Flexible Model Research Articles

Conformal Prediction and Large Language Models for Medical Coding

Abstract The assignment of current procedure terminology (CPT) codes to medical events is a highly cumbersome, logistic challenge for many healthcare organizations, as well as a significant contributor to medical expenses. Improvement in the allocation of medical resources and expenses dedicated to such tasks can be achieved through automation. However, because of the complex nature of medical records, automation of procedure terminologies is just now developing with the advent of machine learning methods. In this study, we develop a fine-tuned large language model (LLaMA-3B) as a high-reliability predictor for CPT codes. As input, we use 2018 pathology report text data, including gross report, microscopic description, brief medical history, and final diagnosis. We define our dataset with the top five most common technical component CPT codes, which account for 85% of all samples. As a (meta) predictor of the veracity of the large language model itself, we use the prediction’s softmax scalar value of the classification model, borrowing from similar recent approaches in conformal prediction. Specifically, the validation set, which is distinct from both the train and test set, is used to establish a prediction threshold below which the model withholds judgement. We show that, by fine tuning an off-the-shelf language model on pathology report text alone, we can achieve 95% prediction accuracy of the top 5 most common CPT codes on our dataset. We also then demonstrate the utility of combining large language models with conformal prediction. The two in combination raise our accuracy to 99.5% when we allow the model to abstain on making a prediction on 30% of the data, as is determined by a separate threshold on the scalar value of the predictor from the aforementioned validation set. We therefore present a highly flexible model for medical coding, which is simultaneously provably-reliable. Large language models can as such serve as a powerful tool for overcoming challenges in medical billing, thereby improving healthcare efficiency and reducing medical costs.

Application of Fractional Modified Taylor Wavelets in the Dynamical Analysis of Fractional Electrical Circuits under Generalized Caputo Fractional Derivative

Abstract This study examines the application of fractional calculus in the analysis and modeling of electrical circuits of fractional order, highlighting its potential to explain the behaviour of complex electrical circuits accurately. In the domain of electrical circuits, fractional differential equations are employed in the analysis and simulation of systems that consist of resistors, capacitors and inductors. In the present paper, a novel approach utilizing fractional order modified Taylor wavelets is implemented to solve the fractional model of RL, LC, RC and RLC electrical circuits under generalized Caputo fractional derivative which offers precise and flexible modeling of non-locality and hereditary characteristics in complex systems. Furthermore, an additional parameter $\sigma$ (time scale parameter) is incorporated in fractional circuit dynamics to maintain the physical dimensionality. The considered wavelets with the collocation technique offer an efficient solution by converting the fractional model of electrical circuits into a set of algebraic equations which are further solved by using the Newton iteration method. Moreover, this study discusses the significance of Ulam-Hyers stability, emphasizing its role in ensuring stable and reliable circuit performance. The impact of fractional order on the dynamics of the electric circuit model is presented by tables and graphs. The approximate solutions obtained by the proposed method are well comparable with exact solutions and some other existing wavelet-based techniques. The residual errors are also evaluated under various model parameters for fractional orders. Furthermore, the graphs illustrate that the error progressively decreases as the number of wavelets basis increases.

Unique exploration method of electronic component failure time and yield strengths of circular tubes under complete flexible model.

The aim of this study is to develop new exponential weighted moving average control charts based on a flexible model. These control charts created through least square and weighted least square estimators of the shape parameter of the new Kumaraswamy Pareto distribution. Exponential weighted moving average control charts based on least square and weighted least square estimators are compared for checking the performance of control charts. The results were not only explored through numerical values but also explored through half a dozen plots. The numerical results and plots exposed that the exponential weighted moving average control chart based on weighted least square estimator has better performance than the other proposed chart. Some key findings are discussed which are obtained from the comparative analysis of EWMA control charts. The simulation study of proposed charts is also reported in detail. The two data sets further demonstrate the effectiveness of the proposed charts. The reported results, for real data sets, are not only displayed in normal plots but also displayed in three-dimension plots. We recommend that the proposed method can be adapted for different types of distributions, and also suggest some future research directions. The concluding remarks are reported at the end of this manuscript.

