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This paper investigates methods that leverage physical contact between a robot's structure and its environment to enhance task performance, with a primary emphasis on improving precision. Two main approaches are examined: solving the inverse kinematics problem and employing quadratic programming, which offers computational efficiency by utilizing forward kinematics. Additionally, geometrical methods are explored to simplify robot assembly and reduce the complexity of control calculations. These approaches are implemented on a physical robotic platform and evaluated in real-time applications to assess their effectiveness. Through experimental evaluation, this study aims to understand how environmental contact can be utilized to enhance performance across various conditions, offering valuable insights for practical applications in robotics. [ABSTRACT FROM AUTHOR]

With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users' intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts. [ABSTRACT FROM AUTHOR]

Featured Application: The Trail Making Test in Virtual Reality (TMT-VR) provides a highly adaptable and immersive tool for cognitive and neuropsychological assessments. Its application extends beyond traditional clinical settings, offering potential use in remote assessments, telemedicine, and longitudinal studies that require repeated measures. The fully automated, randomized, and naturalistic environment of the TMT-VR ensures precise and consistent evaluation across diverse populations, making it particularly useful for assessing cognitive functions in older adults, individuals with neurological conditions, and populations with limited technological exposure. Additionally, the TMT-VR could serve as a valuable tool in cognitive rehabilitation programs, where immersive and engaging tasks are crucial for patient motivation and adherence. However, validation studies are required for scrutinizing its utility in older adults, clinical populations, and settings. Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18–35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes as follows: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. The TMT-VR demonstrates high usability, acceptability, and user experience among participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations. [ABSTRACT FROM AUTHOR]

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement. [ABSTRACT FROM AUTHOR]

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in a performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. We propose a Chinese Joint Entity and Relation Extraction Model based on progressive contrastive learning and heterogeneous feature fusion (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, our model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel. [ABSTRACT FROM AUTHOR]

Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick–handover–place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate ( 84.5 % ). [ABSTRACT FROM AUTHOR]

(1) Exoskeletons offer potential benefits for overhead working tasks, but gender effects or differences are unclear. This study aimed to compare the performance as well as subjective body strain and comfort of men and women using an upper-body exoskeleton. (2) n = 20 female and n = 16 male participants performed an overhead drilling task with and without a passive upper-body exoskeleton in a randomized cross-over study. The task performance of different movement phases, perceived exertion, and ease of use were measured to compare gender differences. One- and two-way analyses were used to compare genders in the different conditions. The body mass index (BMI) was included as a covariate. (3) Gender differences in task performance were found for error integrals (p < 0.001) with higher values in male participants. Moreover, there was a significant interaction effect for gender x exoskeleton use. While females showed performance decrements in aiming with exoskeleton use, the males' performance increased (p = 0.025). No other gender differences were observed. (4) Gender differences in task performance using an upper-body industrial exoskeleton were less detectable than expected, indicating that body composition and anthropometrics might be valuable indicators for performance including assisting devices. Moreover, future studies should also integrate the examination of muscle activity to gain more insights into potential gender movement control patterns. [ABSTRACT FROM AUTHOR]

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families. [ABSTRACT FROM AUTHOR]

Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path diffusion model. This module employs soft attention to extract style features and integrate them with content features. Additionally, a parameter-free Similarity Attention Mechanism (SimAM) is employed within the image feature space to facilitate the transfer of style image textures and colors, while simultaneously minimizing the loss of structural content information. The fusion of these dual attention mechanisms enables us to achieve style transfer in texture and color without sacrificing content integrity. The experimental results indicate that our approach exceeds existing methods in several evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: Working memory (WM) involves temporarily storing and manipulating information. Research on the impact of aging on WM has shown inconsistent results regarding the decline in visual and verbal WM, with a lack of studies on tactile WM. This study aimed to assess the effects of aging on WM across verbal, visuospatial, and tactile modalities using span tasks of forward (storage) and backward (manipulation) stages. Methods: A total of 130 participants, divided into four age groups of 20–29, 60–69, 70–79, and 80–89, completed the Digit, Visuospatial, and Tactual Spans. Performance was analyzed using a 3 (Task) × 4 (Group) × 2 (Stage) mixed design repeated measures ANOVA. Results: The analysis revealed significant main effects for modality (p < 0.001, ηp2 = 0.15), age (p < 0.001, ηp2 = 0.48), and stage (p < 0.001, ηp2 = 0.30). Digit Span outperformed the other modalities, while Tactual Span showed the worst performance. Additionally, task performance declined with age, and the forward stage was superior to the backward stage. Interaction effects indicated that Digit Span was less affected by aging compared to the Visuospatial and Tactual Spans (p = 0.004, ηp2 = 0.07). Post hoc analyses further revealed that the Digit Span consistently outperformed the other modalities in both stages, with more pronounced differences observed in the forward stage. Conclusions: Verbal WM is more resilient to aging compared to the other modalities while tactile WM declines with age in a manner similar to verbal and visuospatial WM, suggesting a modality-specific impact of aging on WM. [ABSTRACT FROM AUTHOR]

This study investigates the cognitive mechanisms underlying vigilance and pattern recognition using a novel adaptation of Mackworth's Clock Test. We aimed to quantify the time it takes for temporal patterns detected unconsciously through implicit learning to surface in the conscious mind within a dynamic vigilance task environment. Forty-eight participants detected random and non-disclosed rhythmic anomalous clock hand movements in this setting. Our results indicate significant variability in detection accuracy, reaction times, and the ability to recognize the hidden pattern among participants. Notably, 23% of all participants and 56% of those who consciously reported the pattern exhibited statistically lower reaction times indicative of knowledge of the pattern 40 s before conscious identification. These findings provide valuable insights into the transition from unconscious to conscious detection, highlighting the complexity of sustained attention and pattern recognition. The study's implications extend to designing training programs and tasks for high-stakes professions requiring prolonged vigilance. Future research should further explore the cognitive and neural correlates of these processes and the impact of task complexity on performance. [ABSTRACT FROM AUTHOR]

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human–robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot's behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles. [ABSTRACT FROM AUTHOR]

Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic Support (ES) systems. Existing recognition methods focus more on algorithmic designs, such as neural network structure designs, to improve recognition performance. However, in open electromagnetic environments with increased flexibility in radar transmission, these methods would suffer performance degradation due to domain shifts between training and testing datasets. To address this issue, this study proposes a robust radar inter-pulse modulation feature extraction and recognition method based on disentangled representation learning. At first, inspired by the Representation Learning Theory (RLT), the received radar pulse sequences can be disentangled into three explanatory factors related to (i) modulation types, (ii) modulation parameters, and (iii) measurement characteristics, such as measurement noise. Then, an explainable radar pulse sequence disentanglement network is proposed based on auto-encoding variational Bayes. The features extracted through the proposed method can effectively represent the key latent factors related to recognition tasks and maintain performance under domain shift conditions. Experiments on both ideal and non-ideal situations demonstrate the effectiveness, robustness, and superiority of the proposed method in comparison with other methods. [ABSTRACT FROM AUTHOR]

Force-feedback devices enhance task performance in most robot teleoperations. However, their increased size with additional degrees of freedom can limit the robot's applicability. To address this, an interface that visually presents force feedback is proposed, eliminating the need for bulky physical devices. Our telepresence system renders robotic hands transparent in the camera image while displaying virtual hands. The forces applied to the robot deform these virtual hands. The deformation creates an illusion that the operator's hands are deforming, thus providing pseudo-haptic feedback. We conducted a weight comparison experiment in a virtual reality environment to evaluate force sensitivity. In addition, we conducted an object touch experiment to assess the speed of contact detection in a robot teleoperation setting. The results demonstrate that our method significantly surpasses conventional pseudo-haptic feedback in conveying force differences. Operators detected object touch 24.7% faster using virtual hand deformation compared to conditions without feedback. This matches the response times of physical force-feedback devices. This interface not only increases the operator's force sensitivity but also matches the performance of conventional force-feedback devices without physically constraining the operator. Therefore, the interface enhances both task performance and the experience of teleoperation. [ABSTRACT FROM AUTHOR]

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics. [ABSTRACT FROM AUTHOR]

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations. [ABSTRACT FROM AUTHOR]

Current research on game difficulty prediction mainly uses heuristic functions or physiological signals. The former does not consider user data, while the latter easily causes interference to the user. This paper proposes a difficulty prediction method based on multiple facial cues and game performance. Specifically, we first utilize various computer vision methods to detect players' facial expressions, gaze directions, and head poses. Then, we build a dataset by combining these three kinds of data and game performance as inputs, with the subjective difficulty ratings as labels. Finally, we compare the performance of several machine learning methods on this dataset using two classification tasks. The experimental results showed that the multilayer perceptron classifier (abbreviated as MLP) achieved the highest performance on these tasks, and its accuracy increased with the increase in input feature dimensions. These results demonstrate the effectiveness of our method. The proposed method could assist in improving game design and user experience. [ABSTRACT FROM AUTHOR]

The current research landscape in ontology visualization has largely focused on tool development, yielding an extensive array of visualization tools. Although many existing solutions provide multiple ontology visualization layouts, there is limited research in adapting to an individual user's performance, despite successful applications of adaptive technologies in related fields, including information visualization. In an effort to innovate beyond traditional one-size-fits-all visualizations, this paper contributes one step towards realizing user adaptive visualization by recognizing timely moments when users may potentially need intervention, as real-time adaptation can only occur if it is possible to correctly predict user success and failure during an interaction in the first place. In addition, an open-source, reusable, and extensible software: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze) is made available to the broader scientific community interested in descriptive and predictive gaze analytics. Building on a wealth of research in eye tracking, this paper compares four approaches to predictive gaze analytics through a series of experiments that utilize scheduled gaze digests, irregular gaze events, the last known gaze status, as well as all gaze captured for a user at a given moment in time. The results from a set of experimental trials suggest that irregular gaze events are most informative of early predictions of user performance, whereas cognitive workload appears to be most indicative of overall user performance in the task scenario presented in this paper. These empirical findings highlight the importance of an analytical approach to gaze on user predictions and indicate careful consideration when applying. [ABSTRACT FROM AUTHOR]

Background/Objectives: Emotional intelligence may help nurses to cope with demanding work environments where the need to improve the quality and safety of the care provided, as well as the care of the chronically ill, prevails. Although it is well known that emotional intelligence is positively related to work performance, the literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. Methods: We conducted a cross-sectional study with 318 nurses. We collected data from a convenience sample of nurses during January 2024. Since we conducted an online survey through social media, our sample could not be representative of all nurses in Greece. For instance, older nurses may be underrepresented in our study due to limited access on social media. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Results: We found that the four streams of emotional intelligence (i.e., well-being, self-control, emotionality, sociability) increased nurses' work performance. In particular, we found a positive relationship between well-being and task performance (adjusted beta = 0.210, 95% CI = 0.140 to 0.281, p-value < 0.001) and contextual performance (adjusted beta = 0.135, 95% CI = 0.050 to 0.221, p-value = 0.002). Similarly, there was a positive relationship between self-control and task performance (adjusted beta = 0.136, 95% CI = 0.030 to 0.241, p-value = 0.012). Additionally, sociability increased task performance (adjusted beta = 0.223, 95% CI = 0.151 to 0.295, p-value < 0.001) and contextual performance (adjusted beta = 0.198, 95% CI = 0.111 to 0.286, p-value < 0.001). Moreover, emotionality (adjusted beta = −0.198, 95% CI = −0.319 to −0.076, p-value = 0.002) and sociability (adjusted beta = −0.133, 95% CI = −0.221 to −0.044, p-value = 0.003) reduced counterproductive work behavior. Conclusions: Our multivariable models identified a positive impact of emotional intelligence on nurses' work performance. Nurse managers and healthcare organizations should adopt appropriate interventions to improve nurses' emotional intelligence. Enhancing emotional intelligence among nurses can improve work performance and, thus, healthcare outcomes. Moreover, higher levels of emotional intelligence may empower nurses' compassion and resilience, fostering a supportive work environment. In this context, the well-being of both nurses and patients may improve. [ABSTRACT FROM AUTHOR]

