Analysis Of Behavior Research Articles

Experimental and numerical analysis of the fluid flow behavior of a tank corner impacting a water surface

The interaction of a tank impacting a water surface is an extremely complex nonlinear multiphase flow phenomenon. In this study, experiments and numerical simulations are used to systematically investigate the flow physics and load characteristics of a tank corner impacting a water surface. Free surface flow at different fall heights (200–800 mm) and inclination angles (0°–15°) was obtained through free fall experiments. The volume of fluids method and overset grid technology were used to simulate the water impact process of a three-dimensional structure accurately. For typical bubble flows, the numerical and experimental results agree well. On the basis of the three-dimensional flow characteristics and pressure distribution, flow behaviors, such as fluid climbing, corrugation disturbances, and air cavity effects, are analyzed. Bubble flow has a significant effect on the behavior mode of the impact load. In particular, the bubbles at the upper wall play a key role in the load characteristics at different locations. In addition, the influences of corrugations inside the tank's corner and the impact velocity on fluid flow were investigated. These results provide beneficial references for an in-depth understanding of the fluid flow and load characteristics between a tank and fluid.

FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of Aedes aegypti mosquitoes.

Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.

Academia as a driver of change: a bibliometric analysis of pro-environmental behavior in higher education institutions

Academia as a driver of change: a bibliometric analysis of pro-environmental behavior in higher education institutions

Study on the mechanical behavior of millennium-ancient bricks based on microscopic characteristics

The wide performance variation and inability for destructive testing make accurately determining ancient brick mechanical properties crucial for assessing structural safety in ancient masonry. This paper uses multiscale modeling with microscopic finite element analysis to quantitatively assess ancient brick macroscopic stress–strain relationship and compressive strength based on microstructural characteristics. The basic parameters such as porosity, pore size distribution, mineral composition and volume ratio, and microstructure characteristics of ancient bricks were obtained by corresponding methods. The Mori-Tanaka homogenization theory was applied to derive a three-phase equivalent brick matrix using elastic moduli and Poisson’s ratios of quartz, kaolinite, and montmorillonite at the microscale. The Representative Volume Element model with pores was created based on this matrix and pores, integrating Drucker-Prager plastic damage for finite element analysis of ancient brick mechanical behavior. The results indicate that stress–strain curves from the multi-scale micromechanics model resemble macroscopic experimental curves and maintain consistent peak strength. This shows that combining microscopic testing with finite element simulation for analyzing the mechanical properties of ancient bricks is feasible, providing a new non-destructive means of obtaining these properties.

Academic writing in the age of AI: Comparing the reliability of ChatGPT and Bard with Scopus and Web of Science

ChatGPT and Bard (now known as Gemini) are becoming indispensable resources for researchers, academicians and diverse stakeholders within the academic landscape. At the same time, traditional digital tools such as scholarly databases continue to be widely used. Web of Science and Scopus are the most extensive academic databases and are generally regarded as consistently reliable scholarly research resources. With the increasing acceptance of artificial intelligence (AI) in academic writing, this study focuses on understanding the reliability of the new AI models compared to Scopus and Web of Science. The study includes a bibliometric analysis of green, sustainable and ecological buying behaviour, covering the period from 1 January 2011 to 21 May 2023. These results are used to compare the results from the AI and the traditional scholarly databases on several parameters. Overall, the findings suggest that AI models like ChatGPT and Bard are not yet reliable for academic writing tasks. It appears to be too early to depend on AI for such tasks.

Three-dimensional mesoscale analysis of the dynamic tensile behavior of concrete with heterogeneous mesostructure

This paper investigates the dynamic tensile behavior of concrete under high strain rates. An optimized random concrete mesostructure generation procedure is established using the 3D entrance block (3D E(A, B)) algorithm to account for the intrinsic material heterogeneity. This approach avoids repetitive and complicated polyhedral aggregate overlapping checks in traditional methods, resulting in a highly efficient aggregate packing process. The nucleation, propagation, and coalescence of cracks are captured by a properly developed rate-dependent cohesive constitutive law. The mesoscale model is validated through comparison with experimental results. The crack evolution in the concrete under dynamic direct tensile loading conditions is explicitly presented, and the effects of the aggregate volume fraction and ITZ properties on the dynamic tensile strength enhancements are studied. The results indicate that material heterogeneity significantly influences the fracturing process and damage distribution. The dynamic tensile strength of concrete exhibits a two-strain rate regime dependence on the aggregate volume fraction concerning the strain rate. The influence of the mechanical properties of the ITZ on the dynamic tensile strength of concrete increases with the increasing strain rate.

