Analysis Of Behavior Research Articles

Uncertainty and sensitivity analysis of the fission product behaviour in the Phébus FPT1 test with the system code AC2

The Phèbus FP (Fission Product) research program, conducted by IRSN and CEA, aimed to improve the understanding of the phenomena governing core melt down, fission product release and transport, as well as the fission product behaviour in the containment. The test facility represents a 900 MWe PWR at a 1/5000 scale. The second test, FPT1, was carried out in 1996 and utilised an Ag-In-Cd alloy control rod. There is a growing interest in Best Estimate Plus Uncertainty analysis both in the research, as well as in the licensing. It has a long-standing history in case of thermal–hydraulic system codes focusing on design basis accidents, but its application is limited on the severe accident domain. In recent times however an increasing attention has been paid to evaluating severe accident codes’ uncertainties. This study presents an uncertainty and sensitivity analysis (UaSA) of the FPT1 test, focusing on fission product behavior modeling within the severe accident code system AC2. The detailed application of the coupled codes ATHLET-CD and COCOSYS in this context is novelty. Detailed description of the methodological and practical approach is provided. The uncertain input parameters chosen and quantified are directly related to the phenomena describing the fission product transport and retention phenomena both in the primary circuit, as well as in the containment, while thermal–hydraulic conditions within the primary circuit were kept as much as possible constant. Finally, the study has been complemented by a sensitivity analysis deriving the Spearman's rank correlation coefficient for each uncertain input parameter.

AI-Powered Home Cage System for Real-Time Tracking and Analysis of Rodent Behavior

AI-Powered Home Cage System for Real-Time Tracking and Analysis of Rodent Behavior

Study of Optoelectronic, Magnetic, and Thermoelectric Properties of Sr2XOsO6 (X = Y, Lu, Sc) Double Perovskites for Spintronic and Data Storage Devices

Advancement in electronic device miniaturization coupled with the precise control over their electromagnetic and transport properties, holds great promise for realizing practical quantum computing devices. Double perovskites, known for their stability, non-toxicity, and spin-polarized characteristics, are particularly appealing for spintronics applications. In this communication, we investigate the role of 6d-electrons of Os in regulating the magnetic properties in Sr2XOsO6 (X = Y, Lu, Sc) using theoretical methods with the WIEN2k code. The computed formation energies (-3.04, -2.91, and -2.82 eV) confirm the thermodynamic stability of these compositions. Analysis of the band structures and DOS exhibit ferromagnetic semiconducting character, elucidated through exchange constants and energies. This computation highlights the essential role of basic hybridization processes, crystal field effects, and exchange energy in generating ferromagnetic character driven by electronic spin. The analysis of optical behavior against photon energy (eV) reveals the maximum absorption in the UV region. Furthermore, the calculated values of σ/τ, ke/τ, S, χ, PF, and ZT in the temperature range of 200-800 K highlight the potential of these compounds for thermoelectric applications. This comprehensive analysis underscores the suitability of Sr2XOsO6 (X = Y, Lu, Sc) for advanced electronic and spintronic technologies.

Analysis of Structural, Optical, Morphological and Magnetic Behaviour of Lead Nanoferrite by Two Different Methods

Sol gel and the hydrothermal approach are two distinct techniques that have been successfully used to synthesise lead nanoferrite (PbFe2O4). To determine which of the two approaches is superior, the features of the sample prepared by each have been examined. Vibration Sample Magnetometer (VSM), Fourier Transform Infrared Spectroscopy, UV-VIS Spectral investigations, Scanning Electron Microscopy (SEM), X-Ray Diffraction, and Size, Band Gap Energies, Morphology, and Magnetic Properties of Nanostructures were used to determine and compare its various aspects. The mean diameters of the synthesised lead ferrite nanocrystalline were found to be 33 nm for the sol gel method and 20 nm for the hydrothermal method, respectively, based on X-Ray Diffraction analysis using Debye-Scherer’s formula with the peak. FTIR spectra are used to analyse the vibration characteristics of nanomaterials. The optical characteristics of the synthesised nanomaterials, such as their Energy Band Gap, were determined using UV-VIS Spectral investigations. SEM is used for surface morphological investigations, allowing the structure of the synthesised nanomaterials to be studied. Using VSM, the magnetisation of the produced nanomaterials was examined, and the hysteresis loops were used to calculate the saturation magnetisation (Ms), coercivity (Hc), and retainivity (Mr).

