Development Of Model Research Articles

MRI quantitative hemodynamic parameter histogram assement of hepatocellular carcinoma development in a rabbit VX2 liver cancer model

BackgroundNowadays, liver cancer is the second leading cause of cancer death in the world. Therefore, it is very important to understand tumor progression and evaluate tumor microcirculation for treatment strategy.ObjectiveTo explore the application of MRI quantitative hemodynamic parameter histogram analysis in the development of a rabbit VX2 liver cancer.MethodsFive New Zealand white rabbits were randomly selected each time for liver Dynamic contrast- enhanced MR perfusion (DCE-MRI) imaging at the 1st, 2nd, 3rd, 4th and 5th weeks after liver tumor implantation. The dynamic changes and the significant differences between the tumor volume and related derived parameters during tumor progression were compared and analyzed.ResultsIn the development of the VX2 liver cancer model, the tumor volume between the end of 1 ~ 5w group (F = 44.11; P = 0.000) was statistically significant (P < 0.01), and the significant differences were found between the 5th week and the 1st, 2nd, 3rd, 4th week, the 4th week and the 1st, 2nd week, the 3rd week and the 5th week (P < 0.01), respectively. The skewness and kurtosis of the volume transport constant (Ktrans) value, the maximum, 90th percentile and skewness of the reverse reflux rate constant (Kep) value, and the skewness, kurtosis, 75th and 90th percentiles of the plasma fraction (Vp) value were statistically significant (P < 0.05) at the end of each week. The 10th, 25th, 50th percentiles of the Kep value, the 10th, 25th percentiles of the extracellular space (EES) fraction (Ve) value, the 10th percentile of the Vp value was positively correlated with tumor volume (r > 0, P < 0.05). The expression of α-smooth muscle actin (α-SMA) was negatively correlated with the mean, 75th percentile of the Ktrans value, the 50th, 75th percentiles of the Kep value, the mean, minimum of the Vp value (r < 0, P<0.05). It was positively correlated with the skewness, kurtosis of the Ktrans value, the skewness of the Vp value (r > 0, P<0.05).ConclusionsMRI quantitative hemodynamic parameter histogram analysis can reflect the progress of the rabbit VX2 liver cancer. In particular, the skewness, kurtosis and percentiles of Ktrans, Kep and Vp can reflect the microvascular information and can be used as biomarkers to assess the development of Hepatocellular Carcinoma(HCC).

Development of a Self-Evaluation Model Based on an Intelligent Tutoring System to Increase Student Learning Motivation in Basic Programming Subjects

Learning evaluation is a series of efforts to improve the quality of learning. Evaluation activities will have a good impact on students to encourage themselves in making improvements that affect their learning achievement. However, the implementation of learning evaluation in the field is not always easy. As the results of field studies conducted at SMK BPI Bandung, there are obstacles in the continuity of learning evaluations which the implementation has not been optimal. This study aims to develop a self-evaluation model based on an intelligent learning system (ITS) as a support in the learning process, where students can actively and independently evaluate their abilities, thereby increasing learning motivation which has an impact on their learning achievement. From the results of the research conducted using a nonequivalent pretest-posttest control group design, it was concluded that: 1) The evaluation media based on the ITS got a percentage value of 97.78% in the "very good" category and was feasible to use. 2) ITS evaluation media proved to increase learning motivation in the experimental class, marked by an increase in the average value of pre-test to post-test of 0.83% and showed differences in motivation with the control class. 3) Students respond to the ITS evaluation media, namely the media has a "very good" category with a percentage of 83.58%.

Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures

This paper introduces a three-dimensional (3D) semi-explicit probabilistic numerical model for simulating crack propagation within the framework of the finite element method. The model specifically addresses macrocrack propagation using linear volume elements. The criteria governing the macrocrack propagation is based on the softening behavior observed in concrete under uniaxial tension. This softening behavior corresponds to a dissipated cracking energy that is equal to the mode I critical fracture energy (GIC) used in the Linear Elastic Fracture Mechanics theory (LEFM). The probabilistic nature of this model revolves around the random distribution of two mechanical properties: tensile strength (ft) and fracture energy, which varies based on the volume of finite elements. The scattering of the fracture energy increases as the volume of finite elements decreases in order to consider the strong heterogeneity of the material. This work primarily aims to estimate the relationship between the standard deviation of GIC and the volume of finite elements through the development of the numerical model. For this purpose, an inverse analysis is conducted based on a fracture mechanical test simulation. This test involves macrocrack propagation in a large Double Cantilever Beam (DCB) specimen with a crack length exceeding two meters. The proposed inverse analysis procedure yields highly significant results, indicating that the numerical model effectively evaluates both crack length and crack opening during propagation.

