Analysis Of Behavior Research Articles

Pressure influenced electronic phase transition, mechanical, optoelectronic, and transport characteristics of double perovskite Rb2AgSbCl6: A first-principles investigation

In this work, we used density functional theory (DFT) simulations to investigate the structural, elastic, electronic, optical, and transport characteristics of pressure-induced Rb2AgSbCl6. By calculating the octahedral and the tolerance factors, the phase stability has been verified. The calculated formation energy and Gibbs free energy values have demonstrated the thermodynamic stability of the material. The outcomes of the mechanical behavior analysis revealed that the pressure-driven Rb2AgSbCl6 is mechanically stable and sustains ductility as well as anisotropy. Additionally, as the pressure is increased from 0 GPa to 100 GPa, the band gap has been reduced from 2.64 eV to 0 eV. The hydrostatic pressure of 100 GPa was the critical pressure at which the material revealed a metallic nature. The optical properties such as absorbance and optical conductivity shift towards lower energy and comparatively higher at increased pressure with minimal energy loss. The thermoelectric characteristics are examined utilizing the BoltzTraP code at different pressures. The figure of merit (ZT) value was increased from 0.76 to 0.80 with the increase in pressure at room temperature. These characteristics render pressure-influenced Rb2AgSbCl6 favorable candidates for utilization in solar cells as well as thermoelectric devices.

Analysis of the Mechanical Behavior and Joint Shear Capacity Optimization of Glued Keys in Segmental U-Shaped Bridges

This paper presents an in-depth analysis of the mechanical behavior and joint shear capacity optimization of segmental U-shaped bridges, with a focus on the application of precast segmental techniques in the construction of U-beam bridges widely used in urban rail transit networks. This study further explores the roles of key position distribution and size in the overall stability and service behavior of such structures. Considering the critical case study of the Colombia Bogotá Metro Line 1 project, finite element modeling was carried out using ABAQUS 6.14 to simulate concrete material behaviors and to evaluate the stress–strain relationship in accordance with the concrete plastic damage model and existing standards. This research identifies the significant contribution of keys in minimizing deformation and enhancing shear capacity, demonstrating the pivotal influence of shear key design on the mechanical behavior of segmental bridges. By calculating the shear capacity under different cases, this study provides recommendations on key distributions and dimensions that optimize joint shear capacities, indicating that augmenting key size within the web plate section decisively reinforces the bridge’s mechanical resilience.

Experimental and numerical analysis of the fracture behavior of an epoxy-based marine coating under static tension and accelerated aging effect in NaCl solution

Experimental and numerical analysis of the fracture behavior of an epoxy-based marine coating under static tension and accelerated aging effect in NaCl solution

Influence of rolling defects on fatigue crack initiation of leaf spring

ABSTRACT Leaf springs, as vital suspension components in trucks, endure various forces such as torsion, compression, tension and impact, rendering them highly susceptible to fatigue and fracture risks, thereby posing a significant threat to vehicle safety. During the production process of leaf springs, surface decarburisation can easily occur, leading to the formation of numerous surface rolling defects. These defects exert varying influences on the initiation of fatigue crack. This study investigates the influence of various forms of rolling defects caused by decarburisation on fatigue crack initiation in 50CrMnMoNbAT leaf spring through experimental and numerical analysis of fatigue fracture behaviour. The results indicate that fracture locations in the leaf springs are predominantly concentrated approximately 297 mm and 698 mm from the ends. Furthermore, the occurrence of surface decarburisation during the rolling process engenders a significant number of surface rolling defects. Surface defects in leaf springs can be categorised into three forms: Parallel defect, Parcel defect and Occlusal defect. In terms of defect propagation rate, rolling defects on the concave surface are most prone to initiate fatigue cracks, with Occlusal defects exhibiting the highest likelihood of developing internal cracks and the fastest crack propagation rate.

