Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes, and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with intractable epilepsy, and we observed an increasing difference in NF signal between conditions (Up−Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF's potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF's application to memory and provides valuable insights for developing more efficient memory enhancement strategies.

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.

Understanding the complex relationship between sleep and memory is a major challenge in neuroscience. Many studies on memory consolidation in humans suggest that sleep triggers offline memory processes, resulting in less forgetting of declarative memory and performance stabilization in non-declarative memory. However, issues related to non-optimal experimental designs, task characteristics and measurements, and inappropriate data analysis practices can significantly affect the interpretation of the effect of sleep on memory. In this article, we discuss these issues and suggest constructive solutions to address them. We believe that implementing these solutions in future sleep and memory research will significantly advance this field by improving the understanding of the specific role of sleep in memory consolidation.

In the last years, a recent growing line of research has been developed with fruitful results by using fractional and conformable derivatives in the iterative procedures of classical methods for solving nonlinear equations. In that sense, the use of conformable derivatives has shown better behavior than fractional ones, not only in the theory, but also in the practice. In this work, we adapt the approximation of conformable derivatives in order to design the first conformable derivative-free iterative schemes to solve nonlinear equations: a Steffensen’s type method and a Secant type method; the latter with memory. Convergence analysis is made, preserving the order of classical cases, and the numerical performance is studied in order to confirm the results in the theory. It is shown that these methods can present some numerical advantages versus their classical partners, with wide sets of converging initial estimations.

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.

The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

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.

Heusler Ni-Mn-Ga alloys with martensitic transformation being multiferroic materials are touted for magneto- and multi-caloric effect (MCE) and moreover, they exhibit magnetic shape memory (MSM) phenomena. MCE benefits from high saturation magnetization and also large variability and sensitivity of transformation temperatures on alloying. In contrast, MSM phenomena are limited by low temperatures of both ferromagnetic and ferroelastic transformations. In an attempt to increase the transformation temperatures and to understand the physical fundamentals beyond transformation variations and magnetic ordering, we experimentally investigate the magnetic and transformation properties in Ni2MnGa alloyed with transitional elements spanning from Cr to Cu. These elements are consecutively substituted on Ni, Mn, and Ga sites, to establish the effect of positional substitution and elemental evolution. The elemental dependence of saturation magnetization, magnetocrystalline anisotropy, Curie temperature, and martensitic transformation temperatures are compared with (non-)stoichiometric Ni-Mn-Ga Heusler compounds. The observed behavior is complex and there are no clear trends of transformation temperatures as a function of the number of valence (non-bonding) electrons per atom, however, the saturation magnetization at 10K and Curie temperature seems to follow the Pauli-Slater curve. Obtained results can serve as useful rectification for the theoretical prediction of new Ni-Mn-Ga-X Heusler alloys.

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.

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.

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.

(1) Background: Many processes play a key role in how language functions, and these processes are strongly related to reading. The aim of our research was to explore the relationship between reading impairment associated with adult acquired cognitive-linguistic impairment (aphasia) and the components of verbal working memory involved in language functioning. (2) Methods: We measured the reading abilities of a total of twenty-two adults diagnosed with aphasia using the Adult Acquired Reading Assessment. To assess working memory, we applied the measurement procedures available in Hungarian for measuring verbal working memory. Correlation analyses were performed to investigate the relationship between reading and the components of working memory involved in processing verbal information. In order to explore this relationship as comprehensively as possible, the results were analysed quantitatively through group-level analyses and qualitatively through case studies. (3) Results: The results suggest that the functioning of phonological short-term memory and verbal working memory is crucial for the reading of vowels, for certain word reading sub-processes such as reading complex words and pseudowords, and for text comprehension tasks, in particular for the processing of implicit text-level infor-mation. (4) Conclusions: The data support a strong relationship between reading and working memory, but not all mechanisms are related with the same weighting. The data will contribute to the understanding of the relationship between reading and working memory, which is important in order to see which cognitive components are dominant in reading instruction and which other cognitive mechanisms are affected in poor readers.

An impaired healing response underlies diabetic foot wounds chronicity, frequently translating in amputation, disability, and early mortality. In addition, diabetics frequently suffer of the underappreciated episode of post-epithelization ulcer recurrence. Recurrence epidemiological data are alarmingly high; so that the ulcer is considered in “remission” and not healed for the time it remains epithelialized. Recurrence may result from the conspiracy of behavioral and biological factors. Although the damaging role of behavioral, clinical predisposing factors is undebatable, the role of endogenous biological signalers that prime the residual scar tissue to recurrence has remained elusive. We propose that ulcer recurrence is deeply impinged by chronic hyperglycemia and its downstream biological effectors, which originate epigenetic drivers that enforce abnormal pathologic phenotypes to dermal fibroblasts, and keratinocytes as memory cells. Hyperglycemia-derived cytotoxic reactants accumulate and modify dermal proteins, reduce scar tissue mechanical tolerance, and disrupt fibroblasts-secretory activity. Accordingly, the combination of epigenetic and local and systemic cytotoxic signalers, induce the onset of “at-risk phenotypes” as premature skin cells aging, dysmetabolism, inflammatory, pro-degradative, and oxidative programs that may ultimately converge to scar cells demise. Unfortunately, post epithelialization recurrence rates data are missing in clinical studies of accepted ulcer healing therapies during follow-up periods. Intra-ulcer infiltration of epidermal growth factor exhibits the most consistent remission data with the lowest recurrences during 12-month follow-up. Recurrence data should be regarded as a valuable clinical endpoint during the investigational period for each emergent healing candidate.

Liquid crystal elastomers (LCEs) are shape morphing materials whose large and reversible shape transformation results from the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape changing behaviors under the stimuli are largely directed by LC orientations, and therefore various strategies have been developed to spatially modulate the LC alignments. But most of the methods suffer from complex fabrication technologies or intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some types of LCEs, such as polysiloxane side-chain LCEs or thiol-acrylate main-chain LCEs, etc, have been achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2D and 3D shape changing performances achieved by mechanically programming the polydomain LCE between the two crosslinking steps. The resulting LCEs exhibited reversible thermal induced shape transformation between the initial and programmed shapes due to the two-way memory between the first network structure and the second network structure. Our work would be potential for expanding the applications of LCE materials in actuators, soft robotics and smart structures where arbitrary and easily programmed shape morphings are needed.

Norepinephrine plays an important role in modulating memory consolidation and evocation through its beta-adrenergic receptors (Adrβ: β1, β2 and β3), which are expressed in the hippocampus (HC) and amygdala (AMY). Here we hypothesized that multisensory stimulation would reverse the memory impairment caused by the inactivation of the Adrβ3 with consequent inhibition of sustained glial-mediated inflammation and glutamatergic depuration. To test this, 21- and 86-day-old Adrβ3KO mice and respective controls underwent to (i) gustative and olfactive stimuli of positive and negative valence associated with (ii) intellectual challenges to reach the food in addition to (iii) objects in hidden places (iv) foraging for 8 weeks followed by (v) analysis of GFAP, Iba-1 and EAAT2 protein expression in the HC and AMY. While this protocol restored the memory impairment when applied to Adrβ3KO animals immediately after weaning, it had no effect when applied to adult animals. In fact, we observed that aging worsens the memory of Adrβ3KO mice. Although no significant expression of GFAP and Iba1 were observed in HC of young and old mice, Adrβ3KO increased EAAT2 expression HC of old mice, while MS didn’t change EAAT2 in young mice but enhanced it expression in older. Relative to AMY of old mice Adrβ3KO increased GFAP expression compared whit WT mice and MS sustained the GFAP expression and increased the EAAT2 expression compared with WT group. These results suggest that a richer and more diverse environment helps to correct memory impairment when applied right after weaning Adrβ3KO animals, also show that the changes in GFAP, Iba1 and EAAT2 expression levels in young and old mice indicate a functional significance in the process of learning and memory and that the control of neuroinflammation in limbic areas mediates this response. They also reinforce the idea that disruption of noradrenergic signaling could be involved in the cognitive impairment observed later in life.

