Effective Model Research Articles

A Fault-Tolerant Soft Sensor Algorithm Based on Long Short-Term Memory Network for Uneven Batch Process

Batch processing is a widely utilized technique in the manufacturing of high-value products. Traditional methods for quality assessment in batch processes often lead to productivity and yield losses because of offline measurement of quality variables. The use of soft sensors enhances product quality and increases production efficiency. However, due to the uneven batch data, the variation in processing times presents a significant challenge for building effective soft sensor models. Moreover, sensor failures, exacerbated by the manufacturing environment, complicate the accurate modeling of process variables. Existing soft sensor approaches inadequately address sensor malfunctions, resulting in significant prediction inaccuracies. This study proposes a fault-tolerant soft sensor algorithm that integrates two Long Short-Term Memory (LSTM) networks. The algorithm focuses on modeling process variables and compensating for sensor failures using historical batch quality data. It introduces a novel method for converting quality variables into process rates to align uneven batch data. A case study on simulated penicillin production validates the superiority of the proposed algorithm over conventional methods, showing its capacity for precise endpoint detection and effectiveness in addressing the challenges of batch process quality assurance. This study offers a robust solution to the issues of soft sensor reliability and data variability in industrial manufacturing.

Software Implementation of Methods for Modeling the Interaction of Electromagnetic Radiation with Carbon Nanocomposites

Methods for modeling the interaction of electromagnetic radiation with a carbon nanostructured composite containing various structural elements, such as carbon fibers, nanoparticles, air gaps, and transition shells are considered. It is shown that for effective and adequate modeling of the interaction of electromagnetic radiation with a carbon nanostructured composite, it is necessary to create software due to the presence of a large set of input parameters, as well as the complexity of calculations. Functional requirements for the software are formulated, reflecting the choice of models and the presence of a convenient interactive interface. The architecture of the software that implements models of interaction of electromagnetic radiation with a carbon nanostructured composite is described, and the software implementation (class structure, methods, user interface) is considered. The developed code is based on proven physical and mathematical models, which make it possible to obtain results close to experimental data. The software architecture allows to effectively manage the modeling process, and also includes the ability to modify models and take into account new structural elements. An interactive interface for users has been developed that allows to easily manage the model parameters and analyze the results of numerical experiments.

Insights on the Use of Transgenic Mice Models in Alzheimer's Disease Research.

Alzheimer's disease (AD), the leading cause of dementia, presents a significant global health challenge with no known cure to date. Central to our understanding of AD pathogenesis is the β-amyloid cascade hypothesis, which underlies drug research and discovery efforts. Despite extensive studies, no animal models of AD have completely validated this hypothesis. Effective AD models are essential for accurately replicating key pathological features of the disease, notably the formation of β-amyloid plaques and neurofibrillary tangles. These pathological markers are primarily driven by mutations in the amyloid precursor protein (APP) and presenilin 1 (PS1) genes in familial AD (FAD) and by tau protein mutations for the tangle pathology. Transgenic mice models have been instrumental in AD research, heavily relying on the overexpression of mutated APP genes to simulate disease conditions. However, these models do not entirely replicate the human condition of AD. This review aims to provide a comprehensive evaluation of the historical and ongoing research efforts in AD, particularly through the use of transgenic mice models. It is focused on the benefits gathered from these transgenic mice models in understanding β-amyloid toxicity and the broader biological underpinnings of AD. Additionally, the review critically assesses the application of these models in the preclinical testing of new therapeutic interventions, highlighting the gap between animal models and human clinical realities. This analysis underscores the need for refinement in AD research methodologies to bridge this gap and enhance the translational value of preclinical studies.

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Background:Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. Objective:This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Methods:Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results:Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Conclusion:Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

Temporal cluster-based local deep learning or signal processing-temporal convolutional transformer for daily runoff prediction?

