Task Proficiency Research Articles

The relationships between balanced-bilingual proficiency, executive function, and rational thinking: a dissociative finding

ABSTRACT The cognitive advantages of bilingualism have accumulated significant support in various domains, and executive function is suggested to be one of their operative mechanisms. However, empirical evidence of the relationships among bilingualism, executive function, and rational thinking is still lacking. The present study extends this understanding. With a refined operational definition of bilingualism and by controlling for possible confounding variables, we recruited 114 university participants to perform a balanced bilingual proficiency task; a modified Simon executive function (EF) task (to assess inhibition and updating components of EFs); a syllogism task; a critical thinking test; and measures of intelligence, socioeconomic status (SES), and the openness-to-experience personality trait. The results showed that balanced bilingual proficiency positively and robustly predicted participants’ performance in syllogism and critical thinking tasks after we controlled for other variables. The relationships between EF components and rational thinking showed a dissociative result: Inhibition and updating ability contributed to syllogistic and critical thinking differently. These results shed further light on the educational application needs of bilingual learning and concluded in a discussion of future investigations of the theoretical accounts and mechanisms of bilingual advantages.

Advancements in Data Augmentation and Transfer Learning: A Comprehensive Survey to Address Data Scarcity Challenges

Abstract: Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms. : Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.

3D vs. 2D-4 K: Performance and self-perception of laparoscopic novices in a randomized prospective teaching intervention using standard tasks and box trainers

ObjectiveThe use of three-dimensional (3D) laparoscopy in surgical practice and training has been an area of research and discussion. Studies have suggested that 3D vision can improve speed and precision compared to traditional two-dimensional (2D) displays, while other authors found no benefits on the learning curves of laparoscopic novices. Modern two-dimensional laparoscopy with a resolution of 3840 × 2160 pixels (2D-4 K) seems to improve laparoscopic view and helps learners orient without stereopsis. However, evidence comparing these systems for laparoscopic training is limited. Therefore, the impact of viewing mode (2D-4 K vs. 3D) on learning and task proficiency remains unclear.DesignWe performed a two-hour teaching intervention on basic laparoscopic skills for novices. In this parallel group randomized study, we randomly assigned learners to 2D-4 K or 3D teaching and performed tasks of increasing difficulty and complexity using standard laparoscopy box trainers. Before the last and most challenging task, learners had to crossover to the other laparoscopy setup. Our hypothesis was that learners would be faster and more precise when using a 3D setup. The primary endpoint was task proficiency measured by speed and failure rate. Secondary outcomes were performance using the viewing mode of the other group without familiarization, self-perception, and career aspirations before and after the teaching intervention, expressed on a Likert scale.SettingThe study was performed by the Department of General, Visceral and Thoracic Surgery at the German Armed Forces Hospital Ulm, which is an academic teaching hospital of the University of Ulm.ParticipantsThirty-eight laparoscopic novices, including medical students and junior residents, participated voluntarily in this teaching intervention. Group allocation was performed via the virtual coin flip method. Apparently, participants and tutors were not blinded to group assignment. No formal approval by the ethics committee was needed for this noninvasive study in compliance with the World Medical Association Declaration of Helsinki as discussed with the ethics committee of the University of Ulm.ResultsThirty-eight laparoscopy novices were randomized in the study. The 3D group (n = 19) was significantly faster than the 2D-4 K group (n = 19) (p = .008) in a standard box trainer model, with 134.45 ± 41.45 s vs. 174.99 ± 54.03 s for task 1 and 195.97 ± 49.78 s vs. 276.56 ± 139.20 s for task 2, and the effect was consistent throughout the learning curve. The failure rate was not significantly affected by the viewing mode. After crossover to the other laparoscopy system, precision and time were not significantly different between the groups. Learners rated the difficulty of laparoscopy lower on a Likert scale after having two hours of basic laparoscopy training. The study was funded by the hospital’s teaching budget.ConclusionsLaparoscopic novices can benefit from a 3D laparoscopy training setup. Exclusive 3D training prior to a complex task on a 2D-4 K setup does not negatively affect the learner’s performance.

