Current Models Research Articles

Accuracy of death risk prediction models for acute coronary syndrome patients: a systematic review and meta-analysis.

This study systematically evaluates the accuracy of several death risk prediction models for patients with acute coronary syndrome (ACS) through evidence-based methods. We identify the most accurate and effective ACS death risk prediction model and provide an evidence-based basis for clinical healthcare personnel to evaluate their choice of death risk prediction model for ACS patients. An evidence-based approach was used to study the current death risk prediction model for ACS. First, a literature search was carried out using computer-based and manual searching. The literature databases searched include Cochrane Library, MEDLINE, EMBASE, PubMed, Web of Science, WanFang Data, CNKI, VPCS, and SinoMed. The search period was limited to 2009 to 2022. Screening, quality evaluation and data extraction were carried out for the included articles. The PROBAST was used to conduct a migration risk assessment. RevMan 5.3 and Meta-DiSc 1.4 were used in combination to determine the model effect sizes. A descriptive analysis was conducted for the data that could not be meta-analyzed. A total of 8277 articles were initially included in this study. After screening, 25 articles were finally included, involving 11 different risk prediction models. A total of 306,390 patients with ACS were included of which 158,080 (51.6%) were male and 147,793 (48.4%) were female. The patients stemmed from 11 different countries (e.g., China, the USA, Spain, the UK, etc.). The total number of deaths was 23,601. The sensitivity of the GRACE risk prediction model was 0.78, with a specificity of 0.76 and an AUC value of 0.86. The sensitivity of the CAMI risk prediction model was 0.78, with a specificity of 0.70 and an AUC value of 0.85. The sensitivity of the TIMI risk prediction model was 0.51, with a specificity of 0.81, and an AUC value of 0.64. The sensitivity of the REMS risk prediction model was 0.78, with a specificity of 0.46 and an AUC value of 0.41. Eight different risk prediction models (EPICOR, CRUSADE, SAMI, GWTG, LNS, SYNTAX II, APACHE II) that could not be combined with the effect size were also included, with sensitivities ranging from 0.77-0.95, specificities ranging from 0.22-0.99, and AUC values ranging from 0.71-0.92. The GRACE and CAMI risk prediction models demonstrate good accuracy for evaluating the death risk of ACS patients. The accuracy of the TIMI risk prediction model is similar to that of the REMS risk prediction model. The APACHE II, SYNTAX II, EPICOR, and CAMI risk prediction models also show good accuracy for estimating the risk of death in ACS patients, although further validation is needed due to limited evidence. For improved predictive accuracy and to help advance medical interventions, the author recommends that clinical medical staff use the GRACE model to predict the death risk of ACS patients.

A fast modeling method of pulsed eddy current with double-coil based on model-driven BP neural network

ABSTRACT The analytical model serves as an important guiding role in practical testing. Currently, there is scarce research on the building of the analytical model for pulsed eddy current (PEC) with double-coil excited by a voltage source. This paper derives a frequency-domain model for PEC from a double-coil’s equivalent circuit. To tackle low modeling efficiency due to complex impedance changes and mutual inductance changes, a BP neural network (BPNN) maps lift-off, thickness, and frequency to impedance changes and mutual inductance changes. Training samples for the BPNN come from analytical models of impedance change and mutual inductance change. The study finds that a BPNN with four hidden layers and Bayesian regularization significantly improves prediction accuracy. Finally, the correctness of the BPNN method is verified. The PEC signals obtained by the BPNN method, the analytical model, and the Comsol finite element method are compared under different thicknesses and lift-offs, and it is found that the consistency of the PEC signals of these three methods is very well. And, the amplitudes of the residual signals between the BPNN method and the analytical model of the PEC signals are relatively small, and the relative errors at the peaks are no more than 0.4%.

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.

Recently, brain networks have been widely adopted to study brain dynamics, brain development, and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. First, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes' predictions. Moreover, few of the current graph learning models are interpretable, which may not be capable of providing biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning (HSGPL) model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. To further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using data from human connectome project (HCP) and open access series of imaging studies (OASIS). Our results from extensive experiments demonstrate the superiority of the proposed model compared with several state-of-the-art techniques. In addition, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers.