Water scarcity poses a major obstacle to sustainable development, and precise discharge prediction plays a vital role in enabling effective water resource management. This study investigated improved prediction techniques for nonstationary time series. The study evaluated the effect of signal processing techniques and blending approaches on the performance of deep learning models for daily discharge prediction. It also compared the performance of cluster-based local modeling with hybrid signal processing-deep learning approaches. Two robust deep learning methods, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), along with a powerful signal processing approach called discrete wavelet transform, were utilized for prediction of daily discharge. Three blending approaches were assessed: 1) decomposing both inputs and target, 2) decomposing only the target, and 3) decomposing only the inputs and then blending them with deep learning models. Also, a new hybrid deep learning based model namely discrete wavelet transform-Temporal Convolutional Transformer (DWT-TCT) was developed. The results showed that a single-output wavelet transform-deep learning model (3rd blending approach) outperformed multi-output models, demonstrating a relative enhancement of up to 56% for the LSTM model and 51% for the CNN model. Furthermore, temporal cluster-based local modeling displayed promising performance, resulting in an improvement of up to 18% in NRMSE compared to the wavelet transform-deep learning model, while also requiring less computational cost. The successful results of the temporal cluster-based local modeling approach provide a beneficial alternative to hybrid signal processing-deep learning models. In addition the results showed that the proposed DWT-TCT model outperformed all other models with NRMSE ranges from 6.8% to 16.2% in the study areas. The results have implications for hydrology and water resources management, as they can be used to develop more precise and effective models for predicting discharge in view of nonstationarity.

Atomic soliton transmission and induced collapse in scattering from a narrow barrier

We report systematic numerical simulations of the collision of a bright matter-wave soliton made of Bose-condensed alkali-metal atoms through a narrow potential barrier by using the three-dimensional Gross–Pitaevskii equation. In this way, we determine how the transmission coefficient depends on the soliton impact velocity and the barrier height. Quite remarkably, we also obtain the regions of parameters where there is the collapse of the bright soliton induced by the collision. We compare these three-dimensional results with the ones obtained by three different one-dimensional nonlinear Schrödinger equations. We find that a specifically modified nonpolynomial Schrödinger equation is able to accurately assess the transmission coefficient even in a region in which the usual nonpolynomial Schrödinger equation collapses. In particular, this simplified but very effective one-dimensional model takes into account the transverse width dynamics of the soliton with an ordinary differential equation coupled to the partial differential equation of the axial wave function of the Bose–Einstein condensate.

PCTDepth: Exploiting Parallel CNNs and Transformer via Dual Attention for Monocular Depth Estimation

Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection

In real-world engineering, uncertainty is ubiquitous within material properties, structural geometry, load conditions, and the like. These uncertainties have substantial impacts on the estimation of structural performance. Furthermore, information or datasets in real life commonly contain imperfections, e.g., noise, outliers, or missing data. To quantify these impacts induced by uncertainties on structural behaviours and reduce the effects of data imperfections simultaneously, a machine learning-aided stochastic analysis framework is proposed. A novel supervised machine learning technique, namely the Capped Extended Support Vector Regression (CX-SVR) technique, is developed to effectively suppress the effects of outliers and noise in datasets. Its inherent convexity in optimization and capped strategy theoretically supports the accuracy of CX-SVR, especially in handling datasets with imperfections. Once the effective surrogate model is established, subsequent analyses, like sampling-based methods, can circumvent the cumbersome physical model, which is potentially the nest of computational burden and errors in engineering applications. The high robustness of the proposed approach can be summarized in four main aspects: unrestrictive selection of the system inputs and their statistical information, ‘perfect’ or ‘imperfect’ data, enough statistical information (including statistical moments, probability density functions, and cumulative distribution functions) of the system outputs, and physical problems from various engineering fields.

Dosage-dependent effects of FGFR2W290R mutation on craniofacial shape and cellular dynamics of the basicranial synchondroses.

