Modeling Methods Research Articles

Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics

Brain imaging genetics is an evolving neuroscience topic aiming to identify genetic variations related to neuroimaging measurements of interest. Traditional linear regression methods have shown success, but their reliance on individual-level imaging and genetic data limits their applicability. Herein, we proposed S-GsMTLR, a group sparse multi-task linear regression method designed to harness summary statistics from genome-wide association studies (GWAS) of neuroimaging quantitative traits. S-GsMTLR directly employs GWAS summary statistics, bypassing the requirement for raw imaging genetic data, and applies multivariate multi-task sparse learning to these univariate GWAS results. It amalgamates the strengths of conventional sparse learning methods, including sophisticated modeling techniques and efficient feature selection. Additionally, we implemented a rapid optimization strategy to alleviate computational burdens by identifying genetic variants associated with phenotypes of interest across the entire chromosome. We first evaluated S-GsMTLR using summary statistics derived from the Alzheimer's Disease Neuroimaging Initiative. The results were remarkably encouraging, demonstrating its comparability to conventional methods in modeling and identification of risk loci. Furthermore, our method was evaluated with two additional GWAS summary statistics datasets: One focused on white matter microstructures and the other on whole brain imaging phenotypes, where the original individual-level data was unavailable. The results not only highlighted S-GsMTLR's ability to pinpoint significant loci but also revealed intriguing structures within genetic variations and loci that went unnoticed by GWAS. These findings suggest that S-GsMTLR is a promising multivariate sparse learning method in brain imaging genetics. It eliminates the need for original individual-level imaging and genetic data while demonstrating commendable modeling and feature selection capabilities.

Выбор элемента(ов) прототипа модели медицинской организации в целях оптимизации процессов регионального проекта с использованием методов динамического программирования.

Changes in federal legislation regulating the provision of patients suffering from cardiovascular diseases initiate a revision of the regional legislation, and inevitably raises questions of practical adaptation and optimization of the processes of ongoing projects, taking into account resources in the established management model in medical organizations. Finding elements of a prototype model of a medical organization implementing a regional project as objects of influence for theoretical proof of hypotheses using the dynamic programming method (callback function, recursion) is the task of optimizing the project processes considered in this article. The purposeof the research question was to optimize the processes of a regional project using the dynamic programming method, which is focused on finding the objective function F(i) – the number of ways to return the function. The subgoal was to justify the use of dynamic programming methods. Materials and methods. The information base for the study was federal and regional regulatory legal acts published in the legislative reference and legal systems of the Russian Federation, and electronic resources. The study was carried out using methods of dynamic programming, optimization, recursion, and modeling. Results. We did not find similar studies in the available literature. This study was carried out using the dynamic programming method, which is designed to solve the problem of finding the target function(s) F(i) of regional projects. The authors attempted to find a prototype of an operating procedure at the level of project executors, which is indivisible and elementary for subsequent optimization of the variable (non-constant) elements of the prototype of a medical organization model from the found objective function. Finding F(i) by categories of patients (object of influence) registered at the dispensary, obtained using the callback function F(0), F(1), F(2), F(3), …F(k); F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion) consists of decomposing the problem into smaller subproblems, finding the optimal solution to the subproblems recursively and using the resulting solution subtasks for constructing a solution to the original problem – optimizing an element of a prototype model of a medical organization, a regional project as a whole. Conclusion. The main tasks associated with project optimization are the tasks of minimizing the cost of the project for a given deadline for its implementation and minimizing (maximizing) the project completion period for a given marginal cost of its implementation. In search of a solution to optimization issues, the authors proposed finding F(i) by categories of patients (object of influence) registered as “D”, obtained using the callback function F (0) ←F (1) ←F (2) ← F (3) ……F(n‑1) ← Fn (recursion).

