Analysis Of Behavior Research Articles

Enhanced behavioral impact of optimized bupropion-encapsulated bilosomes over traditional niosomes treating depression.

Bupropion (Bpn), an FDA-approved NDRI (norepinephrine-dopamine reuptake inhibitor), poses risks of seizures and liver failure due to its stimulant properties, necessitating the development of alternative formulations. This research aims to develop a Bpn nanoformulation within bilosomal vesicles to enhance therapeutic efficacy at lower doses, using three bile salts, span 20 surfactants, and cholesterol via thin-film hydration. Optimization of bilosomal stability is achieved by trialing various ingredient concentrations, identifying a surfactant-to-cholesterol-to-bile salt ratio of 1.5:1:0.17µM, with sodium cholate (B.SCF) yielding the most stable system. Bpn encapsulated in the optimized bilosomal vesicle (Bpn@B.SC F) demonstrated high encapsulation efficiency of 78.142 ± 11.07% and drug-retaining capacity compared to the niosomal system. The in vitro and in vivo toxicity profile of the product is superior to the niosomal system and shows negligible toxicity with a viability rate of not less than 95%, with a sustained release profile in artificial cerebrospinal fluid (ACSF). In vivo, behavioral analysis on zebrafish revealed that Bpn@B.SC F treatment more effectively improved depressive behavior compared to free Bpn and other treatments, evidenced by increased exploration rates and reduced irregular movements, as shown through statistical and trajectory data. Hence, it is concluded that the bilosomal structure, compared to the niosomal system, is a better carrier of drugs to achieve brain delivery and improve mood.

Alteration in rheological and digestive properties of O/W emulsions using controlled aggregation of konjac glucomannan and xanthan gum in aqueous phases

The modulation of lipid digestion and emulsion stability remains a pivotal area of research, crucial for developing healthier food products with enhanced sensory and nutritional attributes. This study investigates the influence of konjac glucomannan (KGM) and xanthan gum (XG) on oil-in-water (O/W) emulsions throughout simulated gastrointestinal tract (GIT) digestion, focusing on rheological properties, microstructure evolution, and lipid release kinetics. Three distinct O/W emulsions were formulated with 20 wt% oil content, incorporating either 0.8 wt% XG, a 1:1 mixture of KGM and XG (KGM/XG), or a blend of KGM-XG. Rheological assessments revealed all emulsions exhibited shear-thinning behavior across applied shear rates (0.1–100 1/s). During gastric and small intestinal digestion phases, the KGM-XG emulsion exhibited significantly higher viscosity at higher shear rates (2.512–100 1/s), indicating robust molecular interactions between KGM and XG under digestive conditions. The analysis of microstructure, particle size, and flow behavior showed that the KGM-XG emulsion exhibited significantly (p < 0.05) higher viscosity and smaller particle sizes compared to other systems throughout the digestion process. This finding indicates that KGM-XG emulsion demonstrates a strong resistance to enzymes and digestive fluid during the transition from oral to small intestinal phases. Importantly, the extent of free fatty acid (FFA) release at the end of the small intestinal phase was highest for XG (34.4%), followed by KGM/XG (15.65%) and KGM-XG (13.23%). These findings highlight the potential of KGM-XG emulsions to modulate lipid digestion kinetics, offering applications in designing emulsion-based foods, such as sauces, beverages, and reduced-fat foods.

Implementation of Artificial Neural Networks as a Method for Early Detection of Tax Evasion Behavior in Indonesia