Dynamics of changes in the temperature coefficient of electrical conductivity of distilled water in conductometric cells during heating and cooling

The problem of taking into account the influence of air on the properties of distilled water is considered, namely the lack of a single generally accepted method for calculating such an influence. The sensitivity of the structure of water to the influence of external factors is described and the possibility of recording and studying such factors by changes in the temperature coefficient of electrical conductivity of water is shown. The dependences of the temperature coefficient of electrical conductivity of distilled water on the rate of change in water temperature, the degree of filling of conductometric cells, as well as on the intensity of the exchange of carbon dioxide between water and air across their interface have been studied. It is noted that these metabolic processes are currently insufficiently studied. A hardware-software measuring complex has been developed and manufactured to study the temperature coefficient of electrical conductivity of water when its temperature changes within the range of 20–55 °C. The temperature coefficient of electrical conductivity of water in sealed conductometric cells was measured at different degrees of filling the cells with distilled water and the rate of heating and cooling of water. The degree of filling of the cells varied within the range of 10–100 %, the rate of change in water temperature varied within the range of 0.04–2.00 °C/min. With a constant heating and cooling time of 15 minutes in all experiments, the change in speed was achieved by changing the temperature of the heating element. The integral temperature coefficient of electrical conductivity is calculated based on the initial and final values of electrical conductivity and water temperature in each measurement cycle. The dependences of the temperature coefficient of electrical conductivity on the rate of change in water temperature at several constant degrees of cell filling were obtained. It has been shown that with a constant ratio of the volumes of water and air in the cell and an increase in the rate of heating of water, the temperature coefficient of electrical conductivity of water decreases by 19–22 %. It has been established that at a constant rate of water heating, with a decrease in the volume of water in the cell, the temperature coefficient of electrical conductivity of water decreases by 40–42 %. The results obtained can be used to quantify the dissociation coefficient of carbonic acid, the mobility of hydrogen ions, as well as the intensity of the gas exchange process under various external influences on water. Refinement of data on the electrical conductive properties of water and processes at the water/air interface is necessary for the development of models of atmospheric phenomena and climate change, as well as for the creation of sensors for weak changes in environmental parameters for the purposes of both environmental monitoring and medical diagnostics.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Development and verification of mechanistic vaginal absorption and metabolism model to predict systemic exposure after vaginal ring and gel application.

The current work describes the development of mechanistic vaginal absorption and metabolism model within Simcyp Simulator to predict systemic concentrations following vaginal application of ring and gel formulations. Vaginal and cervix physiology parameters were incorporated in the model development. The study highlights the model assumptions including simulation results comparing systemic concentrations of 5 different compounds, namely, dapivirine, tenofovir, lidocaine, ethinylestradiol and etonogestrel, administered as vaginal ring or gel. Due to lack of data, the vaginal absorption parameters were calculated based on assumptions or optimized. The model uses release rate/in vitro release profiles with formulation characteristics to predict drug mass transfer across vaginal tissue into the systemic circulation. For lidocaine and tenofovir vaginal gel, the predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits. The average fold error (AFE) and absolute AFE indicating bias and precision of predictions range from 0.62 to 1.61. For dapivirine, the pharmacokinetic parameters are under and overpredicted in some studies due to lack of formulation composition details and relevance of release rate used in ring model. The predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits for etonogestrel and ethinylestradiol vaginal ring (AFEs and absolute AFEs from 0.84 to 1.83). The current study provides first of its kind physiologically based pharmacokinetic framework integrating physiology, population and formulation data to carry out in silico mechanistic vaginal absorption studies, with the potential for virtual bioequivalence assessment in the future.

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

Data model-based toolpath generation techniques for CNC milling machines

Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.

