Advanced Model Research Articles

Professional Advancement Models for Nurse Practitioners and Physician Associates/Assistants: A historical overview and opportunities for future growth.

Professional advancement models (PAM) are paramount in the growth of nurse practitioners (NP) and physician associates/assistants (PA) who seek professional challenges, clinical recognition, job satisfaction, upward mobility, and incentives for retention. Professional advancement models for NPs and PAs were born out of literature on clinical career ladders that date back to the 1970s for the nursing work force. Over time, PAMs have evolved to offer NPs and PAs structured pathways to guide and reward professional growth, education, advocacy, research, and quality improvement (QI). Although the first institutional account of PAM development for NPs and PAs was published in 1998, there has been a recent spike in case reports of PAMs from individual medical centers. This article (1) provides a historical timeline of published data on the development and implementation of PAMs for NPs and PAs, (2) critiques features of existing PAMs, and (3) offers recommendations regarding PAM innovation for more uniform adoption in medical institutions across the country.

The beginning of the Neolithic in the central Balkans

Since 2020 a wealth of new data has been generated on the beginning of the Neolithic in the central Balkans. The picture that has emerged is broadly consistent with the Wave of Advance model, with the first farmers arriving in the region around 6250 BC and expanding gradually towards the north. In this paper, an updated review of the evidence and interpretations is presented, and potentially problematic or ambiguous aspects of the current interpretations of the Neolithic expansion in the Balkans are identified. Alternative hypotheses and means of their testing are also discussed.

The Stress Effects of Mandibular Movements on the Temporomandibular Joint With Sagittal Split Ramus Osteotomy.

The aim of this study was to evaluate the stress changes in the temporomandibular joint (TMJ) after sagittal split ramus osteotomy (SSRO) with finite element analysis (FEA). 5 and 10mm mandibular setback and advancement were applied to models by using SSRO and a control model without osteotomy evaluated. The articular disc was modeled as superelastic, and the stresses on the articular fossa, disc, and condyle were evaluated. The stresses on the cartilage were 1.150 MPa on the 5mm advancement model and 1.506 MPa on the 10mm advancement model. The stresses on the disc were 11.56 MPa on the 5mm advancement model, 7.94 MPa on the 10mm advancement model and the amount were significantly higher than other models. The stress, especially in the cartilage, increased with the amount of advancement. In the setback models, the stresses on the condylar cartilage and the disc were higher than in the control model, and the stress in the condylar cartilage increased with the amount of setback. SSRO increases the stresses in the TMJ components and stresses increase depending on the amount of advancement and setback and may cause the development of joint problems.

FBANet: An Effective Data Mining Method for Food Olfactory EEG Recognition.

At present, the sensory evaluation of food mostly depends on artificial sensory evaluation and machine perception, but artificial sensory evaluation is greatly interfered with by subjective factors, and machine perception is difficult to reflect human feelings. In this article, a frequency band attention network (FBANet) for olfactory electroencephalogram (EEG) was proposed to distinguish the difference in food odor. First, the olfactory EEG evoked experiment was designed to collect the olfactory EEG, and the preprocessing of olfactory EEG, such as frequency division, was completed. Second, the FBANet consisted of frequency band feature mining and frequency band feature self-attention, in which frequency band feature mining can effectively mine multiband features of olfactory EEG with different scales, and frequency band feature self-attention can integrate the extracted multiband features and realize classification. Finally, compared with other advanced models, the performance of the FBANet was evaluated. The results show that FBANet was better than the state-of-the-art techniques. In conclusion, FBANet effectively mined the olfactory EEG data information and distinguished the differences between the eight food odors, which proposed a new idea for food sensory evaluation based on multiband olfactory EEG analysis.

Development of a Professional Advancement Model for Perfusionists

Improvement in professional advancement opportunities may reduce turnover and improve retention for perfusionists. However, the current literature lacks examples of professional advancement models (PAMs) for perfusionists. This review looks at examples from other healthcare fields to provide the rationale and develop a framework for such a model. The review results led to the development of a point-based PAM that included four levels: perfusionist I, II, III, and IV. Each level is associated with its own point requirement, experience level, and salary increase. Points can be acquired through four defined categories. Perfusion programs needing professional advancement can use these results as a foundation for implementing a PAM for perfusionists.