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.

Obstructive sleep apnea (OSA) is a chronic condition characterized by intermittent hypoxia (IH) and sleep fragmentation (SF). In murine models, chronic SF can impair endothelial function and induce cognitive declines. These deficits are likely mediated, at least in part, by alterations in Blood-Brain Barrier (BBB) integrity. Male C57Bl/6J mice were randomly assigned to SF or sleep control (SC) conditions for 4 or 9 weeks, and in a subset 2 or 6 weeks of normal sleep recovery. Presence of inflammation and microglia activation were evaluated. Explicit memory function was assessed with the novel object recognition (NOR) test, while BBB permeability was determined by systemic dextran-4kDA-FITC injection and Claudin 5 expression. SF exposures resulted in decreased NOR performance and in increased inflammatory markers and microglial activation as well as enhanced BBB permeability. Explicit memory and BBB permeability were significantly associated. BBB permeability remained elevated after 2 weeks of sleep recovery (p<0.01) and returned to baseline values only after 6 weeks. Chronic SF exposures mimicking the fragmentation of sleep that characterizes patient with OSA elicits evidence of inflammation in brain regions and explicit memory impairments in mice. Similarly, SF is also associated with increased BBB permeability, the magnitude of which is closely associated with cognitive functional losses. Despite normalization of sleep patterns, BBB functional recovery is a protracted process that merits further investigation.

Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.

Nature provides plenty of good examples for simple and very efficient joint assemblies. One example is the enormously flexible cervical spine of American barn owls, which consists of 14 cervical vertebrae. Each pair of vertebrae produces a comparatively small individual movement in order to provide a large overall movement of the entire cervical spine. The biomimetic replication of such joints is difficult due to the delicate and geometric unrestricted joint shapes as well as the muscles that have to be mimicked. Using X-ray as well as micro computed tomography images and with the utilisation of additive manufacturing, it is possible to produce the owl neck vertebrae in scaled-up form, to analyse them and then to transfer them into technically usable joint assemblies. The muscle substitution of these joints is realised by smart materials actuators in the form of shape memory alloy wire actuators. This actuator technology is outstanding for its muscle-like movement and for its enormous energy density [1,2]. The disadvantage of this wire actuator technology is the low rate of contraction, which means that a large length of wire has to be installed to generate adequate movement. For this reason, the actuator wires are integrated into additively manufactured carrier components to mimic the biological joints. This results in joint designs that compensate for the disadvantages of the small contraction of the actuators by intelligently installing large wire lengths on comparatively small installation spaces, while also providing a sufficient force output. With the help of a test rig, the developed technical joint variants are examined and evaluated. This demonstrates the technical applicability of this bionic joints.

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.

Working memory (WM) is a brain system for short-term storage and manipulation of information and plays an important role in complex cognitive tasks. In the synaptic theory of WM memorized elements are stored in the form of short-term potentiated connections in a sample population of neurons. In this paper, we show that such populations can be formed due to the mechanisms of spike-timing-dependent plasticity (STDP) – the phase dependence associated with the ratio of the pulse times of the interacting neurons. We propose a WM model considering two types of plasticity: short-term plasticity and STDP. We have shown formation of neuronal clusters encoding items in the WM model, that can be formed by external stimulation of a group of neurons due to the mechanisms of STDP and hold and reactivated by short-term plasticity mechanisms. The dynamic formation of neuronal clusters instead of pre-formed clusters gives additional flexibility to the model.

Background: Despite hearing aids adequately compensate for hearing loss, a substantial proportion of the population leave their hearing difficulties untreated. Even though this is a well-known clinical issue, the optimal approach to address this issue during the hearing rehabilitation process is still unclear. Purpose: The present study aims to characterise behavioural and neurophysiological auditory and cognitive processing skills in experienced hearing aid users versus those with normal hearing, aimed at providing clinicians with the evidence required to adequately manage the expectations of their clients, thus indirectly reinforcing hearing-aid adoption within the adult population with hearing loss. Research design: Behavioural tests included auditory, cognitive, and speech-in-noise tasks; and neurophysiological testing included cortical auditory evoked potentials evoked by a /da/ stimulus presented at 65 dB SPL. The tests were selected based on previous literature supporting specific speech-understanding skills. Study sample: Ten participants (7 female, 21—68 years) with bilateral, mild-moderate to moderately-severe sensorineural hearing loss (HL), and 10 with clinically normal hearing (NH, 8 female, 19—62 years) participated in the study. Data collection and analysis: Behavioural data was analysed using multivariate analysis of variance (MANOVA) and neurophysiological analysis used measurements such as independent t-test, time-frequency analysis and inter-trial phase coherence. Results: The NH and HL groups presented similar scores in all the behavioural tasks. Time-frequency analysis revealed a statistically significantly reduction in alpha (8–12 Hz) synchronisation at the centro-frontal electrodes in the HL group – a brain activity pattern that has been associated with listening effort, inhibition and selective attention. Conclusions and significance: Results support the conclusion that hearing aids are effective in compensating for the audibility of their users, enabling them to perform at similar levels than their normal-hearing peers. However, the reduced alpha synchronisation observed in the HL population indicates that adequate audibility does not extend to improved neural responses. Future studies need to investigate the induced activity in speech understanding paradigms to explore the auditory processing differences at cortical level. The results are only from a small sample size but the findings have the potential to support clinicians in managing adequately the expectations of their clients in regards the benefits of hearing aid technologies.

The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

Conclusive evidence that specic long-term memory formation relies on den- dritic growth and structural synaptic changes has proven elusive. Connec- tionist models of memory based on this hypothesis are confronted with the so-called plasticity stability dilemma or catastrophic interference. Other fun- damental limitations of these models are the feature binding problem, the speed of learning, the capacity of the memory, the localisation in time of an event and the problem of spatio-temporal pattern generation. This paper suggests that the generalisation and long-term memory mechanisms are not correlated. Only the development and the improvement of the feature ex- tractors in the cortex involves structural synaptic changes. We suggest the long-term memory has a separate mechanism which involves protein synthe- sis to encode the information into the structure of these proteins. A model of memory should be capable of explaining the dierence between memorisation and learning. Learning has in our approach two dierent mechanisms. The generalisation in the brain is handled by the proper development of the links between neurons via synapses. The Hebbian learning rule could be applied only for this part of learning. Storing an internal ring pattern involves, in our approach, a new mechanism which puts the information regarding this ring pattern into the structure of special proteins in such a way that it can be retrieved later. The hypotheses introduced in this article includes a physiological assumption which has not been yet verified because it is not currently experimentally accessible. Keywords: Waves, Protein Synthesis, Resonance, Long Term Memory Preprint submitted to Neural Networks

The current theories of consciousness do not address the two major problems:What is the solution to the hard problem of consciousness or What is the experience of self-existence? and How is conscious perception computed? We address these questions using our hypothesized theory:The Self Simulation theory and propose a model of computation of consciousness.The components of the model are an interconnected network of auxiliary processors or Auxiliary Neural Networks(AN) which receives input from the external sensory modalities,computes a representation of the information and relays it to the NCC(Neural Correlates of Consciousness), the NCC – a processing unit which can be neuro anatomically distributed in location but computationally connected to each other and to the Auxiliary Networks,and a working memory which the NCC references for further computation of information.Computation of conscious perception is achieved via the decoding of the content of the memory by the NCC.The self simulation theory accounts not only for the various phenomenal experiences but also experimentation paradigms conducted in the past.

