Basic Tasks Research Articles

Performance of instrumental activities of daily living in patients with haemophilic arthropathy. A cross-sectional cohort study.

The development of haemophilic arthropathy causes joint damage that leads to functional impairment that limits the performance of activities in patients with haemophilia. The aim was to identify the best predictive model for performing instrumental activities of daily living in adult patients with haemophilia arthropathy. Cross-sectional cohort study. 102 patients were recruited. The dependent variable was the performance of instrumental activities of daily living (Lawton and Brody scale). The dependence on the performance of activities of daily living was the dependent endpoint (Barthel scale). The secondary variables were joint damage (Hemophilia Joint Health Score), pain intensity, and clinical, anthropometric, and sociodemographic variables. The degree of dependence, joint damage, pain intensity, and marital status (Cp=5.60) were the variables that best explain the variability in the performance of instrumental activities of daily living (R2 adj=0.51). Loss of predictive capacity is acceptable with good mean internal (R2 mean=0.40) and external (R2-r2=0.09) validation. According to the predictive pattern obtained, patients with haemophilia, who were married, without joint pain or damage, and independent in their day-to-day lives, had a score of 7.91 points (95% CI: 7.42; 8.39) in the performance of instrumental activities of daily living. The predictive model for the functional capacity of instrumental activities of daily living in haemophilia patients encompasses factors such as level of autonomy, joint impairment, pain severity, and marital status. Notably, despite the presence of joint damage, individuals with haemophilia exhibit a significant level of independence in carrying out both basic daily tasks and instrumental activities of daily living. Id NCT04715100.

Road Boundary Extraction Method from Mobile Laser Scanning Point Clouds

Abstract With the rapid development of mobile laser scanning (MLS) technology, high-precision three-dimensional point cloud data has shown great potential in different fields such as topographic mapping, road asset management and smart city construc-tion. Three-dimensional point cloud data contains not only position information, but also the shape and attributes of the target, which is very convenient for obtaining road information. Accurate extraction of the road boundary is a basic task for obtaining road infrastructural data, which can support the generation of high-precision maps, vehicle navigation and auton-omous driving. However, road boundary extraction in urban environments is easily occluded such as vehicles and pedestri-ans on the road, which leads to problems such as difficulty and incomplete extraction of MLS point cloud road boundaries. To address this problem, this study proposes a road boundary extraction method that integrates pavement edge information, accurately considers the position of the road boundary from two dimensions, eliminates false boundaries, and completes the missing boundary through the extracted boundary spatial relationship. First, grid elevation filtering is used to remove high-level non-ground points. Then the pavement edges and curb stone points are extracted from the preprocessed point cloud, and they are superimposed to remove false boundary points to obtain accurate road boundaries. Finally, based on the spatial relationship of road boundaries, missing parts are detected and repaired to obtain complete road boundaries. Experimental results show that the accuracy on real road scenes exceeds 98%, the completeness rate is above 91%, and the extraction quality is above 90%, which verifies the effectiveness and accuracy of this method.

The early visual processing of faces in a basic classification task: An ERP study of spatial frequency, other-race and other species effect

ABSTRACT This study investigates the neural processing of other-race and other-species faces, using spatial frequencies (SFs) to probe into early visual processing mechanisms. We conducted a basic classification task to reveal fundamental aspects of facial processing without the complexity of higher-order recognition tasks. This approach utilized SFs set at low (LSF, 0.53 cycles per degree [cpd]), broadband (BB), and high (HSF, 2.1 cpd) SFs to examine their impact on the both P100 and N170 components of event-related potentials. Our findings revealed a significant latency difference in the N170 component under HSF conditions, indicating race-specific efficiency in processing fine details between Asian and Caucasian faces. Furthermore, N170 amplitudes were consistently larger for human than monkey faces, with this difference more pronounced in LSF and BB conditions. This finding indicates that the other-species effect in early visual processing is more pronounced in holistic processing (LSF and BB conditions) rather than in fine-detail processing.

Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder

Previous studies indicate that transcranial direct current stimulation (tDCS) is a promising emerging treatment option for autism spectrum disorder (ASD) and its efficacy could be augmented using concurrent training. However, no intrastimulation social cognition training for ASD has been developed so far. The objective of this two-armed, double-blind, randomized, sham-controlled clinical trial is to investigate the effects of tDCS combined with a newly developed intrastimulation social cognition training on adolescents with ASD. Twenty-two male adolescents with ASD were randomly assigned to receive 10 sessions of either anodal or sham tDCS at F3/right supraorbital region together with online intrastimulation training comprising basic and complex emotion recognition tasks. Using baseline magnetic resonance imaging data, individual electric field distributions were simulated, and brain activation patterns of the training tasks were analyzed. Additionally, questionnaires were administered at baseline and following the intervention. Compared to sham tDCS, anodal tDCS significantly improved dynamic emotion recognition over the course of the sessions. This task also showed the highest activations in face processing regions. Moreover, the improvement was associated with electric field density at the medial prefrontal cortex and social awareness in exploratory analyses. Both groups showed high tolerability and acceptability of tDCS, and significant improvement in overall ASD symptoms. Taken together, multisession tDCS improved dynamic emotion recognition in adolescents with ASD using a task that activates brain regions associated with the social brain network. The variability in the electric field might diminish tDCS effects and future studies should investigate individualized approaches.

A Glossary of Terms in Artificial Intelligence for Healthcare

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

PGY-2 emergency medicine residents are more efficient when paired with an early clinical medical student.

There is a concern that provide increased extraneous cognitive load when paired with residents on shift. However, this may be offset by the decrease in extraneous load they may provide to the residents they are paired with by offloading basic patient care tasks. We hypothesized that these forces may not be balanced. We conducted a retrospective observational analysis of PGY-2 emergency medicine residents and junior medical students at a single academic emergency department (ED) in the Midwest. A series of efficiency metrics (relative value unit [RVUs], patients per hour [PPH], time to note completion, and resident assignment to disposition [RATD]) as well as one quality metric (number of return ED visits; "bouncebacks") were compared for resident shifts in which a student was paired with the resident as well those in which no student was paired utilizing a regression model. A total of 1844 records met the inclusion criteria (214 shifts with a paired medical student and 1630 without). After covariates were adjusted for, medical student shift status was a statistically significant predictor of increases in PPH (p < 0.0001) and RVUs (p = 0.0161) but was not significantly associated with RATD (p = 0.6941), log-time to note completion (p = 0.1604), or bounceback status (p = 0.9840). Shifts where residents were paired with medical students were predicted to see an additional 1.131 (95% confidence interval [CI] 0.660-1.602) PPH and produce an additional 1.923 RVUs (95% CI 1.130-3.273) per shift relative to shifts without medical students. When junior medical students were paired with a PGY-2 resident on ED shifts, there was a significant increase in the PPH and RVUs generated when compared with shifts in which no medical student was paired with them.

Posterior comparison of model dynamics in several hybrid turbulence model forms

Hybrid turbulence models that can accurately reproduce unsteady three-dimensional flow physics across the entire range of grid scales and turbulence dynamics from Reynolds-averaged Navier–Stokes (RANS), through large-eddy simulation (LES), down to direct numerical simulations (DNS) are of increasing interest to the turbulence modeling community. However, despite decades of research and development, the basic tasks of eliminating poor-performing hybrid RANS-LES models and accelerating adoption of superior models through well-designed validation and verification have yet to occur. As a step in this direction, in this work we evaluate thirteen different hybrid RANS-LES models via systematic grid refinement of decaying homogeneous isotropic turbulence. We further derive a novel mathematical framework for assessing the energy partitioning dynamics of each Hybrid RANS-LES model, wherein model-to-model variations in energy partitioning can be interpreted as different feedback mechanisms operating on a low-dimensional nonlinear dynamical system. We found that model forms similar to the flow simulation methodology—also often termed very-large eddy simulation—are dynamically inconsistent with DNS at all resolutions. Additionally, we found a strong dynamical similarity in the feedback mechanisms of all models related to detached eddy simulation and partially averaged Navier–Stokes that is inherent to their general model forms.

