Multiple Formats Research Articles

Feasibility and Satisfaction with Virtual Trainings for Evidence-Based Child Trauma Treatments during the COVID-19 Pandemic

ABSTRACT The COVID-19 pandemic necessitated a change to virtual formats for evidence-based treatment trainings, including child trauma treatments. However, there has been limited research on trainee perceptions of virtual training. The present study examined trainee experiences in statewide dissemination of Child-Parent Psychotherapy (CPP), Trauma-Focused Cognitive Behavior Therapy (TF-CBT) and Parent–Child Interaction Therapy (PCIT) in both virtual and in-person formats. In all models, participants reported several benefits of virtual trainings related to pragmatics, balancing personal obligations, overall physical and emotional comfort, and increased access. Within treatment models, there were some relative weaknesses noted about virtual training compared to the in-person format. Virtual PCIT participants perceived lower knowledge gains and stated that the teaching format was not as conducive to learning. Virtual TF-CBT participants noted lower intention to use the treatment model and assessment measures. Virtual CPP participants did not note any perceived weaknesses. Despite these relative weaknesses noted, overall feasibility and satisfaction remained high across both formats, supporting virtual training as a feasible and acceptable format. Written feedback demonstrated significant variability in format preference, suggesting that multiple format options could create a more inclusive and accessible space. Lessons learned include increasing technological competencies and breaks, acknowledging the benefits and challenges of virtual training, and holding benevolence for trainees and trainers. The results of this study have implications for clinicians, trainers, and stakeholders seeking virtual dissemination and implementation methods.

Human preferences toward algorithmic advice in a word association task

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even show complacency. In other cases, humans are mistrustful of algorithmic advice, or will hold algorithms to higher standards of performance. Given the increasing use of algorithms to support creative work such as text generation and brainstorming, it is important to understand how humans will respond to algorithms in those scenarios—will they show appreciation or aversion? This study tests the effects of algorithmic advice for a word association task, the remote associates test (RAT). The RAT task is an established instrument for testing critical and creative thinking with respect to multiple word association. We conducted a preregistered online experiment (154 participants, 2772 observations) to investigate whether humans had stronger reactions to algorithmic or crowd advice when completing multiple instances of the RAT. We used an experimental format in which subjects see a question, answer the question, then receive advice and answer the question a second time. Advice was provided in multiple formats, with advice varying in quality and questions varying in difficulty. We found that individuals receiving algorithmic advice changed their responses 13% more frequently (chi ^{2} = 59.06, p < 0.001) and reported greater confidence in their final solutions. However, individuals receiving algorithmic advice also were 13% less likely to identify the correct solution (chi ^{2} = 58.79, p < 0.001). This study highlights both the promises and pitfalls of leveraging algorithms to support creative work.

Understanding South Australia’s blood products usage patterns and outcomes, using data linkage.

ObjectivesThe purpose of this analytical activity was to ensure confidence in the technical capability for extracting, linking, and integrating public hospital inpatient data, public pathology blood transfusions records and blood tests, to optimise records linkage allowing patterns and trends to be then analysed with confidence. ApproachThe SURE secure data platform was essential to ensure data governance and security requirements were met while integrating health data spanning 18 months (January 2018 - June 2019). Data sources came in multiple formats of varying quality. R was chosen for its data wrangling abilities and reproducibility. The phases were: Source data loading and cleaning Linking hospital inpatient and blood transfusions records Summarising linked transfusion data Linking inpatient and blood tests data Summarising linked tests data Integrating hospital data with summarised transfusion and summarised tests data Deriving additional variables based on summarised data ResultsFrom 143,192 transfusion records, 55,053 (38.4%) were excluded as they did not meet the inclusion criteria (e.g., hospital or blood product out-of-scope). From 7,897,451 blood test records, 238,013 (3.0%) were excluded, mostly of poor quality (missing/invalid hospital code). Initially 91.4% of transfusion records were matched with hospital inpatient records. The linkage rate for state-wide blood test records was 62.3% for tests records, noting the low match rate was attributed to tests not performed on public hospital patients, as the blood test data was statewide. Linkage process was improved by adding additional patient codes from public pathology’s internal patient identifiers. The linkage rate improved to 95.5% for transfusion records and 64.4% for test records. Conclusion12 different data sources, with differing file types and formats, needed coding to achieve standardised results, enabling future reproducibility. Over one hundred business rules were implemented to produce a robust solution for future data updates. End results were analysed, and it was determined that linkage and integration quality exceeded previous similar attempts in terms of match rate and accuracy.

