Short Memory Research Articles

Cognitive Dysfunction in Hospitalized Patient with Moderate-to-Severe COVID-19: A 1-Year Prospective Observational Study.

To screen the neurocognitive impairment persistent post-COVID-19. We assessed the neuropsychiatric disorders associated with COVID-19 in a prospective study, by "Mini-Mental State Examination" (MMSE) and Montreal Cognitive Assessment (MoCA) questionnaires, applied in the discharge to COVID-19 hospitalized patients for moderate and severe forms of disease. They were followed-up in 6 and 12 months. The tests were performed in the baseline and were reevaluated after 6 and 12 months. Baseline cognitive dysfunction was found in 12.4% of patients, according to the MMSE test and in 19.7% by the MoCA scores. Overall cognitive dysfunction in COVID-19 was returned to normal after 6 months, although some tasks are more severe and persistently impaired, such as attention, concentration, short memory, and execution skills. The male gender and the degree of hypoxia, related to the severity of COVID-19 infection, were related to cognitive dysfunction in the study group. Cognitive domain impairments related to COVID-19 could persist over 6 months post-acute infectious episode requiring systematic screening for early diagnosis of progressive brain pathologies and rehabilitation.

Enhancement of Text Recognizing Exploitation in Phishing Websites using LSTM in Comparison with CNN based on Improving the Accuracy Rate

The objective of the work is to predict the accuracy of phishing websites based on exploitation of text recognition using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). To achieve accuracy, a novel np.random function was used. Method and Materials : Accuracy and Loss are performed with DATA dataset from the keras library. The total sample size is 20. The two groups Convolutional Neural Network (N=10) and Long Short Memory (N=10). Result : The result proved that Convolutional Neural Network (CNN) with better accuracy of 97.3% than Long Short Term Memory (LSTM) accuracy of 93.2%. Finally CNN appears significantly better than LSTM. The two algorithms CNN and LSTM are statistically satisfied with the independent sample T-Test value (p<0.001) with a confidence level of 95%. Conclusion : Detecting the phishing website significantly seems to be better in CNN (Std.Error Mean = .0632) than LSTM (Std.Error Mean = .0678).

Extraction and analysis of factors influencing college students’ mental health based on deep learning model

Abstract This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.

Bound to a spider without its web: Task-type modulates the retrieval of affective information in subsequent responses

Action control theories assume that upon responding to a stimulus response and stimulus features are integrated into a short episodic memory trace; repeating any component spurs on retrieval, affecting subsequent performance. The resulting so-called “binding effects” are reliably observed in discrimination tasks. In contrast, in localization performance, these effects are absent and only inhibition of return (IOR) emerges – a location change benefit. Affective information has been found to modulate binding effects; yet a modulation of IOR has led to mixed results, with many finding no influence at all. In the current study, participants discriminated letters (Experiment 1) or localized dots (Experiment 2) on a touchpad in prime-probe sequences. During the prime display two images – one with fruits and one with a spider – appeared, one of which spatially congruent with the to-be-touched area. In the discrimination task, previously touching a spider compared to a fruit slowed down response repetitions. In contrast, the localization task only showed IOR. This suggests that task-irrelevant valence is integrated with the response and affects subsequent responses due to retrieval. However, this is not ubiquitous but depends on task type. The results shed further light on the impact of affective information on actions.

Stability and dynamics of a stochastic discrete fractional-order chaotic system with short memory

In this paper, a stochastic discrete fractional-order chaotic system with short memory is proposed, which possesses two equilibrium points. With the help of the Lyapunov function theory, some sufficient conditions for the stability in probability of the two equilibrium points are given. Secondly, the effects of fractional order and memory steps on the stability of the system are discussed. Finally, the path dynamical behavior of the system is investigated using numerical methods such as Lyapunov exponents, bifurcation diagram, phase diagram, and 0–1 test. The numerical simulation results validate the findings.

