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An adjoint‐based procedure to determine weaknesses, or, more generally, the material properties of structures is developed and tested. Given a series of load cases and corresponding displacement/strain measurements, the material properties are obtained by minimizing the weighted differences between the measured and computed values. The present paper proposes and tests techniques to minimize the number of load cases and sensors. Several examples show the viability, accuracy and efficiency of the proposed methodology and its potential use for high fidelity digital twins. [ABSTRACT FROM AUTHOR]

ABSTRACT This study describes the identification and characterisation of a new mesophilic Aeromonas salmonicida strain, named HMes1 isolated from Atlantic salmon (Salmo salar L.) in Tasmania. Isolates were identified as Aeromonas salmonicida through phenotypic, phylogenetic and genetic characterisation. After characterisation, the diagnostic phenotypic identification system MicroSys A24 was updated and a new multiplex conventional PCR was developed to enable rapid and inexpensive identification of atypical A. salmonicida, and exclusion of the exotic strain, A. salmonicida ssp. salmonicida. [ABSTRACT FROM AUTHOR]

Introduction: The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency. Methods: This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures. Results: In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model's ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the "Permanent pasture" class. The F1-score was 94%, indicating a balanced measure of the model's performance. Discussion: The integration of advanced technologies in the CAP's control mechanisms, as demonstrated, has the potential to automate the verification of farmers' declarations and subsequent subsidy payments. [ABSTRACT FROM AUTHOR]

A growing and increasingly globalised human population, requiring the movement of goods and commodities, is placing increasing demands on the maritime industry, resulting in a concurrent increase in global shipping activities. This has consequences for the marine environment, particularly for species vulnerable to the impacts of vessel traffic. For example, vessel collisions can result in sub‐lethal or fatal injuries for marine mammals, whilst vessel noise can cause acoustic masking that effectively reduces an animal's listening space, potentially impacting their communication, navigation and foraging capacity.While a number of parallel approaches to mapping collision risk to large whales have arisen, these methods vary in their focus, usually on either co‐occurrence, collision probability, or probability of mortality. However, little attention has been given to the implications of methodological choice and data selection on subsequent risk predictions.To assess differences between these approaches, we used a standardised input dataset comprised of telemetry‐point data from tagged bowhead whales, and satellite‐based Automated Identification System (AIS) data of spatial vessel movements covering the Davis‐Baffin Arctic Marine Area. We applied this data to eight different, previously published analyses for deriving areas of vessel risk.We found that the choice of risk mapping approach affected the location, and total area, identified as 'high risk', and that more computationally complex approaches did not necessarily equate to different predictions. There was considerable variation in the total area of 'high risk' predicted within each map (range = 20–42,246 km2).Synthesis and Applications. The results underscore the importance of methodological transparency, informed data selection and careful interpretation when predicting collision risk. We provide practical recommendations for enhancing transparency when predicting risk, and discuss choice of approach suitable for different situations or management applications. It is critical that managers and policy makers are aware of the implications of applying different approaches when interpreting risk evaluation outputs. [ABSTRACT FROM AUTHOR]

Dynamic testing of systems is most realistic of real-world conditions when multiple input and multiple output (MIMO) techniques are used. To replicate measured environmental conditions, a series of desired outputs (responses) on the system must be realized by inverting the frequency response functions (FRF) matrix for input estimation. System identification is dependent on the number of inputs and outputs, which affect the type of solutions. In input estimation, the auto spectra of the determined inputs are affected by both the auto spectra and cross spectra of the desired outputs. This research evaluates errors and investigates two sources in an experimental setting. The first significant error source is test-to-test variability in FRFs from system identification tests, quantified by magnitude variability from long-duration tests. The second significant error source is the realization of time histories from frequency domain inputs. The spectral content of the realized random process deviates from the desired content and is also quantified. The structure is a linear three-story frame fixed at the base; two inputs are provided uniaxially with the use of suspended electrodynamic shakers. The proposed model for error sources was found to be effective in predicting errors in a SISO (single-input single-output) and a square (2-input 2-output) MIMO test. [ABSTRACT FROM AUTHOR]

Efficient traceability is paramount for Australia's biosecurity system and market access. Electronic identification (eID) offers higher traceability performance than that achieved with visual and mob‐based identification used for the sheep and goats National Livestock Identification System (NLIS). The current study aims to gain an understanding of the barriers and motivations of using eID for NLIS sheep and goats in New South Wales (NSW) and to provide recommendations to support the transition to eID. A social research study using a mixed‐methods approach was used through semi‐structured interviews and a cross‐sectional epidemiological and behavioural study among sheep and goat producers. The COM‐B behaviour change model based on capabilities, opportunities and motivations was used as a framework for the study. A total of 269 participants informed this study: 25 interviews were conducted with government, industry and private stakeholders, and 184 and 58 sheep and goat producers participated in the cross‐sectional study, respectively. The study identified poor understanding of the purpose and importance of NLIS among producers, with over two‐thirds not supporting eID implementation. The main barriers identified to the eID implementation were practical, including costs, technology quality and increased workload. Attitudinal, behavioural and knowledge barriers, such as the perception of the current system providing efficient traceability and the perceived lack of benefits of eID were also identified. This study provides an in‐depth analysis of practices and perceptions of stakeholders and producers on sheep and goat traceability and recommendations to address barriers identified, based on education and appropriate behavioural and technical support. [ABSTRACT FROM AUTHOR]