Ocean negative carbon emissions: A new UN Decade program

Ocean negative carbon emissions: A new UN Decade program

Evaluating Student Perceptions and Performance of Active Learning Strategies in a Pharmacotherapy Course

Learning-preferences vary and thus it is beneficial for students to experience a variety of active-learning pedagogies for meaningful learning and long-term retention. This novel study attempted to evaluate systematically and longitudinally, student perceptions of multiple formats of active-learning strategies. Four different active-learning strategies were utilized in a course - team-based-learning (TBL), case-study, flipped-classroom, and interactive-technology. Student perspectives indicated active-learning strategies were helpful for learning. Faculty in higher-education could implement active-learning strategies in their courses for enhanced student-engagement, long-term retention, and success. This easily replicable method of collecting student perceptions on learning-experience anonymously enables learner-centered course design and promotes continuous-quality-improvement in higher-education.

Bioelectrical Impedance Spectroscopy for Monitoring Mammalian Cells and Tissues under Different Frequency Domains: A Review.

Bioelectrical impedance analysis and bioelectrical impedance spectroscopy (BIA/BIS) of tissues reveal important information on molecular composition and physical structure that is useful in diagnostics and prognostics. The heterogeneity in structural elements of cells, tissues, organs, and the whole human body, the variability in molecular composition arising from the dynamics of biochemical reactions, and the contributions of inherently electroresponsive components, such as ions, proteins, and polarized membranes, have rendered bioimpedance challenging to interpret but also a powerful evaluation and monitoring technique in biomedicine. BIA/BIS has thus become the basis for a wide range of diagnostic and monitoring systems such as plethysmography and tomography. The use of BIA/BIS arises from (i) being a noninvasive and safe measurement modality, (ii) its ease of miniaturization, and (iii) multiple technological formats for its biomedical implementation. Considering the dependency of the absolute and relative values of impedance on frequency, and the uniqueness of the origins of the α-, β-, δ-, and γ-dispersions, this targeted review discusses biological events and underlying principles that are employed to analyze the impedance data based on the frequency range. The emergence of BIA/BIS in wearable devices and its relevance to the Internet of Medical Things (IoMT) are introduced and discussed.

HabitEst3D: A User-Friendly Software for Estimating Mixed Crystal Habits from Two-Dimensional Sections in Igneous Rocks

Crystal habit in igneous rocks provides a window to understand magmatic processes or reveal crystallization environments. Generally, we can obtain the two-dimensional (2D) crystal habits directly from the thin section, which is easy to access. However, the three-dimensional (3D) habit cannot be directly observed in thin sections and needs the stereological conversion from 2D habits. Statistical methods have been developed for stereological conversion, but they cannot identify mixed habits. Our study uses the distributions of the cut-sections of pre-set habits to match the unknown sample and enumerates habit combinations to find the best-match results for mixed habits. The specialized program, HabitEst3D, is developed according to our model in this study. The program is written in Python and is a cross-platform with a user-friendly graphical interface. The input data are the aspect ratio of 2D sections. After setting the parameters, the program finds the best-match estimations fitting the sample, visualizes the results, and saves them in multiple file formats. The program is robust and is not sensitive to outliers to obtain more accurate results. It traverses all possible combinations and needs memory and time but effectively explores the mixed crystal habits in the sample, contributing to investigating magmatic processes in more detail.

