Analysis Of Behavior Research Articles

NONLINEAR MODELING AND ANALYSIS OF DAMAGED REINFORCED CONCRETE BEAMS USING ANSYS AND LIRA-SAPR SOFTWARE PACKAGES

This article presents a comparative analysis of the nonlinear behavior of a reinforced concrete beam with damaged reinforcement in the tension zone under a gradually increasing load until failure. The experimental beam, measuring 2100 mm × 200 mm × 100 mm, consists of a 20 mm diameter rebar in the tension zone, two 6 mm diameter rebars in the compression zone, and 6 mm diameter stirrups spaced 75 mm apart for transverse reinforcement. Nonlinear calculations were performed using ANSYS and LIRA-SAPR, with identical initial conditions applied to both models for accurate comparison. The study focuses on key aspects such as result accuracy, ease of use, and time required for nonlinear calculations, including material and geometric nonlinearity. By highlighting the strengths and weaknesses of each software, the research offers insights for engineers and researchers working on complex structural modeling of reinforced concrete.

Review of Electrical Steels with their Properties and Recent Trends for Improvement

Objectives: This review study provides a thorough analysis of the magnetic behavior and characteristics of electrical steels, including established and novel materials (3%wt Silicon). Methods: We investigate how composition, microstructure, and processing methods affect the magnetic performance of these steels as well as the basic principles guiding their behavior. Thermomechanical processing can be observed for its impact on the material’s microstructure and magnetic properties. This review discusses testing over samples for conventional processes for different % of cold worked or strain hardening and annealing at higher temperatures i.e., 1200°C. For uncertainties regarding the impact of impurities or inclusions on their magnetic properties, the study was carried out to know the effect of cerium addition on the material’s magnetic properties of the material. Findings: It was observed that rolling temperature has significant effect over magnetic properties of the material. A notable improvement in the magnetic permeability of the material and its critical performance metric for soft magnetic materials is indicated by the increase in “Gamma max”. Similarly, the effects of compressive stresses, doping effect, grain size, annealing, and other manufacturing processes were studied, and observed their impact on magnetic properties. This review discusses the literature on future scope with the latest trends. This helps clarify the trade-offs associated with maximizing electrical steels for certain uses. Novelty: This study presents a novel approach towards electrical steel and its magnetic properties by incorporating different alloying elements and optimizing rolling & annealing parameters. A key innovation is the application of machine learning and neural network to predict and optimize material behavior, core loss and provide methods to improve magnetic properties of electrical steels. Significance: Electrical steels are crucial in transformers and electric motors, enhancing efficiency by minimizing energy losses and improving magnetic performance. This can be achieved by changing manufacturing parameters and operating conditions with the help of latest trends. Keywords: Electrical steel, Magnetic properties, Machine learning, Composition, Microstructure, Core loss

Aging Behavior Analysis of Al-Ce-Based Alloys Using Hardness and Microstructural Quantification

Aging Behavior Analysis of Al-Ce-Based Alloys Using Hardness and Microstructural Quantification

Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds.

An Analysis and Comparison of the Hydrodynamic Behavior of Ships Using Mesh-Based and Meshless Computational Fluid Dynamics Simulations

This paper presents a comparison of two turbulence models implemented in two different frameworks (Eulerian and Lagrangian) in order to simulate the motion in calm water of a displacement hull. The hydrodynamic resistance is calculated using two open-source Computational Fluid Dynamics (CFD) software packages: OpenFOAM and DualSPHysics. These two packages are employed with two different numerical treatments to introduce turbulence closure effects. The methodology includes rigorous validation using a Wigley hull with experimental data taken from the literature. Then, the validated frameworks are applied to model a ship hull with a 30 m length overall (LOA), and their results discussed, outlining the advantages and disadvantages of the two turbulence treatments. In conclusion, the resistance calculated with OpenFOAM offers the best compactness of results and a shorter simulation time, whereas DualSPHysics can better capture the free-surface deformations, preserving similar accuracy.

