Neurons In Layer Research Articles

Design of an artificial neural network and proportional‐integral‐derivative controller using particle swarm optimization for Boeing 747‐400 aircraft pitch control

AbstractThis paper presents the design of an artificial neural network (ANN) and proportional integral derivative (PID) controller using particle swarm optimization (PSO) for Boeing 747‐400 aircraft pitch control (APC). The combinations of disturbance, open loop unstable and nonlinear dynamics are major problems in a Boeing 747‐400 commercial aircraft. This paper investigates the control mechanism of pitch angle control of Boeing 747‐400 with small disturbance theory linearization methods and ANN based non‐linear controllers. A PID controller is tuned by PSO, whereas the PID is tuned by graphical user interface (GUI) when compared with an ANN controller. The controller for this system was designed using an ANN controller and PID tuned using a recent optimization technique such as the PSO method with integral square error (ISE) as an objective function. A comparative study of the time domain performances of the pitch control of the Boeing 747‐400 commercial aircraft was presented. The ANN controller outperformed the PID‐PSO and PID‐GUI controllers in terms of system performance, including rising time (tr), settling time (ts), percentage overshoot (percent OS), and steady state error, across various elevator deflection angles. Basically, the percentage overshoot and steady state error were 0% and 0 respectively, indicating that the ANN controller achieved an improvement of 100%. Various parameters were compared with the PID‐GUI, PID‐PSO, and ANN controllers for pitch control of the Boeing 747‐400 air craft. The ANN controller architecture comprises of two input neurons, two hidden layer neurons, and one output layer neuron. The simulation was performed using Matlab/Simulink. The results show that the PID‐PSO controller was improved by the ANN controller and the performance specifications of the aircraft obtained by the ANN controller were satisfactory.

At the onset of active whisking, the input layer of barrel cortex exhibits a 24 h window of increased excitability that depends on prior experience.

The development of motor control over sensory organs is a critical milestone in sensory processing, enabling active exploration and shaping of the sensory environment. However, whether the onset of sensory organ motor control directly influences the development of corresponding sensory cortices remains unknown. Here, we exploit the late onset of whisking behavior in mice to address this question in the somatosensory system. Using ex vivo electrophysiology, we discovered a transient increase in the intrinsic excitability of excitatory neurons in layer IV of the barrel cortex, which processes whisker input, precisely coinciding with the onset of active whisking at postnatal day 14 (P14). This increase in neuronal gain was specific to layer IV, independent of changes in synaptic strength, and required prior sensory experience. Strikingly, the effect was not observed in layer II/III of the barrel cortex or in the visual cortex upon eye opening, suggesting a unique interaction between the development of active sensing and the thalamocortical input layer in the somatosensory system. Predictive modeling indicated that changes in active membrane conductances alone could reliably distinguish P14 neurons in control but not whisker-deprived hemispheres. Our findings demonstrate an experience-dependent, lamina-specific refinement of neuronal excitability tightly linked to the emergence of active whisking. This transient increase in the gain of the thalamic input layer coincides with a critical period for synaptic plasticity in downstream layers, suggesting a role in facilitating cortical maturation and sensory processing. Together, our results provide evidence for a direct interaction between the development of motor control and sensory cortex, offering new insights into the experience-dependent development and refinement of sensory systems. These findings have broad implications for understanding the interplay between motor and sensory development, and how the mechanisms of perception cooperate with behavior.

Correlation between the number of interstitial neurons of the white matter and number of neurons within cortical layers: Histological analyses in postnatal macaque.

We have examined the number and distribution of NeuN-immunoreactive cortical white matter interstitial cells (WMICs) and compared them to the neurons in layers 1-6 across the overlying cortex in coronal sections from postnatal macaques. The data have been gathered from over 300 selected regions at gyral crowns, at sulci, and at linear regions of the cortex where we also determined cortical layer thicknesses: standard thicknesses and tangential thicknesses. Cortical thicknesses and cell numbers showed variability according to gyral, linear, or sulcal regions. In spite of these variations, our standardized cell numbers in layers 1 to 6b and interstitial cells underlying layer 6b-white matter boundary have shown a consistent correlation between the number of WMICs and the number of layer 5 and 6a cortical neurons on all cortical regions studied: for each WMIC, there are on the order of five cortical neurons in layer 5 and approximately three cortical neurons in layer 6a, irrespective of the origins of the selected cortical area or whether they are from gyral, linear, or sulcal regions. We propose that the number of interstitial neurons in the postnatal macaque cortex is correlated to the density of neurons within layers 5 and 6a and, from a clinical perspective, the change in density or distribution of interstitial neurons in schizophrenia or epilepsy may in fact be linked to the number of layers 5 and 6a neurons.