An Explainable Multichannel Model for COVID-19 Time Series Prediction

Introduction: The COVID-19 pandemic has affected every country and changed people's lives. Accurate prediction of COVID-19 trends can help prevent the further spread of the outbreak. However, the changing environment affects the COVID-19 prediction performance, and previous models are limited in practical applications. Methods: An explainable multichannel deep learning model with spatial, temporal and environmental channels for time series prediction, STE-COVIDNet, was proposed. The time series data of COVID-19 infection, weather, in-state population mobility, and vaccination were collected from May, 2020, to October, 2021, in the USA. In the environmental channel of STE-COVIDNet, an attention mechanism was applied to extract significant environmental factors related to the spread of COVID-19. In addition, the attention weights of these factors were analyzed with the actual situation. Results: STE-COVIDNet was found to be superior to other advanced prediction models of COVID-19 infection cases. The analysis results of attention weight were reported to be consistent with existing studies and reports. It was found that the same environmental factors that influence the spread of COVID-19 can vary across time and region, which explains why findings of previous studies on the relationship between the environment and COVID-19 vary by region and time. Conclusion: STE-COVIDNet is an explainable model that can adapt to environmental changes and thus improve predictive performance.

Detecting and Grounding Multi-Modal Media Manipulation and Beyond.

Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various DeepFake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM 4). DGM 4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM 4 dataset, where image-text pairs are manipulated by various approaches, with rich annotation of diverse manipulations. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs: 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. To exploit more fine-grained contrastive learning for cross-modal semantic alignment, we further integrate Manipulation-Aware Contrastive Loss with Local View and construct a more advanced model HAMMER++. Finally, we build an extensive benchmark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of HAMMER and HAMMER++; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation.

Development of E-LKPD based on Problem Based Instruction on Human Digestive System Material for Class XI Students of SMA Negeri 5 Metro

The examination was directed to figure out Issue Based Guidance Understudy Worksheets (LKPD) in science examples and to figure out understudies' reactions. The kind of exploration being done by specialists is Improvement (Innovative work). This examination utilizes the Borg and Nerve advancement model embraced by Sugiyono. The instruments utilized are validator and understudy surveys. Then, the information got will be broke down from every appraisal by validator specialists and understudies. The nature of understudy worksheets is completed by approving understudy worksheet items in light of issue based guidance, in particular media specialists, material specialists and language specialists. In light of media specialists, a typical rate was gotten of 84%, material specialists acquired a normal level of 90%, and language specialists got a typical level of 75%. Then, the understudy worksheet item is tried. To figure out understudies' reactions to understudy worksheet items, limited scope preliminaries and huge scope preliminaries were done. Limited scope preliminaries got a typical level of 87% with the standards "Entirely Possible" while in enormous scope preliminaries got a typical level of 87 % with the rules "Truly Qualified" accordingly the understudy worksheet depends on issue based guidance in science examples. created appropriate for use.

Chinese Modernization in the Eyes of African Students

This article explores the perceptions and experiences of African students studying in China regarding Chinese modernization efforts. Through qualitative interviews and surveys conducted among African students in Chinese universities, the study examines how these students perceive China’s rapid development and modernization initiatives. The findings reveal that African students view China as a model for economic growth, technological advancement, and infrastructure development. Many students appreciate China’s emphasis on innovation, entrepreneurship, and scientific research, which have contributed to its transformation into a global economic powerhouse. They admire China’s progress in key sectors such as telecommunications, transportation, renewable energy, and artificial intelligence, highlighting the country’s achievements in bridging the digital divide and fostering sustainable development. Moreover, African student’s express admiration for China’s commitment to poverty alleviation, social welfare, and environmental sustainability. They note China’s efforts to lift millions of people out of poverty, improve living standards, and promote ecological conservation through green initiatives and clean technology. Many students commend China’s Belt and Road Initiative (BRI) for promoting connectivity, trade, and cultural exchange between China and Africa, thereby fostering mutual benefits and cooperation. In conclusion, the article highlights the significance of Chinese modernization in shaping the perceptions and experiences of African students in China. It calls for continued dialogue, collaboration, and partnership building to foster a deeper understanding of Chinese modernization and its implications for Africa’s development trajectory. The voices of African students serve as a catalyst for meaningful engagement, exchange of ideas, and building a shared future of prosperity and cooperation between China and Africa.

Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis

Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis.In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation.The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making.