Analysis of the Scientific Production on Direct Consumer Behavior

The objective of the research was to analyze the scientific contributions that underpin the behavior of the direct consumer. The methodology was basic, non-experimental design, exploratory-explanatory scope, whose sample consisted of 30 articles. The results showed that the analysis of behaviors can also be developed through the collection of information about the preferences and interests of consumers; there are also various variables that intervene in the behavior of consumers, which is why the analysis of these aspects must be taken into account for the formulation of precise strategies. Concluding that, it is possible to get to know the consumer and the reasons, both internal and external, that involve their decision making, therefore it is significant to be able to factor them to allow it to be visualized with a broad panorama and to know how it impacts them and its evolution.

Analyzing the Impact of Multiple Foundation Stiffness Correlation on the Natural Frequency of Offshore wind turbines

The analysis of wind turbine behavior should avoid unplanned resonance, as it can increase fatigue damage. It is essential to rigorously evaluate the natural frequency of wind turbines in the initial design stage. This estimation can be done through two steps, the first one is the computing of the fixed base natural frequency of the wind turbine. The second is the estimation of the foundation stiffness effect. This paper examines the error margin of four correlations of foundation stiffness namely Randolph, Davies and Budhu, DNV, and Higgins in the estimation of the natural frequency of wind turbines. The fixed base natural frequency was estimated in this paper using a special-purpose finite element computer program called TurbiSoft through beam and volume elements. A reference 5.0 MW offshore wind turbine and 9 other turbines from different wind farms have been analyzed. For the fixed base natural frequency, obtained results demonstrate the reliability of the finite element program TurbiSoft through an error margin between 1-4% for the reference 5.0 MW offshore wind turbine. For the natural frequency of the whole system, the estimated error marking was between 9-20% which is an important value.This significant error is attributed to the low accuracy of these four correlation formulas for predicting the foundation flexibility contribution. The results from the four correlation formulas used are quite similar, with a difference in the error margin of less than 1%. However, the largest error margins are observed with the Higgins formula, which has been originally developed for short monopiles, even though the analyzed monopiles can be considered short, with monopile slenderness ratios less than 8.

Influence of dynamic complexity on serial robots with joint clearance based on a nonlinearity measure

Conditional monitoring and diagnosis of serial robots depend not only on the method used but also on the dynamic complexity of the robotic system itself. Previously, researchers have focused mainly on the analysis of dynamic behaviors with different parameters of a mechanical system with clearance. Although the indicators such as Root Mean Square (RMS), spectrum kurtosis (SK), Entropy have the potentiality to express the specific trend by different parameters, it can be noticed that they mainly depend on the values of the dynamic responses and neglect the information of the nonlinear characteristics in the responses of mechanical systems. In this paper, a nonlinearity measure-based method is proposed to analyze the impact of dynamic complexity on serial robots with joint clearance. The kinetic equations of a serial robot with joint clearance are established by considering the contact forces in the joint. The dynamic model of the robotic system is validated by experiments. A quantization method for dynamic complexity analysis is established based on the nonlinearity measure, and the influences of parameters on the dynamic complexity are analyzed via numerical computations for the validated dynamical models of the robotic system. A relationship is developed among the clearance radius, friction coefficient, and dynamic complexity. This shows that a larger clearance radius will lead to a more complex system and that friction coefficients within a certain range can reduce the dynamic complexity of the system. These results can guide methodological choices for condition monitoring and diagnosis of serial robotic systems.

Swarm Intelligence and Unmanned Systems: The Potentıal Impact of The Principles of Swarm Intelligence and Collective Behaviour in Nature On Unmanned Systems and Autonomous Organizational Structures

This paper examines the potential implications of the principles of swarm intelligence and collective behavior in nature for unmanned systems and autonomous organizational structures. Swarm intelligence is inspired by natural systems in which individual units interact according to simple rules to form a complex and organized whole. These principles can be observed in a wide range of situations, from the synchronized flight of flocks of birds to the harmonized swimming behavior of schools of fish. The study emphasizes that swarm intelligence principles have the potential to create more flexible, resilient and efficient systems with decentralized control mechanisms and autonomous decision-making processes. Furthermore, it is suggested that these approaches can find applications in many fields, from military operations to agricultural and environmental monitoring, from disaster response to urban planning. The study provides a detailed analysis of swarm behavior in nature and discusses how these behaviors can be emulated and optimized in unmanned systems. In this context, the potential impacts of swarm intelligence and collective behavior principles on unmanned systems are evaluated in terms of increasing their adaptability, optimizing energy efficiency and maximizing mission success. It is also argued that these principles can contribute to making unmanned systems more resilient to contingencies and changing environmental conditions. This theoretical study suggests that swarm intelligence principles can provide innovative solutions for future unmanned systems and autonomous organizational structures.

