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The Good Behavior Game (GBG) is an interdependent group-oriented contingency management system successfully used in school settings to promote positive student behaviors. As a classroom management intervention, there is a large body of evidence for it increasing desirable classroom behaviors and decreasing problem behaviors across a range of student populations. Recent studies have also demonstrated that a positive reinforcement-focused version of the GBG can be successfully implemented using a software application. The purpose of this article is to provide a brief overview of the GBG and its evidence base and describe the steps for implementing the GBG with a freely available software application for classroom management. [ABSTRACT FROM AUTHOR]

The significant impact of outdoor school environments on student behaviour highlights the need to study how students interact with their surroundings. This has become a central focus in recent educational research, leading scholars to use various methods to gain insight into student movement behaviour. This research introduces a novel data-driven spatial gridding technique, employing thermal comfort and space syntax evaluation through ENVI-met and depthMapX software. It validates the extracted climatic and spatial heatmaps using real-time drone footage and Python programming to map and visualize student movement patterns. The findings from the Pearson correlation analysis in the Orange Software suggest that the correlated spatial-thermal heatmap better represents students' actual behaviour with a high correlation coefficient of 0.84 (p = 0.84). Furthermore, this study sheds light on how students' dynamic and static behaviours are linked to the schoolyard's thermal and spatial characteristics. In conclusion, this research innovatively combines real-time monitoring with dual-simulations to analyze student movement behaviour, providing valuable insights into specific schoolyard dynamics. [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]

Recently, the need to separate truth from lies has motivated lie detection as a constant human endeavor; therefore there is a need to develop lie detection techniques and focus on the new area of lie detection utilizing facial expression. Human faces are a powerful repository of emotions in the complicated interaction between verbal and non-verbal clues that characterize human communication. From this micro-expression, the transitory emotion discloses the more prominent indicators that precede deceitful behavior, which makes the tapestry rich in information that can be harnessed to detect a lie. Historically, the development of deceiving lies passed through many developments to find the best way to get high performance, but the development of artificial intelligence and face recognition has further altered the landscape of lie detection. In this paper, the reason for lie detection is revealed with the techniques used to detect lies. The paper aims to present and survey the techniques with comparison used to detect lies, which will highlight the importance of this topic and urge researchers to develop current techniques or find other related techniques that serve the issue. The presentation of the techniques in this research revealed that the lie detection technique using facial expressions is considered the best technique to achieve the detection of lies. Facial expression is the most efficient because it does not require physical contact and because they are visual of real internal feelings and not voluntary movements, and computer vision and artificial intelligence have had an effective role in supporting this method and exploiting it optimally. Finally, the paper shows the limitations and achievements that the researchers found in their research to help researchers in this field. [ABSTRACT FROM AUTHOR]

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience. [ABSTRACT FROM AUTHOR]

The disequilibrium theory suggests restricting a behavior below baseline levels will induce response deficit and make that behavior a more impactful reinforcer. This reinforcement principle was incorporated into a behavior change project for eight students, where the instrumental behavior was homework, and the contingent behavior was social media (SM) access. Students self‐selected their level of SM access deficit and completed both a baseline and treatment phase during the first 8 weeks of an undergraduate learning and behavior course. Most students increased daily homework rates during treatment relative to baseline, although the average increase was not statistically significant. Daily SM rates were significantly decreased during treatment relative to baseline, which was evidence of response deficit. Students rated the behavior change project high on most social validity measures. These results indicate that behavior change projects based on the disequilibrium theory are a viable way to induce changes in socially significant behaviors. [ABSTRACT FROM AUTHOR]

Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify 'out-profiled' consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. [ABSTRACT FROM AUTHOR]

This research paper explores the behavior analysis of a repairable 2-out-of-4 system utilizing an evolutionary algorithm approach. The 2-out-of-4 system configuration is a critical setup widely employed in various engineering applications, necessitating thorough understanding and optimization for reliability and performance enhancement. By integrating evolutionary algorithms with system analysis, this paper aims to optimize system parameters, such as redundancy allocation and maintenance scheduling, to improve reliability and availability. The proposed methodology offers a novel approach to address the challenges associated with the complex behavior of repairable 2-out-of-4 systems, providing insights for system designers and engineers.

