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The reaction-diffusion equations have been studied in various aspects of nature, such as heat transfer and biological dynamic modelling. In this paper, we study a nonlocal reaction-diffusion model with time delay associated with the density and diffusion behaviour of biological populations. Based on our previous conclusions and numerical strategy of the direct problem, we continue to study the inverse problem about the diffusion coefficient as well as the parameters in the birth function, and also perform numerical simulations with error analysis. In addition, we further generalize the constant diffusion coefficient to the position-dependent diffusion rate, which is intended to improve the generalizability to multiple organisms diffusion in nature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Salmonellosis is the second-most commonly reported foodborne gastrointestinal infection in the European Union and a major contributor to foodborne outbreaks globally. Salmonella serotyping differentiates typhoidal strains requiring antibiotic therapy (e.g., serovars Typhi, Paratyphi A, Paratyphi B-d-tartrate negative, Paratyphi C) from typically self-limiting non-typhoidal Salmonella (NTS) strains, making precise identification essential for appropriate treatment and epidemiological tracking. At the same time, the ability to identify the serogroup of Salmonella, regardless of which of the above two groups it belongs to, provides an important initial epidemiological indication that is useful for case management by competent health authorities. This study evaluates the effectiveness of ATR-FTIR spectroscopy coupled with a machine learning algorithm to identify four key Salmonella enterica serogroups (B, C1, D1—including typhoidal strains such as S. Typhi—and E1) directly from solid monomicrobial cultures without sample pretreatment. The system was paired with I-dOne software v2.2 already able to detect Salmonella spp., possibly leading to the characterisation of both the species and serotype from one colony. The multivariate classification model was trained and validated with 248 strains, with an overall accuracy of >98% over 113 samples. This approach offers a potential rapid alternative for clinical labs without serotyping facilities. [ABSTRACT FROM AUTHOR]

Relaxation time and frequency spectra are not directly available by measurement. To determine them, an ill-posed inverse problem must be solved based on relaxation stress or oscillatory shear relaxation data. Therefore, the quality of spectra models has only been assessed indirectly by examining the fit of the experiment data to the relaxation modulus or dynamic moduli models. As the measures of data fitting, the mean sum of the moduli square errors were usually used, the minimization of which was an essential step of the identification algorithms. The aim of this paper was to determine a relaxation spectrum model that best approximates the real unknown spectrum in a direct manner. It was assumed that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. A modified relaxation frequency spectrum was defined as a quotient of the real relaxation spectrum and relaxation frequency and expanded into a series of linearly independent exponential functions that are known to constitute a basis of the space of square-integrable functions. The spectrum model, given by a finite series of these basis functions, was assumed. An integral-square error between the real unknown modified spectrum and the spectrum model was taken as a measure of the model quality. This index was proved to be expressed in terms of the measurable relaxation modulus at uniquely defined sampling instants. Next, an empirical identification index was introduced in which the values of the real relaxation modulus are replaced by their noisy measurements. The identification consists of determining the spectrum model that minimizes this empirical index. Tikhonov regularization was applied to guarantee model smoothness and noise robustness. A simple analytical formula was derived to calculate the optimal model parameters and expressed in terms of the singular value decomposition. A complete identification algorithm was developed. The analysis of the model smoothness and model accuracy for noisy measurements was carried out. The equivalence of the direct identification of the relaxation frequency and time spectra has been demonstrated when the time spectrum is modeled by a series of functions given by the product of the relaxation frequency and its exponential function. The direct identification concept can be applied to both viscoelastic fluids and solids; however, some limitations to its applicability have been pointed out. Numerical studies have shown that the proposed identification algorithm can be successfully used to identify Gaussian-like and Kohlrausch–Williams–Watt relaxation spectra. The applicability of this approach to determining other commonly used classes of relaxation spectra was also examined. [ABSTRACT FROM AUTHOR]

With the growing demand for energy and increasing environmental concerns, accurate modeling and efficient optimization of thermodynamic systems have become key research areas in engineering. In recent years, the application of machine learning techniques in thermodynamic systems has demonstrated significant potential, greatly improving modeling precision and optimization efficiency. However, existing methods face challenges in handling temperature delay phenomena, limiting the accuracy and effectiveness of system modeling and control. This paper introduces a temperature delay identification algorithm to enhance the accuracy of delay representation. Additionally, it develops a predictive control model for thermodynamic systems that incorporates temperature delay. These two components aim to improve the precision and efficiency of thermodynamic system modeling, providing new insights and methods for the intelligent management of complex thermodynamic systems. [ABSTRACT FROM AUTHOR]