Self-Directed Learning (SDL) Model on Pentigraph Writing Skills Among Primary School Students

Students' waning interest in writing personal narratives and the rise of readily available online examples have led them to take shortcuts, hindering their creative development. Traditional writing instruction, often limited to theoretical explanations, fails to engage students and lacks innovative strategies. This research investigates the effectiveness of the Self-Directed Learning (SDL) model in improving the pentigraph (three-paragraph) writing skills of fifth-grade students. Employing a quasi-experimental design with a quantitative approach and a Posttest-Only Control Design, the study involved 82 fifth-graders from Taman Kota Elementary School in Madiun, Indonesia. Convenience sampling selected two classes (VB and VC) with 20 students each. Data collection utilized questionnaires, tests, and documentation. Findings revealed a positive impact of the SDL model on students' Indonesian pentigraph writing skills. Through independent learning, students demonstrated improvement, progressing from basic tasks to planning, controlling, and self-assessment. The SDL model empowers students to develop their writing based on their unique learning styles. This research highlights the effectiveness of the SDL approach in enhancing pentigraph writing and student ownership of the learning process

DIAGNOSTICS OF THE DEVELOPMENT OF INFORMATION AND DIGITAL COMPETENCE OF FUTURE OFFICERS

Formulation of the problem. Today the problem of developing information and digital competence (IDC) of future officers in the context of non-formal education and the problem of designing a diagnostic apparatus to measure such development is relevant. The paper aims to develop a diagnostic apparatus (criteria and indicators) to measure the development of information and digital competence of future officers Methods. To achieve this goal, we used the analysis and generalization of scientific sources, structural and logical analysis to identify and substantiate the indicators of the development of the IDC of future officers, as well as content analysis and comparative analysis to characterize the levels of development of the IDC of future officers. Results. To assess the levels of development of the IDC of future officers, the criteria (motivational and value, cognitive and activity) and the relevant indicators (the presence of motives, value attitudes for self-development, knowledge about IT &amp; DT in general and in the industry, the ability to use DT and IT to solve professional problems) have been determined. The qualitative assessment of the indicators made it possible to characterize the levels of development of future officers' IDC. Each level (low, medium, and high) reflects a different degree of IDC development, and, accordingly, a different degree of development of motives, knowledge, and skills to use IT and DT in professional activities. A low level may be sufficient for basic tasks, but medium and high levels are critical for managing complex operations and adapting to the performance of professional duties. Conclusions. This diagnostic apparatus has been developed, and levels of development of future officers' information and digital competence have determined the ability to use modern technologies to perform official duties effectively. With the help of the diagnostic apparatus, it becomes possible to test the effectiveness of pedagogical models of future officers' IDC development.

A problem-based approach in the formation of the self-educational competence of schoolchildren (based on the use of digital technology resources)

The article reveals the possibilities of the problem-based approach in solving the problem of the formation of the self-educational competence of schoolchildren. Various ways of influencing the development of their self-educational competence through the preparation of specially selected tasks are offered in lessons, in homework and in individual work with schoolchildren. It is substantiated that in lessons it is expedient from the point of view of the formation of self-educational competence to use typical tasks similar to typical tasks for independent work and creative tasks. It is shown that the use of educational, training and research tasks is productive for organizing homework. It has been revealed that in individual work it will be effective to perform preparatory, trial, basic tasks. At the same time, it has been proven that all the specified types of tasks involve the active use of digital technologies, which will allow for effective organization of educational activities, improve the motivation and cognitive interest of schoolchildren, ensure individualization of learning and create prerequisites for the development of independence of schoolchildren. In lessons, it is advisable to use simple digital applications, digital resources for creating your own content, digital activity environments and digital resources for online communication. It is effective to use digital resources-sources of educational information, tools for self-monitoring and self-diagnosis, as well as resources for creating your own digital content in homework. Digital resources-sources of information, tools for self-monitoring and self-diagnosis, digital activity environments and resources for personalized learning are useful for the individual work of schoolchildren. The article examines the potential of the problem-based approach to the formation of the self-educational competence of schoolchildren. The main components of self-educational competence are considered, namely: motivational-value, substantive-procedural and control-reflective. The impact of the problem-based approach on the development of each of these components is analyzed. It has been determined that the problem-based approach helps to increase students' motivation to study, stimulates their cognitive activity and develops critical thinking

Leveraging Oracle HCM for Enhanced Employee Engagement

As the current corporate environment continues to evolve, employee engagement has emerged as a critical component in determining the success of an organisation. Oracle Human Capital Management (HCM) systems provide a revolutionary chance to improve employee engagement by providing a full suite of tools that are geared to handle different areas of workforce management. This opportunity presents itself as a result of the availability of these systems. In order to provide a more unified and responsive approach to employee engagement, Oracle Human Capital Management (HCM) offers an integrated platform that incorporates basic HR tasks, talent management, workforce management, and analytics. In this article, we investigate how the capabilities of Oracle Human Capital Management (HCM) may be strategically used to cultivate a staff that is more engaged and motivated. The cloud-based design of the system guarantees accessibility and real-time data processing, both of which are crucial for interactions with workers that are timely and relevant. A variety of features that may be adapted to assist engagement activities are provided by the modules that make up Oracle Human Capital Management (HCM). These modules include Employee Central, Talent Management, and Learning Management. For example, the Talent Management module helps to connect employee goals with organisational objectives, which in turn improves both work happiness and performance. While this is going on, the Learning Management module offers individualised training and development possibilities, according to the workers' requirements for professional advancement and their goals for their careers.

