Short Memory Research Articles

A Gaussian Process-Enhanced Non-Linear Function and Bayesian Convolution–Bayesian Long Term Short Memory Based Ultra-Wideband Range Error Mitigation Method for Line of Sight and Non-Line of Sight Scenarios

Relative positioning accuracy between two devices is dependent on the precise range measurements. Ultra-wideband (UWB) technology is one of the popular and widely used technologies to achieve centimeter-level accuracy in range measurement. Nevertheless, harsh indoor environments, multipath issues, reflections, and bias due to antenna delay degrade the range measurement performance in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This article proposes an efficient and robust method to mitigate range measurement error in LOS and NLOS conditions by combining the latest artificial intelligence technology. A GP-enhanced non-linear function is proposed to mitigate the range bias in LOS scenarios. Moreover, NLOS identification based on the sliding window and Bayesian Conv-BLSTM method is utilized to mitigate range error due to the non-line-of-sight conditions. A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. Furthermore, moving average and min-max removing methods are utilized to minimize the standard deviation in the range measurements in both scenarios. Extensive experimentation with different settings and configurations has proven the effectiveness of our methodology and demonstrated the feasibility of our robust UWB range error mitigation for LOS and NLOS scenarios.

Effect of Shape Memory on the Mechanical Properties of Concrete Reinforced with Short Shape Memory Alloy Fibers

Effect of Shape Memory on the Mechanical Properties of Concrete Reinforced with Short Shape Memory Alloy Fibers

The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks

With the rapid development of artificial intelligence and computer vision technology, dance action recognition and quality assessment have become a challenging and important research field. Traditional dance movement recognition methods often rely on manual feature extraction and expert scoring, which have problems such as strong subjectivity, low efficiency, and difficulty in handling complex movements. To address these issues, this study proposes a dance action recognition and quality assessment method based on Spatio Temporal Convolutional Neural Network (ST-CNN). Specifically, it includes a feature extraction network based on 3D spatiotemporal description operators and a multi-scale aggregated long short memory network for extracting spatial features of single frame images and fusing video sequence features composed of all frames. This approach can effectively capture dynamic changes in dance movements by combining information from spatial and temporal dimensions, thereby achieving more accurate recognition and evaluation. In addition, to address the high storage and computational requirements of convolutional neural networks during training, we integrated dance video sequences into an IoT architecture to address parallel recognition and evaluation of multiple videos. Finally, we validated the proposed method on the public datasets NTU-RGB 60 and NTU-RGB 120, and the experimental results demonstrated the effectiveness of the method.

Integrating clustering and sequential analysis for improving the spectral density estimation and dependency structure of time series

Time series models (TSMs) are used to forecast events based on verified historical data that are widely applied to describe natural and social phenomena. The spectral density function (SDF) of the TSM has the benefit to determine trends, periodic patterns and behavior of observable events and is used in degree of memory (DOM) which is used to identify independent and dependent structures. Therefore, paying attention to the estimator of SDF (SDE) is the cornerstone and desired goal in the statistical properties of TSMs. This paper gives a novel perspective for improving the SDE then dependency structure of TSMs by integrating clustering and sequential analysis. The accuracy of the lag window estimator (LWE) and periodogram estimator (PE), which are commonly used as SDEs in practice, is examined both with and without integrating the new proposed technique in order to figure out the efficacy of the new technique. Several circumstances, including independent and dependent data structures, TSMs with long and short DOMs, different numbers of clusters, and different dataset sizes, are used to carry out the investigation. The main findings indicate that: (i) The SDEs (PE and LWE) perform better when combined with the new proposed technique than when used alone. (ii) Using the new technique in conjunction with the SDEs results in absolutely perfect estimators for independent processes. (iii) High efficiency estimators for short memory TSMs arise when the new proposed technique is combined with the SDEs in the dependent processes; however, high efficiency in long memory TSMs (LMTSMs) is not guaranteed. (iv) Using the new technique, the correlation estimator behavior between SDEs and frequencies improves significantly with increasing DOM values of LMTSMs. (v) By applying the new technique for LMTSMs, the DOM estimator provides results indicating significant dependence. This study demonstrates that there are other efficient ways to improve the estimator than estimate techniques.