This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

A Shape Memory Alloy (SMA) enabled Overtopping Wave Energy Converter (OWEC) that maximizes its overtopping discharge, and thus energy output under different wave conditions is presented. Among all the parameters affecting the overtopping discharge rate, the crest freeboard height is the most influential one to control the overtopping discharge rate and the stored overtopping volume behind it. Currently, all the OWEC crest freeboard heights are fixed by design to maximize the discharge rate on one particular sea state. In the present study, we show that the SMA can adjust the crest freeboard height through a control system based on the sea state and achieve an optimal overtopping discharge rate. A scaled OWEC model is built in the lab with its crest freeboard height controlled by springs made of SMA. The length (and thus tension) of the springs is controlled by temperature changes by changing the passing current through the springs. By adjusting the length of the springs based on the incoming wave condition, we adjust the freeboard to an optimal height known to generate a maximum overtopping discharge rate for energy conversion. This smart material-enabled design can maximize the overtopping discharge and thus the output power of the OWEC under various wave conditions. Furthermore, the simplicity of using SMA springs as the actuator leads to the minimum number of moving mechanical parts, which can remarkably decrease maintenance costs. As the proof of concept, two types of tests are conducted in the laboratory using the same OWEC model under several random wave trains generated from spectra with different significant wave heights - one type with a fixed crest freeboard height and the other type featuring the adjustable crest freeboard height controlled by the springs. The substantial increase of harvested output power in the OWEC with the adjustable crest freeboard height may pave the way for more efficient wave energy conversion systems.

Directed movement towards a target requires spatial working memory, including processing of sensory inputs and motivational drive. In a stimulus-driven, operant conditioning paradigm designed to train zebrafish, we present a pulse of light via LED’s and/or sounds via an underwater transducer. A webcam placed below a glass tank records fish swimming behavior. During operant conditioning, a fish must interrupt an infrared beam at one location to obtain a small food reward at the same or different location. A timing-gated interrupt activates robotic-arm and feeder stepper motors via custom software controlling a microprocessor (Arduino). “Ardulink”, a JAVA facility, implements Arduino-computer communication protocols. In this way, full automation of stimulus-conditioned directional swimming is achieved. Precise multi­day scheduling of training, including timing, location and intensity of stimulus parameters, and feeder control is accomplished via a user-friendly interface. Our training paradigm permits tracking of learning by monitoring, turning, location, response times and directional swimming of individual fish. This facilitates comparison of performance within and across a cohort of animals. We demonstrate the ability to train and test zebrafish using visual and auditory stimuli. Current methods used for associative conditioning often involve human intervention, which is labor intensive, stressful to animals, and introduces noise in the data. Our relatively simple yet flexible paradigm requires a simple apparatus and minimal human intervention. Our scheduling and control software and apparatus (NemoTrainer) can be used to screen neurologic drugs and test the effects of CRISPR­-based and optogenetic modification of neural circuits on sensation, locomotion, learning and memory.

This study examined the relationship among the three domains of executive function (EF; cognitive shifting, inhibitory control, and working memory) to test the applicability of the unity-diversity model in preschoolers using both behavioral and fNIRS approaches. Altogether 58 Chinese preschoolers (34 boys, 24 girls, Mage = 5.86 years, SD = 0.53, Age range = 4.83-6.67 years) were administered the Dimensional Card Change Sort (DCCS), go/no-go, and missing scan task. Their brain activations in the prefrontal cortex during the tasks were examined using fNIRS. First, the behavioral results indicated that the missing scan task scores (working memory) correlated with the DCCS (cognitive shifting) and go/no-go tasks (inhibitory control). But the latter two did not correlate with each other. Second, the fNIRS results demonstrated that the prefrontal activations during the working memory task correlated with those in the same regions during the cognitive shifting and inhibitory control tasks. Still, the latter two did not correlate. The behavioral and neuroimaging evidence jointly indicates that the unity-diversity model of EF does apply to Chinese preschoolers.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and past gradients, with step size adjusted for each parameter. We also combine Incorporating Multiple Clustering Centers and a bootstrap-aggregated decision trees ensemble, which is shown to further boost classification performance. In addition, we provide an ablation study for assessing the performance improvement that each module of our ensemble produces. Multiple experiments on a large set of datasets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be made available at https://github.com/LorisNanni.

The deformation monitoring information of concrete dams contains some high-frequency com-ponents, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The example analysis shows that the model has good calculation speed, fitting and prediction accuracy and it can effectively mine the date char-acteristics inherent in the measured deformation, and reduce the influence of noise components on the modeling accuracy.

This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

Chimeric Antigen Receptor (CAR)-T cell immunotherapy revolutionized cancer treatment and consists of the genetic modification of T lymphocytes with a CAR gene, aiming to increase their ability to recognize and kill antigen-specific tumor cells. The dynamics of CAR-T cell responses in patients presents a multiphasic kinetics with distribution, expansion, contraction, and persistence phases. The characteristics and duration of each phase depend on the tumor type, the infused product, and on patient-specific characteristics. We present a mathematical model which describes the multiphasic CAR-T cell dynamics resulting from the interplay between CAR-T and tumor cells, considering patient and product heterogeneities. The CAR-T cell population is divided into functional (distributed and effector), memory, and exhausted CAR-T cell phenotypes. The model is able to describe the diversity of CAR-T cell dynamic behaviors in different patients and hematological cancers as well as their therapy outcomes. Our results indicate that the joint assessment of the area under the concentration-time curve in the first 28 days and the corresponding fraction of non-exhausted CAR-T cells may be considered as potential markers to classify therapy responses. Overall, the analysis of different CAR-T cell phenotypes can be a key aspect for a better understanding of the whole CAR-T cell dynamics.

The shape memory alloys belong to the smart materials thanks to their thermomechanical proprieties' reply to thermal or to mechanical loading. These materials can change shape, stiffness, displacement, natural frequency, and many mechanical characteristics in response to stress or to heat such as conduction, convection or radiation. However, heating by convection or conduction are the most useful and studied methods unlike radiation. Therefore, this paper aims to study the radiation effect on the shape memory alloy behavior

The COVID-19 pandemic exposed the vulnerability of current vaccine technologies characterized by a slow onset of action and antigen-specific immune response. Although parental vaccines offer long-term protection against homologous strains, they rely exclusively on adaptive immune memory to produce neutralizing antibodies that are ineffective against new vaccine variants. Moreover, growing evidence highlights the multifaceted functions of trained immunity to elicit a rapid and enhanced innate response against unrelated stimuli or pathogens to subsequent triggers. This review discusses the protective role of trained immunity against respiratory pathogens and the experimental models essential for evaluating novel inducers of trained immunity. We further elaborate on the potential of trained immunity to leverage protection against emerging pathogens via recognition of diverse antigens by pathogen recognition receptors (PPRs) on innate immune cells. We also propose integrating trained- with adaptive- immunity to shape next-generation vaccines by coupling each one's unique characteristics.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article we will leverage a computational neuron-astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we will demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage.