The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables. For a high dimensional nonparametric nonlinear system, however, identifying whether a variable contributes or not is not easy. Therefore, based on the Fourier spectrum of density-weighted derivative, one novel variable selection approach is developed, which does not suffer from the dimensionality curse and improves the identification accuracy. Furthermore, a necessary and sufficient condition for testing a variable whether it contributes or not is provided. The proposed approach does not require strong assumptions on the distribution, such as elliptical distribution. The simulation study verifies the effectiveness of the novel variable selection algorithm. [ABSTRACT FROM AUTHOR]

The widely linear complex-valued least mean square (WL-CNLMS) algorithm is extensively used for processing complex-valued signals, but it exists performance compromise between convergence rate and steady-state misadjustment. In response to this problem, we incorporate the idea of switching step-size (SSS), that is, selecting an optimal step-size at each iteration by comparing the mean-square deviation trends of the WL-CNLMS algorithm with pre-set different step-sizes and then proposing the SSS based WL-NLMS algorithm. Meanwhile, to keep the robustness of the algorithm in the impulsive noise environment, a robust variant of it is proposed by utilizing the modified Huber function instead of the quadratic function. Through extensive simulations in the contexts of system identification and beamforming, we have verified the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonlinear Box–Jenkins systems with saturation nonlinearity. The input saturation nonlinearity is presented as a linear parametric expression through the application of a switching function, then the identification model of the system is derived by using the key term separation technique. Based on this model and the data filtering technique, the filtering identification model of the system is given by changing the system structure without changing the relationship between the input and output, which can reduce the interference of the colored noise and improve the identification accuracy. Then a data filtering-based maximum likelihood gradient-based iterative algorithm is proposed to estimate the unknown parameters. The maximum likelihood gradient-based iterative algorithm is provided for comparison. The feasibility and superiority of the proposed approach are emphasized by a simulation example. [ABSTRACT FROM AUTHOR]

To solve the aircraft dynamics modeling problem in the entire envelope range, this work proposes a closed-loop system identification method based on deep learning. A closed-loop flight test was designed, under the framework of the closed-loop flight test, the motion mode of the aircraft was fully stimulated by the input signal of the control rudder surface and the airspeed and position commands. The lateral and longitudinal aerodynamic coefficients were solved from the flight test data, and the black box relationship between the aerodynamic coefficients and their influencing factors was established based on the deep network technology. The aerodynamic coefficient black box model was combined with the dynamics and kinematic equations of the aircraft to form a deep network dynamic model of the aircraft, which belongs to a gray box dynamics model. The deep network can easily and uniformly process different batches of flight test data, thus combining the flight test data at different flight state points, and finally building a complete aerodynamic model within the entire envelope range. Three groups of flight tests were performed: the first group of tests was used for model set training, the second group of test data was used for the selection of the best model, and the third group of flight tests was used for model validation. The model verification was completed from two aspects: the prediction of the aerodynamic coefficient and the prediction of the flight state variables. The results show that the deep network model can complete high-precision modeling of aerodynamic coefficients; and the gray box dynamic model can complete the modeling of aircraft dynamics within the entire envelope, and can be used as a long-term, high-precision flight simulator. [ABSTRACT FROM AUTHOR]

Low-frequency vibration is the core problem that hinders high-precision equipment's positioning accuracy and control accuracy. However, the response of a nonlinear electrical-mechanical coupling system is nonlinear and quite complicated under the ambient random vibrating environment. This paper presents a vibration suppression method through NARX (Nonlinear autoregressive with external input) neural network and its controller. The experiment platform is designed, and its dynamic model is obtained through system identification. The Neural network direct dynamic inverse control system is established, and the controller is designed. Both the simulation and experimental results show that the NARX identification and the controller can effectively achieve vibration suppression, and the experiment's highest vibration rejection ratio is 90.8%. The vibration suppression technology can be extensively applied to low-frequency vibration systems, such as aerospace equipment and high-precision equipment. [ABSTRACT FROM AUTHOR]

This paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application. [ABSTRACT FROM AUTHOR]

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model's performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE. [ABSTRACT FROM AUTHOR]