Curve-Modelling and Machine Learning for a Better COPD Diagnosis.

Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

M2 receptors are required for spatiotemporal sequence learning in mouse primary visual cortex.

Acetylcholine is a neurotransmitter that plays a variety of roles in the central nervous system. It was previously shown that blocking muscarinic receptors with a nonselective antagonist prevents a form of experience-dependent plasticity termed "spatiotemporal sequence learning" in the mouse primary visual cortex (V1). Muscarinic signaling is a complex process involving the combined activities of five different G protein-coupled receptors, M1-M5, all of which are expressed in the murine brain but differ from each other functionally and in anatomical localization. Here we present electrophysiological evidence that M2, but not M1, receptors are required for spatiotemporal sequence learning in mouse V1. We show in male mice that M2 is highly expressed in the neuropil in V1, especially in thalamorecipient layer 4, and colocalizes with the soma in a subset of somatostatin-expressing neurons in deep layers. We also show that expression of M2 receptors is higher in the monocular region of V1 than it is in the binocular region but that the amount of experience-dependent sequence potentiation is similar in both regions and that blocking muscarinic signaling after visual stimulation does not prevent plasticity. This work establishes a new functional role for M2-type receptors in processing temporal information and demonstrates that monocular circuits are modified by experience in a manner similar to binocular circuits.NEW & NOTEWORTHY Muscarinic acetylcholine receptors are required for multiple forms of plasticity in the brain and support perceptual functions, but the precise role of the five subtypes (M1-M5) are unclear. Here we show that the M2 receptor is specifically required to encode experience-dependent representations of spatiotemporal relationships in both monocular and binocular regions of mouse V1. This work identifies a novel functional role for M2 receptors in coding temporal information into cortical circuits.

Nonlinear Decoupling Control With PIλ Dμ Neural Network for MIMO Systems.

In this brief, a fractional order proportional-integral-differential neural network (PIDNN) controller based on the beetle swarm optimization algorithm (BSO-PI [Formula: see text]NN) is proposed for multi-input multi-output (MIMO) systems with strong coupling. First, the fractional order PID operator is introduced to the hidden layer neurons of the neural network, where long memory characteristics of the fractional order neurons can improve the control accuracy and convergence speed. Second, a sufficient condition on the learning rate is established to ensure the stability of the controller by the Lyapunov theory. Third, the PI [Formula: see text]NN is initialized by the BSO algorithm to prevent weights from falling into local optima. The proposed fractional order PIDNN controller can eliminate the coupling between variables and achieve desirable control performance without specific system models. To the authors' best knowledge, this is the first work that the fractional order PI [Formula: see text] neurons are employed in neural network. Two simulation examples verify the effectiveness and superiority of the proposed controller.

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers.

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

Under high noise conditions and random impacts, which constitute strong interference, models often exhibit limited capability in capturing long-term dependencies, leading to lower accuracy in predicting the remaining useful life (RUL) of bearings. To address this issue, a spatiotemporal fusion network capable of ultra-long-term feature analysis is proposed to enhance the accuracy of bearing RUL prediction under substantial interference. This network utilizes a dilated convolution-based lightweight vision transformer encoder to extract spatial features reflecting the short-term degradation state of the bearing. Then, these features are sequentially fed into an adaptive tiered memory unit, based on the multiple attention mechanism and the neuron layering mechanism, to analyze temporal features indicative of long-term degradation. Subsequently, short-term spatial and long-term temporal features are fused for RUL prediction. To validate the robustness and predictive accuracy of the proposed approach under strong interference, a gearbox-rolling bearing accelerated platform is constructed, simulating high noise and random impact conditions. Experiments confirm the high robustness and predictive accuracy of the proposed method under strong interference conditions.