Effect of Cu/Bi ratio on the photoelectrochemical performance of CuBi2O4/CuO nanocomposite films for CO2 reduction

Abstract Solar energy is free, clean, and virtually limitless; however, its conversion into a storable form presents technological challenges. One avenue towards solar energy utilization is the photoelectrochemical (PEC) reduction of CO2 to one- or two-carbon fuels, employing a semiconductor configured as an electrode. A potential material for this application is the p-type copper bismuth oxide (CuBi2O4) with a band gap capable of visible light absorption and a conduction band edge position suitable for CO2 reduction. In this study, CuBi2O4 nanocomposite films of varying Cu/Bi ratios (0.25, 0.51, 0.68, 0.94, 2.04) were prepared via an electrodeposition-spray deposition-annealing route. Where the Cu/Bi ratio exceeded the stoichiometric value of 0.5, a bilayered film composed of a copper (II) oxide (CuO) phase on top of CuBi2O4 was formed, creating a planar heterojunction between the two oxide layers. With increasing Cu/Bi ratio, the light absorption range of the films broadened due to the CuO phase. Analysis of the photocurrent-potential behavior of the films under visible-light illumination showed a 4–7-fold increase in the photocurrent from an inert electrolyte to a CO2-saturated electrolyte, confirming potential activity for CO2 reduction of the CuBi2O4/CuO films. A higher Cu/Bi ratio resulted to an improved charge separation efficiency, enhancing the photocurrent generation. However, the transient photocurrent response of the films showed a 70-80% decrease in the photocurrent after only 15 mins of testing. When tested in an electrolyte with an electron scavenger, the percent decrease was lowered to <10%, indicating that the instability of the films resulted from poor interfacial kinetics. While the CuBi2O4/CuO nanocomposite films can accomplish CO2 reduction, further strategies to improve their efficiency and stability are needed to realize practical application.

MECHANICAL PERFORMANCE OF FIBER-REINFORCED SELF-COMPACTING CONCRETE: COMPARATIVE ANALYSIS AND STRUCTURAL IMPLICATIONS

The primary focus of this paper's analysis of the mechanical static behavior of concrete reinforced with steel fibers is its classification based on differences in mix design and fiber concentration. To find out the uniaxial compressive and tensile strengths, experimental experiments were run. Different mix patterns and fiber lengths were combined to create a variety of combinations with fiber percentages varying from 1% to 2% by volume. Uniaxial compression testing was used to mechanically characterize the SFRC in order to determine its ultimate compressive strength. Furthermore, ductility and first crack strength indices were evaluated using bending test with four-point consisted of the notched specimens. The evaluation of SFRC's tensile strength was conducted using both analytical modeling and experimental methods. The results of the experiments showed that the SFRC behaved differently depending on the length and fiber content. Direct tensile strength was calculated theoretically using an analytical model from the literature and compared to the experimental findings. The comparison showed a generally good agreement, confirming the results of the investigation.

Analysis of Investment Behavior Among Filipinos: Integration of Social Exchange Theory (SET) and the Theory of Planned Behavior (TPB)

Despite the emergence of more accessible and modern forms of investment, the ever competitive and volatile market remains subject to anomalous irrationalities caused by investors. To this day, predicting their behavior remains difficult with lacking information, and poses a problem for investment platforms to effectively adjust to their predispositions. Therefore, this study aimed to comprehensively analyze the factors that have influenced investors’ behaviors using the integrated construct of the Social Exchange Theory and the Theory of Planned Behavior. With consideration of convenience sampling, a total number of 10,725 data points were collected and analyzed through machine learning algorithms of decision tree and neural network. Specifically, the comparison between long short-term memory (LSTM) and neural network, and random forest classifier and LightGBM were considered. It was found that the investor’s attitude, accessibility to financial services, and perceived economic benefits were the most influential predictors to their behavior, while six other factors also showed varying levels of significance. This study aimed to provide a unique framework which could be utilized by investment platforms to cater to the different behavioral factors expressed by investors. In line with these findings, it is recommended that platforms create flexible solutions that are based on their intentions and preferences, and more user-friendly through the implementation of new technologies. In addition, they are suggested to appeal to novice investors by reducing the burden of costs, promising future benefits, and promoting financial education. The results of this study proved the reliability of the integrated model as a social and behavioral framework, and consequently, LSTM overpowering other tools on accurate forecast made, followed by neural network, and random forest.

Long-term tracking of social structure in groups of rats

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.

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.

New paradigms shift in buildings: experimental application of Digital Twin for safety and well-being