Large fingerprint databases can make the automated search process tedious and time‐consuming. Fingerprint pattern classification is a significant step in the identification system's complexity in terms of time and speed. Although several fingerprint algorithms have been developed for classification tasks, further improvements in performance and efficiency are still required. Most of the fingerprint algorithms use deep learning techniques. However, some deep learning techniques can be resource‐intensive and computationally expensive, while others can disregard the spatial relationships between the features used in classifying fingerprint patterns. This study proposes using lightweight deep learning models (i.e., MobileNet and EfficientNet‐B0) integrated with attention modules to classify fingerprint patterns. The two lightweight models are modified, yielding MobileNet+ and EfficientNet‐B0+ models. The lightweight deep learning models can help achieve optimal performance and reduce computational complexity. The attention modules focus on distinctive features for classification. Our proposed approach integrates four attention modules for fingerprint pattern classification into two lightweight deep learning models, that is, MobileNet+ and EfficientNet‐B0+. To evaluate our approach, we use two publicly available fingerprint datasets, that is, the NIST special database 301 dataset and the LivDet dataset. The evaluation results show that the EfficientNet‐B0+ model achieves the highest classification accuracy of 97% with only 854,086 training parameters. As a conclusion, we consider the training parameters small enough for the EfficientNet‐B0+ model to be deployed on low‐resource devices. [ABSTRACT FROM AUTHOR]

Summary Prevention of ABO‐incompatible red cell transfusions (ABO‐it) requires accurate donor and patient identification and correct application of processes for transfusion safety. In France and Germany, a bedside identity check and ABO compatibility test are performed. In the UK, an identity check, often structured as a bedside checklist, is performed with or without electronic patient identification (ePID). To compare the efficacy of ABO‐it bedside preventive measures, frequencies and causes of ABO‐it between 2013 and 2022 were investigated in all three countries. Despite differing bedside safety measures, similar average ABO‐it frequencies were observed in France (0.19 [SD:0.09]/100 000 issued red cell units) and in the UK (0.28 [SD:0.17]/100 000), whereas a higher frequency (0.71 [SD:0.23]/100 000) was observed in Germany which has similar bedside safety measures to France. ABO‐it resulted mostly from erroneous patient identification and transfusion of a red cell unit intended for another patient. In France and Germany, all ABO‐it were associated with incorrectly performed identity check and ABO compatibility test. In the UK, most ABO‐it were associated with incorrectly performed identity checks. Current measures to prevent ABO‐it are not fully effective. Further development and implementation of effective patient identification systems, including electronic information systems, across the entire transfusion process, should be considered. [ABSTRACT FROM AUTHOR]

The mountainous region of central Shandong Province in eastern China is renowned for its high frequency of rainstorms, which often result in devastating flood disasters and have profoundly affected the evolution of local ancient civilizations. To identify ancient flood disaster events, recognizing sediment signatures via multiple indices is important. Here, we investigated the 2018 flood deposits in the Danhe River Basin and sampled short core DH2, on which the grain size, total organic matter (TOM) content, carbonate content, magnetic susceptibility (MS) and pollen were measured. The fine grain size of the flood sedimentary layer reveals that the flood energy in the alluvial plain area is usually weak. The pollen species and concentration and the tree pollen content in the flood layer are significantly greater than those in the soil layer, suggesting that the flood sediment mainly originates from the mountainous areas in the upper reaches of the river, which provides more forest vegetation information. The MS of flood deposits is lower than that of the soil layer, which is mainly related to the intensity of pedogenesis. The research results indicate that the flood sediments in the downstream floodplain areas of small watersheds are predominantly composed of fine-grained components, with characteristics of high loss on ignition, low magnetic susceptibility, high pollen abundance and diverse species. These findings establish a multi-index identification system for paleoflood sedimentation, which has important reference significance for the study of paleoflood sedimentology and hydrology. [ABSTRACT FROM AUTHOR]

This paper presents novel and low computational complexity spline adaptive filtering (SAF) algorithms based on partial coefficients selection and adaption. They are addressed partial update SAF normalized least mean squares (PU-SAF-NLMS) algorithms. The PU-SAF-NLMS equations are established via the steepest gradient descent constraint to the proposed cost function. In the proposed algorithms, the filter coefficients are updated based on periodic, sequential, and selective rules, leading to reduction in computational complexity. Furthermore, the convergence speed of PU-SAF-NLMS algorithms is close to the conventional SAF-NLMS. Moreover, the convergence analysis of algorithms is studied. Ultimately, the performance of the presented algorithms is appraised through several experimental results. [ABSTRACT FROM AUTHOR]