Craniosynostosis is a common yet complex birth defect, characterized by premature fusion of the cranial sutures that can be syndromic or nonsyndromic. With over 180 syndromic associations, reaching genetic diagnoses and understanding variations in underlying cellular mechanisms remains a challenge. Variants of FGFR2 are highly associated with craniosynostosis and warrant further investigation. Using the missense mutation FGFR2W290R , an effective mouse model of Crouzon syndrome, craniofacial features were analyzed using geometric morphometrics across developmental time (E10.5-adulthood, n = 665 total). Given the interrelationship between the cranial vault and basicranium in craniosynostosis patients, the basicranium and synchondroses were analyzed in perinates. Embryonic time points showed minimal significant shape differences. However, hetero- and homozygous mutant perinates and adults showed significant differences in shape and size of the cranial vault, face, and basicranium, which were associated with cranial doming and shortening of the basicranium and skull. Although there were also significant shape and size differences associated with the basicranial bones and clear reductions in basicranial ossification in cleared whole-mount samples, there were no significant alterations in chondrocyte cell shape, size, or orientation along the spheno-occipital synchondrosis. Finally, shape differences in the cranial vault and basicranium were interrelated at perinatal stages. These results point toward the possibility that facial shape phenotypes in craniosynostosis may result in part from pleiotropic effects of the causative mutations rather than only from the secondary consequences of the sutural defects, indicating a novel direction of research that may shed light on the etiology of the broad changes in craniofacial morphology observed in craniosynostosis syndromes.

Features of Pedagogical Modeling of the Educational Process in the System of Additional Professional Education of Medical Workers

The modern system of additional professional education offers new principles and approaches to organising the pedagogical process. In this regard, the issue of choosing the most appropriate technologies and tools for organising training and building an effective model of the educational process in the system of additional professional education of medical workers is relevant. The purpose of the study is to identify the features of pedagogical modeling of the educational process in the system of additional professional education of medical workers. Research methodology and methods. The article discusses theoretical and conceptual modeling ideas presented in domestic and foreign literature. The study was conducted on the basis of the Federal State Budgetary Educational Institution of Higher Education “Novosibirsk State Medical University” of the Ministry of Health of Russia in the Centre for Distance Educational Technologies of the Department of Postgraduate Education. Research Results. During the study, it was revealed that pedagogical modeling of the educational process in the system of additional professional education of medical specialists has its own characteristics. Conclusion. When modeling the educational process in the system of additional professional education, the current requirements and needs of medical specialists must be taken into account, which makes it possible to adapt the content and methodology of training to specific professional tasks. Besides, it is important to consider the practice-focused nature of medical education: internships, training practical skills on simulators, active use of digital educational technologies, the possibility of individualising training so that all students could develop their professional skills according to their needs. The system is for assessing academic performance and monitoring student satisfaction with training, which allows to adjust the educational process and ensure high quality education is an important part of pedagogical modeling.

High harmonic generation in graphene quantum dots

We study theoretically the generation of high harmonics in disk graphene quantum dots placed in linearly polarized short pulse. The quantum dots (QD) are described within an effective model of the Dirac type and the length gauge was used to describe the interaction of quantum dots with an optical pulse. The generated radiation spectra of graphene quantum dots can be controlled by varying the quantum dot size, i.e. its radius. With increasing the quantum dot radius, the intensities of low harmonics mainly decrease, while the cutoff frequency increases. The sensitivity of the cutoff frequency to the QD size increases with the intensity of the pulse.

Primary study of the relative and compound biological effectiveness model for boron neutron capture therapy based on nanodosimetry.

The current radiobiological model employed for boron neutron capture therapy (BNCT) treatment planning, which relies on microdosimetry, fails to provide an accurate representation the biological effects of BNCT. The precision in calculating the relative biological effectiveness (RBE) and compound biological effectiveness (CBE) plays a pivotal role in determining the therapeutic efficacy of BNCT. Therefore, this study focuses on how to improve the accuracy of the biological effects of BNCT. The purpose of this study is to propose new radiation biology models based on nanodosimetry to accurately assess RBE and CBE for BNCT. Nanodosimetry, rooted in ionization cluster size distributions (ICSD), introduces a novel approach to characterize radiation quality by effectively delineating RBE through the ion track structure at the nanoscale. In the context of prior research, this study presents a computational model for the nanoscale assessment of RBE and CBE. We establish a simplified model of DNA chromatin fiber using the Monte Carlo code TOPAS-nBio to evaluate the applicability of ICSD to BNCT and compute nanodosimetric parameters. Our investigation reveals that both homogeneous and heterogeneous nanodosimetric parameters, as well as the corresponding biological model coefficients α and β, along with RBE values, exhibit variations in response to varying intracellular 10B concentrations. Notably, the nanodosimetric parameter effectively captures the fluctuations in model coefficients α and RBE. Our model facilitates a nanoscale analysis of BNCT, enabling predictions of nanodosimetric quantities for secondary ions as well as RBE, CBE, and other essential biological metrics related to the distribution of boron. This contribution significantly enhances the precision of RBE calculations and holds substantial promise for future applications in treatment planning.