The relaxation spectrum is a fundamental viscoelastic characteristic from which other material functions used to describe the rheological properties of polymers can be determined. The spectrum is recovered from relaxation stress or oscillatory shear data. Since the problem of the relaxation spectrum identification is ill-posed, in the known methods, different mechanisms are built-in to obtain a smooth enough and noise-robust relaxation spectrum model. The regularization of the original problem and/or limit of the set of admissible solutions are the most commonly used remedies. Here, the problem of determining an optimally smoothed continuous relaxation time spectrum is directly stated and solved for the first time, assuming that discrete-time noise-corrupted measurements of a relaxation modulus obtained in the stress relaxation experiment are available for identification. The relaxation time spectrum model that reproduces the relaxation modulus measurements and is the best smoothed in the class of continuous square-integrable functions is sought. Based on the Hilbert projection theorem, the best-smoothed relaxation spectrum model is found to be described by a finite sum of specific exponential–hyperbolic basis functions. For noise-corrupted measurements, a quadratic with respect to the Lagrange multipliers term is introduced into the Lagrangian functional of the model's best smoothing problem. As a result, a small model error of the relaxation modulus model is obtained, which increases the model's robustness. The necessary and sufficient optimality conditions are derived whose unique solution yields a direct analytical formula of the best-smoothed relaxation spectrum model. The related model of the relaxation modulus is given. A computational identification algorithm using the singular value decomposition is presented, which can be easily implemented in any computing environment. The approximation error, model smoothness, noise robustness, and identifiability of the polymer real spectrum are studied analytically. It is demonstrated by numerical studies that the algorithm proposed can be successfully applied for the identification of one- and two-mode Gaussian-like relaxation spectra. The applicability of this approach to determining the Baumgaertel, Schausberger, and Winter spectrum is also examined, and it is shown that it is well approximated for higher frequencies and, in particular, in the neighborhood of the local maximum. However, the comparison of the asymptotic properties of the best-smoothed spectrum model and the BSW model a priori excludes a good approximation of the spectrum in the close neighborhood of zero-relaxation time. [ABSTRACT FROM AUTHOR]

Background: The Medical Information Database Network (MID-NET®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET®. Methods: This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated. Results: A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%). Conclusion: This study developed valuable GIP identification algorithms for MID-NET®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms. [ABSTRACT FROM AUTHOR]

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.

Salmonellosis is the second-most commonly reported foodborne gastrointestinal infection in the European Union and a major contributor to foodborne outbreaks globally. Salmonella serotyping differentiates typhoidal strains requiring antibiotic therapy (e.g., serovars Typhi, Paratyphi A, Paratyphi B-d-tartrate negative, Paratyphi C) from typically self-limiting non-typhoidal Salmonella (NTS) strains, making precise identification essential for appropriate treatment and epidemiological tracking. At the same time, the ability to identify the serogroup of Salmonella, regardless of which of the above two groups it belongs to, provides an important initial epidemiological indication that is useful for case management by competent health authorities. This study evaluates the effectiveness of ATR-FTIR spectroscopy coupled with a machine learning algorithm to identify four key Salmonella enterica serogroups (B, C1, D1—including typhoidal strains such as S. Typhi—and E1) directly from solid monomicrobial cultures without sample pretreatment. The system was paired with I-dOne software v2.2 already able to detect Salmonella spp., possibly leading to the characterisation of both the species and serotype from one colony. The multivariate classification model was trained and validated with 248 strains, with an overall accuracy of >98% over 113 samples. This approach offers a potential rapid alternative for clinical labs without serotyping facilities.