Numerical analysis of bubble behavior in proton exchange membrane water electrolyzer flow field with serpentine channel

Green hydrogen, produced using renewable energy sources, represents a critical component in the transition to sustainable energy systems due to its clean and versatile nature. This research investigates the dynamic behavior of bubbles within the serpentine flow field of a Proton Exchange Membrane Water Electrolysis (PEMWE) cell, aiming to enhance the understanding of two-phase flow dynamics and improve the efficiency of green hydrogen production. Utilizing the Volume of Fluid (VOF) method, a three-dimensional unsteady model was developed to simulate the flow dynamics at the anode of a PEMWE system. The study explores the transition of bubbles from bubbly flow to slug and annular flow, highlighting the significant impact of bubble formation on mass transport and overall cell performance. The results demonstrate that larger bubbles impede liquid water delivery to reaction sites and cause unstable pressure drops. The investigation also examines the influence of wall contact angles on bubble behavior, revealing that hydrophobic surfaces lead to increased gas coverage and more oxygen accumulation inside the channel, which hinders mass transport. These findings underscore the necessity for optimized flow channel designs and enhanced surface treatments to mitigate bubble coalescence and improve PEMWE performance.

Thermal Behaviour, Chemical Composition and Morphological analysis ofCotton Stalk for Efficient Biochar Production

The escalating concern over air pollution from crop stubble burning necessitates responsible biomass disposal or utilisation. In this study, characterisation of cotton stalk was carried out to assess its feasibility for biochar production. The proximate analysis revealed that cotton stalk had moisture content of 8.65%, bulk density of 215.8 kg m-3, volatile matter of 71.65%, and fixed carbon content of 16.03%. The ultimate analysis showed that cotton stalks were predominantly composed of carbon (64.88%), hydrogen (6.58%) , and oxygen (2.55%), with a low (0.66%) nitrogen content . Thermo-gravimetric analysis (TGA) highlighted the pyrolysis process, identifying a peak temperature of approximately 500°C and distinct stages of thermal degradation: moisture loss up to 150°C, hemicellulose decomposition between 200°C and 300°C, cellulose decomposition from 300°C to 400°C, and lignin decomposition from 400°C to 500°C. X-ray diffraction (XRD) analysis confirmed the presence of crystalline cellulose with a diffraction peak at 2θ = 22.5°, indicating significant structural stability during pyrolysis for biochar production. Fourier-transform infrared (FTIR) spectroscopy revealed characteristic peaks associated with cellulose and lignin, underscoring the retention of essential structural components in biochar during biomass conversion. These analyses demonstrated the potential of cotton stalks as a viable feedstock for biochar production, providing a sustainable method for agricultural waste management and soil health improvement.

Seismic behavior and failure analysis of prefabricated foamed concrete composite walls for passive houses

In view of the lack of research on the failure mode and seismic behavior of high-performance insulation walls utilizing lightweight concrete as the structural material, this study presents experimental findings from a seismic behavior investigation of prefabricate foamed concrete composite walls (PFCCWs) for passive house in China. The PFCCWs incorporate low thermal conductivity and high-strength foamed concrete, polyurethane, decorative magnesium straw slabs to provide excellent thermal and mechanical properties for prefabricated passive houses in hot summer and cold winter area. The seismic behavior of PFCCWs under different influence factors were elaborated using quasi-static test data and observation of external damage. The lightweight and porous nature of foamed concrete leads to significant crack formation during failure. The findings suggest that the skin effect of magnesium straw slabs and polyurethane impedes crack development and improve the load-bearing capacity with ductility enhancement. Additionally, reducing the aspect ratio and substituting post-cast column concrete with common concrete are effective to improve the seismic behavior. The window opening changes the worst damage area of the wall and notably weakens the seismic behavior. The enhanced integrity of cast-in-place wall leads to the difference in the failure mechanism and increases in load-bearing and deformation capacities. Meanwhile, design suggestions for the foamed concrete insulation walls are also proposed by comparing the failure mode and hysteretic response of various wall types.

