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The Repetitive Behaviours Questionnaire for Adults (RBQ-2A) measures two factors of restricted and repetitive behaviours (RRBs) associated with autism. However, the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides four criteria for RRBs: repetitive motor behaviours, insistence on sameness, restricted interests, and interest in sensory aspects of the environment (or atypical sensitivity). The current paper aimed to examine whether the RBQ-2A is a psychometrically sound measure of these four factors. Study 1 had university students (N = 368) complete the RBQ-2A and other related measures online and revealed that the RBQ-2A can assess the factors highlighted in the DSM-5 and that these four factors comprise a general RRB construct. Study 2 had individuals disclosing a diagnosis of autism (N = 283) complete the RBQ-2A and other related measures online and supported that this four-factor structure provided good psychometric properties. While the current paper provides findings for an online autistic population, further research is needed to generalize these findings to autistic individuals less likely to partake in online studies (e.g., those with intellectual or language disabilities). Overall, the results suggest that the RBQ-2A reliably and validly assesses RRBs. [ABSTRACT FROM AUTHOR]

A significant proportion of malaria infections in humans exhibit no symptoms, but it is a reservoir for maintaining malaria transmission. A time periodic reaction-diffusion model for malaria spread is introduced in this paper, incorporating spatial heterogeneity, incubation periods, symptomatic and asymptomatic carriers. This paper introduces the concept of the basic reproduction number R 0 , which is defined as the spectral radius of the next generation operator, and we present some preliminary results by elementary analysis. The threshold dynamic behavior analysis shows that when R 0 < 1 , the disease is extinct, and when R 0 > 1 , the disease is persistent. We investigate the case of constant system parameters, focusing on the global asymptotic stability of the disease-free steady state when R 0 = 1 . In the numerical simulation section, we validate the theoretical results obtained, and then use elasticity analysis methods to explore the influence of parameters on the output solution. In addition, sensitivity analysis of the basic reproduction number under homogeneous conditions indicates direction of controlling malaria transmission. And several control measures are evaluated in the following steps. [ABSTRACT FROM AUTHOR]

The recent series of papers between Sampaio and Haydu (Behavior and Social Issues 32(1):115–133, 2023a; Behavior and Social Issues 32(1):141–146, 2023b) and Ardila-Sánchez and Hayes (Behavior and Social Issues 32(1):134–140, 2023) on the cultural milieu construct is of critical import for culturo-behavior science. By constructing a revised version of Houmanfar and colleagues' (Ardila-Sánchez et al., Behavior and Social Issues 28(1):298–315, 2019: Houmanfar, Ardila-Sánchez, & Alavosius, Behavior science perspectives on culture and community, pp. 151–170, Springer, 2020: Houmanfar & Rodrigues, Behavior and Social Issues, 15(1):13–30, 2006: Houmanfar, Rodrigues, & Ward, Behavior and Social Issues 19(1):78–103, 2010) elaborated metacontingency model, Sampaio and Haydu address confusion inherent in the concept of the cultural milieu by separating it into two different components—cultural antecedents and selecting environment variables—with different functional properties. This allows the cultural milieu to be conceptualized more coherently within a behavior-analytic framework, a framework into which the cultural milieu does not quite fit when described in interbehavioral terms. However, given differences in integrated field and contingency logic, an interbehaviorally conceptualized cultural milieu does not belong in any metacontingency model, elaborated or otherwise. The purpose of this paper is to address this issue—the elephant in the field—and confusing aspects of the cultural milieu that Sampaio and Haydu do not address when one considers the elaborated metacontingency model from an interbehavioral perspective. In doing so, I discuss issues with the model warranting attention and the prospect of a unified culturo-behavior science. [ABSTRACT FROM AUTHOR]

Due to the digitization of data and the dynamic requirements of the users, cloud computing is one of the most used technologies in the present scenario. Cloud computing provides a platform to store, process, and share data remotely for heterogeneous users and provides services according to the requests generated by those users. However, its rapid growth has led to one of the major challenges in the environment; the security and privacy of the data. To address the security and privacy concerns, in this paper, our major contribution is a trusted computing framework namely Secure Framework using Behavior and Role Analysis (SFBRA) for cloud data security. The framework utilizes user log monitoring data, pattern recognition algorithms, and role-based access mechanisms to detect malicious and suspicious activities of different users. Our proposed framework provides two levels of security for cloud users. In Level-1, we calculate the trust value of the logged-in users by analyzing the existing log table and pattern of request access. In Level-2, we calculate the trust of the request (storage, processing, sharing) data packet using behavior analysis of the user and a role-based access mechanism and finally detect the malicious activities. The efficacy of our proposed framework is demonstrated through experimentation, where we compare our framework with existing research works. The results show 95% accuracy in potential attack detection and prevention,approximately 8 Mbps throughput, and 0.003% packet drop on average. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing the lack of Braille-embossed labels in Chinese pharmaceutical product packaging. To this end, this study constructs an evolutionary game model of multi-participant behaviour in drug production regulation and conducts simulation based on the perspective of stakeholders in order to study the evolutionary steady state of regulatory authorities, pharmaceutical companies, and visually impaired individuals and the influence of each parameter variable on the choice of strategic behaviour of the participants. The simulation results show that in the legal framework of China, administrative penalties, the production subsidy rate, and the Basic Medical Insurance (BMI) reimbursement rate are the main drivers of the system's evolution towards a steady state. Reduced costs of drugs with Braille-embossed packaging is a prerequisite for the visually impaired to purchase such drugs, and the key for the whole system to converge to a stable state therefore lies in the BMI reimbursement rate for drugs with Braille-embossed packaging. This paper suggests that the Chinese government should improve the law enforcement mechanism for the regulation of the production of drugs with Braille-embossed packaging; increase the type and intensity of administrative penalties as well as production subsidies, tax incentives, and compliance incentives for pharmaceutical companies; and provide higher BMI reimbursement rates for the visually impaired. The results of the study provide valuable insights to improve accessibility to drugs and build a barrier-free environment for ensuring medication safety in China and other developing countries. [ABSTRACT FROM AUTHOR]