AMPA receptor potentiation alleviates NLRP3 knockout-induced fear generalization in mice

Genetic knockout and pharmaceutical inhibition of the NLRP3 inflammasome enhances the extinction of contextual fear memory, which is attributed to its role in neuronal and synaptic dysregulation, concurrent with neurotransmitter function disturbances. This study aimed to determine whether NLRP3 plays a role in generalizing fear via the inflammatory axis. We established the NLRP3 KO mice model, followed by behavioral and biochemical analyses. The NLRP3 KO mice displayed impaired fear generalization, lower neuroinflammation levels, and dysregulated neurotransmitter function. Additionally, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, but not the inhibition of NMDA or 5-HT2C receptors, resulted in fear generalization in NLRP3 KO mice because TAT-GluA2 3Y, but not SB242084 and D-cycloserine, treated blocked NLRP3 deprivation effects on fear generalization. Thus, global knockout of NLRP3 is associated with aberrant fear generalization, possibly through AMPA receptor signaling.

Educators’ knowledge of and perceptions towards students with autism in Nigeria

Global research about autism spectrum disorder (ASD) is highly important as the disorder occurs across cultures and geographical regions. Literature indicates that there were no studies conducted among Nigerian educators about their knowledge of and perceptions towards ASD. This study surveyed 477 secondary school Nigerian educators to examine the knowledge and perceptions of ASD. The results showed that educators had high levels of awareness in the knowledge domain and low levels of awareness in the perception domain. In addition, results demonstrated a significant positive relationship between educators who received prior training or college education in ASD and their current knowledge of ASD. Furthermore, there was a significant positive relationship between educators who are certified in Applied Behaviour Analysis and the knowledge of ASD. Conversely, the relationship between current educators of students with ASD (who have experience as their only training), and the knowledge of ASD was significantly negatively related. Results are discussed and implications for research and practice are provided.

Perceptions of Machine Learning among Therapists Practicing Applied Behavior Analysis: A National Survey

Perceptions of Machine Learning among Therapists Practicing Applied Behavior Analysis: A National Survey

Tensile, flexural and fracture properties of MEX-printed PLA-based composites

Due to the continuous development of material extrusion-additive manufacturing (MEX-AM) technology, the fracture behavior of polymeric parts, and especially of their associated composites, presents some shortcomings and also a challenge in the field. In this paper, the fracture properties of non-reinforced (NR) and reinforced Polylactic Acid (PLA) samples manufactured by MEX-AM process are investigated through tensile, flexural and fracture mechanics tests. The samples are reinforced with glass fibers (GF-PLA), carbon fibers (CF-PLA) and bronze particles (BP-PLA), and tested parallel (named FLAT for all tests) and normal (named UPRIGHT for tensile test, and ON-EDGE for flexural and fracture mechanics tests) to the layer growth (LG). It was found that regardless of the type of test, the LG and the material significantly influence the mechanical properties and collapse modes of the MEX-printed samples. It was obtained that the parallel-LG load is favorable for the tensile and flexural tests, and the normal-LG for the fracture mechanics tests. Overall, the NR-PLA material gives the best strength, strain and energy absorption properties, CF-PLA stands out as the stiffest material, while BP-PLA presents the minimum performances for almost all properties. The macroscopic analyzes of the fractured samples clearly highlight distinct failure mechanisms associated with each type of test, material and LG. From the analysis of the mechanical behavior and macrostructural images, CF-PLA was identified as the most brittle material, and NR-PLA the most ductile.

DOLPHIN ASSISTED THERAPY: WELFARE OF BOTH PATIENTS WITH DEPRESSION AND PARTICIPATING DOLPHINS

The main aim of this research was to measure the effect of dolphin assisted therapy on both patients and participating dolphins. The research measured welfare of patients with depression and situational welfare of dolphins which participated in the dolphin assisted therapy programme. Patients with organic depression took part in two-week dolphin assisted therapy sessions. When measuring situational welfare of dolphins, the study sample consisted of nine individual dolphins (Tursiops truncatus ponticus, Lat.): three males and six females ranging from five to twenty years. The main research results revealed that psychosocial characteristics of patients with organic depression disorder as well as patients’ self-evaluation changed statistically significantly, the symptoms of depression decreased. The research revealed that patients were much more physically active during the period of participation in the dolphin assisted therapy process than before. The analysis of dolphin behaviour brought to the light that most of the therapy time dolphins behaved positively. There were noticed no therapy sessions where the dolphin behaviour would be evaluated as more negative than positive or just negative.