Research Aims: This study aims to analyze the ability of the Artificial Neural Network (ANN) model in early detection of tax evasion behavior in Indonesia. Design/methodology/approach: This study uses a qualitative method with a descriptive approach, the types of data used in this study are primary data and secondary data. Primary data is obtained from the results of interviews to obtain the information needed by the researcher. The interview informants came from the DGT sub-Directorate of Potential, Compliance and Tax Revenue with the initials K&amp;H, tax consultants and taxpayers. while secondary data is collected through the DGT online website and through articles and journals on Artificial Intelligence (AI), Artificial Neural Networks (ANN), tax evasion, and financial information data on income, expenses, amount of assets, taxes payable) and behavioral indicators (tax compliance history, frequency of late reporting, significant changes in financial statements). The data analysis technique used is descriptive analysis, namely data collection, data reduction, data presentation and conclusion drawing Research Findings: The results of this study show that the use of a neural network model can be an effective tool to detect symptoms of tax evasion behavior early. This method allows tax authorities to monitor and identify suspicious activity more efficiently and effectively. Theoretical Contribution/Originality: This study makes a significant theoretical contribution by integrating the use of Artificial Neural Networks (ANN) in detecting tax evasion in Indonesia. The originality of this research lies in the application of ANN in the context of Indonesia's tax system, where the use of advanced technology such as ANN is still relatively new. The study also combines the pentagon's fraud theory, which adds elements of arrogance and competence in the analysis of tax evasion behavior, with a machine learning approach, creating a more holistic and innovative framework

Experimental and theoretical analysis of photovoltaic performance and thermal behavior for bifacial PV-Trombe wall system with reversible louvers in summer

The traditional monofacial PV-Trombe wall can harness both solar photovoltaic (PV) and thermal energy in buildings, but its performance is hindered by the need for transparent PV glass panels, which reduces PV power generation performance. To address this issue, this paper introduces a novel bifacial PV-Trombe Wall that leverages the rear-side power generation capability of bifacial PV cells. Theoretical analysis of the PV power generation performance of the system highlighted key factors and relationships, indicating that at a coverage rate of 0.5, the increase in its power generation compared to traditional systems reaches a maximum of 17.46 %. Furthermore, an experimental setup was devised and tested to explore the system's PV performance and thermal behavior during the summer season. The experimental results show that the reflective function of reversible louvers had successfully reduced the maximum temperature and rate of temperature rise on the attached wall, effectively alleviating the issue of solar heat absorption in buildings during summer. Besides, the system with reversible louvers improved the overall PV efficiency from 15.40 % to 16.17 %, with a total power generation increase of 5.04 %. Therefore, the system can enhance PV power generation and efficiently decrease the building's cooling energy consumption during the summer.

Electoral Behavior of Russian Youth of the Digital Generation

The article is devoted to the analysis of the electoral behavior of Russian youth of the digital generation (aged 18–24 years). The empirical basis of the study was made up of data from an interregional youth survey conducted by the authors in 2023, as well as data from a WCIOM study in 2024. The relatively low level of their participation in the electoral process and in general in socio-political activities is considered as a feature of the electoral behavior of young people of the digital generation. At the same time, a steady trend towards a decrease in youth absenteeism has been identified, which is manifested in the formation of an intermediate group of young people with a more positive attitude towards elections. Subject to comprehensive constructive actions on the part of the state and civil society institutions, this group can be included in the electoral process. The study showed that young people of the digital generation are quite well informed about the possibility of online voting, although the vast majority of young people aged 18–25 years do not have practical experience in participating in remote electronic voting. For young people who ignore the institution of elections, the form of voting is not significant. A tendency has been discovered towards a decrease in the electoral activity of girls of the digital generation, who are inferior to young men in other indicators of socio-political activity.

Influence of Forced Field on Blood Flow in an Overlapping Stenosed Arterial Section

This article presents the theoretical analysis of the nonlinear behavior of the blood flow through an angled arterial segment with overlapping stenosis. A mathematically created time-variant stenosis emerges from the formation of arterial narrowing brought on by atheroma. An elastic cylindrical tube with a moving wall is used to represent the artery, and the Casson liquid is used to simulate blood that flows through it. The nonlinear elements that arise in the equations, govern blood flow are taken into account. The impact of the pulsatile pressure gradient that caused by the regular heartbeat on the flow process in the stenosed artery is demonstrated mathematically. By employing the boundary conditions, the present analytical technique can easily compute the velocity profiles, wall shear stress, and flow resistivity. To carry out a systematic quantitative study, the desired quantities are numerically computed. The results are graphically presented in the discussion section. They provide an overview of how the degree of stenosis and the malleability of the artery wall influence blood flow abnormalities. The application of the current model is adequately justified by many significant conclusions.