An analysis of active consumer behavior in a fitness boutique: satisfaction levels, perceived quality dimensions, and dropout reasons

The European fitness market is experiencing continuous growth, prompting enhancements in service quality at boutique gyms. These establishments are recognized for their personalized attention, professional staff, and innovative technology use. The perceived quality in these gyms significantly influences client satisfaction and loyalty, which are critical for retention. The aim of the contribution is to understand satisfaction levels, perceived quality dimensions, and dropout reasons inside this business model. The study sample comprises 67 adults, aged 18 to 70 years, predominantly aged 41-50. The EPOD2 Questionnaire and Dropout Reasons Scale were utilized for data collection. An analysis of a boutique fitness center in Zaragoza (Spain), indicates high demand, particularly among women. Most users positively rate the service, especially commending the staff, though communication is rated lower. Higher satisfaction is associated with increased loyalty, and reasons for dropout include distance and congestion, consistent with previous studies. The study has some limitations, including: sample size, gender imbalance, and a lack of longitudinal data. Further approaches should encompass larger samples, longitudinal designs, and broader comparisons to enhance the understanding of satisfaction and loyalty in boutique fitness centers.

Optical monitoring in southern blazars. Analysis of variability and spectral colour behaviours

Abstract We present the results of the optical monitoring of 18 southern blazars in the V and R Johnson-Cousins bands. Our main objective is to study the variations in the optical flux and their relationship with the colour and spectral variabilities. The optical observations were acquired with the 2.15 m ‘Jorge Sahade’ telescope, CASLEO, Argentina. The whole campaign comprised from 2014 April to 2019 September. In addition, X-ray data were taken from the Chandra X-ray Observatory and the Swift/XRT databases, and γ-ray data was taken from the Fermi-LAT 3FGL catalogue. From the total of 18 blazars, we found variability in each one of the timescales considered for 6 blazars (PKS 0208-512, PKS 1116-46, PKS 1440-389, PKS 1510-089, PKS 2005-489 and PKS 2155-304). In particular, from the colour-magnitude and the multiwavelength analysis, we found that PKS 1510-089 (FSRQ) is undergoing an activity phase. For the case of PKS 2005-489 (BL Lac), this blazar is in a quiescent state, in which it has been for more than a decade, and it is compatible with its BWB (bluer-when-brighter) moderate tendency, possibly due to the presence of shocks within the jet.

Moisture-induced mechanical property changes in natural hybrid composites with mwcnt fillers

ABSTRACT This study investigates the moisture absorption behaviour and its effects on the mechanical properties of polyester composites reinforced with jute and hemp fibres. The differences in moisture absorption between distilled water and saltwater are examined. Tensile and flexural tests are conducted on pure jute fibre-reinforced polyester composite, pure hemp fibre-reinforced polyester composite, and jute (J-P) and hemp (H-P) fibre-reinforced polyester composite. Changes in mechanical properties due to varying moisture conditions are analysed. Furthermore, the impact of a synthetic nano filler, multiwall carbon nanotubes (MWCNTs), on these composites is assessed. The study reveals that MWCNTs enhance mechanical properties, particularly in moist conditions. Fracture behaviour analysis aids in understanding the interaction between natural fibres, the matrix, and nano-reinforcement. At maximum natural fibre weight fractions (40%), moisture absorption reaches 8.17% for the J-P composite in distilled water and 9.61% in saltwater, while the H-P composite shows maximum absorptions of 6.61% and 8.27%, respectively. The hybrid composite records 4.18% and 7.17% under similar conditions. Compared to the plain jute fibre-reinforced composite, the addition of multiwall carbon tubes into the polymer matrix significantly reduces the moisture absorption capacity of the jute composite to 34.89% and 31.30% for distilled water and saltwater, respectively.