Background: Previous studies demonstrated the influential role of sustained attention in the reading comprehension of alphabetic writing systems. However, there is limited understanding of how these cognitive functions contribute to reading comprehension in non-alphabetic systems, such as Japanese. This study seeks to explore this gap, focusing on how sustained attention and response inhibition function in a writing system where some of the characters represent meanings rather than sounds, introducing another layer of difficulty in the complex process of reading; Methods: Seventy-five Japanese 9th grade students performed a task to assess sustained attention and response inhibition. The cognitive test was carried out using tablets to enable feasible parallel group administration while maintaining high comparability with ecological classroom settings. Reading comprehension was measured using an exam that the participants took as part of their educational routine; Results: Our results indicate that both sustained attention and response inhibition significantly contributed to the reading comprehension of Japanese 9th grade students; Conclusions: These results replicate and expand previous studies documenting the contribution of sustained attention on the reading comprehension of alphabetic writing systems to a non-alphabetic system. Moreover, our findings unravel another important cognitive factor, namely response inhibition in reading comprehension. We suggest that response inhibition may play a crucial role in reading non-alphabetic writing systems that pose high cognitive demands, such as Japanese. [ABSTRACT FROM AUTHOR]

Introduction: Specific home tasks and the use of splints seem to positively affect altered structures and functions, as well as the activities and participation, of children with unilateral cerebral palsy (UCP). However, how did families experience the implementation of these therapies? Objective: To describe the experiences as they were perceived by the families of children with UCP before, during, and after a specific task intervention in the home environment, either with or without upper limb splinting. Methods: A qualitative, descriptive, phenomenological study was conducted in a natural environment. Fourteen families caring for children with UCP who participated in a previous randomized controlled trial were included. Data from unstructured and semi-structured interviews were analyzed through a thematic analysis. Results: Fourteen families (17 parents, age 37–47 years) caring for children with UCP (14 children, ages 6–10 years, 64% female) were interviewed. The following three themes emerged: "The project itself", in which families explained that they enrolled because of their trust in therapists; "Results obtained", where the main improvement was the integration of the assisting hand in the body schema; and "Lights and shadows", where families showed what they learned as positive points and some negative aspects related to the assessments and splints. Conclusions: The perceptions of the parents after the specific task intervention in the home environment showed a greater integration of the most affected side. Nevertheless, although the support of a splint on the hand can have beneficial results in terms of performance, other drawbacks leading to the disuse of the splint were highlighted. [ABSTRACT FROM AUTHOR]

Background: Children with developmental coordination disorder (DCD) exhibit reduced interlimb coordination compared to typically developing children (TDC) during complex tasks like running, which requires dynamic postural control. However, the extent of interlimb coordination difficulties in DCD during tasks that demand minimal dynamic balance, such as self-paced and externally auditory-paced tasks, remains unclear. This study aimed to compare interlimb coordination and auditory–motor synchronization between children with DCD and TDC during a seated antiphase coordination task of the lower limbs, which has minimal postural control requirements. Methods: Twenty-one children with DCD and 22 TDC performed an antiphase knee flexion and extension task while seated, in three conditions (baseline silence, metronome discrete, and metronome continuous), for three minutes. The interlimb coordination, synchronization, and spatiotemporal movement parameters were analyzed using a mixed model analysis; Results: Children with DCD displayed less coordinated interlimb movements compared to TDC (p = 0.0140), which was the result of the greater variability in coordinating antiphase knee flexion–extension movements (p < 0.0001). No group differences in spatiotemporal movement parameters were observed. Children with DCD, compared to TDC, had a lower synchronization consistency to metronomes (p = 0.0155). Discrete metronomes enhanced interlimb coordination compared to the baseline silence condition (p = 0.0046); Conclusions: The study highlights an inferior interlimb coordination and auditory–motor synchronization in children with DCD compared to TDC. Implementing metronomes with a discrete temporal structure improved the interlimb coordination of both groups during the used fundamental seated interlimb coordination task, supporting theorical frameworks of event-based timing. [ABSTRACT FROM AUTHOR]

Building instance extraction and recognition (BEAR) extracts and further recognizes building instances in unmanned aerial vehicle (UAV) images, holds with paramount importance in urban understanding applications. To address this challenge, we propose a unified network, BEAR-Former. Given the difficulty of building instance recognition due to the small area and multiple instances in UAV images, we developed a novel multi-view learning method, Cross-Mixer. This method constructs a cross-regional branch and an intra-regional branch to, respectively, extract the global context dependencies and local spatial structural details of buildings. In the cross-regional branch, we cleverly employed cross-attention and polar coordinate relative position encoding to learn more discriminative features. To solve the BEAR problem end to end, we designed a channel group and fusion module (CGFM) as a shared encoder. The CGFM includes a channel group encoder layer to independently extract features and a channel fusion module to dig out the complementary information for multiple tasks. Additionally, an RoI enhancement strategy was designed to improve model performance. Finally, we introduced a new metric, Recall@(K, iou), to evaluate the performance of the BEAR task. Experimental results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully reflect the model's performance. To solve these undesirable effects, we propose a framework for synthesizing minority class samples, GraphSHX, to balance the number of samples of different classes, and integrate the XGBoost model for node classification prediction during the training process. Conventional graph neural networks (GNNs) yielded unsatisfactory results, possibly due to the limited number of newly generated nodes. Therefore, we introduce a meta-mechanism to deal with small-sample problems, and employ the meta-learning approach to enhance performance on small-sample tasks by learning from a large number of tasks. An empirical evaluation of node classification on six publicly available datasets demonstrated that our balanced data set method outperforms existing optimal loss repair methods and synthetic node methods. The addition of the XGBoost model and meta-learning improves the accuracy by more than 5% to 10%, with the overall accuracy of the improved model being 15% higher than that of the baseline method. [ABSTRACT FROM AUTHOR]