Comparative study on bending behavior and damage analysis of 3D-printed sandwich core designs with bio-inspired reinforcements

In this study, novel sandwich core designs with bio-inspired reinforcements were proposed and their bending behaviors were comparatively examined. The geometrical shapes of alligator osteoderm and chambered nautilus shell were utilized as bio-inspired reinforcements for sandwich core structures. Sandwich core structures were produced through the additive manufacturing method. Experimental tests and finite element analysis were performed to determine the bending performances of the proposed sandwich core structures. The load-carrying capacity, deformation ability, damage-tolerant capability, energy absorption, and damage mechanisms of the proposed sandwich core structures were comparatively investigated through experimental and numerical methods. The orthotropic material model and Hashin’s damage criterion were used in the numerical model of 3D-printed sandwich core structures to consider the effect of the filament raster orientation on the elastic and damage behavior of the sandwich core structures. Compared to the classical honeycomb sandwich core structure, while bio-inspired reinforcements improved the load-carrying capacity and damage-tolerant capability of sandwich core structures, they reduced the energy absorption ability of sandwich core structures due to reducing the vertical deformation ability of sandwich core structures. Bio-inspired reinforcements significantly affected the stress distribution and damage behavior of the sandwich core structures. They reduced von Mises stress level at the outer cell edges of the sandwich core structures and caused reinforcement damage instead of outer cell damage.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

Lateral Load Behavior Analysis of a Novel Self-Centering Y-Eccentrically Braced Frames with Slip Connection Shear Link

Lateral Load Behavior Analysis of a Novel Self-Centering Y-Eccentrically Braced Frames with Slip Connection Shear Link

Role of brain monoamines in acetamiprid-induced anxiety-like behavior

Neonicotinoid (NN) pesticides have been linked to increased brain dysfunction in mammals, such as anxiety-like behavior; this is thought to involve monoamines (MA), neurotransmitters that control behavior, memory, and learning. However, the mechanism by which NNs affect the central nervous system is not fully understood. In this study, we aimed to investigate whether MAs affect NNs-induced anxiety-like behavior. Mice were orally administered acetamiprid (ACE), an NN, at the no observed adverse effect level (NOAEL) of mouse (20 mg/kg body mass) set by the Food Safety Commission of Japan, and the elevated zero-maze (EZM) test was performed 30 min after administration. After behavioral analysis, levels of four MA (dopamine, 3-MT, serotonin, and histamine) in selected brain regions were determined by liquid chromatography mass spectrometry (LC/MS/MS). In the exposed group, a trend toward increased anxiety-like behavior was observed, and at least one MA concentration was significantly increased in each region. Further, significant correlations were found between behavioral test results and hippocampal serotonin and striatal dopamine concentrations, as well as between dopamine and serotonin concentrations, in the exposed group. As anxiety can influence activity in the behavioral tests, the activity of neurons in the raphe nuclei (RN), a brain region greatly involved in anxiety via the serotonergic system, was examined by staining with anti-serotonin antibodies, and increased serotonergic activity was observed. Taken together, these results suggest that ACE regulates MA levels, notably serotonin levels in the hippocampus and that RN plays an important role in ACE-induced anxiety-like behavior.

The extended finite element method in endodontics: A scoping review and future directions for cyclic fatigue testing of nickel-titanium instruments.

The present study reviews the current literature regarding the utilization of the extended finite element method (XFEM) in clinical and experimental endodontic studies and the suitability of XFEM in the assessment of cyclic fatigue in rotary endodontic nickel-titanium (NiTi) instruments. An electronic literature search was conducted using the appropriate search terms, and the titles and abstracts were screened for relevance. The search yielded 13 hits after duplicates were removed, and four studies met the inclusion criteria for review. No studies to date have utilized XFEM to study cyclic fatigue or crack propagation in rotary endodontic NiTi instruments. Challenges such as modelling material inputs and fatigue criteria could explain the lack of utilization of XFEM in the analysis of mechanical behavior in NiTi instruments. The review showed that XFEM was seldom employed in endodontic literature. Recent work suggests potential promise in using XFEM for modelling NiTi structures.

A Non-parametric Analysis of Gender Differences With the Impacts of Applied Behavior Analysis on Autistic Individuals.