The aim of the article is developing and analyse an algorithmic method for solution finding of one inverse problem of 2d vortex fluid dynamics. It is identification and prediction of the flow structure evolution of the based on the data on fluid velocity vectors in a set of reference points. Theoretical analysis of convergence and adequacy of the method is difficult due to the ill-posedness typical of inverse problems, these issues studied experimentally. Methods. The proposed method uses a mathematical model of a point vortex dynamics system for identification and prediction flow structures. The parameters of the model system are found by minimising the functional that evaluates the closeness of the original and model vectors fields at the reference points. The prediction of the vortex structure dynamics is based on the solution of the Cauchy problem for a system of ordinary differential equations with the parameters found in the first stage. Results. As a result of the calculations, we found it out: the algorithm converges to the desired minimum from a wide range of initial approximations; the algorithm converges in all cases when the identified structure consists of sufficiently distant vortices; the forecast of the development of the current gives good results with a steady flow; if the above conditions are violated, the part of successful calculations decreases, false identification and an erroneous forecast may occur; with the convergence of the method, the coordinates and circulation of the eddies of the model system are close to the characteristics of the eddies of the test configurations; the structures of the streamlines of the flows are topologically equivalent; convergence depends more on location than on the number of vectors used for identification. Conclusion. An algorithm for solving the problem of identifying and the evolution forecast of a 2d vortex flow structure is proposed when the fluid velocity vectors in a finite set of reference points are known. The method showed its high efficiency when using from 40 to 200 reference points. The results of the study make it possible to recommend the proposed algorithm for identifying flat vortex structures, which consist of vortices separated from each other.

In the practical application of wireless power transfer (WPT) technology, the identification of uncertain secondary side coil is of great significance to the interoperability of the WPT system. Whether the secondary coil can be accurately identified to realize the ideal match of the primary coil and the secondary coil is one of the keys to achieve efficient power transmission. Based on the characteristics of magnetic field image, this paper combines the convolutional neural network (CNN) to realize the recognition of the coil type, and establishes a 4-layer and 6-layer neural network respectively, and the recognition accuracy is as high as 98%. Compared with the existing visual geometry group (VGG) 16 neural network algorithm, the proposed algorithm has lower complexity and is more suitable for the WPT application field.

Artificial intelligence technology has unique advantages in processing multimodal information such as images, videos and sounds, and plays an important role in the process of enhancing the effect of multimodal cultural communication. The Page Rank model is used to identify communication subjects based on the role of key communication subjects in cultural communication, as proposed in this paper. A genetic algorithm is utilized to improve the traditional neural network algorithm, so that it provides more accurate prediction results and countermeasures in the process of promoting cultural communication. The analysis shows that the OLMT algorithm has an average accuracy of 94.56% in a specific identification interval. The difference between the expected and actual output values of the improved neural network algorithm for the test samples was less than 0.7.

Aiming at the problem of power reduction when the transmitter and receiver coils are misaligned in wireless power transfer (WPT) system, a strategy to improve power by controlling the current phase of the array coils is proposed in this paper. Taking the square coil as an example, the expression of the magnetic field strength at a certain point in the array coil is discussed, and the parameters affecting the magnetic field distribution are determined. In order to improve the uniformity of the magnetic field distribution, the non-equidistant coil structure of the array coil is designed. BP neural network is introduced to learn the variation law of the magnetic flux with the current phase of the array coil in the specified area, and the phase difference between the coils when the magnetic flux is maximum is determined. Finally, the above concepts are realized through simulations and experiments. The results show that the proposed non-equidistant coil has more advantages in the uniformity of magnetic field distribution than the equidistant coil. Under the optimal phase configuration, the magnetic flux in the planar area of the array coil can be effectively improved.