Abstract Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

Copyright of Acta Comportamentalia is the property of Instituto de Psicologia y Educacion de la Universidad Veracruzana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Comportamentalia is the property of Instituto de Psicologia y Educacion de la Universidad Veracruzana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research introduces an algorithmic framework for enhancing the security of Internet of Things (IoT) networks. The Enhanced Anomaly Detection (EAD) algorithm initiates the process by detecting anomalies in real-time IoT data, serving as the foundational layer. The Behavior Analysis for Profiling (BAP) algorithm builds upon EAD, adding behavior analysis for profiling and adaptive identification of abnormal behavior. Signature-Based Detection (SBD) involves pre-identified attack signatures, which supports detection of known attacks and provides proactive defense measures against documented threats. The MLID, or the Machine Learning-Based Intrusion Detection, algorithm uses trained machine learning models in order to detect anomalies and the adaptability to changing security risks. The Real-Time Threat Intelligence Integration (RTI) algorithm integrates updated threat intelligence feeds, which improves the framework's responsiveness to emerging threats. The visual representations illustrate once again the idea of the new framework being very accurate at intergration, applicability, and overal security effectiveness. The research makes a standard solution which proves to be a smart and responsive way guarding the IoT networks reducing and even fighting known and potential threats in a real-time mode. [ABSTRACT FROM AUTHOR]

Online education has been expected to be the future of learning; it will never replace the value of traditional classroom experiences fully. Technical problems have less impact on offline education, which gives students more freedom to plan their time and stick to it. In addition, teachers cannot observe their students' behavior and activities during offline education, and they can intervene when necessary. The offline education helps to know the way of behavior analysis of students. Depending upon the analysis student's characteristics and classroom performance can be evaluated by the teachers. The classroom analysis of the students helps in framing the lesson plan easier. The student's activity freedom is also focused on the offline education. The student's behavior and the study characteristics are clearly noticed by offline education classes. The complete educational sector performance is centered on the behavior analysis of the students. As long as students need offline education, it would be a critical component of their overall growth. As educational resources have grown, it has become more crucial to examine and evaluate offline classroom teaching behavior to indicate overall institution performance. A deep learning-student attention recognition framework (DL-SARF) for offline classroom assessment is developed in this article. There are three approaches to professional classroom behavior analysis: the student's intense focus on their side face, head down, and eyes. As the experiments demonstrate, the proposed deep learning-student attention recognition framework can accurately assess student behavior in the classroom and make the administration and implementation of the lesson plan easier. [ABSTRACT FROM AUTHOR]

BACKGROUND: Caenorhabditis elegans is a widely used model animal. Chemotaxis assay is one of the experiments that study the effects of different chemicals on nematodes. It is mainly used to study the effects of different chemicals on the perception behavior of nematodes. By conducting this experiment, not only can the neurotoxicity of chemicals be reflected, but also the impact of chemicals on physiological functions regulated by the nervous system, such as nematode feeding behavior and basic motor ability. OBJECTIVE: The experiment of detecting the response of nematode to chemicals is also a common method of chemical toxicity testing based on nematode models. In the analysis of worm tendency behavior, manual operations are generally used. Manually processing a large number of worms under a microscope is very time-consuming and labor-intensive. The current quantitative methods for nematode chemotaxis experiments are not only time-consuming and labor-intensive, but also biased in experimental results due to differences in judgment standards among experimenters. The automatic and efficient quantification method for nematode chemotaxis experiments is a very important technical difficulty in the field of nematode experiments. METHODS: Here, we have designed an automatic quantification method for nematode chemotaxis experiments by incorporating image acquisition and processing techniques into the nematode experiment. RESULTS: The experimental results show that the Pearson correlation coefficient between manual and automatic counting results is 0.978. CONCLUSION: This proves the effectiveness of our method. Applying the automatic measurement method to replace manual counting by the experimenter can improve work efficiency, and reduce errors in human counting operations. [ABSTRACT FROM AUTHOR]

The Burn Care Quality Platform (BCQP) consolidates data previously collected from the National Burn Repository and the Burn Quality Improvement Program into a single registry. Its data elements and their associated definitions are tailored to create consistency across other national trauma registries, namely the National Trauma Data Bank implemented by the American College of Surgeons Trauma Quality Improvement Program (ACS TQIP). The BCQP now includes 103 participating burn centers and has captured data from 375,000 total patients as of 2021. With 12,000 patients entered under the current data dictionary, the BCQP represents the largest registry of its kind. On behalf of the American Burn Association Research Committee, the aim of this whitepaper is to provide a succinct overview of the BCQP, showcasing its unique features, strengths, limitations, and relevant statistical considerations. This whitepaper will highlight the resources available to the burn research community and offer insight on proper study design when preparing to conduct a large data set investigation for burn care. All recommendations herein were formulated through the consensus of a multidisciplinary committee and based on the available scientific evidence. [ABSTRACT FROM AUTHOR]