ABSTRACT This article is aimed to study the parameter estimation problems of a non‐commensurate fractional‐order system with saturation and dead‐zone nonlinearity. In order to reduce the structural complexity of the system, the model separation scheme is used to decompose the fractional‐order nonlinear system into two subsystems, one includes the parameters of the linear part and the other includes the parameters of the nonlinear part. Then, we derive an auxiliary model separable gradient‐based iterative algorithm with the help of the model separation scheme. In addition, to improve the utilization of the real time information, an auxiliary model separable multi‐innovation gradient‐based iterative algorithm is presented based on the sliding measurement window. Finally, the feasibility of the presented algorithms is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. Therefore, this article aims to propose a multi‐data classification detection model to differentiate the data of regular operation, faults, and FDIAs when the smart grid suffers FDIAs. Due to the unbalanced number of different kinds of samples in the dataset, Affinitive Borderlinen SMOTE is used to pre‐process the data by oversampling to improve the training accuracy. A multi‐data detection model based on the Inception network is established, and the overall structure of the network and the individual Inception modules are given. A small power system is an example of simulating a smart grid suffering from FDIAs. The designed classification detection model is simulated, validated, and compared with two‐dimensional convolutional neural networks and existing research results. The qualitative analysis of the evaluation metrics can show that the Inception network model has high accuracy and real‐time performance for detecting different data. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a method to improve the identification performance of linear dynamic systems by utilizing knowledge from samples of non‐identical distribution systems. Traditional identification methods heavily rely on the quality of the dataset, such as sample length and noise level, which constrains their performance due to the assumption of identical distribution. Motivated by the concept of sample‐based transfer learning, we propose a sample transfer identification method and derive the condition to avoid negative transfer. We develop a fast iterative transfer identification method for low storage costs, considering the computational burden imposed by the sample size from the source system. Additionally, based on the fast iterative transfer identification method, considering the need to update the current measurement data model in real time, a fast iterative online sample transfer identification method is explored. Through simulations, we validate the effectiveness and superiority of the proposed methods. The results show that sample transfer identification is superior to non‐transfer identification and fast iterative sample transfer identification effectively reduces the calculation amount when dealing with low quality measurement data. [ABSTRACT FROM AUTHOR]

Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques. [ABSTRACT FROM AUTHOR]

Frozen shoulder (FS) is a common shoulder condition accompanied by shoulder pain and a loss of shoulder range of motion (ROM). The typical clinical assessment tools such as questionnaires and ROM measurement are susceptible to subjectivity and individual bias. To provide an objective evaluation for clinical assessment, this study proposes an inertial measurement unit (IMU)-based identification system to automatically identify shoulder tasks whether performed by healthy subjects or FS patients. Two groups of features (time-domain statistical features and kinematic features), seven machine learning (ML) techniques, and two deep learning (DL) models are applied in the proposed identification system. For the experiments, 24 FS patients and 20 healthy subjects were recruited to perform five daily shoulder tasks with two IMUs attached to the arm and the wrist. The results demonstrate that the proposed system using deep learning presented the best identification performance using all features. The convolutional neural network achieved the best identification accuracy of 88.26%, and the multilayer perceptron obtained the best F1 score of 89.23%. Further analysis revealed that the identification performance based on wrist features had a higher accuracy compared to that based on arm features. The system's performance using time-domain statistical features has better discriminability in terms of identifying FS compared to using kinematic features. We demonstrate that the implementation of the IMU-based identification system using ML is feasible for FS assessment in clinical practice. [ABSTRACT FROM AUTHOR]

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces. [ABSTRACT FROM AUTHOR]

The development and validation of lateral control strategies for railway running gears with independently rotating driven wheels (IRDWs) are an active research area due to their potential to enhance straight-track centering, curve steering performance, and reduce noise and wheel–rail wear. This paper focuses on the practical application of theoretical models to a 1:5 scaled test rig developed by the German Aerospace Center (DLR), addressing the challenges posed by unmodeled phenomena such as hysteresis, varying damping and parameter identification. The theoretical model from prior work is adapted based on empirical measurements from the test rig, incorporating the varying open-loop stability of the front and rear wheel carriers, hysteresis effects, and other dynamic properties typically neglected in literature. A transparent procedure for identifying dynamic parameters is developed, validated through closed- and open-loop measurements. The refined model informs the design and tuning of a cascaded PI and PD controller, enhancing system stabilization by compensating for hysteresis and damping variations. The proposed approach demonstrates improved robustness and performance in controlling the lateral displacement of IRDWs, contributing to the advancement of safety-critical railway technologies. [ABSTRACT FROM AUTHOR]

Data-driven machine learning models are useful for modeling complex-typically nonlinear-structures based on empirical observations, bypassing the need to generate a physical model in cases where the physics is not well known or easily modeled. One disadvantage of purely data-driven approaches is that they tend to perform poorly in regions outside the original training domain. To mitigate this limitation, physical knowledge about the structure can be embedded in the model architecture via the model topology or numerical constraints in the formulation. We propose a neural network framework based on Hamiltonian mechanics to enforce a physics-informed structure to the model. The Hamiltonian framework allows us to relate the energy of the system to the measured quantities (e.g., accelerations) through the Euler–Lagrange equations of motion. A challenge with this hybrid data-driven, physics-constrained approach is the problem of limited observability, i.e., not being able to measure structural response in a complete coordinate system that is compatible with the physical constraints being enforced. To overcome this issue, we propose combining an invertible neural network autoencoder architecture with knowledge from embedding theory to enrich the limited observable data with time-delay embeddings. From Taken's theorem, we know that a sufficient time-delay embedding is diffeomorphically equivalent to the underlying state space of the system. We use this information to find time-delays of the original data and build the diffeomorphic mapping with a neural network encoder. The approach is demonstrated on computational and experimental examples. [ABSTRACT FROM AUTHOR]

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. [ABSTRACT FROM AUTHOR]