The relaxation spectra, from which other material functions used to describe mechanical properties of materials can be uniquely determined, are important for modeling the rheological properties of polymers used in chemistry, food technology, medicine, cosmetics, and many other industries. The spectrum, being not directly accessible by measurement, is recovered from relaxation stress or oscillatory shear data. Only a few models and identification methods take into account the non-negativity of the real spectra. In this paper, the problem of recovery of non-negative definite relaxation spectra from discrete-time noise-corrupted measurements of relaxation modulus obtained in the stress relaxation test is considered. A new hierarchical identification scheme is developed, being applicable both for relaxation time and frequency spectra. Finite-dimensional parametric classes of models are assumed for the relaxation spectra, described by a finite series of power-exponential and square-exponential basis functions. The related models of relaxation modulus are given by compact analytical formula, described by the products of power of time and the modified Bessel functions of the second kind for the time spectrum, and by recurrence formulas based on products of power of time and complementary error functions for frequency spectrum. The basis functions are non-negative. In result, the identification task was reduced to a finite-dimensional linear-quadratic problem with non-negative unknown model parameters. To stabilize the solution, an additional smoothing constraint is introduced. Dual approach was used to solve the stated optimal identification task resulting in the hierarchical two-stage identification scheme. In the first stage, dual problem is solved in two levels and the vector of non-negative model parameters is computed to provide the best fit of the relaxation modulus to experiment data. Next, in second stage, the optimal non-negative spectrum model is determined. A complete scheme of the hierarchical computations is outlined; it can be easily implemented in available computing environments. The model smoothness is analytically studied, and the applicability ranges are numerically examined. The numerical studies have proved that using developed models and algorithm, it is possible to determine non-negative definite unimodal and bimodal relaxation spectra for a wide class of polymers. However, the examples also demonstrated that if the basis functions are non-negative and the model is properly selected for a given type of the real spectrum (unimodal, multimodal), the optimal model determined without non-negativity constraint can be non-negative in the dominant range of its arguments, especially in the wide neighborhood of the spectrum peaks. [ABSTRACT FROM AUTHOR]

Real risk status detection is an effective way to reflect risky or dangerous driving behaviors and therefore to prevent road traffic accidents. However, a driver's risk status is not only difficult to define but also uncontrollable and uncertain. In this study, a simulated experiment with 30 drivers was conducted using a driving simulator to collect the multi-sensor data of road conditions, humans, and vehicles. The driving risk status was classified into three states (0 - incident, 1 - near crash, or 2 - crash) on the basis of the playback system of the driving simulator. The experimental data were pre-processed using the cubic spline interpolation method and the time-windows theory. A driving risk status identification model was established using the C5.0 decision tree algorithm, and the receiver operating characteristic curve (ROC) was adopted to evaluate the performance of the identification model. The results indicated that respiration (RESP), vehicle speed (SPE), SM_FATIGUE, distance to the left lane (LLD), course angle (CA), and skin conductivity (SC) had a significant correlation (p < 0.05) with the driving risk status. The identification accuracy of the C5.0 decision tree algorithm was 78%, and the areas under the ROC were 0.934, 0.77, and 0.845, respectively. Moreover, compared with other four identification algorithms, the algorithm performance evaluation indexes TPR (0.780), precision (0.753), recall (0.78), F-measure (0.756), and kappa (0.884) of the C5.0 decision tree were all the best. The conclusion can provide reference evidence for danger warning systems and intelligent vehicle design. [ABSTRACT FROM AUTHOR]

Abstract Background In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence. Methods Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review. Results In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan–Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar. Conclusion The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.

The viscoelastic relaxation spectrum provides deep insights into the complex behavior of polymers. The spectrum is not directly measurable and must be recovered from oscillatory shear or relaxation stress data. The paper deals with the problem of recovery of the relaxation spectrum of linear viscoelastic materials from discrete-time noise-corrupted measurements of relaxation modulus obtained in the stress relaxation test. A class of robust algorithms of approximation of the continuous spectrum of relaxation frequencies by finite series of orthonormal functions is proposed. A quadratic identification index, which refers to the measured relaxation modulus, is adopted. Since the problem of relaxation spectrum identification is an ill-posed inverse problem, Tikhonov regularization combined with generalized cross-validation is used to guarantee the stability of the scheme. It is proved that the accuracy of the spectrum approximation depends both on measurement noises and the regularization parameter and on the proper selection of the basis functions. The series expansions using the Laguerre, Legendre, Hermite and Chebyshev functions were studied in this paper as examples. The numerical realization of the scheme by the singular value decomposition technique is discussed and the resulting computer algorithm is outlined. Numerical calculations on model data and relaxation spectrum of polydisperse polymer are presented. Analytical analysis and numerical studies proved that by choosing an appropriate model through selection of orthonormal basis functions from the proposed class of models and using a developed algorithm of least-square regularized identification, it is possible to determine the relaxation spectrum model for a wide class of viscoelastic materials. The model is smoothed and robust on measurement noises; small model approximation errors are obtained. The identification scheme can be easily implemented in available computing environments. [ABSTRACT FROM AUTHOR]

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle's identity and message is determined to protect the vehicle's security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment. [ABSTRACT FROM AUTHOR]

Parameter identification of quantum systems is a fundamental task in developing practical quantum technology. In this article, we study the identification of time-varying decoherence rates for open quantum systems. Given the measurement data of local observables, this can be formulated as an optimization problem. We expand the unknown decoherence rates into Fourier series and take the expansion coefficients as optimization variables. We then convert it into a minimax problem and apply a sequential linear programming technique to solve it. Numerical study on a two-qubit quantum system with a time-varying decoherence rate demonstrates the effectiveness of our algorithm.