Are we assessing motor competence? Evidence-informed constructs for motor competence in preschoolers through an Exploratory Graph Analysis

ABSTRACT Motor competence (MC) is conceptually defined as a multidimensional latent construct that covers the proficient performance in motor skills and its underlying mechanisms This study aimed to statistically provide arguments that MC is a network of interconnected constructs, such as FMS, coordination, and its underlying mechanisms, which are responsible for preschoolers’ proficiency in motor tasks. Participated 102 preschoolers (65 girls, M age = 4.22 ± 0.19) who were assessed for the Test of Gross Motor Development − 2nd edition, the Motor Competence Assessment, and the Supine-to-Stand. Data were explored using Exploratory Graph Analysis, using the EGAnet package in RStudio. A four-dimensional structure (61.2% of interactions) comprising tasks of the different protocols was underlined, in which all the nodes presented stable and adequate indexes (≥0.65; TEFI = –2.67). Four dimensions of MC were highlighted, namely Dimension 1, which combined movements for locomotor patterns; Dimension 2, comprising three process-oriented measures of object control skills to project objects; Dimension 3, which comprised of skills which require body coordination to displace body through space; and Dimension 4, composed by object control skills evaluated through product-oriented measures. For a better understanding of MC, the assessment of these different aspects that comprises MC should be considered.

Assessment of Cochlear Implant Rehabilitation Success by Speech-Language Therapists Using International Classification of Functioning, Disability and Health Criteria.

This study aims to introduce an assessment questionnaire based on the International Classification of Functioning, Disability and Health (ICF) to enable quantifiable clinical documentation. The questionnaire assists speech-language therapists (SLTs) in evaluating both non-audiological and audiological rehabilitation outcomes of patients with cochlear implants (CIs). Six SLTs were recruited to evaluate the effectiveness of CI care using ICF criteria. We carefully selected 20 pertinent ICF criteria, and SLTs received thorough training in how to apply them uniformly. A cohort of 48 patients was evaluated at three distinct time points: presurgery, postsurgery, and 6 months post-initial CI fitting. Patients underwent dedicated speech-language therapy sessions throughout the treatment course. A total of 144 complete datasets were scrutinized using a two-factor analysis of variance for ranks. Following CI care, patients exhibited substantial improvements in auditory perception, characterized by enhanced sound recognition and speech discrimination. Additionally, patients demonstrated improved attentional focus, proficiency in daily tasks and increased conversational engagement over time. Environmental factors, particularly sound perception, improved markedly, correlating with reported reductions in stress levels. Minimal changes were noted in stress management and communication techniques. Articulatory function, speech functions, and pain perception exhibited the least alteration. The data extracted from the ICF-based assessments underscore the vital significance of incorporating speech therapy-based metrics to evaluate both non-audiological and audiological factors in assessing the success of CI care within the SLTs domain. This approach proves to be highly relevant both in terms of demonstration and clinical applicability.

Understanding hate speech: the HateInsights dataset and model interpretability.

The persistence of hate speech continues to pose an obstacle in the realm of online social media. Despite the continuous evolution of advanced models for identifying hate speech, the critical dimensions of interpretability and explainability have not received proportional scholarly attention. In this article, we introduce the HateInsights dataset, a groundbreaking benchmark in the field of hate speech datasets, encompassing diverse aspects of this widespread issue. Within our dataset, each individual post undergoes thorough annotation from dual perspectives: firstly, conforming to the established 3-class classification paradigm that includes hate speech, offensive language, and normal discourse; secondly, incorporating rationales that outline specific segments of a post supporting the assigned label (categorized as hate speech, offensive language, or normal discourse). Our exploration yields a significant finding by harnessing cutting-edge state-of-the-art models: even models demonstrating exceptional proficiency in classification tasks yield suboptimal outcomes in crucial explainability metrics, such as model plausibility and faithfulness. Furthermore, our analysis underscores a promising revelation concerning models trained using human-annotated rationales. To facilitate scholarly progress in this realm, we have made both our dataset and codebase accessible to fellow researchers. This initiative aims to encourage collaborative involvement and inspire the advancement of the hate speech detection approach characterized by increased transparency, clarity, and fairness.