The influence of ChatGPT on thinking skills and creativity of EFL student teachers: a narrative inquiry

ABSTRACT There is an urgent need in the educational community to explore the potential impact of advanced language models powered by artificial intelligence, such as ChatGPT, on thinking skills and creativity. These models hold significant promise for enhancing teaching and learning, but it is crucial to investigate their potential effects on these critical areas of cognitive development. This study employs a narrative inquiry approach, utilising weekly written narratives and qualitative interviews for analysis, to explore and understand student teachers’ experiences, perceptions, and reflections on using ChatGPT in their teaching practices. Exploring the experiences and perspectives of 12 English student teachers, this research scrutinises the influence of ChatGPT on the development of thinking skills and creativity during the practicum part of a teacher education programme. Through narratives and interviews the study uncovers the benefits, challenges, and strategies for optimising the use of ChatGPT in pre-service language teacher education. Key findings reveal the importance of human-computer co-creation, engaging in critical analysis, and fostering collaboration to maximise the educational benefits of ChatGPT. These insights contribute to the ongoing discourse on the effective integration of AI in teacher education and offer practical recommendations for educators and students navigating the complex interplay between AI tools and independent thinking.

Perceptions of Female Administrative Employees on the New Career Advancement Model at Universidade Nacional Timor Lorosa'e

Perceptions of Female Administrative Employees on the New Career Advancement Model at Universidade Nacional Timor Lorosa'e

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Recursive Deformable Pyramid Network for Unsupervised Medical Image Registration.

Complicated deformation problems are frequently encountered in medical image registration tasks. Although various advanced registration models have been proposed, accurate and efficient deformable registration remains challenging, especially for handling the large volumetric deformations. To this end, we propose a novel recursive deformable pyramid (RDP) network for unsupervised non-rigid registration. Our network is a pure convolutional pyramid, which fully utilizes the advantages of the pyramid structure itself, but does not rely on any high-weight attentions or transformers. In particular, our network leverages a step-by-step recursion strategy with the integration of high-level semantics to predict the deformation field from coarse to fine, while ensuring the rationality of the deformation field. Meanwhile, due to the recursive pyramid strategy, our network can effectively attain deformable registration without separate affine pre-alignment. We compare the RDP network with several existing registration methods on three public brain magnetic resonance imaging (MRI) datasets, including LPBA, Mindboggle and IXI. Experimental results demonstrate our network consistently outcompetes state of the art with respect to the metrics of Dice score, average symmetric surface distance, Hausdorff distance, and Jacobian. Even for the data without the affine pre-alignment, our network maintains satisfactory performance on compensating for the large deformation. The code is publicly available at https://github.com/ZAX130/RDP.

Advanced Practice Provider Professional Advancement Model: A 10-Year Experience

In a large 700-bed academic medical center, a professional advancement model for advanced practice providers (APPs) provides a structured process to guide professional growth, development, and promotion. During a 10-year period, 217 APPs applied and 162 were promoted, resulting in a 74% promotion rate. Of the APPs promoted to senior nurse practitioner or senior physician assistant, 140 (86%) are still employed at the medical center. This advancement model provides a structured process to guide the APP workforce in developing clinical expertise and fostering professional development while improving retention and overall satisfaction.

Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization

In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms

The Role of Advanced Modeling in the Assembly and Operation of Tokamak Magnets

The Role of Advanced Modeling in the Assembly and Operation of Tokamak Magnets

A State-of-the-Art Review on Direct Welding of Polymer to Metal for Structural Applications: Part 1 — Promising Processes

Structural lightweighting through the effective use of multiple materials has received increasing attention for fulfilling today’s demands for environmental sustainability in transportation systems. Direct dissimilar material joining methods (versus, e.g., traditional adhesive bonding or mechanical fastening) have become increasingly desirable since they offer process simplicity, production efficiency, and hermetic sealing, among others. In this two-part article, we provide a critical assessment of the state-of-the-art research and promising direct dissimilar material joining techniques reported over the last decades, with a particular emphasis on their potential for structural applications in Part I. As such, recent advances in advanced joint design and modeling methods for enabling optimum joint design for jointability and joint performance are presented along with some detailed examples for demonstrating their potential impacts on industrial applications in Part II. Finally, recommendations on future research and development directions are outlined for supporting the industry’s drive towards multi-material lightweighting.

Characterization of the zebrafish as a model of ATP-sensitive potassium channel hyperinsulinism

IntroductionCongenital hyperinsulinism (HI) is the leading cause of persistent hypoglycemia in infants. Current models to study the most common and severe form of HI resulting from inactivating mutations in the...