Analytical-Numerical Method for Solving the Spectral Problem in a Model of Geostrophic Ocean Currents

Analytical-Numerical Method for Solving the Spectral Problem in a Model of Geostrophic Ocean Currents

Analysis and Modeling Study on the Effect of Working Parameters of Portable Neutron Tubes on Target Current

Abstract As an important parameter index of sealed neutron tubes, their high-pressure side target current directly affects neutron yield. The working parameters of a neutron tube affect the target current. Using self-developed neutron tubes, the influence of operating parameters of neutron tubes on target current was studied. The effect curves of gas pressure, anode high pressure, and target high pressure on neutron yield were obtained, and the secondary electron suppression characteristics of the target current were obtained. Further, we analyze and fit a multi-parameter coupling model for the target current, to provide a theoretical basis for establishing the working state and improving the performance of sealed neutron tubes.

A Comprehensive Review of Preclinical Models for Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is a reproductive, metabolic, and endocrine disorder with unclear aetiology. PCOS, the most common cause of female reproductive and metabolic disorders, is known to affect more than one in ten women globally. PCOS and associated clinical manifestations are probably underdiagnosed despite their high occurrence. Objective: Alternative animal models have been employed to investigate the causes of PCOS or assess potential treatments. In light of this piece of information, it is challenging to create an animal model that accurately captures all components of this condition; nonetheless, the resemblance of an animal model's biology and/or biochemical characteristics to the phenotypes of PCOS in humans may boost its applicability. Result: The key characteristics of these models are closer to human situations when compared to women with PCOS, as shown by this comparison. The creation and testing of drugs for the treatment of PCOS are necessary. Conclusion:: The overview of PCOS, current preclinical models, and appropriate models chosen in different studies to mimic various phenotypes in PCOS studies are all covered in this review paper. Additionally, we have outlined the benefits and drawbacks of PCOS animal models.

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework.

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.

Compositionally Equivariant Representation Learning.

Deep learning models often need sufficient supervision (i.e., labelled data) in order to be trained effectively. By contrast, humans can swiftly learn to identify important anatomy in medical images like MRI and CT scans, with minimal guidance. This recognition capability easily generalises to new images from different medical facilities and to new tasks in different settings. This rapid and generalisable learning ability is largely due to the compositional structure of image patterns in the human brain, which are not well represented in current medical models. In this paper, we study the utilisation of compositionality in learning more interpretable and generalisable representations for medical image segmentation. Overall, we propose that the underlying generative factors that are used to generate the medical images satisfy compositional equivariance property, where each factor is compositional (e.g., corresponds to human anatomy) and also equivariant to the task. Hence, a good representation that approximates well the ground truth factor has to be compositionally equivariant. By modelling the compositional representations with learnable von-Mises-Fisher (vMF) kernels, we explore how different design and learning biases can be used to enforce the representations to be more compositionally equivariant under un-, weakly-, and semi-supervised settings. Extensive results show that our methods achieve the best performance over several strong baselines on the task of semi-supervised domain-generalised medical image segmentation. Code will be made publicly available upon acceptance at https://github.com/vios-s.

An Exploration of Organoid Technology: Present Advancements, Applications, and Obstacles.

Organoids are in vitro models that exhibit a three-dimensional structure and effectively replicate the structural and physiological features of human organs. The capacity to research complex biological processes and disorders in a controlled setting is laid out by these miniature organ-like structures. This work examines the potential applications of organoid technology, as well as the challenges and future directions associated with its implementation. It aims to emphasize the pivotal role of organoids in disease modeling, drug discovery, developmental biology, precision medicine, and fundamental research. The manuscript was put together by conducting a comprehensive literature review, which involved an in-depth evaluation of globally renowned scientific research databases. The field of organoids has generated significant attention due to its potential applications in tissue development and disease modelling, as well as its implications for personalised medicine, drug screening, and cell-based therapies. The utilisation of organoids has proven to be effective in the examination of various conditions, encompassing genetic disorders, cancer, neurodevelopmental disorders, and infectious diseases. The exploration of the wider uses of organoids is still in its early phases. Research shall be conducted to integrate 3D organoid systems as alternatives for current models, potentially improving both fundamental and clinical studies in the future.