Effects of pedagogical intervention on Chinese EFL learners' use of motivational regulation strategies and oral English proficiency improvement

This study investigated the role of motivational regulation strategies (MRSs) in English as a foreign language (EFL) pedagogy, with a focus on Chinese university students. While prior research has explored MRSs, their specific impact on the oral proficiency of Chinese EFL learners remains under-examined. This mixed-methods research offers empirical insights, advocating for the integration of MRSs into EFL instruction to bolster students' oral proficiency. The study is bifurcated, with the first phase dedicated to cataloging MRSs' usage among Chinese university students and crafting the Speaking Strategies for Motivational Regulation Questionnaire (SSMRQ). This initial phase engaged 171 EFL students from a northern Chinese "211 project" university, employing convenience sampling. Factor analysis of participant responses culminated in a 15-item SSMRQ, spanning five dimensions: Environment Structuring (ES), Mastery Self-Talk (MST), Self-Consequating (SC), Self-Oriented Performance Self-Talk (SPST), and Externally-Oriented Performance Self-Talk (EPST). The second phase assessed the efficacy of MRSs-oriented instruction on student application of MRSs and oral English proficiency. This quasi-experimental study involved 22 consenting second-year English majors from the participating university. Data collection instruments encompassed the SSMRQ, oral English proficiency tasks, reflective journals, and semi-structured interviews. The instructional intervention spanned four weekly 30-min sessions, targeting the five MRSs' dimensions and offering feedback on oral performance. Assessments were conducted pre- and post-intervention using the SSMRQ, oral tasks, and reflective journals. Six participants, selected based on their oral English performance, were interviewed in-depth. Results suggest that the instructional intervention had a significant effect on the students' use of MRSs and their oral English proficiency. The study offers pedagogical insights into EFL speaking instruction in the higher education context, underscoring the importance of personalized teaching strategies tailored to individual learner needs. Collectively, this research introduces the SSMRQ and elucidates the pedagogical merits of MRSs-based instruction on oral English proficiency, establishing an empirical base for subsequent inquiry and pedagogical advancement in language education.

Influencing Factors of Spatial Ability for Architecture and Interior Design Students: A Fuzzy DEMATEL and Interpretive Structural Model

Spatial ability is not just a skill but a crucial element for architecture and interior design students, significantly impacting their proficiency in tasks involving 2D drawings, 3D components, and artistic expression. Despite extensive research in this area, a gap remains in the understanding of how to effectively cultivate spatial ability through educational interventions. This study, with its unique approach of identifying key influencing factors and their interrelationships within the fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM), fills this gap. The method visualizes cause-and-effect relationships within a structural model and captures the interdependencies between influencing factors. In a collaborative effort between nine universities in 2023–2024, 17 experts selected through purposeful sampling contributed to the development of a comprehensive list of potential influencing factors. After refinement through filtering, comparison with the existing literature, and expert consensus, seven influencing factors of spatial ability for architecture and interior design students from personal traits and STEAM disciplines were identified, which are sketching and hand drawing skills, mathematical skills, video game practice, descriptive geometry skills, augmented reality practice, spatial talk, and gesturing while talking. Sketching and hand drawing skills, mathematical skills, and video game practice come under cause factors of spatial ability, whereas the rest are effect factors. Proceeding with ISM analysis revealed that sketching and hand drawing skills and mathematical skills are located in the input layer and have a continuous impact on spatial ability. Descriptive geometry skills lie in the transition layer, which are considered as deep influencing factors, the rest of the factors lie in the effect layer. This study delves into the theoretical and practical implications of these findings, offering valuable insights for educational policy and practice.

Exploring the role of Large Language Models in haematology: A focused review of applications, benefits and limitations.

Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human-like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS-2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively.

Expanding the boundaries of leadership development: Propositions for leadership educators.

This article delves into the intricacies of leadership development through the lens of the leadership learning framework (LLF); challenging conventional notions by emphasizing self-awareness, relational dynamics, and values as key elements of the leadership development process. We first explore how the LLF reshapes traditional paradigms, redirecting focus from skills acquisition to personal growth rooted in self-awareness, relational dynamics, and values acquisition. Through recommendations and examples, the discussion seeks to clarify the implications of this perspective for leadership educators, advocating for programs that foster not only task proficiency but also critical reflection and a deep sense of interdependency in relationships with others in the leadership process. Ultimately, it underscores the importance of integrating self-reflection, social awareness, and value-centered approaches into leadership development programming to cultivate leaders capable of navigating the complexities of modern society.

Stepwise Attention-Guided Multiscale Fusion Network for Lightweight and High-Accurate SAR Ship Detection

Many exceptional deep learning networks have demonstrated remarkable proficiency in general object detection tasks. However, the challenge of detecting ships in synthetic aperture radar (SAR) imagery increases due to the complex and various nature of these scenes. Moreover, sophisticated large-scale models necessitate substantial computational resources and hardware expenses. To address these issues, a new framework is proposed called a stepwise attention-guided multiscale feature fusion network (SAFN). Specifically, we introduce a stepwise attention mechanism designed to selectively emphasize relevant information and filter out irrelevant details of objects in a step-by-step manner. Firstly, a novel LGA-FasterNet is proposed, which incorporates a lightweight backbone FasterNet with lightweight global attention (LGA) to realize expressive feature extraction while reducing the model’s parameters. To effectively mitigate the impact of scale and complex background variations, a deformable attention bidirectional fusion network (DA-BFNet) is proposed, which introduces a novel deformable location attention (DLA) block and a novel deformable recognition attention (DRA) block, strategically integrating through bidirectional connections to achieve enhanced features fusion. Finally, we have substantiated the robustness of the new framework through extensive testing on the publicly accessible SAR datasets, HRSID and SSDD. The experimental outcomes demonstrate the competitive performance of our approach, showing a significant enhancement in ship detection accuracy compared to some state-of-the-art methods.

GPT-4 as an X data annotator: Unraveling its performance on a stance classification task.

Data annotation in NLP is a costly and time-consuming task, traditionally handled by human experts who require extensive training to enhance the task-related background knowledge. Besides, labeling social media texts is particularly challenging due to their brevity, informality, creativity, and varying human perceptions regarding the sociocultural context of the world. With the emergence of GPT models and their proficiency in various NLP tasks, this study aims to establish a performance baseline for GPT-4 as a social media text annotator. To achieve this, we employ our own dataset of tweets, expertly labeled for stance detection with full inter-rater agreement among three annotators. We experiment with three techniques: Zero-shot, Few-shot, and Zero-shot with Chain-of-Thoughts to create prompts for the labeling task. We utilize four training sets constructed with different label sets, including human labels, to fine-tune transformer-based large language models and various combinations of traditional machine learning models with embeddings for stance classification. Finally, all fine-tuned models undergo evaluation using a common testing set with human-generated labels. We use the results from models trained on human labels as the benchmark to assess GPT-4's potential as an annotator across the three prompting techniques. Based on the experimental findings, GPT-4 achieves comparable results through the Few-shot and Zero-shot Chain-of-Thoughts prompting methods. However, none of these labeling techniques surpass the top three models fine-tuned on human labels. Moreover, we introduce the Zero-shot Chain-of-Thoughts as an effective strategy for aspect-based social media text labeling, which performs better than the standard Zero-shot and yields results similar to the high-performing yet expensive Few-shot approach.

