Analysis Of Behavior Research Articles

Analysis of Deformation Mechanism Behaviors of Ti-5Mo-1Fe Alloy under Quasi-static and Dynamic Deformation

To evaluate the deformation mechanisms of Ti-5Mo-1Fe alloy, compression tests were performed at strain rates ranging from 10<sup>-3</sup>/sec to 3×10<sup>3</sup>/sec at room temperature. The results showed that Ti-5Mo-1Fe alloy exhibited different deformation mechanisms depending on the strain rate. Under quasi-static strain rates, the Stress-Induced Martensitic (SIM) transformation from the metastable β phase (BCC) to the α" phase was observed through the double yield phenomenon in the stress-strain curve. This contributed to enhanced compressive strength, work hardening, and ductility by absorbing and dispersing deformation energy. Shear bands (SB) were observed near the fracture zone under quasi-static conditions, since the SIM transformation acts as an obstacle to dislocation movement. In contrast, a dynamic strain rate generated adiabatic shear bands (ASB) due to localized heating, leading to coarsened grains near the fracture zone. With increasing strain rates, significant temperature rises were detected in the specimens, leading to increased β phase stability and a reduced chemical driving force for the α" transformation, thereby suppressing the SIM transformation. Consequently, dislocation slip became the dominant deformation mechanism at high strain rates. This study provides insights into the strain rate sensitivity of metastable β-titanium alloys, offering fundamental information for the development of advanced Ti alloys with high strength, good ductility, and impact resistance.

Experiential quality and satisfaction in marine tourism: a gendered analysis of post-visit behavior and frequency of visits in Lampung, Indonesia

Experiential quality and satisfaction in marine tourism: a gendered analysis of post-visit behavior and frequency of visits in Lampung, Indonesia

Consumer Behavior Analysis and Enterprise Marketing Strategy Optimization Based on Decision Tree Model and Association Rule Algorithm

Consumer Behavior Analysis and Enterprise Marketing Strategy Optimization Based on Decision Tree Model and Association Rule Algorithm

Prediction of Pellet Durability Index (PDI) in a Commercial Feed Mill Using Multiple Linear Regression with Variable Selection and Dimensionality Reduction.

Pellet quality, measured as Pellet Durability Index (PDI), is an important key performance indicator (KPI) for commercial feed manufacturing, as it can impact both mill efficiency and downstream performance of animals fed the manufactured diets. However, it is an ongoing challenge for the feed industry to control pellet quality, due to the complexity of feed manufacturing and the large number of variables influencing the process. Previous studies have explored prediction of pellet quality using either simple empirical models with a few variables or machine learning models with many variables. The objective of the current study was to develop statistical regression models to predict PDI, and to describe the relationship between pellet quality and 55 available variables based on a dataset with 2691 observations collected from a commercial feed mill. In the current study, the response variable (PDI) was transformed using the Box-Cox approach into the transformed response variable (tPDI), that was more normally distributed. Three multiple regression models were developed based on subsets of variables processed by variable selection and dimensionality reduction methods: Forward Selection, Principal Component Analysis, and Partial Least Squares. The results indicated that Model 1 (Forward Selection with manual removal of sparse variables), built on 9 variables, performed better than the other two models. It exhibited consistent model prediction performance on the training data and testing data, in terms of MAE (1.93 ± 0.063 versus 1.96), RMSPE (2.45 ± 0.079 versus 2.45), and CCC (0.549 ± 0.0273 versus 0.550), with a better prediction precision based on the fit plot. Expanding Temperature (℃), Fat Content (%), and ADF Content (%), and Indoor Humidity (Pelletizer) (%) were identified as more influential than other variables on the transformed response variable (tPDI) in Model 1, based on a behavior analysis. The models developed in the current study can be helpful to feed mills for predicting and comprehending the effect of a number of commonly measured variables on pellet quality in the commercial setting.

