Identification Method Research Articles

Experimental identification of ion cyclotron emission on HL-2A using YOLO neural network algorithm

Abstract Identification of magnetohydrodynamics (MHD) instabilities with neural networks has been extensively applied in the research of magnetically controlled fusion plasmas. Ion Cyclotron Emission (ICE) is a potential fast ion diagnostic method in burning plasmas. To assess ICE as a fast ion diagnostic for International Thermonuclear Experimental Reactor, real-time identification of ICE is required in the fast ion diagnostic flow. In the present work, we employed YOLO (You Only Look Once) to identify core and edge ICE in a large labeled database of HL-2A discharges, achieving a precision of 85.4% and a recall rate of 77.3%. Subsequent improvements to the YOLO model resulted in a noteworthy 8.3% increment in the recall rate. The developed identification method demonstrates significant potential for real-time application in identifying MHD instabilities.

Ignoring the food route underestimated human health risk from potentially toxic elements in agricultural environments of Ziyang, Shaanxi, China.

Staple food is a crucial exposure route for the human intake of potentially toxic elements (PTEs), but it has been neglected in previous human health risk (HHR) studies. Lack of attention to this issue will lead to an underestimation of HHR caused by PTEs. This study establishes a comprehensive regional identification method for health risk assessment (HRA), namely, soil-maize health risk assessment (SMHRA) and applies it to Ziyang, Shaanxi, which is a typical agricultural county. SMHRA considered the exposure pathway of staple food and utilized Monte Carlo simulation to enhance the accuracy of HRA for PTEs. Results indicated the PTE spatial heterogeneity in a soil-maize system. Introducing staple food exposure pathway would increase HHR values and probabilities 1.57-2.80 and 1.53-5.63 times than that when food route was not considered. Overall, the HHR caused by a single PTE was low, which relatively safe. The introduction of food pathway contributed to accurate estimate the HHR of As and Ni, and the risk probabilities ranged from 0.04% to 12.46%. Few areas had high levels of Ni, which pose health risks: approximately 1.8% for children and higher than 0.5% for adults. Both As and Ni had the highest contribution to HHR among all PTEs, with 33.84%-41.56% TNCR caused by As, and 54.73%-56.90% TCR created by Ni, respectively. For human health risk routes, the staple food exhibited the highest contribution to HHR among all exposure routes, with TNCR of 36.15%-56.73% and the TCR of 44.96%-64.28%. Our research imply that dietary intake of PETs must be considered in the human health risk assessment in agricultural environment, which offers the foundation for subsequent environmental risk prevention and control.

Efficacy of modal curvature damage detection in various pre-damage data assumptions and modal identification techniques

The efficacy of modal curvature approach for damage localization is discussed in the paper in the context of input data. Three modal identification methods, i.e., Eigensystem Realization Algorithm (ERA), Natural Excitation Technique with ERA (NExT-ERA) and Covariance Driven Stochastic Subspace Identification (SSI-Cov), and four methods of determining baseline data, i.e., real measurement of the undamaged state, analytical function, Finite Element (FE) model and approximation of current experimental mode shape, are considered. Practical conclusions are formulated based on analysis of two cases. The first is a laboratory beam with a notch and the second is a stonemasonry historic lighthouse with modern restoration in its upper part. The analysis shows that NExT-ERA and SSI-Cov in combination with approximation of current mode shape provide high efficacy in damage localization alongside relatively straightforward determination of baseline data. It proves that the construction of advanced FE models of a structure can be replaced with a much simpler method of baseline data acquisition. Furthermore, the research shows the structural mode shapes identified with ERA may not always indicate the presence of damage.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Phenotypic and Molecular Identification of M.Canis

The current study was conducted to compare morphological and molecular identification methods for the fungus M.canis isolated from 42 patients with dermatophytosis who were clinically diagnosed by a dermatologist, after review by a consultant dermatologist and venereologist in Al-Muthanna Governorate at Al-Hussein Teaching Hospital. In the period from January to November 2024, using traditional methods of isolation and laboratory identification, and molecular methods using polymerase chain reaction (PCR) technology.

