Analysis Of Behavior Research Articles

Theta and alpha oscillations may underlie improved attention and working memory in musically trained children.

Attention and working memory are key cognitive functions that allow us to select and maintain information in our mind for a short time, being essential for our daily life and, in particular, for learning and academic performance. It has been shown that musical training can improve working memory performance, but it is still unclear if and how the neural mechanisms of working memory and particularly attention are implicated in this process. In this work, we aimed to identify the oscillatory signature of bimodal attention and working memory that contributes to improved working memory in musically trained children. We recruited children with and without musical training and asked them to complete a bimodal (auditory/visual) attention and working memory task, whereas their brain activity was measured using electroencephalography. Behavioral, time-frequency, and source reconstruction analyses were made. Results showed that, overall, musically trained children performed better on the task than children without musical training. When comparing musically trained children with children without musical training, we found modulations in the alpha band pre-stimuli onset and the beginning of stimuli onset in the frontal and parietal regions. These correlated with correct responses to the attended modality. Moreover, during the end phase of stimuli presentation, we found modulations correlating with correct responses independent of attention condition in the theta and alpha bands, in the left frontal and right parietal regions. These results suggest that musically trained children have improved neuronal mechanisms for both attention allocation and memory encoding. Our results can be important for developing interventions for people with attention and working memory difficulties.

Behavior and Performance Analysis of Reinforced Concrete and Steel Infilled-Frame

Behavior and Performance Analysis of Reinforced Concrete and Steel Infilled-Frame

Multiscale residual stress and mechanical behavior analysis in machining of Ti-6Al-4V alloy with advanced microscopic characterization

Machining of polycrystalline metal materials results in metallurgical modifications in the machined surface, and it can introduce multiscale residual stresses (macroscale and microscale residual stress), which may be detrimental to the mechanical properties thereby interfering with the service performance of machined parts. However, most of the current studies only focus on the macroscale residual stress, the microscale residual stresses have always been neglected since the limitations of the commonly used techniques for residual stress measurement in machined surfaces. In this study, the recently developed Focused Ion Beam-digital image correlation (FIB-DIC) ring core method was employed to evaluate the time-resolved strain relaxation and multiscale residual stress of the machined surface under different cutting conditions. The quantitative evaluation of residual stress and microstructural effects on hardness of the machined surface was conducted by combining with FIB incremental milling and nanoindentation technique. It has been observed that although the global tensile strain relaxation was captured in the ring-core region, local microscale compressive strain relaxation also occurred. The hardness of the machined surface was determined by the combined effects of microstructure and residual stress induced by machining. Specifically, the grain refinement and compressive residual stress contributed to hardening, while tensile residual stress resulted in softening of the machined surface. Furthermore, it was also observed the hardening/softening effects were enhanced with increasing intensity of tension and compression.

Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection in Wireless Sensor Networks

There has been a rise in the use of Wireless Sensor Networks (WSNs) in several fields. Depending on the malicious actions, attacks on WSN can take many forms. This research demonstrates how to leverage request and response data from each node to pinpoint the location of malicious nodes. If a node is malevolent, it will prohibit data from being sent to other nodes. They do an excellent job of pretending to be someone else. To enhance route quality, packet dropping can be used to perform a Denial of Service (DoS) assault by rejecting data packets and cutting them off from their destinations. The primary goal of this research is to expose malicious nodes within the network. To avoid being attacked by the false information fed by the adversary through compromised nodes, it is crucial to discover and isolate the nodes that have been compromised. Unfortunately, flat topology networks are difficult to keep safe because of their limited scalability and substantial communication overhead. By computing the packet drop and throughput values with valid nodes, efficient security architecture for the WSN can be proposed, allowing for the detection of malicious activity. If sensor networks are susceptible to attacks, they will be unable to fulfill their duty in a variety of event detection applications. Malicious nodes can cause unacceptable drops in event detection effectiveness and spikes in false alarm rates by producing fake readings and deceptive information. In this research, a method for identifying malicious nodes in WSNs is proposed. This research considers a Trusted Head Node (THN) for monitoring the nodes behaviour in the network to detect the malicious actions in the network. This research proposes a Trusted Head Node for Node Behaviour Analysis for Malicious Node Detection (THN-NBA-MND) in WSN. The proposed model based on node behavior analysis in frequent time intervals; detect the malicious nodes to improve the network performance in data transmission. The proposed model when contrasted with the existing model performs better in malicious node detection.