The world is witnessing the fast replacement of fossil-fuelled vehicles by environment-friendly electric vehicles (EVs). This unprecedented growth of EVs is expected to burden the material resource consumption of batteries constituting metals from the earth. However, circular economy in the electric vehicle battery (EVB) ecosystem can provide a sustainable solution to resource management. This has triggered the need to understand and analyse the EVB ecosystem players. Long-term analyses of behaviours of the different variables under dynamic and complex systems are best carried out by the application of system dynamics (SD)—a continuous simulation methodology. This research paper has contributed in developing an interactive SD-based model for the circular production and consumption flow of EVBs to analyse the behavioural dynamics of the different variables involved. Five scenarios have been simulated for the analyses. The behaviour trends of the variables obtained from the SD-based simulation model have provided useful insights and five recommendations for resource sustainability. The recommendations are: the necessity for adopting Industry-4.0 in EVB manufacturing, awareness creation of circular thinking amongst all stakeholders, enhanced end-of-life (EOL) period of EVB, compelling need for high EVB recollection efficiency after EOL, and improvement in the reprocessing technology of EOL batteries. [ABSTRACT FROM AUTHOR]

Bonica's (Am J Polit Sci 58(2):367–386, 2014) campaign finance-based ideology scores, or CFscores, create an ideological common space that allows researchers to compare a wide variety of actors. Because relatively few citizens donate to candidates, however, the public is not well represented in this common space. This paper addresses that gap. It uses random forest machine learning on data from the 2012 Cooperative Congressional Election Study to impute CFscores for respondents who did not donate to candidates, based on how their policy views compared to those who did. These new scores are robust to differences in issue importance between donors and non-donors, and they outperform other ideological measures in predicting vote choice. The scores are then applied to a substantive exercise. Past research shows that extreme candidates for governor are penalized more by voters than those in lower-profile races. The implied mechanism—that vote choice for governor is more ideologically-driven—can be directly tested with imputed CFscores, since they uniquely allow comparisons between voters and candidates across races. An analysis of voting behavior in 2012 confounds expectations. Ideology appears to factor no more into vote choice for governor than for US House. These novel findings underscore the value of extending CFscores to non-donating survey respondents, and while current efforts are limited by data availability, this study offers encouragement and a roadmap to that end. [ABSTRACT FROM AUTHOR]

A flexible drilling tool is a special drilling tool for ultrashort-radius radial horizontal wells. This tool is composed of many parts and has the characteristics of a multibody system. In this paper, a numerical method for the dynamic analysis of flexible drilling tools is proposed. The flexible drill tool is discretized into spatial beam elements, while the multilayer contact of the flexible drilling tool is represented by the multilayer dynamic gap element, and the dynamic model of the multibody system for the flexible drilling tool's multilayer contact is established, considering the interaction force between the drill bit and the rock. The nonlinear dynamic equation is solved using the Newmark method and Newton–Raphson method. An analysis of the dynamic behavior of a flexible drilling tool is conducted. The results indicate that the flexible drilling tool experiences vortex formation due to the interaction between the flexible drilling pipe and the guide pipe, leading to increased friction and wear. This situation hinders safe drilling operations with flexible drilling tools. The collision force of the flexible drilling tool near the bottom of the hole is more severe than that of the other tool types, which may lead to failure of the connection. [ABSTRACT FROM AUTHOR]

Since the emergence of big data, the popularity of deep learning models has increased and they are being implemented in a wide range of applications, including people detection and counting in congested environments. Detecting and counting people for human behavior analysis in retail stores is a challenging research problem due to the congested and crowded environment. This paper proposes a deep learning approach for detecting and counting people in the presence of occlusions and illuminance variation in a crowded retail environment, utilizing deep CNNs (DCNNs) for semantic segmentation of top-view depth visual data. Semantic segmentation has been implemented using (DCNNs) in recent years since it is a powerful approach. The objective of this paper is to design a novel architecture that consists of an encoder–decoder architecture. We were motivated to use transfer learning to solve the problem of insufficient training data. We used ResNet50 for the encoder, and we built the decoder part as a novel contribution. Our model was trained and evaluated on the TVHeads dataset and the people counting dataset (PCDS) that are available for research purposes. It consists of depth data of people captured from a top-view RGB-D sensor. The segmentation results indicate high accuracy and demonstrate that the proposed model is robust and accurate. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier. [ABSTRACT FROM AUTHOR]

Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model's performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats. [ABSTRACT FROM AUTHOR]

Purpose: The paper's aim is to use the dynamic theory of multi-rigid body systems to establish a dynamic model of the ammunition belt for the gas-operated machine gun. Then, an experimental study is also carried out on the PKMS Kalashnikov 7.62 mm machine gun to verify the reliability of the proposed model. Method: A multi-body mechanical model for dynamic analysis of the ammunition belt is proposed to serve as a foundation for the theoretical calculation problem. In addition, a set of experimental procedures for the PKMS Kalashnikov 7.62 mm machine gun is carried out dynamic analysis problem. Results: The calculation results are compared with the experimental results, including the motion, displacement, and velocity of the ammunition belt and the interaction force between two link joints to confirm the suitability of the proposed model. From here, it can be asserted that our model is an effective one and should be encouraged in research into the dynamics of the automatic machine and the design of the gas-operated machine gun. [ABSTRACT FROM AUTHOR]

This paper describes a strategic science of teaching (SST) and its application to effective education as a form of social justice. SST is grounded in the Comprehensive Application of Behavior Analysis to Schooling (CABAS®), a system-wide approach to teaching. The Accelerated Independent Learner (AIL) model stems from the CABAS® system but operates within public schools and general education classrooms where the science of behavior is applied to establish academic, social, and self-management repertoires for all students. In the AIL model, data from the student's progress drives instruction. The arrangement of the classroom includes classroom management, instructional presentations, curriculum-based measurement, visual displays of individual student mastery of curricular objectives, the interaction between verbal development and what to teach, and acquisition of learning cusps. Another critical aspect of these classrooms includes teacher measures and teacher education. All these components are used to collectively accelerate student learning. Using this model, we attempt to remove the barrier to a one-size-fits-all education system, thus creating a more just educational system for all children. [ABSTRACT FROM AUTHOR]

Many Australian students fail to meet an acceptable standard of reading proficiency. This can negatively impact their academic progress, social, and emotional well-being, and increase their risk of developing challenging behaviors. These risks and challenges have been found to compound over the lifetime of the learner. Unfortunately, the proportion of Australian students who fail to meet reading proficiency standards increases as they move through their years of schooling, and reading difficulties disproportionately affect historically marginalized groups. This has raised concerns about the effectiveness of instructional approaches used within the Australian education system, particularly in reading, and prompted discussions of reform. The purpose of this review paper was to examine the contributions of the science of reading and science of behavior to our collective knowledge regarding reading development and effective reading instruction, and how this knowledge is currently being used in the Australian context. We provide a discussion on the current state of reading instruction and achievement in Australia by considering national trends, inequities, and systemic challenges. Implications and recommendations to address inequities in reading outcomes, using both the science of reading and science of behavior, are discussed. [ABSTRACT FROM AUTHOR]