Introduction Considering the scarcity of research that directly investigates the differences between genders in their response to applied behavior analysis (ABA) therapy for individuals diagnosed with autism spectrum disorder (ASD), the objective of this study is twofold.First, it aims to reinforce the male-to-female ratio reported in existing scientific literature, thereby contributing to a broader understanding of gender distribution in ABA therapy for ASD. Second, it seeks to identify gender-based differences in aggregate target behaviors at various time intervals using three distinct datasets. The goal is to determine if gender influences the effectiveness of ABA therapy for ASD, which could inform future therapeutic strategies. Ultimately, this study strives to enhance our understanding of gender disparities in ABA therapy response among ASD individuals and hopes to improve therapeutic outcomes for all, regardless of gender. Materials and methods Three to five behavioral technicians per child collected daily general target mastery data for 263 individuals with autism. This data was gathered using a large N design through retrospective chart reviews within the "Catalyst" tracking software (DataFinch Technologies, Atlanta,USA). Three separate datasets were collected from June 7, 2023to January 7, 2024. Behavior analysts employed a mixed model of discrete trial training, mass trials, and naturalistic environment treatment over seven months. General target mastery data was assembled for 259 children and four adults, with seven data instances missing. Descriptive statistics encompassed central tendency and dispersion measures, including the data distribution's mean, standard deviation, median, and range. Non-parametric inferential analysis was performed with the Mann-Whitney U test. Results Mann-Whitney U computations resulted in non-significant gender differences on all gender comparisons for the three datasets covering the seven-month timeframe. Dataset #1:Time 1-(U=727.5, p=0.846, ή2=0.0002, Time 2-(U=736, p=0.910, ή2=0.00005), Time 3-(U=687.5, p=0.569, ή2=0.001) Dataset #2:Time 1-(U=781, p=0.383, ή2=0.003), Time 2-(U=819.5, p=0.585, ή2=0.001), Time 3-(U=825, p=0.618, ή2=0.001) Dataset #3:Time 1-(U=395, p=0.198, ή2=0.007),Time 2-(U=373.5, p=0.365, ή2 =0.003),Time 3-(U=363, p=0.471, ή2=0.002),Time 4-(U=366.5, p=0.436, ή2 =0.003),Time 5-(U=371, p=0.391, ή2=0.003),Time 6-(U=394, p=0.208, ή2=0.007),Time 7-(U=373, p=0.373, ή2=0.003),Time 8-(U=371.5, p=0.387, ή2=0.003),Time 9-(U=464.5, p=0.512, ή2=0.002),Time 10-(U=356.5, p=0.546, ή2=0.002),Time 11-(U=357.5, p=0.535, ή2=0.002),Time 12-(U=350.5, p=0.346, ή2=0.004) Conclusions This study suggests no significant gender differences in response to ABA therapy among individuals with autism, indicating its potential effectiveness for both genders. However, these findings should be interpreted cautiously due to statistical uncertainties reflected in the broad confidence intervals as they hint at possible substantial gender differences. Further research, including an extension study, must confirm these results and understand potential gender nuances in ABA therapy response. This could help tailor more effective, personalized therapeutic strategies for individuals with autism.

Machining behaviour analysis of PBF-LB warped components with internal lattice microstructures

Metal additive Powder Bed Fusion, Laser Based (PBF-LB) manufacturing offers significant improvements in the design of complex parts of inner components areas for several sectors, from dies and moulds, to blades and aerofoils. Generating internal structures helps to move fluids, work as heat exchanges, or achieve dramatic weight relief, only possible by additive manufacturing, concretely PBF-LB. Along with these designs, sculptured surfaces and warped surfaces are also common in blades and other aerodynamic pieces. However, it is mandatory that these designs accomplish the final required mechanical properties without surface damage and non-admissible dimensional deviations. In this study, the five-axis milling of a part containing internal structures has been investigated. Two types of PBF-LB Inconel® 718 blades were manufactured, one solid and one with internal microstructures. They were finished using two types of ball-ended mills, ball and barrel-lens tooltip. The analysis of the blade with internal structures shows better machining behaviour, dynamic behaviour, and surface quality.