IFRN: Insensitive feature removal network for zero-shot mechanical fault diagnosis across fault severity

Zero-shot learning is a promising technique for diagnosing mechanical faults in complex and uncertain environments. However, when diagnosing mechanical faults across different severities using zero-shot learning, the impact of insensitive features should be minimized due to the susceptibility of zero-shot prototypes and the vulnerability of vibration signals. To accomplish this, an Insensitive Feature Removal Network (IFRN) with an entire attention mechanism (EAM) module and a denoise autoencoder module is proposed to remove insensitive features hierarchically by dividing them into two categories: common insensitive features (CIF) and private insensitive features (PIF), each with different properties depending on their corresponding sub-labels presence in mechanical faults. Concretely, EAM removes insensitive features that the classifier cannot differentiate with the help of the entire attention weight comparison by attention generation, attention comparison, and attention limitation parts. Then, the denoise autoencoder module with long short memory term (LSTM) is utilized to remove the insensitive features that are not completely removed, especially for the insensitive features that independently arise. The IFRN’s effectiveness is demonstrated through comparative experiments and ablation studies using the Case Western Reserve University (CWRU) dataset, where the experimental result shows that IFRN outperforms conventional zero-shot learning methods. Furthermore, an analysis with prototype distance and sample aggregation is presented to further justify the effectiveness of the proposed method in reducing the prototype shift and improving classification accuracy by removing insensitive features.

Auditing of hadoop log file for dynamic detection of threats using H-ISSM-MIM and convolutional neural network

Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% .

In vivo retinal imaging is associated with cognitive decline, blood-brain barrier disruption and neuroinflammation in type 2 diabetic mice.

Type 2 diabetes (T2D) is associated with chronic inflammation and neurovascular changes that lead to functional impairment and atrophy in neural-derived tissue. A reduction in retinal thickness is an early indicator of diabetic retinopathy (DR), with progressive loss of neuroglia corresponding to DR severity. The brain undergoes similar pathophysiological events as the retina, which contribute to T2D-related cognitive decline. This study explored the relationship between retinal thinning and cognitive decline in the LepR db/db model of T2D. Diabetic db/db and non-diabetic db/+ mice aged 14 and 28 weeks underwent cognitive testing in short and long-term memory domains and in vivo retinal imaging using optical coherence tomography (OCT), followed by plasma metabolic measures and ex vivo quantification of neuroinflammation, oxidative stress and microvascular leakage. At 28 weeks, mice exhibited retinal thinning in the ganglion cell complex and inner nuclear layer, concomitant with diabetic insulin resistance, memory deficits, increased expression of inflammation markers and cerebrovascular leakage. Interestingly, alterations in retinal thickness at both experimental timepoints were correlated with cognitive decline and elevated immune response in the brain and retina. These results suggest that changes in retinal thickness quantified with in vivo OCT imaging may be an indicator of diabetic cognitive dysfunction and neuroinflammation.

Exploring the online interaction model of college English based on deep learning network

Abstract In this paper, we apply a deep learning model to discriminate sentiment in an interactive model of online college English education and propose a fusion model that splices convolutional neural networks and bidirectional long- and short-term memory neural networks horizontally. Convolutional neural networks are good at capturing the sentiment feature vectors using multi-channel convolutional kernels but are unable to extract the sentiment information above and below the sentiment sequence. The short and long-term memory neural network is able to extract the sentiment feature vectors by using recurrent neural networks, which can better compensate for the shortcomings of the convolutional neural networks. The online teaching of college English is selected as the object of analysis, and the improved Flanders interaction analysis system is used to study the online interaction process of college English so as to propose suggestions for the interaction of online teaching of college English. Then the performance of the model is analyzed through simulation experiments. Compared with the traditional TextCNN and BiLSTM, the CNN −BiLSTMATT sentiment analysis model has an accuracy of 0.8611, precision of 0.8471, recall of 0.8691, and F1 of 0.8562, so the CNN − BiLSTMATT sentiment analysis model is more suitable for college English online interaction. This study overcomes the disadvantages of online interaction and continuously improves the efficiency of online teaching interaction.

Fault detection and diagnosis in AHU system using deep learning approach

Energy consumption in buildings increases with the failures of equipment involved in the energy exchange, and control networks in buildings. One of the ways to remedy this issue is to offer high-performance fault detection systems. This article proposes a Fault Detection and Diagnostics (FDD) system based on Convolutional Neural Network (CNN) and Long Term Short Memory (LSTM) neural networks, applied to an AHU and using an hybrid database containing data from simulation and real-world on an actual physical building. The proposed system is designed to effectively identify and categorize faults, whether they occur in the sensors or in the mechanical equipment responsible for critical functions such as heat exchanges, air transfer, and system control. The FDD system provides results with an overall accuracy of around 96.88 %.