Human addiction, as a learned behaviour, has and is constantly being treated psychologically, with specific and timely interventions from Neuroscience. We endorse that human addiction can receive further boost as regards treatment, when we firmly understand how it works from a quantum scale. This is majorly because the dopaminergic pathway (DP) that is well elaborated in the brain of every addict is connected to the memory pathway. This further implies that the recall process in the brain of the addict, as regards his/her addiction is fully functional in line with the pleasure that arises from the element of his/her addiction. This dopamine-led pathway shows itself as prominent in what pertains to addiction, this is because of the role it plays in reward. As a neurotransmitter, dopamine flickers when reward is in the offing. It should be noted that a full understanding of the dimensions of addiction in the human person has not be attained to, therefore, we seek to add to this ongoing research, by considering excerpts arising from Quantum Field Theory. We are introducing excerpts from QFT, because DP, is an attendant element in the process of reward and motivation. In clear terms, we are alluding that it all begins with the memory.

Therapeutic vaccine studies should be designed to elicit durable, high magnitude, and efficacious T cell responses, all of which can be impacted by the choice of vaccination schedule. Here, we compare different prime-boost intervals (PBI) in a human papillomavirus (HPV) model using HPV16E6E7 Venezuelan equine encephalitis virus replicon particle (VRP) vaccination to address the optimal boosting schedule, quality of immune response, and overall in vivo efficacy. Six different vaccine regimens were tested with each group receiving booster vaccinations at different time intervals. Analysis of T-cell responses demonstrated a significant HPV16 E7 specific CD8+T cell response with minimally a one-week PBI between antigen re-exposure. Significant E7-specific in vivo cytotoxicity was also observed with longer PBIs. Additionally, longer PBIs led to an enhanced memory recall response to tumor challenge, which correlated with differential expansion of T cell memory subsets. Our findings imply that when using alphavirus vector platforms as a vaccination strategy, a one-week PBI is sufficient to induce high magnitude effector T cells with potent anti-tumor activity. However, longer PBIs lead to enhanced long-term protective anti-tumor immunity. These findings have implications for therapeutic vaccine clinical trials in which shorter intervals of prime-boost regimens may lead to suboptimal durable immune responses.

Nowadays, technological advancement has transformed traditional vehicles into Au-tonomous Vehicles (A.V.s). In addition, in our daily lives, A.V.s play an important role since they are considered an essential component of smart cities. A.V. is an intelligent vehicle capable of main-taining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, A.V.s collect information about the outside environment using sensors to ensure safe navigation. Furthermore, A.V.s reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, A.V.s could be threatened by cyberattacks, posing risks to human life. For example, re-searchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW AVs. Therefore, more research is needed to detect cyberattacks targeting the components of A.V.s to mitigate their negative consequences. This research will contribute to the security of A.V.s by detecting cyberattacks at the early stages. First, we inject False Data Injection (FDI) attacks into an A.V. simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify FDI attacks targeting the control system of the A.V. through a compromised sensor. We use long short-term memory (LSTM) deep networks to detect FDI attacks in the early stage to ensure the stability of the operation of A.V.s. Our method classifies the collected dataset into two classifications: normal and anomaly data. The ex-perimental result shows that our proposed model's accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

Using of the organic base tetramethylammonium hydroxides (TMAH) is a viable, cheap and fast route, for the MgZnAl-LDH type materials synthesis by both co-precipitation and mechano-chemical methods. TMAH provided several advantages as smaller quantity of water required in the washing step compared to the use of inorganic alkalis, prevention of LDH contamination with alkali cations, acting as template molecule in texture tailoring along with disadvantages as its presence in small quantities in the resulting layered materials. Regardless the use of organic / inorganic bases and co-precipitation / mechano-chemical methods, zincite stable phase was found in all the synthesized solids. The basicity of catalysts followed the trend: mixed oxides &gt; reconstructed &gt; parent LDH. The memory effect of LDH is supported only by the presence of Mg and Al cations, while Zn remains as zincite stable phase. The catalytic activities for Claisen-Schmidt condensation of benzaldehyde with cyclohexanone provided values higher than 90% after 2h, with a total selectivity in 2,6-dibenzylidenecyclohexanone, while in self-condensation of cyclohexanone no more than 7.29% after 5h. These behaviors depended on catalysts basicity as well as the planar rigidity of the compound.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Recent research has suggested that working memory training interventions may benefit children with Developmental Language Disorder (DLD). The current study investigated a short and engaging adaptive working memory intervention that targeted executive skills and aimed to improve both language comprehension and working memory abilities in children with DLD. Forty-seven 6- to 10-year-old children with DLD were randomly allocated to an executive working memory training intervention (n=24) or an active control group (n=23). A pre-test/intervention/post-test/9-month-follow-up design was used. Outcome measures included assessments of language (to evaluate far transfer of the training) and working memory (to evaluate near transfer of the training). Hierarchical multiple regression analyses controlling for pre-intervention performance and age found group to be a significant predictor of sentence comprehension and of performance on six untrained working memory measures at post-intervention and 9-month follow-up. Children in the intervention group showed significantly higher language comprehension and working memory scores at both time points than children in the active control group. The intervention programme showed potential to improve working memory and language comprehension in children with DLD and demonstrated several advantages: it involved short sessions over a short period; caused little disruption in the school day; and was enjoyed by children.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

Depression and anxiety are prevalent, persistent and difficult to treat industrialized world mental health problems. These disorders negatively modify an individual’s life perspective through brain function imbalances, notably in the amygdala and hippocampus, and are primarily treated with pharmaceuticals and psychotherapy. Nevertheless, these mental health issues have only increased in the number of individuals affected and the intensity of their suffering—especially as a result of COVID-19 restrictions and fears. An approach to alleviating depression and anxiety in relation to researchers self-identifying as experiencing burnout is promising. Enhancing resilience, the approach considers depression and anxiety as consequences of the particular method people adopt in ordering their memories, and focuses on narrative development. The method encourages accepting of different perspectives as unique and necessary in creating safe protection from research burnout. Moving from an identification of personal character to prompting plot development of memory, the method promotes resilience by encouraging thoughtful reconsideration of the negative assessments by participants of their circumstances that can lead to depression and anxiety. The method of ordering and group members’ feedback are inspected, including during the period of COVID-19 restrictions, and conclusions are offered regarding further research to encourage burnout resilience to diminish depression and anxiety.

Cardiovascular disease is the leading cause of death worldwide. A key factor in assessing the risk of cardiovascular disease is left ventricular functional evaluation. Left ventricular (LV) systolic function is evaluated by measuring the left ventricular ejection fraction (LVEF) using echocardiography data. Therefore, quick and accurate left ventricle segmentation is important for estimating the LVEF. However, it is difficult to accurately segment the left ventricle due to changes in the shape and area of the left ventricle during cardiac cycles. In this study, we proposed a framework that considers changes in the shape and area of the left ventricle during the cardiac cycle by applying the convolutional long short-term memory (CLSTM) approach. In addition, we evaluated the left ventricular segmentation and multidimensional quantification of the proposed system in comparison to manual and automated segmentation methods. In addition, to assess the validity of CLSTM, the values of multi-dimensional quantification metrics were compared and analyzed using graphs and Bland–Altman plots on a frame-by-frame basis. We demonstrated that the CLSTM method effectively segments the left ventricle by considering the LV activity. In conclusion, we demonstrated that LV segmentation based on our framework may be utilized to accurately estimate LVEF values.