The reactivity effect of metacognitive judgments on first-order task performance has received increased research attention. Previous studies showed that soliciting retrospective confidence ratings (CRs) reactively enhances task performance itself, such as performance in decision making and reasoning tasks, especially for those with high self-confidence. It remains unknown whether CRs can improve students' academic assessment performance in real educational settings. The current study recruited 795 fourth-grade elementary school children to explore if making CRs reactively affects students' academic assessment performance in two main subjects (i.e., Chinese Language and Mathematics). The data were collected across six waves with half-year intervals. From Wave 2, children either provided (CR group) or did not provide CRs (no-CR group) when completing standardized academic assessments. The results showed Bayesian evidence supporting the claim that making CRs does not influence children's academic assessment performance (both the average performance across waves 2–6 and the performance in each wave) in both subjects. Furthermore, children's self-confidence did not moderate the reactive influence of CRs. The results from multilevel regression analyses re-confirmed the above conclusions. Possible explanations for the absence of the reactivity effect of CRs on children's academic assessment performance are discussed. [ABSTRACT FROM AUTHOR]

Background/Objectives: Fibromyalgia (FM) is a chronic condition characterized by widespread musculoskeletal pain, fatigue, and impaired motor performance. This study aimed to investigate the effects of transcranial direct current stimulation (tDCS) during virtual reality (VR) tasks on the motor performance of women with FM. Methods: Participants were divided into two groups: Group A received active tDCS for 10 days followed by sham tDCS for 10 days, while Group B received the opposite sequence. Both groups performed VR tasks using MoveHero software (v. 2.4) during the tDCS sessions. Motor performance was assessed by the number of hits (movement with correct timing to reach the targets) and absolute (accuracy measure) and variable (precision measure) errors during VR tasks. Participants were 21 women, aged 30–50 years, and diagnosed with FM. Results: Group A, which received active tDCS first, presented significant improvements in motor performance (number of hits and absolute and variable errors). The benefits of active tDCS persisted into the sham phase, suggesting a lasting neuroplastic effect. Conclusions: tDCS during VR tasks significantly improved motor performance in women with FM, particularly in complex, extensive movements. These findings indicate that tDCS enhances neuroplasticity, leading to sustained motor improvements, making it a promising therapeutic tool in FM rehabilitation. [ABSTRACT FROM AUTHOR]

The neural basis of tactile processing in humans has been extensively studied; however, the neurophysiological basis of human width discrimination remains relatively unexplored. In particular, the changes that occur in neural networks underlying active tactile width discrimination learning have yet to be described. Here, it is hypothesized that subjects learning to perform the active version of the width discrimination task would present changes in behavioral data and in the neurophysiological activity, specifically in networks of electrodes relevant for tactile and motor processing. The specific hypotheses tested here were that the performance and response latency of subjects would change between the first and the second blocks; the power of the different frequency bands would change between the first and the second blocks; electrode F4 would encode task performance and response latency through changes in the power of the delta, theta, alpha, beta, and low-gamma frequency bands; the relative power in the alpha and beta frequency bands in electrodes C3 and C4 (Interhemispheric Spectral Difference—ISD) would change because of learning between the first and the second blocks. To test this hypothesis, we recorded and analyzed electroencephalographic (EEG) activity while subjects performed a session where they were tested twice (i.e., two different blocks) in an active tactile width discrimination task using their right index finger. Subjects (n = 18) presented high performances (high discrimination accuracy) already in their first block, and therefore no significant improvements were found in the second block. Meanwhile, a reduction in response latency was observed between the two blocks. EEG recordings revealed an increase in power for the low-gamma frequency band (30–45 Hz) for electrodes F3 and C3 from the first to the second block. This change was correlated with neither performance nor latency. Analysis of the neural activity in electrode F4 revealed that the beta frequency band encoded the subjects' performance. Meanwhile, the delta frequency band in the same electrode revealed a complex pattern where blocks appeared clustered in two different patterns: an Upper Pattern (UP), where power and latency were highly correlated (Rho = 0.950), and a sparser and more uncorrelated Lower Pattern (LP). Blocks belonging to the UP or LP patterns did not differ in performance and were not specific to the first or the second block. However, blocks belonging to the LP presented an increase in response latency, increased variability in performance, and an increased ISD in alpha and beta frequency bands for the pair of electrodes C3–C4, suggesting that the LP may reflect a state related to increased cognitive load or task difficulty. These results suggest that changes in performance and latency in an active tactile width discrimination task are encoded in the delta, alpha, beta, and low-gamma frequency bands in a fronto-central network. The main contribution of this study is therefore related to the description of neural dynamics in frontal and central networks involved in the learning process of active tactile width discrimination. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions often involve pain related to specific movements. However, most studies on the impact of experimental pain on motor performance and learning have used tonic pain models. This study aimed to evaluate the effect of experimental phasic pain during the preparation or execution of a reaching task on the acquisition and retention of sensorimotor adaptation. Participants were divided into three groups: no pain, pain during motor preparation, and pain during motor execution. Pain was induced over the scapula with a laser while participants performed a force field adaptation task over two days. To assess the effect of pain on motor performance, two baseline conditions (with or without pain) involving unperturbed pointing movements were also conducted. The results indicated that the timing of the nociceptive stimulus differently affected baseline movement performance. Pain during motor preparation shortened reaction time, while pain during movement execution decreased task performance. However, when these baseline effects were accounted for, no impact of pain on motor adaptation or retention was observed. All groups showed significant improvements in all motor variables for both adaptation and retention. In conclusion, while acute phasic pain during motor preparation or execution can affect the movement itself, it does not interfere with motor acquisition or retention during a motor adaptation task. [ABSTRACT FROM AUTHOR]

Working memory (WM) is crucial for adequate performance execution in effective decision-making, enabling individuals to identify patterns and link information by focusing on current and past situations. This work explored behavioral and electrophysiological (EEG) WM correlates through a novel decision-making task, based on real-life situations, assessing WM workload related to contextual variables. A total of 24 participants performed three task phases (encoding, retrieval, and metacognition) while their EEG activity (delta, theta, alpha, and beta frequency bands) was continuously recorded. From the three phases, three main behavioral indices were computed: Efficiency in complex Decision-making, Tolerance of Decisional Complexity, and Metacognition of Difficulties. Results showed the central role of alpha and beta bands during encoding and retrieval: decreased alpha/beta activity in temporoparietal areas during encoding might indicate activation of regions related to verbal WM performance and a load-related effect, while decreased alpha activity in the same areas and increased beta activity over posterior areas during retrieval might indicate, respectively, active information processing and focused attention. Evidence from correlational analysis between the three indices and EEG bands are also discussed. Integration of behavioral and metacognitive data gathered through this novel task and their interrelation with EEG correlates during task performance proves useful to assess WM workload during complex managerial decision-making. [ABSTRACT FROM AUTHOR]