An exploratory study of early childhood coaches’ practices and professional learning needs

ABSTRACT Coaching is increasingly being used as a mechanism to improve the quality of early childhood education. Yet, for coaching outside of researcher-controlled interventions, limited information details coaches’ reports of their practices’ professional learning needs. We addressed this gap via an exploratory study utilizing online questionnaires of 91 coaches working with educators in a Midwestern US state across 12 coaching initiatives. Most participants had less than 5 years of experience working as an early childhood coach. Almost a third coached for multiple initiatives. Coaching occurred via multiple formats and often addressed behavior management and social emotional development regardless of the coaching initiative. Coaches identified challenges regarding scheduling and working with difficult learners and reported that peer support and being coached themselves were the most beneficial learning experiences. Findings suggest that the perspective of the coach is important in informing training and professional learning for both coaches and educators.

MLNe: Simulating and estimating effective size and migration rate from temporal changes in allele frequencies.

In studies of molecular ecology, conservation biology, and evolutionary biology, the current or recent effective size (Ne) of a population is frequently estimated from the marker genotype data of two or more temporally spaced samples of individuals taken from the population. Despite the developments of numerous Bayesian, likelihood, and moment estimators, only a couple of them can use both temporally and spatially spaced samples of individuals to estimate jointly the effective size (Ne) of and the migration rate (m) into a population. In this note, I describe new implementations of these joint estimators of Ne and m in software MLNe which runs on multiple platforms (Windows, Mac, Linux) with or without a graphical user interface (GUI), and has an integrated simulation module to simulate genotype data for investigating the impacts of various factors (such as sample size and sampling interval) on estimation precision and accuracy, exploits both message passing interface (MPI) and openMP for parallel computations using multiple cores and nodes to speed up analysis. The program does not require data pre-processing and accepts multiple formats of a file of original genotype data and a file of parameters as input. The GUI facilitates data and parameter inputs and produces publication-quality output graphs, while the non-GUI version of software is convenient for batch analysis of multiple datasets as in simulations. MLNe will help advance the analysis of temporal genetic marker data for estimating Ne of and m between populations, which are important parameters that will help biologists for the conservation management of natural and managed populations. MLNe can be downloaded free from the website http://www.zsl.org/science/research/software/.

The Emergency Medicine Resident Retreat: Creating and Sustaining a Transformative and Reflective Experience

Introduction Burnout rates for emergency medicine residents are high. One intervention and initiative to enhance wellness and address burnout is the resident retreat. Retreats have multiple formats and are often designed with an emphasis on social events. This longitudinal retreat curriculum for a three-year residency training program was designed emphasizing rest, a step away from what is familiar, and reflection. MethodsIndividual resident retreats were designed for each year of postgraduate training. The agenda for each is organized and intentional. Activities focused on personal well-being, self-reflection, team building, professional development, and physical activities are coupled with topics unique to class year roles and responsibilities. Retreats are held away from the hospital establishing a separation from the workplace.ResultsThe retreat program has been sustainable for almost decades with trainees evaluating it highly. Faculty and residents enthusiastically participate in the program and consider it a fundamental part of the residency; 93.75% of residents surveyed strongly agreed that the retreats benefit their training while 94.2% strongly agreed that retreats increased their enthusiasm for training.ConclusionsAn emergency medicine resident retreat program focusing on unique elements for each post-graduate year is achievable and sustainable in an emergency medicine residency program. Over time, the retreat has become an integral part of the residency experience with positive experiences for both faculty and trainees.

Redundancy and Reducibility in the Formats of Spatial Representations

Mental representations are the essence of cognition. Yet to understand how the mind works, one must understand not just the content of mental representations (i.e., what information is stored) but also the format of those representations (i.e., how that information is stored). But what does it mean for representations to be formatted? How many formats are there? Is it possible that the mind represents some pieces of information in multiple formats at once? To address these questions, I discuss a “case study” of representational format: the representation of spatial location. I review work (a) across species and across development, (b) across spatial scales, and (c) across levels of analysis (e.g., high-level cognitive format vs. low-level neural format). Along the way, I discuss the possibility that the same information may be organized in multiple formats simultaneously (e.g., that locations may be represented in both Cartesian and polar coordinates). Ultimately, I argue that seemingly “redundant” formats may support the flexible spatial behavior observed in humans and that researchers should approach the study of all mental representations with this possibility in mind.

Face mediated human–robot interaction for remote medical examination

Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.