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value. [ABSTRACT FROM AUTHOR]

The identification and supervision of systemically important financial institutions (SIFIs) are crucial for regulators to address systemic risks to the financial system. Prevalent identification methods are biased because less attention is paid to the indirect connections among financial institutions. This study proposes a novel method for measuring the systemic importance of financial institutions. The proposed method is an identification algorithm based on adjacency information entropy that integrates the direct and the indirect connections among financial institutions. The sizes of financial institutions will not be investigated, and each financial institution is regarded as a node in the financial system network. The adjacency degree of every node is calculated. Subsequently, the information entropy of each node can be determined. The magnitude of the information entropy is taken as the importance measure of these financial institutions in the financial network. We conduct empirical analysis by employing the data of stock prices of 16 listed banks in the Chinese securities market with a sample period from October 2007 to December 2019 and adopt the method presented in this study to rank the systemic importance of these banks. Analysis shows that the ranking is in accordance with the reality, and the algorithm will promote the accuracy of identifying SIFIs. [ABSTRACT FROM AUTHOR]

Background: In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence.Methods: Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review.Results: In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan-Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar.Conclusion: The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis. [ABSTRACT FROM AUTHOR]

Predicting more relevant areas of medical research, with a prediction period starting at five years, is currently done exclusively by experts, while the results of such forecasts are extremely ineffective and differ significantly, depending on their source. Modern development trends in world science require the creation of a universal forecasting tool that can be used as a basic resource—providing objective, system-independent information. This condition justifies the relevance of the present study. This study’s goal was to develop and test an algorithm to identify future innovative research areas in digitalization conditions (using the medical sector as an example). During this research, a prognostic model was developed based on the hype cycle, which makes determining a list of possible areas for hardware development in the medical sector possible, presenting this list as a set of tokens that are decoded using mental analysis and automated through the Python programming language. The process of identifying future innovative research areas comprises the following stages: identifying the aggregator (hub) research results, parsing primary information, translating the analyzed information, forming a set of lexemes, forming an analytical dataframe, constructing regression models for the highlighted lexemes, forming and storing the resulting dataframe, and metathinking the highlighted lexemes. In total, 4,000 study names were analyzed, based on the ResearchGate platform, which made obtaining 28 significant lexemes based on the results of metathinking possible. Next, an associative map was created using the most promising research areas in medicine, namely: diagnosing viral infections, the spread of viral infections, coronaviruses, cardiovascular diseases, and lung diseases. The obtained algorithm for the automatic determination of promising research areas can be modified by choosing different sources of information.

This paper reports the use of an advanced statistical algorithm for the recognition and classification of a set of 30 archaeological metallic objects from the Museum of Malaga. In-situ laser-induced breakdown spectrometry (LIBS) analysis was performed using a portable analyzer. The coordinate-obtaining method provided the statistical weights of each element in the sample. A comparative study between the coordinate-obtaining method and the linear correlation method is also discussed in order to corroborate the applicability of the proposed approach to the field of cultural heritage. The possibility of fast identification based on the simultaneous comparison of all the spectra in the reference LIBS library while allowing the analysis of heterogeneous materials is the main advantage of the method. In addition, statistical analysis (Euclidean distance analysis and binary diagrams) suggested that differentiating between archaeological sites is feasible.

A knowledge-oriented system of functional nutrition is described. Information technologies implemented using multi-criteria optimization and mathematical programming methods will allow adjusting and optimizing food rations according to various criteria, as well as structuring the resulting set of alternatives and determining the optimal adequate version of a food ration.