Executive functions comprise top-down cognitive processes that exert control over information processing, from acquiring information to issuing a behavioural response. These cognitive processes of inhibition, working memory and shifting underpin complex cognitive skills, such as episodic memory and planning, which have been repeatedly investigated in several bird species in recent decades. Until recently, avian executive functions were studied in relatively few bird species, but have gained traction in comparative cognitive research following MacLean and colleagues’ large-scale study (2014). Therefore, in this review paper, relevant previous findings are collected and organized to facilitate further investigations of these core cognitive processes in birds. This review can assist in integrating findings from avian and mammalian cognitive research and further current understanding of executive functions’ significance and evolution.

This paper deals with the analysis of trends in road accidents on major highways in Brazil. Using updated time series techniques, our results indicate that a low degree of long memory was detected in the series with shocks having transitory effects over time. We further find that the number of accidents taking place in Brazil has been reducing over time, though in the presence of negative shocks, the recovery is not going to be immediate due to the long memory nature of the data. Despite the absence of relevant investment relating to infrastructure expansion, it is worth mentioning the consolidation of a nationwide tolled road system in Brazil involving concessions to private administrators, alongside more severe traffic laws that can impose limitations on driving licences.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

Today, more disciplines are intercepting each other, giving rise to “cross-disciplinary” research. Technological advancements in material science and device structure and production have paved the way towards development of new classes of multi-purpose sensory devices. Organic phototransistors (OPTs) are photo-activated sensors based on organic field-effect transistors that convert incident light signals into electrical signals. The organic semiconductor (OSC) layer and three-electrode structure of an OPT offer great advantages for light detection compared to conventional photodetectors and photodiodes, due to their signal amplification and noise reduction characteristics. Solution processing of the active layer enables mass production of OPT devices at significantly reduced cost. The chemical structure of OSCs can be modified accordingly to fulfil detection at various wavelengths for different purposes. Organic phototransistors have attracted substantial interest in a variety of fields, namely biomedical, medical diagnostics, healthcare, energy, security, and environmental monitoring. Lightweight and mechanically flexible and wearable OPTs are suitable alternatives not only at clinical levels but also for point-of-care and home-assisted usage. In this review, we aim to explain different types, working mechanism and figures of merit of organic phototransistors and highlight the recent advances from the literature on development and implementation of OPTs for a broad range of research and real-life applications.

Neutrophils as innate immune cells primarily act as first responders in acute infection and directly maintain inflammatory responses. However, a growing body of evidence suggests that neutrophils also bear the potential to mediate chronic inflammation by exhibiting memory-like features. We recently showed that priming by serial doses of lipopolysaccharide (LPS) from gram-negative bacteria can trigger opposing memory-like responses (exaggerated inflammation, i.e. trained sensitivity or suppression of inflammation, i.e. tolerance) depending on the LPS-dose. We now asked whether this observation could also hold true for lipoteichoic acid (LTA) from gram-positive S. aureus. We found comparable effects of LTA on neutrophil priming as seen for LPS. Low-dose (1 ng/mL) LTA-priming promoted increased production of pro-inflammatory mediators (i.e., TNF-α, IL-6, ROS), whereas high-dose (10 µg/mL) results in contrary reactions supporting anti-inflammatory responses by increased IL-10 and declined pro-inflammatory capacity. In vitro neutrophil recruitment was similarly regulated by LTA -priming. Investigation of signalling patterns revealed TLR2/MyD88-mediated regulation of NFκB-p65 through intermediate PI3Ks/MAPK. Collectively, our data suggest a previously unknown capacity of neutrophils to be differentially primed by varying doses of LTA, endorsing memory-like features in neutrophils.

Wernicke-Korsakoff syndrome (WKS) is induced by thiamine deficiency (TD) and mainly related to alcohol consumption. Frontal cortex dysfunction has been associated to impulsivity and disinhibition in WKS patients. The pathophysiology involves oxidative stress, excitotoxicity and inflammatory responses leading to neuronal death, but the relative contributions of each factor (alcohol and TD, isolate or in interaction) to these phenomena are still poorly understood. A rat model was used by forced consumption of 20% (w/v) alcohol for 9 months (CA), TD hit (TD diet + pyrithiamine 0.25 mg/kg, i.p. daily injections the last 12 days of experimentation; TDD), and both combined treatments (CA+TDD). Motor and cognitive performance and cortical damage were examined. CA caused hyperlocomotion as a possible sensitization of ethanol-induced excitatory effects and recognition memory deficits. In addition, CA+TDD animals showed a disinhibited-like behavior, which appears to be dependent on TDD. Also, combined treatment led to more pronounced alterations in nitrosative stress, lipid peroxidation, apoptosis and cell damage markers. Correlations between injury signals and disinhibition suggest that CA+TDD disrupts behaviors dependent on the frontal cortex. Our study sheds light on the potential disease-specific mechanisms, reinforcing the need for neuroprotective therapeutic approaches along with preventive treatments for the nutritional deficiency in WKS.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

The present study explored the expression of CD62L and CD44 by bovine peripheral blood mononuclear cells (PBMCs) and WC1.1+ γδ T cells under Staphylococcus aureus cell culture stimu-lation. In this study, peripheral blood cells were isolated from ten dairy cows and cocultured with S. aureus. Afterward, the γδ T cell subpopulation and the expression of CD44, CD62L and prolif-erative (Ki67+) cells were evaluated by flow cytometry. Our results showed that the percentages of proliferative PBMCs and WC1.1+ γδ T cells were higher when stimulated with S. aureus. The percentage of CD44+ cells increased in S. aureus-stimulated cultured PMBCs and WC1.1+ γδ T cells, as did the CD44 geometric mean fluorescence intensity (GMFI). The rate of CD62L cells did not differ among groups for either PBMCs or WC1.1+ γδ T cells. A higher GMFI of CD62L in prolif-erative PBMCs than nonproliferative PBMCs upon stimulation with S. aureus was detected, whereas no impact on the GMFI of CD62L was observed in WC1.1+ cells. In summary, our study identified that S. aureus was associated with high expression of CD44 in overall PBMCs and WC1.1+ γδ T cells, and they could generate memory WC1.1+ γδ T cells, preferably central memory cells.