OSNR-Aware Modeling and Optimization of Multi-Core Fiber-Based Spectrally–Spatially Flexible Optical Networks

We focus on the optical signal-to-noise ratio (OSNR)-aware optical path (lightpath) planning problem in spectrally and spatially flexible optical networks (SS-FONs) connected using weakly coupled multi-core fibers (MCFs) in which distance-adaptive and super-channel transmission is realized using multiple modulation formats. In the SS-FON considered, the quality of transmission (QoT) of optical signals is degraded due to the inter-core crosstalk (XT) effect occurring in MCFs. To account for the XT impairment when planning lightpath connections, we make use of a reliable QoT model based on the OSNR estimation, in which the XT impairment is integrated with other physical-layer impairments. To model the lightpath planning optimization problem, we develop a novel mixed-integer programming (MIP) formulation that incorporates the OSNR model. In addition, we propose an efficient heuristic method that is capable of solving larger instances of the optimization problem considered. The results of numerical experiments indicate the low scalability of the MIP method due to the presence of XT and high effectiveness of the heuristic method. The analysis of three different network topologies and two types of MCFs shows a high impact of XT on network performance and limited performance gains from the presence of the central core in a 7-core MCF.

Visual Question Answering

We propose the task of free-form and open- ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open- ended answers contain only a few words or a closed set of answers that can be provided in a multiple choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers and discuss the information it provides. Numerous baseline for VQA are provided and compared with human performance. In this model, we have exclusively introduced a feature of voice to text using Speech recognition, Google Text-to-Speech and pygame module.

An Integrated HBIM Framework for the Management of Heritage Buildings

Heritage buildings are subject to severe damage due to their exposure to dynamics such as environmental changes, earthquakes, structural loads, etc., thus needing a proper maintenance and management system. However, during the restoration, maintenance, and management process, heritage building practitioners face numerous challenges, such as inefficient project management, financial loss, and project delay. These problems arise due to a lack of digital documentation and updated information management systems. Heritage building management still uses traditional management techniques where heritage information is managed using multiple format systems by different professionals, thus missing collaboration, information integration, and interoperability. Building information modeling (BIM) is presented here as a supporting tool to address these issues. The aim of this study is to develop a novel HBIM framework to manage heritage buildings in an integrated and interoperable environment that supports 3D digital documentation to conserve a heritage building and facilitate restoration planning and facility management (FM) activities. Moreover, we demonstrate FM integration with BIM to promote this digital technique. The results indicate that the proposed framework is feasible and effective in providing information integration and communication between stakeholders in managing heritage buildings.

Perceptions of an eHealth family-based cardiovascular disease risk reduction intervention: a mixed methods study

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Department for the Economy, Northern Ireland Introduction Controllable cardiovascular disease (CVD) risk factors are often initiated in the family home, yet risk reduction is seldom targeted toward families. Participatory design of eHealth interventions with end-users has potential to increase acceptability and adherence of such interventions. Despite their shared risk of developing CVD, few eHealth interventions target both parents and children at CVD risk. Therein lies an evidence gap; the need for coproduction of a family-based eHealth intervention aimed at early CVD prevention. Purpose The aim of this study was to identify the perceptions of families towards the design, functionality and acceptability of an eHealth, family-based, CVD prevention intervention. Methods Thee online focus groups comprising six families were conducted between April and May 2021. Families consisted of at least one parent who met CVD risk factor criteria, and at least one child aged five to 17 years. Content analysis and narrative synthesis were used to identify categories and subcategories regarding development of and engagement with the proposed intervention. Additionally, quantitative, and demographic measures were used to determine psychosocial and health profiles. Results Three categories were identified from the focus groups: 1) previous experiences of using health-related apps or devices 2) expectations of a newly developed eHealth family-based CVD prevention intervention 3) motivators for engagement with the intervention Goal setting, rewards, accountability, adaptability, recording achievements and competition were considered crucial motivators for engagement for both parents and children. Participants welcomed the intervention, and advised they would prefer a free, personalised, easy-to-use, non-time-consuming intervention, with multiple content formats, and additional information available should they seek to access it. Parents conveyed satisfactory general self-efficacy and quality of life, low anxiety and depression and high perceived social support. Conclusions Overall, families were constructive and forthcoming towards the idea of an eHealth, family-based CVD prevention intervention. These findings informed a prototype of a family-based eHealth intervention aimed at early CVD prevention. The prototype is currently being developed with plans for piloting via a randomised controlled trial in the home environment, by families as an early intervention for the prevention of CVD.