We study the autowave front propagation in a medium with discontinuous characteristics and the conditions for its stabilization to a stationary solution with a large gradient at the interface between media in the one-dimensional case. The asymptotic method of differential inequalities, based on constructing an asymptotic approximation of the solution, is the main method of study. We develop an algorithm for constructing such an approximation for the solution of the moving front form in a medium with discontinuous characteristics. The application of such an algorithm requires a detailed analysis of the behavior of the solution in neighborhoods of two singular points: the front localization point and the medium discontinuity point. As a result, we obtain a system of equations for the front propagation speed; this distinguishes this paper from the previously published ones. The developed algorithm can be used to describe autowave propagation in layered media. The results can also be extended to the multidimensional case. [ABSTRACT FROM AUTHOR]

This paper plans to propose a general numerical model for investigating the free and forced vibration behaviors of the multi-stepped functionally graded porous graphene platelet-reinforced composite (FGP-GPLRC) curved beam with various boundary conditions based on the first-order shear deformation Timoshenko beam theory by employing Rayleigh–Ritz method in conjunction with Jacobi polynomials. Different porosity and GPL distribution types are considered. The general boundary conditions of the multi-stepped FGP-GPLRC curved beam and connecting conditions of the FGP-GPLRC curved beam elements are simulated by employing boundary springs and connecting springs, respectively. The convergence and validation of the proposed numerical model are demonstrated by comparing the proposed results with the corresponding results which come from open literature and finite element software ABAQUS. The free and forced vibration behavior analysis of multi-stepped FGP-GPLRC curved beam is carried out by investigating the influences of material property and geometric parameters on the natural frequency and displacement response in the frequency and time domains of multi-stepped FGP-GPLRC curved beam structure. The investigation results can offer the technique guidance for the design and manufacture of the multi-stepped FGP-GPLRC curved beam. [ABSTRACT FROM AUTHOR]

Modern Recommender Systems (RSs) compete to maintain rich user profiles that can accurately reflect user behavior, interests, and service contexts. While benefiting from an online service supported by an RS, user preferences and interests may rapidly change over time. To keep up with the changes from the user perspective, an RS should maintain the making of effective personalization as supported by robust profile construction methods. Building an effective user profile database requires exhaustive data and behavior analysis over extended periods. In this paper, we delve into traditional RS architectures to identify limitations, gaps, and opportunities for improvements in existing user profile mechanisms. To that end, a Global User Profile Framework (GUPF) is proposed towards achieving increased effectiveness. Furthermore, the adoption of the developed framework is exemplified by presenting different potential scenarios. The presented work concludes with the identification of important venues and research directions that are enabled by the proposed GUPF. [ABSTRACT FROM AUTHOR]

This paper focuses on a human motion image detection and tracking method that utilizes a Gaussian mixture model and light sensor. The objective is to develop an accurate and efficient approach for detecting and tracking human motion images in various applications such as human behavior analysis and intelligent monitoring. To achieve this, the method utilizes a Gaussian mixture model to establish a background model of the scene. The background model is used to estimate the static elements in the scene. By computing the background difference of the current image, a binary image of the foreground region is obtained. Additionally, data from the light sensor is incorporated to further segment and filter the foreground area, removing noise and irrelevant motion. The detection and tracking of human motion is realized through trajectory analysis and matching techniques. Experimental evaluation confirms the effectiveness and accuracy of the proposed method. The results demonstrate that the method achieves high accuracy and robustness in human motion image detection and tracking. Compared to traditional methods, this approach significantly improves the accuracy of recognizing human body boundaries and tracking motion. [ABSTRACT FROM AUTHOR]

Person and suspicious activity detection is a major challenge for image-based surveillance systems. However, the accuracy of person detection is affected by several factors, such as the presence of the person, his trajectory, posture, complex background, and object distortion. In this work, we developed a person-focused dataset that includes various behaviors of students in an educational institution, such as cheating, theft of lab equipment, fights, and threatening situations. This dataset ensures consistent and standardized identification annotations for individuals, making it suitable for detection, tracking, and behavioral analysis of individuals. In addition, we have increased the detection accuracy through an improved architecture called YOLOv5 and introduced an efficient method for detecting global and local anomalous behaviors. This method extracts motion features that accurately describe the person's movement, speed, and direction. To evaluate the effectiveness of our proposed approach, we validated it against our proposed database and publicly available benchmark datasets. Our method achieves state-of-the-art detection accuracy, namely 96.12%, with an error rate of 6.68% compared to existing methods. The empirical results show a significant improvement in anomalous activity detection. Our paper concludes with a summary and a discussion of possible future research directions. [ABSTRACT FROM AUTHOR]

In this paper, the durability, the wear resistance, the tribological behavior, and the wear particles flow of the Anodic Aluminum Oxide Film 5083 were studied under cyclic loading. Different anodizing durations (reaction times (RT)) and applied currents (J) were tested. Besides, a tribological model with a correlation between the friction coefficient and the wear mechanisms of the oxide layer was established. Postmortem analyses were investigated using scanning electron microscopy (SEM), energy-dispersive x-rays (EDX), and profilometry analyses. Experimental results were shown that the increase in the RT improves the durability of the 5083-aluminum alloy oxide layer. Moreover, when RT increased, the stability of friction coefficient was improved, and the surfaced pores were more filled with micro-sized wear particles, inducing the establishment of a compacted layer. Consequently, the 5083-aluminum alloy oxide layer was shown important tribological characteristics such as the durability and wear resistance after hundreds of rubbing cycles. It was concluded that three stages characterized the friction behavior and the wear particle flow under cycling: firstly, a friction coefficient stable stage with the establishment of an internal flow that feeds the contact (internal source flow); the second stage is the progressive increase in the friction coefficient, while the wear particles were crushed and compacted to form a smooth layer, constituting the continuous internal flow; and the third stage is the return of the stable state of the friction coefficient, where the smooth layers spread, and the pores filled with the powder. [ABSTRACT FROM AUTHOR]

