed to study longitudinal changes in intelligence from 6 to 8 years of age in a sample of 39 preterm children with a history of risk of brain damage and a control group of 35 children born at term. The Wechsler Intelligence Scale (WISC-IV) was used for cognitive measurement. The results showed that the preterm group obtained significantly lower scores than the control group. The working memory indicator significantly affected the interaction between age and prematurity. We consider that it is crucial to expand the knowledge we have about the neurocognitive development of premature infants both in specific cognitive domains and in age ranges so that the information obtained helps to predict the probability of presenting cognitive alterations from early stages and, therefore, to implement intervention strategies and programs based on scientific evidence and complemented in their design by clinical experience, empirical and theoretical knowledge of the different professionals involved in infant cognitive intervention.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

We previously reported nano-pulse treatment (NPT), a pulsed power technology, resulted in 4T1-luc mammary tumor elimination and a strong in situ vaccination, thereby completely protecting tumor-free animals against a second live tumor challenge. The mechanism whereby NPT mounts effective antitumor immune responses in the 4T1 breast cancer predominantly immunosuppressive tumor microenvironment (TME) remains unanswered. In this study, orthotopic 4T1 mouse breast tumors were treated with NPT (100 ns, 50 kV/cm, 1000 pulses, 3 Hz). Blood, spleen, draining lymph nodes and tumors were harvested at 4-hour, 8-hour, 1-, 3-, 7-day. Additional studies were conducted over 3-month posttreatment intervals for the analysis of frequencies, death and functional markers of various immune cells in addition to suppressor function of regulatory T cells (Tregs). NPT was verified to elicit strong in situ vaccination (ISV) against breast cancer and promoted both acute and long-term T cell memory. NPT abolished immunosuppressive dominance systemically and in the TME by substantially reducing Tregs, myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs). NPT induced apoptosis in Tregs and TAMs. It also functionally diminished the Treg suppression capacity, likely explained by the downregulation of activation markers, particularly 4-1BB and TGFβ, and a phenotypic shift from predominantly activated (CD44+CD62L-) to naïve (CD44-CD62L+) Tregs. Importantly, NPT selectively induced apoptosis in activated Tregs and spared effector CD4+ and CD8+ T cells. These changes were followed by a concomitant rise in CD8+CD103+ tissue-resident memory T cells and TAM M1 polarization. These findings indicate that NPT effectively switches the TME and secondary lymphatic systems from an immunosuppressive to an immunostimulatory state allowing cytotoxic T-cell function and immune memory formation to eliminate cancer cells and account for the NPT in situ vaccination.