Analysis Of Behavior Research Articles

Characterizing the development of gravity-driven slug flows using high-speed imaging and PIV-PLIF techniques

Although developing gravity-driven slug flow frequently occurs in oil and gas, and energy systems, its development dynamics remain underexplored; this gap, in turn, has left the underlying relationships between flow evolution and transport phenomena in these applications inadequately characterized as well. The present study experimentally investigates the spatiotemporal-spectral development of gravity-driven air–water and CO2-water slug flows in a vertical 25.4 mm ID pipe. Enhanced flow visualization techniques, utilizing high-speed imaging and particle image velocimetry-planar laser induced fluorescence (PIV-PLIF), were employed to determine the behaviors of gas and liquid phases and interactions at four positions along the pipe axis. A machine vision-based algorithm was employed to extract slug unit cell characteristics and instantaneous void fraction signals, allowing for a comprehensive statistical analysis of gas phase behavior across the flow domain. A novel algorithm was also developed to preprocess raw PIV-PLIF images, facilitating phase discrimination and noise reduction before PIV cross-correlation analyses are conducted. The results showed a logarithmic growth in the lengths of Taylor bubbles, liquid slugs, and slug unit cells along the pipe, with liquid slugs constituting nearly 60 % of slug units downstream. Taylor bubble length distributions correlated well with log-normal fits, while liquid slug and unit cell lengths transitioned from log-normal patterns upstream to near-normal distributions downstream with broader and less peaked shapes. Taylor bubble velocities and appearance frequencies of the flow structures declined exponentially along the pipe, with Taylor bubble velocities showing narrower and more peaked near-normal distributions downstream. Instantaneous void fraction signals exhibited fewer, wider peaks and troughs with reduced small-amplitude oscillations downstream. The analysis of the signals indicated a complete bubbly-to-slug transition at Z/D=30. Gas-liquid phase interactions, classified as almost-zero, weak, and strong, impacted liquid phase velocity profiles and the behavior of Taylor bubbles, with minimum stable liquid slug lengths of 8–9 D and a wake length of 1.8 D observed. Empirical correlations were developed to represent the spatiotemporal-spectral aspects of flow development, with spectral parameters, particularly liquid slug frequency, identified as the most reliable indicators of the fully developed region, predicting entrance lengths of 114.0 D and 113.4 D for air–water and CO2-water, respectively. Gas density was found to strongly influence flow characteristics and transition, accelerating the approach to the fully developed region.

Molten-Salt-Chemistry-Assisted Synthesis of Heterostructured g-C3N4/BiOI Nanocomposites with Enhanced Sunlight Absorption Properties for Efficient Solar-to-Chemical Energy Conversion

The g-C3N4 nanosheets coupled BiOI (g-C3N4/BiOI) nanocomposites with unique heterojunction structure and excellent wide-spectrum sunlight absorption properties have been developed via molten-salt-chemistry-assisted strategy. The heterostructured g-C3N4/BiOI shows enhanced solar to the thermal effect, improved electron mobility and carrier lifetime for efficient photocatalytic dye degradation. The engineered g-C3N4/BiOI catalyst exhibits outstanding photocatalytic activity for methyl orange (MO) removal with 100% degradation conversion and excellent stability under solar light irradiation, which is respectively 2.69 and 1.27 times higher than those of g-C3N4 and BiOI counterparts. The broad scope toward other dyes degraded by g-C3N4/BiOI and the generality of the molten-salt-chemistry-assisted strategy for synthesizing g-C3N4/BiOCl and g-C3N4/BiOBr with considerable photodegradation efficiency are proved. The analysis of kinetic behaviors, electron spin resonance, and nanosecond transient absorption spectra for structure-activity relationship, key intermediate of •O2− species, and excited state lifetime of g-C3N4/BiOI heterojunction material in the photocatalytic MO degradation processes are also demonstrated.

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.

Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

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.

Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer): V. Validation with mineral samples and reference materials.

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA's JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d'Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2-3nm, and spectral smile variability by +/1nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion.

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.