Digital screener of socio-motor agency balancing motor autonomy and motor control

Dyadic social interactions evoke complex dynamics between two agents that, while exchanging unequal levels of body autonomy and motor control, may find a fine balance to synergize, take turns, and gradually build social rapport. To study the evolution of such complex interactions, we currently rely exclusively on subjective pencil and paper means. Here, we complement this approach with objective biometrics of socio-motor behaviors conducive to socio-motor agency. Using a common clinical test as the backdrop of our study to probe social interactions between a child and a clinician, we demonstrate new ways to streamline the detection of social readiness potential in both typically developing and autistic children by uncovering a handful of tasks that enable quantification of levels of motor autonomy and levels of motor control. Using these biometrics of autonomy and control, we further highlight differences between males and females and uncover a new data type amenable to generalizing our results to any social setting. The new methods convert continuous dyadic bodily biorhythmic activity into spike trains and demonstrate that in the context of dyadic behavioral analyses, they are well characterized by a continuous Gamma process that can classify individual levels of our thus defined socio-motor agency during a dyadic exchange. Finally, we apply signal detection processing tools in a machine learning approach to show the validity of the streamlined version of the digitized ADOS test. We offer a new framework that combines stochastic analyses, non-linear dynamics, and information theory to streamline and facilitate scaling the screening and tracking of social interactions with applications to autism.

Low-velocity impact behavior analysis on composite double curved-shell reinforced with graphene platelet

ABSTRACT This research investigates the low-velocity impact behavior of a spherical rigid body on a composite shell reinforced with graphene platelets (GPLs), in which GPL have been uniformly dispersed and randomly arranged within each individual layer. The weight proportion of GPL has been altered across the thickness. The modified Halpin Tsai model is employed to analyze the Young's modulus and the effective material characteristics of a composite reinforced with graphene platelets (GPLRC). The mass density and Poisson's ratio of the viscoelastic multilayer composite are determined using the rule of mixture. The material parameters of each layer are defined according to the Kelvin–Voigt model. The governing equations have been derived by applying the first order shear deformation shell theory, Hamilton's principle, and modifying the Hertz contact law. The PDE problems have been transformed into ODE equations through the utilization of the Galerkin method. Subsequently, the ordinary equations have been solved utilizing the Newmark method. A parametric study is conducted to examine the effects of various factors, such as fraction weight, GPL distribution pattern, impactor mass, radius, viscoelastic modulus, and initial velocity, on the low velocity impact response of a composite shell reinforced with GPL.

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Consumer Behavior Analysis as a Form of Pre-market Survey for the Black Pule (Alstonia spectabilis) Antimalarial Tablet Prototype

Background: Malaria a disease caused by the protozoan parasite Plasmodium with a high incidence of spread that is still found in remote tropical places, especially areas around gardens, swamps, forests, and mines such as eastern Indonesia. The commonly used antimalarial drug chloroquine has experienced resistance, encouraging researchers to look for effective alternative treatments. Formulation development of Standardized Herbal Medicine (OHT) from black pule was carried out as a potential new alternative treatment as an antimalarial. Objectives: The purpose of this study was to determine the level of liking and quality assessment or characteristics of the prototype of antimalarial OHT tablets. Methods: The method used in this research is descriptive quantitative which aims to describe the panelists' perspective on the product and to analyze product marketing. In this research method includes hedonic test and hedonic quality test which is a series of organoleptic tests and consumer acceptance tests by tasting and observing the product followed by filling out a form according to what is felt and observed. Results: The results obtained from this research, namely the preference test of the overall characteristics of the prototype of antimalarial black pule tablets (Alstonia spectabilis) showed a good sensory impression and characteristics. The positive results obtained from all aspects have illustrated that the panelists have considerable interest in the prototype sample and assess that the prototype tablets are suitable for dissemination to the public. Conclusions: Based on the research, it can be concluded that the prototype of black pule (Alstonia spectabilis) tablets tested through hedonic and hedonic quality tests produced overall good results, characterized by the tendency of panelists to choose "Like" in the hedonic test of sensory impressions and positive values in the hedonic quality test.