The Emerging biomedical applications such as electrocardiography, electroencephalogram, wireless implantable devices have required optimized power-based SAR ADC in them to reduce package cost and to extend battery life. In view of power optimized SAR ADC, a dynamic controlled comparator with novel static power reduction logic incorporated with common mode kickback noise reduction technique is designed at 45 nm technology in CADENCE Virtuoso in this paper. Designed comparator is analyzed in terms of performance parameters; static power dissipation, transient power dissipation, maximum sampling rate, offset voltage and delay. Simulation and behaviour modeling analyses of proposed dynamic comparator has resulted in five times reduction in static power dissipation along with optimization of transient power and sampling rate as well compared to previously available double tail dynamic comparator. Moreover, it has been mentioned that proposed dynamic comparator is dissipating 2.36 pW of static power and 3.59 nW of transient power with sampling rate of 240 KS/s at 0.5 V. Further, the kickback noise of proposed design is limited to 4.7 mV with maximum delay of 124 ps. Finally, the novel outcomes of proposed comparator are the optimization in required parameters such as power, delay, offset voltage and kickback noise for biomedical applications as compared to double tail dynamic comparator. The proposed consideration for SAR ADC can become a backbone for low power biomedical applications. [ABSTRACT FROM AUTHOR]

In this paper, the mechanical and physical behavior study of hybrid aluminum metal matrix composites has been carried out. Aluminum 7175/Si3N4-based hybrid composites have been developed by infusing MoS2 and Si3N4 particles via vacuum-assisted bottom pouring stir casting technique. Mechanical and physical behavioral synthesis was carried out by varying weight percentages of Si3N4 (0–9%) and MoS2 (3, 5 and 7) particles as reinforcement. Apart from mechanical and physical behavioral evaluations, the microstructural features were investigated. The microstructural analysis showed a grain refinement with the addition of reinforcing content. The hybrid composites results exhibited micro-hardness, tensile strength and compressive strength with 7 wt% of Si3N4 and 5 wt% MoS2. The density of HAMMCs was also improved by 2.77% as compared to monolithic base alloy. During the micro-hardness evaluation of cast samples, influence of variation in indentation load and dwell time was examined. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to investigate the microstructural, mechanical, and wear behaviour of AZ31/TiC surface composites fabricated through friction stir processing (FSP). TiC particles are reinforced onto the surface of AZ31 magnesium alloy to enhance its mechanical properties for demanding industrial applications. The FSP technique is employed to achieve a uniform dispersion of TiC particles and grain refinement in the surface composite. Microstructural characterization, mechanical testing (hardness and tensile strength), and wear behaviour evaluation under different operating conditions are performed. Response surface methodology (RSM) is utilized to optimize the wear rate by considering the effects of process parameters. The results reveal a significant improvement in hardness (41.3%) and tensile strength (39.1%) of the FSP-TiC composite compared to the base alloy, attributed to the refined grain structure (6–10 μm) and uniform distribution of TiC particles. The proposed regression model accurately predicts the wear rate, with a confirmation test validating an error percentage within ± 4%. Worn surface analysis elucidates the wear mechanisms, such as shallow grooves, delamination, and oxide layer formation, influenced by the applied load, sliding distance, and sliding velocity. The enhanced mechanical properties and wear resistance are attributed to the synergistic effects of grain refinement, particle-accelerated nucleation, the barrier effect of TiC particles, and improved interfacial bonding achieved through FSP. The optimized FSP-TiC composites exhibit potential for applications in industries demanding high strength, hardness, and wear resistance. [ABSTRACT FROM AUTHOR]

Among infrastructure, concrete bridges are the most exposed to various environmental effects. Structural degradation occurs due to natural and artificial influences shortening the lifespan of the structure. Therefore, bridges need to be reinforced over time. The structures used in this study are re-formed using aged bridge floor decks that have been used for 50 years, approximately. The fiber-reinforced polymer (FRP) adhesion method, using sheets and plate forms, was selected among various reinforcement methods to investigate the performance of reinforced structures. We have tested various reinforcement scenarios including one and two layers FRP sheets and FRP plates. The mechanical properties of the reinforced structures were evaluated experimentally through tensile strength and flexural test experiments. In contrast to most available literature focused on model-based studies, our present work represents an experimental test validation of structural reinforcement on an actual bridge. Our results indicate that fiber-based reinforcement in sheet form exhibits higher performances of the reinforced structure compared to reinforcement using the plate form. This study is intended to provide sufficient data for reinforcing bridge floors that could be used for reference at future construction sites. [ABSTRACT FROM AUTHOR]

We studied clinical AI-supported decision-making as an example of a high-stakes setting in which explainable AI (XAI) has been proposed as useful (by theoretically providing physicians with context for the AI suggestion and thereby helping them to reject unsafe AI recommendations). Here, we used objective neurobehavioural measures (eye-tracking) to see how physicians respond to XAI with N = 19 ICU physicians in a hospital's clinical simulation suite. Prescription decisions were made both pre- and post-reveal of either a safe or unsafe AI recommendation and four different types of simultaneously presented XAI. We used overt visual attention as a marker for where physician mental attention was directed during the simulations. Unsafe AI recommendations attracted significantly greater attention than safe AI recommendations. However, there was no appreciably higher level of attention placed onto any of the four types of explanation during unsafe AI scenarios (i.e. XAI did not appear to 'rescue' decision-makers). Furthermore, self-reported usefulness of explanations by physicians did not correlate with the level of attention they devoted to the explanations reinforcing the notion that using self-reports alone to evaluate XAI tools misses key aspects of the interaction behaviour between human and machine. [ABSTRACT FROM AUTHOR]

Self-injurious behavior (SIB) is a well-known phenomenon in patients with chronic tic disorders (CTD). To investigate prospectively symptomatology of SIB in adults with CTD, we developed and validated the self-injurious behavior scale for tic disorders (SIBS-T). Patients completed the SIBS-T and a variety of assessments for tics and comorbidities. We investigated SIB frequency, internal consistency of the SIBS-T, and carried out an exploratory factor analysis (EFA). We enrolled n = 123 adult patients with CTD. SIB was reported by n = 103 patients (83.7%). The most frequently reported SIB were beating/pushing/throwing and were found in 79.6% of cases. Patients with SIB had significantly higher tic severity measured with the Adult Tic Questionnaire (ATQ) (p = 0.002) as well as higher severity of psychiatric comorbidities such as obsessive–compulsive symptoms (OCS) (p < 0.001,), attention deficit/hyperactivity disorder (ADHD) (p < 0.001,), and anxiety (p = 0.001). In addition, patients with SIB had significantly lower quality of life (p = 0.002). Pearson correlations demonstrated significant associations between SIB and severity of tics (p < 0.001), depression (p = 0.005), ADHD (p = 0.008), and borderline personality traits (p = 0.014). Consequently, higher SIBS-T also correlated with greater impairment of quality of life (p < 0.001). The internal consistency of the SIBS-T was good (α = 0.88). The EFA confirmed a single factor underlying the SIBS-T. [ABSTRACT FROM AUTHOR]