Sexual Orientation and Gender Identity (SOGI): A Tutorial on Ethical Data Practices

Abstract In the U.S., approximately 13 million individuals identify as part of a sexual and gender minority (SGM). This broad spectrum includes sexual orientation identities such as gay, lesbian, bisexual, queer, pansexual, and same-gender-loving, as well as gender identities such as transgender, gender non-conforming, bigender, and two-spirit. As behavior analysts heed the call to engage in culturally competent practices that address diverse sexual orientation and gender identities (SOGI), they will likely consider collecting SOGI data as part of their practice. The benefits of SOGI data collection certainly exist. However, the historical oppression and increased vulnerability of SGM populations require a careful and thorough evaluation of ethical data collection practices to avoid harm and to ensure respectful and inclusive practices. The present tutorial aims to begin the discussion of ethical and effective SOGI data collection practices within behavior analysis by offering initial guidelines and considerations. We highlight methods that improve cultural sensitivity, and caution against traditional methods that could harm respondents or contribute to a non-inclusive environment.

Assessment of the Usage of Innovative Integrated Photogrammetry Method in Structural Behaviour Analysis: The Case of Mardin Melik Mahmut Mosque

The integration of photogrammetric modeling with the finite element method has emerged as a valuable approach in the practical analysis and determination of structural behavior. Photogrammetry, a technique that utilizes the analysis and processing of photographs to create three-dimensional models of real-world objects, offers a powerful tool for capturing accurate geometric data. On the other hand, the finite element method provides a numerical framework for the mathematical modeling and analysis of complex structural systems. This article presents a methodology that integrates these two modeling techniques by comparing their performance with integrated and classical models. Classical modelling is a technique performed in a finite element software and has been applied for many years. Accordingly, this study aims to reveal the limitations of photogrammetry, develop solutions to these limitations, and provide integrated model generation. The material definitions and load assignments were kept consistent across both modeling techniques for observing the differences in analysis. The study used SAP2000 and modal analysis was conducted to analyse the behaviour and to compare the geometrical properties of the models independent of region and time. The obtained results reveal a high level of similarity, with the natural vibration periods of the models demonstrating 98%, the stress values exhibiting 90%, and deformation values 90% similarity a result of the dynamic loads applied in the X-Y direction. Consequently, this study highlights the potential for further improvement in the integrated model generation process by identifying the advantages and disadvantages of each modeling technique.

Impact of fluoxetine exposure on Lymnaea stagnalis and its developing eggs: integrating untargeted lipidomics, targeted metabolomics, and classical risk assessment

Pharmaceuticals such as selective serotonin reuptake inhibitors (SSRIs), are increasingly detected in aquatic environments, posing potential risks to non-target organisms, because many of those substances are widely shared neuromodulator. In this study, we investigated the effects of SSRI antidepressant, namely, fluoxetine, exposure on the freshwater snail L. stagnalis, focusing on egg development, neurochemical pathways, and lipid metabolism. Snails were exposed to a range of 51–434 µg fluoxetine L⁻1 for 7 days, followed by analysis of survival, feeding behaviour, reproduction, and metabolomic changes in the central nervous system (CNS), albumen gland, and eggs. Although no significant effects were observed on survival or fecundity, fluoxetine exposure significantly impaired egg development in a dose-dependent manner, reducing hatching rates with an EC50 of 126 µg fluoxetine L⁻1. Removal of eggs from the contaminated environment partially reversed these developmental effects, suggesting potential recovery if fluoxetine levels decrease. Molecular analysis revealed several neurochemical and lipidomic alterations. In the CNS, elevated levels of catecholamines, phosphatidylcholines (PC), and ceramides were linked to disruptions in neurotransmission, membrane integrity, and impaired embryo development. In the albumen gland, we detected a decrease of key lipid classes, including sphingomyelins and fatty acids, which can be linked with impaired egg quality. Additionally, a decrease in histamine in both the albumen gland and eggs suggested further disruption of egg development, potentially affecting metamorphosis success. Moreover, the dose-dependent increase in choline, along with PC and oxidized PC, indicated oxidative stress and lipid peroxidation in the CNS and exposed eggs of Lymnaea stagnalis. Our findings highlight the benefits of combining behavioral assessments with metabolomic profiling to better understand the mechanistic pathways underlying fluoxetine’s adverse effects.