Persistence in greenhouse gas emissions: Evidence from European countries

The objective of this study is to analyse the persistence of carbon dioxide along with other greenhouse gas emissions for the EU27 countries. The time trends of series are assessed by applying fractional integration methods to per capita greenhouse gases in EU member states to ascertain whether shocks in the series show transitory or permanent impacts. The key findings indicate significant negative trends in 19 countries along with the EU27 as a whole, and reveal that Lithuania is the only state in which greenhouse gas emissions show a positive trend. The results also show that Bulgaria, Croatia, Hungary, Ireland, Latvia, Poland, Portugal and Romania are the states where the time trend is deemed to be negligible. Long memory is present in nine countries with mean reverting shocks. The EU27 as a whole shows only short memory behaviour. Accordingly, the requirements for policy changes within the EU27 is less pressing but there is, to some extent, a need for policy change in Lithuania.

Incidence and risk factors of Post-intensive care syndrome (PICS) in surgical ICU survivors: a prospective Chinese cohort study

BackgroundPost-intensive care syndrome (PICS) is a term coined by the Society of Critical Care Medicine to describe the psychological, cognitive, and physical dysfunction that ICU survivors may experience. Although surgical patients represent a substantial proportion of ICU survivors, studies describing PICS in this specific population remain limited. This study aims to determine the incidence and independent risk factors associated with PICS among surgical ICU survivors in a Chinese cohort.MethodsThe study was a prospective cohort study of critically ill surgical patients who were discharged from the ICU at the First Affiliated Hospital of Sun Yat-sen University between August 2021 and June 2022. Demographic characteristics, disease-related information, and ICU treatment were collected, and enrolled participants were followed up within six months after ICU discharge. The Chinese version of the Medical Outcomes Study 36-item Short-Form Health Survey (SF-36) and the Short Memory Questionnaire (SMQ) were used to assess PICS. The physical component summary (PCS) and the mental component summary (MCS) were averaged from the corresponding four-dimension scores in the SF-36. PICS diagnosis was determined based on the presence of at least one of the following: physiological dysfunction (defined as PCS reductions greater than 10), psychological dysfunction (defined as MCS reductions greater than 10), or cognitive dysfunction (defined as SMQ reductions and scores less than 40 at six months). PICS diagnosis was based on the presence of at least one of the following: physiological, psychological, or cognitive dysfunction.ResultsA total of 565 patients were screened in this study, and 83 were enrolled after applying the inclusion and exclusion criteria. Overall, 65 surgical ICU survivors developed PICS within six months, with an incidence rate of 78.3%. The prevalences were 55.4% and 27.7% at the end of 3 and 6 months after ICU discharge, respectively. Univariate analysis showed that there was a correlation between the occurrence of PICS and the total bilirubin and creatinine levels at ICU admission, APACHE II score, ICU length of stay, and the presence of dialysis (P < 0.05). ICU length of stay was identified as an independent risk factor for the occurrence of PICS in surgical ICU survivors after adjusting for confounders.ConclusionThe overall PICS incidence in surgical ICU survivors was 78.3%, with prevalence gradually decreasing over time to 27.7% within 6 months. For surgical survivors requiring ICU care, the longer the ICU stay, the more likely to develop PICS.Implications for clinical practiceThe findings offer valuable insights into the incidence and risk factors of PICS in surgical ICU survivors, which can help healthcare professionals identify surgical cases at high risk of developing PICS and tailor the treatment effectively.