Aggregate drying is a crucial step in asphalt mixture production. Enhancing the model accuracy and controller performance of aggregate dryer burners is essential for consistent flame characteristics, reducing NOx emissions, and minimizing fuel consumption. This paper introduces a second-order nonlinear parametric model for coal powder burners that includes delay and noise. Model parameters were determined through experimental data using the Salp Swarm Algorithm, showing higher accuracy than models based on the least square method. A dual-layer model predictive control (MPC) based on this mathematical model was developed to improve the economic efficiency of the aggregate drying process. Simulation results showed that the dual-layer MPC saves 1.08 tons of coal every 10 h compared to a standard MPC. A full-scale prototype demonstrated average flame length, flame temperature, and NOx emissions of 4242.6 mm, 1729.4 °C, and 460.2 mg/m³, respectively, validating the accuracy of the proposed mathematical model and controller. [ABSTRACT FROM AUTHOR]

At the present stage of transient ultra-high-temperature energy release of boron-containing warm-pressure explosives, single thermocouples are often used for multi-point measurements in the process of their temperature field changes, and the results of their temperature field reconstruction are not satisfactory due to the limited consistency of the thermocouples. Aiming at the above-mentioned problems, a C-type two-thermocouple suitable for transient temperature measurement in high-temperature environments is designed; the system characterization of the two-thermocouple is carried out by using the blind system identification method of the inter-relationships; the identification process is evaluated by a new cost function; and the optimal solution on the new cost function is realized by using the gradient descent method. The temperature reconstruction of the two-thermocouple output excited by the simulated heat source is carried out by using the auto-regressive with extra inputs model, and the feasibility of the reconstruction results is verified. In the experimental part, a thermocouple dynamic characteristic calibration system based on a high-temperature furnace is constructed and experimental validation is carried out in the high-temperature furnace to compare the effects of different exposure lengths and different wire diameters on the output of the two-thermocouple, and as a result, the outputs are corrected and analyzed. The results show that two-thermocouple methods with different combinations of wire diameters are better for temperature measurement, with a reconstructed root mean squared error of 0.0162 and a goodness of fit of 89.13%. [ABSTRACT FROM AUTHOR]

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values. [ABSTRACT FROM AUTHOR]

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter's parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system's input and the IIR filter's output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value. [ABSTRACT FROM AUTHOR]

This article addresses the identification of unknown nonlinear continuous‐time systems through a linear time‐varying (LTV) approximation as a starting point. The mathematical form of the nonlinear system is unknown and is reconstructed by use of a well‐designed experiment, followed by LTV and linear parameter‐varying (LPV) estimations, and an integration step. The experiment used allows for a linearization of the unknown nonlinear system around a time‐varying operating point (system trajectory), resulting in an LTV approximation. After estimating the LTV model, an LPV model is identified, where the parameter‐varying (PV) coefficients represent partial derivatives of the unknown nonlinear system evaluated at the trajectory. We demonstrate a structural relation in the LPV model structure that ensures that the LPV coefficient vector is the gradient of the unknown nonlinear system. The nonlinear model of the system is then reconstructed through symbolic integration of the PV coefficients. This identification method enables the estimation of the unknown nonlinear system and its mathematical form using input–output measurements. The article concludes by illustrating the method on simulation examples. [ABSTRACT FROM AUTHOR]

In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two‐stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation model and the identified mathematical model. Moreover, through the construction of a single imputation model, analytical expressions of predictive distributions can be given for missing entries across all missingness patterns. Furthermore, analytical expressions of the expectation and variance of distribution are provided to impute missing values and quantify uncertainty, respectively. This uncertainty is incorporated into a single mathematical model by mitigating the influence of samples with imputations during training and testing. The framework is applied to three applications, including a simulated example and two real applications on structural health monitoring and seismic attenuation modeling. Results reveal a minimum reduction of 21% in root mean squared error values, compared to those achieved by directly removing incomplete samples. [ABSTRACT FROM AUTHOR]

This study introduces three types of multivariable fragility surfaces, integrating effective structural features to improve damage assessment. The incorporation of additional information such as building occupancies, structural responses, and underlying soil types enhances the accuracy of conventional fragility curve predictions. Additionally, three modification factors are proposed to further refine conventional fragility curves and provide more precise predictions. The multivariable fragility surfaces are developed for eccentric brace frames modeled in Opensees software which is validated by experimental results and subjected to incremental dynamic analysis with 44 far-field ground motions. The influence of soil flexibilities on structural responses is incorporated through Winkler springs, representing soil-structure interaction. Diverse occupancies, such as hospitals, museums, and residential structures, are assessed using various peak floor acceleration thresholds and story drift ratios, employing multidimensional limit state functions to consider both structural and nonstructural losses. To account for uncertainties in structural responses and a single intensity measurement, a damage-sensitive feature derived from roof acceleration response, obtained through signal processing and system identification techniques, is introduced. The results for the proposed multivariable fragility surfaces indicate that the spectral acceleration corresponding to a 50% probability of exceedance could vary between 10.2 and 89%, in comparison to the corresponding conventional fragility curves. Finally, to evaluate the application of the enhanced fragility surface and modification factors, two instrumented EBF buildings, a 4-story EBF building, and a real 5-story hospital EBF, are selected as case studies. With additional details on soil types, occupancies, and structural responses, the process of employing modification factors resulting in enhanced fragility curves is demonstrated. [ABSTRACT FROM AUTHOR]