CHARACTERISTICS OF INTEGRATION BETWEEN STATISTICAL MODELS AND MATHEMATICAL MODELS

This study focuses on integrating mathematical and statistical modeling, where a statistical model estimates the parameters of a mathematical model, or a mathematical model generates data to train a statistical model. This integration benefits both approaches: mathematical models improve the accuracy of statistical models, while statistical models help reduce bias in mathematical ones. The findings demonstrate that this combination is a valuable tool for understanding and predicting dynamic systems, offering more accurate and flexible models. Research consistently shows that integrating these models is an ideal approach for solving complex problems and understanding various systems.

Application of the flexible and polarisable hydration ion model to study Th(IV) aqueous solutions

Th(IV) hydration in aqueous solution is studied by means of molecular dynamics simulations based on a polarisable Th(IV)- H 2 O interaction potential developed in the framework of the hydrated ion concept. Molecular Dynamics (MD) simulations provide as the only in-solution representative motif the ennea-aqua ion, [ Th ( H 2 O ) 9 ] 4 + , with first shell water molecules at an average distance of 2.47 Å from the ion, in accordance with most of the experimental estimates. A well-defined second hydration shell is also identified, hosting ca. 19 solvent molecules at 4.65 Å. Non-structural aspects of the Th(IV) hydration phenomenon, such hydration enthalpy and ion diffusion are well reproduced by the simulation results. The analysis of the O-Th-O angle distribution suggests that the capped square antiprism arrangement prevails over the trigonal tricapped prism. The well-balanced definition of the ion–water and water–water interactions in such a demanding ion neighbourhood is confirmed by the EXAFS and XANES spectroscopies. The theoretical spectra, computed from the MD trajectory, are in fine agreement with some of the experimentally recorded. The sensitivity of the XAS spectroscopy to structural changes confirms how the presented potential manages in a proper way the response of the water molecules to the highly polarising electric field generated by the tetravalent ion.

High- accuracy chaotic time series prediction of the flexible beam-ring model based on P$$\_$$CNN-BiLSTM ED network

High- accuracy chaotic time series prediction of the flexible beam-ring model based on P$$\_$$CNN-BiLSTM ED network

Error performance of DF cooperative SMTs with I/Q imbalance over Beckmann fading channels

The direct down-conversion principle, which has generally been used in the design of multiple-input multiple-output (MIMO) schemes, including space modulation techniques (SMTs), is attractive to researchers because of its low cost, low power consumption with fewer components, flexible and simple structure. However, hardware imperfections such as in-phase (I) and quadrature-phase (Q) imbalance (IQI) negatively affect the performance of the systems with direct down-conversion in practice. On the other hand, cooperative communication is a promising technology that can be utilized in the design of future wireless networks due to its significant advantages such as increasing system reliability, extending network coverage, reducing channel degradation, and providing high quality of service. In this study, SMT-based methods are integrated into cooperative systems, and a flexible and comprehensive model is presented that is applicable to many channel structures. Specifically, the error performance analysis of space shift keying (SSK), spatial modulation (SM), and quadrature SM (QSM) systems in the presence of IQI in decode-and-forward (DF) cooperative communication is carried out by analytical derivations and computer simulations over generalized Beckmann fading channels. The obtained results show that the performance of SMT-based DF cooperative systems is superior to the conventional schemes, and the effects of receiver IQI can be eliminated by optimal detector designs.

Developing a flexible learning model for an applied science college and institute in Saudi Arabia: A conceptual articulation

Developing a flexible learning model for an applied science college and institute in Saudi Arabia: A conceptual articulation

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster than linearly in the sample size when used in combination with direct solver methods such as the Cholesky decomposition. Computations with Vecchia-Laplace approximations can thus become prohibitively slow precisely when the approximations are usually the most accurate, i.e., on large data sets. In this article, we present iterative methods to overcome this drawback. Among other things, we introduce and analyze several preconditioners, derive new convergence results, and propose novel methods for accurately approximating predictive variances. We analyze our proposed methods theoretically and in experiments with simulated and real-world data. In particular, we obtain a speed-up of an order of magnitude compared to Cholesky-based calculations and a threefold increase in prediction accuracy in terms of the continuous ranked probability score compared to a state-of-the-art method on a large satellite data set. All methods are implemented in a free C++ software library with high-level Python and R packages. 4