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students' physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923. [ABSTRACT FROM AUTHOR]

Skin ulceration syndrome of sea cucumbers is one of the most serious diseases in intensive aquaculture, and it is the most effective way of preventing the spread of this disease to detect the abnormal behavior of sea cucumbers in time and take corresponding measures. However, the detection and tracking of multi-object is a hard problem in sea cucumber behavior analysis. To solve this problem, this paper first proposes a novel one-stage algorithm SUS-YOLOv5 for multi-object detection and tracking of sea cucumbers. The proposed SUS-YOLOv5 optimizes the maximum suppression algorithm in the overlapping region of the object detection box. Next, the SE-BiFPN feature fusion structure is proposed to enhance the transmission efficiency of feature information between deep and shallow layers of the network. Then, a MO-Tracking algorithm is proposed integrated with DeepSORT to achieve real-time multi-object tracking. Experimental results show that the mAP@0.5 and mAP@0.5:0.95 of the proposed object detector reach 95.40% and 83.80%, respectively, which are 3.30% and 4.10% higher than the original YOLOv5s. Compared with the traditional SSD, YOLOv3, and YOLOv4, the mAP of SUS-YOLOv5 is improved by 5.49%, 1.57%, and 3.76%, respectively. This research can realize the multiobject detection and tracking, which lays the foundation for the prediction of skin ulceration syndrome in sea cucumbers and has a certain practical application value for improving the intelligence level of aquaculture. [ABSTRACT FROM AUTHOR]

This review synthesizes the literature base evaluating the effects of behavior analytic interventions delivered via augmented reality (AR) technology for individuals with autism. We also conducted a quality review. We identified 14 studies with a majority of the studies (n = 11; 79%) utilizing single-case research design. Of the 14 studies, three met the quality criteria to be classified as "strong" and can offer evidence on the integration of AR technology into the practice of behavior analysis. All three studies taught a functional skill (i.e., tooth brushing and navigation). The remaining studies focused on skill acquisition with zero studies utilizing AR within the context of behavior assessment or behavior reduction interventions. This literature base is emerging with additional research required. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study was to develop and evaluate the application of computerized programming to teach how to study expository texts. Principles of programmed instruction were used, and different target behaviors were focused on: inspecting the text, locating and highlighting important information, paraphrasing, building schemes, and conceptual maps, and elaborating questions on the topic. The activities were designed and carried out using Google Forms, Socrative, and Wordwall applications. Nine students from the 3rd to the 6th year of elementary school, from public and private schools, participated in this study. Participation was online and remote. The results showed an improvement in the performance of the participants in seven of the ten selected objectives for evaluation. In conclusion, it is possible to teach how to study expository texts through programmed instruction based on a clear description of the objectives involved in this repertoire.

The Good Behavior Game (GBG) is an interdependent group-oriented contingency management system successfully used in school settings to promote positive student behaviors. As a classroom management intervention, there is a large body of evidence for it increasing desirable classroom behaviors and decreasing problem behaviors across a range of student populations. Recent studies have also demonstrated that a positive reinforcement-focused version of the GBG can be successfully implemented using a software application. The purpose of this article is to provide a brief overview of the GBG and its evidence base and describe the steps for implementing the GBG with a freely available software application for classroom management.

The modelling and forecasting of personal conduct depend on the records that individuals contribute at some stage in the shared network. It is essential to conduct this analysis to forecast the pursuits, feelings, and personal options, ultimately resulting in a development in service efficiency. However, to get accurate predictions, it’s far essential to triumph over the mission of information segregation and retaining precision in evaluation. This paper offers the preference-based predictive behaviour analysis (PPBA) approach to address this problem. This approach aims to achieve the best possible accuracy stage in the desired identity. A study primarily based on analysis is done on mutated and segregated data in the proposed methodology, which uses transfer learning. A more sophisticated understanding of consumer options is made possible through the system of segregation, which is completed by studying desire deviations from the attitude of many inputs. Diverse mutations in deviation sites are recognized during the learning method, which aligns options for various statistics. State validations are completed primarily based on an individual’s preceding options and any novel deviations observed inside the present state of affairs. This involves figuring out novel deviations from preceding behaviours, which can be rooted in various person options, which, in the long run, results in the refinement of user conduct prediction and modelling across quite a few applications primarily based on social networks. The proposed PPBA achieves 14.82% high detection accuracy for different input values, 9.41% less analysis time, 7.49% less false positives, 9.5% less complexity, and 10.34% high preference ratio.

