Analysis Of Behavior Research Articles

Digital twins for electric propulsion technologies

As the space industry is undergoing an evolution, the current approaches toward design, development, and qualification of Electric Propulsion (EP) systems largely based on empirical “trial-and-error” methodologies are falling short of addressing the emerging needs and keeping abreast of the rapid changes in market trends. Furthermore, with the proliferation of Artificial Intelligence (AI) within the space industry toward next-generation autonomous satellites and spacecrafts, the conventional EP monitoring and control strategies become inadequate and need to give way to approaches compatible with satellite-level autonomy requirements. A digital twin (DT) – a technology capable of providing an accurate dynamically adapting virtual representation of a physical asset – is a game-changing concept that catalyzes the transcendence of the EP industry past its pressing challenges today. In this paper, we aim to: (i) define the DT concept, highlighting how it surpasses traditional modelling, (ii) enumerate the DT’s breakthrough promises for the EP industry, and (iii) specify the challenges to realize practical and scalable EP DTs. Additionally, we report on the technical progress achieved and/or planned at Imperial Plasma Propulsion Laboratory to fill the foundational gaps in three building block elements of DTs, namely, (i) a cost-effective kinetic model to generate extensive high-fidelity databases for machine learning (ML), (ii) ML-enabled models for prediction and analysis of performance and operational behavior, and (iii) a DT architecture that integrates the numerical models in terms of a computing infrastructure and provides data pipelines and interfaces for the DT’s data exchanges with the real world, its dynamic updating, and uncertainty quantification.

Covalent Organic Frameworks (COFs): A New Class of Materials for Multivalent Metal‐Ion Energy Storage Systems

The rise of electronic societies is driving a surge in the demand for energy storage solutions, particularly in the realm of renewable energy technologies like batteries, which rely heavily on efficient electrode materials and separators. As an answer to this necessity, Covalent Organic Frameworks (COFs) are emerging and a highly intriguing class of materials, garnering increased attention in recent years for their extensive properties and possible applications. This review addresses the remarkable versatility and boundless potential of COFs in scientific fields, mainly focusing on multivalent metal ion batteries (MMIBs), which include AIB (Aluminium‐ion batteries), MIB (Magnesium‐ion battery), CIB (Calcium‐ion battery), and ZIB (Zinc‐ion battery), as both electrode materials and separators across a spectrum of battery technology. Inclusive of their approaches, merits, and reaction mechanisms, this review offers an extensive summary of COFs concerning multivalent ion batteries. By providing a rigorous analysis of COF attributes, electrochemical behaviour, and methodologies, our explanation contributes to a deeper understanding of their potential in advancing battery technology.

Consistency in verbal expression of death row criminals: concluding life with emotions

IntroductionThere is a lack of both data and, more significantly, computational analyses of the linguistic behavior near an individual's final moments of life. The present study is aimed to reduce human bias and save time in psycholinguistic studies by providing data-backed insights.MethodsA novel machine learning based pipeline proposed, using elements of semantic similarity (BERT and transformers) and emotion extraction in collaboration, to analyze the final statements of death row inmates to understand the consistency in their verbal expression moments before their death. A new method of analysis was proposed in this study to explore the notions inherent in the statements. A large database of 466 final statements from death row inmates in Texas was utilized in this study. Manual notion analysis was validated by a computational method of notion inferencing.ResultsBasic emotions of Anger and Fear majorly dominated the statements, constituting 54% of the whole, while 21% of all statements were of emotional states of Happiness and Serenity.DiscussionThe outcomes of this study are expected to contribute to psychological analyses of humans, moments before death, and provide insights to criminology researchers to formulate better strategies of rehabilitation and debate the death penalty.