Creativity Through Embodied Movement in Expressive Activities: Conceptualising a New Pedagogical Model

ABSTRACT This work aimed to outline the first stages for the development of a pedagogical model for teaching-learning of expressive content within physical education (creative body movement). Drawing on several suggestions from previous models’ conceptualizations, we explore and present the theoretical foundations, practical considerations, and assumptions that align and support their core features. The major theme of the model is that students explore new expressive movements and develop capabilities through expressive games or tasks that nurture their creativity and ease their embodied movements. This implies that teachers should provide students with several resources and stimuli to nurture their creativity. Also, teachers should promote students’ embodiment through the exploration of new movements that allow knowing themselves in different movement situations. The affective and cognitive domains will be prominent in this model. The teachers’ and students’ assumptions, implementation needs, and enactment about how to apply this model are also proposed.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with anormalized root mean squared error(N-RMSE)of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Modeling spatial groundwater level patterns of Bangladesh using physio-climatic variables and machine learning algorithms

Groundwater monitoring is essential for sustainable groundwater resource management in a country like Bangladesh, where this precious resource is gradually declining due to over-extraction. However, traditional methods of acquiring groundwater level (GWL) data over a large area are time-consuming and expensive. To address this, this study proposes an alternative approach using freely available daily groundwater storage (GWS) gridded data from the Global Land Data Assimilation System (GLDAS) and other data sources such as population, rainfall, temperature, irrigation, and elevation. By employing different regression and machine learning models like multiple linear regression (MLR), regression trees, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANN), the study aimed to model GWL data for Bangladesh at a spatial resolution of 0.25° × 0.25°. In-situ weekly GWL data collected from 844 locations across the country were used for model development. The results indicated that GWS data alone was insufficient to estimate the spatial variability and trend of groundwater in Bangladesh. However, the comparison of different models showed that the ANN model performed better, with an overall correlation coefficient (R) of 0.95 and mean squared error (MSE) of 0.64 m2 when estimating GWL using GWS and other data. The study also identified population and rainfall as the most influential factors in determining GWL. The developed ANN model can be utilized to estimate GWL at locations where observation data are unavailable, enabling the monitoring of GWL for sustainable groundwater management in Bangladesh.

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

Cryptocurrency is a virtual currency that can be used as a financial or economic standard, foreign currency reserve, and as a means of payment in some countries. The value that goes up and down every time is not easy to predict using logic. This is a problem for investors, besides that investors lack knowledge about the direction of crypto money movement. In addition, there is no system that can predict the price of Bitcoin, so this can cause investors to take the wrong steps in transactions and can cause losses. To avoid this risk, a system is needed that can predict bitcoin prices using data mining techniques, namely forecasting, the algorithms used are CNN and LSTM. The data used is Bitcoin closing price data from January 1, 2017, to April 26, 2023. The data is divided into 80% training data and 20% testing data. The prediction results are evaluated using MAPE which gets a MAPE value of 0.037 or 3.7% in the CNN algorithm, while the LSTM algorithm gets a value of 0.065 or 6.5%. The MAPE results of the two algorithms are in the MAPE range &lt;10%, so it can be said that the ability of the forecasting model is very good so that it can be used as a reference to determine the prediction of bitcoin prices in the next few periods.

Evolution in Development of a Predictive Deep-Learning Model for Total Hip Replacement Based on Radiographs: Commentary on an article by Yi Xu, MD, et al.: "Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs. The Total Hip Replacement Prediction (THREP) Model".

Evolution in Development of a Predictive Deep-Learning Model for Total Hip Replacement Based on Radiographs: Commentary on an article by Yi Xu, MD, et al.: "Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs. The Total Hip Replacement Prediction (THREP) Model".

Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement

A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts’ reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a thick data analytics (TDA) framework for eliciting and combining expert-human insight into the evaluation of models. The insight is threefold: (1) statistical methods are limited to using joint distributions of observable quantities for predictions but often there is more information available in a real-world than what is usable for algorithms, (2) domain experts can access more information (e.g., patient files) than an algorithm and bring additional knowledge into their assessments through leveraging insights and experiences, and (3) experts can re-frame and re-evaluate prediction problems to suit real-world situations. Here, we revisit an example of predicting temporal risk for intensive care admission within 24 hours of hospitalization. We propose a sampling procedure for identifying informative cases for deeper inspection. Expert feedback is used to understand sources of information to improve model development and deployment. We recommend model assessment based on objective evaluation metrics derived from subjective evaluations of the problem formulation. TDA insights facilitate iterative model development towards safer, actionable, and acceptable risk predictions.

Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer

BackgroundDeep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated.MethodsA combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve.ResultsDL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation.ConclusionsDL-based model was established to accurately predict the survival rate of postoperative patients with GC.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&amp;R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

Ensuring sustainable development by prioritizing national economic interests

Aim. To develop recommendations on the formation of a national model of sustainable development of the Russian economy based on the identification of its key vectors, components in the conditions of external and internal shocks and changes caused by the transformation of strategic guidelines for sustainable development.Objectives. To analyze the peculiarities of the implementation of the global concept of sustainable development of the United Nations (UN) under the influence of deglobalization processes; to substantiate the need to transform the established foundations of sustainable development of economic systems in order to ensure the national economic interests of Russia; to propose the author’s approach to the formation of the structure and content of the national model of sustainable development of economic systems; to build a scheme of interaction between different levels of the economic system, ensuring the development of the national model of sustainable development of economic systems.Methods. In the course of the research we applied general scientific methods and techniques: analysis and synthesis, generalization of practical experience, expert assessments, comparison, abstraction and concretization, monitoring, modeling.Results. Based on the analysis of modern processes of deglobalization, the necessity of changing the structure and content of the concept of sustainable development of the Russian economy in the conditions of external shocks caused by the transformation of the global agenda of sustainable development was substantiated. The author’s approach to the formation of the structure and content of the national model of sustainable development of economic systems is proposed, which supplements the current triad of sustainable development (economy, ecology and social sphere) with innovation and institutional components and is scalable to the meso(regions) and micro-level (enterprises). The structure of the model sets socio-economic security, innovation security and the quality of the institutional environment in the conditions of external and internal shocks as controllable parameters, focusing on the creation of conditions for convergent convergence of meso-level systems. The scheme of organization of interaction between macro-, mesoand microlevels of the economic system is constructed, in which at the macrolevel the foundations of sustainable development of the country’s economy on the basis of national interests are set, management and control of mechanisms of sustainable development of economic systems of other levels are organized, and at the mesolevel the multiplier effect is provided for in the implementation of economic mechanisms of interaction of all elements of the regional ecosystem; the key task of the microlevel is the implementation of effective management of the regional ecosystem.Conclusions. Sustainable development of the Russian economy in the current conditions of global challenges is a complex task, the solution of which requires a systemic and holistic approach based on the prioritization of national economic interests. The proposed model, which determines the general guidelines at the macro level, suggests the organization of interaction of all three levels of the economy (macro-, mesoand micro-) with the provision of multiplier effect at the meso level, which requires the elaboration of adaptation mechanisms to the conditions of regional ecosystems and the specifics of business entities, suggests the continuation of the study.

AI in Finance Disruptive Technologies and Emerging Opportunities

The integration of Artificial Intelligence (AI) in the financial sector has ushered in disruptive technologies and unlocked a plethora of emerging opportunities. This paper provides an in-depth exploration of the transformative role of AI in finance, delineating its impact on various facets including investment strategies, risk assessment, fraud detection, customer service, and regulatory compliance. Leveraging machine learning algorithms, natural language processing, and predictive analytics, AI empowers financial institutions to process vast datasets, derive actionable insights, and automate decision-making processes with unprecedented precision and efficiency. Furthermore, AI-driven innovations facilitate personalized financial services, streamline operations, and catalyze the development of novel business models, thereby reshaping the competitive landscape of the finance industry. Nevertheless, the adoption of AI in finance necessitates careful consideration of ethical, privacy, and regulatory implications to ensure responsible and sustainable deployment. Through comprehensive analysis and case studies, this paper illuminates the disruptive potential and emerging opportunities afforded by AI in finance, paving the way for informed decision-making and strategic investment in this rapidly evolving domain.

Development and validation of a nomogram model for accurately predicting severe fatigue in maintenance hemodialysis patients: A multicenter cross-sectional study in China.

This study aims to analyze the risk factors for severe fatigue in maintenance hemodialysis (MHD) patients and develop a clinical prediction model to help doctors and patients prevent severe fatigue. Multicentre MHD patients were included in this study. The objective was to investigate the risk factors for severe fatigue in MHD patients and develop a prediction model. A total of 243 MHD patients were included in the study, and the incidence of severe fatigue was found to be 20.99%. Using age, body mass index, total cholesterol, and albumin levels, a predictive nomogram for fatigue was constructed. In the training set, the nomogram had an area under the curve of 0.851, sensitivity of 82.86%, specificity of 81.76%, and c-index of 0.851. The nomogram was accurate in calibration and proved to be clinically useful. The nomogram developed in this study is a practical and reliable tool for quickly identifying severe fatigue in MHD patients.