#2534 Classification of patients with acute kidney damage on the base of blood pressure value as a risk factor using a neural network

Abstract Background and Aims In acute kidney damage, the monitoring of hemodynamic status is best achieved by determining the blood pressure value. It is considered that the values of diastolic blood pressure in young and healthy people are constant from the aorta to the periphery, and the values of systolic and pulse pressure increase from the periphery to the aorta. The frequency of hypertension correlates with the presence of a cardiovascular precipitating risk factor in acute kidney injury, hypertension is also a modifying risk factor in the second and third stages of acute kidney injury, while the presence of hypotension on admission can be a clinical indicator of its cause. In patients with acute kidney damage and in patients with sepsis, the mechanism of autoregulation of mean arterial pressure, which is affected by stroke volume and heart rate, is not effective. The aim of the paper is to examine the classification of hospitalized patients with acute kidney injury based on blood pressure as a risk factor. Method The study included 86 patients over 18 years of age of both sexes (39 men 45.3% and 47 women 54.7%) with an average age of 66.85 ± 15.30 years who were diagnosed with acute kidney injury on admission to hospital treatment. Patients were divided into groups according to the stages of renal insufficiency. The study was designed as a cross-sectional comparative study. The assessment of the existence of acute kidney damage, as well as the determination of the stage of the disease, was based on the diagnostic criteria proposed by the K/DIGO expert group for acute kidney damage. The study did not include patients who had disease progression after acute kidney failure and were treated with dialysis for more than three months. Basic clinical, biochemical and hemodynamic parameters were determined in all subjects at the beginning of treatment, except for the analysis of nitrogen status, which was determined both before and at the end of treatment. During the formation of the neural network (pattern recognition Mat Lab), the parameters of systolic arterial pressure, diastolic arterial pressure normalized by min-max normalization to values from 0 to 1 were included. The data were randomly distributed into three categories: 80% for training, 10% for test, 10% for validation, which means that the parameters of 68 patients were used for training, 9 patients for validation and another 9 patients for the test. The number of input parameters is 33 to 39, while the number of output parameters is 2 in relation to treatment. Formed neural networks with different included blood pressure parameters, structures of 25 neurons in the hidden layer, with 55 and 15 epochs and with accuracy of 97.7% and 90.7% of patient classification. Results Our results show that 40.3% of patients in the third stage of acute kidney injury have hypertension. The lowest average values of systolic (108.44 ± 22.81 mmHg) and diastolic (65.56 ± 15.29 mmHg) blood pressure were experienced by patients in the first stage of acute kidney damage, and the lowest values of mean arterial pressure existed in the second stage of acute kidney damage (68.20 ± 44.96 mmHg) .Statistically significant risk factors for the development of the third stage of acute kidney damage in the multivariate analysis were patients older than 65 years, and mean arterial pressure values less than 65 mmHg. The confusion matrices showed that the classifiers adapted to the minimum and maximum values of systolic, diastolic and mean arterial blood pressure are well adapted and achieve satisfactory accuracy with 97.7% and 90.7%. Conclusion By comparing blood pressure parameters in hospitalized patients in relation to the stage of acute kidney damage, no statistically significant deviations were found. Univariate logistic regression analysis showed that age over 65 was a significant independent risk factor for stage three acute kidney injury, while the multivariate model showed that risk factors for the development of stage three acute kidney injury were patients same age and middle arterial blood pressure less than 65 mmHg. Satisfactory accuracy of formed neural networks when classifying patients during validation training and test was also demonstrated.

Artificial Neural Network (ANN)-Based Water Quality Index (WQI) for Assessing Spatiotemporal Trends in Surface Water Quality—A Case Study of South African River Basins

Artificial neural networks (ANNs) are powerful data-oriented “black-box” algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained.

Expression of guanylate cyclase C in human prefrontal cortex depends on sex and feeding status.