The purposes of this study were to explore students’ learning performance, knowledge construction, and behavioral patterns in computer-supported collaborative learning (CSCL) online discussions with/without using Form+Theme+Context (FTC) model guidance scaffolding in visual imagery education. In the online learning activities, the control group did not use the FTC model guidance scaffolding, while the experimental group did. This study employed quantitative content analysis and sequential analysis to investigate the discussion content and behavioral patterns of 63 students from a private university in Taiwan during online discussion learning activities. Results showed that the learning performance of the students in the experimental group outperformed that of students in the control group. Moreover, the study revealed that the two groups of students were primarily sharing or comparing information during discussion. More behaviors of exploring opinions and concepts and communicating or constructing knowledge among group members were observed in the experimental group. Secondly, students in the experimental group participated more in knowledge construction than did students in the control group, and their behavioral patterns were more diverse. Accordingly, this study shows that incorporating the FTC model into learning with sufficient guidance from the instructor could be useful for improving students’ visual imagery analysis abilities.

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.

Introduction: The current research aimed to determine the dominant draft scenario of the life of women with bipolar I disorder. Materials and Methods: This qualitative research was a phenomenological study, and we collected the data using Colaizzi's method. We interviewed 21 female patients with bipolar I disorder admitted to the neuropsychiatric department of three hospitals in Isfahan City, Iran, in 2020, and the life scenarios of the subjects were investigated. The triangulation method was used to obtain the validity and reliability of the research findings. Results: The scenarios were categorized into high, medium, and low repetition. The most frequent codes included suicide (26 repetitions) with the subcategories of suicidal thoughts, despair of life, and feeling close to death. Codes with moderate repetition included negative emotions (18 repetitions) with the subcategories of feeling like others are strangers to you, feeling rejected, uninterested in life, liking dark colors, and feeling empty. The less frequent codes included negative actions (11 repetitions) with the subcategories of self-sacrifice, suppression of hearing from others, sexual abuse, self-mutilation, running away from home, substance use, and difficulty in life. Conclusion: The findings showed that the dominant scenario in women with bipolar disorder, based on high frequency, was the "do not exist" scenario. [ABSTRACT FROM AUTHOR]

Cyber security situation awareness, has become a hotpot of research. However, the existing cyber security situation awareness methods are difficult to extract high-order features from network traffic data. In this work, we present an improved cyber security situation awareness method based on ResMLP and LSTM network from a new perspective. Our work focus on cyber attack behavior analysis, that is a key research content of cyber security situation awareness. It introduces the Residual Multi-Layer Perceptrons in deep learning into the network structure of long-short term memory. It can effectively extract the spatial and temporal characteristics of network traffic data, reduce the computational complexity, and improve the accuracy of cyber security situation awareness. Firstly, we extract the spatial features using the ResMLP network. Secondly, we extract the temporal characteristics using the LSTM network. The architecture of the ResMLP network replaces the self-noticing layer with a linear interaction layer, and this design architecture allows the model to guarantee accurate cyber attack behavior analysis performance while balancing the computational cost of the model, which can improve the detection efficiency of the model. Considering that the network data are fed into the model in the form of time series after processing, the model incorporates LSTM networks to avoid the gradient problem while better bringing up the temporal characteristics in the data.The experimental results show that the proposed method can model the future cyber security situation in a network environment more accurately than other similar methods. [ABSTRACT FROM AUTHOR]

Copyright of Acta Comportamentalia is the property of Instituto de Psicologia y Educacion de la Universidad Veracruzana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are limited resources outlining age‐referenced rates of problem behavior displayed by neurotypical children. Such information is important for practitioners as a basis for social comparison when they are targeting behavior reduction goals for neurodivergent clients. We distributed a survey to parents of children aged 1–10 years without a developmental diagnosis in which parents reported frequency of five problem behaviors across a 24‐h period, as well as commonly targeted replacement behaviors. Problem behavior was reported across all age groups to varying degrees based on topography and age. Replacement behaviors, such as waiting and tolerating denials generally improved as children increased in age but still largely remained below 80% of opportunities. The present study may serve as a reference for researchers and clinicians to set goals that are developmentally appropriate. [ABSTRACT FROM AUTHOR]