Background: Thrombocytopenia is a common side effect of cytotoxic chemotherapies, which is often dose-limiting. Predicting an individual’s risk is of high clinical importance, as otherwise, a small subgroup of patients limits dosages for the overall population for safety reasons. Methods: We aim to predict individual platelet dynamics using non-linear auto-regressive networks with exogenous inputs (NARX). We consider different architectures of the NARX networks, namely feed-forward networks (FNN) and gated recurrent units (GRU). To cope with the relative sparsity of individual patient data, we employ transfer learning (TL) approaches based on a semi-mechanistic model of hematotoxicity. We use a large data set of patients with high-grade non-Hodgkin’s lymphoma to learn the respective models on an individual scale and to compare prediction performances with that of the semi-mechanistic model. Results: Of the examined network models, the NARX with GRU architecture performs best. In comparison to the semi-mechanistic model, the network model can result in a substantial improvement of prediction accuracy for patients with irregular dynamics, given well-spaced measurements. TL improves individual prediction performances. Conclusion: NARX networks can be utilized to predict an individual’s thrombotoxic response to cytotoxic chemotherapy treatment. For reasonable model learning, we recommend at least three well-spaced measurements per cycle: at baseline, during the nadir phase and during the recovery phase. We aim at generalizing our approach to other treatment scenarios and blood lineages in the future. [ABSTRACT FROM AUTHOR]

The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying the interactions of neurons. Estimating parameters of this class of generative model allows one to predict the system's dynamics and responses under changing inputs and, indeed, changing parameters. We first assume a set of known state-space equations and address the problem of inferring the lumped parameters from observed time series. Crucially, we consider this problem in the setting of bistability, random fluctuations in system dynamics, and partial observations, in which some states are hidden. To identify the most efficient estimation or inversion scheme in this particular system identification, we benchmark against state-of-the-art optimization and Bayesian estimation algorithms, highlighting their strengths and weaknesses. Additionally, we explore how well the statistical relationships between parameters are maintained across different scales. We found that deep neural density estimators outperform other algorithms in the inversion scheme, despite potentially resulting in overestimated uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. We then eschew the mean-field approximation and employ deep neural ODEs on spiking neurons, illustrating prediction of system dynamics and vector fields from microscopic states. Overall, this study affords an opportunity to predict brain dynamics and responses to various perturbations or pharmacological interventions using deep neural networks. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm. [ABSTRACT FROM AUTHOR]

Summary: In the era of digitalization boom, cyber‐physical system (CPS) has been widely used in several fields. However, malicious data tampering in communication networks may lead to degradation of the state estimation performance, which may affect the control decision and cause significant losses. In this paper, for the identification of finite impluse response (FIR) systems with binary‐valued observations under data tampering attack, an optimal attack strategy based on the average entropy is designed from the perspective of the attacker. In the case of unknown parameters, the regression matrix is used to give the estimation method of the system parameters, the algorithmic flow of the data tampering attack for the implementation of the on‐line attack is designed. Finally, the effectiveness of the algorithm and the reliability of the conclusions is verified through the examples. [ABSTRACT FROM AUTHOR]

To contain the spread of the virus at ports, many countries have implemented quarantine policies for vessels from abroad during COVID-19. In response, vessels chose to skip the port to save time or undergo a 14-day quarantine to ensure critical supplies, both of which significantly affected the performance of the port network. However, due to the combined effect of many factors, data analysis techniques can hardly identify the impact of quarantine policies on the outcomes. Therefore, to enable both networkwide performance assessment and detailed evaluation for individual vessels and ports under such an unprecedented policy, a microscopic simulation model for the global port network (GPN) is desired. The proposed simulation method is based on real-world vessel movement data from Automatic Identification Systems (AIS) combined with a port database. It is found that the effect of the quarantine policy on a particular port consists of two parts, i.e. the direct impact caused by vessels' port skipping and the indirect impact caused by network interaction, which is further determined by the location of, and the policy implemented by the port. Furthermore, the ability of the global port network to maintain its performance under different levels of pandemic situations and different rates for vessels to skip the ports requiring quarantine is investigated. Interestingly, in most cases, a moderate port skipping rate (mostly between 20% and 50%) could help improve network performance. The results and presented simulation method can assist policymakers in coping with COVID-19 and potential global catastrophes. [ABSTRACT FROM AUTHOR]