Memory is improved and reflex maturation accelerated in the progeny of rat dams that consumed pequi (Caryocar Brasiliense)

This study evaluated the influence of maternal consumption of pequi pulp and nuts during pregnancy and lactation on reflex and somatic maturation and memory performance in rat offspring. The pups were divided into three groups: Control, treated with distilled water; a Pulp group and a Nuts group, treated with 2000 mg/kg of body weight of pequi pulp and nuts, respectively. Reflex and somatic maturation of the offspring were evaluated during lactation. Short and long-term memory consolidation in adolescent offspring were measured by evaluating habituation to the open field and object recognition. Data showed an acceleration of reflex maturation in three of the seven parameters evaluated in the Nuts group and four parameters in the Pulp group compared to the control. There was an acceleration in the appearance of somatic maturation in one parameter in the Nuts group and three in the pulp group compared to the control. The long-term object recognition rate was higher in the Nuts group compared to the control and highest in the Pulp group. Maternal levels of malonaldehyde in the brain were measured and their levels in the Pulp and Nuts groups were lower than those in the Control group. We can conclude that consumption of both pulp and pequi nuts promoted a reduction in oxidative stress in the maternal brain, promoted acceleration of reflex and somatic maturation, and improved long-term memory in the offspring, with these effects being more intense in the Pulp group.

Micrometeorological drivers affecting the variability of CO2 uptake in the Himalayan Oak and Pine dominated ecosystems: An assessment of causal relationships

Micrometeorological variability significantly impacts the structures, functions, and dynamics of ecosystems. However, the assessment of feedback and causal relationships among microclimatic drivers and various ecosystems in the Himalayan region is rarely evaluated. Here, we studied the micrometeorological drivers controlling the variability in the net ecosystem exchange (NEE) of Himalayan Oak (Banj-Oak/Quercus leucotrichophora) and Pine (Chir-Pine/Pinus roxburghii) dominated ecosystems, as NEE is an indicator of ecosystem functioning. We used half-hourly eddy covariance flux data of CO2 fluxes from two sites established over Pine and Oak dominated ecosystems in Uttarakhand, India. We conducted the analysis with the information theory-based Temporal Information Partitioning Networks (TIPNets) approach to generate weekly process networks. TIPNets represent directed lag-structured causal graphs to identify the causal relationships and capture the temporal association among the variables. Our analysis aimed to capture fluctuations in variables with up to 6 h of memory. Based on the data availability, we generated the weekly networks at both the sites for the monsoon and post-monsoon seasons of 2016 and 2017. In both ecosystems, the sub-daily scale variations among the micrometeorological variables are responsible for the fluctuations in NEE. The Pine ecosystem is found to be more sensitive to changes in air temperature (TA) and uptakes more CO2 as compared to the Oak ecosystem throughout the study period. The transfer entropy links show that the NEE of the Oak ecosystem is moisture-driven (precipitation and relative humidity), while the Pine ecosystem is heat-driven (TA and net solar radiation) in both seasons. The influence of precipitation is not observed within a short memory of 6 h in the Pine ecosystem. This is because lesser fine roots take time to show the precipitation signature on NEE through infiltration, soil moisture, and root water uptake, compared to Oak. However, the impacts of moisture stress are evident in the network structure of both ecosystems, with more causal links observed in the network during dry periods compared to wet periods.

Gender-modulated influence of BDNF concentration and Val66Met polymorphism on cognitive outcomes in chronic limb ischemia patients.

While numerous studies have established associations between brain-derived neurotrophic factor (BDNF) and cognitive functioning, limited research has delved into the role of BDNF concerning cognitive outcomes in atherosclerosis-related conditions. This study aimed to investigate the correlations between cognitive performance, serum BDNF levels, and the BDNF Val66Met polymorphism in individuals diagnosed with chronic limb ischemia (CLI). The study encompassed 159 CLI patients (52 females, 107 males) aged 59-73 years. Genetic analysis involved assessing the BDNF Val66Met single-nucleotide polymorphism using a TaqMan SNP Genotyping Assay and the ViiA™ 7 Real-Time PCR System. Serum BDNF levels were quantified. Cognitive functioning was evaluated through a computerized battery of tests, including the simple reaction test (SRT) for speed and accuracy assessment, verbal memory test (VMT) for short and long-term memory evaluation, and the GoNoGo test for cognitive control and inhibition. Gender differences in cognitive performance were observed, with women excelling in VMT, while men demonstrated superior performance in SRT and the GoNoGo test. No statistically significant differences were noted between the Met/Met or Met/Val and Val/Val subgroups. However, notable differences emerged in female Met carriers, exhibiting superior VMT scores but more incorrect Go responses in the GoNoGo test. Conversely, female Val homozygotes showed significantly more incorrect NoGo responses compared to male Val homozygotes. In men carrying the Met allele, higher BDNF concentrations correlated with improved GoNoGo test results (r = 0.248, p = 0.01). Conversely, in women with the Val/Val variant, lower BDNF concentrations were associated with better VMT scores. This study underscores the sex-specific impact of BDNF serum levels and the BDNF polymorphism on cognitive processes among CLI patients. The findings highlight the nuanced influence of BDNF in shaping cognitive functioning, emphasizing the need for further research into these sex-dependent associations.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Usage-based Grammar Induction from Minimal Cognitive Principles