Mechanical behavior analysis of sandwich composite structures with different core geometries under three-point bending

This study presents a comprehensive investigation into the mechanical behavior of sandwich composite structures featuring six distinct core geometries under three-point bending conditions. Through detailed finite element analysis (FEA), the research examines the performance characteristics of I-shaped, traditional honeycomb (NIDA), O-shaped, X-shaped, semi-circular, and modified core geometries. The analysis focused on stress distribution patterns, displacement responses, and overall structural integrity. Results demonstrate that the semi-circular core geometry exhibits superior mechanical properties, achieving an 84.66% improvement in positive stress resistance and 74.79% enhancement in negative stress resistance compared to the traditional honeycomb reference model. The study revealed consistent linear elastic behavior across all models within the tested displacement range, with the semi-circular configuration showing optimal stress distribution and minimal core deformation. Using aluminum for facings and core materials, the research evaluated mechanical responses through displacement-controlled loading conditions, measuring stress concentrations at critical points and analyzing deformation patterns. This research provides valuable insights into the relationship between core geometry and mechanical performance, offering practical implications for engineering applications requiring high strength-to-weight ratios and enhanced flexural resistance in modern structural design.

Parent-Led Applied Behavior Analysis to Impact Clinical Outcomes for Individuals on the Autism Spectrum: Retrospective Chart Review.

Autism spectrum disorder (ASD) can have traits that impact multiple domains of functioning and quality of life, which can persevere throughout life. To mitigate the impact of ASD on the long-term trajectory of an individual's life, it is imperative to seek early and adequate treatment via scientifically validated approaches, of which applied behavior analysis (ABA) is the gold standard. ABA treatment must be delivered via a behavior technician with oversight from a board-certified behavior analyst. However, shortages in certified ABA therapists create treatment access barriers for individuals on the autism spectrum. Increased ASD prevalence demands innovations for treatment delivery. Parent-led treatment models for neurodevelopmental conditions are effective yet underutilized and may be used to fill this care gap. This study reports findings from a retrospective chart review of clinical outcomes for children that received parent-led ABA treatment and intends to examine the sustained impact that modifications to ABA delivery have had on a subset of patients of Montera, Inc. dba Forta ("Forta"), as measured by progress toward skill acquisition within multiple focus areas (FAs). Parents received ≥40 hours of training in ABA prior to initiating treatment, and patients were prescribed focused (<25 hours/week) or comprehensive (>25-40 hours/week) treatment plans. Retrospective data were evaluated over ≥90 days for 30 patients. The clinical outcomes of patients were additionally assessed by age (2-5 years, 6-12 years, 13-22 years) and utilization of prescribed treatment. Treatment encompassed skill acquisition goals; to facilitate data collection consistency, successful attempts were logged within a software application built in-house. Improved goal achievement success between weeks 1-20 was observed for older age, all utilization, and both treatment plan type cohorts. Success rates increased over time for most FAs, with the exception of executive functioning in the youngest cohort and comprehensive plan cohort. Goal achievement experienced peaks and declines from week to week, as expected for ABA treatment; however, overall trends indicated increased skill acquisition success rates. Of 40 unique combinations of analysis cohorts and FAs, 20 showed statistically significant positive linear relationships (P<.05). Statistically significant positive linear relationships were observed in the high utilization cohort (communication with P=.04, social skills with P=.02); in the fair and full utilization cohorts (overall success with P=.03 for the fair utilization cohort and P=.001 for the full utilization cohort, and success in emotional regulation with P<.001 for the fair utilization cohort and P<.001 for the full utilization cohort); and in the comprehensive treatment cohort (communication with P=.001, emotional regulation with P=.045). Parent-led ABA can lead to goal achievement and improved clinical outcomes and may be a viable solution to overcome treatment access barriers that delay initiation or continuation of care.