Guanylate cyclase C (GC-C) has been detected in the rodent brain in neurons of the cerebral cortex, amygdala, midbrain, hypothalamus, and cerebellum. In this study we determined GC-C protein expression in Brodmann areas (BA) 9, BA10, BA11, and BA32 of the human prefrontal cortex involved in regulation of feeding behavior, as well as in the cerebellar cortex, arcuate nucleus of hypothalamus and substantia nigra in brain samples of human 21 male and 13 female brains by ELISA with postmortem delay < 24 h. GC-C was found in all tested brain areas and it was expressed in neurons of the third cortical layer of BA9. The regulation of GC-C expression by feeding was found in male BA11 and BA10-M, where GC-C expression was in negative correlation to the volume of stomach content during autopsy. In female BA11 there was no correlation detected, while in BA10-M there was even positive correlation. This suggests sex differences in GC-C expression regulation in BA11 and BA10-M. The amount of GC-C was higher in female BA9 only when the death occurred shortly after a meal, while expression of GC-C was higher in BA10-O only when the stomach was empty. The expression of GC-C in female hypothalamus was lower when compared to male hypothalamus only when the stomach was full, suggesting possibly lower satiety effects of GC-C agonists in women. These results point toward the possible role of GC-C in regulation of feeding behavior. Since, this is first study of GC-C regulation and its possible function in prefrontal cortex, to determine exact role of GC-C in different region of prefrontal cortex, especially in humans, need further studies.

ANN and CFD driven research on main performance characteristics of solar chimney power plants: Impact of chimney and collector angle

Solar energy systems operate directly connected to the sun. Solar chimney power plants are privileged systems that can provide power output even in cloudy weather and during hours when there is no sun. The design and sizing of this system, which researchers focused on after its first application in the 1980s, is very effective on its performance. In this study, the collector slope and chimney slope that give maximum power output for the Manzanares pilot plant are investigated with a 3D CFD model. Simulations made using the RNG k-e turbulence model and the DO (discrete ordinates) solar ray tracing algorithm provide results that are in high compatibility with experimental data and literature. It is understood that the system provides maximum power at 0.6° collector slope and 1.5° chimney divergence angle. It is seen that the system, which gives a power output of approximately 46 kW in the reference case, exceeds the power output by 4.5 times and reaches 216.853 kW in the design that includes the collector and chimney slope. The effects of the main elements of the system on the performance are also included by changing the collector radius and chimney height while preserving these inclination angles. More than the power output in the reference case, 49.233 kW, can be achieved with the inclined design, with a collector radius of 73.2 m and a chimney height of 155.68 m. Although the effect of increasing the chimney height on power output continues after 1.2 floors, its effect decreases. In the study, it is seen that increasing the chimney height and changing the collector radius provide a greater increase in power output. Furthermore, the scope extends to the incorporation of an Artificial Neural Network (ANN) model, presenting a novel approach to predicting SCPP system performance. The findings ascertain the utilisation of 9 neurons in the hidden layer of the ANN, demonstrating a precise alignment with the study data.

DeepMetaDroid: Real-Time Android Malware Detection Using Deep Learning and Metadata Features

The increasing prevalence of Android malware poses significant risks to mobile devices and user privacy. The traditional detection methods have limitations in keeping up with the evolving landscape of malware attacks, necessitating the development of more effective solutions. In this paper, we present DeepMetaDroid, a real-time detection approach for Android malware that leverages metadata features. By analyzing crucial metadata, including APK size, download size, permissions, certificates, and DEX files, the proposed method enables effective identification of malware and enhances mobile security. Using deep learning techniques, a lightweight Android real-time monitoring system is equipped with the trained model. These methods include long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and other ensemble models. Utilizing the rectified linear unit (ReLU) as the activation function, the DNN model is constructed with 32 neurons in the input layer. A one-dimensional convolutional layer with 32 neurons and a filter size of three is used as the input layer in the CNN model. The LSTM model is designed with an input layer consisting of 16 neurons. The GRU model with 32 neurons is employed in the input layer. Additionally, ensemble models that combined several architectures were developed. The proposed method offers a faster and more scalable solution for malware detection by consuming fewer resources like memory and CPU. This work ensures device security by providing real-time monitoring on Android devices to prevent users from installing malicious applications and, thus, enhance user privacy and security.

