Adaptive Identification Research Articles

Beyond Peto's Paradox: Expanding the Study of Cancer Resistance Across Species.

Peto's paradox, which highlights the lower-than-expected cancer rates in larger and/or longer-lived species, is a cornerstone of discussions at the intersection of ecology, evolution, and cancer research. It prompts investigations into how species with traits that theoretically increase cancer risk manage to exhibit cancer resistance, with the ultimate goal of uncovering novel therapies for humans. Building on these foundational insights, we propose expanding the research focus to species that, despite possessing traits beyond size and longevity that theoretically increase their cancer risk, exhibit unexpected cancer resistance. Testing Peto's paradox without interference from transient dynamics also requires considering species that are at an equilibrium between cancer risks and defenses, which is increasingly challenging due to anthropogenic activities. Additionally, we argue that transmissible cancers could significantly help in understanding how the metastatic process might be naturally suppressed. This research perspective is timely and aims to support the continued and in-depth identification of anti-cancer adaptations retained throughout evolution in the animal kingdom.

Design and implementation of anti-mapping security access technology based on illegal scanning

Abstract In the current field of information security, illegal network scanning activities are prevalent, and such behaviors are usually aimed at detecting security vulnerabilities in network systems and preparing for future attack activities. This study proposes a secure access system based on anti-mapping technology, which aims to effectively block illegal scanning behaviors while ensuring that the normal access of legitimate users is not affected. The system integrates advanced behavioral analysis algorithms that utilize machine learning techniques for deep learning and pattern recognition of network traffic, and is able to accurately distinguish between normal user activities and malicious scanning attempts. At the core of the system is a set of dynamic adaptive identification mechanisms that update the detection algorithms in real time to adapt to emerging scanning techniques and attack strategies by continuously learning from changes in network traffic. In addition, the system employs role-based access control (RBAC) policies to enhance the protection of sensitive resources. The Secure Access Gateway is deployed at the boundary of the network to monitor and filter all ingress traffic, effectively intercepting unauthorized scanning activities by comprehensively evaluating the source, behavior and frequency of traffic. Experimental results show that the proposed two-layer network structure performs well in detecting common threats such as port scanning, DDoS attacks, and SQL injections, with an accuracy rate of over 95%. Especially for complex and covert APT (advanced persistent threat) attacks, the system can significantly reduce the false alarm rate and effectively improve the detection speed. However, when dealing with some highly customized malware, the system's recognition ability still needs to be improved, which indicates that future research needs to focus more on enhancing the ability to learn and adapt to unknown threats.

Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5%.

Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient’s performance improves as the order, β, increases with β>1, leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

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.

Study of Noise Reduction and Identification of Internal Damage Signals in Wire Ropes

Mining wire rope, a frequently used load-bearing element, suffers various forms of damage over extended periods of operation. Damage occurring within the wire rope, which is not visible to the naked eye and is difficult to detect accurately with current technology, is of particular concern. Consequently, the identification of internal damage assumes paramount importance in ensuring mine safety. This study proposes a wire rope internal damage noise reduction and identification method, first of all, through a three-dimensional magnetic dipole model to achieve the detection and analysis of the internal damage of the wire rope. Simultaneously, a sensor system capable of accurately detecting the internal damage of wire rope is developed and validated through experimentation. A novel approach is proposed to address the noise reduction issue in the design process. This approach utilizes a particle swarm optimization variational modal decomposition method to enhance the signal-to-noise ratio. Additionally, a dual-attention mode, which combines channel attention and spatial attention, is integrated into the CNN-GRU network model. This network model is specifically designed for the detection of internal damage in steel wire ropes. The proposed method successfully achieves quantitative identification of internal damage in steel wire ropes. The experimental findings demonstrate that this approach is capable of efficiently detecting internal damage in wire rope and possesses the capacity to quantitatively identify such damage, enabling adaptive identification of wire rope.

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy

Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.

Multi-receptor skin with highly sensitive tele-perception somatosensory.

The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a ΔV/Δd sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.

Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

A linearly constrained framework for the analysis of the deficient length least-mean square algorithm

Most adaptive system identification analyses assume the length of the adaptive filter to match the length of the unknown system response. This assumption tends to be unrealistic, and its conclusions may not apply to some practical applications. The behavior of deficient length adaptive filters has been studied using different approaches. This work formulates the deficient length adaptive system problem using a Linearly Constrained Minimum Mean Squared Error (LCMMSE) framework. This new formulation leads to a very interesting interpretation that allows the utilization of results from the study of constrained adaptive filters to understand the behavior of the deficient length LMS adaptive filter. The reduced number of coefficients is formulated as a linear optimization constraint that defines a projection onto the feasible space for the adaptive weight vector. We derive analytical models for the mean and mean-square behavior of the adaptive weights. In addition, we derive an analytical model for the variance of the steady-state squared estimation error which provides important design information. Simulation results show excellent matching between theory and actual algorithm behavior.

LSTM-BIGRU based Spectrum Sensing for Cognitive Radio

There is a shortage of wireless spectrum due to developments in the area of wireless communications as well as the number of users that are using resources. Spectrum sensing is a method that solves the issue of shortage. Deep learning surpasses classical methods in spectrum sensing by enabling autonomous feature learning, which enables the adaptive identification of complicated patterns in radio frequency data for cognitive radio in wireless sensor networks. This innovation increases the system's capacity to manage dynamic, real-time circumstances, resulting in increased accuracy over traditional approaches. Spectrum sensing (SS) using LSTM-BIGRU with gaussian noise has been suggested in this article. Long-term dependencies in sequential data are well- preserved by LSTM due to its dedicated memory cells. In addressing and man- aging long-term dependencies in sequential data, BIGRU's integration enhances the efficacy of the model as a whole. To conduct the investigation, RadioML2016.04C.multisnr open-source dataset was utilized. Whereas, by using RadioML2016.10b open-source dataset, QAM64, QPSK and QAM16 performance evaluation has been investigated. The experimental findings demonstrate that the suggested Spectrum Sensing has better accuracy on the dataset particularly at lower SNRs. The improved spectrum sensing (SS) performance of our suggested model is shown by the evaluation of performance indicators, such as the F1 Score, CKC and Matthew's correlation coefficient, highlighting its potency in the field of spectrum sensing applications.

Remaining useful life prediction across operating conditions based on deep subdomain adaptation network considering the weighted multi-source domain

As one of the core contents of transfer learning, domain adaptation (DA) has been widely used to predict of the remaining useful life (RUL) of rolling bearings across operating conditions. However, existing RUL prediction methods often adopt single-source DA and do not consider the sample's drift phenomenon and migration degree in the source domain. At the same time, the distribution of bearing samples in different subdomains (degradation stages) differs, forcing global adaptation to inevitably lead to negative migration. Therefore, a deep subdomain adaptation network considering weighted multi-source domain (DSAN-WM) is proposed to improve the prediction accuracy of RUL across operating conditions. Firstly, a migration metric is developed to measure the migration degree of samples in the source domain, and a high-quality multi-source domain sample set is constructed. Secondly, a fine-grained feature distribution alignment strategy for subdomains based on adaptive degradation stage identification is proposed to reduce the difference in sample distribution in the same subdomain. Thirdly, for each decision boundary obtained by the multi-source domain model, consistent alignment and degradation constraints based on physical knowledge are established to improve the consistency and prediction accuracy of each regressor output. Lastly, the effectiveness of the proposed method is verified using eight migration scenarios of two bearing data sets, and the superiority of the method is proven through a comparison with advanced deep learning and transfer learning approaches.

Bracing for the next wave: A critical incident study of frontline decision-making, adaptation and learning in ambulance care during COVID-19.