Rapid bacterial identification through volatile organic compound analysis and deep learning

BackgroundThe increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms.ResultsAlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively.ConclusionThis work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

An Application Programming Interface (API) Sensitive Data Identification Method Based on the Federated Large Language Model

The traditional methods for identifying sensitive data in APIs mainly encompass rule-based and machine learning-based approaches. However, these methods suffer from inadequacies in terms of security and robustness, exhibit high false positive rates, and struggle to cope with evolving threat landscapes. This paper proposes a method for detecting sensitive data in APIs based on the Federated Large Language Model (FedAPILLM). This method applies the large language model Qwen2.5 and the LoRA instruction tuning technique within the framework of federated learning (FL) to the field of data security. Under the premise of protecting data privacy, a domain-specific corpus and knowledge base are constructed for pre-training and fine-tuning, resulting in a large language model specifically designed for identifying sensitive data in APIs. This paper conducts comparative experiments involving Llama3 8B, Llama3.1 8B, and Qwen2.5 14B. The results demonstrate that Qwen2.5 14B can achieve similar or better performance levels compared to the Llama3.1 8B model with fewer training iterations.

Measurable morphological features of single circulating tumor cells in selected solid tumors-A pilot study.

Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2-3 markers-based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label-free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n = 29; ovarian, n = 24, breast, n = 54; and prostate, n = 33). Epithelial (pan-keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial-mesenchymal transition-related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open-source software QuPath. On average, detected CTCs (n = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells (n = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer-specific variations, the largest CTCs were identified in lung cancer, whereas the smallest-in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%-0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial-mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size-dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial-mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

YOLOX-S-TKECB: A Holstein Cow Identification Detection Algorithm

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms.

How Severe Was the 2022 Flash Drought in the Yangtze River Basin?

Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate.

Dynamic Testing and Finite Element Model Adjustment of the Ancient Wooden Structure Under Traffic Excitation

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes.

MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases.

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CTx MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor's response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.

Medical image recognition based on multilayer neural network

As a common accidental injury, accurate identification of burns and scalded injuries is of great significance to the development of treatment programs and prognosis assessment. Traditional identification methods are subjective and inaccurate, this paper proposes a burns and scalds grade identification technique based on multilayer neural network. The three-degree classification standard of burns and scalds is described, and the structure and principle of multilayer neural network are introduced, including the network structure composed of input layer, hidden layer and output layer, forward propagation, back propagation and training process. The implementation steps of burns and scald grade recognition technology based on multi-layer neural network are discussed in detail, such as data acquisition, image pre-processing, feature extraction, classifier design, model training and evaluation. Through experiments, the model is tested using a dataset containing 362 burns and scalds images, and the model achieves more than 90% accuracy on the test set with high accuracy and reliability. The multi-layer neural network model is used to classify the burn and scald data, which improves the diagnostic accuracy, shortens the delay time of the disease, reduces the pressure of professional doctors, and helps patients have a preliminary understanding of the degree of burn.

Computer-Vision-Aided Deflection Influences Line Identification of Concrete Bridge Enhanced by Edge Detection and Time-Domain Forward Inference

To enhance the accuracy and efficiency of the deflection response measurement of concrete bridges with a non-contact scheme and address the ill-conditioned nature of the inverse problem in influence line (IL) identification, this study introduces a computer-vision-aided deflection IL identification method that integrates edge detection and time-domain forward inference (TDFI). The methodology proposed in this research leverages computer vision technology with edge detection to surpass traditional contact-based measurement methods, greatly enhancing the operational efficiency and applicability of IL identification and, in particular, addressing the challenge of accurately measuring small deflections in concrete bridges. To mitigate the limitations of the Lucas–Kanade (LK) optical flow method, such as unclear feature points within the camera’s field of view and occasional point loss in certain video frames, an edge detection technique is employed to identify maximum values in the first-order derivatives of the image, creating virtual tracking points at the bridge edges through image processing. By precisely defining the bridge boundaries, only the essential structural attributes are preserved to enhance the reliability of minimal deflection deformations under vehicular loads. To tackle the ill-posed nature of the inverse problem, a TDFI model is introduced to identify IL, recursively capturing the static bridge response generated by the bridge under the influence of successive axles of a multi-axle vehicle. The IL is then computed by dividing the response by the weight of the preceding axle. Furthermore, an axle weight ratio reduction coefficient is proposed to mitigate noise amplification issues, ensuring that the weight of the preceding axle surpasses that of any other axle. To validate the accuracy and robustness of the proposed method, it is applied to numerical examples of a simply supported concrete beam, indoor experiments on a similar beam, and field tests on a three-span continuous concrete beam bridge.