DEFORMATION MECHANISMS IN CASE OF DESTRUCTION OF THE TUBING NOZZLE MADE OF FERRITE-PEARLITE STEEL

In the work by methods of experimental studies and computer modeling, deformation mechanisms were analyzed during the destruction of the steel suspended branch pipe of the tubing. Tubing rupture occurred during production well operation for 306 days as a result of hydrogen sulfide stress corrosion cracking (SSCC), which was preceded by hydrogen embrittlement of the metal and cyclic action of external forces. Scanning electron microscopy (SEM) shows that the suspended branch pipe is characterized by a ferrite-pearlite structure, and in the area of its fracture, which occurred in the threaded part, there is an increased content of degenerated pearlite grains and traces of hydrogen blisters. Analysis of the crystallographic texture of the original nozzle showed the presence of Br {110} < 112 >, G {110} < 001 >, TG1 {111} < 110 >, TG2 {111} < 011 >, RG {110} < 110 > and RC {001} < 110 > grain texture components. It was found that after operation of the nozzle in well conditions, in the area far from the fracture, the grain orientations related to Cube {001} < 100 >, TG1 {111} < 110 >, TG2 {111} < 011 >, TB3 {331} < 136 > texture components increase. It was found that in the fracture area, in addition to the grain orientations characteristic of the nozzle body, there are also Br {110} < 112 >, G {110} < 001 >, RC {001} < 110 >, RG {110} < 110 >, TC {112} < 110 >, TB1 {111} < 112 > and TB2 {554} < 225 > texture components It is shown that Cube and grain orientation TB3 in the branch pipe after operation accelerate the process of hydrogen embrittlement of metal, and the presence of TB1 orientation indicates the process of return and/or recrystallization in the product. It was found that the process of hydrogen embrittlement led to a decrease in the strength properties and ductility of the steel of the branch pipe. Within the framework of viscoplastic self-consistent (VPCC) model, textures and deformation behavior of samples cut from different sections of the nozzle (before and after operation) were simulated. It was found that at the initial stages of tension the samples of the new branch pipe, the most active is the operation of {110} < 111 > slip systems. In samples of the nozzle after operation, the activity of {110} < 111 > slip systems decreases. At the same time, the activity of {123} < 111 > slip systems increases, and the nature of the operation < 112 > {111} of the slip system does not change. It was found that in all studied states before destruction the activity of {123} < 111 > systems increases ~ 1.4 times. It was concluded that the growth of TG2 {111} < 011 >, {332} < 110 > and {557} < 110 > texture components, as well as the activity of < 123 > {111} slip systems are associated with the effect of shear deformations that generate microcracks in pearlitic grains, and the applied forces contribute to the development of cracks and subsequent destruction of the product. It has been shown that by controlling the crystallographic texture and, accordingly, deformation mechanisms, it is possible to produce new pipeline steels that are more resistant to SSCC.

Integrated study of prediction and optimization performance of PBI-HTPEM fuel cell using deep learning, machine learning and statistical correlation

This paper uses 3D modeling and artificial intelligence methods to predict, and find optimal point in high-temperature proton exchange membrane (HTPEM) fuel cells. The main objective is to obtain maximum power and current density at the optimum node of the HTPEM fuel cell. The response surface method (RSM) is used to prevent excessive duplication and ensure adequate data coverage for determining input parameters. Also, for the first time, the correlation presented was compared with AI-based metaheuristic optimization methods i.e., including support vector regression (SVR), Gaussian process regression (GPR), and deep neural networks (DNN) with a dropout layer, alongside metaheuristic algorithms such as whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), firefly algorithm (FF), and the genetic algorithm (GA). The results show that SVR, GPR, and DNN methods have excellent performance, with mean absolute percentage error (MAPE) of 0.81 % for DNN, 0.83 % for SVR, and 2.24 % for GPR. Most optimization algorithms exhibit errors below 8 %. The DNN-GOA, SVR-WOA, SVR-GA, and GPR-GOA algorithms have the lowest errors among them. Correlations have a lower computational cost for obtaining maximum power and current density at the optimum node compared to optimization algorithms, with a relative error of less than 6 % in most cases.