Standing long jump is known as one of the important skills in the success of athletes in most sports. In addition, one of the most effective factors that can affect standing long jump distance and kinematics is the focus of attention used by the athlete. Therefore, the aim of the present study was to compare the effect of internal, external, and holistic focus of attention instructions on standing long jump performance and kinematics. The participants were 30 novices (all males; mean age = 21.70 ± 2.21 years; mean height = 175.73 ± 6.09 cm; and mean weight = 73.76 ± 11.77 kg) who performed 12 standing long jumps in four focus of attention conditions. Internal focus, external focus, holistic focus, and control conditions were implemented in a counterbalanced order. Jump distance and maximum knee flexion angle before take-off were recorded in all trials. The results showed that in relation to the standing long jump performance, the distance was similar in external and holistic focus conditions, and both were superior to internal or control conditions. There was no difference between control and internal focus of attention conditions. The results related to movement kinematics, however, did not report a difference between the maximum flexion angles before take-off. This study replicates the benefits of external and holistic focus instructions for jump distance, but this difference was not a product of different maximum knee flexion angles. It is suggested that coaches implement external and/or holistic focus cues to maximize athlete performance in jumping tasks. [ABSTRACT FROM AUTHOR]

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses. [ABSTRACT FROM AUTHOR]

This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an attention mechanism for predicting students' academic performance. The research is motivated by the need for efficient tools to enhance student assessment and support tailored educational interventions. The model tackles two tasks: predicting overall performance (total score) as a regression task and classifying performance levels (remarks) as a classification task. By handling both tasks simultaneously, it improves computational efficiency and resource utilization. The dataset includes metrics such as Continuous Assessment, Practical Skills, Presentation Quality, Attendance, and Participation. The model achieved strong results, with a Mean Absolute Error (MAE) of 0.0249, Mean Squared Error (MSE) of 0.0012, and Root Mean Squared Error (RMSE) of 0.0346 for the regression task. For the classification task, it achieved perfect scores with an accuracy, precision, recall, and F1 score of 1.0. The attention mechanism enhanced performance by focusing on the most relevant features. This study demonstrates the effectiveness of the Multi-Task LSTM model with an attention mechanism in educational data analysis, offering a reliable and efficient tool for predicting student performance. [ABSTRACT FROM AUTHOR]

Dyslexia, often referred to as a specific reading disability, affects many students around the world. It is a neurological disorder that affects the ability to recognise words, and it causes difficulties in writing and reading comprehension. Previous computer-based methods for the automatic detection of dyslexia in children have had low efficiency due to the complexity of the test administration process and the low measurement reliability of the attention measures used. This paper proposes the use of a student's mobile device to record the spatio-temporal trajectory of attention, which is then analysed by deep neural network long short-term memory (LSTM). The study involved 145 participants (66 girls and 79 boys), all of whom were children aged 9 years. The input signal for the neural network consisted of recorded observation sessions, which were packets containing the child's spatio-temporal attention trajectories generated during task performance. The training set was developed using stimuli from Benton tests and an expert opinion from a specialist in early childhood psychology. The coefficients of determination of R 2 ∼ 0.992 were obtained for the proposed model, giving an accuracy of 97.7% for the test set. The ease of implementation of this approach in school settings and its non-stressful nature make it suitable for use with children of different ages and developmental stages, including those who have not yet learned to read. This enables early intervention, which is essential for effective educational and emotional support for children with dyslexia. [ABSTRACT FROM AUTHOR]

Background: Visual impairment is a global problem and, regardless of the cause, it substantially impacts people's daily lives. Navigating towns and cities can be one of the most difficult tasks for someone with a visual impairment. This is because our streetscapes are often inaccessible for navigating safely and independently by people with a visual impairment. Barriers include street clutter, bollards, pavement parking, and shared spaces. Methodology: Participants with varying levels of diabetic retinopathy (DR) and retinitis pigmentosa (RP) were recruited. Each participant completed a clinical visit and a 1-mile walk. Participants discussed confidence, anxiety, difficulty, and any barriers encountered while completing the walkaround. Participants completed quality of life (RetDQol), diabetes distress scales, and a study questionnaire. They also underwent retinal imaging and visual function testing. Retinal imaging and visual function results were compared with confidence, difficulty, and anxiety levels during the walkaround using Spearman's correlation. Results: Thirty-three participants took part in the study, 22 with diabetes and 11 with RP. Results showed that average confidence was correlated with visual acuity, RetDQol, mean visual fields, and vertical peripheral diameter visual fields. Average difficulty was associated with visual acuity, RetDQol, dark adaptation, mean visual fields, percentage of the retina, and both horizontal and vertical diameter visual fields. In addition, some of the barriers discussed were pavement issues, bollards, parked cars, uneven pavements, alfresco dining, light levels, and street features such as tree roots, poles, A-boards, and street clutter. Conclusions: People with RP and treated DR faced common barriers while navigating the walkaround. The removal of these common barriers would make our streetscapes more accessible for all and will allow for more independence in those with visual impairments. [ABSTRACT FROM AUTHOR]