Test of change point versus long‐range dependence in functional time series

In the context of functional time series, we propose a significance test to distinguish between short memory with a change point and long range dependence. The test is based on coefficients of projections onto an optimal direction that captures the dependence structure of the latent stationary functions that are not observable due to a potential change point. The optimal direction must be estimated as well. The test statistic is constructed using the local Whittle estimator applied to these coefficients. It has standard normal distribution under the null hypothesis (change point) and diverges to infinity under the alternative (long range dependence). The article includes asymptotic theory, a simulation study and an application to curve‐valued time series derived from intraday asset prices.

A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model

In order to improve the quantitative inversion accuracy of soil organic matter in arid areas, the soils affected by different human disturbances in Xinjiang were used as research objects (Regions I, II and III). This study compared and analyzed the preprocessing hyperspectral data effects of GMFOD (global memory fractional order derivative) with 1759 memory length and SMFOD (short memory fractional order derivative) with different memory lengths (L = 1500, 1200, 1000, 800, 500, 300, 250, 200, 150, 100), machine learning models based on RBF (radial basis function) and ELM (extreme learning machine) were used to explore the best model for predicting soil organic matter content. Simulation results showed that (1) SMFOD had shorter computational runtime compared to GMFOD, and the 100 SMFOD had the shortest running time. When all orders of fractional order derivative were calculated, the time required for 100 SMFOD was 115.27 s, while the time required for GMFOD was 1025.32 s. These indicated that the running time of the former was 789.49% shorter than that of GMFOD. (2) Comparing the correlation coefficients of hyperspectral and soil organic matter in all fractional orders calculated based on GMFOD and SMFOD, it was found that the maximum correlation coefficient of 100 SMFOD and GMFOD differed by 0.0103, 0.0058 and 0.0287 in Regions I, II and III, which means that GMFOD only increased by 0.94%, 1.71% and 4.25%. These indicated that the maximum correlation coefficient of SMFOD and GMFOD was very similar at each fractional order. (3) The best model for inversion of soil organic matter in all three regions was SMFOD-CC1-RBF (CC1 denotes bands passed the 0.01 significance test), which was located at the fractional orders of 1.4, 1.8 and 1.1, respectively. Meanwhile, R2 (coefficient of determination), RMSE (root mean square error), RPD (ratio of the performance to deviation) were 0.8249, 0.7656, 1.9256 in Region I; were 0.7630, 1.4422, 1.9561 in Region II; and were 0.6799, 1.5343,1.8513 in Region III, which indicated that the higher-order SMFOD had better performance in inverting organic matter than the lower-order SMFOD, and the prediction ability of this model for soil organic matter in the three regions was good.

Side effects based network construction and drug repositioning of ropinirole as a potential molecule for Alzheimer’s disease: an in-silico, in-vitro, and in-vivo study

Alzheimer’s disease (AD) is the leading cause of dementia in older adults. Drug repositioning is a process of finding new therapeutic applications for existing drugs. One of the methods in drug repositioning is to use the side-effect profile of a drug to identify a new therapeutic indication. The drugs with similar side-effects may act on similar biological targets and could affect the same biochemical process. In this study, we explored the Food and Drug Administration-approved drugs using PROMISCUOUS database to find those that have adverse effects profile comparable with the ligands being studied or used to treat AD. Here, we found that the ropinirole, a dopamine receptor agonist, shared a maximum number of side-effects with the drugs proven beneficial for treating AD. Furthermore, molecular modelling demonstrated that ropinirole exhibited strong binding affinity (−9.313 kcal/mol) and best ligand efficiency (0.49) with sigma-1 receptor. Here, we observed that the quaternary amino group of ropinirole is essential for binding with sigma-1 receptor. Molecular dynamic simulation indicated that the movement of the carboxy-terminal helices (α4/α5) could play a major role in the receptor’s physiological functions. The neurotoxicity induced by Aβ25–35 in SH-SY5Y cells was reduced by ropinirole at concentrations 10, 30, and 50 µM. The effect on spatial learning and memory was examined in mice with Aβ25–35 induced memory deficit using the radial arm maze. Ropinirole (10 and 20 mg/kg) significantly improved the short and long-term memories in the radial arm maze test. Our results suggest that ropinirole has the potential to be repositioned for AD treatment. Communicated by Ramaswamy H. Sarma

Research on Cloud Platform Software Aging Prediction Method Based on VMD-ARIMA-BilSTM Combined Model

When the cloud platform runs under heavy load for a long time, internal resources will be consumed and errors will accumulate continuously. As a result, the software aging phenomenon occurs, which ultimately degrades the performance and reliability of the software system. Aiming at the above problems, this paper proposes a hybrid model based on integrated variational mode decomposition, moving average free regression and long and short memory network (VMD-ARIMA-BILSTM) to predict the software aging problem. Firstly, the original resource utilization rate is decomposed into stationary time series and non-stationary time series by variational mode decomposition. Then, the advantages of moving average free regression and bidirectional long short-term memory network are used to predict stationary and non-stationary series respectively. Finally, the prediction results are reconstructed to obtain the final prediction results. Experimental results show that compared with single ARIMA and BI-LSTM, the hybrid model designed in this paper has higher prediction accuracy and faster convergence speed.