Landscape Character and Management Zones in Polder Landscapes: A Case Study of the Dongting Lake Area

Polder landscapes, as a representative of lowland agriculture landscapes, possess diverse natural and cultural values. However, polder landscapes are facing multiple challenges arising from economic and climate changes, including increasing flood threats and the fragmentation of landscape patterns. These issues have severely threatened the safety and regional characteristics of the polder landscape. Therefore, this study aims to preserve the regional characteristics of polder landscapes while promoting the sustainable development of lowland environments, and the objective is to provide a foundation and baseline for the management and planning of polder landscapes. In this study, the Dongting Lake Area (DLA) was selected as a representative case of polder landscapes. To identify the landscape character and delineate the landscape management zones, this study employed the Landscape Character Assessment (LCA) method in conjunction with a polder typology method at two scales. As a result, the polder landscape types were identified and visualized at two different scales. At the Lake Scale, a comprehensive identification method for polder unit types and polder landscape regions was established, achieving a stepwise transition from polder areas to polder clusters and ultimately to individual polder units. At the Local Scale, an integrative analysis of landscape character diversity (LCD) and land construction suitability (LCS) was employed to establish five landscape management zones for polder landscapes, thereby realizing a balance between the conservation of polder landscape diversity with the sustainable development of wetland environments. This study contributes a set of cross-scale ideas and methods for identifying landscape characters and delineating management zones in polder landscapes, which could be expanded to other lowland agricultural landscapes.

Experimental Evaluation of Under Slab Mats (USMs) Made from End-of-Life Tires for Ballastless Tram Track Applications.

The growing population of urban areas results in the need to deal with the noise pollution from the transportation system. This study presents experimental test results of static and dynamic elastic characteristics of under slab mats (USMs) according to the procedure of DIN 45673-7. Prototype USMs based on recycled elastomeric materials, i.e., SBR granules and fibres produced from waste car tires, are analysed. Vibration isolation mats with different thicknesses (10, 15, 20, 25, 30, and 40 mm), densities (500 and 600 kg/m3), and different degrees of space filling (no holes, medium holes, large holes) are considered. Moreover, a practical application of the laboratory test results of USMs in the design of ballastless track structures of two different types (with a concrete slab and longitudinal beams) is presented. Deflections of the rail and the floating slab system, as well as stresses acting on the mat, are determined according to EN 16432-2. The use of shredded rubber from recycled car tires as a material component of sustainable and environmentally friendly tram track structures may be one of the most effective ways to manage rubber waste within the current trend toward a circular economy, and this study intends to introduce methods for experimental identification and analytical selection of basic static and dynamic parameters of prototype USMs.

Perceptual Pigeon Galvanized Optimization of Multi-objective CNN on the Identification and Classification of Mango Leaves Disease

Aims The aim of this study is to develop a strong Multi-objective Convolutional Neural Network (MOCNN) optimized using Perceptual Pigeon Galvanized Optimization (PPGO) for accurate identification and classification of mango leaf diseases. This approach aims to increase classification accuracy, computational efficiency, and generalization ability. The ultimate goal is to improve disease management in mango crops through advanced image-based diagnostic techniques. Background Demands for the consumption of mango (Mangifera indica) fruit and its leaves were growing exponentially in all parts of the world due to its large health benefits for various organs in the human body. However, these plants were largely exposed to various kinds of microbial diseases during cultivation despite the application of pesticides. Hence, it is becoming a significant threat to the farmers and to the food industry. Objective The objectives of this study are to develop reliable methods for the early identification of mango leaf diseases, enabling prompt intervention and reducing crop damage. Additionally, the study aims to provide effective disease management applications that will help farmers minimize crop losses and maintain their economic stability. Methods PPGO is an advanced optimization algorithm inspired by the natural foraging behavior of pigeons. It integrates perceptual hashing and galvanic responses to adaptively adjust the search process, allowing for efficient exploration and exploitation of the solution space. The multi-objective convolutional neural network is trained to minimize a composite loss function that considers classification accuracy, computational efficiency, and generalization error. Results The Perceptual Pigeon Galvanized Optimization (PPGO) with a Multi-objective Convolutional Neural Network (MOCNN) demonstrates superior performance compared to traditional CNN optimization techniques. The results show an accuracy of 96%, recall of 94%, precision of 92%, and an F1 score of 92%. These metrics surpass those of existing methods such as Efficient Supervised Learning based on Deep Neural Network (ESDNN), Hierarchical Deep Learning Support Vector Machine (HDLSVM), Ordinal Regression Neural Network (ORNN), Deep Convolutional Generative Adversarial Network (DCGAN), Convolutional Neural Network (CNN), Local Contrast Normalization Convolutional Neural Network (LCNN), and Visual Geometry Group Network (VGGNET 19). Conclusion The integration of Perceptual Pigeon Galvanized Optimization with a Multi-objective Convolutional Neural Network offers a powerful approach for identifying and classifying mango leaf diseases. The proposed method effectively balances multiple performance metrics, leading to a robust and efficient model suitable for real-world agricultural applications.