Programmable MCMC with Soundly Composed Guide Programs

Probabilistic programming languages (PPLs) provide language support for expressing flexible probabilistic models and solving Bayesian inference problems. PPLs with programmable inference make it possible for users to obtain improved results by customizing inference engines using guide programs that are tailored to a corresponding model program. However, errors in guide programs can compromise the statistical soundness of the inference. This article introduces a novel coroutine-based framework for verifying the correctness of user-written guide programs for a broad class of Markov chain Monte Carlo (MCMC) inference algorithms. Our approach rests on a novel type system for describing communication protocols between a model program and a sequence of guides that each update only a subset of random variables. We prove that, by translating guide types to context-free processes with finite norms, it is possible to check structural type equality between models and guides in polynomial time. This connection gives rise to an efficient type-inference algorithm for probabilistic programs with flexible constructs such as general recursion and branching. We also contribute a coverage-checking algorithm that verifies the support of sequentially composed guide programs agrees with that of the model program, which is a key soundness condition for MCMC inference with multiple guides. Evaluations on diverse benchmarks show that our type-inference and coverage-checking algorithms efficiently infer types and detect sound and unsound guides for programs that existing static analyses cannot handle.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators

AbstractWith the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high‐power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high‐power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi‐user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Leakage Model-flexible Deep Learning-based Side-channel Analysis

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well. In this paper, we introduce a deep learning approach with a flexible leakage model, referred to as the multi-bit model. Instead of trying to learn a pre-determined representation of the target intermediate data, we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multi-bit model can adjust to existing leakage models like the Hamming weight and Most Significant Bit while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multi-bit model to profile all 16 subkey bytes simultaneously, which requires negligible computational effort. The experimental results show that the proposed methods can efficiently break all key bytes across four considered datasets while the conventional leakage models fail. Our work signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency with the proposed leakage model.

Improving organisation to improve care: ERN ReCONNET organisational reference model for systemic sclerosis patients’ care pathway

Objective: To optimise the organisation of care and encourage the adoption of good clinical practices, the RarERN Path© methodology was designed within ERN ReCONNET. The aim of our work was to report the application of RarERN Path© on systemic sclerosis within the ERN ReCONNET centres, providing a feasible and flexible organisational reference model for optimising the systemic sclerosis care pathway in different countries. Methods: RarERN Path© is a six-phase methodology which enables the creation of a reference organisational model co-designed on the basis of the expertise of different stakeholders. It foresees six phases, ranging from the map of existing patients’ care pathways and patients’ stories, to the consensus on a common organisational patient care pathways, and its key performance indicators definition. Results: The agreed reference model highlights the importance of having an organisational flow for referrals that foresees how patients may access directly the specialised unit from the different referrals. Specific specialised visits were considered as mandatory to be organised and they included cardiologist, pneumologist, gastroenterologist, psychologist, nephrologist, dermatologist, wound care specialist/nurses and other healthcare professionals (such as nurses, social workers and nutritional counselling). Moreover, specific services related to therapy were highlighted as strongly recommended to be organised, mainly represented by infusion therapy and wound care, as well as occupation therapy and physiotherapy. Conclusion: The organisational model emerged from our investigation emphasises that the organisation of specific services for systemic sclerosis treatment should be organised as a solid support for implementing the existing recommendations on systemic sclerosis management in real life.

Modeling of in-plane floor flexibility in existing reinforced concrete buildings

The paper addresses the problem of determining the mechanical characteristics of an orthotropic slab that best represents the in-plane behavior of a complex reinforced concrete (RC) floor system. To represent the structural behavior of the real RC floor, a reference model composed of solid finite elements is developed. The mechanical parameters of the equivalent orthotropic slab are determined by different homogenization techniques. In particular, two numerical algorithms and an analytical procedure to derive the elastic parameters of the equivalent slab representing the in-plane behavior of a single floor cell are proposed. Then, to determine which homogenization technique best predicts the structural behavior, representative one-story and six-story structures are modeled using both the equivalent slabs obtained using the three proposed techniques and the reference floor model. A discussion of the suitability of modeling floor in-plane flexibility taking account of only the top RC slab, for the considered case studies, is also provided.