Investigating the wave attenuation capabilities of rectangular pile head breakwater: A physical modelling approach

The study provides a comprehensive examination of single row Rectangular Pile Head Breakwaters (RPHB), encompassing both non-perforated and perforated variations. In the non-perforated RPHB category, the investigation delves into the effects of pile head height and width, and wave climate. For perforated RPHB structures, the study analyses the influence of percentage of perforations, perforation size, and depth of water. Further, the research includes a comparative assessment between non-perforated and perforated RPHB structures. Additionally, the research conducts a comparative analysis with similar structures. In the case of non-perforated RPHB, the configuration with relative pile head diameter (D/d) of 2.4 and relative pile head height (Y/Hmax) of 1.5 stood out as the most effective model. Similarly, the perforated RPHB demonstrated its maximum wave attenuation potential with percentage of perforations (P) of 24% with relative size of perforations (S/D) of 0.25. This optimal configuration achieved a minimal wave transmission coefficient (Kt) of 0.53, reflection coefficient (Kr) of 0.33, and energy dissipation coefficient (Kd) of 0.79 at a relative water depth (h/H) 0.865. Notably, the introduction of perforations on the RPHB structure led to an improvement in wave attenuation performance by 4–8%, resulting in lower reflection and higher energy dissipation. Comparatively, the RPHB structure outperformed the Enlarged (cylindrical) Pile Head Breakwater (EPHB) and Conical Pile Head Breakwater (CPHB) structures in terms of wave attenuation, exhibiting higher reflection and superior energy dissipation characteristics. The consistent outcome of these investigations reveals that the RPHB exhibits superior hydrodynamic performance characteristics and design suitability, making it a promising choice for breakwater applications.

DCCN: A dual-cross contrastive neural network for 3D point cloud representation learning

The proliferation of depth cameras and LiDAR sensors in actual industrial environments has fueled the pursuit of an effective and efficient 3D point cloud model that enables us to perceive and interact with the physical world. However, the intrinsic complexity of 3D semantic information poses significant challenges to model design, including spatial rotation invariance and irregular point cloud structure, which fundamentally impact the representation and behavior of 3D point cloud systems. Existing have either heavily relied on labeling information in a supervised learning setting or failed to effectively capture the inherent patterns of the 3D point clouds within a self-supervised learning framework, leading to poor performance in specific downstream tasks. To address these limitations, this paper introduces a self-supervised framework, Dual-Cross Contrastive Neural Network (DCCN) for 3D point cloud self-supervised representation learning. DCCN leverages cross-view, cross-network, and domain-specific knowledge distillation to enhance the extraction of hidden features from point clouds and fully exploit the capabilities of the encoder. Our DCCN employs a pseudo-Siamese network consisting of an online network and a target network, facilitating knowledge interaction and distillation. The method extracts internal states from augmented 3D point cloud by learning cross-view relationships and optimizes model parameters through intra-modal cross-network learning. We incorporate a momentum-updating mechanism without shared weights in the Siamese network architecture to distill knowledge and enhance the role differentiation the online and target networks. Experimental results demonstrate that our approach outperforms a range of supervised and self-supervised learning methods across a series of downstream tasks consisting of four tasks in three representative datasets. Ablation studies validate the component-wise effectiveness of cross-view, cross-network, and moment-updating learning objectives in achieving superior point cloud representation. The overall findings establish our method, DCCN, as an effective solution for 3D point cloud representation learning in real-world applications.

Preserving privacy in big data research: the role of federated learning in spine surgery.

Integrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery. The authors reviewed the literature. FL has promising applications in spine surgery, including telesurgery, AI-based prediction models, and medical image segmentation. Implementing FL requires careful consideration of infrastructure, data quality, and standardization, but it holds the potential to revolutionize orthopedic surgery while ensuring patient privacy and data control. Federated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care.