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved.

Exploring Deep Learning Approaches for Short-Term Passenger Demand Prediction

An accurate short-term passenger demand forecast makes a contribution to the coordination of traffic supply and demand. Forecasting the short-term passenger demand for the on-demand transportation service platform is of utmost significance since it might incentivize empty cars to relocate from over-supply regions to over-demand regions. Yet, because spatial, temporal, and exogenous dependencies need to be evaluated concurrently, short-term passenger demand forecasting may be rather difficult. This article aims to investigate several methods that can be utilized to forecast short-term traffic demand, with a primary emphasis on deep learning approaches. We examine varying degrees of temporal aggregation and how these levels affect various architectural configurations. In addition, by analyzing 22 models representing 5 distinct architectural configurations, we illustrate the influence of varying layer configurations within each architecture. The findings indicate that the long-term short memory (LSTM) structures perform the best for short-term time series forecasting, but more complex architectures do not significantly enhance the outcomes. Moreover, considering the spatiotemporal aspects results in an improvement in the prediction of more than fifty percent. In addition, we investigate the vectorization of time, also known as Time2Vec, as a way of embedding to make it possible for a selected algorithm to recognize periodic characteristics in time series, and we show that the outcome is improved by fifteen percent.

Long Memory Cointegration in the Analysis of Maximum, Minimum and Range Temperatures in Africa: Implications for Climate Change

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa.

For Whom the Platter Tolls

Abstract This paper deals with memory in medieval Rus. Previous scholarship has focused on organised liturgical commemoration (memoria), which relied on complex canonised texts and sophisticated church rituals. This article concerns less formal types of medieval remembrance, like commemorative graffiti, colophons, and simple commemorative rituals. Such memorial devices can be called paraliturgical because they either facilitated liturgical commemoration or derived from liturgical texts and ceremonies, but technically were not part of the liturgy. Paraliturgical remembrance offered a peculiar version of the past. It combined short family memory, which went back to one or two generations, and the mythologised past, which was based on Scripture and inspirational legends, like that about Riurik. Paraliturgical memory was future orientated as it carried information required for salvation, including records of charity and donations. This type of memory also conveyed emotions, genealogical knowledge, and royal mythology. Paraliturgical remembrance crossed institutional and regional boundaries, creating communities of rememberers.

The computational power of the human brain.

At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.

Beta-caryophyllene mitigates the cognitive impairment caused by repeated exposure to aspartame in rats: Putative role of BDNF-TrKB signaling pathway and acetylcholinesterase activity

Aspartame (ASP) is a common sweetener, but studies show it can harm the nervous system, causing learning and memory deficits. β-caryophyllene (BCP), a natural compound found in foods, including bread, coffee, alcoholic beverages, and spices, has already described as a neuroprotector agent. Remarkably, ASP and BCP are commonly consumed, including in the same meal. Therefore, considering that (a) the BCP displays plenty of beneficial effects; (b) the ASP toxicity; and (c) that they can be consumed in the same meal, this study sought to investigate if the BCP would mitigate the memory impairment induced by ASP in rats and investigate the involvement of the brain-derived neurotrophic factor (BDNF)/ tropomyosin receptor kinase B (TrKB) signaling pathway and acetylcholinesterase (AChE) activity. Young male Wistar rats received ASP (75 mg/kg; i.g.) and/or BCP (100 mg/kg; i.p.) once daily, for 14 days. At the end of the treatment, the animals were evaluated in the open field and object recognition tests. The cerebral cortex and hippocampus samples were collected for biochemical and molecular analyses. Results showed that the BCP effectively protected against the cognitive damage caused by ASP in short and long-term memories. In addition, BCP mitigated the increase in AChE activity caused by ASP. Molecular insights revealed augmented BDNF and TrKB levels in the hippocampus of rats treated with BCP, indicating greater activation of this pathway. In conclusion, BCP protected against ASP-induced memory impairment. AChE activity and the BDNF/TrkB signaling pathway seem to be potential targets of BCP modulatory role in this study.