Asymptotic behavior of solutions of nonlinear discrete systems in critical cases

AbstractThe paper is dedicated to the analysis of the asymptotic behavior of essentially nonlinear discrete systems whose linearization possesses eigenvalues on the unit circle. For these systems, the paper establishes sufficient conditions for the asymptotic stability regardless of terms higher than the third order. Moreover, it derives an asymptotic stability criterium for a reduced critical subsystem. The proof incorporates a constructive approach for deriving Lyapunov functions using the center manifold reduction and normal form method. The key result of the paper asserts that, given the obtained stability conditions, the solutions of the system converge to the origin with a polynomial decay rate. The paper also introduces a method for evaluating the coefficients of the decay rate estimate. To illustrate these findings, we apply the obtained result to analyzing nonlinear business cycle and predator-pray models.

THE EFFECT OF THE ABA (APPLIED BEHAVIOR ANALYSIS) METHOD ON THE KNOWLEDGE AND EFFICACY OF TOOTHBRUSHING IN AUTICAL CHILDREN

The dental and oral health of autistic children is generally not good. This is exacerbated by the inability to brush teeth properly, due to disruption of the child's concentration and interaction, making it difficult to receive instructions on how to brush teeth. Dental and oral health problems in autistic children are the same as dental health problems in normal children. The difference is the behavior that autistic children display where they are unable to follow instructions or are uncooperative. Objective: To determine the effect of dental health education using the Applied Behavior Analysis (ABA) method on the knowledge and efficacy of toothbrushing in autistic children. Method: The type of research design is quasi-experimental with a pretest and posttest non-equivalent control group design utilizing intervention and control groups with a purposive sampling technique totaling 15 intervention groups and 15 control groups. The instrument used for this research was a questionnaire to assess the knowledge and efficacy of brushing teeth in children with autism. Results: There was an influence and increase in knowledge and efficacy of toothbrushing in both groups with a value of (P<0.05), however the increase was higher in the intervention group. Conclusion: There is an influence of dental health education using the ABA (Applied Behavior Analysis) method on the knowledge and efficacy of toothbrushing in autistic children

Consumer Behavior Analysis through Advanced-Data Techniques: A Review for Utilities in Retail Marketing

This review offers a comprehensive analysis of the use of cutting-edge data strategies, including Big Data analytics, Machine Learning (ML), and Artificial Intelligence (AI), etc. in understanding and predicting consumer behavior. The convergence of these technologies has enabled marketers to uncover more detailed knowledge about consumer preferences and enhance customer engagement & satisfaction. By reviewing various methodologies, this study showcases the ways in which these tools are leveraged for segment consumers, predict consumer buying habits, and optimize marketing strategies. Additionally, the paper explores the Social media data utilization in real-time consumer analytics, demonstrating their effectiveness in personalizing marketing efforts and improving customer experience. The challenges of data privacy, ethical considerations, and the necessity for more robust analytical tools are also discussed. The review concludes by identifying potential research paths that could drive forward the field of consumer behavior analysis in retail marketing. Key Words: Artificial Intelligence, Big Data, Consumer Behavior, Machine Learning, Retail Marketing.

Field-road classification for agricultural vehicles in China based on pre-trained visual model

Field-road classification that automatically identifies the activity (either in-field or on-road) of each point in Global Navigation Satellite System (GNSS) trajectories is a critical process in the behavior analysis of agricultural vehicles. To capture movement patterns specific to agricultural operations, we propose a multi-view field-road classification method, which extracts a physical and a visual feature vector to represent a trajectory point. We propose a task-specific approach using a pre-trained visual model to effectively extract visual features. Firstly, an image is generated based on a point plus its neighboring points to provide the contextual information of the point. Then, an image recognition model, a fine-tuned ResNet model is developed using the pretraining-finetuning paradigm. In such a paradigm, a pre-training process is used to train an image recognition model (ResNet) with natural image datasets (e.g., ImageNet), and a fine-tuning process is applied to update the parameters of the pre-trained model using the trajectory point images, enabling the model to have both general knowledge and task-specific knowledge. Finally, a visual feature is extracted for a point by the fine-tuned model, thereby overcoming the limitations caused by the small-scale generated images. To validate the effectiveness of our multi-view field-road classification, we conducted experiments on four trajectory datasets (Wheat 2021, Paddy, Wheat 2023, and Wheat 2024). The results demonstrated that the proposed method achieves competitive accuracy performance, i.e., 92.56%, 87.91%, 90.31%, and 94.23% on four trajectory datasets, respectively. Extensive experiments demonstrate that our approach can consistently perform better than the existing state-of-the-art method on the four trajectory datasets by 2.99%, 4.42%, 2.88%, and 2.77% in the F1-score, respectively. In addition, we conduct an in-depth analysis to verify the necessity and effectiveness of our method.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Phase behavior analysis of methane confined in nanopores using molecular simulation

