Analysis Of Behavior Research Articles

The analysis of the overall failure of practical Zn−Ni battery

Zn−Ni batteries are viewed as promising power sources for electric tools and electric vehicles (EVs) due to their benefits of high safety, high working voltage and attractive rate performance. However, the practical applications of the Zn−Ni batteries are hampered by poor cycling performance, posing significant challenges for their widespread utilization. To effectively address this pressing concern, a profound investigation of the intrinsic failure mechanisms underlying Zn−Ni batteries holds paramount importance. This paper carries out a thorough analysis of battery behavior to simulate industrial application scenarios using two different assembly methods of Zn−Ni batteries. One type is a battery composed of one Ni(OH)2 cathode and one Zn anode (abbreviated as OO). The other type is the battery assembled with one Ni(OH)2 cathode and two Zn anodes (abbreviated as OT). Ultimately, it is revealed that the principal factors of Zn anode failure in OO battery are the growth of zinc dendrites, passivation and uneven current distribution. In contrast, for OT battery, the cathode emerges as the failed electrode. Specifically, the rupture of spheres on the positive electrode surface and the detached powder from the substrate result in the decline in capacity and finally the failure of the battery. Two classical battery assembly methodologies were employed to investigate in-depth the failure mechanisms of Zn−Ni batteries, ultimately revealing that the reasons for battery failure mechanism differed, thus providing valuable and practical guidance for future research aimed at enhancing battery lifespan.

Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis

Background: Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. Methods: To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model’s interpretability but also provides valuable insights by highlighting the most influential features through attention weights. Results: Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%. Conclusions: This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.

Tensile strength of friction stir additive manufactured laminated AA 6061/TiC/GS composites

Abstract With the fast progress of industrial manufacturing, friction stir additive manufacturing has fascinated wide-ranging consideration in the industry due to high material consumption rate. Friction stir additive manufacturing (FSAM), a newly developed solid-phase additive manufacturing technique was employed to fabricate AA6061/TiC/GS composite. The process parameters like tool rotational speed, transverse speed, tool tilt angle and type of tools used in friction stir additive manufacturing were analyzed. Taguchi’s L16 orthogonal array and ANOVA method was used to find the optimum process parameters for the tensile strength. Development characteristics of stirred zone, recrystallization and mixing of reinforced particles will significantly improve the mechanical properties of the fabricated composites. Microstructural investigation and fractography was done by using optical microscopy and scanning electron microscopy (SEM). Corrosion, wear behavior and elemental analysis through EDS was also performed for the fabricated material. The maximum tensile strength of 385.74 MPa was attained under optimal parameters of the tool rotational speed 1,200 rpm, transverse speed 55 mm min−1, and tool tilt angle of 1° for scrolled tapered octagonal tool pin. The findings of the linear regression model showed a minor variance between model and experimental values. Prominent results of the experiment were compared by few other researcher’s findings working in similar area.

Kendra's journey: an intrinsic case study of a deaf autistic child.

This study used an intrinsic case study to investigate how interventions from applied behavior analysis impacted language development, academic progress, and the reduction of severe challenging behaviors for an 8-year-old child who is Deaf and autistic. 3 main themes were identified: language acquisition, behavioral change strategies, and academic performance. Additionally, 2 common threads wove in and out of each and connected all 3 themes: language access and qualified providers, including Deaf professionals. This study expands the literature about using behavioral strategies with children who are Deaf and autistic and discusses implications for clinical and educational practice.

Real-Time Collaborative Programming in Undergraduate Education: A Comprehensive Empirical Analysis of Its Impact on Knowledge, Behaviors, and Attitudes

Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when using RCP. This study utilized a quasi-experimental design to investigate the impact of RCP on undergraduates’ programming knowledge and skills, behaviors, and attitudes toward collaboration. Eighty-two participants were randomly assigned to either the RCP group or the Traditional Collaborative Programming (TCP) group. The data analysis yielded noteworthy findings. First, RCP significantly improved learners’ problem-solving skills and led to higher knowledge test scores compared to TCP. Second, there were no significant differences in attitudes toward collaboration between the two groups. Third, analysis of programming behaviors indicated that the RCP group exhibited more efficient coding practices, including producing a greater volume of code and executing it more successfully. Additionally, the RCP group’s code-testing activities were predominantly concentrated at the first half of the class sessions. Based on the empirical research results, three major pedagogical implications were proposed. These findings highlight the benefits of integrating RCP into programming education and suggest avenues for future research into collaborative learning dynamics.