1. The production of goose eggs holds significant economic value on a global scale and the quality of fertilised eggs is crucial for the successful hatching and sustained development of the poultry industry. Developing a low-cost fertilised egg identification system that is suitable for large-scale testing is of great significance. However, existing methods are expensive and have high environmental detection requirements, which limit their promotion. 2. To address this issue, an improved object detection model called FEDM based on YOLOv5 is proposed, which has been shown to be outstanding among nine models. The main network of YOLOv5 is enhanced with the SENet attention mechanism to improve the feature selection capability. The C3_DCNv3 is introduced to enhance the detection ability of blood vessels in the fertilised eggs. The application of Dyhead significantly improved the representation capacity of the object detection head without any computational overhead. The loss function is replaced with MPDIoU to simplify the calculation process. 3. Experimental results from the augmented dataset showed that the average precision of the FEDM reached 96.7%, which is a 5.5% improvement compared to the YOLOv5s model. FEDM exhibited better detection performance on eggs from different shooting angles than the YOLOv5 algorithm and achieves high detection speed. 4. The FEDM secured significant advancement on the detection rate of the fourth day fertilised egg compared to the YOLOv5 algorithm. Based on this result, savings and space utilisation can be made, which has practical application value. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusions A 45‐min interval from injury to intubation has been proposed as a performance indicator for severe trauma patient management. In the Sydney pre‐hospital system a previous change in case identification systems was associated with activation delay. We aimed to determine if this also decreased the proportion of patients intubated within this benchmark.Retrospective cohort study of patients intubated by a helicopter emergency medical service (HEMS) over two time periods. Period 1 dispatch was via HEMS crew directly screening the computerised dispatch system, and period 2 was via paramedics in a central control room. Times from emergency call to intubation were compared.In the HEMS crew screening period 46/58 (79.31%) intubations met the target, compared with 137/314 (43.6%) in the central control period (P < 0.001). The median (interquartile range) time to intubation in the direct crew screening period was 33 (25–41) min, versus the central control period at 47 (38–60) min (P < 0.001).On multivariate modelling, distance to the scene was related to time to intubation (P < 0.001; Incident Rate Ratio = 1.018, 95% confidence interval 1.015–1.020) as was dispatch system, entrapment/access difficulty and indication for intubation (all P < 0.001).Time from emergency call to intubation was significantly shorter in the HEMS screening period where all non‐trapped cases less than 50 km distant were intubated within the 45‐min benchmark. There was no distance where intubation within 45 min could be assured for non‐trapped patients in the central control period due to dispatch delays. [ABSTRACT FROM AUTHOR]

In order to provide environmental solutions for the treatment of oily residues (slop oil) from refineries, formulations with surfactants were proposed according to the type of dispersed system of the slop (crude oil emulsified with water and/or solid particles). Samples were collected in 12 tanks located at the Oxialquilados Venezolanos C.A., Barcelona Plant (Anzoátegui, Venezuela), performing physicochemical tests for the identification of the dispersed system (solubility, wettability, stability, water content) and microscopic observations (morphology and particle size). The results indicated heterogeneous slop samples with a water content of 2%–60% vol/vol consisting of single or multiple emulsions, with droplet sizes between approximately 10 and 30 micron; others formed by sludge or suspension (solids/oil/water). For the destabilization of these systems, aqueous and organic cleaning solutions were designed, determining the percentage of oil separated in the bottle tests at optimal and simulated field conditions. Field tests with volume scaling of the slop tank reported efficiencies of 63%, 85%, and 100% when treating the slop TK‐17, TK‐36, EE, respectively; for the mixture (Pulmon‐2) an efficiency of 47%. By combining heat treatment and two demulsifiers was possible to treat the slop TK‐17 (multiple emulsion). A considerable volume of water can be drained from the tanks for use in the plant. In conclusion, the surfactant formulas were effective in the recovery of a significant amount of crude oil, making it feasible to return it to the generating entity. [ABSTRACT FROM AUTHOR]

DNA barcoding is an effective identification tool for tree ferns with heteromorphic generations with morphologically simple gametophytic phase. A complete reference dataset which includes coverage of the target local flora is necessary for accurate identification. Three different Cyathea sp.pl. were investigated to test the utility of plastid DNA barcode regions rbcL with the intention of developing an identification system for native pteridophytes. In addition, the inter-specific and inter-generic variations among the Cyathea sp.pl. were also focused. BLAST analysis revealed the similar rbcL sequences related to C. nilgirensis Holttum, C. gigantea (Wallich ex Hook. f.) Holttum and C. crinita (Hook.) Copel and a sum of 143 sequences viz., Cyathea sp.pl. (121), Gymnosphaera sp.pl.(4), Sphaeropteris sp.pl. (1) and Alsophila sp.pl. (17). The sequences of 143 plants were retrieved from GenBank in FASTA format. The sequences were aligned using multiple sequence alignment tool MUSCLE. The inter-specific and inter-generic distance percentage were calculated using Maximum Likelihood, Minimum Evolution, UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and Neighbor-Joining method. The analyses were constructed using MEGA X software. The rbcL gene provided sequence variation with its strong resolving power. This study demonstrates the overall effectiveness of DNA barcodes for species identification of tree fern genus Cyathea in the pteridophyte flora of Southern Western Ghats. [ABSTRACT FROM AUTHOR]