Abstract Enhancing durability and service life of buildings and components is pivotal for sustainable development. It constitutes an opportunity to reduce energy consumption, carbon emissions and life cycle impact of buildings in a climate neutral perspective. Issues strongly introduced and set as priorities by EU policies that highlight the urgent need to tackle them also through deeply heritage renovation and digital transformation in the building sector. Digitalization is a cornerstone of this transformation, instrumental in facilitating and enabling it sustainably. The Key Enabling Technologies (KETs) are directly associated with new technological potential and emerging technologies. Digital Twin (DT) approach appears alongside these. Its experimentation and widespread application are gaining prominence in innovating Life Cycle Management (LCM) and sustainability practices of buildings. In this scenario the paper explores the role and potentialities of DT approach presenting the experimental application of the DT4SEM. A digital infrastructure that, by integrating Internet of Things (IoT), synchronizes a physical building with its virtual counterpart. The two realities (physical and virtual) remain interconnected through the mutual exchange of data, both in real-time and asynchronously enabling proactive monitoring and analysis of seismic behaviour. The experimental setup involves simultaneously Big Data analytics, simulation tools and deploying sensors within the sample building. Data is then fed into the virtual DT model, allowing for continuous comparison and analysis to detect anomalies and predict potential risks. This approach facilitates enhanced decision-making, performance optimization and sustainability improvements across the building’s lifecycle: a new vision for built environment management that evolves from a process resulting in a sequence of operative phases into a complex digital infrastructure. The design of the DT4SEM and the current application for seismic monitoring in buildings, results from a collaborative effort involving the academic spinoff BIG srl, the startup Sysdev, the company Berna Engineering srl, and ACCA Software spa.

Numerical analysis of flexural behaviour of UHPC-CFTS columns as enclosure supports for electrical substations

Abstract This study establishes a finite element analysis model of ultra-high-performance concrete (UHPC) encased concrete filled steel tube (CFST) composite columns under combined compression and bending. Appropriate constitutive models and element types are selected as the basis. The finite element model simulates the test results of steel-concrete composite columns, and the applicability of the model is verified by comparison with existing test data of UHPC encased CFSTs. On the basis of demonstrating model reliability, the full-range load-deformation curves of UHPC encased CFSTs with different concrete strengths under compression-bending are compared. The failure modes and material stress distributions are analyzed to elucidate the influence mechanism of the UHPC on the flexural performance of UHPC encased CFSTs under combined compression and bending. The results show that compared to normal concrete, UHPC can effectively improve the flexural capacity of UHPC encased CFST columns. Owing to the addition of steel fibers, the failure mode of UHPC encased CFSTs under combined compression and bending is more integral compared to normal concrete. The research results of this study can provide references for the performance evaluation and design of UHPC encased CFSTs in the structural enclosures for electrical equipment in high/low voltage substations.

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.

Computationally cost-efficient analysis of the flow behavior of twin-fluid atomizers using computational fluid dynamics

In industrial-scale operations, wet electrostatic precipitators (WESPs) are used to minimize particulate matter, employing atomizers such as single-fluid and twin-fluid atomizers (TFAs). While TFAs provide several benefits over single-fluid atomizers, quantifying their spray characteristics is more complex, necessitating comprehensive case studies to design the internal structure of the spray and achieve desired properties. This study employed computational fluid dynamics (CFD) to simulate the internal and external flow phenomena of TFAs in industrial-scale WESPs, aiming to facilitate various parametric studies by reducing the high computational costs associated with analyzing high-speed internal flows and particle dynamics within the spray system. To decrease computational costs, the simulation was divided into two parts using stepwise segregated scenarios: Part I focused on the high-cost internal flow analysis, examining the spatiotemporal evolution of internal flow until it is fully developed, followed by droplet size distribution estimation at the nozzle. Part II computed the external flow of the spray, assessed potential cost reductions by examining the interactions between dispersed droplets, and validated the spray angle, penetration, and coverage against experimental data. The segregated strategy employed mapping techniques to integrate the two parts seamlessly. The simulation results closely matched the experimental benchmarks for spray angle, penetration, and coverage within a minimal error margin (<5%), demonstrating the model’s accuracy in capturing actual spray phenomena in TFAs. This approach significantly reduced the computational cost by more than twentyfold compared to conventional one-step solvers, offering a viable method for conducting various case studies in spray CFD simulations.

Retrospective Analysis of Membranoproliferative Glomerulonephritis and C3 Behavior in a Large Brazilian Center

Retrospective Analysis of Membranoproliferative Glomerulonephritis and C3 Behavior in a Large Brazilian Center

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

Enhancing dynamic mechanical properties of cemented lithium mica tailings backfill with alkaline rice straw fibers: Experimental investigation and microscopic analysis

Cemented lithium mica tailings backfill (CLMTB) faces dynamic loads from mining operations, affecting its stability. This study investigates the use of alkaline rice straw fibers (ARSF) to enhance CLMTB's dynamic properties. Dynamic impact tests were conducted on CLMTB specimens with varying ARSF contents, revealing that dynamic compressive strength (DCS) increased with ARSF up to 0.45% before decreasing. DCS improved with rising strain rates. The research includes analyses of stress-strain behavior, failure patterns, and energy dissipation during impacts, with scanning electron microscopy revealing the microstructural effects of ARSF on CLMTB. The findings suggest that ARSF effectively mitigates the damaging impacts of loads, significantly reducing crack development in CLMTB.

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.

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.