Роль банків у процесах тінізації економіки України

Introduction. The relevance of the article is due to the growing role of banking institutions in the process of shadowing the Ukrainian economy, which negatively affects the financial stability of the state. Problem Statement. The shadow economy has been identified as one of the main threats to the economic stability and security of the state. The study of the role of banking institutions in illegal transactions by entities whose purpose is money laundering, using various schemes and methods of financial fraud, is considered from the standpoint of substantiating directions and effective tools for countering shadowing of the economy. The implementation of such measures at the national and corporate level in accordance with international norms will make it possible to strengthen control over banking transactions, increase stability of the banking and financial systems and, ultimately, improve the level of economic security of the country. Purpose. Analysis of the role of banking institutions in the shadow economy, justification of directions and tools for counteracting the participation of banks in the shadow economy. Methods. The methodological basis of the study is the fundamental provisions of economic theory, theories of management and state regulation of the economy, economic and mathematical modeling, the theory of money, finance and credit, scientific achievements on the shadow economy and measures to counteract its manifestations. Results. The study identifies the role of banking institutions and proposes a typology of forms of their participation in the shadow economy, including: legalization of proceeds of crime; use of correspondent accounts; use of fictitious companies and front persons; offshore accounts; lending and deposit operations of banks; securities manipulation; and other financial fraud. The impact of these mechanisms on the economic stability of the state and the banking system is analyzed. A comparative analysis of international experience in combating money laundering is carried out, and effective tools for preventing shadow operations are identified. The trends and factors of shadowing the Ukrainian economy are analyzed. To assess the impact of banking institutions on the spread of the shadow economy, it is proposed to use the Simpson distribution for shadow cash flows and discount rates with the participation of banking institutions. The instruments of counteracting the participation of banks in the processes of shadowing the Ukrainian economy are substantiated. Conclusions. Further research is planned to focus on the impact of modern digital technologies, such as blockchain and cryptocurrencies, on money laundering through banking institutions. A promising area is the development of innovative methods of financial monitoring and identification of suspicious transactions, in particular with the use of artificial intelligence and machine learning. It is also important to study the effectiveness of international cooperation and develop recommendations for improving the legislative framework for combating money laundering through banking institutions.

An improved transfer learning method for rotor unbalance position identification from simulated data to experimental data

In the study of dynamic balancing for flexible rotors operating at high speeds, determining the unbalanced position of the rotor has consistently posed significant challenges. Accurate identification of the unbalanced position enables low-speed dynamic balancing to serve as a viable alternative to high-speed methods, ultimately reducing costs and enhancing operational efficiency. Although deep learning techniques utilizing limited labeled data have shown promising results in identifying unbalanced positions, the challenge of gathering a sufficient amount of labeled data for effectively training diagnostic models remains substantial. To address this issue, we propose a Pre-Adaptive Transfer Learning (PATL) approach that employs sample reconstruction through frequency domain correlation analysis. This technique facilitates cross-domain deep transfer recognition of rotor unbalanced positions by transferring insights from simulated dynamic model data to experimental datasets. Compared to existing methodologies, the proposed approach demonstrates significant improvements in both the accuracy and generalization of unbalanced position identification. The novelty of this research lies in the introduction of pre-adaptive transfer learning, which effectively minimizes the disparity between experimental and simulation data, thereby enhancing the model's recognition capabilities. Experimental results indicate that the proposed method achieves effective balancing outcomes across various operating conditions.