An Empirical Analysis of Loan Repayment Behavior and Default Rates on Digital Lending Platforms: Evidence from an Emerging Market

The rapid growth of digital lending platforms has revolutionized access to credit in emerging economies, providing financial opportunities to underserved individuals. Despite these advancements, high loan default rates remain a substantial challenge, threatening the sustainability of these lending platforms and the broader financial ecosystem. This study examines the determinants of loan default among users of mobile-based digital lending platforms in Kenya, a leading adopter of digital financial services. Employing a sample of 161 responses from borrowers, the analysis uses a probit regression model to identify primary predictors of loan default. The study focuses on borrower demographics like age and place of residence and loan characteristics, such as repayment term (period) and interest rates. The results reveal that older customers and those in rural areas are more likely to default on their loans, whereas longer repayment periods reduce the probability of default. Interestingly, the interest rate does not significantly affect default behavior, implying that borrowers prioritize access to credit over its cost. These results underscore the essence of tailoring digital lending practices to the diverse borrower needs and circumstances. By addressing the factors that drive loan defaults, digital platform lenders can improve risk assessment frameworks, enhance financial inclusion, and enhance the sustainability of digital lending platforms. The findings provide actionable insights for policymakers, lenders, and other industry players seeking to mitigate default risks and foster a more robust digital financial ecosystem.

Courtship behavior of a secretive forest tinamou, Tinamus major (Great Tinamou)

Abstract The mating systems of many rainforest birds remain poorly understood, especially those that elude observation in dense forest understories. Here we documented mating behaviors in a pair of Tinamus major (Great Tinamou), a secretive species with a wide distribution across the lowland Central and South American tropics. Despite anecdotal preconceptions that males court females, we observed predominant courtship displays from the putative female tinamou. In this mating ritual recorded in the birds’ natural habitat, only the putative female vocalized in the form of clucking and soft-rolling songs, and courted the putative male with a suite of courtship displays, such as neck-elongation, tail-raising, crouching, and feather-fluffing. In contrast, the putative male watched while standing still, approached, tail-raised, walked away, and mounted the putative female a few times. The clucking song of the putative female had not been described previously but we repeatedly observed this song type immediately before the soft-rolling songs during the courtship interaction. Clucking was of lower frequency and around 10 times shorter than the soft-rolling songs. The clucking and soft-rolling songs sung by the putative female were of lower frequency than the common morning territorial songs recorded in the vicinity. This behavioral auditory analysis of the secretive mating ritual of a rainforest tinamou reveals a previously underappreciated female role in their courtship displays.

Understanding school refusal behavior using SRAS‐R and its application to behavioral consultation techniques

Unauthorised student absences from school occur around the world. Given the importance of school, it's crucial to understand why this occurs and how the situation might be improved. Existing measures include psycho-educational, psycho-analytic and behavioral approaches but the issue is growing and more needs to be done to better understand non-attendance. If understanding can be improved, more effective measures can be put in place to encourage children to attend their classes. Associate Professor Yoshinori Yano leads a team based within the Department of Early Childhood Education at Sakushin Gakuin University Womenâ–™s College in Japan applying a variety of techniques and approaches to better understanding school refusal behavior. Yano is particularly interested in special needs education and applied behavior analysis. He is applying the concept of applied behavior analysis to his work to understand the actions of people and visualise achievement status. He is looking at truant behavior from the perspectives of special support education and student guidance. Yano is using the School Refusal Assessment Scale (SRAS-R) to understand school absences. There are two types of the scale: one for professional grading and one for parental grading. The four functions of the SRAS-R are: avoidance of unpleasant stimuli related to school; avoidance of evaluation and social situations (ESE); attention-seeking behavior of others; and having fun outside of school. Studies have been conducted using the Japanese version of SRAS‐R, the SRAS‐R‐J, but none in clinical settings outside of school.