Analysis of hydrodynamic behavior in response to diverse pile arrangements adjacent to an impermeable dike

ABSTRACT Intense energy flow near the dike head significantly influences the development of the dike field zone (DFZ), momentum exchange zone (MEZ), and mainstream zone (MSZ), leading to potential partial or full failure of the dike in the MEZ. To address this, laboratory experiments were conducted to observe flow changes and rate of energy reduction around a single impermeable dike, with alterations made to the pile group length (HL: half-length = 11.5 cm and FL: full length = 23 cm), location (U: upstream, D: downstream), and shape (C: Circular, DV: Delta Van, ST: Streamlined tapered, APF: Angled Plate footing). Modifications in the MEZ flow structure were studied to mitigate concerns about intense energy vortices and dike head flows. These piles helped to assess factors like flow deflection, backwater rise, energy reduction rate, velocity fluctuations, and discharge distribution percentages across the zones. The data revealed that when the pile group length was increased from HL to FL, there was an inverse effect on depth-averaged velocity, MEZ discharge distribution percentage, and flow deflection toward the DFZ. Conversely, there was a direct influence on the rise in backwaters, rate of energy reduction, and discharge distribution percentages in the DFZ. The U-FL-APF pile dike showcased optimal results, displaying a reduction in maximum energy by 52% and streamwise velocity by 96%, while also increasing the backwater rise and discharge distribution by 22% and of only 5% in the MEZ, respectively, compared to a single impermeable dike. During flood events, these suggested measures can mitigate the intensified swirls formed in the DFZ, protect the dike head from direct momentum exchanges in the MEZ, and enhance flow deflection into the MSZ.

Comparative Study of High-Cycle Fatigue and Failure Mechanisms in Ultrahigh-Strength CrNiMoWMnV Low-Alloy Steels

This study provides a thorough analysis of the fatigue resistance of two low-alloy ultrahigh-strength steels (UHSSs): Steel A (fully martensitic) and Steel B (martensitic–bainitic). The investigation focused on the fatigue behaviour, damage mechanisms, and failure modes across different microstructures. Fatigue strength was determined through fully reversed tension–compression stress-controlled fatigue tests. Microstructural evolution, fracture surface characteristics, and crack-initiation mechanisms were investigated using laser scanning confocal microscopy and scanning electron microscopy. Microindentation hardness (HIT) tests were conducted to examine the cyclic hardening and softening of the steels. The experimental results revealed that Steel A exhibited superior fatigue resistance compared to Steel B, with fatigue limits of 550 and 500 MPa, respectively. Fracture surface analysis identified non-metallic inclusions (NMIs) comprising the complex MnO-SiO2 as critical sites for crack initiation during cyclic loading in both steels. The HIT results after fatigue indicated significant cyclic softening for Steel A, with HIT values decreasing from 7.7 ± 0.36 to 5.66 ± 0.26 GPa. In contrast, Steel B exhibited slight cyclic hardening, with HIT values increasing from 5.24 ± 0.23 to 5.41 ± 0.31 GPa. Furthermore, the martensitic steel demonstrated superior yield and tensile strengths of 1145 and 1870 MPa, respectively. Analysis of the fatigue behaviour revealed the superior fatigue resistance of martensitic steel. The complex morphology and shape of the NMIs, examined using the 3D microstructure characterisation technique, demonstrated their role as stress concentrators, leading to localised plastic deformation and crack initiation.

Zoographics in the Journal of the Experimental Analysis of Behavior: Increasing inclusion of female animals.