Design of Fast Mining Algorithm for Educational Sport Course Data Based on Cluster Analysis

In this age of big data, education researchers are reconceptualizing and re-evaluating the value of education data. Therefore, we need to use educational data mining methods for data analysis to better guide teaching. The informatization level of colleges and universities is improving year by year, and the entire training data of students from enrollment to graduation is stored. These datasets are collected, stored, and kept by different departments, contain a large amount of regular and relevant information, and truly record the growth footprint of students. Traditional educational decision-making has not yet fully explored and used the valuable information hidden in data resources. Although some scholars have carried out research related to campus data mining at this stage, there are still many problems that have not yet been solved in the application of decision-making in colleges and universities. This paper is based on the idea of data-driven decision-making, combined with the data characteristics of campus big data, and establishes a model solution for student behavior analysis and behavior prediction by applying multiple machine learning algorithms. On the basis of the analysis of students’ academic behavior performance in the context of multi-category educational data, we proposed a cluster analysis framework for processing multi-type campus big data, and described the group characteristics of the clustering results. By introducing the K-prototype algorithm, we effectively solved the multi-category problem where traditional clustering algorithms (such as K-Means, etc.) cannot adapt to the attributes of educational data. The research results show that innovative educational decision-making models and methods are based on the idea of “data-prediction-decision”, which promotes the application research of big data science in the area of education.

Analysis of nonlinear bending behavior of nano-switches considering surface effects

Nano-switch structures are important control elements in nanoelectromechanical systems and have potential applications in future nanodevices. This paper analyzes the effects of surface effects, geometric nonlinearity, electrostatic forces, and intermolecular forces on the nonlinear bending behavior and adhesion stability of nano-switches. Based on the Von Karman geometric nonlinearity theory, four types of boundary conditions for the nano-switch structure were specifically calculated. The results show that surface effects have a significant impact on the nonlinear bending and adhesion stability of nano-switches. Surface effects increase the adhesion voltage of the nano-switch and decrease its adhesion displacement, and as the size of the nano-switch structure increases, the impact of surface effects decreases. A comparative analysis of the linear theory and the nonlinear theory results shows that the adhesion voltage predicted by the linear theory is smaller than that predicted by the nonlinear theory. The effect of geometric nonlinearity increases as the size of the nano-switch structure increases, as the distance between the electrodes increases, and as the aspect ratio of the nano-switch structure increases. These findings provide theoretical support and reference for the design and use of future nanodevices and nanoelectromechanical systems.

Structural Strength Analysis and Optimization of Commercial Aircraft Nose Landing Gear under Towing Taxi-Out Conditions Using Finite Element Simulation and Modal Testing

In the field of civil aviation, the nose landing gear is a critical component that is prone to damage during taxiing. With the advent of new technologies such as towing taxi-out and hub motors, the nose landing gear faces increasingly complex operational environments, thereby imposing higher performance demands. Ensuring the structural safety of the nose landing gear is fundamental for the successful application of these technologies. However, current research on aircraft nose landing gear under these new conditions is somewhat lacking, particularly in terms of reliable analysis models for real-world scenarios. This study focuses on a typical Class C aircraft, specifically the B-727 model, for which a finite element model of the nose landing gear is developed. Modal testing of the aircraft’s nose landing gear is conducted using the impact hammer method, and the results are compared with those from the simulations. The experimental data indicate that the error range for the first seven natural frequencies is between 0.23% and 9.27%, confirming the high accuracy of the developed landing gear model. Furthermore, with towing taxi-out as the primary scenario, a dynamic model of the aircraft towing system is established, and an analysis on the structural strength and topological optimization of the nose landing gear under various conditions, including high speeds and heavy loads, is performed. The results show that the developed model can effectively support the analysis and prediction of the mechanical behavior of the nose landing gear. Under high-speed, heavy-load conditions, the nose landing gear experiences significantly increased loads, with the maximum deformation primarily occurring at the lower section of the shock strut’s outer cylinder. However, no damage occurred. Additionally, under these conditions, an optimized structural design for the landing gear was identified, which, while ensuring structural strength, achieves a 22.32% reduction in the mass of the outer cylinder, also ensuring safety in towing taxi-out conditions.