Bayesian modeling and optimization for split-plot experiments with multiple responses

In many industrial processes, cost or time constraints make some input variables harder to change or control than others. An appropriate experimental design method is restricted randomization, which results in split-plot experiments. Empirical models that connect multiple quality characteristics with input variables play a crucial role in robust parameter design for split-plot experiments. At present, many modeling methods typically adopt the single response model for analyzing industrial processes in the split-plot experiments without considering correlation among multiple responses, correlation among whole plots, and uncertainty of model parameters. However, ignoring these issues can lead to poor product or process design. To solve these issues, this paper suggests a novel Bayesian modeling and optimization approach. We first construct a Bayesian multi-response linear mixed-effects model and obtain the posterior distribution for model parameters by employing Bayesian theorem. Then, the Gibbs sampling procedure is employed for the estimation of model parameters. Finally, the overall weighted desirability optimization function meeting the specification is developed to avoid acquiring ideal input settings with outliers. A simulation and an engineering case study demonstrate the validity of the proposed method. In comparison to existing methods, the optimization results given the proposed method are more robust and reliable.

COMBINED SUPERCHARGING SYSTEM OF DOMESTIC HIGH-PRESSURE TWO-STROKE SPECIAL PURPOSE DIESEL

Modern trends in engine construction are inextricably linked to the improvement of power units by modernizing existing models. The introduction of a forced to 1100 kW diesel engine of the DN type allows to increase the efficiency and bring the equipment in line with modern requirements in a short time and with lower costs. The key method of improvement is the modernization of the supercharging system, which increases power without increasing the dimensions and weight of the engine, as well as improves technical and economic characteristics. A combined supercharging system with a low-pressure turbocharger (LPT) and a high-pressure drive compressor (HPT) is proposed, which work together or separately depending on the operating mode. The use of a mathematical model of the workflow made it possible to carry out a computational and experimental study of the proposed system in the modes of maximum power and maximum torque, confirming its high efficiency. The use of modern technologies and materials, as well as the introduction of innovative design and modeling methods, allows to create more reliable and durable power units. Modernization of the supercharging system is one of the most important directions in the development of modern engine construction, which allows to achieve an increase in the level of specific power, improvement of fuel economy and environmental characteristics of engines by ensuring optimal conditions for their operation in various operating conditions and in a wide range of operating modes. The paper presents the research results of the proposed two-stage supercharging scheme of the DN type diesel engine, which makes it possible to achieve a power of 1100 kW. It is shown that this scheme has advantages over the traditional one, as it makes it possible to provide a higher boost pressure in a wider range of crankshaft RPM, as well as to reduce power consumption from the crankshaft to drive the compressor at modes close to the nominal ones. These factors collectively lead to a significant improvement in the characteristics of the diesel due to the introduction of the two-stage supercharging system.

Improving streamflow forecasting in semi-arid basins by combining data segmentation and attention-based deep learning

The increasing threats of flash floods and water scarcity in semi-arid regions necessitate high-quality streamflow forecasting with process-based or data-driven models. However, temporal heterogeneity of hydrological patterns and infrequent flood events present challenges for accurate streamflow forecasting. To address this issue, we purpose an effective modeling approach that combines a novel data segmentation method, BPX, with an attention-based deep learning (DL) model. BPX integrates Bai-Perron analysis and XGBoost to accurately identify dry and wet periods from hydro-meteorological variables both in the past and future times. The DL model introduces an attention mechanism to temporal convolutional network (TCN) to enhance its ability in handling heterogeneous time-series data. We demonstrate the effectiveness of our approach using nearly 20 years of hydro-meteorological data from the Wei River basin in China. Various modeling methods are compared, including two process-based models (Xinanjiang and GR4J) and four DL models (classical RNN, GRU, LSTM and TCN) with or without the attention module. After systematic benchmarking, it is found that both the BPX segmentation and the attention mechanism can improve streamflow forecasting with DL. Among the various modeling approaches, BPX-TCN exhibits the best overall performance throughout the prediction period, achieving the highest Kling-Gupta efficiency of 0.91 (compared to 0.45–0.88 for the other models), while BPX-TCN-attention provides more accurate predictions of floods and multi-step predictions. In similar applications in semi-arid regions, our approach may serve as a valuable reference where temporal heterogeneity is significant and poses challenges to traditional modeling methods.

Who is afraid of modelling time as a continuous variable?