An agenda for establishing the DSM-5 alternative model of personality disorder (AMPD) trait model in adolescence: Comment on Clark and Watson (2022).

In the years since the publication of the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association, 2013), considerable progress has been made in understanding the structure of the traits included in the Alternative Model of Personality Disorders (AMPD) in adulthood. In the next phase of research, the structure of the pathological trait model needs to be established more firmly in adolescence as well. There is promising evidence from studies of the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) that the structure of pathological traits in adolescents shows considerable overlap with the structure in adult samples, but it is premature to conclude that the structure is entirely the same. To make additional progress in assessing personality pathology in youth, measures should be developed in multiple formats and for multiple informants; normal-range personality trait constructs should be included in measures for research and clinical use in young people; and research should incorporate Clark and Watson's (2022) recommendations to add new facets and the domain of Anankastia to adolescent measures of personality pathology. Adolescents stand to benefit from a trait taxonomy in the AMPD that more explicitly includes them. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Simple Multiple Precision Algorithms for Exponential Functions [Tips &amp; Tricks

Exponential functions are essential in many areas of science and engineering. Fast and efficient computing of such functions in multiple floating-point formats is a complex task for all classes of processing units—general purpose CPUs, digital signal processors, GPUs, intelligent processing units, and tensor processors (TPU)—developed recently for neural computing and deep machine learning.

Orientation aware weapons detection in visual data: a benchmark dataset

Automatic detection of weapons is significant for improving security and well being of individuals, nonetheless, it is a difficult task due to large variety of size, shape and appearance of weapons. View point variations and occlusion also are reasons which makes this task more difficult. Further, the current object detection algorithms process rectangular areas, however a slender and long rifle may really cover just a little portion of area and the rest may contain unessential details. To overcome these problem, we propose a CNN architecture for Orientation Aware Weapons Detection, which provides oriented bounding box with improved weapons detection performance. The proposed model provides orientation not only using angle as classification problem by dividing angle into eight classes but also angle as regression problem. For training our model for weapon detection a new dataset comprising of total 7801 weapons images is gathered from the web and then manually annotated with position oriented bounding boxes. Our dataset provides not only oriented bounding box as ground truth but also horizontal bounding box. We also provide our dataset in multiple formats of modern object detectors for further research in this area. The proposed model is evaluated on this dataset, and the comparative analysis with off-the shelf object detectors yields superior performance of proposed model, measured with standard evaluation strategies. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF .

Atomic Belt and Road: China’s international nuclear market entry

Rapid development of China’s internal nuclear market, together with its rise on the international arena and the declared transition to green economy, gave a crucial impetus to its aspirations to enter the international atomic market as a global exporter of nuclear technology. The aim of this article is to define the political and ideological foundations underpinning such an ambitious endeavor, as well as predict the related development in the short and mid-term perspective. The author utilizes both general scientific methods as well as statistical and comparative analysis to single out the directions of China’s nuclear market outreach, both regionally and globally. Pakistan, widely presented as a flagship example of China’s exporting its know-how in nuclear engineering, remains the only partner of a kind in the broader Asia-Pacific. This article demonstrates that Belt and Road Initiative (specifically, the Green Silk Road), out of all multiple formats, was arguably chosen by the Chinese government as the most suitable framework for expanding its influence in the nuclear domain and filling a certain gap which formed after Westinghouse and Areva (now Orano) got reorganised after encountering financial problems. At the same time, China’s bid for leadership in the sphere of nuclear technology is obstructed by some impediments. Among them are Russia’s dominance in breakthrough areas of nuclear engineering and Rosatom’s large market share, as well as lack of political will in some countries’ elites to build ties with China in this strategic realm, opting for competition instead.