Applied behavior analysis (ABA) focuses on providing socially significant treatments and practices. Recent advancements in ethically-minded research have encouraged practitioners to think beyond the technical skills required for implementing behavior-analytic practices and consider how these practices are delivered. Compassionate care in ABA has recently been conceptually defined and subsequently researched to explore ways to explicitly and systematically teach compassionate-care-based skills within the scope of ABA. Researchers have noted barriers specific to the time required to teach compassionate-care-based skills using behavior skills training (BST) especially when working to train groups in compassionate care skills. Further, these studies have focused primarily on using compassionate care towards interactions with caregivers as communication partners. This study operationalized the compassionate care-based skill of receiving feedback within a behavioral analytic lens and taught it to eight master's students enrolled in an ABA course. An experimental case study approach was used to evaluate the relative benefit of modified behavioral skills training (BST) versions, including conditions with and without rehearsal opportunities and individualized performance feedback. Results indicated that all participants demonstrated significant skill increases across multiple communication partners (i.e., client, caregiver, colleague) compared to baseline sessions, with conditions including opportunities for rehearsal and performance feedback resulting in higher skill acquisition for seven of the eight participants. Conditions that required the participants to generalize to novel scenarios and novel communication partners resulted in lower competencies; however, all participant skills across all communication partners remained higher than baseline measures. Limitations included practicing skills within role-play scenarios only, the application of compassionate care-based skills in a variety of cultures, and limits related to using an experimental case study as a methodological approach. Implications for future research and practitioner-based applications are discussed. [ABSTRACT FROM AUTHOR]

The composite attachment loss during orthodontic clear aligner therapy is an adverse event that commonly happens in clinical practice and can affect the overall outcome and length of treatment. The aim of our research is to provide a basis for the further study of an innovative digital protocol and application method for orthodontic aligner attachments. Two 3D models were designed, one based on the proposed protocol and the other on the conventional method for aligner attachment application. Four attachment shapes were used to identify the maximum values for the von Mises equivalent stresses, the maximum displacements values and the areas in which these values were recorded through FEM analysis. The results of the mechanical simulation show lower values of von Mises stress recorded in the 3D printed attachments assemblies, independent of their shape, when simulated under the same boundary and load conditions. The trapezoidal prism shaped 3D printed model has a 3.7 times smaller displacement value (0.088 [mm]) compared to the adhesive resin model (0.326 [mm]). In conclusion, the proposed protocol for aligner attachments and the introduction of innovative materials is a promising method of solving conventional attachment problems in current orthodontic treatments. [ABSTRACT FROM AUTHOR]

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10–100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation. Quantifying animal behavior is crucial in various fields such as neuroscience and ecology, yet we lack data-efficient methods to perform behavioral quantification. Here, the authors provide new unified models across 45+ species without manual labeling, thus enhancing analysis in behavioral studies. [ABSTRACT FROM AUTHOR]

This research is based on computational fluid dynamics simulations of water and microbubble flow within the tank of a lamellar DAF (L-DAF) clarification system operating under high-rate DAF conditions (12–30 m/h). Firstly, performance of the DAF tank with lamellae was evaluated under two operating conditions in which the flow was either short-circuited or stratified in the absence of lamellae. In addition, the improvement in bubble removal efficiency achieved by the incorporation of lamellae in each scenario was assessed. Secondly, an in-depth analysis was conducted of the flow that develops in the separation zone as a result of placing the lamella pack in that part of the tank. The significant density difference that the lamellae cause to exist between the bubble blanket and clarified water below is responsible for the complex three-dimensional flow observed between the two regions. Analysis of this flow showed a previously undescribed mechanism in which the density gradient plays a crucial role in preventing bubbles from passing through the lamellae and ultimately escaping with the effluent. Finally, the effect of hydraulic loading on the bubble removal efficiency of the L-DAF tank under consideration was researched, and it was found that an L-DAF with a height/length ratio of 0.72 is able to operate at hydraulic loading close to 30 m/h, evidencing good debubbling performance. [ABSTRACT FROM AUTHOR]

The telehealth model can allow for cost-effective supports in areas where limited applied behavior analysis (ABA) services are available. As a result, e-learning programs for parents of children with autism have become part of the telehealth paradigm. In the Czech Republic, one of the many places where there are limited ABA supports and no available ABA e-learning courses for families, the development of an e-learning course that is freely available, and linguistically and culturally appropriate, is needed. It was anticipated that this course could mirror some of the free e-learning materials available to English-speaking caregivers and provide foundational knowledge to later support more intensive ABA services. Therefore, a pilot study was developed to assess an ABA e-learning course for caregivers in the Czech Republic with the aim of validating its efficacy and assessing whether a synchronous component was needed for the best outcomes. The materials were not meant to replace best practices in more intensive caregiver training but provide foundational skills for later more successful local services. Results demonstrated that a total of 33 caregivers completed the course, making statistically significant gains in their ABA knowledge and rating the course highly. Interestingly, the addition of the synchronous component only impacted the knowledge gain outcome. Suggestions for supporting caregivers in e-learning course completion and directions for future research are additionally explored. [ABSTRACT FROM AUTHOR]

Researchers increasingly identify virtual reality (VR) simulations as a potentially effective professional development tool. However, simulations used in education and behavior analysis typically require active oversight from technicians and instructors. "Smart" VR integrated with artificial intelligence could independently administer simulation components, alleviate logistical challenges associated with high-quality professional development such as behavioral skills training (BST), and provide trainees with opportunities to extensively practice skills across a range of disciplines. The limited research in this area has yet to demonstrate a functional smart VR application with the ability to provide instruction related to a primarily vocal skill. The current study used a randomized, combined multiple probe across behaviors and participants design to examine a smart VR application's ability to deliver components of BST and assess participants (n = 2) acquisition of a mathematical questioning strategy designed to examine covert student problem solving in general education settings. Results suggest that automated assessment of participants corresponded with results of direct observation. Although insufficient to demonstrate a functional relation between training and participant performance, the iterative experiment provides qualified support for the use of automated BST as a tool for skill acquisition. Findings indicate smart VR represents a promising means of improving professional development and a fruitful area of interdisciplinary collaboration. [ABSTRACT FROM AUTHOR]