Exploring the Cellulose Content on the Thermal and Mechanical Properties of Banana Peel Blend Tapioca Starch Bioplastic

Petrochemical polymers are convenient but harmful to humans and the environment. In response to growing environmental concerns over petroleum-based polymers, this study developed a biodegradable plastic made from tapioca and banana peel starch. Tapioca flour, banana peel starch, and 3g, 6g, and 9g of cellulose were used via solvent cast technique to fabricate bioplastic films. Fourier transform infrared spectroscopy shows strong peaks at 3400-3200 cm-1, corresponding to OH bond stretching vibrations for all obtained films. The bioplastics film displayed good performance in tensile testing when 6g of cellulose was used as compared to other formulations. Thermogravimetric analysis showed that adding cellulose enhanced the thermal properties where the temperature of 139.0394 ℃ (T20) and 140.16 ℃ (T50) shifted to a higher temperature when 9g cellulose was incorporated with the blend matrix. Interesting to note that 3g of cellulose absorbs the most water however 9g loses the least weight. This phenomenon is due to an increase in the cellulose-hydroxyl hydrogen bonding. On the other hand, the modified films are insoluble in acetic acid, moderately soluble in ammonia, and entirely soluble in sulphuric acid for solubility behaviour analysis. The banana peel starch blend and tapioca powder film modified with cellulose bioplastics have the potential to be applied ideally for food packaging.

Cinnamaldehyde induces a TRPA1-mediated nociceptive behavior in planarians.

Nociception is defined as "the neural process of encoding noxious stimuli" by the International Association for the Study of Pain (IASP). Nociception relies on detecting noxious stimuli arising from a potentially or actually tissue-damaging event via specialized cells called nociceptors. In planarians, nociceptive behavior is often indicated by a 'scrunching' gait, in contrast to the usual gliding behavior displayed in normal conditions. The present study extends our previous study Reho et al. (2024) by testing a new potentially irritant molecule, Cinnamaldehyde (CA), which could induce scrunching gaits. We reproduced the nociceptive chemical tests from our previous study using CA instead of Allyl isothiocyanate (AITC) on Girardia dorotocephala (Gd) implementing an open field behavioral analysis. CA induced a dose-dependent increase in scrunching gait similar to the action of AITC and was expectedly partially suppressed by morphine and meloxicam. Knocking down the expression of the Gd-TRPA1 ion channel by RNA interference also suppressed the behavioral reaction to the molecule. In conclusion, we demonstrated that CA induced a nociceptive behavior in planarians through an action on the ion channel TRPA1. SIGNIFICANCE STATEMENT: In this article, we provide evidence that cinnamaldehyde induces a nociceptive behavior through a direct action in an invertebrate model (flatworm) much in the same way that in vertebrates.

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers’ cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the “fatigued” spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Chemical form dependent iodine transfer from seawater into seaweed and abalone.

Nuclear fuel reprocessing plant releases several kinds of radioactive nuclides, mainly 3H and 129I, into the oceans. Radio iodine causes thyroid dose. Iodine accumulates in several marine species such as wakame and abalone, which are food materials. Therefore, an analysis of iodine behavior in the marine environment is important to assess the impact of 129I. Iodine in seawater exists mainly in two chemical forms: iodide (I-) and iodate (IO3-). Their environmental behaviors are different. Thus, understanding these behaviors is important to assess the environmental behavior of total iodine. In this study, iodate and iodide transfer between seawater and abalone (Haliotis discus hannai) with 125I tracer was observed.

Smart Integration of Renewable Energy Sources Employing Setpoint Frequency Control—An Analysis on the Grid Cost of Balancing

The increasing installation of Renewable Energy Sources (RES) presents significant challenges to the stability and reliability of power systems. This paper introduces an advanced control method to mitigate the adverse effects of intermittent generation from RES on the power system frequency stability. The proposed approach emphasizes the critical role of Battery Energy Storage Systems (BESS) and RES in enhancing the resilience of modern power networks. The Generation Export Management Schemes (GEMS) are employed to curtail the excessive export of RES, thereby contributing to improved frequency stability. This research involves a comprehensive analysis of the dynamic behavior of the network under various operational scenarios, particularly focusing on power exchanges between RES, BESS, and synchronous generation units. Furthermore, this paper focuses on the economic implications of integrating RES into the grid, with a detailed cost of balancing (COB) modelling and analysis conducted to assess the financial viability of the proposed frequency management solutions. The analysis encompasses both short-term and long-term perspectives, providing insights into the development of economically sustainable smart power networks that effectively integrate renewable energy and storage technologies while maintaining system stability.