Twice-exceptional learners face a unique set of challenges arising from the intersection of extraordinary talent and disability. Neurobiology research has the capacity to complement pedagogical research and provide support for twice-exceptional learners. Very few studies have attempted to specifically address the neurobiological underpinnings of twice-exceptionality. However, neurobiologists have built a broad base of knowledge in nervous system function spanning from the level of neural circuits to the molecular basis of behavior. It is known that distinct neural circuits mediate different neural functions, which suggests that 2e learning may result from enhancement in one circuit and disruption in another. Neural circuits are known to adapt and change in response to experience, a cellular process known as neuroplasticity. Plasticity is controlled by a bidirectional connection between the synapse, where neural signals are received, and the nucleus, where regulated gene expression can return to alter synaptic function. Complex molecular mechanisms compose this connection in distinct neural circuits, and genetic alterations in these mechanisms are associated with both memory enhancements and psychiatric disorder. Understanding the consequences of these changes at the molecular, cellular, and circuit levels will provide critical insights into the neurobiological bases of twice-exceptional learning.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. Based on atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level using the pulse width. Therefore, we also fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme based on the phase transition mechanism to compare to the (GeTe/Sb2Te3)16 iPCM. We designed an identical pulse scheme that implements linear and symmetrical LTP and LTD based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing gradual conductance modulation, a nonlinearity value of 0.32, and LTP/LTD 40 conductance states using identical pulses trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next generation non-volatile memory.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

In a collaborative forecasting task, experts may have unequal authority levels. However, this has rarely been considered reasonably in the existing fuzzy collaborative forecasting methods. In addition, experts may not be willing to discriminate their authority levels. To address these issues, an auto-weighting fuzzy weighted intersection (FWI) fuzzy collaborative intelligence approach is proposed in this study. In the proposed auto-weighting FWI fuzzy collaborative intelligence approach, experts’ authority levels are automatically and reasonably assigned based on their past forecasting performances. Subsequently, the auto-weighting FWI mechanism is established to aggregate experts’ fuzzy forecasts. The theoretical properties of the auto-weighting FWI mechanism have been discussed and compared with those of the existing fuzzy aggregation operators. After applying the auto-weighting FWI fuzzy collaborative intelligence approach to a case of forecasting the yield of a DRAM product from the literature, its advantages over several existing methods were clearly illustrated.

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long–range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long–range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations and agent–based models. Reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first–passage time distributions. Research has lead us to question whether the observed long–range memory is a result of actual long–range memory process or just a consequence of non–linearity of Markov processes. As our most recent result we discuss the long–range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the ARFIMA sample series. Our newly obtained results seem indicate that new estimators of self–similarity and long–range memory for analyzing systems with non–Gaussian distributions have to be developed.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

An association of food sources with odors prominently guides foraging behavior in animals. To understand the interaction of olfactory memory and food preferences, we used glucose-averse (GA) German cockroaches. Multiple populations of cockroaches evolved a gustatory polymorphism where glucose is perceived as a deterrent and enables GA cockroaches to avoid eating glucose-containing toxic baits. Comparative behavioral analysis using an operant conditioning paradigm revealed that learning and memory guide foraging decisions. Cockroaches learned to associate specific food odors with fructose (phagostimulant, reward) within only a 1 hr conditioning session, and with caffeine (deterrent, punishment) after only three 1 hr conditioning sessions. Glucose acted as reward in wild type (WT) cockroaches, but GA cockroaches learned to avoid an innately attractive odor that was associated with glucose. Olfactory memory was retained for at least 3 days after three 1 hr conditioning sessions. Our results reveal that specific tastants can serve as potent reward or punishment in olfactory associative learning, which reinforces gustatory food preferences. Olfactory learning therefore reinforces behavioral resistance of GA cockroaches to sugar-containing toxic baits. Cockroaches may also generalize their olfactory learning to baits that contain the same or similar attractive odors even if they do not contain glucose.

Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

Functional Connectivity analysis using Electroencephalography signals is common. The EEG signals are converted to networks by transforming the signals into a correlation matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented on the correlation matrix data to classify them either on their psychometric assessment or the effect of therapy; The EEG data is trail-based/event-related. The classifications based on RNN provided higher accuracy( 74-88%) than the other three models( 50-78%). Instead of using individual graph features, a correlation matrix provides an initial test of the data. When compared with the time-resolved correlation matrix, it offered a 4-5% higher accuracy. The time-resolved correlation matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the correlation matrix, a static feature.

Neurological disorders, such as amyotrophic lateral sclerosis, Parkinson’s disease, and Alzheimer’s disease, are commonly associated with persistent neuro-inflammation, and there is an urgent need to discover new therapeutic agents that may target the various pathways involved in neurodegeneration. In this study, we investigated the therapeutic potential of folecitin, a flavonoid isolated from Hypericum oblongifolium, against lipopolysaccharide (LPS)-induced oxidative stress associated with neurodegeneration, amyloidogenic Aβ production pathway, and memory dysfunction in mice. LPS was administered i.p. at 250 µg/kg/day for 3 weeks, followed by the administration of folecitin at a dose of 30 mg/kg/day for the last two weeks. A Western blot technique was used to assess the expression of different proteins involved in oxidative stress, neurodegeneration, and neuronal synapse. Results indicated that folecitin significantly reduced LPS-induced apoptotic neurodegeneration, including the expression of BAX, Caspase-3, and PARP-1 proteins, inhibited BACE1, and the amyloidogenic Aβ production pathway. Folecitin improved both pre- and post-neuronal synapse, as well as memory dysfunction. Furthermore, folecitin significantly activated endogenous antioxidant proteins such as Nrf-2 and HO-1 via stimulating the phosphorylation of Akt proteins. These findings suggest that folecitin may be a suitable lead to design new drugs for neurotoxin-triggered neurodegenerative disorders.

The purpose of this study was to explore the association of cognition with hazardous drinking and alcohol related disorder in persons with bipolar disorder (BD). The study population included 1,268 persons from Finland with bipolar disorder. Alcohol use was assessed through hazardous drinking and alcohol related disorder including alcohol use disorder (AUD). Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Alcohol related disorder diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task, or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association between RT-test and alcohol use was analyzed with log-linear regression, and eβ with 95% confidence intervals (CI) are reported. PAL first trial memory score was analyzed with linear regression, and β with 95% CI are reported. PAL total errors adjusted was analyzed with logistic regression and odds ratios (OR) with 95% CI are reported. After adjustment for age, education and housing status, hazardous drinking was associated with lower median and less variable RT in females while AUD was associated with a poorer PAL test performance in terms of the total errors adjusted scores in females. Our findings of positive associations between alcohol use and cognition in persons with bipolar disorder are unique.

Energy harvesting from wind turbines has been explored by researchers for more than a century from conventional turbines up to the latest bladeless turbines. Amongst these bladeless turbines, vortex bladeless wind turbine (VBT) harvests energy from oscillation of a turbine body. Due to the novelty of this science and the widespread researches around the world, one of the most important issues is to optimize and predict produced power. To enhance the produced output electrical power of VBT, the fluid-solid interactions (FSI) were analyzed to collect a dataset for predicting procedure. Long short-term memory (LSTM) method has been used to predict the produced power of VBT from the collected data. The reason of choosing LSTM from various artificial neural network methods is that the parameters of VBT study are all time- dependent and the LSTM is one of the most accruable algorithms for predicting time series data. In order to find the relationship between the parameter and the variables used in this research, a correlation matrix was presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and its prediction shows that the LSTM method is very accurate for these types of research. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of 2 and a half hours to two minutes. Also, one of the most important achievements of this study is to suggest a mathematical relation of VBT output power which helps to extend it in a different size of VBT with a high range of parameter variations.

Inside Central Nervous System (CNS) appears neurons and glia cells. There are more glial cells than neurons and have more functions than neurons. Glia name represents different kind of cells, ones from neural origin (astrocytes, radial glia, and oligodendroglia), and others from blood monocytes (microglia). During ontogeny, neurons appear first (rat fetal 15th) and after astrocytes (rat fetal 21th) indicating a bigger importance function in the CNS. Also, during the phylogeny, reptiles have less astrocytes compared to neurons and in humans, astrocytes are double in number than neurons. This data, perhaps means that astrocytes are more special cells and work in memory and learning? Astrocytes have an important role in different mechanisms protecting CNS across the production of antioxidant and anti-inflammatory proteins, cleaning extracellular medium and helping neurons to communicate with each other correctly. Inflammatory mediators production are important to prevent changes in normal physiology. But, excessive or continue production leads to many diseases, such as Alzheimer's disease (AD), Sclerosis Lateral Amyotrophic (ELA), Multiple sclerosis (MS), and neurodevelopment diseases, like Bipolar disorder, Schizophrenia, and Autism's symptomatology. Different drugs and thecniques can reverse oxidative stress and/or inflammatory excess. This review is intended to serve as an approximation to the field.