We propose a probabilistic representation of the ground states of massive and massless Schrödinger operators with a potential well in which the behaviour inside the well is described in terms of the moment-generating function of the first exit time from the well and the outside behaviour in terms of the Laplace transform of the first entrance time into the well. This allows an analysis of their behaviour at short to mid-range from the origin. In a first part, we derive precise estimates on these two functionals for stable and relativistic stable processes. Next, by combining scaling properties and heat kernel estimates, we derive explicit local rates of the ground states of the given family of non-local Schrödinger operators both inside and outside the well. We also show how this approach extends to fully supported decaying potentials. By an analysis close-by to the edge of the potential well, we furthermore show that the ground state changes regularity, which depends qualitatively on the fractional power of the non-local operator. [ABSTRACT FROM AUTHOR]

In an explosion test using a shock tube, the behavior of pressure waves can be reproduced with high reliability. However, the explosion in a shock tube occurs in a confined space. It is difficult to predict the behavior of pressure waves and its effect on various concrete specimens by using the research findings related to free-field explosions. Moreover, few studies have focused on explosive-driven shock tubes. In this study, the behavior of pressure waves in a shock tube was numerically analyzed using a finite-element analysis program. The explosive used to generate the pressure waves was an ammonium nitrate fuel oil (ANFO), which exhibits non-ideal explosion characteristics. The Jones–Wilkins–Lee (JWL) and ignition-and-growth (I&G) equations of state were used for blast-pressure calculation. The analysis results were affected by factors such as the release rate of explosive energy and the development of the pressure waves in the confined explosion. The blast behaviors, such as the low release rate of explosive energy and the resulting increase in the impulse, were analyzed using the ignition-and-growth equation. The impulse produced during the development of waves reflected by the block installed at the tube inlet exceeded that produced by the tube wall. Such behaviors that occurred at the beginning of a blast affected the process of wave propagation along the shock tube and the wave reflection due to the test specimen at the outlet of the shock tube. In this study, the blast behavior in the shock tube, which could be referenced for the analysis of blast overpressure and its effect on concrete specimens, was numerically analyzed. Further research on the structural behaviors of concrete specimens due to blast overpressure is needed. [ABSTRACT FROM AUTHOR]

Traffic cyclic loading is the key factor that leads to the deterioration of the long-term service behavior of subgrade. A series of cyclic triaxial tests was carried out by the large-scale dynamic and static triaxial apparatus (LSDSTA) to study the dynamic behaviors of coal gangue subgrade filler (CGSF) under multi-step cyclic loading using the morphological characteristics of hysteretic curves (MCHC). MCHC was quantitatively characterized by four parameters, i.e., the unclosed degree (εphl), inclination of long axis degree (khl), area (Shl) and fullness degree (αhl). With the increase of dynamic strain, εphl increases exponentially. khl of the coal gangue sample first decreases and then shows an increasing trend with the increasing dynamic strain. The values of Shl are close to each other, and the energy dissipation in the sample is small. However, with the increase of dynamic strain, the specimen failure degree is increased, Shl increases exponentially, and the damping ratio increases. With the increase of dynamic strain, αhl increases approximately linearly. Confining pressure has a certain effect on the four parameters. There parameters can be recommended and used for quantitative analysis the dynamic behaviors of subgrade filler under traffic cyclic loading. [ABSTRACT FROM AUTHOR]

Background: Red onion husk, a readily available agricultural waste material, contains diverse bioactive compounds with potential health benefits. This study aimed to assess the safety and therapeutic potential of red onion husk extract in managing manic-like symptoms and associated neurochemical dysfunctions. Methods: Acute and repeated oral dose studies were conducted in mice and rats to evaluate the safety profile of the extract. FT-IR analysis identified functional groups in the extract, while GC-MS analysis identified specific bioactive compounds in the flavonoid-rich fraction. A ketamine-induced manic behaviour model in Wistar rats was employed to assess the extract's efficacy in attenuating manic-like symptoms. Behavioural and neurochemical analyses were performed to further investigate the extract's effects. Results: The extract demonstrated a favourable safety profile in both acute and repeated dose studies. FT-IR analysis revealed a complex mixture of organic compounds, including hydroxyl groups, alkynes/nitriles, aromatic and non-aromatic C = C bonds, amines, and polysaccharides. GC-MS analysis identified 17 bioactive compounds, including five-methyl-2-phenylindolizine, methadone N-oxide, and 3-phenylthiane, S-oxide. Ketamine administration significantly increased oxidative stress markers, TBARS, and suppressed antioxidant enzyme activities (SOD, GPx, CAT) in both the cerebral cortex and hippocampus, alongside elevated acetylcholinesterase (AchE) activity, indicating enhanced neuronal excitability. Pre-treatment with FRF (25 mg/kg) effectively mitigated ketamine-induced oxidative stress, as evidenced by reduced TBARS levels and partially restored SOD and GPx activities. Interestingly, FRF significantly increased CAT activity (p < 0.001), potentially suggesting an additional compensatory mechanism. Notably, FRF pre-treatment also counteracted ketamine-upregulated AchE activity, offering neuroprotection against heightened neuronal excitability. Conclusion: Red onion husk extract exhibits a favourable safety profile and exerts potent antioxidant and neuroprotective effects, possibly through modulating Nrf2 signalling pathways. Its ability to counteract ketamine-induced oxidative stress and neuronal hyperactivity highlights its potential as a complementary therapeutic strategy for managing manic episodes in bipolar disorder. Further research is warranted to elucidate the precise molecular mechanisms underlying FRF's action and explore its clinical efficacy in human studies. [ABSTRACT FROM AUTHOR]