Objectives: A nuanced examination of human behavior can yield valuable insights into whether a mindfulness-based intervention (MBI) promotes meditation practices and to what degree in various populations. This study aimed to offer a comprehensive analysis of meditation behaviors exhibited by individuals in response to a MBI in the context of an intensive residential treatment program for addiction recovery. Method: A total of 100 participants enrolled in a residential treatment program participated in an added MBI component to their routine care. We quantified the type, frequency, and duration of meditation practices outside of scheduled MBI sessions and conducted a descriptive analysis to investigate how these practices during the intervention predicted meditation behaviors 7 months later. Results: All seven common types of mindfulness meditation assessed, except the body scan, were performed an average of once per day during the MBI. The longest average duration of meditation practice was observed in the case of walking meditation, during both the MBI (M=20.30, SD=16.66 min) and 7-month follow-up (M=25.43, SD=20.85 min). Out-of-session meditation frequency (unstandardized beta, B=0.56, p<0.001) and duration (B=0.45, p=0.02) during the MBI significantly predicted subsequent meditation behaviors 7 months later, even after adjusting for treatment site discharge status and other clinical variables. Conclusions: Most participants in this addiction recovery sample consistently engaged in a variety of meditation practices outside of formal MBI class sessions while in residential living, performing them approximately once a day, with a particularly robust response to walking meditation. The persistence of meditation practice 7 months later suggests that the learned behaviors endure throughout addiction recovery. Preregistration: This original trial is registered with clinicaltrials.gov (NCT02977988) [ABSTRACT FROM AUTHOR]

Background: The area of social skills is broad, in theory and in practice. For social skills training, various clinical practices have been applied in group sessions, as have motivational resources such as role-playing games (RPGs). In recent years, the need arose to assess the clinical impact of the pandemic. The objective of this study was to determine the impact that the pandemic has had on in-person and online social skills training. Methods: We evaluated six subjects with autism spectrum disorder, with or without another, similar disorder, each of whom attended a total of 12 two-hour RPG sessions over a 12-month period. The original (Portugueselanguage) version of the Social Skills Inventory for Adolescents was applied at three different time points (pre-, mid-, and post-intervention). Results: After six in-person tabletop RPG sessions, there was an increase in the mean frequency scores and a decrease in the mean difficulty scores. However, during the pandemic, the remaining six sessions were conducted online and the effect was the opposite. Conclusion: Our data indicate that there is a need for further studies assessing social skills training in online contexts. [ABSTRACT FROM AUTHOR]

This clinical practice guideline for the diagnosis and treatment of acute bacterial arthritis (ABA) in children was developed by a multidisciplinary panel representing the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA). This guideline is intended for use by healthcare professionals who care for children with ABA, including specialists in pediatric infectious diseases and orthopedics. The panel's recommendations for the diagnosis and treatment of ABA are based upon evidence derived from topic-specific systematic literature reviews. Summarized below are the recommendations for the diagnosis and treatment of ABA in children. The panel followed a systematic process used in the development of other IDSA and PIDS clinical practice guidelines, which included a standardized methodology for rating the certainty of the evidence and strength of recommendation using the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation) (see Figure 1). A detailed description of background, methods, evidence summary and rationale that support each recommendation, and knowledge gaps can be found online in the full text. [ABSTRACT FROM AUTHOR]

Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%. [ABSTRACT FROM AUTHOR]

While racial, ethnic, and socioeconomic disparities in burn care have been identified in the literature, there is a paucity of research into specific underlying causes of these disparities. Here, we sought to characterize whether time to initial burn consult might contribute to racial, ethnic, and socioeconomic differences in burn care outcomes. We performed a retrospective review of all patients evaluated by the burn surgery service at a single regional ABA-verified burn center between June 2020 and April 2022. Patients without data for the time of onset of burn injury were excluded. Time to burn consult was defined as the time from onset of burn injury to the time of first burn consult. Three hundred and sixty-five patients met the inclusion criteria. Average age was 33.3 years, and 65.8% of patients were male. Average time to burn consult for all patients was 17 hours and 07 minutes. There were no significant differences in this variable among our cohort when stratified by race, ethnicity, or insurance status. Rates of surgical management (Chi-squared P = 0.05) and length of stay (ANOVA P < 0.0001) significantly differed by insurance status, but not among racial or ethnic groups. Medicare patients had the highest rates of surgical intervention and longer hospital stays; patients without insurance had the lowest rates of surgical intervention and shorter hospital stays. These results indicate that time from burn onset to burn consult is unlikely to contribute meaningfully to racial, ethnic, and socioeconomic disparities in burn care. Further studies are needed to better understand other aspects of burn care that may contribute to the noted disparities. [ABSTRACT FROM AUTHOR]