Background: Medical personnel carrying out electrical cardioversion (EC) procedures must remember to have the R-wave sync mode switched on, use the correct energy and maintain personal safety. The defibrillators used by medical response teams most often switch out of cardioversion mode once a shock is delivered. Therefore, this mode must be switched on again before subsequent shocks are delivered. The main aim of the study was to assess the ability of emergency medical teams participating in emergency medicine championships to perform EC. Methods: The research was a retrospective observational study and was based on an analysis of the evaluation sheets from two tasks simulating the management of a patient with unstable tachycardia conducted during the International Winter Emergency Medicine Championships. Three-person teams consisting of paramedics and representing the Polish emergency services were included in the study. The team representing the championship organiser and the few foreign teams participating in the competition were excluded from the study. Results: The decision to conduct EC was taken by 36 teams (83.72%) in 2015 and 27 teams (87.10%) in 2019. In both editions of the championships, during consecutive shocks, the percentage of actions performed correctly decreased significantly—switching on synchronisation mode in 2015 (94.4%, 83.33%, 72.22%) and in 2019 (100%, 88.89%, 81.48%); correct energies in 2015 (91.67%, 80.56%, 77.78%) and in 2019 (92.59%, 85.19%, 81.48%); shocks in a safe manner in 2015 (94.44%, 94.44%, 91.67%) and in 2019 (100%, 96.30%, 96.30%). Conclusions: Teams participating in the assessed tasks in a significant majority of cases correctly qualified the patient for EC, and correctly carried out the actions required for this procedure. It is of particular note that with every subsequent shock, the percentage of shocks carried out without the sync mode increased significantly. [ABSTRACT FROM AUTHOR]

Background: This study aimed to examine how concurrent working memory (WM) loads affect selective attention, and to explore developmental differences between young adults and children/adolescents aged 10 to 14 years. Methods: We employed a modified Stroop task with verbal or spatial WM loads to assess their impact on attention. Results: In adults, we found increased Stroop effects when WM load overlapped with target processing and decreased Stroop effects when WM load overlapped with distractor processing. Conversely, in children/adolescents, WM loads did not significantly impact target or distractor processing, indicating no change in Stroop effects under dual-task conditions. Interestingly, results from the correlational analyses revealed that as participants' ages increase, the interference effect under the WM load that shares resources with distractor processing in the attention task decreases. Conclusions: Our findings suggest that the interaction between attention and WM differs across developmental stages. While adults showed distinctive effects of concurrent WM loads on attention processing depending on the cognitive resources utilized, children/adolescents failed to show the interaction between the two cognitive systems. Furthermore, a significant relationship between age and the effects of WM load on attention was observed, providing insights into the development of the interaction between WM and attention. [ABSTRACT FROM AUTHOR]

Organizations need high performance from their employees to achieve their goals, provide specialized services and products, and ultimately secure a competitive edge. Performance is also a source of satisfaction for employees, as it creates feelings of mastery and pride. Different leadership styles positively influence both employee performance and organizational excellence; thus, the present study aimed to analyze the relationship between a novel leadership style based on attachment theory, secure base leadership, and job performance (i.e., task and conceptual performance and counterproductive work behaviors). Additionally, a passive-avoidant leadership relationship with performance dimensions is analyzed. Using partial least squares structural equation modeling with self-reported data from 422 Spanish employees, the results show that secure base leadership is positively related to task and contextual performance, while it is negatively related to counterproductive work behaviors. The opposite pattern is found for passive-avoidant leadership (except for the link between passive-avoidant leadership and contextual performance, which is not significant). The promotion of secure base leadership within organizations allows for the cultivation of a supportive environment that favors work behaviors that are aligned with organizational objectives, and since leadership can be trained, these results are relevant for practitioners in organizations. [ABSTRACT FROM AUTHOR]

The First-Person Shooter is a very popular genre in electronic sports (esports), where there are different aiming techniques such as Aim Flicking and Aim Tracking. Although the mouse grip is suggested as one of the most important factors in gaming performance, there is a lack of evidence on this topic. Therefore, the purpose of the present study was to determine the performances of three types of mouse grips (palm grip, claw grip, and fingertip grip) in two different tasks (flicking and tracking tasks) by analyzing kinematic and scoring variables. Twenty-two participants performed the tasks on a computer with the same monitor and mouse, each using their preferred grip: palm grip, claw grip, and fingertip grip. The aim400kg was used to perform the tasks, and a camera system (Optitrack's Flex 3) was used to capture the mouse movement. The results from the flicking task indicated significant differences in the minimum cursor trajectory, with higher values observed in the claw grip compared to both the palm and fingertip grips. However, no significant differences were observed in the tracking task in terms of velocity, acceleration, or hand movement. Moreover, only high correlations were observed in the flicking task between Score and Reaction Time (r = −0.911) and between Score and Total Distance (r = 0.724). In conclusion, the mouse grip does not affect the Aim Flicking or Aim Tracking task performance. This study has revealed interesting results related to performance, including correlations between the Score, Reaction Time, and Total Distance in flicking tasks. [ABSTRACT FROM AUTHOR]

Gait dysfunction (GD) is a common impairment of Parkinson's disease (PD), which negatively impacts patients' quality of life. Among the most recent rehabilitation technologies, a lower-limb powered exoskeleton (LLEXO) arises as a useful instrument for gait training in several neurological conditions, including PD. However, some questions relating to methods of use, achievable results, and usefulness compared to traditional rehabilitation methodologies still require clear answers. Therefore, in this review, we aim to summarise and analyse all the studies that have applied an LLEXO to train gait in PD patients. Literature research on PubMed and Scopus retrieved five articles, comprising 46 PD participants stable on medications (age: 71.7 ± 3.7 years, 24 males, Hoehn and Yahr: 2.1 ± 0.6). Compared to traditional rehabilitation, low-profile lower-limb exoskeleton (lp-LLEXO) training brought major improvements towards walking capacity and gait speed, while there are no clear major benefits regarding the dual-task gait cost index and freezing of gait symptoms. Importantly, the results suggest that lp-LLEXO training is more beneficial for patients with an intermediate-to-severe level of disease severity (Hoehn and Yahr > 2.5). This review could provide a novel framework for implementing LLEXO in clinical practise, highlighting its benefits and limitations towards gait training. [ABSTRACT FROM AUTHOR]