Peer feedback literacy is becoming increasingly important in higher education as peer feedback has substantially grown as a pedagogical approach. However, quality of produced feedback, a key behavioral aspect of peer feedback literacy, lacks a systematic and evidence-based conceptualization to guide research, instruction, and system design. We introduce a novel framework involving six conceptual dimensions of peer feedback quality that can be measured and supported in online peer feedback contexts: reviewing process, rating accuracy, feedback amount, perceived comment quality, actual comment quality, and feedback content. We then test the underlying dimensionality of student competencies through correlational analysis, Multidimensional Scaling, and cluster analysis, using data from 844 students engaged in online peer feedback in a university-level course. The separability of the conceptual dimensions is largely supported in the cluster analysis. However, the cluster analysis also suggests restructuring perceived and actual comment quality in terms of initial impact and ultimate impact. The Multi-Dimensional Scaling suggests the dimensions of peer feedback can be conceptualized in terms of relative emphasis on expertise vs. effort and on overall review quality vs. individual comment quality. The findings provide a new road map for meta-analyses, empirical studies, and system design work focused on peer feedback literacy. [ABSTRACT FROM AUTHOR]

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior. [ABSTRACT FROM AUTHOR]

Threats on computer networks have been increasing rapidly, and irresponsible parties are always trying to exploit vulnerabilities in the network to do various dangerous things. One way to exploit vulnerabilities in a computer network is by employing malware. Botnets are a type of malware that infects and attacks targets in groups. Botnets develop quickly; the characteristics of initially sporadic attacks have grown into periodic and simultaneous. This rapid development has proved that the botnet is advanced and requires more attention and proper handling. Many studies have introduced detection models for botnet attack activity on computer networks. Apart from detecting the presence of botnet attacks, those studies have attempted to explore the characteristics of botnets, such as attack intensity, relationships between activities, and time segment analysis. However, there has been no research that explicitly detects those characteristics. On the other hand, each botnet characteristic requires different handling, while recognizing the characteristics of the botnet can help network administrators make appropriate decisions. Based on these reasons, this research builds a detection model that can recognize botnet characteristics using sequential traffic mining and similarity analysis. The proposed method consists of two main processes. The first is training to build a knowledge base, and the second is testing to detect botnet activity and attack characteristics. It involves dynamic thresholds to improve the model sensitivity in recognizing attack characteristics through similarity analysis. The novelty includes developing and combining analytical techniques of sequential traffic mining, similarity analysis, and dynamic threshold to detect and recognize the characteristics of botnet attacks explicitly on actual behavior in network traffic. Extensive experiments have been conducted for the evaluation using three different datasets whose results show better performance than others. [ABSTRACT FROM AUTHOR]

Little is known about the impact of teachers' questions on child bilingual's heritage language reading process and outcomes. This study examined the role of adults' questions in English-Mandarin bilingual preschoolers' Mandarin word learning, story comprehension, and reading engagement. Ninety-nine 4- to 5-year-old preschoolers in Singapore were assigned to one of the three reading conditions: (a) reading with contextualized questions (e.g., labelling), (b) reading with decontextualized questions (e.g., inference), and (c) reading without questions. The experimenters read three storybooks to the children three times over 2 weeks. Children's general Mandarin proficiency was tested before the intervention, and their target words knowledge and story comprehension were tested before and after the intervention. Children's reading engagement in each reading was assessed with a modified Child Behavior Rating Scale. The results demonstrate that not all aspects of Mandarin performance and reading engagement have benefitted from the experimenter's questions. Contextualized questions were found to significantly enhance children's word meaning explanation and story retelling. Contextualized and decontextualized questions lead to higher increase in social-cognitive engagement but resulted in faster decrease in behavioral and affective engagement over repetitive readings. Furthermore, children's initial Mandarin proficiency influences their reading process and outcomes. Generally, the better their Mandarin vocabulary knowledge was, the more they could enjoy and benefit from the reading, whether they were asked questions or not. [ABSTRACT FROM AUTHOR]

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis. [ABSTRACT FROM AUTHOR]

The current investigation delves into a comparative evaluation of the thermophysical behavior observed in 2-methyl-1-pentanol and short-chain alcohols (C4-C7), from 1-butanol to 1-heptanol over a temperature range of 293.15 to 333.15 K. The primary emphasis of this research centers on analyzing the excess molar volumes and viscosity deviations of these compounds. The findings indicate positive excess molar volumes in the mixtures, which escalate with the elongation of the alkyl chain in the alcohols. Viscosity measurements exhibit deviations from ideal behavior, showing a negative trend that intensifies with longer alkyl chains, indicative of weak molecular interactions between 2-methyl-1-pentanol and the alcohols. Additionally, the study employs the Cubic-Plus-Association (CPA) model to establish correlations among the densities of these mixtures. Remarkably, the model closely aligns with experimental findings, demonstrating a maximum disparity of only 0.57% in the 2-methyl-1-pentanol + 1-pentanol mixture. [ABSTRACT FROM AUTHOR]

Rodent model systems using lipopolysaccharide (LPS) administration have been developed to examine the effect of postnatal inflammation on neuropsychiatric behaviors. In this study, we investigated the effect of 250 μg/kg of LPS administration in male and female Wistar rats at three different postnatal periods. Rats were divided into four groups: a control group which received an intraperitoneal (IP) injection of phosphate buffer saline (PBS) on postnatal day 1 and three experimental groups which received an IP injection of LPS (250 μg/kg) at three different postnatal periods: day 1, day 3 and day 5. Each group consisted of 12 rats and had an equal gender distribution. At three months, rats were subjected to neurobehavioral assessments and biochemical oxidative and inflammatory assays. As a trend, LPS administration generates uniform anxiogenic behaviors, a depressive response, and increased central oxidative and inflammatory stress across all groups as compared to controls. Males receiving an LPS injection at postnatal days 1 and 3 and females receiving it at days 3 and 5 displayed more anxiogenic and depressive responses than their gender counterparts. Females had higher biomarker levels, with results being more pronounced in the group receiving injection on day 3. Postnatal LPS induces emotional disturbances along with oxidative stress and inflammation in brain tissues. Response to LPS injection seems to be gender-dependent and modulated by several variables, including its time of administration. Further investigations that take into consideration these variables, among others, are needed. [ABSTRACT FROM AUTHOR]