(1) Background: Substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a mild Traumatic Brain Injury (mTBI) is presented. We show that if timing deficits are remediated using low-level movement discrimination training, then high-level cognitive skills, including reading, attention, processing speed, and working memory improve substantially. (2) Methods: Two case studies are presented using MEG source imaging on an adult dyslexic, and a healthy older adult observed before and after training on movement discrimination two times/week for 8 weeks for adult dyslexic. (3) Results: We found improvements in reading, attention, processing speed, and working memory on neuropsychological tests. Substantial MEG signal increases in visual Motion Networks (V1, V3, MT, MST), Attention Networks (ACC, dlPFC, vlPFC and precuneous/ PCC areas) and Memory Networks (dlPFC). (4) Conclusions: Improving neural timing deficits before cognitive exercises to improve specific cognitive skills provides a rapid and effective method to improve cognitive skills. Improving the timing and sensitivity of low-level dorsal pathways, improving feedforward and feedback pathways, is essential to improve high-level cognitive skills. This adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person’s quality of life rapidly and effectively.

During the last years, epigenetic processes have emerged as important factors for many neurodegenerative diseases, such as Alzheimer’s diseases (AD). These complex diseases seem to have a heritable component; however, genome-wide association studies failed to identify the genetic loci involved in the eatiology. So, how can these changes be transmitted from one gen-eration to the next? Answering this question would allow us to understand how the environ-ment can affect human populations for multiple generations and explain the high prevalence of neurodegenerative diseases, such as AD. This review pays particular attention to the relationship among epigenetics, cognition, and neurodegeneration across generations, deepening the under-standing of the relevance of heritability in neurodegenerative diseases. In fact, we highlight some recent examples of EI induced by experiences, focusing on their contribution of processes in learning and memory, to point out new targets for therapeutic interventions. Here, we first describe the prominent role of epigenetic factors in memory processing. Then, we briefly discuss aspects of EI. And ends, we summarize evidence of how epigenetic marks inherited by experi-ence and/or environmental stimuli contribute to cognitive status offspring, since better knowledge of EI can provide clues in the appearance and development of age-related cognitive decline and AD.

Learning disorders (LD) are diagnosed in children impaired in the academic skills of reading, writing and/or mathematics. Children with LD usually show a slower resting-state electroencephalogram (EEG), with EEG patterns corresponding to a neurodevelopmental lag. LD-children also show a consistent cognitive impairment in working memory (WM), including an abnormal task-related EEG with an overall slower EEG activity of more delta and theta power, and less gamma activity in posterior sites; task-related EEG patterns considered indices of an inefficient neural resource management. Neurofeedback (NFB) treatments aimed at normalizing the resting-state EEG of LD-children have shown improvements in cognitive-behavioral indices and diminished EEG abnormalities. Given the typical findings of a WM impairment in LD-children; we aimed to explore the effects of a NFB treatment in the WM of children with LD, by analyzing the WM-related EEG power-spectrum. We recruited 18 children with LD (8-10 years old). They performed a Sternberg-type WM-task synchronized with an EEG of 19 leads (10-20 system) twice in pre-post treatment conditions. They went through either 30 sessions of a NFB treatment (NFB-group, n= 10); or through 30 sessions of a placebo-sham treatment (Sham-group, n= 8). We analyzed the before-after treatment group differences for the behavioral performance and the WM-related power-spectrum. The NFB group showed faster response times in the WM-task post-treatment. They also showed an increased gamma power at posterior sites and a decreased beta power. We explain these findings in terms of NFB improving the maintenance of memory representations coupled with a reduction of anxiety.

Shape-memory hydrogels (SMH) are as multifunctional, actively-moving polymers of interest in biomedicine. In loosely crosslinked polymer networks gelatin chains may form triple helices, which can act as temporary netpoints in SMH, depending on the presence of salts. Here, we show programming and initiation of the shape-memory effect of such networks based on a thermomechanical process compatible with the physiological environment. The SMH were synthesized by reaction of glycidylmethacrylated gelatin with OEG α,ω-dithiols of varying crosslinker length and amount. Triple helicalization of gelatin chains is shown directly by wide-angle X-ray scattering and indirectly via the mechanical behavior at different temperatures. The ability to form triple helices increased with the molar mass of the crosslinker. Hydrogels had storage moduli of 0.27-23 kPa and Young’s moduli of 215-360 kPa at 4 °C. The hydrogels were hydrolytically degradable, with full degradation to water soluble products within one week at 37 °C and pH = 7.4. A thermally-induced shape-memory effect is demonstrated in bending as well as in compression tests, in which shape recovery with excellent shape recovery rates Rr close to 100% were observed. In the future, the material presented here could be applied e.g. as self-anchoring devices mechanically resembling the extracellular matrix.

HLA class-I (HLA-I) polyreactive monoclonal antibodies (mAbs) reacting to all HLA-I alleles were developed by immunizing HLA-E monomeric heavy chain (HC) (Open Conformers, OCs). Two of the mAbs (TFL-006 and TFL-007) bound to the HC’s coated on a solid matrix. The binding was inhibited by a peptide 117AYDGKDY123, present in all alleles of the six HLA-I isoforms but masked by 2-microglobulin -m) in intact HLA-I trimers (Closed Conformers, CCs). Identical HLA-I polyreactivity is observed in IVIg administered to lower anti-HLA antibodies (Abs) in HLA-sensitized patients, but the mechanism is unknown. We hypothesized that the mAbs that mimic IVIg HLA-I polyreactivity might mimic the immunomodulatory functions of IVIg. We tested the relative binding affinity of the mAbs and IVIg for both OCs- and CCs and compared their effects on (a) the phytohemagglutinin (PHA)-activation T-cells, (b) the production of anti-HLA-II antibody (Ab) by B-memory cells, and anti-HLA-I Ab by immortalized B-cells, and (c) the upregulation of CD4+, CD25+, and Fox P3+ T-regs. The mAbs bound only to OCs, whereas IVIg is bound to both CCs and OCs. The mAbs suppressed blastogenesis and proliferation of PHA-activated T-cells, anti-HLA Ab production by B-cells and expanded the T-regs, better than IVIg. We conclude that a humanized version of the TFL-mAbs could be an ideal therapeutic IVIg-mimetic.

The effect of stress on task performance is complex, too much or too little negatively affects performance; and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor and workload related stress were more dominant in the medial and lateral PFC respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of negative affect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

An evaluation of image contrast in a controlled foggy environment has been made by comparison between intensity imaging and different modes of polarimetric imaging. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A linear polarimetric camera of division of focal plane and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson’s contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier is to detect and segment the targets of interest that are surrounded by fog. It has been proved quantitatively that in the studied situations polarimetric images present an improvement in contrast when compared to conventional intensity images.