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method. [ABSTRACT FROM AUTHOR]

Background Administrative data have limited sensitivity for case finding of atrial fibrillation/atrial flutter (AF/AFL). Linkage with clinical repositories of interpreted ECGs may enhance diagnostic yield of AF/AFL. Methods and Results We retrieved 369 ECGs from the institutional Marquette Universal System for Electrocardiography (MUSE) repository as validation samples, with rhythm coded as AF (n=49), AFL (n=50), or other competing rhythm diagnoses (n=270). With blinded, duplicate review of ECGs as the reference comparison, we compared multiple MUSE coding definitions for identifying AF/AFL. We tested the agreement between MUSE diagnosis and reference comparison, and calculated the sensitivity and specificity. Using a data set linking clinical registries, administrative data, and the MUSE repository (n=11 662), we assessed the incremental diagnostic yield of AF/AFL by incorporating ECG data to administrative data‐based algorithms. The agreement between MUSE diagnosis and reference comparison depended on the coding definitions applied, with the Cohen κ ranging from 0.57 to 0.75. Sensitivity ranged from 60.6% to 79.1%, and specificity ranged from 93.2% to 98.0%. A coding definition with AF/AFL appearing in the first 3 ECG statements had the highest sensitivity (79.1%), with little loss of specificity (94.5%). Compared with the algorithms with only administrative data, incorporating ECG data increased the diagnostic yield of preexisting AF/AFL by 14.5% and incident AF/AFL by 7.5% to 16.1%. Conclusions Routine ECG interpretation using MUSE coding is highly specific and moderately sensitive for AF/AFL detection. Inclusion of MUSE ECG data in AF/AFL case identification algorithms can identify cases missed using administrative data‐based algorithms alone.

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.

In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their results and express conclusions. Today this process is mainly carried out by human examiners referring to guidelines or provided quality scales. The largest the set of fingermarks (e.g., hundreds, thousands), the longest and the most labor‐intensive this task becomes. Moreover, it is difficult to guarantee a fully objective process since the subjectivity of each individual is almost impossible to avoid, especially with regards to the interpretation of the quality scale levels or when facing fingermarks detected in an inhomogeneous manner. In this paper, the possibility of automatizing the quality assessment step is explored. The choice has been made to consider the use of quality assessment algorithms currently applied in an identification context. 150 natural fingermarks from ten donors were deposited on three different supports. These marks were detected using 1,2‐indanedione/zinc or cyanoacrylate fumigation depending on the support. Then, their quality was assessed by five examiners, according to the UNIL scale, and by seven algorithms (i.e., Lights Out, Latent Fingerprint Image Quality 1 and 2, Latent Quality Metric, Expected Score Likelihood Ratio, NIST Fingerprint Image Quality, MINDTCT). Spearman and Pearson correlations were calculated, and the distribution of scores for each algorithm was charted (using boxplots) against the results provided by the human examiners. The most promising results were obtained with the LQM algorithm, more specifically with the fingermark clarity metric. [ABSTRACT FROM AUTHOR]

Predicting more relevant areas of medical research, with a prediction period starting at five years, is currently done exclusively by experts, while the results of such forecasts are extremely ineffective and differ significantly, depending on their source. Modern development trends in world science require the creation of a universal forecasting tool that can be used as a basic resource— providing objective, system-independent information. This condition justifies the relevance of the present study. This study’s goal was to develop and test an algorithm to identify future innovative research areas in digitalization conditions (using the medical sector as an example). During this research, a prognostic model was developed based on the hype cycle, which makes determining a list of possible areas for hardware development in the medical sector possible, presenting this list as a set of tokens that are decoded using mental analysis and automated through the Python programming language. The process of identifying future innovative research areas comprises the following stages: identifying the aggregator (hub) research results, parsing primary information, translating the analyzed information, forming a set of lexemes, forming an analytical dataframe, constructing regression models for the highlighted lexemes, forming and storing the resulting dataframe, and metathinking the highlighted lexemes. In total, 4,000 study names were analyzed, based on the ResearchGate platform, which made obtaining 28 significant lexemes based on the results of metathinking possible. Next, an associative map was created using the most promising research areas in medicine, namely: diagnosing viral infections, the spread of viral infections, coronaviruses, cardiovascular diseases, and lung diseases. The obtained algorithm for the automatic determination of promising research areas can be modified by choosing different sources of information. [ABSTRACT FROM AUTHOR]