Interest in the phase behavior of hydrocarbons in shale reservoirs has grown in recent years. Petroleum fluid phase behavior has been observed to differ significantly between conventional reservoirs and shale reservoirs. Within shale reservoirs, notable surface-fluid interactions can lead to non-uniform molecule distribution and an alteration in fluid phase behavior, primarily caused by the existence of nano-scale porous materials. In this work, we study the phase behavior of methane in single cylindrical pore models. We apply the gauge Gibbs ensemble Monte Carlo (gauge-GEMC) simulation technique to investigate the phase behavior of methane in 4–10 nm single nanopores and calculate the saturation pressures at various temperatures using the grand canonical Monte Carlo (GCMC) simulation technique. A shift in the phase diagram has been found for methane in nanopores. As pore size decreases, the shift becomes more significant.

Infralimbic Cortex and Behavioral Responses to Mild Stress after Repeated Social Stress Exposure

Major Depressive Disorder (MDD) is an emotional impairment that can have debilitating consequences for afflicted individuals. Social stress, including isolation or alienation, may be an important factor that contributes to MDD. In two experiments, repeated social defeat was utilized in rodents to induce social stress, modeling the social stressors that are thought to contribute to MDD in humans. Behavioral analyses revealed that dominance and submissive behaviors in the social defeat sessions were quite stable across repeated sessions. Accordingly, there appeared to be little variability between the rats in their social stress experience. This validates the model and allows for the study to draw inferences concerning the impacts of the stress experience from means testing of behavioral and physiological outcomes. The second experiment evaluated the impacts of repeated social defeat on cortical responses to the mild stress of a novel environment. RNAscope was utilized evaluate expression of c-Fos, GAD65, and VGluT1 mRNA in the infralimbic cortex. mRNA for c-Fos was labeled to identify which neurons were active in the infralimbic cortex. This was combined with labels for vGluT1 and GAD65 mRNAs to evaluate if those neurons were excitatory (glutamatergic) or inhibitory (GABAergic). The socially stressed rats exhibited increased mRNA expression for GAD65 in the infralimbic cortex. This suggests increased stress-induced biosynthesis of GABA in inhibitory neurons of the infralimbic cortex.

Effects of Mozart–Orff parent–child music therapy among mothers and their preschool children with autism spectrum disorder: A mixed-methods randomised controlled trial

BackgroundAutism spectrum disorder (ASD) negatively impacts mental health, particularly in mothers of autistic children who experience heightened stress. Applied behaviour analysis (ABA) and music therapy are recognised interventions for improving ASD symptoms. However, the specific benefits of parent–child music therapy and ABA for autistic children and their mothers remain uncertain. This study evaluated the effects of parent–child music therapy on preschool autistic children and their mothers.MethodA randomised controlled trial was conducted with 100 mother–child pairs assigned to either the control group receiving ABA or the intervention group receiving both music therapy and ABA. Qualitative interviews were conducted post-intervention for 12 mothers.ResultsChildren in the intervention group exhibited lower scores for ASD symptoms than those in the control group. Moreover, mothers in the intervention group demonstrated reduced dysfunctional parent–child interaction, lower overall parental stress, significantly improved family functioning, and increased levels of hope compared with those in the control group. Mothers held positive views regarding music therapy.ConclusionsCombining ABA with parent–child music therapy can alleviate ASD symptoms in children and reduce stress in mothers. Improved parent–child interaction and enhanced family functioning further support the benefits of this combined approach. Parent–child music therapy, combined with ABA demonstrated positive outcomes for autistic children, including reduced ASD symptoms, improved parent–child interaction, decreased parental stress, enhanced family functioning, and increased hope. These findings highlight the potential of incorporating music therapy as a valuable component in the comprehensive treatment of ASD.Trial registrationThis study was registered in the Chinese Clinical Trial registry (05/07/2021, ChiCTR2100048261, https://www.chictr.org.cn/showproj.html?proj=128957). Ethical approval was obtained from the Research Ethics Committee of Fujian Medical University and the study hospital (Fujian Provincial Maternity and Child Health Hospital; 2017 − 105), and informed consent was obtained from all subjects and/or their legal guardian(s).