The importance of night sleep for maintaining good physical and cognitive health is well documented as well as its negative changes during aging. Since Mild Cognitive Impairment (MCI) patients bear additional disturbances in their sleep, this study aimed at examining whether there are potential mixed effects of sleep and afternoon time of day (ToD) on the storage, processing, and updating components of working memory (WM) capacity in older adults with MCI. In particular, the study compared patients' performance in the three working memory components, in two-time conditions: "early in the morning and after night sleep", and "in the afternoon and after many hours since night sleep". The Working Memory Capacity & Updating Task from the R4Alz battery was administered twice to 50 older adults diagnosed with MCI. The repeated measures analysis showed statistically significant higher performance in the morning condition for the working memory updating component (p < 0.001). Based on the findings, it seems that the afternoon ToD condition negatively affects tasks with high cognitive demands such as the WM updating task in MCI patients. These findings could determine the optimal timing for cognitive rehabilitation programs for MCI patients and the necessary sleep duration when they are engaged in cognitively demanding daily activities. [ABSTRACT FROM AUTHOR]

Long-duration bedrest impairs upright postural and locomotor control, prompting the need for assessment tools to predict the effects of deconditioning on post-bedrest outcome measures. We developed a tilt board mounted vertically with a horizontal air-bearing sled as a potential supine assessment tool for a future bedrest study. The purpose of this pilot study was to examine the association between supine proprioceptive assessments on the tilt board and upright functional mobility. Seventeen healthy participants completed variations of a supine tilt board task and an upright functional mobility task (FMT), which is an established obstacle avoidance course. During the supine tasks, participants lay on the air-bearing sled with axial loading toward the tilt board. Participants tilted the board to capture virtual targets on an overhead monitor during 30 s trials. The tasks included two dynamic tasks (i.e., double-leg stance matching mediolateral tilt targets over ±3° or ±9° ranges) and two static tasks (i.e., single-leg stance maintaining a central target position). The performances during the dynamic tasks were significantly correlated with the FMT time to completion. The dominant-leg static task performance showed a moderate trend with the FMT time to completion. The results indicate that supine proprioceptive assessments may be associated with upright ambulation performance, and thus, support the proposed application in bedrest studies. [ABSTRACT FROM AUTHOR]

Simple Summary: Noise can positively and negatively affect the systems it interacts with. When the right amount of noise is added to a weak signal, it can make it easier to detect, a phenomenon known as stochastic resonance. Our research focused on applying noise to improve human reaction times. We observed a significant decrease in the reaction times after placing subjects in the beneficial noise branch close to the optimal point. These findings suggest a novel approach to enhancing human performance in tasks that require faster reaction times, such as sports. Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports. [ABSTRACT FROM AUTHOR]

Intelligent urban perception is one of the hot topics. Most previous urban perception models based on semantic segmentation mainly used RGB images as unimodal inputs. However, in natural urban scenes, the interplay of light and shadow often leads to confused RGB features, which diminish the model's perception ability. Multimodal polarization data encompass information dimensions beyond RGB, which can enhance the representation of shadow regions, serving as additional data for assistance. Additionally, in recent years, transformers have achieved outstanding performance in visual tasks, and their large, effective receptive field can provide more discriminative cues for shadow regions. For these reasons, this study proposes a novel semantic segmentation model called MixImages, which can combine polarization data for pixel-level perception. We conducted comprehensive experiments on a polarization dataset of urban scenes. The results showed that the proposed MixImages can achieve an accuracy advantage of 3.43% over the control group model using only RGB images in the unimodal benchmark while gaining a performance improvement of 4.29% in the multimodal benchmark. Additionally, to provide a reference for specific downstream tasks, we also tested the impact of different combinations of polarization types on the overall segmentation accuracy. The proposed MixImages can be a new option for conducting urban scene perception tasks. [ABSTRACT FROM AUTHOR]

Propulsive power is one of the factors that determine the performance of sprint cycling. Pedaling rate is related to power output, and stiffness is associated with improving performance in athletic tasks. Purpose: to investigate the relationship between musculoarticular stiffness and pedaling rate. Methods: twenty-two healthy, untrained male volunteers (19 ± 2 years, 175 ± 6 cm, 74 ± 16 kg) were divided into two groups after their musculoarticular (MA) stiffness was tested, and these groups were the stiffness group (SG) and compliant group (CG). A 6-s maximal cycling test was conducted in four cycling modes, which were levels 5 and 10 air-resistance, and levels 3 and 7 magnetic-resistance. Peak and average cadence, peak power output (POpeak), crank force (CFpeak), peak rate of crank force development (RCFD), and the angle of peak crank force were collected. The significance of differences between the two groups for these variables was assessed using an independent samples t-test. Pearson product–moment correlations were calculated to analyze the relationship between MA stiffness and each performance variable. Results: the SG had significantly higher peak cadence and average cadence at level 3 magnetic-resistance, peak crank force, and peak power output at level 10 air-resistance, peak rate of crank force development at levels 5 air-resistance, 10 air-resistance, and 3 magnetic-resistance (p < 0.05). MA stiffness was significantly correlated with average cadence at levels 5 and 10 air-resistance, peak crank force in all 4 modes, and RCFD and peak power output at level 10 air-resistance. There were no significant relationships between MA stiffness and the angle of peak crank force in each cycling mode. Conclusion: results indicate that participants with relatively higher MA stiffness seemed to have a higher pedaling rate during a 6-s sprint cycling in these conditions. They also performed a superior crank force and rate of crank force development, producing greater power output when sprint cycling. Optimizing cycling resistance or gear ratio to enhance both RCFD and musculotendinous stiffness may be crucial for improving sprint cycling performance. [ABSTRACT FROM AUTHOR]

Simple Summary: Detecting key areas in tissue samples is crucial for accurate and timely cancer diagnosis. This study focuses on using advanced deep learning techniques to analyze images of skin tumors, specifically melanoma and nevi. By training our deep learning-based model on a dataset of these images, we aimed to detect key areas in each image and simultaneously classify whether a given image shows melanoma or a benign nevus. Our method achieved an accuracy of 92.3% in classifying the images from a test set, meaning it correctly identified most cases. This approach not only helps in distinguishing between these types of skin tumors but also shows promise for broader applications in diagnosing various cancers, potentially improving clinical outcomes and reducing healthcare costs. Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort which contains 160 hematoxylin and eosin whole slide images of primary melanoma (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep learning method to allow for classification, at the slide level, of nevi and melanoma. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on a skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors. [ABSTRACT FROM AUTHOR]