We examined the zoographics, or the characteristics of nonhuman animal subjects, reported for the entirety of the Journal of the Experimental Analysis of Behavior (JEAB) through the most recent complete year (1958-2023). Animal sex in particular was evaluated to determine whether the lack of inclusion of female subjects in other disciplines extends to JEAB. Through systematic coding of all nonhuman empirical articles, we found consistent underreporting of most zoographics and a disproportionate use of male subjects relative to female subjects. Additionally, animal sex was commonly unreported and the inclusion of both male and female subjects was sparse. Recent years show some improvement, but greater inclusion is required. Lack of female subjects in research as well as underreporting of zoographics can generate unrepresentative results and hamper replication, generalization, and translation. We provide resources to guide future research and reporting suggestions such as equal inclusion and disaggregation of data by sex. We also clarify misunderstandings about the use of both sexes in research such as beliefs that it necessarily increases the cost of research.

Data-Driven Personalization : Revolutionizing User Experience

Data-driven personalization has emerged as a transformative approach in digital user experience design, leveraging advanced analytics and machine learning to tailor content and interfaces to individual users. This article explores five key aspects of data-driven personalization: user behavior analysis, segmentation and targeting, machine learning algorithms, real-time adaptation, and privacy and ethical considerations. It examines the significant impact of personalization on business outcomes, including increased revenue and customer engagement, while also addressing implementation challenges, such as technological complexity and privacy concerns. The article provides insights into the methodologies, processes, and best practices for effective personalization, supported by industry statistics and case studies, offering a comprehensive overview of how organizations can harness this powerful approach to create more engaging, relevant, and effective digital experiences.

Multi-perspective API call sequence behavior analysis and fusion for malware classification

The growing variety of malicious software, i.e., malware, has caused great damage and economic loss to computer systems. The API call sequence of malware reflects its dynamic behavior during execution, which is difficult to disguise. Therefore, API call sequence can serve as a robust feature for the detection and classification of malware. The statistical analysis presented in this paper reveals two distinct characteristics within the API call sequences of different malware: (1) the API existence feature caused by frequent calls to the APIs with some special functions, and (2) the API transition feature caused by frequent calls to some special API subsequence patterns. Based on these two characteristics, this paper proposes MINES, a Multi-perspective apI call sequeNce bEhavior fuSion malware classification Method. Specifically, the API existence features from different perspectives are described by two graphs that model diverse rich and complex existence relationships between APIs, and we adopt the graph contrastive learning framework to extract the consistent shared API existence feature from two graphs. Similarly, the API transition features of different hops are described by the multi-order transition probability matrices. By treat each order as a channel, a CNN-based contrastive learning framework is adopted to extract the API transition feature. Finally, the two kinds of extracted features are fused to classify malware. Experiments on five datasets demonstrate the superiority of MINES over various state-of-the-arts by a large margin.

MICROSTRUCTURE AND WEAR BEHAVIOR CHARACTERIZATION OF STIR-CAST ALUMINUM 6063 ALLOY AND ITS FOAM: A COMPARATIVE STUDY

In this research work, aluminum 6063 alloy foam was synthesized through stir casting using varying amounts of titanium hydride as a foaming agent. A comparative microstructural and wear behavior analysis of stir-cast aluminum 6063 alloy and its foam is presented. The oxide and aluminide phases present as intermetallic compounds in the stir-cast aluminum 6063 alloy foam ensure the stability of the synthesized foam. In order to optimize the effect of wear parameters on the tribological properties of stir-cast aluminum 6063 alloy foam, the L933 orthogonal array (OA) was designed and modeled statistically. The tribological properties of stir-cast aluminum 6063 alloy and its foam were evaluated by pin-on-disc tests. The optimal tribological properties of the synthesized foam are 0.29 coefficient of friction, 8.6 N frictional force, and 0.00025 mm3/N-m specific wear rate. The tribological properties of stir-cast aluminum 6063 alloy foam are superior to those of aluminum 6063 alloy and comparable to other aluminum foams described in the literature.