• Measurement of the tyre belt radial displacement during cleat test. • Wave propagative solution for tyre radial vibration. • Identification of the waves propagating along the tyre circumference. • Comparison of the identification results with in-plane tyre flexible ring model. Road vehicle NVH performances can be improved acting on tyres, which play a fundamental role in the transmission of structure-borne noise and vibration into the cabin, up to 500 Hz. This paper aims at investigating the in-plane dynamics of a rolling tyre in this frequency range. Tyre radial vibration measurements are carried out through a dedicated experimental setup, based on a laser triangulation sensor scanning the tread of a rolling tyre. The experimental data are collected during cleat tests, considering different rolling speeds and inflation pressures, to investigate their influence on tyre dynamics. Then, a data processing algorithm is proposed with the aim of identifying the progressive and regressive waves propagating along the tyre circumference after the cleat impact. Finally, to interpret the results, the identified tyre response has been compared to the analytical results obtained through a simplified in-plane tyre model. [ABSTRACT FROM AUTHOR]

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level.

In this study, the authors consider the identification of auto‐regressive (AR) models for time‐series from one‐bit quantised observation sequences. The only available information is the fact that the samples of the time‐series are lower or higher than a threshold of quantisation. This threshold may be different from zero. An identification algorithm is presented and analysed. A recursive formulation is proposed, an extension for the identification of a non‐linear time‐series is also proposed. [ABSTRACT FROM AUTHOR]

Most identification algorithms do not consider the prescribed error bound on the parameter estimation error information, which may result in the poor transient performance of parameter estimation during the identification process. In this study, a novel identification scheme is presented for the extended Wiener–Hammerstein systems with backlash non‐linearity, which is implemented by using prescribed performance function and error transformation technique. To improve the transient performance of parameter estimation, the prescribed performance function is designed to prescribe estimation error bound. Then, the error transformation technique is applied to transform the prescribed estimation error problem into an equivalent generalised error problem, which can simplify the design of identification algorithm. By developing the parameter updating law, the convergence of generalised error problem can be guaranteed, and the parameter estimation of considered system can be achieved. The numerical example and experiment results validate that the proposed scheme can provide more accurate parameter estimation and better transient performance than the available identification approaches. [ABSTRACT FROM AUTHOR]

Here are proposed robustness characteristics of the algorithm of identification of the type of the dynamic object (DO) and the law of probability distribution of identification sufficient statistics, formed by the algorithm under prior uncertainty. The law is applied for verification and validation of the algorithm. Wavelet fractal correlation algorithm (WFCA) implements vectorial criterion of ratio of likelihood functions of simple alternative hypotheses—types of DOs, this criterion being invariant to specific features of DO motion trajectories. The likelihood functions are reconstructed by simulation according to sufficiently representative complexes of implementations of fractal dimensions, energies, wavelet spectra and maximum eigenvalues of biased correlation matrices as functional of the measured coordinates of spatial attitude of various types of real DOs located by the optoelectron device (OED). The simulation proved robustness and high efficiency of the algorithm of identification of the type of DOs. [ABSTRACT FROM AUTHOR]

This study investigates the quantised‐input FIR (finite impulse response) system identification under quantised and event‐triggered output observations. For the binary‐valued quantisation, they introduce a so‐called either–or communication scheme to save communication resources. Based on the received data and the triggered indicator, an identification algorithm is proposed to estimate the unknown parameter by use of the empirical measure and weighted least squares. The convergence performance is established and the communication rate is derived. For the multi‐threshold quantisation, it can be seen as a vector‐valued observation in which each component represents a binary‐valued output, and the corresponding identification problem is discussed. A numerical example is included to illustrate the main results obtained. [ABSTRACT FROM AUTHOR]

This paper studied loan transaction data of an Internet financial platform, and identified the high-risk members by analyzing the propagation behavior of the loan network. It established the SIS model and SIR model based on threshold propagation and random propagation, respectively. After that, it generated an algorithm to evaluate the users’ risk value. Furthermore, it compared it with the actual defaulting data. In terms of the top 5% and 10% high risk group division, the results show that it can achieve high recall rate and good structural correlation with the algorithm. [ABSTRACT FROM AUTHOR]

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