Abstract This study explores the cognitive mechanisms underlying human language acquisition through grammar induction by a minimal cognitive architecture, with a short and flexible sequence memory as its most central feature. We use reinforcement learning for the task of identifying sentences in a stream of words from artificial languages. Results demonstrate the model’s ability to identify frequent and informative multi-word chunks, reproducing characteristics of natural language acquisition. The model successfully navigates varying degrees of linguistic complexity, exposing efficient adaptation to combinatorial challenges through the reuse of sequential patterns. The emergence of parsimonious tree structures suggests an optimization for the sentence identification task, balancing economy and information. The cognitive architecture reflects aspects of human memory systems and decision-making processes, enhancing its cognitive plausibility. While the model exhibits limitations in generalization and semantic representation, its minimalist nature offers insights into some fundamental mechanisms of language learning. Our study demonstrates the power of this simple architecture and stresses the importance of sequence memory in language learning. Since other animals do not seem to have faithful sequence memory, this may be a key to understanding why only humans have developed complex languages.

Fractal-Fractional-Order Modeling of Liver Fibrosis Disease and Its Mathematical Results with Subinterval Transitions

In this paper, we study human liver disease with a different approach of interval-based investigation by introducing subintervals. This investigation may be referred to as a short memory investigation. Such concepts are useful in problems where a transition is observed when transitioning from one subinterval to the other one. We use the classical and fractal-fractional-order derivative in each subinterval. We study the existence of solutions by using Banach’s and Krasnoselskii’s fixed-point theorems. Their stability is analyzed by adopting the Hyers–Ulam (H-U) stability approach. Also, using the extended Adams–Bashforth–Moulton (ABM) method, we simulate the results that visually present the numerical solutions for different fractal-fractional-order values.

Frequency domain local bootstrap in short and long memory time series

Bootstrap methods in the frequency domain are effective instruments to approximate the distribution of many statistics of weakly dependent (short memory) series but their validity with long memory remains mostly unsolved. This article proposes a frequency domain local bootstrap (FDLB) based on resampling a locally studentized version of the periodogram in a neighborhood of the frequency of interest. A bound of the Mallows distance between the distributions of the periodogram and its FDLB bootstrap counterpart is offered for anti-persistent, weakly dependent, stationary, and non stationary long memory series. This result is in turn used to justify the use of the FDLB for some statistics that are weighted averages of periodogram ordinates. Finally, the validity of the FDLB to estimate the distribution of the local Whittle estimator is proved and its finite sample behavior analyzed in a Monte Carlo and in an empirical application, comparing its performance with rival alternatives.

Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000-2022.