Are selection history effects limited to implicit forms of memory? Evidence from intertrial repetition.

Selection history effects in visual attention are typically considered implicit memory effects. In three experiments, we investigated if a key selection history effect, intertrial priming, could be based on the incidental application of explicit memory. In the basic search task (Experiment 1), participants searched for real-world objects from different categories. We examined nonpredictive, intertrial repetition at two levels: (1) the repetition of target location from trial N-1 to trial N and (2) the repetition of target location and color within a category. Reliable repetition advantages were observed at both levels. In Experiments 2-4, we examined whether participants had explicit access to the target values driving the selection history effects here. In Experiments 2 and 3, participants could reliably report the properties of the immediately preceding search target. In Experiment 4, participants could reliably report the properties of the last target exemplar they had found in each of the 36 categories. These data indicate that guidance by selection history was based on the nonstrategic application of memory representations that could be explicitly retrieved and reported. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

WP8.13 - Validating Remote Basic Surgical Skills Certification: A Randomised Controlled Trial

Abstract Aims Simulation based training comprises a fundamental component of modern surgical education, necessitating the development and advancement of affordable, functional, and validated simulators. This study explored the effectiveness of using the Inovus Basic Surgical Skills (BSS) simulator in improving core surgical skills amongst junior trainees. Methods This was a multicentre, RCT between August 2022 – November 2022 involving junior trainees yet to complete a validated basic surgical skills course. All participants completed an initial series of basic surgical tasks, including a range of suturing and knot-tying techniques. Three parameters (objective scoring tool, time taken for task completion, self-assessment questionnaire) were used to evaluate each surgical skill. Subsequently, participants were randomly allocated to the intervention group (BSS simulator) for at home training, or the control group receiving no further training. Results 5 domains were assessed for suturing tasks through respect for tissue, motion and efficiency, visuo-spatial, suture quality and time taken. Knot-tying was assessed by quality and time taken. Independent samples t-test revealed no significant difference between control and intervention groups (p&amp;gt;0.05) despite improvement in task quality and reduced completion time. Self-assessment questionnaires revealed a significant (p&amp;lt;0.05) improvement in confidence performing more complex tasks in intervention groups. Conclusions Study results demonstrate an improvement in both the quality of basic surgical skills and reduced completion time with use of the BSS simulator. Participants report increased confidence post-intervention following use of the BSS simulator. Despite the limitation of a small sample size, these findings underpin the potential benefits of BSS simulator use for surgical education amongst trainees, increasing accessibility to surgical training through an exceptionally realistic remote training environment.

Comparative Study for Virtual Personal Assistants (VPA) and State-of-the-Art Speech Recognition Technology

Numerous types of virtual assistants have emerged as a result of the widespread use of smartphones, the expansion of their services, the tremendous advancements in automatic speech recognition and AI, and the growing reliance on virtual personal assistants (VPAs) for basic daily tasks like playing music, sending texts, making restaurant reservations, and getting weather updates. The popularity of virtual personal assistants is largely attributable to their convenient blend of user-friendliness and natural language interaction. This study comprehensively examines various virtual personal assistants powered by AI. It briefly overviews each, such as Microsoft Cortana, Samsung Bixby, Apple SIRI, Google Assistant, and Amazon Alexa. This study also includes a comprehensive overview of the state-of-the-art speech recognition used in virtual personal assistants. The findings show that each Virtual Personal Assistant has advantages, and a user may select any of them depending on his preferences and needs.

Crafting culture in the Adriatic: a case of woodcarvers and painters in Quattrocento Dubrovnik

The well-preserved archival documentation in Dubrovnik provides valuable insights into various subjects, including production of art. This study delves into the collaboration between woodcarvers and painters in late medieval Dubrovnik, focusing on the mid-15th century, when artistic production was greatly influenced by the exceptional stability of the commune. Contribution challenges conventional assumptions regarding artist specialization during this era. Contracts from this period often delineated the responsibilities of painters in supervising carpentry work during artwork fabrication, while some documents suggest woodcarvers executed basic painting tasks. Discoveries are consistent with the knowledge of Italian art production, affirmed through meticulous examination of exceptionally well-preserved archival sources.