Static and dynamic conductivity of amorphous nanogranular composites (CoTaNb)x(MgO)1-x

The paper presents the results of study of amorphous nanogranular composite films CoTaNbxMgO1−x, (21.73≤ x ≤73.27 at%) with a thickness of 311–1040 nm on a lavsan substrate with a thickness of about 20 μm. The results of experimental studies of microstructure, static conductivity (at direct current), dynamic conductivity (50 kHz – 15 MHz), determined from measurements of the total impedance and phase angle, and dynamic microwave conductivity (8–12 GHz), calculated from measurements microwave reflectance, were presented. The spectra of dynamic conductivity as a function of frequency, the dependence of static conductivity and the ratio of dynamic to static conductivity on the content of the metal phase and on the ratio of the sizes of granules to the spaces between granules were obtained. Using the mechanisms of closed and open circuit models of intragranular currents and an algorithm for calculating structural and conductive characteristics from measured values of microwave reflectance, the conductivity of granules was calculated and its contribution to the dynamic microwave conductivity of composites was assessed.

Modeling and Analysis of Turbidity Currents in a Reservoir with the Dredged Guiding Channel

Modeling and Analysis of Turbidity Currents in a Reservoir with the Dredged Guiding Channel

A novel ballistic model for the subthreshold behavior of nanosheet transistors

A novel ballistic model for the subthreshold current of nanosheet transistors is successfully developed based on the Landauer approach with the three-dimensional number of channels. The ballistic threshold voltage can also be achieved through the calculated free charge density induced by the three-dimensional density-of-states equal to the substrate doping concentration. It indicates that under the low drain voltage corresponding to the Fermi distribution function, the subthreshold current is mainly governed by the low contact potential. However, under the high drain voltage corresponding to the Fermi distribution function, the thermal voltage, instead of the contact potential between the source and drain, initiates the subthreshold current. Besides subthreshold current and threshold voltage, the ballistic conductance and subthreshold swing are also revealed in the subthreshold conduction. It indicates that the thin silicon, thin gate oxide, heavy substrate doping density, and high work function will alleviate the ballistic effects, decrease the subthreshold current/swing, and increase the threshold voltage/ballistic resistance.

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

Zero-Resistance-Branch Electrical Drainage System to Solve the Adverse Influence of Stray Current on Buried Pipelines

The buried pipeline's ground potential may be seriously shifted by the stray current leaking from the rail transit, leading to the problem of severe electrochemical corrosion on buried pipeline. For this, a zero-resistance- branch electrical drainage system is proposed to solve the adverse influence of stray current on buried pipeline just like the APF technology in power systems, mainly consisting of a negative resistance compensating converter (NRCC) and a grounding electrode, which can directly drain the stray current to the soil without connecting the rail system. Moreover, the equivalent interference circuit model of stray current is established, and the corrosion rule is analyzed in detail. Furthermore, based on the NRCC, the zero-resistance active drainage control method is proposed to realize the absorption of stray current. As the optimization objective of the control method, the disturbance component of pipe-to-soil potential is regulated to be approximately 0, and the zero-resistance branch at the disturbance frequency can be realized without directly detecting the value of the stray current. Then, the stray current can be transferred to the new drainage branch to mitigate its electrochemical corrosion on buried pipelines. Finally, the experimental results verify the effectiveness of the proposed electrical drainage system.

Weight Decay With Tailored Adam on Scale-Invariant Weights for Better Generalization.

Weight decay (WD) is a fundamental and practical regularization technique in improving generalization of current deep learning models. However, it is observed that the WD does not work effectively for an adaptive optimization algorithm (such as Adam), as it works for SGD. Specifically, the solution found by Adam with the WD often generalizes unsatisfactorily. Though efforts have been made to mitigate this issue, the reason for such deficiency is still vague. In this article, we first show that when using the Adam optimizer, the weight norm increases very fast along with the training procedure, which is in contrast to SGD where the weight norm increases relatively slower and tends to converge. The fast increase of weight norm is adverse to WD; in consequence, the Adam optimizer will lose efficacy in finding solution that generalizes well. To resolve this problem, we propose to tailor Adam by introducing a regularization term on the adaptive learning rate, such that it is friendly to WD. Meanwhile, we introduce first moment on the WD to further enhance the regularization effect. We show that the proposed method is able to find solution with small norm and generalizes better than SGD. We test the proposed method on general image classification and fine-grained image classification tasks with different networks. Experimental results on all these cases substantiate the effectiveness of the proposed method in help improving the generalization. Specifically, the proposed method improves the test accuracy of Adam by a large margin and even improves the performance of SGD by 0.84% on CIFAR 10 and 1.03 % on CIFAR 100 with ResNet-50. The code of this article is public available at xxx.

Denoised Non-Local Neural Network for Semantic Segmentation.