Recent evidence has highlighted the critical role of memory cells in maintaining lifelong food allergies, thereby identifying these cells as therapeutic targets. IgG⁺ memory B cells replenish pools of IgE-secreting cells upon allergen exposure, which contract thereafter due to the short lifespan of tightly regulated IgE-expressing cells. Advances in the detection and highly dimensional analysis of allergen-specific B and T cells from allergic patients have provided insight on their phenotype and function. The newly identified Th2A and Tfh13 populations represent a leap in our understanding of allergen-specific T cell phenotypes, though how these populations contribute to IgE memory responses remains poorly understood. Within, we discuss the mechanisms by which memory B and T cells are activated, integrating knowledge from human systems and fundamental research. We then focus on memory reactivation; specifically, on the pathways of secondary IgE responses. Throughout, we identify areas of future research which will help identify immunotargets for a transformative therapy for food allergy.

Abstract Women victims of abuse can suffer neuropsychological sequelae that affect memory and attention, as well psychopathological disorders such as depression. These consequences affect their daily life and physical and psychological health. Objective: To analyze sequelae that affect attention and memory, as well a possible association of these sequelae to depression. Method: A total of 68 women victims of gender-based violence participated in the study. The participants were between 15 and 62 years of age and resided in Spain at the time of data collection. The Luria DNA Battery (Neuropsychological Diagnosis of Adults) by Manga and Ramos (2000); and the Beck Depression Inventory were applied. Results: Women victims of gender-based violence suffer neuropsychological sequelae, presenting low short-term memory and attentional control; and score low on the Luria-DNA battery. Of these women, 60% suffer from some relevant type of depression, and there are significant differences according to their degree of memory. Through the HJ-Biplot, a direct relationship was found between memory and attentional control with the total score of the Luria battery. On the other hand, an inverse relationship was found between short-term memory and depression. Lastly, three well-differentiated gender clusters of women victims of gender-based violence were identified. Conclusions: A lower rate of depression is observed in women victims of abuse when they have a more intact short-term memory.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

Purpose of the study was to explore the association of cognition with hazardous drinking, binge drinking and alcohol use disorder in schizophrenia and schizoaffective disorder. Cognitive deficits are common in schizophrenia. Alcohol might be associated with additional cognitive impairment in schizophrenia patients. The study population included 3362 schizophrenia and schizoaffective disorder patients in Finland. Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Binge drinking was obtained from the AUDIT-C. Alcohol use disorder (AUD) diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task (5-CSRTT), or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association of alcohol use with RT test and PAL test was analyzed with log-linear regression and logistic regression, respectively. After adjustment for age, education and age at first psychotic episode, hazardous drinking in females was associated with lower median RT. Compared to never binge drinkers, male and female participants drinking 6 or more doses of alcohol monthly or less had lower median RT. In the PAL test both first trial memory score (FTMS) and total errors adjusted score (TEAS) were associated with better performance in males drinking 6 or more doses of alcohol weekly or more and in females drinking 6 or more doses monthly or less. Higher PAL TEAS was associated with AUD in females Some positive associations between alcohol and cognition were found in male and female schizophrenia and schizoaffective disorder patients with hazardous drinking and binge drinking.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens increasing tumor elimination efficiency. In the last years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate on patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable to be mathematically modeled. In this work, we developed a three population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities were considered as uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on some specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reduce and optimize the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such in silico laboratory was made available in a Shiny R-based platform called CARTmath. It is an open-source, easy to run simulator, available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br, containing this manuscript results as examples and documentation. The developed model, together with the CARTmath platform, provides potential use for assessing different CAR-T cell immunotherapy protocols and associated efficacy, becoming an accessory towards in silico trials.

Re-creation of the past of historical buildings sits at the intersection of the spatio-temporal manifestation of cultural memories, socio-cultural meanings, values and identity re-moulds and refines the existing understanding and sense of place. Digital technologies have become a popular tool in re-creation of the past by creating a new body of knowledge and historical discourse based on identifying the gaps within our written histories. Designers and policymakers around the world have been exploring various tools and technologies such as diachronic modelling yet there is a gap in evidence-based understanding regarding the actual functioning and success of application for place making. This paper, therefore, sets out to scrutinise the role of digital technologies in facilitating digital place making. To do so, it investigates the potential of a new “digital heritage” narrative in the revival of the lost architectural narrative of the Dennys Lascelles Wool Store, Geelong. The proposed paper aims to investigate the potential of a new “digital heritage” narrative and story-telling as a means towards digital place making framework. While exploring the new and unique capabilities provided by the digital narrative in capturing, simulating and disseminating ‘lost’ heritage it will further imbue a sense of place by connecting the everyday city dweller.

Alcohol and nicotine are widely-abused legal substances worldwide. Relapse to alcohol or tobacco seeking and consumption after abstinence is a major clinical challenge, and is often evoked by cue-induced craving. Therefore, disruption of the memory for the cue-drug association is expected to suppress relapse. Memories have been postulated to become labile shortly after their retrieval, during a “memory reconsolidation” process. Interference with the reconsolidation of drug-associated memories has been suggested as a possible strategy to reduce or even prevent cue-induced craving and relapse. Here, we surveyed the growing body of studies in animal models and in humans assessing the effectiveness of pharmacological or behavioral manipulations in reducing relapse by interfering with the reconsolidation of alcohol and nicotine/tobacco memories. Our review points to the potential of targeting the reconsolidation of these memories as a strategy to suppress relapse to alcohol drinking and tobacco smoking. However, we discuss several critical limitations and boundary conditions, which should be considered to improve the consistency and replicability in the field, and for development of an efficient reconsolidation-based relapse prevention therapy.

We address the dynamics of two-dimensional Navier-Stokes models with infinite delay and hereditary memory, whose kernels are a much larger class of functions than the one considered in the literature, on a bounded domain. We prove the existence and uniqueness of weak solutions by means of Faedo-Galerkin method. Moreover, we establish the existence of global attractor for the system with the existence of a bounded absorbing set and asymptotic compact property.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

Although working memory (WM) is crucial for intellectual abilities, not much is known about its brain underpinnings, especially the structural connectivity. We used diffusion tensor imaging (DTI) to look across the whole brain for the white matter integrity correlates of the individual differences in the reading span (verbal WM capacity during reading) in healthy adults. Right-handed healthy native Russian speakers (N = 67) underwent DTI on a 3T Philips Ingenia scanner. Verbal WM was assessed with the Daneman-Carpenter reading span test (Russian version). Fractional anisotropy maps from each participant were entered into the group tract-based spatial statistics analysis with the reading span as a covariate; the results were TFCE-corrected. After taking into account effects of age, sex, education and handedness, reading span positively correlated with the white matter integrity in multiple sites: the body, the genu and the splenium of corpus callosum; bilateral corona radiata (anterior, posterior, and superior); bilateral superior longitudinal fasciculus; several tracts in the right hemisphere only, including the internal and external capsule; bilateral superior parietal and frontal white matter. Although the left hemisphere is central for verbal processing, we revealed the important role of the right hemisphere white matter for the verbal WM capacity. Our finding indicates that larger verbal working memory span may originate from additional processing resources of the right hemisphere.

In recent years, music education in Ibero-America has been losing ground within the school environment in favor of the development of curricular systems that benefit academic results in standardized tests. Despite this, several studies in the field of cognitive neurosciences have found evidence of great relevance and in which it can be observed how music education can favor cognitive development and performance in practically all stages of human development, with important results in language tasks, attention, and executive functions such as planning, inhibition, cognitive flexibility and working memory.