IoT and Machine Learning Techniques for Precision Beekeeping: A Review

Integrating Internet of Things (IoT) devices and machine learning (ML) techniques holds immense potential for transforming beekeeping practices. This review paper offers a critical analysis of state-of-the-art IoT-enabled precision beekeeping systems. It examines the diverse sensor technologies deployed for honeybee data acquisition, delving into their strengths and limitations, particularly regarding accuracy, reliability, energy sustainability, transmission range, feasibility, and scalability. Furthermore, this paper dissects prevalent ML models used for bee behaviour analysis, disease detection, and colony monitoring tasks. This paper evaluates their methodologies, performance metrics, and the challenges involved in selecting appropriate machine learning algorithms. It also examines the influence of sensing devices, computational complexity, dataset limitations, validation procedures, evaluation metrics, and the effects of pre-processing techniques on these models’ outcomes. Building upon this analysis, this paper identifies key research gaps and proposes promising avenues for future investigation. The focus is on the synergistic use of IoT and ML to address colony health management challenges and the overall sustainability of the beekeeping industry.

Wide range dielectric behavior characterization of ternary composite (Re-BT-CaO)

This article's primary objective is to investigate a ternary composite's dielectric behavior using time domain spectroscopy (TDS). A heterogeneous mixture of barium titanate (BT) and calcium oxide (CaO) in an epoxy resin matrix (RE) with a predetermined volume fraction level (70%) makes up this type of material. The remaining components (BT and CaO) are variable in increments of 07.50% and complete one another in order to achieve the final percentage, which is 30%. The different composite samples are created using the aforementioned stoichiometric ratios at room temperature and below atmospheric pressure. These mixes are put through a dielectric behavior analysis over a broad frequency range, from DC to 10 GHz. This study intends to highlight the impact of calcium oxide and barium titanate on the composite's dielectric behavior and on the form factor provided by the modified Lichtenecker equation (MLL). It follows a numerical method for optimizing of the predictive model of this law based on a better smoothing of this shape factor in order to allow a better concordance among the predicted values and practical ones. A comparison between the experimental data and the MLL values is performed in order to highlight the proposed model performance on the ternary composite dielectric permittivity estimation quality. This research's focus is on how these materials are used in micro- and nanoelectronics as well as telecommunication components fabrication.

A comprehensive analysis of molecular characteristics of hot and cold tumor of gastric cancer.

The advent of immunotherapy has revolutionized the treatment paradigm for gastric cancer (GC), offering unprecedented clinical benefits. However, a detailed molecular characterization of the tumor immune microenvironment in GC is essential to further optimize these therapies and enhance their efficacy. Consensus clustering was utilized to classify GC patients into distinct immune states, followed by an in-depth analysis of differences in mutation profiles, copy number variations, and DNA methylation patterns. Weighted gene co-expression network analysis (WGCNA) and correlation analysis were applied to identify gene modules underlying the classification of immune "hot" and "cold" tumors. Subsequently, 101 machine learning algorithm combinations were employed to construct a prognostic model based on the identified gene modules. Single-cell analysis was conducted to investigate cellular interactions associated with the immune-determinant gene module. Finally, immunofluorescence staining for CD8, CD45, and CXCR4 was performed on human GC tissue samples. A total of 1,298 GC patients were included in this comprehensive analysis. For the first time, we identified and characterized immune "hot" and "cold" tumors in GC patients, revealing distinct molecular features associated with these tumor types. Immune "hot" tumor-related genes were identified, and their functional roles were validated through biological behavior analysis. A prognostic signature, termed the hot tumor top regulators (HTTR), was developed using 101 machine learning algorithm combinations. The HTTR signature emerged as an independent prognostic factor, effectively stratifying patients into low- and high-risk groups with significant differences in overall survival. High-risk groups demonstrated strong associations with immune checkpoint regulation, antigen presentation, and inhibitory pathways. Notably, single-cell analysis revealed that HTTR genes were highly active in CD8 + T cells, with the CXCL12-CXCR4 axis playing a critical role in mediating interactions between CD8 + T cells and endothelial cells. In conclusion, the HTTR signature served as a robust prognostic biomarker for GC patients and effectively identified those with immune "hot" tumors. This finding provided valuable insights into the molecular mechanisms of tumor immunity in GC, offering potential avenues for targeted therapeutic interventions.