Emergence of integrated behaviors through direct optimization for homeostasis

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

Synthesis and Evaluation of Benzo[1–3]selenadiazole-Isoxazoles as Corrosion Inhibitors for Copper in NaCl: An Integrated Experimental and Theoretical Approach

In this study, we investigate the protective capabilities of two synthetic compounds, 5-methyl-5-(4-methylbenzo[d][1,2,3]selenadiazol-7-yl)-3-phenyl-4,5-dihydro-isoxazole (BHP) and 5-methyl-5-(4-methylbenzo[d][1,2,3]selenadiazol-7-yl)-3-(4-nitrophenyl)-4,5-dihydro-isoxazole (BNP), as corrosion inhibitors for copper in a 3.5wt. % NaCl solution. The chemical structure of these benzo[1,2,3]selenadiazolyl-isoxazole derivatives combines a benzo[1,2,3]selenadiazole core, which to our knowledge has not been previously studied for its anticorrosive activity on copper, with an isoxazole nucleus known in the literature as one of the most effective azoles in preventing copper corrosion. The efficiency and mechanism of copper corrosion inhibition were evaluated through electrochemical, theoretical, and morphological studies. Potentiodynamic polarization (PDP) showed that both BHP and BNP were effective in inhibiting copper corrosion. At 10-3M, BHP showed a maximum inhibition efficiency of 80.24%, while BNP achieved an even higher efficiency of 89.83%. Adsorption behavior analysis indicated that both compounds followed the Langmuir isotherm. In addition, the high value of Kads (103M-1) indicates a strong adsorption capacity of our molecules on the copper surface. The negative adsorption free energy (ΔG°ads=−34.14kJ/mol for BHP and ΔG°ads=−34.00kJ/mol for BNP) indicates a spontaneous adsorption process. Electrochemical impedance spectroscopy (EIS) showed that both compounds formed a thin protective layer on the copper surface, a finding further supported by surface morphology studies using scanning electron microscopy (SEM-EDX) and ATR-FTIR analysis.

GABA transporter mGat4 is involved in multiple neural functions in mice

γ-Aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the central nervous system. The termination of GABA transmission is through the action of GABA transporters (GATs). mGAT4 (encoded by Slc6a11) is another GAT besides GAT1 (encoded by Slc6a1) that functions in GABA reuptake in CNS. Research on the function of mGAT4 is still in its infancy. We developed an mGat4 knockout mouse model (mGat4−/− mice) and performed a series of behavioral analyses for the first time to study the effect of mGat4 on biological processes in CNS. Our results indicated that homozygous mGat4−/− mice had less depression, anxiety-like behavior and more social activities than their wild-type littermate controls. However, they had weight loss and showed motor incoordination and imbalance. Meanwhile, mGat4−/− mice showed increased pain threshold and hypoalgesia behavior in nociceptive stimulus and learning and memory impairments. The expression of multiple components of the GABAergic system including GAD67, GABAA and KCC2 was altered. There is little or no compensatory change in mGat1. In a word, mGat4 may play a key role in normal motor coordination, sensation, emotion, learning and memory and could be the potential target of neurological disorders.

The effect of Dy-Cu co-deposition and grain boundary diffusion on the microstructure and magnetic properties of sintered NdFeB magnets