To explore frontline decision-making, adaptation, and learning in ambulance care during the evolving COVID-19 pandemic. Descriptive and interpretative qualitative study. Twenty-eight registered nurses from the Swedish ambulance services described 56 critical incidents during the COVID-19 pandemic through free-text questionnaires. The material was analysed using the Critical Incident Technique and Interpretive Description through the lens of potential for resilient performance. The findings were synthesized into four themes: 'Navigating uncharted waters under never-ending pressure', 'Balancing on the brink of an abyss', 'Sacrificing the few to save the many' and 'Bracing for the next wave'. Frontline decision-making during a pandemic contribute to ethical dilemmas while necessitating difficult prioritizations to adapt and respond to limited resources. Learning was manifested through effective information sharing and the identification of successful adaptations as compared to maladaptations. During pandemics or under other extreme conditions, decisions must be made promptly, even amidst emerging chaos, potentially necessitating the use of untested methods and ad-hoc solutions due to initial lack of knowledge and guidelines. Within ambulance care, dynamic leadership becomes imperative, combining autonomous frontline decision-making with support from management. Strengthening ethical competence and fostering ethical discourse may enhance confidence in decision-making, particularly under ethically challenging circumstances. Performance under extreme conditions can elevate the risk of suboptimal decision-making and adverse outcomes, with older adults being especially vulnerable. Thus, requiring targeted decision support and interventions. Enhancing patient safety in ambulance care during such conditions demands active participation and governance from management, along with decision support and guidelines. Vertical communication and collaboration between management and frontline professionals are essential to ensure that critical information, guidelines, and resources are effectively disseminated and implemented. Further research is needed into management and leadership in ambulance care, alongside the ethical challenges in frontline decision-making under extreme conditions. Findings are reported per consolidated criteria for reporting qualitative research (COREQ). No Patient or Public Contribution.

Adaptive two-dimensional subspace identification for monitoring batch processes with limited batch data

Data-driven based batch process monitoring is of critical importance in ensuring stable operating processes and consistent product quality. For long-duration batch processes, it is unrealistic to involve expensive data to train a statistical model for monitoring. To model the inherently batch-wise and variable-wise dynamics, nonlinearity, and time-varying characteristics, this paper proposes a local learning-based two-dimensional subspace identification (LL-2D-SID) scheme based on the similarity between the ongoing batch and the previous batches. The similarity is estimated by the extended extrapolative time-warping. Unlike the conventional statistical models using rich batch data, LL-2D-SID through online optimizing mechanism using limited batch data still has good prediction performance. The application of the sintering process in the polytetrafluoroethylene production has demonstrated that the LL-2D-SID based process monitoring scheme can not only accurately track temperature changes but also timely give fault alarms with a lower error alarm rate than the other SID-based process monitoring schemes.

Enhancing Climate Change Resilience: Assessing Adaptation Needs, and Significance of Monitoring and Evaluation Systems

Losses and damages from climate change related extreme weather events and disasters require the development of adaptation measures to increase resilience to the adverse impacts of climate change. In line with the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement's Global Goal on Adaptation, Parties have developed strategies that include adaptation actions, but there are significant gaps in identification of adaptation needs and the monitoring and evaluation (M&E) of actions to address them. Adaptation M&E systems are critical for measuring the success of adaptation actions, providing feedback from the implementation process, and identifying new actions. There is no global methodology for adaptation M&E. At international climate change negotiations in 2023, it was agreed that countries should operationalize their national adaptation M&E systems by 2030. The aim of this study is to evaluate adaptation M&E methodologies developed by countries at different development levels and to present future policy recommendations for the adaptation M&E system planned to be established in Turkey. The study reveals the necessity of up-to-date socio-economic data as well as climate data in determining adaptation needs and adaptation M&E systems. In Turkey, which is vulnerable to the impacts of climate change, for the success of adaptation actions, besides the rapid operationalization of adaptation M&E system, establishment of the system with an approach that includes all stakeholders in the process and considers adaptation actions as integrated with disaster risk management actions is an important requirement in the context of Turkey's international commitments, national security, and development.

Determinants of climate change awareness and the incidence across farming systems and agroecological zones in Sierra Leone