Prospects and Challenges to ICT Adoption in Teaching and Learning at Rural South African Universities: A Systematic Review

This study aimed to systematically review the prospects and challenges associated with adopting Information and Communication Technology (ICT) in teaching and learning at rural universities in South Africa. To achieve this aim, a systematic review methodology was employed. The systematic review findings revealed a multifaceted landscape wherein opportunities for enhancing education through ICT exist alongside significant challenges. Prospects include the potential for technology to bridge educational gaps, increase accessibility, and provide innovative learning experiences. However, challenges such as inadequate infrastructure, limited connectivity, and varying levels of digital literacy pose significant impediments. The study emphasizes the need for targeted interventions to address these challenges, including infrastructure development, capacity building, and localized content creation. Additionally, recommendations for flexible learning models and collaborative partnerships are outlined to facilitate effective ICT adoption in the unique context of rural South African universities. Continuous research and evaluation are advocated to inform future strategies and ensure sustainable improvements in rural education through ICT integration.

Enhancing business resilience: Innovation and adaptation during and after the global pandemic

Purpose: In response to the unprecedented challenges posed by the global pandemic, this study rigorously examines the strategic interplay between innovation and adaptation as catalysts for business resilience. Method: The research investigates the influence of two independent variables – innovation in business and adaptive strategies – on the dependent variable of business resilience, establishing a robust conceptual framework based on a thorough literature review. Employing a mixed-methods approach, incorporating case studies, surveys, and interviews, the study meticulously explores the multifaceted dimensions of business responses during the pandemic. Results: A detailed analysis of innovation encompasses technological, process, and product innovations, while simultaneously scrutinizing adaptive strategies such as flexible business models, employee empowerment, and customer-centric approaches. The research reveals that businesses embracing process and technological innovation alongside radical shifts in their business models achieved higher levels of resilience. Qualitative narratives assess the performance metrics and success stories of these resilient businesses. The examination extends to the post-pandemic landscape, evaluating the sustainability of innovation and adaptation in a stable environment. Emphasis is placed on the enduring relevance of these strategies and their integration into organizational culture, thereby shaping long-term business resilience. Limitations: The study identifies and addresses challenges and barriers hindering innovation and adaptation, including economic constraints, technological barriers, and organizational resistance. Contribution: This study offers a nuanced and insightful exploration of the intricate dynamics between innovation, adaptation, and business resilience during and after the pandemic. The findings not only contribute to the academic discourse but also provide actionable insights for businesses seeking to fortify their resilience amidst ongoing global uncertainties.

Agile Governance Public Information Management at the Communication, Informatics, and Statistics Service of Riau Province

This study aims to analyze the implementation of agile governance in public information management at the Riau Province Communication, Informatics and Statistics Service (Diskominfotik). Agile governance is a governance approach that emphasizes flexibility, responsiveness, and rapid collaboration in providing public services. The research method used is qualitative descriptive with an explanatory approach. Data collection was carried out through observation, interviews, and document studies analyzed using Nvivo 14 software. The results of the study indicate that the implementation of agile governance at Diskominfotik Riau is still faced with a number of obstacles, including limited knowledge and capacity of human resources (HR) in understanding the concept and a bureaucratic structure that does not fully support flexibility. In addition, performance evaluations that are not yet based on quick wins slow down the adaptation process. Nevertheless, Diskominfotik Riau has utilized various digital platforms to provide access to public information, although improvements need to be made in terms of responsiveness and timeliness of service. Recommendations from this study include increasing training for HR, implementing a more flexible governance model, and periodic evaluations based on short-term results. The implementation of agile governance is expected to improve the quality of public services and strengthen public trust in government transparency.