Comparative Analysis of 24-h Movement Behaviours in Non-Overweight and Overweight/Obese Children: Findings from the FAMIly Physical Activity, Sedentary Behaviour, and Sleep (FAMIPASS)

Background: Childhood overweight and obesity are global health concerns associated with insufficient physical activity (PA), excessive sedentary behaviour (SB), and inadequate sleep. This study aimed to determine whether differences exist in 24 h movement behaviours between Czech non-overweight children and children with overweight/obesity aged 3–10 years, with respect to their gender, age, or family socioeconomic status (SES). Methods: A total of 381 children (49.9% girls), aged 3–10 years, participated. Their PA, SB, and sleep were continuously monitored over a regular week using wrist-worn accelerometers. Weight status was determined by BMI z-scores, according to World Health Organization standards. SES was assessed using the Family Affluence Scale. Results: Non-overweight children had averages of 414 min of PA, 472 min of SB, and 554 min of sleep per day, while children with overweight/obesity had averages of 392 min of PA, 503 min of SB, and 545 min of sleep. Non-overweight children engaged in significantly more PA (22 min per day; p = 0.014) and significantly less SB (31 min per day; p = 0.002) than children with overweight/obesity. No significant differences were found between the weight groups in gender distribution, age, family SES, or maternal and paternal obesity. Conclusions: Non-overweight children exhibited higher levels of PA and lower levels of SB compared to their counterparts with overweight/obesity, independent of gender, age, or family SES. These findings highlight an association between daily movement behaviours and weight status in young children. Further research is needed to explore the impact of modifying PA and SB on weight outcomes.

MPE-HRNetL: A Lightweight High-Resolution Network for Multispecies Animal Pose Estimation.

Animal pose estimation is crucial for animal health assessment, species protection, and behavior analysis. It is an inevitable and unstoppable trend to apply deep learning to animal pose estimation. In many practical application scenarios, pose estimation models must be deployed on edge devices with limited resource. Therefore, it is essential to strike a balance between model complexity and accuracy. To address this issue, we propose a lightweight network model, i.e., MPE-HRNet.L, by improving Lite-HRNet. The improvements are threefold. Firstly, we improve Spatial Pyramid Pooling-Fast and apply it and the improved version to different branches. Secondly, we construct a feature extraction module based on a mixed pooling module and a dual spatial and channel attention mechanism, and take the feature extraction module as the basic module of MPE-HRNet.L. Thirdly, we introduce a feature enhancement stage to enhance important features. The experimental results on the AP-10K dataset and the Animal Pose dataset verify the effectiveness and efficiency of MPE-HRNet.L.

AI-driven adaptive analysis for finding emergent behavior in military capability design

This article introduces and demonstrates a new methodology for searching for emergent behavior in simulation-based analysis as rare, extreme events using adaptive techniques enabled by recent advances in Bayesian machine learning (ML). The new methodology, Low-cost Adaptive exploratioN to Track down Extreme, Rare events using Numerical optimization (LANTERN), supports analysis activities in defense planning that iteratively build up the understanding of new technology and concept alternatives in complex military scenarios. Central to this process is emergent behavior—hard-to-predict but highly important behaviors that present problems or opportunities. These unexpected behaviors can be generated in complex military scenarios and are crucial to decision-making, but are often difficult to find and work with due to the expense and cost of the existing approaches for working with high-fidelity military simulation. To address this challenge, LANTERN is formulated to accelerate the discovery of emergent behavior as rare, extreme events by combining human expert understanding with new artificial intelligence (AI)-driven adaptive experimentation techniques in iterative analysis. A demonstration of the methodology is presented using military agent-based simulation scenarios developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). The demonstration highlights how analysis can focus directly on searching for emergent behavior and shows substantial improvements over brute-force Monte Carlo approaches.

Investigation of Tensile Properties and In Situ Analysis of Fracture Behavior in High-Porosity Open-Cell Nickel Foam.

Nickel foam offers excellent conductivity, a high surface area, and lightweight structure, making it ideal for applications, like battery electrodes, catalysts, and filtration systems. Its durability and corrosion resistance further enhance its performance in various industries. However, few studies focus on the tensile anisotropy of nickel foam and its tensile fracture process. In this study, the anisotropic tensile behavior of nickel foam with varying relative densities has been investigated, along with its tensile fracture behavior using in situ techniques. The tensile properties of nickel foams show strong anisotropy due to the flattening process in the production process. The results show that the tensile properties, including the yield strength, tensile strength, and elastic modulus, increase with the increasing relative density, while the elongation percentage has no relationship with the relative density. The experiment data on tensile strength are in agreement with Gibson's formula and Liu's formula. In situ tensile tests are conducted to explore the microscopic fracture mechanism of nickel foam. The results show that the struts of nickel foam are tensile fractures or shear fractures near the joints, and the fracture process of struts is clearly recorded and analyzed. This study is significant as it provides critical insights into the anisotropic tensile behavior of nickel foam and fracture mechanism, enabling the optimization of production processes and broadening its potential applications.