ESTraS, an easy and simple tracking system for assessing rodent behavior

Animal behavior analysis software has become an essential tool in the life sciences. However, the currently available tools have some significant shortcomings that limit their use by individuals without programming skills, access to higher informatics resources, or the capability to invest large sums of money. We have developed and validated an easy-to-use and straightforward tracking system named ESTraS to address this issue. This freeware software allows researchers to track and analyze rodent behaviors, offering additional options such as trajectory and angle analysis. Through ESTraS, researchers can utilize unsupervised clustering techniques, such as k-means or hierarchical clustering, to further explore the obtained results. This clustering enables the classification of results based on observed similarities among subjects. The data of this manuscript shows that ESTraS can prove to be extremely valuable, not only by providing essential behavioral analysis tools but also by offering specific data analysis options with just one click.

Dynamic trust management framework using blockchain for zero-trust-based authentication in BYOD environments

ABSTRACT With evolving work patterns, employees access company resources from various devices, making traditional management approaches obsolete. Ensuring secure access is crucial. We propose a blockchain-based trust management framework utilizing smart contracts for continuous verification and behavior analysis. Suspicious activity triggers dynamic access adjustments. Blockchain ensures immutability and traceability in authentication. Our design mitigates centralization risks and enhances granularity. Experimental results show minimal time costs during verification, maintaining efficiency despite additional security checks.

Limiting hearing loss in transgenic mouse models.

Transgenic mice provide unprecedented access to manipulate and visualize neural circuits, however, those on a C57BL/6 background develop progressive hearing loss, significantly confounding systems-level and behavioral analysis. While outbreeding can limit hearing loss, it introduces strain variability and complicates the generation of complex genotypes. Here, we propose an approach to preserve hearing by crossing transgenic mice with congenic B6.CAST-Cdh23Ahl+ mice, which maintain low-threshold hearing into adulthood. Widefield and two-photon imaging of the auditory cortex revealed that 2.5-month-old C57BL/6 mice exhibit elevated thresholds to high frequency tones and widespread cortical reorganization, with most neurons responding best to lower frequencies. In contrast, Ahl+ C57BL/6 mice exhibited robust neural responses across tested frequencies and sound levels (4-64 kHz, 30-90 dB SPL) and retained low thresholds into adulthood. Our approach offers a cost-effective solution for generating complex genotypes and facilitates more interpretable systems neuroscience research by eliminating confounding effects from hearing loss.Significance statement Common C57BL/6 mice exhibit severe progressive hearing loss, which is a serious confound for systems-level and behavioral studies. While outbreeding to an unaffected strain can help, this is cost-prohibitive if multiple transgenes are introduced. We propose and validate a simple approach that preserves hearing in transgenic mice by crossing them with congenic B6.CAST-Cdh23Ahl+ mice. Using widefield and two-photon imaging of the auditory cortex, we demonstrate that Ahl+ C57BL/6 mice maintain robust neural responses across a range of frequencies and sound levels into adulthood. We also offer a rapid method to genotype offspring without the need to sequence. Our approach offers a cost-effective solution for generating complex genotypes while preserving hearing, thereby facilitating more accurate, reproducible, and interpretable neuroscience research.

Floral Designs and their Use in Product Items

This study investigated the cultural significance of the butterfly bush (Buddleja paniculata) in the construction of Thai meaning, belief, ritual, wisdom, and tradition with the goal of developing and implementing a model for a creative process to co-design related cultural capital products. The researchers synthesized the aesthetic and cultural characteristics of the butterfly bush for application to creative pattern designs and products and identified two cultural dimensions composed of meaning and belief that could be adapted into design patterns along with the flower’s colors and composition. The synthesized patterns were applied to three types of women’s bags based on an analysis of women's bag use behaviors, and the final products were evaluated by 3 flower experts, 3 design experts, 100 customers and fabric and bag producers. The results showed high satisfaction with the representativeness, attractiveness, and usability of the fabric and bag designs. The results highlight the value of cultural capital for producing attractive goods that align with market demand and contribute to sustainable livelihoods, as well as the critical roles of consumers, designers, and producers in the collaborative design of such products.