In this paper, Dy-Cu coating was electrodeposited on the surface of commercial sintered NdFeB magnet as a diffusion source and the grain boundary diffusion was carried out on the sintered NdFeB magnet. The experimental results show that the coercivity of the diffusion magnet is increased from 993 kA/m to 1241 kA/m, an increase of 24.95 %, and the remanence is only reduced to 1.366 T, a decrease of 0.44 % compared with the original magnet. The electrochemical behavior analysis shows that with the addition of Cu2+, the reduction potential of Dy3+ appears a large positive shift, and the Dy-Cu co-deposition process is a three-dimensional instantaneous nucleation process. The SEM images show that the Dy-Cu deposition layer has a dense and flat surface with small grain size. The energy spectrum analysis shows that the Dy-Cu elements obtained by electrodeposition are uniformly distributed, and there is no massive agglomeration phenomenon. SEM-EDS line scan observation shows that the Dy elements diffused along the grain boundary phase mainly accumulate in the grain boundary phase and the epitaxial layer of the main grain. EPMA shows that Cu and Dy elements are evenly distributed in the grain boundary phase after grain boundary diffusion. SEM-BSE shows that the near-surface grain boundary phase structure is significantly optimized after grain boundary diffusion. XRD shows that Dy partially replaces the NdFeB main phase, leading to the migration of the main peak to the large angle direction. At the same time, due to the existence of low melting point metal, Dy preferentially enters the magnet internal, resulting in the decrease of the content of (Dy, Nd)2Fe14B phase. The optimization of the microstructure and grain boundary phase composition and the formation of (Dy, Nd)2Fe14B structure are the main reasons for the significant enhancement of the coercivity According to Kronmüller plots, the coercivity mechanism of diffusion magnet is still the magnetic domain nucleation inversion mechanism. After the Tafel test, it is found that the diffusion Dy magnet has a relatively positive corrosion voltage Ecorr and a relatively low corrosion current density Icorr, and its corrosion resistance is greatly improved.

Research on Modal Behavior of Large Francis Turbine Runner

The water-added-mass can affect the modal behavior of the Francis turbine runner largely. The sealing rings in the band-chamber and the crown-chamber outside of the runner are utilized to prevent leakage of water around the Francis turbine. The sealing rings and the chambers filled with water can also affect the modal behavior of the turbine. When the turbine runner operates in the power plant at various operating conditions, the seal clearances of the band-chamber and the crown-chamber filled with water may change the modal behavior of the large Francis turbine runner. Firstly, the 3D geometrical structure of the studied large-scale Francis turbine runner was constructed, and then the CAD model was meshed to establish the finite-element model for numerical analysis. Next, the modal shapes and the natural-frequencies of runners in the air were calculated, which is the traditional method used for the structural modal behavior analysis of the turbine runners. Finally, this paper investigates the modal behavior of a large-scale Francis runner prototype with the combined effects of sealing rings and water added-mass by direct water-structure coupled analysis via the finite element method (FEM) to obtain more realistic results. The results reveal that the effects of water-added-mass and sealing rings cause a significant decrease in the natural-frequency of the large Francis turbine runner, which may lead to an increase in the amplitude of vibrations and even cracks. Technical suggestions are proposed to improve the monitoring and maintenance in the power stations to maintain the safe and efficient operation of the large Francis turbine units.

Development of Evolutionary Gravity Neocognitron Neural Network Model for Behavioral Studies in Rodents

From the past decades, rodent models have played role in evaluating the use of several drugs for the treatment of brain diseases. Generally, these tests are performed by recoding a video and examine to carry out various annotation about the behavior and activities of the rodents. However, the video must be executed continuously to ensure proper annotation that causes time complexity and increases the human observation error. Conventional techniques for rodent behavioral analysis process are not affordable for the research purpose due to increase cost and poor interpretability. To tackle this issue, a new and effective deep learning (DL) technique is introduced to analyze the multiclass behaviors in rodents under real-time scenario. At first, the video captured from camera is preprocessed by performing frame conversion and noise removal process. For removing the noise, the Butterworth-amended unsharp mask filtering (B_UMF) technique is emphasized thereby improving the image quality. Finally, the Evolutionary Gravity Neocognitron Neural Network (EGravity-NCNN) model is proposed to classify multiple rodent behaviours using adaptive feature learning. The simulation process for the developed method is carried out via the Python platform and various performance like accuracy, precision and recall are scrutinized and compared with conventional schemes. The developed method achieved the overall accuracy of 97.33 %, precision of 96.29 %, and recall of 97.02 % for the classification of rodent behaviours accurately.