EXPRESS CALCULATION OF OXYGEN REGIME IN WASTEWATER FACILITIES

Problem statement. The efficiency of biological water purification depends significantly on the concentration of dissolved oxygen in the volume of the bioreactor. But during the operation of bioreactors, the oxygen regime changes, so it is important to predict the oxygen concentration for different modes of operation of treatment plants. Simplified mathematical models are used to calculate the oxygen regime, but these models are designed for typical bioreactors. An important task is the development of effective multivariate numerical models for evaluating the oxygen regime in reactors of different geometric shapes, which differ from “classical” reactors. The purpose of the article. Development of a CFD model for operational evaluation of the oxygen regime in bioreactors. Methodology. For mathematical modeling of the oxygen regime in the bioreactor, a two-dimensional mass transfer equation is used, which takes into account the place of oxygen supply from the aerator located in the building, the hydrodynamics of the flow in the bioreactor, and the diffusion process. The potential motion model was used to solve the hydrodynamic problem. Numerical integration of modeling equations is carried out by using finite-difference splitting schemes. At each splitting step, the difference equations are solved using explicit schemes. This allows a simple computer implementation of the numerical model. Scientific novelty. A multifactor two-dimensional CFD model was developed, which allows for a quick assessment of the oxygen regime in a bioreactor used for wastewater treatment. Practical significance. The developed CFD model and computer code can be used at the stage of designing or re-engineering bioreactors in order to find the optimal location of aerators in the structure. Conclusions. The software implementation of the developed numerical model was carried out. The results of a computational experiment on the study of the wastewater treatment process in a sedimentation tank are presented.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data.

Pyroptosis impacts the development of malignant tumors, yet its role in colorectal cancer (CRC) prognosis remains uncertain. To assess the prognostic significance of pyroptosis-related genes and their association with CRC immune infiltration. Gene expression data were obtained from The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing dataset GSE178341 from the Gene Expression Omnibus (GEO). Pyroptosis-related gene expression in cell clusters was analyzed, and enrichment analysis was conducted. A pyroptosis-related risk model was developed using the LASSO regression algorithm, with prediction accuracy assessed through K-M and receiver operating characteristic analyses. A nomogram predicting survival was created, and the correlation between the risk model and immune infiltration was analyzed using CIBERSORTx calculations. Finally, the differential expression of the 8 prognostic genes between CRC and normal samples was verified by analyzing TCGA-COADREAD data from the UCSC database. An effective pyroptosis-related risk model was constructed using 8 genes-CHMP2B, SDHB, BST2, UBE2D2, GJA1, AIM2, PDCD6IP, and SEZ6L2 (P < 0.05). Seven of these genes exhibited differential expression between CRC and normal samples based on TCGA database analysis (P < 0.05). Patients with higher risk scores demonstrated increased death risk and reduced overall survival (P < 0.05). Significant differences in immune infiltration were observed between low- and high-risk groups, correlating with pyroptosis-related gene expression. We developed a pyroptosis-related prognostic model for CRC, affirming its correlation with immune infiltration. This model may prove useful for CRC prognostic evaluation.

Multi-Tiered Systems of Educator Professional Development: A Systematic Literature Review of Responsive, Tiered Professional Development Models

Teachers and clinicians report feeling underprepared to implement evidence-based behavior supports. As such, effective models of professional development that lead to improved outcomes for all individuals are required. Critical components of high-quality professional development include explicit instruction, modeling, practice, direct feedback, and, potentially, individual coaching. However, delivering all components may be resource intensive. Professional development approaches that extend the tiered logic of multi-tiered systems of support to adult learning may address these challenges. In such models, universal professional development is provided to whole-staff groups on specific skills using explicit instruction, modeling, practice, and feedback. Data are then collected and used to inform targeted, individualized supports. The current systematic literature review addressed a gap in the literature by identifying, summarizing, and appraising 20 published studies that examined tiered, responsive professional development models. While consistent practices were identified (e.g., group didactic training at Tier 1 and coaching at Tiers 2 and 3), the form and shape of specific interventions at each tier differed across studies. The same was observed in the data-based decision-making processes employed to determine the need for additional trainee support. Practical implications to inform the ongoing examination of tiered responsive professional development models are discussed.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.