High-Caloric Diets in Adolescence Impair Specific GABAergic Subpopulations, Neurogenesis, and Alter Astrocyte Morphology

We compared the effects of two different high-caloric diets administered to 4-week-old rats for 12 weeks: a diet rich in sugar (30% sucrose) and a cafeteria diet rich in sugar and high-fat foods. We focused on the hippocampus, particularly on the gamma-aminobutyric acid (GABA)ergic system, including the Ca2+-binding proteins parvalbumin (PV), calretinin (CR), calbindin (CB), and the neuropeptides somatostatin (SST) and neuropeptide Y (NPY). We also analyzed the density of cholinergic varicosities, brain-derived neurotrophic factor (BDNF), reelin (RELN), and cyclin-dependent kinase-5 (CDK-5) mRNA levels, and glial fibrillary acidic protein (GFAP) expression. The cafeteria diet reduced PV-positive neurons in the granular layer, hilus, and CA1, as well as NPY-positive neurons in the hilus, without altering other GABAergic populations or overall GABA levels. The high-sugar diet induced a decrease in the number of PV-positive cells in CA3 and an increase in CB-positive cells in the hilus and CA1. No alterations were observed in the cholinergic varicosities. The cafeteria diet also reduced the relative mRNA expression of RELN without significant changes in BDNF and CDK5 levels. The cafeteria diet increased the number but reduced the length of the astrocyte processes. These data highlight the significance of determining the mechanisms mediating the observed effects of these diets and imply that the cognitive impairments previously found might be related to both the neuroinflammation process and the reduction in PV, NPY, and RELN expression in the hippocampal formation.

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

&lt;p&gt;Understanding the retention of organic compounds (OCs) is critical for membrane applications in water recycling. The objective of this study was to create an optimized model using Artificial Neural Networks for Quantitative Structure-Property Relationship (QSPR-ANN) to predict the effect of adsorption on the retention of organic compounds (OCs) by nanofiltration (NF) and reverse osmosis (RO). An optimal model (QSPR-ANNoptimal) characterized by a similar structure (13 neurons in the inputs layer, 11 neurons in the hidden layer, and 1 neuron in the output layer) is constructed to predict the effect of adsorption on the retention of organic compounds by membranes. A set of 273 data points was used to test the neural network. the data set was used 70% for training, 15% for validation, and 15% for testing. For the most promising neural network model, the calculated retention values were compared to the experimental retention values, and good correlations were found (the determination coefficient "R2 = 0.9872" and the root mean squared error "RMSE = 2.2743%" for the test phase). This indicates the good robustness of the established QSPR-ANN model and the possibility of predicting the various parameters that characterize the retention of OCs by RO/NF. Sensitivity analysis revealed that the effect of adsorption retention of organic compounds by reverses osmosis and nanofiltration membranes depends more precisely on two important interactions (hydrophobic/adsorption and steric hindrance).&lt;/p&gt;

Activity-dependent dendrite patterning in the postnatal barrel cortex.

For neural circuit construction in the brain, coarse neuronal connections are assembled prenatally following genetic programs, being reorganized postnatally by activity-dependent mechanisms to implement area-specific computational functions. Activity-dependent dendrite patterning is a critical component of neural circuit reorganization, whereby individual neurons rearrange and optimize their presynaptic partners. In the rodent primary somatosensory cortex (barrel cortex), driven by thalamocortical inputs, layer 4 (L4) excitatory neurons extensively remodel their basal dendrites at neonatal stages to ensure specific responses of barrels to the corresponding individual whiskers. This feature of barrel cortex L4 neurons makes them an excellent model, significantly contributing to unveiling the activity-dependent nature of dendrite patterning and circuit reorganization. In this review, we summarize recent advances in our understanding of the activity-dependent mechanisms underlying dendrite patterning. Our focus lays on the mechanisms revealed by in vivo time-lapse imaging, and the role of activity-dependent Golgi apparatus polarity regulation in dendrite patterning. We also discuss the type of neuronal activity that could contribute to dendrite patterning and hence connectivity.

Machine learning approach for prediction of ionospheric irregularities on ROTI index over the Northern anomaly crest in Egypt during solar cycle 24