Upcycling EPS waste and mineral wool to produce new lightweight gypsum composites with improved thermal performance

Energy efficiency and waste management are crucial for sustainability of construction sector. In this research, a new gypsum composite material is presented in which traditional raw materials have been partially replaced by thermal insulation waste from facades energy retrofitting projects, using an innovative process to recover this waste. Consequently, a novel lightweight gypsum composite has been developed with a replacement of up to 14.7% by weight of the original material with EPS waste, adding mineral wool as reinforcement fibres (0.375%wt.). Thus, thermal behaviour analysis of these new composites has been conducted, both material itself and of its performance as part of a real construction system, including finite element analysis of the construction system. In addition, the physicochemical, physical and mechanical characterisation of the new composites has been carried out. Results show that the developed new material has a density 20.3% lower than the traditional gypsum composite, resulting in a 30.4% reduction in thermal conductivity. Furthermore, the use of these new lightened gypsum composites as finishing boards in lightweight steel frame (LSF) wall systems reduces the overall thermal resistance of the wall by up to 10.6% with just 25mm thickness. On the other hand, the mechanical resistance of this new material exceeds the reference values established by current standards, ranging between 4.18-1.87MPa for flexural strength and between 7.87-4.27MPa for compressive stresses. Additionally, the developed composites have shown a reduction in both total and capillary water absorption compared to traditional gypsum by 19.6% and 40.0% respectively, enhancing the material’s durability and its excellent thermal properties throughout its lifespan.

Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for Nocturnal Activity Tracking

This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed by comparison with manually scoring, before being applied to seven nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data were used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in behavioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.

Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach

Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(V8-Sort) for the automatic monitoring of the different behaviors of group-housed pigs. We addressed common challenges such as variable lighting, occlusion, and clustering between pigs, which often lead to significant errors in long-term behavioral monitoring. Our approach offers a reliable solution for real-time behavior tracking, contributing to improved health and welfare management in smart farming systems. First, the YOLOv8 is employed for the real-time detection and behavior classification of pigs under variable light and occlusion scenes. Second, the OC-SORT is utilized to track each pig to reduce the impact of pigs clustering together and occlusion on tracking. And, when a target is lost during tracking, the OC-SORT can recover the lost trajectory and re-track the target. Finally, to implement the automatic long-time monitoring of behaviors for each pig, we created an automatic behavior analysis algorithm that integrates the behavioral information from detection and the tracking results from OC-SORT. On the one-minute video datasets for pig tracking, the proposed MOT method outperforms JDE, Trackformer, and TransTrack, achieving the highest HOTA, MOTA, and IDF1 scores of 82.0%, 96.3%, and 96.8%, respectively. And, it achieved scores of 69.0% for HOTA, 99.7% for MOTA, and 75.1% for IDF1 on sixty-minute video datasets. In terms of pig behavior analysis, the proposed automatic behavior analysis algorithm can record the duration of four types of behaviors for each pig in each pen based on behavior classification and ID information to represent the pigs’ health status and welfare. These results demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, providing technical support for prompt anomaly detection and health status monitoring for pig farming managers.

Cinereous Vulture (Aegypius monachus) Welfare Monitoring in a Breeding Center during the Breeding Season

ABSTRACT One of the conservation measures of the globally near-threatened cinereous vulture is restoring populations via translocations, with some vultures originating from breeding centers. These centers need to have reproductive success, and securing good welfare levels should be a priority due to the negative implications a compromised welfare has on reproduction. Thus, assessing welfare in breeding centers is essential. Remote methods should be preferred, such as behavior analysis and use of space using camera systems. The study’s first objective was to develop an activity budget that could be used as a baseline for detecting behavior irregularities. The second was to determine if behavior analysis and use of space could detect potential welfare issues in a breeding center. The study developed an activity budget that could be used as a standard for behavior analysis for the captive population. Behavior irregularities were detected, and the potential need to improve the enclosure and its surroundings were noticed. Altogether, the proposed methodology and results will contribute to the detection of possible welfare issues in breeding centers and, consequently, improve reproductive success and species’ conservation.