On the Identification and Use of Social versus Nonsocial Reinforcers: A Review of Research Practices

AbstractRecent research has developed efficacious methods for identifying individualized social reinforcers and utilizing social reinforcers may be beneficial for several reasons. However, the relative likelihood of utilizing social versus nonsocial reinforcers in behavior-analytic research remains unclear. The aim of this study was to evaluate how likely behavior analysts are to employ social versus nonsocial reinforcers in the context of research. We pursued this aim by evaluating the types of reinforcers utilized in research published during the past 9 years in five applied behavior-analytic journals. Results suggest that researchers in applied behavior analysis have been more likely to use individualized nonsocial reinforcers than individualized social reinforcers. Moreover, when social reinforcers were employed, they were much more likely to be generic and not individualized. These data suggest there is room for improvement in the types of programmed reinforcement contingencies we use in research and ways of facilitating such improvement are discussed. Implications and directions for future research evaluating current practices, demonstrating the utility of social reinforcers, and comparing the utility of social versus nonsocial reinforcers are also discussed.

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach.

Accurately and efficiently predicting the infrared (IR) spectra of a molecule can provide insights into the structure-properties relationships of molecular species, which has led to a proliferation of machine learning tools designed for this purpose. However, earlier studies have focused primarily on obtaining normalized IR spectra, which limits their potential for a comprehensive analysis of molecular behavior in the IR range. For instance, to fully understand and predict the optical properties, such as the transparency characteristics, it is necessary to predict the molar absorptivity IR spectra instead. Here, we propose a graph-based communicative message passing neural network algorithm that can predict both the peak positions and absolute intensities corresponding to density functional theory calculated molar absorptivities in the IR domain. By modifying existing spectral loss functions, we show that our method is able to predict with DFT-accuracy level the IR molar absorptivities of a series of hydrocarbons containing up to ten carbon atoms and apply the model to a set of larger molecules. We also compare the predicted spectra with those generated by the direct message passing neural network. The results suggest that both algorithms demonstrate similar predictive capabilities for hydrocarbons, indicating that either model could be effectively used in future research on spectral prediction for such systems.

Evaluation of elastic constants of M23C6 and M7C3 embedded in Fe-Cr-C alloys using in-situ XRD tensile test and self-consistent model

This paper investigates the elastic properties of the model Fe-Cr-C alloys, specifically focusing on M23C6 and M7C3. The study uses in-situ synchrotron X-ray data during tensile deformation to determine the individual elastic characteristics of the matrix and iron/chromium carbides. The experimental results obtained from in-situ X-ray diffraction (XRD) are compared to the elastic constants of carbides that were reported in previous studies and derived using density function theory (DFT). The appropriate elastic constants for M23C6 and M7C3 were selected based on the self-consistent code executing with reported elastic constants. The directional elastic modulus and Poisson's ratio of iron/chromium carbides are calculated, and the anisotropy of the elastic constants is evaluated using the XRD lattice deformations under loading. The elastic modulus of carbide varies with the volume fraction of carbide in the effective medium. The study finds that the hexagonal structure is more probable than orthorhombic structure for M7C3 due to well-matched estimations of the directional elastic modulus and Poisson's ratio obtained from in-situ XRD data and the self-consistent calculations. In-situ XRD analysis of elastic behavior of each diffraction can be used to demonstrate the elastic constants of carbides and shows potential for obtaining precise elastic constants by integrating with self-consistent modeling and DFT.

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

AbstractTravel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

Analysis of Farmers' Entrepreneurial Behavior, and its Effect on Farm Productivity

AbstractThis study aims to analyze the entrepreneurial behavior of farmers from the aspect of formal education and farming experience and its influence on farm productivity. The research population was rice paddy farmers in Cialam Jaya Village, Konda Subdistrict, South Konawe District, Southeast Sulawesi Province totaling 215. The number of samples was 140 farmers who were determined proportionally based on the area of cultivated land with the Slovin method. This research is quantitative using path analysis to analyze the direct and indirect effects of exogenous variables on endogenous variables. The results showed that farmers' formal education and farming experience had a positive effect on entrepreneurial behavior. Formal education of farmers has no direct effect on farm productivity but influences entrepreneurial behavior. Farming experience has a direct effect on farm productivity and indirectly through entrepreneurial behavior. Entrepreneurial behavior has a positive effect on farm productivity. Keywords:Education, Experience, Entrepreneurial Behavior, Productivity