Analysis of Behavioral Acceptance and Security Behavior on Implementation of Digital Workplace

Analysis of Behavioral Acceptance and Security Behavior on Implementation of Digital Workplace

Sexual receptivity of females on Neotropical scorpion Tityus pusillus Pocock, 1893 in response to male reproductive advances

AbstractDue to the high reproductive investment, females are the decisive sex when making decisions about whether or not to reproduce. Generally, female scorpions display aggressive sexual behavior in response to male reproductive advances when they are not receptive. However, female receptivity can vary over time, depending on environmental factors favorable to reproduction Thus, the current study aimed to examine the correlation between the number of reproductive attempts by male Tityus pusillus scorpion and mating success over time, considering the female's behavioral response. Behavioral analyses were monthly conducted on 150 T. pusillus field-caught pairs over a five-month period. Our results indicate that despite females reacting aggressively to male advances, the persistence of the male has a positive effect in courtship probability. Furthermore, we observed variations in female behavior throughout the studied period, suggesting that female receptivity might be linked to the reproductive season. Therefore, environmental factors may play a significant role in regulating scorpion reproduction in Neotropical forests.

Real Time Threat Intelligence System for Malware Detection

Abstract: We introduce a framework that represents real-time threat intelligence, a Python Project employing techniques for machine learning and behavioral analysis to quickly identify potential malware in scanned documents. The system integrates behavioral analysis for the purpose of detecting malware to improve security with machine learning, continuously evaluate new data, identify risks and isolate suspicious information. When an unsafe file is detected, the user interface will display the corresponding message

Enhancing threat detection in Identity and Access Management (IAM) systems

Identity and Access Management (IAM) systems play a pivotal role in safeguarding organizational resources by controlling access to sensitive information. However, these systems face evolving threats that can compromise security and privacy. This paper proposes a comprehensive approach to enhance threat detection within IAM systems. By integrating advanced techniques such as anomaly detection, machine learning, and behavior analysis, organizations can better identify and respond to suspicious activities. This paper discusses the challenges associated with threat detection in IAM systems and presents practical solutions to mitigate these risks. Furthermore, the paper highlights the importance of continuous monitoring and adaptation effectively combat emerging threats.

DNS vs. LES: Turbulent flow in square duct with heated foil boundary

Turbulent heat transfer in a square duct, where a finite section of a single duct boundary is heated with an infinitely thin foil, was analyzed with LES and DNS approaches. Both computations were performed with OpenFOAM code with passive scalar approximation, using second order accurate schemes. Results are obtained for the key velocity and temperature statistics in the duct, with an emphasis on the temperature profiles and temperature fluctuations at the foil boundary, which appear due to the imposed constant heat flux boundary condition at the foil. Comparison of DNS and standard LES-WALE model show 20% higher foil temperatures and temperature fluctuations in LES results. These differences are explained with detailed analysis of the LES-WALE model near-wall behavior. It has been demonstrated that LES-WALE yields highly accurate results only when employed with a finely resolved mesh. However, the computational costs associated with such refinement may not be necessarily justified especially with respect to DNS.

Varying degrees of human-animal interaction elicit weak evidence of a temporally stable behavioral trait in rangeland breeding ewes.