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

The continuous memory: A neural network with ordinary differential equations for continuous-time series analysis

Continuous-time series analysis has garnered significant research interest due to its extensive applications; however, it remains a challenging endeavor. In recent years, deep learning methods have achieved remarkable success in tasks such as time series classification and forecasting. Nevertheless, the inherent continuity of time series data has not been fully addressed, presenting a persistent obstacle to performance. Recent studies have highlighted a natural similarity between differential equations and continuous-time series, as both inherently encapsulate the concept of continuity. However, several critical issues with differential equations hinder their direct applicability to time series analysis. These issues include the elusive nature of analytical solutions, the indirect observability of the effects of differential equation parameters, and the dependence of numerical solutions on initial conditions. In this context, we propose the integration of differential equations into neural networks to serve as the continuous memory of the model. This integration imparts a continuous nature to the model, resulting in the development of an efficient deep learning architecture known as Long Short Deep Memory (LSDM). Furthermore, we analyze the characteristics of differential equations when employed as the memory component in neural networks, which leads to the proposal of a novel pre-training approach that incorporates an innate memory mechanism into these networks. Additionally, we utilize LSDM to construct a stacked architecture for processing very long time series data. The proposed model can naturally accommodate arbitrary time gaps between observations, thereby enhancing its effectiveness and suitability for continuous time series analysis tasks. Extensive experiments conducted on various real-world datasets demonstrate that the proposed model outperforms existing methods, providing a new solution to the challenges associated with continuous time series analysis.

Studying at Medical School

What was your experience studying during medical school? I belonged to the last of the “old” curriculum cohort in Glasgow. In MB 1 and 2: we had daily lectures / labs covering all the basic sciences 9-5 almost every day. These continued into MB3 (Pathology) when Clinical Medicine (MB3-5) started. We had summative exams every term. So studying for long hours during term hours and at home was normal for me and my peers. It was very intense. But I learnt a lot and, despite what you often hear, it put me on very good footing to be a good doctor in my medical practice and helping me pass the necessary and numerous post graduate exams. What techniques/resources did you use? I read books, made notes from those books, and then amalgamated those with the lecture notes I had taken contemporaneously =&gt; generating a 3rd set of notes. This last bit streamlined everything I read/studied and made revising the large volume of information just before exams much more manageable. My book list had 1 book covering each of the main subjects that year. The decision was often made by cross-referencing Glasgow’s list with my friends’ list in Singapore – to pick the “best” book. I also spent many hours during clinical years on the wards, in theatres, etc to practice taking history, examining patients, and helping ward doctors. Occasionally kindly classmates passed on bits of past papers - which helped redirect my revision. What worked/didn’t work? I think this technique worked quite well for me – I didn’t fail any subject (although I was always terrified I would and thought I had for my MB5 finals!) and even graduated with commendation - and I continue to use this technique to this day. Studying in the libraries didn’t work well for me in the lead-up to the “big exams” – I was too easily distracted when surrounded by my friends! Now being on the other side, how would you recommend students study/find the study technique that works for them? I think that medical students are clearly expert study-ers and exam-passers to have made it to Medical School. They all know how to study. The trick is, I think, being (1) consistent, (2) systematic, and (3) methodical in your approach. Do a little bit of work every day. Build a timetable around how covering what you need might look like and take it from there. Try and study in a way that makes sense within the subject, so new information and knowledge can build layer by layer logically. For this reason – I think having books help provide that structured way of learning. Also – learn with friends/classmates. Not only is it more sociable, fun, and good for your mental health – they are also great for filling in gaps you didn’t know you had! In terms of prioritizing what to study first/most when “everything is important” – that is where spending time on the wards, speaking to real doctors, senior year students, finding mentors, and reaching out to wider networks of students and other stakeholders really come into their own. The latter has never been easier with the advent of the internet and consequent open access to so many resources. What are the common pitfalls you see students falling into when it comes to studying in medical school? A common one is studying for exams using past papers or MCQ books. Remember: medicine is a vocation where even consultants are continually learning, building on basic foundational knowledge. So the short-term “efficient” learning to pass medical school exams is not sustainable and not a good way to learn how to be a good doctor. Another is thinking you can “learn everything by highlighting every line” in a paper/book/notes. The act of highlighting itself does not transfer knowledge from the book into either your short or long-term memory. It is really by interacting with the item to be learned, that makes it meaningful and allows it to embed as knowledge and learned memory. Dr Carolina Kuepper-Tetzel expands on this more eloquently in this BBC piece: https://www.bbc.co.uk/bitesize/articles/z8vhfdm Resources - Try reading a little every day instead of cramming for exams and work towards MLA’s syllabus (https://www.gmc-uk.org/-/media/documents/mla-content-map-_pdf-85707770.pdf)