External motivational stimuli have been shown to improve athletic performance. However, the neurophysiological mechanisms underlying this improvement remain poorly understood. This randomized crossover study investigated the effects of music and verbal encouragement on measures of muscle excitation and myoelectric manifestations of fatigue in the biceps brachii and brachioradialis muscles during an endurance task. Fifteen untrained (mean age 29.57 ± 2.77 years) and 13 trained individuals (mean age 32.92 ± 2.90 years) were included. The endurance task, performed to exhaustion, consisted of keeping the dominant arm flexed to 90 degrees while holding a dumbbell loaded to 80% of 1RM with a supine grip in three randomized conditions: standard, with self-selected music, and with verbal encouragement. The untrained subjects showed an increase in task duration of 15.26% (p < 0.003) with music and 15.85% (p < 0.002) with verbal encouragement compared to the condition without external stimuli. There were no significant differences in the myoelectric manifestations of fatigue between the different conditions. Regarding the muscle excitation metrics, although the mean amplitude, peak value, and area under the curve remained unchanged across conditions, a significant reduction in the trend coefficient, indicating motor unit recruitment over time, was observed with both music (biceps brachii: −10.39%, p < 0.001; brachioradialis: −9.40%, p < 0.001) and verbal encouragement (biceps brachii: −7.61%, p < 0.001; brachioradialis: −6.51%, p < 0.001) compared to the standard condition. For the trained participants, no significant differences were observed between conditions in terms of task duration and outcome measures related to muscle excitation and myoelectric manifestations of fatigue, suggesting the possible presence of a ceiling effect on motivation. These results highlight the important role of external motivational stimuli, such as music and verbal encouragement, in improving task performance in untrained subjects, probably through more effective and efficient recruitment of motor units. [ABSTRACT FROM AUTHOR]

Air Traffic Controllers' (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky. [ABSTRACT FROM AUTHOR]

The purpose of this study was to compare the workload of a maximal treadmill test (TREAD) and a fire suppression task (BURN) in firefighters and to examine their relationships to fitness as measured by body mass index (BMI), percent body fat (BF%), and peak aerobic capacity (VO2PEAK). The amount of time spent in the heart rate (HR) intensity ranges of 50–59% HRMAX (ZONE1), 60–69% HRMAX (ZONE2), 70–79% HRMAX (ZONE3), 80–89% HRMAX (ZONE4), and ≥90% HRMAX (ZONE5) quantified the workload as the Edward's Training Impulse for TREAD (ETRIMPTREAD) and BURN (ETRIMPBURN). The ETRIMPTREAD was significantly less than ETRIMPBURN. For TREAD, ZONE5 > ZONE2 and ZONE3. For BURN, ZONE4 > ZONE1, ZONE2, and ZONE5 > ZONE1, ZONE2, and ZONE3. A lower BF% and greater VO2PEAK were related to a greater ETRIMPTREAD and unrelated to ETRIMPBURN. For BURN only, a lower BF% and greater VO2PEAK were related to less time in ZONE5. BMI was unrelated to all workload measures. Laboratory-based maximal exercise testing does not adequately reflect the workload of simulated fire suppression and therefore may not be indicative of firefighter readiness to meet job demands. Less-fit firefighters rely on higher cardiovascular intensities to complete the same workload, and practitioners should consider this when selecting strategies to reduce job-associated cardiovascular risk. [ABSTRACT FROM AUTHOR]

Background/Objectives: With the rising ubiquity of digital media and screens in everyday life, toddlers are increasingly exposed to different screens from an early age (i.e., television, computer, tablet, phone). However, few studies have examined the effect of these screens on toddlers' perceptual development. Since tactile exploration skills are necessary for environmental discovery and overall development, the current research investigates the links between screen-use habits and the tactile exploration skills (with visual control) of 6- to 36-month-old toddlers. Methods: The study involved observing the interactions of 135 toddlers with various objects and assessing the complexity of their visuo-tactile exploration strategies through two original experimental tasks. Data concerning screen habits and other relevant factors, such as socio-economic level, were collected using a parental questionnaire. Results: Toddlers with greater screen exposure time demonstrated weaker tactile exploration skills and employed less age-appropriate exploration strategies. Socio-economic factors and parental engagement in alternative activities significantly influenced these developmental outcomes. Conclusions: These findings emphasize the importance of reducing screen time and promoting interactive co-viewing and alternative activities to mitigate the negative effects of screen exposure. Further longitudinal research is needed to determine the long-term impacts of early screen exposure on tactile exploration and overall psychological development. [ABSTRACT FROM AUTHOR]

Background: SmartAction-VR uses virtual reality to simulate daily life tasks and assess cognitive performance based on the multi-errand paradigm. This study explored whether this new task could provide insights into the executive functioning of children and adolescents with ADHD in their everyday activities. Methods: A cross-sectional study was conducted between November 2021 and December 2022. It consisted of one session and was divided into two parts (cognitive tests; and SmartAction-VR). The sample comprised 76 children and adolescents with a median age (IQR) of 13 (11–14) years and an age range of 9–17 years. Of these participants, 60.50% (n = 46) were males. Out of this sample, 40 participants were in the ADHD group and 36 were in the neurotypical group. The following instruments were used: Waisman Activities of Daily Living Scale, Assessment of Sensory Processing and Executive Functioning, Pediatric Simulator Disease Questionnaire, Digit span subtest, Stroop test, NEPSY-II Subtest of Auditory Attention and Cognitive Flexibility, Trail Making Test, Zoo Map Test, and SmartAction-VR. Results: The ADHD group demonstrated lower accuracy (U = 406, p = 0.010), higher values for total errors (U = 292, p = 0.001), more commissions (U = 417, p = 0.003), new actions (U = 470, p = 0.014), and forgetting actions (U = 406, p = 0.010), as well as fewer perseverations compared to the neurotypical group (U = 540.5, p = 0.029). Additionally, participants who forgot more actions were found to have lower independence in daily life (r = −0.281, p = 0.024). Conclusions: The correlations between the results of SmartAction-VR and activities of daily living, as well as cognitive tests, suggest that this new task could be useful for evaluating executive functioning in daily life. [ABSTRACT FROM AUTHOR]