Pragmatic Application of Risk Ratios in Behavior Analysis

Pragmatic Application of Risk Ratios in Behavior Analysis

A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations

Data are becoming more important in education since they allow for the analysis and prediction of future behaviour to improve academic performance and quality at educational institutions. However, academic performance is affected by regions’ conditions, such as demographic, psychographic, socioeconomic and behavioural variables, especially in lagging regions. This paper presents a methodology based on applying nine classification algorithms and Shapley values to identify the variables that influence the performance of the Colombian standardised test: the Saber 11 exam. This study is innovative because, unlike others, it applies to lagging regions and combines the use of EDM and Shapley values to predict students’ academic performance and analyse the influence of each variable on academic performance. The results show that the algorithms with the best accuracy are Extreme Gradient Boosting Machine, Light Gradient Boosting Machine, and Gradient Boosting Machine. According to the Shapley values, the most influential variables are the socioeconomic level index, gender, region, location of the educational institution, and age. For Colombia, the results showed that male students from urban educational institutions over 18 years have the best academic performance. Moreover, there are differences in educational quality among the lagging regions. Students from Nariño have advantages over ones from other departments. The proposed methodology allows for generating public policies better aligned with the reality of lagging regions and achieving equity in access to education.

Deconstructing 'Greenwashing' and 'Anti-Greenwashing' in Corporate ESG Investments: Based on Behavioral Identification, Motivation Analysis and Effect Evaluation

Abstract: With the increasing corporate attention to Environmental, Social, and Governance (ESG), some companies that do not have ESG investment strengths have also joined the ESG practice. As firms intensively integrate ESG into their operational ethos, discrepancies between proclaimed and actual ESG commitments have given rise to "greenwashing" behaviors. At present, China's research on corporate "greenwashing" is still in its infancy, and it is difficult to accurately identify corporate "greenwashing" behaviors and clarify their motives and impacts, so as to achieve the "anti-greenwashing" effect of precise governance. This paper examines the manifestations, motivations, and effects of "greenwashing" through actual case studies as well as theoretical and comparative literature research in order to propose a comprehensive framework for analyzing the nature of "greenwashing." To counteract "greenwashing," the paper combines the reality of problems to extract the insights and shortcomings of the existing research and propose multi-tiered strategies tailored to policy, corporate, and societal levels to look forward to the direction of future research on "anti-greenwashing." The aim of this paper is to promote the development of "greenwash" research and strengthen the society's awareness of "greenwash" behavior and the understanding of corporate ESG behavior, underscores the theoretical and practical significance of addressing "greenwashing." It calls for sustained research to optimize the ESG investment landscape.

Biomarker and adverse outcome pathway responses of Tubifex tubifex(sludge worm) exposed to environmentally-relevant levels of acenaphthene: insights from behavioral, physiological, and chemical structure-activity analyses.

Polycyclic aromatic hydrocarbons (PAHs), including acenaphthene, pose a significant threat to aquatic ecosystems by harming vital organisms such as benthic invertebrates. This study evaluated the impact of environmentally relevant concentrations of acenaphthene on Tubifex tubifex, focusing on sublethal acute toxicity and subchronic biomarker responses. Key biomarkers assessed included histopathological changes and the modulation of antioxidant enzymes: catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST), and malondialdehyde (MDA). Additionally, the study examined structure-activity relationships and species sensitivity distribution (SSD). Concentrations exceeding the solubility threshold of acenaphthene (3.9 mg/L) triggered distinct, concentration-dependent behavioral responses in Tubifex tubifex, such as clumping, mucus secretion, and body wrinkling. Prolonged exposure exacerbated these behavioral dysfunctions, while subchronic exposure resulted in significant histopathological alterations, including epithelial hyperplasia, inflammation, edema, fibrosis, and degenerative changes. The edematic appearance of the body wall suggested a potential immune response to exposure. Furthermore, increased activities of CAT, SOD, and GST indicated oxidative stress in the worms. The study found a 1.5-fold increase in CAT and GST activity, a fivefold increase in SOD, and a striking 100-fold increase in MDA levels compared to controls, signifying an overwhelmed antioxidant defense system and potential cellular disruption. The SSD curve revealed hazard concentrations (HC50 and HC90), indicating that Tubifex tubifex exhibited lower sensitivity to acenaphthene compared to other taxa. In silico analysis and read-across models confirmed the potential of acenaphthene to induce significant oxidative stress upon exposure. The correlation between biomarker responses and structure-activity relationship analysis highlighted the aromatic nature of acenaphthene as a key factor in generating reactive metabolites, inhibiting antioxidant enzymes, and promoting redox cycling, ultimately contributing to adverse outcomes. These findings, coupled with behavioral responses and SSD curve inferences, underscore the importance of the solubility threshold of acenaphthene as a critical benchmark for evaluating its ecological impact in aquatic environments.