Increased trial-to-trial similarity and reduced temporal overlap of muscle synergy activation coefficients manifest during learning and with increasing movement proficiency

Muscle synergy analyses are used to enhance our understanding of motor control. Spatially fixed synergy weights coordinate multiple co-active muscles through activation commands, known as activation coefficients. To gain a more comprehensive understanding of motor learning, it is essential to understand how activation coefficients vary during a learning task and at different levels of movement proficiency. Participants walked on a line, a beam, and learned to walk on a tightrope—tasks that represent different levels of proficiency. Muscle synergies were extracted from electromyography signals across all conditions and the number of synergies was determined by the knee-point of the total variance accounted for (tVAF) curve. The results indicated that the tVAF of one synergy decreased with task proficiency, with the tightrope task resulting in the highest tVAF compared to the line and beam tasks. Furthermore, with increasing proficiency and after a learning process, trial-to-trial similarity increased and temporal overlap of synergy activation coefficients decreased. Consequently, we propose that precise adjustment and refinement of synergy activation coefficients play a pivotal role in motor learning.

Patterns of neural activity in prelimbic cortex neurons correlate with attentional behavior in the rodent continuous performance test.

Sustained attention, the ability to focus on a stimulus or task over extended periods, is crucial for higher level cognition, and is impaired in individuals diagnosed with neuropsychiatric and neurodevelopmental disorders, including attention-deficit/hyperactivity disorder, schizophrenia, and depression. Translational tasks like the rodent continuous performance test (rCPT) can be used to study the cellular mechanisms underlying sustained attention. Accumulating evidence points to a role for the prelimbic cortex (PrL) in sustained attention, as electrophysiological single unit and local field (LFPs) recordings reflect changes in neural activity in the PrL in mice performing sustained attention tasks. While the evidence correlating PrL electrical activity with sustained attention is compelling, limitations inherent to electrophysiological recording techniques, including low sampling in single unit recordings and source ambivalence for LFPs, impede the ability to fully resolve the cellular mechanisms in the PrL that contribute to sustained attention. In vivo endoscopic calcium imaging using genetically encoded calcium sensors in behaving animals can address these questions by simultaneously recording up to hundreds of neurons at single cell resolution. Here, we used in vivo endoscopic calcium imaging to record patterns of neuronal activity in PrL neurons using the genetically encoded calcium sensor GCaMP6f in mice performing the rCPT at three timepoints requiring differing levels of cognitive demand and task proficiency. A higher proportion of PrL neurons were recruited during correct responses in sessions requiring high cognitive demand and task proficiency, and mice intercalated non-responsive-disengaged periods with responsive-engaged periods that resemble attention lapses. During disengaged periods, the correlation of calcium activity between PrL neurons was higher compared to engaged periods, suggesting a neuronal network state change during attention lapses in the PrL. Overall, these findings illustrate that cognitive demand, task proficiency, and task engagement differentially recruit activity in a subset of PrL neurons during sustained attention.

Experienced veterinary surgeons require multiple attempts but gain proficiency rapidly in robotic simulator tasks.

To assess attempts to proficiency of experienced veterinary surgeons for 2 surgical tasks when using a robotic simulator (Mimic dV-Trainer; Surgical Sciences) and determine factors associated with the successful performance of these tasks. Veterinary surgeons with rigid, minimally invasive surgery experience performed 2 tasks ("pick and place" and "knot the ring 1") using the simulator until they attained proficiency. Individual performance variables were recorded. The number of attempts to proficiency was recorded. Performance variables were also assessed for effect on proficiency by the Kendall tau correlation and hierarchical multiple linear regression. The study period was from July 25, 2022, through December 14, 2022. The 18 surgeons enrolled required a median of 8.5 attempts (95% CI, 7 to 12; range, 6 to 22) to reach proficiency for the basic task versus 27 attempts (95% CI, 21 to 38; range, 10 to 63) for the advanced task. Surgeons took a median of 6 minutes (range, 3 to 11 minutes) to complete training for the basic task and 12 minutes (range, 4 to 46 minutes) for the advanced task. The number of attempts to reach proficiency correlated strongly with economy of motion (tau = 0.72), instrument collisions (tau = 0.72), and time to completion (tau = 0.96). Although experienced surgeons required a high number of attempts to gain proficiency in robotic simulator tasks, they did achieve proficiency quickly, encouraging future investigations into their use for training. Specific motion metrics were identified which improved efficiency during training.