Analysis of stress behavior of drill tool by using ANSYS-2020 R2

Analysis of stress behavior of drill tool by using ANSYS-2020 R2

The evaluation of competition effect on rail fares using the difference-in-difference method through symmetric and lagged spans

Long-distance railway markets in Europe have become more competitive since the EU liberalisation packages. Countries like Austria, Czechia, Sweden, and Italy have experienced sustained competition, providing valuable insights into how open-access competition works. Italy's non-subsidised high-speed competition is especially interesting to study. While some researchers have studied the minimum price changes before and after a newcomer enters a route, more is needed about the duration of the effect and the impact on all fare types. Our paper estimates the competition's impact by observing the price dynamics of the incumbent company (Trenitalia) for different fare types over an extended period. We examine the Turin-Milan-Venice route where the competitor (Italo) entered the market during observation. We distinguish between different fare types through detailed descriptive analyses of the incumbent company's price behaviour. Using the difference-in-difference method, we obtain estimates of the effect for different time spans and check their robustness.Two methods are used to design difference-in-difference models: a “classical approach” that compares symmetrical intervals before and after entry and another approach based on the comparison of lagged intervals. The second method compares the specific span with the corresponding one, a year before to exclude the seasonal component. Our findings reveal significant price-reduction effects compared to the price dynamics in other market routes where competition had been introduced.

Dysfunctional copper homeostasis in Caenorhabditis elegans affects genomic and neuronal stability

While copper (Cu) is an essential trace element for biological systems due to its redox properties, excess levels may lead to adverse effects partly due to overproduction of reactive species. Thus, a tightly regulated Cu homeostasis is crucial for health. Cu dyshomeostasis and elevated labile Cu levels are associated with oxidative stress and neurodegenerative disorders, but the underlying mechanisms have yet to be fully characterized. Here, we used Caenorhabditis elegans loss-of-function mutants of the Cu chaperone ortholog atox-1 and the Cu binding protein ortholog ceruloplasmin to model Cu dyshomeostasis, as they display a shifted ratio of total Cu towards labile Cu. We applied highly selective and sensitive techniques to quantify metabolites associated to oxidative stress with focus on mitochondrial integrity, oxidative DNA damage and neurodegeneration all in the context of a disrupted Cu homeostasis. Our novel data reveal elevated oxidative stress, compromised mitochondria displaying reduced ATP levels and cardiolipin content. Cu dyshomeostasis further induced oxidative DNA damage and impaired DNA damage response as well as neurodegeneration characterized by behavior and neurotransmitter analysis. Our study underscores the essentiality of a tightly regulated Cu homeostasis as well as mitochondrial integrity for both genomic and neuronal stability.