Bifurcation analysis of a diffusive radio-dependent model with spatial memory

The cognitive abilities of animals, such as memory, have a significant impact on their movement in space. In this paper, we consider a radio-dependent model with memory-based diffusion under the conditions of Neumann boundary. The stability of a positive equilibrium and the existence of the Turing–Hopf bifurcation induced by memory diffusion and memory delay are carried out in details. Notably, our findings indicate that with a relatively short average memory period, the large memory diffusion can stabilize an otherwise unstable equilibrium. In addition, the third-order truncated normal form for the Turing–Hopf bifurcation restricted to the central manifold is derived, which can reveal the generation of some steady-state and time-periodic solutions with spatial heterogeneity. The coefficients within the normal form are systematically determined through matrix operations, and these results can also be applied to other models with memory diffusion. Ultimately, leveraging the theoretical findings, we elucidated the intricate spatial-temporal dynamics and their associated parameter scopes caused by Turing–Hopf bifurcation through numerical simulations.

Nigella sativa extract abrogates traumatic brain injury-induced memory impairment in adult mice

BackgroundTraumatic brain injury (TBI) is an increasing widespread cause of disability and mortality, typically leading to dementia and memory impairment. ObjectiveThis study aims to investigate the neuroprotective potential of Nigella sativa extract against TBI induced memory impairment in adult albino mice. MethodsAdult male mice were divided into four groups randomly: Control, Nigella sativa extract alone, TBI alone and TBI plus Nigella sativa extract. TBI induction was carried out in mice using a weight dropping method then Nigella sativa extract (10 mg/kg) was administered intraperitoneally for two weeks. Morris water maze and Y-maze tests were used to measure memory improvement ability and Western blot technique was used to analyse the neuroinflammatory and synaptic protein markers. ResultsNigella sativa extract significantly decreased phosphorylated c-Jun N-terminal kinase (p-JNK), Tumor necrosis factor-alpha (TNF-α), and nuclear factor kappa B (NF-κB) proteins to reduce TBI-induced neuroinflammation accompanied by the restoration of both pre- and post-synaptic protein expression in adult mice model. Furthermore, Nigella sativa extract enhanced both short and long-term spatial memory against TBI in adult mice model. ConclusionNigella sativa extract abrogated neuroinflammation mediated memory impairment in TBI mice model. Further research is needed to determine Nigella sativa extract ingredients detail completely and to understand its mechanisms of neuroprotection in reducing memory impairments associated with traumatic brain injury and other neurodegenerative diseases.

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period

ObjectiveAt different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.MethodsDaily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.ResultsIn periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.ConclusionsThe varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

The Comparison of Deep Learning Models for Indonesian Political Hoax News Detection

Indonesia is the world’s fourth most populous country and has a diverse sociopolitical landscape. Political fake news exacerbates existing social divisions and causes political polarization in Indonesian society. Hence, studying it as a specific challenge can contribute to broader discussions on the impact of fake news in different contexts. The researchers propose a hoax news detection system by developing a deep learning model with various lapses against a data set preprocessed using term-frequency and token filtering to represent the most prominent words in each class. The researchers compare the layers with the potential to have high performance in predicting the falsity of Indonesian political news data by observing the models based on training history plots, model specification, and performance metrics in the classification report module. The deep learning models include One-Dimensional Convolution Neural Networks (1D CNN), Long-Term Short Memory (LSTM), and Gated Recurrent Unit (GRU). The news data are obtained from the Kaggle site, containing 41.726 rows of data. Based on the experiments with the text data that has been preprocessed in the form of vectors and the specific parameters before starting, the results show that GRU achieves the highest performance value in accuracy, recall, precision, and F1 score. Although GRU becomes the model with the smallest file size, it is the slowest model to generate predictions from text news data. It also has a higher potential to be an overfitted model due to parameters than a simple RNN.