Analog Sequential Hippocampal Memory Model for Trajectory Learning and Recalling: A Robustness Analysis Overview

The rapid expansion of information systems in all areas of society demands more powerful, efficient, and low‐energy consumption computing systems. Neuromorphic engineering has emerged as a solution that attempts to mimic the brain to incorporate its capabilities to solve complex problems in a computationally and energy‐efficient way in real time. Within neuromorphic computing, building systems to efficiently store the information is still a challenge. Among all the brain regions, the hippocampus stands out as a short‐term memory capable of learning and recalling large amounts of information quickly and efficiently. Herein, a spike‐based bio‐inspired hippocampus sequential memory model is proposed that makes use of the benefits of analog computing and spiking neural networks (SNNs): noise robustness, improved real‐time operation, and energy efficiency. This model is applied to robotic navigation to learn and recall trajectories that lead to a goal position within a known grid environment. The model is implemented on the special‐purpose SNNs mixed‐signal DYNAP‐SE hardware platform. Through extensive experimentation together with an extensive analysis of the model's behavior in the presence of external noise sources, its correct functioning is demonstrated, proving the robustness and consistency of the proposed neuromorphic sequential memory system.

Pooled Analysis of Physical Activity, Sedentary Behavior, and Sleep Among Children From 33 Countries.

The prevalence estimates of physical activity, sedentary behavior, and sleep (collectively known as movement behaviors) in 3- and 4-year-old children worldwide remains uncertain. To report the proportion of 3- and 4-year-old children who met the World Health Organization guidelines for physical activity, sedentary behavior, and sleep across 33 countries. Pooled analysis of data from 14 cross-sectional studies (July 2008 to September 2022) identified through systematic reviews and personal networks. Thirty-three countries of varying income levels across 6 geographical regions. Each study site needed to have at least 40 children aged 3.0 to 4.9 years with valid accelerometry and parent-/caregiver-reported screen time and sleep duration data. Data were analyzed from October 2022 to February 2023. Time spent in physical activity was assessed by reanalyzing accelerometry data using a harmonized data-processing protocol. Screen time and sleep duration were proxy reported by parents or caregivers. The proportion of children who met the World Health Organization guidelines for physical activity (≥180 min/d of total physical activity and ≥60 min/d of moderate- to vigorous-intensity physical activity), screen time (≤1 h/d), and sleep duration (10-13 h/d) was estimated across countries and by World Bank income group and geographical region using meta-analysis. Of the 7017 children (mean [SD] age, 4.1 [0.5] years; 3585 [51.1%] boys and 3432 [48.9%] girls) in this pooled analysis, 14.3% (95% CI, 9.7-20.7) met the overall guidelines for physical activity, screen time, and sleep duration. There was no clear pattern according to income group: the proportion meeting the guidelines was 16.6% (95% CI, 10.4-25.3) in low- and lower-middle-income countries, 11.9% (95% CI, 5.9-22.5) in upper-middle-income countries, and 14.4% (95% CI, 9.6-21.1) in high-income countries. The region with the highest proportion meeting the guidelines was Africa (23.9%; 95% CI, 11.6-43.0), while the lowest proportion was in North and South America (7.7%; 95% CI, 3.6-15.8). Most 3- and 4-year-old children in this pooled analysis did not meet the current World Health Organization guidelines for physical activity, sedentary behavior, and sleep. Priority must be given to understanding factors that influence these behaviors in this age group and to implementing contextually appropriate programs and policies proven to be effective in promoting healthy levels of movement behaviors.

The Structural Analysis of Fitness Center Participation Behavior Using Planned Behavior Theory

The Structural Analysis of Fitness Center Participation Behavior Using Planned Behavior Theory