Abstract Most models of ecological and eco‐evolutionary processes involve creating trajectories of something, be it population densities, average trait values, or environmental states, over time. This requires decision‐making regarding how to represent the flow of time in models. Most ecologists are exposed to continuous‐time models (typically in the form of ordinary differential equations) as part of their training, especially since the famous Lotka‐Volterra predator–prey dynamics are formulated this way. However, few appear sufficiently well trained to produce their own work with continuous‐time models and may lack exposure to the true versatility of available methods. Specifically, knowledge that discrete individuals can be modelled in continuous time using the Gillespie algorithm is not as widespread as it should be. I will illustrate the flexibility of continous‐time modelling methods such that researchers can make informed choices, and not resort to discretizing time as a ‘default’ without a clear biological motivation to do so. I provide three example‐based tutorials: (1) a comparison of deterministic and stochastic dynamics of the Lotka‐Volterra predator–prey model, (2) an evaluation of matelessness in a hypothetical insect population (and of selection to mate more often by either searching more efficiently or by shortening the ‘time out’ after each mating) and (3) within‐season density dependence followed by a birth pulse leading to Beverton‐Holt or Ricker dynamics depending on whether the deaths of conspecifics help reduce the mortality of others or not (compensatory mortality). I highlight properties of the exponential distribution that, while counter‐intuitive, are good to know when deriving expected lifetime reproductive success or other similar quantities. I also give guidance on how to proceed if the so‐called memorylessness assumption does not hold in a given situation, and show how continuous and discrete times can be freely mixed if the biological situation dictates this to be the preferred option. Continuous‐time models can also be empirically fitted to data, and I review briefly the insight this gives into the so‐called ‘do hares eat lynx?’ paradox that has been plaguing the interpretation of the Hudson Bay hare and lynx dataset.

Federated Learning with Multi-Method Adaptive Aggregation for Enhanced Defect Detection in Power Systems

The detection and identification of defects in transmission lines using computer vision techniques is essential for maintaining the safety and reliability of power supply systems. However, existing training methods for transmission line defect detection models predominantly rely on single-node training, potentially limiting the enhancement of detection accuracy. To tackle this issue, this paper proposes a server-side adaptive parameter aggregation algorithm based on multi-method fusion (SAPAA-MMF) and formulates the corresponding objective function. Within the federated learning framework proposed in this paper, each client executes distributed synchronous training in alignment with the fundamental process of federated learning. The hierarchical difference between the global model, aggregated using the improved joint mean algorithm, and the global model from the previous iteration is computed and utilized as the pseudo-gradient for the adaptive aggregation algorithm. This enables the adaptive aggregation to produce a new global model with improved performance. To evaluate the potential of SAPAA-MMF, comprehensive experiments were conducted on five datasets, involving comparisons with several algorithms. The experimental results are analyzed independently for both the server and client sides. The findings indicate that SAPAA-MMF outperforms existing federated learning algorithms on both the server and client sides.

Correlation Statistics and Parameter Optimization Algorithms for RIS-Assisted Marine Wireless Communication Systems

Reconfigurable intelligent surfaces (RISs), as one of the potential key technologies in sixth generation (6G) mobile communications, feature low costs, low energy consumption, and ease of deployment. In this paper, we introduce the RIS technology into the maritime wireless communication scenario, which can transform the wireless transmission environment from uncontrollable to controllable. In the considered communication model, we derive the complex channel impulse response for the RIS propagation link and non-line-of-sight propagation link, respectively. This can be used to capture the physical properties of a communication model from different perspectives. Furthermore, based on the designed communication model, we investigate the correlation properties of propagation links in the space and time domains; they are the space correlation statistics and time correlation statistics. The provided framework addresses the technical bottleneck of the existing RIS channel modeling methods that fail to balance precision and efficiency, improves the channel model matching efficiency in the design process of RIS-enabled near-field maritime communication systems, and provides technical support for the rapid development of the 6G mobile communication industry.

Multiscale Modeling Approach to Understand Mechanism of Deposit Control by Sulfonate-Based Lubricant Detergents.