This paper reports the prediction results of the ionospheric irregularities represented by the ROTI index close to the equatorial ionization anomaly's Northern peak. A feedforward backpropagation Neural Network (NN) approach is implemented using a time series Nonlinear Autoregressive approach with Exogenous inputs (NARX). We used the data from a dual frequency GPS-SCINDA station located at Helwan (geographic coordinates of 29.86°N, 31.32°E, and MLAT of 29.94°N) in Egypt. To allow the model to be independently tested during varying levels of solar activities, we collected a 5-minute resolution of ROTI data for the solar cycle 24 from 2009 to 2017. The factors that influence the development of ionospheric irregularities are involved in the established model representing the diurnal and seasonal variations as well as solar and geomagnetic activity parameters. To support learning, we included IRI-foF2 and IRI-hmF2 parameters in the input layer neurons to improve the model learning about the behavior of the ionospheric F layer. The results show that the NN-ROTI values precisely match the GPS-ROTI values with an RMSE of 0.106 TECU/min and a prediction efficiency of 95%. The predicted values are highly correlated with the originals, with a regression of 0.89. Furthermore, the irregularities were more prevalent during the equinox months than during the solstice months. It is also observed that predicted NN-ROTI values in all seasons have RMSEs less than 0.03 and 0.05 TECU/min for the low and high solar activity, respectively. The results also showed a clear correlation between solar activity and the occurrence percentage of ionospheric irregularities indicating a solar cycle dependency. The prediction values of the NN-ROTI occurrence % demonstrated a remarkable correlation with the observed GPS-ROTI occurrence % across SC24.

Prediction of emission characteristics of diesel/n-hexanol/graphene oxide blended fuels based on fast outlier detection-sparrow search algorithm-bidirectional recurrent neural network

This study aims to utilize deep learning (DL) technology to address the issue of carbon soot emission in traditional internal combustion engines. Spraying and combustion experiments were conducted in a constant volume combustion (CVC) chamber using n-hexanol and graphene oxide nanoparticles as partial substitutes for diesel, with the goal of predicting their emission characteristics based on experimental data. Initially, spray and combustion data under different operating conditions were collected using the CVC experimental setup, and outliers were removed using the fast outlier detection (FOD) algorithm. Subsequently, the learning rate and the number of hidden layer neurons in the Bidirectional Recurrent Neural Network (BiRNN) model were optimized using the Sparrow Search Algorithm (SSA) to enhance the model's accuracy and generalization ability. Unlike previous research methods, the novel FOD-coupled SSA optimization method used in this study ensures the optimal parameter configuration of the speed prediction model while accelerating the convergence speed, which ultimately improves the stability of the model. In addition, the optimal output variables were determined based on the Spearman and Pearson correlation coefficient rules. Finally, the effectiveness of the predictive model was validated using evaluation metrics such as root mean square error (RMSE) and R-squared (R2). The results indicate that the KL coefficient for D80H20GO20 fuel in the CVC device is minimal. The FOD-SSA-BiRNN model achieved R2 values of 0.99409, 0.99529, and 0.99692 for D100, D80H20, and D80H20GO20 fuels, respectively. Therefore, this study provides an effective method for the online accurate prediction of carbon soot emissions in mixed fuels.

Spiral wave dynamics in a neuronal network model

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, ,we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R < 20 µm, only asynchronous activity is observed due to small number of connections. The coupling strength ( gsyn ) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained.

USE OF MACHINE LEARNING, FIXED AND MIXED MODELS FOR VOLUME ESTIMATION IN FLOODPLAIN FOREST IN THE AMAZON ESTUARY

The present study aimed to evaluate the performance of artificial neural networks (ANNs), support vector machines (SVM), and fixed and mixed models to estimate the volume of individual trees in an Amazonian estuary floodplain forest. The forest inventory was conducted in an area located in the district of Itatupã - Gurupá, Pará, Brazil. The Schumacher and Hall linear model was used in its fixed and mixed form to estimate the volume. A total of 240 ANNs and 32 SVM configurations were trained, with 4 variations of the input variables: diameter at breast height (D), commercial height (hm), diameter classes (CCD), commercial height classes (CChm),) and species (SP), with the volume (V) being the output variable. The ANNs were trained in the Neuro 4.0.6 software program, and the analyses of the fixed and mixed regression models and SVM were performed in the R software program. The AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) information criteria were used for the regression models, while the following were used for comparisons of all models: correlation coefficient (rYŶ), bias, root mean squared error (RMSE), and residual distribution analysis. The best statistical metrics were obtained by the ANN of the RPROP- algorithm with D+hm+CCD+CChm inputs, with eight neurons in the hidden layer and hyperbolic tangent activation function, with 0.9827 of rYŶ RMSE of 0.2439, bias of 0.0080 and residual graphic distribution tending to homogeneity.