Adaptive learning-based hybrid recommender system for deception in Internet of Thing

In the rapidly evolving Internet of Things (IoT) security domain, device vulnerabilities pose significant risks, frequently exploited by cyberattackers. Traditional reactive security measures like patching often fall short against advanced threats. This paper introduces a proactive deception system enhanced by an innovative Adaptive Learning-based Hybrid Recommender System (AL-HRS), utilizing the vulnerability and attack repository for IoT (VARIoT) database. This advanced system identifies existing vulnerabilities and dynamically recommends additional deceptive vulnerabilities based on real-time analysis of attacker behavior and historical exploit data. These recommended vulnerabilities mislead attackers into engaging with controlled environments such as honeypots, effectively neutralizing potential threats. The AL-HRS combines the predictive strengths of content-based filtering (CBF) and collaborative filtering (CF) with an adaptive learning mechanism that adjusts recommendations based on ongoing attacker interactions, ensuring the system’s efficacy amidst changing attack patterns. Our approach innovatively combines these methodologies to provide a continuously evolving security strategy, significantly enhancing the deception capability of IoT systems. Initial evaluations demonstrate a potential reduction in device compromise, highlighting the effectiveness and strategic relevance of this adaptive deception framework in IoT cybersecurity.

Hot extrusion, tensile deformation behavior and deformation mechanism analysis of a wrought superalloy with a high γ' phase volume fraction of 64%

A hundred kilograms of GH4975 extrusion bar, featuring fine equiaxed grains, were successfully fabricated using electron beam smelting layer solidification(EBSL) and hot extrusion techniques. Subsequent analysis focused on the microstructure and tensile properties of the extruded bar. The study also delved into the influence of temperature on tensile deformation behavior and mechanisms of the alloy after standard heat treatment(SHT). The results revealed uniformity in precipitated phase size and morphology across the extruded bar, with grain size being the largest at the center and smallest at the edge. Tensile tests conducted at 20°C and 650°C show an obvious work hardening phenomenon, and the deformation mechanism is dominated by APB coupled dislocation pairs shearing. With the increase of deformation temperature, the work-hardening phenomenon gradually weakens, and SFs emerge as the primary deformation mechanism at 750°C. As the deformation temperature further rises, cross slip, Orowan bypass and dislocation climbing gradually dominate. The fracture mode changes from ductile-brittle mixed fracture to pure ductile fracture, and ultimately to brittle fracture. Compared with the traditional preparation method, the hundred kilograms of GH4975 extrusion bar prepared by this technology still show better mechanical properties.

The amidoxime-functionalized magnetic dendritic fibrous nano-silica with a core–shell structure for the efficient capture of U(VI)

The U(VI) extraction from nuclear wastewater through adsorption techniques is vital for reclaiming uranium resources and mitigating environmental pollution. Herein, amidoxime-functionalized dendritic fibrous nano-silica with magnetic reponsiveness and core–shell structure (Fe3O4@DFNS-AO) was synthesized. The rapid absorption of U(VI) by Fe3O4@DFNS-AO achieved equilibrium within 40 min. Fe3O4@DFNS-AO showed great adsorption efficiency, as well as good reusability and selectivity, with a capacity of 175.4 mg/g at optimal pH 6.0. Furthermore, the mechanism was disclosed based upon the study of adsorption behavior and XPS analysis. The study of kinetic and isotherm indicated chemisorption dominated the adsorption and U(VI) could be adsorbed onto Fe3O4@DFNS-AO by monolayer. The amidoxime groups in Fe3O4@DFNS-AO acted as electron donors and formed coordination bonds with uranyl ions, leading to the efficient capture of U(VI). Overall, Fe3O4@DFNS-AO shows great promise in extracting uranium from wastewater.

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.

AI in Neuromarketing: Understanding Consumer Emotions and Behavior through Machine Learning

This paper explores the integration of artificial intelligence (AI) and machine learning in the emerging field of neuromarketing, which combines neuroscience with marketing strategies to decode consumer emotions and behaviors. By employing advanced techniques such as EEG, fMRI, and facial recognition, the research highlights how these technologies can analyze subconscious responses to marketing stimuli, providing deeper insights than traditional methods. The study emphasizes the transformative role of AI in enhancing predictive analytics, real-time personalization, and customer segmentation. Furthermore, it discusses the potential benefits of AI-driven insights for crafting effective marketing campaigns that resonate with consumers and improve engagement. The paper also addresses the challenges faced in this field and proposes future research directions to explore the intersection of AI and consumer behavior analysis.

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design