Farmers' awareness and the incidence of climate change are crucial inputs to effectively scale up interventions to mitigate the effects of climate change. This is because incidence leads to awareness due to observation of occurrences. The purpose of this paper is to examine the determinants of climate change awareness and incidence across farming systems and agroecological zones in Sierra Leone. An ex-post-facto research design was applied, while a multistage sampling procedure was used to select 865 smallholder farmers across agroecological zones and farming systems. Data were collected with a structured questionnaire subjected to face validity and split-half reliability tests. This data analyzed frequency counts, percentages, multiple regression, and principal component analysis. The results show that farmers in the coastal plain, savannah woodland, and transitional rainforest had greater awareness and incidence of climate change across the crop, livestock, and fishery farming systems. The significant determinants of awareness and incidence of climate change among farmers are the adoption of crop smart practices (t = 4.192; p < 0.01); information needs on water smart practices (t = −5.581; p < 0.01); adoption of nutrient smart practices (t = 10.592; p < 0.01); adoption of energy/carbon smart practices (t = 3.206; p < 0.01); adoption of livestock smart practices (t = 3.608; p < 0.01); information needs on weather smart practices (t = 3.505; p < 0.01); incidence of climate change (t = 16.282; p < 0.01); and constraints on nutrient smart practices (t = −2.669; p < 0.01). The Principal Component analysis identified four factors, namely Factor 1 (Impact), Factor 2 (Occurrence), Factor 3 (Evidence), and Factor 4 (Threat), and accounted for 14.96%, 8.27%, 6.41%, 3.50% of the variance, respectively, with a cumulative variance of 33.14%. The study concludes that farmers are aware of the incidence of climate change and are adopting different techniques in response to the different climate changes observed. This study also recommends the identification of specific climate change adaptations and the scaling of interventions for adaptation and mitigation.

Adaptive identification of critical nodes for fault‐on voltage support in islanded microgrids

Adaptive identification of critical nodes for fault‐on voltage support in islanded microgrids

Adaptation of complex interventions for people with long-term conditions: a scoping review.

Adaptation seeks to transfer and implement healthcare interventions developed and evaluated in one context to another. The aim of this scoping review was to understand current approaches to the adaptation of complex interventions for people with long-term conditions (LTCs) and to identify issues for studies performed in low- and middle-income countries (LMICs). Bibliographic databases were searched from 2000 to October 2022. This review involved five stages: (i) definition of the research question(s); (ii) identifying relevant studies; (iii) study selection; (iv) data charting; and (v) data synthesis. Extraction included an assessment of the: rationale for adaptation; stages and levels of adaptation; use of theoretical frameworks, and quality of reporting using a checklist based on the 2021 ADAPT guidance. Twenty-five studies were included from across 21 LTCs and a range of complex interventions. The majority (16 studies) focused on macro (national or international) level interventions. The rationale for adaptation included intervention transfer across geographical settings [high-income country (HIC) to LMIC: six studies, one HIC to another: eight studies, one LMIC to another: two studies], or transfer across socio-economic/racial groups (five studies), or transfer between different health settings within a single country (one study). Overall, studies were judged to be of moderate reporting quality (median score 23, maximum 46), and typically focused on early stages of adaptation (identification and development) with limited outcome evaluation or implementation assessment of the adapted version of the intervention. Improved reporting of the adaptation for complex interventions targeted at LTCs is needed. Development of future adaptation methods guidance needs to consider the needs and priorities of the LMIC context.

Adaptive identification of linear infinite-dimensional systems

We propose an adaptive algorithm for identifying the unknown parameter (possibly vector-valued) in a linear stable single-input single-output infinite-dimensional system. We assume that the transfer function of the infinite-dimensional system can be expressed as a ratio of two infinite series in s (the Laplace variable). We also assume that certain identifiability conditions, which include a persistency of excitation condition, hold. For a fixed integer n, we propose an adaptive update law (a differential equation in time) driven by real-time input-output data for estimating the first n + 1 coefficients in the numerator and the denominator of the transfer function. We show that the estimates for the transfer function coefficients generated by the update law are close to the true values at large times provided n is sufficiently large (the estimates converge to the true values as time and n tend to infinity). The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter. We also provide a numerical scheme for verifying the identifiability conditions and for choosing n sufficiently large so that the value of the reconstructed parameter is close to the true value. The class of systems to which our approach is applicable includes many partial differential equations with constant/spatially-varying coefficients and distributed/boundary input and output. We illustrate the efficacy of our approach using three examples: a delay system with four unknown scalars, a 1D heat equation with two unknown scalars and a 1D wave equation with an unknown linearly-varying (in space) coefficient.

Convex optimization topology identification of distribution network under distributed conditions

Abstract The distributed phenomenon will occupy a very important proportion in the future distribution network. In order to ensure the stable operation of the power system, it is very important to study the topological adaptability identification of new distribution networks. In this paper, an adaptive topology identification regression matrix for a low-voltage distribution network is proposed, which is combined with a convex optimization algorithm to build a topology identification model for a low-voltage distribution network. This method can identify the topology of the distribution network accurately, which is helpful to improve the operation efficiency and safety of the power system.