Study on corrosion behavior and first-principle analysis of M50 steel in lubricating oil contaminated by salt water

The corrosive wear of M50 steel in lubricating oil contaminated with saline is a form of wear that often occurs within the main shaft bearings of aviation engines carried by aircraft working at sea. In the present paper, the corrosion behavior of M50 steel in lubricating oil contaminated with saline was studied. The open-circuit potential, polarization curve and impedance spectrum were analyzed by rotating electrochemical corrosion wear test. A three-layer interface microscopic molecular model was established with Materials Studio to simulate the dynamic corrosion evolution process of M50 steel self-matching pairs and the adsorption energy was calculated. The results show that the corrosion type is pitting corrosion. With the increase of saline concentration, the corrosion area becomes wider and shallower, and the size of the pitting core gradually decreases. The weight loss of M50 steel increases over time, but the trend slows down, which is also shown in the MS simulation results. Electrochemical tests show that the tribocorrosion of M50 steel is related to load, speed. As the load increases, the corrosion rate decreases. And, as the speed increases, the corrosion rate increases. This has practical significance for extending the understanding of tribocorrosion of M50 steel in marine atmospheric environments.

Inferring the Accurate Locations of Noise Records in Mobile Phone Location Data

ABSTRACTThe positioning uncertainty of mobile phone location (MPL) data greatly influences location services and crowd behavior analysis. Although many achievements have been made in controlling its main sources (signal drift and ping‐pong effects), several problems, such as single‐oscillation patterns, insufficient position optimization, and a lack of effective evaluation, remain. In this study, a set of MPL data quality optimization methods are proposed. First, the characteristics of drift records and the oscillation patterns of ping‐pong records are discussed. The quality of the MPL data is subsequently controlled with the proposed feature‐based drift‐record detection method, complex oscillation pattern‐based ping‐pong‐record detection method, and cumulative duration weighting‐based ping‐pong‐record optimization method. These methods are applied to the MPL dataset of a major operator in Nanjing city, and the optimization effect is evaluated with GPS data collected synchronously. The results show that the proposed detection and optimization methods can effectively improve the accuracy of MPL data.

EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals.

Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.

Experimental Analysis of the Mechanical Behavior of Shear Connectors for Precast Sandwich Wall Panels When Subjected to the Push-Out Tests

Precast concrete sandwich panels consist of two outer layers connected by a central connector and an inner insulating layer that enhances thermal and acoustic performance. A key challenge with these panels is eliminating thermal bridges caused by metallic connectors, which reduce energy efficiency. PERFOFRP connectors, made from perforated glass fiber-reinforced polymer (GFRP) sheets, have been proposed to address this issue. These connectors feature holes that allow concrete to pass through, creating anchoring pins that enhance shear resistance and prevent the separation of the concrete layers. This research aimed to evaluate the effect of the diameter and number of holes on the mechanical strength of PERFOFRP connectors. Three diameters not previously reported in the literature were selected: 12.70 mm, 15.88 mm, and 19.05 mm. A total of 18 specimens, encompassing 6 different configurations with varying numbers of holes, underwent push-out tests. The most significant resistance increase was a 15% gain over non-perforated connectors, observed in the configuration featuring three holes of 19.05 mm. The connections exhibited rigid and nearly linear behavior until failure.

Analysis of Flow Pattern and Bubble Behavior in Slab Continuous Casting with Mold Electromagnetic Stirring

The electromagnetic stirring (EMS) and argon blowing are the widely recognized and extensively employed technologies to control the flow pattern in slab continuous casting, and their interaction is directly linked to the final product. To investigate the flow pattern, bubble characteristics under mold EMS and argon blowing, a mathematical model coupling the electromagnetic field, two‐phase flow is constructed. The population balance model (PBM) under the Eulerian frame is applied to describe the bubbles breakage and coalescence. The results show that the flow regime under the interaction of argon blowing and EMS transfers from bubble ascending flow to intermediate flow to horizontal recirculation flow as EMS current increases, and the large argon flow rate could suppress the formation of horizontal recirculation flow. Meanwhile, EMS can promote the coalescence of bubbles, with the increasing EMS current, the bubble numbers decrease first and then increase, and the bubble mean diameter increases first and then decreases. For obtaining the horizontal recirculation flow, the suitable argon flow rate and EMS current should be matched suitably.

Using multivariate partial least squares on fNIRS data to examine load-dependent brain-behaviour relationships in aging.

Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.