Protecting the material surfaces from deposits and insoluble sludge particles extends the engine life and reduces waste. Lubricant detergents in engine oils are essential additive technologies that prevent deposit formation in internal combustion engines. In this study, the effect of sulfonate detergent on deposit formation in a passenger car engine is investigated with experimental and multiscale molecular modeling methods to present a unified approach. First principles density-functional theory calculations, statistical sampling methods, all-atom molecular dynamics simulations, and coarse-grained simulations are examined to elucidate deposit control mechanism of sulfonate detergents. Analysis of the results reveals that sludge particles in the drain oil are similar in structure to piston deposits, and they might be the precursors of the piston deposits. Main factor for controlling the sludge particle deposition is the prevention of their colloidal aggregation at the microscale in the base oil matrix. Aggregation can be mitigated by the intercalation of detergent polar groups between the particles. This is followed by the extension of hydrophobic tails into the oil phase, which decreases further aggregation via formation of a repulsive layer.

Internal Short Circuit and Dynamic Response of Large-Format Prismatic Lithium-Ion Battery Under Mechanical Abuse

Abstract Prismatic lithium-ion batteries (LIBs) are becoming the most prevalent battery type in electric vehicles, and their mechanical safety is garnering increased attention. Understanding the mechanical response and internal short circuit (ISC) of prismatic LIBs during dynamic impact is important for enhancing the safety and reliability of electric vehicles. Thanks to the pioneer's works on the cylindrical and pouch LIB, prismatic LIB can draw on relevant experimental and numerical modeling methods. However, there is still a lack of research on the dynamic effects of prismatic LIB in various loading directions. To address this disparity, the current research utilizes quasi-static and dynamic impact experiments on prismatic LIBs as a foundation. First, the mechanical response of a sizable prismatic LIB under quasi-static conditions and the dynamic effects are examined when subjected to mechanical abuse from various loading directions. Second, an anisotropic finite element model that considers dynamic strain rates are developed, enabling it to accurately represent the mechanical response to both quasi-static and dynamic impact loads. At last, we performed an analysis of ISC occurring under dynamic loading conditions combining the experimental and simulated results. The experimental results as well as the established model can provide reference for the safe design, application, and analysis of prismatic LIBs.

Ligand and structure-based virtual screening approaches in drug discovery: minireview.

The compilation of ligand and structure-based molecular modeling methods has become an important practice in virtual screening applied to drug discovery. This systematic review addresses and ranks various virtual screening strategies to drive the selection of the optimal method for studies that have as their starting point a multi-ligand investigation and investigation based on the protein structure of a therapeutic target. This study shows examples of applications and an evaluation based on the objective and problematic of a series of virtual screening studies present in the ScienceDirect® database. The results showed that the molecular docking technique is widely used in scientific production, indicating that approaches that use protein structure as a starting point are the most promising strategy for drug discovery that relies on virtual screening-based research.

A Conceptual Framework for Optimizing Performance in Sustainable Supply Chain Management and Digital Transformation towards Industry 5.0

The economic growth of developed or emerging countries through globalization has prompted them to increase their supply chain performance. A large number of concepts, tools, and methodologies have been proposed in support of this performance improvement. They are mainly based on the use of classical optimization or enterprise modeling methods. However, environmental and social issues, not to mention digital transformation, are often ignored or not sufficiently integrated. Indeed, the world geopolitical situation, the increase in oil prices, and the commitment to protect our earth require the integration of sustainability aspects and Industry 4.0 concepts like digital twin and artificial intelligence in transforming the supply chain. This paper focuses on defining a conceptual framework to support sustainable supply chain management and digital transformation. It aims to exploit the sustainability and digital maturity of companies to transform their supply chains and enhance their performance to meet the challenges of Industry 5.0. Several practices related to sustainability, as well as two use cases on optimization and digital twin, are presented to illustrate this framework. Finally, based on the previous practices and use cases, an adapted framework for the supply chain manager to support the transition from Industry 4.0 to Industry 5.0 has been developed, as well as a performance dashboard.