“I lock myself in my room and cry in frustration”: an analysis of adolescent behaviors of child-to-parent violence

Child-to-parent violence (CPV) encompasses behaviors such as hitting, insulting, or threatening parents. Over the past decade, the number of CPV cases has increased significantly. While previous research has largely focused on classifying CPV behaviors and examining their causes, there is limited investigation into what happens immediately after CPV episodes. This study aims to describe the behaviors of adolescents following violent actions in both mild and severe cases of CPV. A randomized sample of 1,067 Spanish adolescents, participating in a national CPV project, was studied using the Child-to-Parent Aggression Questionnaire. Among them, 410 adolescents (41.91% boys and 57.84% girls) reported engaging in some form of CPV behavior in the past year, and 47 adolescents met the criteria for severe and repeated CPV. Specifically, 26 adolescents exhibited psychological and 27 exhibited physical CPV toward their mother, while 21 adolescents exhibited psychological and 15 physical CPV toward their father. Adolescents were asked, “What do you do after?” Following thematic analysis, adolescents’ responses were categorized into 6 themes and 17 sub-themes in mild cases. A possible sequence in adolescents’ responses was identified, divided into three phases. In the first phase, adolescents felt bad after their violent behavior, expressed remorse, reflected on it, and sought a safe place. In the second phase, apologizing to parents was the most common response, reported by 70.52% of adolescents. Finally, in the third phase, in addition to apologizing, adolescents attempted to talk with their parents, gave them a hug, or committed to not repeating the behavior. In contrast, 8% of adolescents normalized their behavior, joked about it, or justified their actions. In severe cases, most subthemes were consistent with those observed in mild CPV cases. However, in contrast to milder cases, severe cases showed a lower percentage of adolescents who felt bad or apologized and a higher proportion who normalized, avoided talking about, or justified their violent actions (23.4% of adolescents with severe CPV). This study highlights practical implications for interventions, such as the importance of helping them express their emotions, recognize the harm caused, identify safe spaces, people, or activities, and support them in the process of apologizing.

Bamboo–PCM: Comparative Analysis of Phase Change Material-Impregnated Dendrocalamus giganteus Culm Behavior Exposed to Thermal Variation in Wind Tunnel Assay

The construction industry’s pursuit of eco-friendly materials has sparked interest in bamboo, a renewable resource with exceptional physical and mechanical properties. This study analyzed the integration of Dendrocalamus giganteus bamboo with phase change materials (PCMs) to enhance thermal energy storage in building applications, aiming to improve temperature regulation and reduce energy consumption for climate control. The study compared the performance of bamboo impregnated with an industrial PCM or coconut oil, used in conjunction with a polyurethane resin (PU) coating treatment, assessing their thermal regulation performance against traditional building materials such as ceramic tiles, fiber cement, and metal sheets. From an anatomical perspective, the pores within bamboo culms offered ample space for PCM storage, resulting in a substantial heat storage capacity. Thermal behavior tests conducted in a wind tunnel revealed that the impregnated bamboo samples effectively mitigate temperature fluctuations by aligning them with the PCM’s phase change temperature. Additionally, it was observed that air flow velocity had an impact on this phenomenon. The study concluded that bamboo culms impregnated with PCM hold promise for temperature regulation in construction applications, with variations in airflow exerting an impact on the outcomes obtained.

Online Payment Fraud Detection Model Using Machine Learning

The dependability and safety of UPI-based financial transactions are vital for maintaining public confidence, facilitating seamless digital payments, and ensuring economic stability. This paper introduces a software model aimed at identifying and predicting fraudulent behavior in UPI transactions through the use of advanced technologies, which encompass machine learning algorithms, real-time monitoring, and behavioral analysis. The Use of the software is to reduce financial risks by implementing a fraud assessment system that relies on historical transaction data and current information. By utilizing machine learning algorithms along with rule-based methods, the system is able to recognize abnormal transaction patterns, evaluate user behavior, and identify irregularities. The Prediction of model find the risk of transactions based on factors such as the amount, frequency, location, and behavioral patterns of users to highlight potentially suspicious activities. The suggested system functions by implementing four main phases: user engagement, analysis of historical data, continuous monitoring of real-time data, and fraud prediction analysis. This methodology produces comprehensive reports on the risks associated with transactions and offers proactive measures to identify and thwart fraudulent actions within UPI transactions Key Words UPI Transactions, Fraud Detection, Machine Learning, Real-Time Monitoring, Behavioral Analysis, Risk Assessment,