Do white-footed mice, the main reservoir of the Lyme disease pathogen in the United States, clinically respond to the borrelial tenancy?

As white-footed mice, Peromyscus leucopus, are considered the primary animal reservoir of Borreliella burgdorferi sensu stricto (Bb), the main agent of Lyme disease (LD) in the United States, these animals represent the most relevant model to study borrelial spirochetes in the context of their natural life cycle. Previous studies have consistently demonstrated that although white-footed mice respond immunologically to the invasion of the Lyme pathogen, P. leucopus adults do not develop a clinically detectable disease. This tolerance, which is common for mammalian reservoirs of different pathogens, contrasts with detrimental anti-borrelial responses of C3H mice, a widely used animal model of LD, which always result in a clinical manifestation (e.g., arthritis). The current investigation is a follow-up of our recent study that already showed a relative quiescence of the spleen transcriptome for Bb-infected white-footed mice compared to the infected C3H mice. In an effort to identify the mechanism behind this tolerance, in this study, we have evaluated an extensive list of hematological and biochemical parameters measured in white-footed mice after their 70-day-long borrelial infection. Despite missing reference intervals for Peromyscus mice, our sex- and age-matched uninfected controls allowed us to assess the blood and serum parameters. In addition, for our assessment, we also utilized behavioral, immunological, and histological analyses. Collectively, by using the metrics reported herein, the present results have demonstrated clinical unresponsiveness of P. leucopus mice to the borrelial infection, presenting no restriction to a long-term host-pathogen co-existence.

Paragangliomas of the head and neck

Paragangliomas represent a heterogeneous group of rare neuroendocrine tumors with marked variability in symptoms and disease course. Due to the close proximity to neurovascular structures, paragangliomas of the head and neck region can cause a variety of symptoms. To this day, there are no reliable prognostic factors that can predict apotentially malignant course. All patients with newly diagnosed paragangliomas should undergo an early diagnostic workup and regular follow-up examinations in specialized centers. While radical resection was previously regarded as standard treatment for paragangliomas, radiotherapy and active surveillance (watch-and-scan strategy) have become equally important over the years. Low-threshold techniques for molecular pathology analysis of the mutation-specific behavior of paragangliomas are nowadays available.

Characterization and Outcomes of Profoundly and Non-profoundly Autistic Individuals Admitted to a Specialized Psychiatric Inpatient Unit.

The prevalence of autism spectrum disorder (ASD) is currently estimated to be 1 in 36 children. While much is known about the impact of ASD on family and community functioning as well as treatment outcomes, less is known about a relatively new categorization of ASD, called "profound autism." The current study evaluated 14 consecutively admitted patients meeting criteria for profound autism to a specialized psychiatric inpatient and partial hospitalization program. We then selected 15 non-profoundly autistic youth admitted immediately before or after each profoundly autistic youth to serve as a comparison. Patient caregivers completed measures about parental stress and the severity and frequency of patient problem behavior, and patients participated in functional analyses of problem behavior and function-based treatment programs during their admission. Results showed profoundly autistic youth spent an average of 14 more days admitted to the psychiatric unit, and they engaged in higher parent-reported severity and frequency of self-injurious and stereotypic behavior. Functional analysis results for profoundly autistic youth showed a higher occurrence of automatically reinforced problem behavior but otherwise similar results. As well, profoundly autistic youth showed a lower percentage reduction in problem behavior at the end of the psychiatric hospitalization than their non-profoundly autistic peers. Profoundly autistic youth seem to require a high level of behavioral support, even after psychiatric hospitalization. Insurance companies and therapists need to recognize these challenges and invest sufficient resources to meet their treatment needs.