The non-local (NL) network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most of the current popular NL models tend to ignore the phenomenon that the calculated attention map appears to be very noisy, containing interclass and intraclass inconsistencies, which lowers the accuracy and reliability of the NL methods. In this article, we figuratively denote these inconsistencies as attention noises and explore the solutions to denoise them. Specifically, we inventively propose a denoised NL network, which consists of two primary modules, i.e., the global rectifying (GR) block and the local retention (LR) block, to eliminate the interclass and intraclass noises, respectively. First, GR adopts the class-level predictions to capture a binary map to distinguish whether the selected two pixels belong to the same category. Second, LR captures the ignored local dependencies and further uses them to rectify the unwanted hollows in the attention map. The experimental results on two challenging semantic segmentation datasets demonstrate the superior performance of our model. Without any external training data, our proposed denoised NL can achieve the state-of-the-art performance of 83.5% and 46.69% mean of classwise intersection over union (mIoU) on Cityscapes and ADE20K, respectively.

Dynamics and potential origins of decimeter-sized particles around comet 67P/Churyumov-Gerasimenko

Context. One of the primary goals of the European Space Agency’s Rosetta mission to comet 67P/Churyumov-Gerasimenko was to investigate the mechanisms responsible for cometary activity. Aims. Our aim is to learn more about the ejection process of large refractory material by studying the dynamics of decimeter-sized dust particles in the coma of 67P and estimating their potential source regions. Methods. We algorithmically tracked thousands of individual particles through four OSIRIS/NAC image sequences of 67P’s near-nucleus coma. We then traced concentrated particle groups back to the nucleus surface, and estimated their potential source regions, size distributions, and projected dynamical parameters. Finally, we compared the observed activity to dust coma simulations. Results. We traced back 409 decimeter-sized particles to four suspected source regions. The regions strongly overlap and are mostly confined to the Khonsu-Atum-Anubis area. The activity may be linked to rugged terrain, and the erosion of fine dust and the ejection of large boulders may be mutually exclusive. Power-law indices fitted to the particle size–frequency distributions range from 3.4 ± 0.3 to 3.8 ± 0.4. Gas drag fits to the radial particle accelerations provide an estimate for the local gas production rates (Qg = 3.6 × 10−5 kg s−1 m−2), which is several times higher than our model predictions based on purely insolation-driven water ice sublimation. Our observational results and our modeling results both reveal that our particles were likely ejected with substantial nonzero initial velocities of around 0.5–0.6 m s−1. Conclusions. Our findings strongly suggest that the observed ejection of decimeter-sized particles cannot be explained by water ice sublimation and favorable illumination conditions alone. Instead, the local structures and compositions of the source regions likely play a major role. In line with current ejection models of decimeter-sized particles, we deem an overabundance of CO2 ice and its sublimation to be the most probable driver. In addition, because of the significant initial velocities, we suspect the ejection events to be considerably more energetic than gradual liftoffs.

Dynamic Characterization of SynRM With Dual-Axis Hybrid Excitation Self-Commissioning

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition.

How to handle large multidimensional datasets, such as hyperspectral images and video information, efficiently and effectively plays a critical role in big-data processing. The characteristics of low-rank tensor decomposition in recent years demonstrate the essentials in describing the tensor rank, which often leads to promising approaches. However, most current tensor decomposition models consider the rank-1 component simply to be the vector outer product, which may not fully capture the correlated spatial information effectively for large-scale and high-order multidimensional datasets. In this article, we develop a new novel tensor decomposition model by extending it to the matrix outer product or called Bhattacharya-Mesner product, to form an effective dataset decomposition. The fundamental idea is to decompose tensors structurally in a compact manner as much as possible while retaining data spatial characteristics in a tractable way. By incorporating the framework of the Bayesian inference, a new tensor decomposition model on the subtle matrix unfolding outer product is established for both tensor completion and robust principal component analysis problems, including hyperspectral image completion and denoising, traffic data imputation, and video background subtraction. Numerical experiments on real-world datasets demonstrate the highly desirable effectiveness of the proposed approach.

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

This article presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream Natural Language Processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. First, we offer an introduction and brief summary of current language models. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, generation tasks, emergent abilities, and considerations for specific tasks. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at https://github.com/Mooler0410/LLMsPracticalGuide . An LLMs evolutionary tree, editable yet regularly updated, can be found at llmtree.ai .