On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications

AbstractBlockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage. Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.

Keypoint-Based Bee Orientation Estimation and Ramp Detection at the Hive Entrance for Bee Behavior Identification System

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in 1024×576 px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on 640×384 px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications.

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

Behavioural Analysis of Hatchetman Attacker for Detection & Prevention in Low Power and Lossy IoT Network

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

TIBA: A web application for the visual analysis of temporal occurrences, interactions, and transitions of animal behavior.

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.

Comparative Behavior Analysis of Cy5-Pyrimidine Nucleotides in the Rolling Circle Amplification

Two pairs of Cy-5-labeled dU and dC triphosphates with similar electroneutral fluorophore structures differing in the length of the hydrocarbon linker between the fluorophore and the nitrogenous base were synthesized. A comparative analysis of their substrate behavior in the rolling circle amplification (RCA) using Bst 3.0 DNA polymerase was carried out. It was found that nucleotides with a long linker between the fluorophore and pyrimidine base are more efficiently incorporated into the growing DNA chain, while nucleotides with a short linker inhibit RCA less. In each of the dU and dC pairs with similar fluorophores and linkers, fluorescently labeled uridine derivatives demonstrated a high embedding density. It was found that with simultaneous incorporation of labeled dU and dC, the inhibitory effect does not summarise. This gives grounds for a more careful study of various Cy5-dC variants in order to increase a sensitivity of the analysis with simultaneous introduction of labeled dU and dC.

Poliumoside inhibits apoptosis, oxidative stress and neuro-inflammation to prevent intracerebroventricular Streptozotocin-induced cognitive dysfunction in Sprague-Dawley Rats: in in-vivo, in-vitro and in-silico study.

Alzheimer's disease (AD) is a severe neurological illness causes cognitive decline and mortality if not treated early. However, the current therapeutic modalities are inefficient to manage the cognitive dysfunction of AD. Therefore, in the present manuscript, we have enumerated the pharmacological benefit of Poliumoside in the Streptozotocin-induced cognitive dysfunction in Sprague-Dawley (SD) rats. Initially, the cognitive dysfunction in rats was induced by the intracerebroventricular administration of Streptozotocin, then rats received PMD (5 mg and 10 mg/kg body weight) was given. Various behavioural analysis, such as Morris water maze (MWM), and object recognition tests (ORT), and locomotor analysis was conducted in PMD treated group. Various biochemical analysis was conducted to analyze the effect of PMD on hippocampus oxidative-nitrosative stress and pro-inflammatory cytokines. MTT assay and annexin V/PI staining was performed to analyse the effect of PMD on the cell viability and neuronal toxicity of PC12 cells, respectively. Molecular docking analysis was also conducted with crystal structure of human AChE. PMD treatment improved cognitive capacity in rats in MWM and ORT. Compared to STZ rats, PMD-treated rats had significantly higher locomotor activity and lower AChE activity. PMD also restores dopamine, 5-HT, and NE levels and reduces metabolic their deactivation as evidenced by increased levels of DOPAC, HVA, 5-HIAA. Nitrite, MDA, SOD, CAT, and GSH levels were restored near normal in PMD-treated rats, reducing hippocampus oxidative-nitrosative stress. Pro-inflammatory cytokines were similarly lowered in PMD-treated rats. In in-vitro studies, PMD did not affect PC12 cell survival at the maximal dose of 10 µM. In addition, PMD concentration-dependently prevents H₂O₂-induced neuronal death in PC12 cells. The in-silico docking analysis showed that the PMD fit snugly into the active site of human AChE by engaging with the anionic domain and the catalytic triad of Trp86, Tyr337, Phe338, and Gly121 residues. In conclusion, our study demonstrated that PMD have significant impact on AD by inhibiting ACheE and restoring neurotransmitter levels, which enhances Ach levels in rats and improves cognitive impairment in STZ rats.