Artificial Intelligence and medical specialties: support or substitution?

The rapid advancement of artificial intelligence (AI) in healthcare has spurred extensive debate regarding its potential to replace human expertise across various medical specialties. This narrative review critically examines the integration of AI within diverse medical specialties to discern its role as a substitute or supporter. The analysis encompasses AI's impact on diagnostic precision, treatment planning, and patient care. Although AI systems have demonstrated remarkable proficiency in tasks reliant on data analysis and pattern recognition, they fall short in areas necessitating nuanced decision-making, empathetic communication, and the application of human medical expertise in diagnosis and treatment planning. The rapid evolution of AI applications within medical specialties is propelled by the swift advancements in both hardware and software technologies, fostering a dynamic synergy that continues to redefine the boundaries of precision and efficiency in healthcare delivery. While AI demonstrates remarkable capabilities in automating tasks, it is underscored that its integration in complex domains necessitates a balanced approach that preserves the indispensable contributions of human activity.

Design and actuation of a state transformable amorphous soft robot

Traditional solid-state magnetic robots have limited deformability due to their fixed structures, while liquid robots sacrifice stiffness and stability for deformability. To overcome these limitations, a novel state transformable amorphous soft robot, referred to as the AMS-Robot, has been developed utilizing magnetorheological fluid (MRF). This robot can swiftly transition from a Newtonian fluid state (in a weak magnetic field) to a solid Bingham plastic state (in a strong magnetic field) in response to varying magnetic fields. The locomotion of our AMS-Robot, steered by a gradient magnetic field acting on magnetic droplets, converts magnetic energy into kinetic energy and viscous dissipation, enabling functions like deformation, navigation, split-merger, and gradient crawling. Remarkably, the robot exhibits the ability to transport objects weighing approximately 150 times its own weight under a strong magnetic field. Through simulations of its movement within blood vessels, the potential of the AMS-Robot in medical applications is highlighted, showcasing its proficiency in tasks like precision drug delivery and thrombus removal. This groundbreaking approach holds significant promise for advancements in the field of medical applications.

Impact of high-quality, mixed-domain data on the performance of medical language models.

To optimize the training strategy of large language models for medical applications, focusing on creating clinically relevant systems that efficiently integrate into healthcare settings, while ensuring high standards of accuracy and reliability. We curated a comprehensive collection of high-quality, domain-specific data and used it to train several models, each with different subsets of this data. These models were rigorously evaluated against standard medical benchmarks, such as the USMLE, to measure their performance. Furthermore, for a thorough effectiveness assessment, they were compared with other state-of-the-art medical models of comparable size. The models trained with a mix of high-quality, domain-specific, and general data showed superior performance over those trained on larger, less clinically relevant datasets (P < .001). Our 7-billion-parameter model Med5 scores 60.5% on MedQA, outperforming the previous best of 49.3% from comparable models, and becomes the first of its size to achieve a passing score on the USMLE. Additionally, this model retained its proficiency in general domain tasks, comparable to state-of-the-art general domain models of similar size. Our findings underscore the importance of integrating high-quality, domain-specific data in training large language models for medical purposes. The balanced approach between specialized and general data significantly enhances the model's clinical relevance and performance. This study sets a new standard in medical language models, proving that a strategically trained, smaller model can outperform larger ones in clinical relevance and general proficiency, highlighting the importance of data quality and expert curation in generative artificial intelligence for healthcare applications.

Retraction for Wright et al., Animal personality aligns task specialization and task proficiency in a spider society

Retraction for Wright et al., Animal personality aligns task specialization and task proficiency in a spider society

Detection of Diabetic Foot Ulcers Using Dense Net Framework

Diabetic Foot Ulcers (DFUs) pose a significant health care challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. This study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management.