BADM: Boundary-Assisted Diffusion Model for Skin Lesion Segmentation

Skin lesion segmentation is one of the basic tasks in the field of medical image analysis. Deep learning approaches have emerged as crucial tools for segmenting skin lesion images, and their accuracy in processing abnormalities influences the ultimate diagnostic outcomes. Skin lesion images suffer from problems such as variability in shape and size and fuzzy boundaries between lesions and background, making it difficult to accurately segment lesions from images. To address this challenge, we propose Boundary-assisted Diffusion Model for Skin Lesion Segmentation that incorporates boundary information into the denoising learning process of the diffusion model by introducing an additional boundary-aware task. We design a Boundary-Aware Fusion Model to capture boundary information from deep networks and incorporate boundary information into the learning process of the diffusion model. A Scale-Coordinated Interaction Model is proposed, which effectively transmits and enhances the information of different scale features, and improves the performance of the model when dealing with lesions of different sizes. We evaluate our approach on four different datasets and compare it with other methods. Encouragingly, our method achieves significant improvements in performance without losing novelty.

Assessing patient work system factors for medication management during transition of care among older adults: an observational study

ObjectiveTo develop and evaluate measures of patient work system factors in medication management that may be modifiable for improvement during the care transition from hospital to home among older adults.Design,...

The tasks of the stages of the annual cycle, the solution of which is the basis for improving physical qualities, which ensures the effectiveness of middle-distance running of athletes at the stage of specialized basic training

The current training method focuses on the athlete at the stage of specialized basic training to further achieve a consistent level of development of special strength, as well as basic strength and strength abilities, It is fundamentally necessary to thoroughly understand the technical and tactical abilities of a mid-distance runner in the minds of a high level for The highest level of basic tasks is to achieve a high level of diversity and the interconnection of all types of preparation - physical, technical, tactical, psychological, moral-volitional, etc. Based on periodization systems, we improved the annual training macrocycle for middle-distance runners in the third year of specialized basic training, which includes: 2 autumn-winter and spring-summer macrocycles, 6 main periods and 16 training stages, which include 18 mesocycles and 52 microcycles. Considering the stages of preparation of the annual training cycle as the basis of the training process of middle-distance runners at the stage of specialized basic training, we developed groups of exercises with different predominant orientation, and on this basis, model microcycles of each stage were built depending on the tasks. On the basis of the annual training macrocycle of qualified middle-distance runners in the third year of training at the stage of specialized basic training, the main tasks of the stages of the annual training cycle of different predominant orientations were developed, which are the basis for improving the components of physical qualities. The developed tasks of the stages of the annual training cycle are aimed at increasing the capabilities of the aerobic system, the aerobic-anaerobic system, as well as the anaerobic energy supply system, which will make it possible to lay an aerobic-anaerobic base on which middle-distance runners will successfully perform large volumes of special work, carry loads more easily, and recover faster after them at the stage of specialized basic training.

Edge-Light: Exploiting Luminescent Solar Concentrators for Ambient Light Communication

A recent advance in embedded Internet of Things (IoT) exploits ambient light for wireless communication. This new paradigm enables highly efficient links via simple light modulation, but the design space has a fundamental constraint: in most State of the Art (SoA) studies, the link can only follow the propagation direction of ambient light. Consider, for example, a swarm of drones and ground robots that want to communicate with sunlight. Drone-to-robot communication could be possible because sunlight travels downwards from the air (drone) to the ground (robot), allowing drones to modulate light to send information to robots beneath them. Robot-to-robot communication, however, is not possible because sunlight does not travel sideways (parallel to the ground). To allow 'lateral communication' with ambient light, we propose using Luminescent Solar Concentrators (LSC). These optical components receive ambient light on their surface and re-direct part of the spectra towards their edges. Considering this optical property of LSC, our work has three main contributions. First, we benchmark various optical properties of LSC to assess their performance for ambient light communication. Second, we combine LSC with liquid crystal (LC) shutters to form lateral links with ambient light. Third, we test our links indoors and outdoors with artificial and natural ambient light, by enhancing two robots with our LSC transceivers and showing that they can exchange basic commands and coordinate tasks by communicating only with sunlight.