Methods of Teaching Russian Derivation as a Basis for Expanding the Vocabulary of Foreign Medical Students

The article raises the pedagogical problem of creating an integrated system of training foreign students receiving higher medical education using an intermediary language (English) for the state final certification, which takes place in Russian. The analysis of the texts of situational tasks in examination disciplines (therapy, surgery, obstetrics and gynecology, pediatrics) allowed us to identify their standard structure with the allocation of a finite number of typical syntactic blocks in the tasks, which allowed us to focus on their lexical content and, above all, on the systemic word-formation analysis of different parts of speech, understanding the meanings of words and their significant parts. The formation of students’ systematic understanding of language occurs through the use of functional-communicative, structural-semantic, component methods, modeling methods in word-formation refraction, methods of systematization and classification of word-formation material. This is facilitated by the innovative generalizing course “Scientific activity of a doctor” (8–10 semesters) developed by the authors, the purpose of which is to create motivational attitudes that ensure the use of system data of derivatology in teaching Russian as a foreign language. It is concluded that the creation of an idea of the Russian word structure and its assimilation, form the ability to correlate the basics of different types, to identify word-formation morphemes that make it possible to predict the meaning of previously unknown words and facilitate the understanding of a complex special text of clinical situational tasks, which contributes to the formation of a professional terminological dictionary of a medical university student. It is confirmed that methodological techniques that ensure knowledge of the semantics of communicatively significant derived lexical units make it possible to fully perceive and generate speech. Keywords: methods of teaching, derivation, linguodidactics, professional communication, bilingual education

Budget impact models for lung cancer interventions: A systematic literature review.

Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy. To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs. We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines. A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time. The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.

Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis

PurposeTo evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches. MethodA systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis. Bias risk was assessed using QUADAS-2. A bivariate mixed-effects model was used for the meta-analysis. Meta-analyses were performed according to five task types (vertebral fractures, osteoporotic vertebral fractures, differentiation of benign and malignant vertebral fractures, differentiation of acute and chronic vertebral fractures, and prediction of vertebral fractures). Subgroup analyses were conducted by different ML models (including ML and DL) and modeling methods (including CT, X-ray, MRI, and clinical features). ResultsEighty-one studies were included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and CT (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a sensitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0.99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebral fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (SROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures, ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. ConclusionsML, especially DL models applied to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailored prevention strategies. Further research and validation are required to confirm ML’s clinical efficacy.

Research progress on cardiac arrest-cardiopulmonary resuscitation model in rats

The animal model of cardiac arrest-cardiopulmonary resuscitation (CA-CPR) is the cornerstone of resuscitation research, serving as a tool to address adverse outcomes after cardiac arrest (CA). However, due to the different causes of CA, the pathophysiological changes of CA are not identical. Therefore, the preparation of similar animal models for different types of CA is the basis for studying the pathophysiological changes of CA. Rats are favored by researchers because of their low experimental cost, easy operation, and similar hemodynamic parameters and pathophysiological changes during resuscitation. This paper reviewed the common methods of animal models of CA-CPR in rats. In this review, the methods, criteria, advantages, disadvantages and precautions of each model were analyzed, which would provide useful reference for cardiopulmonary resuscitation (CPR) researchers.

Статистический анализ влияния инноваций на производительность труда в субъектах ЦФО

The relevance of the research topic is due to the contradiction between the innovation activity of organisations and their contribution to regional socio-economic development. Innovation should lead to productivity growth and contribute to the creation of high-productivity jobs. However, the situation in many Russian regions is paradoxical one – despite of high innovation activity of small and medium-sized enterprises their labour productivity remains at the same level or decreases. The purpose of the research is to assess the impact of innovations on labour productivity for the Russian economy on the example of the Central Federal District regions. The paper verifies the hypothesis on the presence of statistically significant relationship between the indicators characterising the innovation activity of small and medium-sized enterprises, the index of labour productivity, and the growth of high-productive jobs in the Central Federal District regions through the methods of economic and mathematical modelling. According to the research results, the parameters of small and medium-sized enterprises innovation activity do not have a significant impact on the dynamics of socio-economic development of the Central Federal District regions.

Failure mechanism and model study of Link16 data link limiters caused by the electromagnetic pulse

Abstract Electromagnetic pulse can easily cause interference or damage to the circuit. This study used TCAD device-level modeling and device-circuit mixed simulation methods to investigate the impact of high-power microwaves on the radar front-end limiter. In addition, the variations of the electron concentration, the hole concentration, and the external drain current of the limiter with the electromagnetic pulse radiation are given by establishing a simulation model, and the damage mechanism of the limiter under the electromagnetic pulses (HPM) radiation is revealed. This study provides an important theoretical basis for the protection and reinforcement of PIN limiters.