Coherent patterns of inter-related behaviors that occur across time and contexts, also known as personality or consistent individual differences (CIDs), are important to identify among livestock as they carry valuable information for selective breeding purposes. Repeated testing is necessary to reveal behavioral patterns, yet temporal stability of behavioral traits is often assumed. In this study, three human-animal interaction (HAI) tests were employed within a flock of sheep maintained in the Sierra-Nevada foothills to assess intra-individual consistency and inter- relatedness of behavioral responses. Groups of five (four or six when needed) ewes were placed in three HAI tests (restrained Human Contact, unrestrained Human Presence and unrestrained Human Approach) at the time of post-breeding, gestation and weaning (3 x per year; over 2 years). PCs were created using principal component analysis (PCA) of behaviors from the unrestrained Human Presence test and were compared to the behaviors from the two other HAI tests to evaluate cross-contextual relationships. Frequency of ‘environmental vigilance’ (EV), ‘investigating fence’ (IF) and duration of ‘head down’ (HD) were the only variables to achieve a moderate repeatability estimate [EV: 0.32 ± 0.09, [CI (0.21, 0.48])]; IF: 0.29 ± 0.13, [CI (0.14, 0.48)]; HD: 0.44 ± 0.15, [CI (0.22, 0.68)]. PCA revealed a single component in all Human Presence tests that was characterized by loadings (>0.45) for duration of ‘grazing’ and ‘environmental vigilance’. Spearman’s rank order analysis revealed no significant correlations between PC1 of each replicate. There was also no significant inter-relatedness identified between behaviors of each HAI test. Taken together, it is suggested that the rangeland ewes of this study exhibited consistency of some individual behaviors within the Human Contact and Human Presence test, however, did not demonstrate a temporally stable trait that can be generalized across multiple contexts. Future work should evaluate the influence of extensive versus intensive management styles on the development and maintenance of human-oriented behavioral traits.

Biosensor-Enhanced Organ-on-a-Chip Models for Investigating Glioblastoma Tumor Microenvironment Dynamics.

Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC models, providing a comprehensive platform for investigating glioblastoma tumor microenvironment dynamics. The applications of this combined approach in studying glioblastoma dynamics are critically scrutinized, emphasizing its potential to bridge the gap between simplistic models and the intricate in vivo conditions. Furthermore, the article discusses the implications of biosensor-enhanced OOC models in elucidating the dynamic features of the tumor microenvironment, encompassing cell migration, proliferation, and interactions. By furnishing real-time insights, these models significantly contribute to unraveling the complex biology of glioblastoma, thereby influencing the development of more accurate diagnostic and therapeutic strategies.

Fluorescence-Based Image Analysis of Seepage Behavior in Drip Irrigation: Exploring Varied Fractal Grading in Media Permeability

In the recycling of low-value metallic elements, heap leaching is commonly employed. The particle size distribution is a crucial parameter in heap leaching implementation, and the percolation behavior of a heap has always been a focal point in heap leaching technology. This paper utilizes a novel form of ultraviolet fluorescence image acquisition and fluorescence image analysis to investigate the percolation process with different fractal dimension particle size distributions, where the maximum particle diameter is 10 mm. The ore used was low-grade copper ore. The results indicate that the new fluorescence image analysis method can reveal different percolation regions during the heap leaching process, aiding in a better understanding of heap leaching behavior. The combined study found that under irrigation conditions of 10 mL/min, seepage was more uniform under the heap structure formed by a particle gradation with a fractal dimension of 2.2.

Individual chaotic behaviour of the S-stars in the Galactic centre

Located at the core of the Galactic centre, the S-star cluster serves as a remarkable illustration of chaos in dynamical systems. The long-term chaotic behaviour of this system can be studied with gravitational N-body simulations. By applying a small perturbation to the initial position of star S5, we can compare the evolution of this system to its unperturbed evolution. This results in two solutions that diverge exponentially, defined by the separation in position space δr, with an average Lyapunov timescale of ∼420 yr, corresponding to the largest positive Lyapunov exponent. Even though the general trend of the chaotic evolution is governed in part by the supermassive black hole Sagittarius A∗ (Sgr A∗), individual differences between the stars can be noted in the behaviour of their phase-space curves. We present an analysis of the individual behaviour of the stars in this Newtonian chaotic dynamical system. The individuality of their behaviour is evident from offsets in the position space separation curves of the S-stars and the black hole. We propose that the offsets originate from the initial orbital elements of the S-stars, where Sgr A∗ is considered in one of the focal points of the Keplerian orbits. Methods were considered to find a relation between these elements and the separation in position space. Symbolic regression provides the clearest diagnostics for finding an interpretable expression for the problem. Our symbolic regression model indicates that ⟨δr⟩ ∝ e2.3, implying that the time-averaged individual separation in position space increases rapidly with the initial eccentricity of the S-stars.

A Deformation Analysis Method for Sluice Structure Based on Panel Data

Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice’s deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice’s case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research.