Mounting empirical support for early intensive behavioral intervention (EIBI) has increased demand for early intervention services for children diagnosed with autism spectrum disorder (ASD), which may include a variety of components such as discrete-trial instruction (DTI) or natural environment teaching (NET). Many caregivers are now learning EIBI techniques and becoming active agents in their child's ASD treatment. Behavioral skills training (BST) has been frequently used to teach individuals to correctly perform a variety of skills, including DTI (Lafasakis & Sturmey, Lafasakis and Sturmey, Journal of Applied Behavior Analysis 40:685–689, 2007). In this study, caregivers were trained to conduct a DTI procedure with a single-component BST method (i.e., real-time feedback). A concurrent, multiple baseline across caregivers design was used to evaluate the efficacy of real-time feedback on the caregivers' implementation of the DTI procedure with confederates. Results showed that real-time feedback yielded short caregiver training times and few sessions to meet the mastery criterion. In addition, caregivers expressed high satisfaction with the real-time feedback training method, and they were able to implement the procedures with their children with high integrity during treatment extension probes. [ABSTRACT FROM AUTHOR]

Operational definitions have a significant history in applied behavior analysis. The practice's importance stems from the role operational definitions play in detecting an event, human thought, or action. While operationalizing target behaviors has enjoyed widespread practice, some concerns have recently arisen with translation validity and detection accuracy. Additionally, a review of the literature produces few articles assessing the validity of operational definitions. Pinpoints represent an alternative for describing target behaviors. A pinpoint has a formula for construction that includes using an action verb, an object, or event that receives the action, and a comprehensibly defined context where the observation of the action verb + object or event occurs. Pinpoints also have few empirical studies demonstrating their validity. The following experiment compared the detection accuracy of an operational definition for self-injurious behavior and a corresponding pinpoint across professionals who worked in a school that served clients with autism spectrum disorder. The results indicate lower accuracy scores for the operational definition when compared to the pinpoint. Additionally, the consistency of scores varied more for the operational definition than the pinpoint. [ABSTRACT FROM AUTHOR]

Frost heave action is a major issue in permafrost regions, leading to various geotechnical engineering problems. In this study, we assess the mechanical behavior of a concrete retaining wall subjected to frost heave under different ground conditions. The assessment utilizes ABAQUS integrated with several user subroutines. The numerical simulation model employs a thermo-mechanical coupled analysis with a porosity rate function, which enables to simulate time-dependent variations in porosity and frost heave of the backfill soil. After verification of the predictive reliability of the simulation model, the frost heave action in the soil and mechanical response of the retaining wall were evaluated regarding the initial groundwater level and presence of a drainage material on the backside of the retaining wall. According to the simulation results, as the initial groundwater level decreased in the backfill soil, the area susceptible to frost heave decreased. However, the von Mises stresses applied to the retaining wall increased. Under the same ground conditions, when the drainage material was installed on the backside of the retaining wall, the frost heave pressure acting on the wall significantly decreased, and less deformation and distortion of the retaining wall occurred. [ABSTRACT FROM AUTHOR]

ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners’ programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students’ programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students’ programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students’ perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT. [ABSTRACT FROM AUTHOR]

In the midst of the deteriorating air pollution and collective stress, people pay close attention to risk mitigation measures such as keeping indoor and purchasing anti-smog products. Through impact evaluations, factors regarding health protective behavior can be identified. However, limited research is available regarding probabilistic interdependencies between the factors and protective behavior and largely relies on subjective diagnosis. These concerns have led us to adopt a data-driven static Bayesian network (BN) and Dynamic BN model to help explore multidimensional factors that may influence the public's health protective behavior of buying anti-smog air purifiers and examine the dependencies among network nodes. Using the city-level aggregate data from an online shopping platform, the results shed new light on relationships existing among 11 factors and protective behavior of buying air purifiers. Furthermore, taking into account the dynamic nature of protective behavior, we add time-related factors on the basis of static BN to construct the dynamic BN model. Results indicate that PM2.5 concentration and product price are the two leading factors affecting the consumption behavior for air purifiers. Additionally, media-related factors play an important role in the consumption behavior. This study contributes to the fields of impact evaluation of protective consumption behavior analysis and links environment risk with public consumption by identifying key factors. [ABSTRACT FROM AUTHOR]

Research evidence indicated that a specific type of augmented reality–assisted (AR-assisted) science learning design or support might not suit or be effective for all students because students' cognitive load might differ according to their experiences and individual characteristics. Thus, this study aimed to identify undergraduate students' profiles of cognitive load in AR-assisted science learning and to examine the role of their distinct profiles in self-efficacy together with associated behavior patterns in science learning. After ensuring the validity and reliability of each measure, a latent profile analysis confirmed that 365 Chinese undergraduates carried diverse dimensions of cognitive load simultaneously. The latent profile analysis findings revealed four fundamental profiles: Low Engagement, Immersive, Dabbling, and Organized, characterized as carrying various respective cognitive loads. The multivariate analysis of variance findings revealed different levels of the six AR science learning self-efficacy dimensions across profiles. Low Engagement students displayed the lowest self-efficacy among all dimensions. Organized students recorded better conceptual understanding and higher-order cognitive skills than Dabbling ones. Students with the Immersive profile had the highest science learning self-efficacy. The lag sequential analysis results showed significant differences in behavior patterns among profiles. Among them, profiles with social interaction, test, and reviewing feedback behavior had a significantly higher score for self-efficacy than those patterns mainly based on test learning and resource visits. This finding provides a unified consideration of students' diverse profiles and can inform interventions for effective design of AR-assisted science learning to match appropriate strategies to facilitate the science learning effect. [ABSTRACT FROM AUTHOR]

Transportation is an unavoidable part of every human's life. The mobility system handles the transport of humans from different places using various transport modes. According to a station in a populated area, the main problem is the presence of traffic in peak hours and wasting their valuable time on the road. The only medium which runs above the traffic is metro rails/subways. For these reasons, metro rails become a point of interest for each researcher's prophecy and provide valuable recommendations for the smooth functioning of services. Even though, in many cases, the metro systems are affected by abnormal passenger flow. So, this study handles abnormal passenger flow detection and station clustering for the behavior study of a passenger flow system. The research compares outlier detection and anomaly identification for the behavioral analysis of the metro rail passenger flow. The study use data from Kochi Metro Rail Limited for the period 2017 to 2019. Outlier removal has used in passenger flow data before building a forecasting system. In pattern recognition algorithm those components which lie outside the patterns can be considered abnormal (anomaly).The outliers are the component falling apart from the region of interest. The effect of removing the outlier from the time-series pattern is studied against the outlier included pattern to show the improvement. [ABSTRACT FROM AUTHOR]