The industrial standard in the design and development process of NVH(Noise Vibration Harshness) characteristic of brakes is the application of Finite Element(FE) models with a high number of degrees of freedom in the range of one or several millions. Nevertheless, parallel experimental investigations are still indispensable. On the other hand, minimal models with, due to the inclusion of the self-excitation process, at least two degrees of freedom are well known to be capable to explain qualitatively phenomena as instability of the desired non-vibrating solution or limit cycle oscillation but are in general very inaccurate in predicting the dynamics of a specific real brake. This is because the underlying physical assumptions are already too restrictive and model parameters (especially those referring to nonlinearities) are widely unknown. To overcome this problem, the data-driven modeling approach SINDy(Sparse Identification of Nonlinear Dynamics) is applied to identify appropriate nonlinear functions for a brake squeal minimal model. A problem thereby is the limited database. It turns out that the naive implementation of the method yielding the lowest possible residuum does not necessarily provide physically meaningful models and results, respectively. Instead, a constrained model that incorporates physical knowledge is used to robustly identify parameters and reproduce realistic dynamic behavior. Thereby, several appropriate models with coexisting limit cycles and stationary equilibrium are identified. In particular, it was found that the angular position of the brake drum has a significant influence on the model parameters and therefore must be taken into account in a model with long-term validity. [ABSTRACT FROM AUTHOR]

Regarding to the complex chaotic non-linear behaviour in nature and the impossibility of their accurate modelling with current chaotic models, the development of Cascade Hyper-Chaotic System is necessary. Therefore, in this paper, new cascade hyper chaotic systems are proposed. Cascade hyper-chaotic systems have more parameters than the above-mentioned systems, and in most cases, they show more complex behaviour. On the other hand, due to the complexity of these systems and their high parameters, the identification of this kind of systems is difficult. For this reason, a new Adaptive Chaotic Grey Wolf Optimisation algorithm is presented in this article in order to estimate the parameters of these systems. Numerical simulations have been performed on the above-mentioned systems of Lu-Chen, Chen-Lorenz and Lu-Lorenz cascading. The results show that the systems have more complex behaviour than the seed systems and the proposed grey wolf algorithm is also an effective tool for estimating the parameter of the above-mentioned cascade Hyper-Chaotic System. [ABSTRACT FROM AUTHOR]

Urban-suburban-rural (U-S-R) zones exhibit distinctive transitional characteristics in interaction between human and nature. U-S-R transition zones (U-S-RTZ) are also highlighting the function diversity and landscape heterogeneity across territorial spaces. As a super megacity in western China, Chengdu's rapid urbanization has driven the evolution of U-S-R spaces, resulting in a sequential structure. To promote the high-quality spatial development of urban-rural region in a structured and efficient manner, it is essential to conduct a scientific examination of the multidimensional interconnection within the U-S-RTZ framework. By proposing a novel identification method of U-S-RTZ and taking Chengdu, China as a case study, grounded in a blender of natural and humanistic factors, this study quantitatively delineated and explored the spatial evolutions of U-S-RTZ and stated the optimization orientation and sustainable development strategies of the production-living-ecological spaces along the U-S-R gradients. The results show that: 1) it is suitable for the quantitative analysis of U-S-RTZ by established three-dimensional identification system in this study. 2) In 1990–2020, the urban-suburban transition zones (U-STZ) in Chengdu have continuously undergone a substantial increase, and the scale of the suburban-rural transition zones (S-RTZ) has continued to expand slightly, while the space of rural-ecological transition zones (R-ETZ) has noticeably compressed. 3) The landuse dynamics within U-S-RTZ has gradually increased in 1990–2020. The main direction of landuse transition was from farmland to construction land or woodlands, with the expansion of construction land being the most significant. 4) R-ETZ primarily focus on ecological functions, and there is a trade-off relationship between the production-ecological function within the S-RTZ, and in the U-STZ, production-living composite functions are prioritized. This study emphasizes the importance of elastic planning and precise governance within the U-S-RTZ in a rapid urbanization region, particularly highlighting the role of suburbs as landscape corridors and service hubs in urban-rural integration. It elucidates to the practical implications for achieving high-quality development of integrated U-S-R territorial spaces. [ABSTRACT FROM AUTHOR]

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank–Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The proposed method can handle sparsely sampled data and unmeasurable nodes. Monte Carlo simulations indicate that the proposed method outperforms state-of-the-art methods with various network topologies. The developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks and communication systems. [ABSTRACT FROM AUTHOR]

While the practical application of adaptive filters has indeed garnered substantial attention, two pressing issues persist that have a profound impact on their performance—system sparsity and the presence of contaminated Gaussian impulsive noise. In this research paper, we propose a novel approach to tackle both of these issues simultaneously by introducing the concept of a proportionate matrix. Specifically, we present a proportionate maximum total complex correntropy algorithm based on the errors-in-variables model. The paper presents a theoretical analysis of the steady-state weight error power under the influence of impulsive noise. Furthermore, it discusses the performance comparison in system identification and highlights the robustness of the proposed algorithm. To validate its effectiveness, a simulation involving stereophonic acoustic echo cancellation is conducted, and the results confirm the clear advantages of the proposed Proportionate Maximum Total Complex Correntropy algorithm. [ABSTRACT FROM AUTHOR]