Response Of System Research Articles

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests.

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes.

Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.

A comprehensive analysis to optimizing national-scale protected area systems under climate change

With the intensification of climate change, incorporating climate information into protected areas planning has become crucial in reducing biodiversity loss. However, the current natural reserve system in China does not take climate information into account. Therefore, we assessed the effectiveness of existing protected areas through climate refuge and connectivity rankings, and Zonation software was used to identify the ecological priority zone in China by combining climate indicators and human footprint. The results show that the current natural protected areas in China have certain limitations in dealing with climate change, and some protected areas may struggle to maintain their value in biodiversity conservation under climate change. Moreover, China still has lots of important areas that can maintain biodiversity under climate change, but most of them are not covered by protected areas. The results provide support for the planning of China's nature protected area system in response to climate change.

Dynamic Characteristics of M-Shape Metal Rubber-Coated Pipeline System: Numerical Modeling and Experimental Analysis

As a high-temperature resistant damping material, reducing vibration by coating with M-shape metal rubber (MMR) in a pipeline system is a promising solution due to its energy dissipation induced by micro dry friction between metallic wires. The main challenge for dynamic calculation and performance evaluation of elastic-porous metal rubber (MR) is derived from the intricate spatial network structure. In this work, the dynamic properties including acceleration admittance and insertion loss of the MMR-coated pipeline system were conducted by numerical simulation and experimental analysis. The constitutive models used to characterize hysteresis phenomena, including Yeoh and Bergström–Boyce models, were identified with different density parameters and adopted for steady-state dynamic numerical analysis. The sine sweep frequency test was conducted to verify the accuracy of the developed numerical model. The results indicate that the maximum error of stress–strain curve between numerical prediction and experimental measurement is 10.7%. In the frequency range of 0–1 500[Formula: see text]Hz, the insertion loss of the MMR-coated pipeline system is positively correlated with the density of MMR, as opposed to the coating distance of pipeline clamps and the influence of excitation force is minimal. Furthermore, the error of dynamic response of the pipeline system in low frequency between the experiment and simulation is 4.7%, indicating that the accuracy of the hysteresis model in predicting the dynamic characteristic of MR materials is effective.

Digital shadows: confronting the rise of technology-facilitated sexual violence and the quest for systemic solutions

ABSTRACT This article explores Technology Facilitated Sexual Violence (TFSV), a rising issue in the digital era. TFSV encompasses online harassment, cyberstalking, and image-based exploitation via digital technologies. Unlike previous studies focusing on youth, this one includes adults. Surveys in Australia and the U.S. show TFSV’s high occurrence in adults. Using a qualitative method, the study involves legal and mental health experts to discuss TFSV’s effects, challenges, and solutions. Findings indicate frequent use of social media for abuse, causing victims significant mental distress like fear and trauma. The study points out the gap between fast-paced technological growth and the slower response of legal systems, resulting in ill-prepared professionals for handling digital abuse. It urges immediate, multi-pronged action against TFSV, including training for legal and law enforcement, education for practitioners and victims, and cross-sector collaboration. The article emphasizes the necessity of a better, comprehensive response to TFSV, advocating for an informed and inclusive approach in addressing digital sexual violence.

Immunomodulatory Effects of Ethanol Extract of Lime Peel on Phagocytosis Activity and Delayed-Type Hypersensitivity Response

The natural human environment is inhabited by various organisms, including those harmful to the body such as bacteria, viruses, fungi, and other pathogens. This condition weakens the immune system's response to pathogens, making the body more susceptible to diseases such as rheumatoid arthritis, cancer, and other inflammations. In some cases, this condition can be fatal, leading to death. This study aims to examine the immunomodulatory effects of Ethanol Extract of Lime Peel on phagocytosis activity and delayed-type hypersensitivity response in experimental animals. The research methods include sample collection and processing, plant identification, extract preparation, and immunomodulatory effect testing. Phytochemical screening results showed the presence of alkaloids, glycosides, flavonoids, tannins, and steroids in the extract of the lime peel. Simplicia and ethanol extract of lime peel characterization yielded results meet the standards of the Indonesian Herbal Pharmacopoeia 2nd edition. Immunomodulatory effect testing was conducted through phagocytosis activity and delayed-type hypersensitivity response assays. The results showed that the extract at doses of 25, 50, and 100 mg/kgBW had immunostimulatory effects in enhancing phagocytosis activity, while doses of 17.5 mg/kgBW, 35 mg/kgBW, and 70 mg/kgBW significantly increased delayed-type hypersensitivity response compared to the negative control. These findings indicate the potential of ethanol extract of Citrus aurantifolia as an immunomodulator that can enhance the body's immune system. This study provides an important contribution to the development of immunomodulatory therapy derived from natural ingredients such as lime peel for managing immunity-related diseases with lower side effects.

Understanding the transport properties of perovskite compounds as a function of temperature, frequency and DC-bias voltage using experimental measurements and appropriate theoretical models.

The present paper provides interesting measurements and methodologies to investigate the key factors controlling the electrical response of perovskite systems. The studied system was successfully prepared using a solid-state route. The chemical analysis and the X-ray diffraction results confirm the formation of the desired perovskite phase. As a result, the DC-resistivity analysis shows that the transport properties are governed by hopping mechanisms above the transition temperature. In this case, thermal agitation allows the charge-carriers to hop across the insulating barrier in the form of grain boundaries. Then, the dominance of the grain boundary contribution is proved. AC-resistivity spectra are investigated in terms of numerous power laws (UDR, SPL and NCL). Accordingly, the decrease in the resistivity values at high frequencies is explained through hopping and tunneling processes. Indeed, it is proved that the electrical transport phenomena are governed by QMT and CBH models. The coexistence of direct (C-C) and indirect (C-A-C) interactions explains the multi-behavior of the AC-resistivity. The scaling representation displays a single muster curve describing the universal dynamic response (UDR). Thus, it reveals the universality of the electrical resistivity of the system. However, the double Schottky barrier model is used to explain the grain boundary effects. A critical frequency "νc" is detected under temperature and DC-bias voltage effects. These observations disclose, in a different way, the separate contributions of the electro-active regions of the sample in the conduction phenomenon.

Targeting small druggable compounds against 3RZE histamine H1 receptor as potential of anti-allergic drug applying molecular modeling approach

BackgroundAllergic disorders, prevalent global health concerns, afflict a substantial portion of the world’s population. These maladies result from an exaggerated immune system response to ordinarily innocuous substances, such as pollen, dust mites, and specific dietary components. Clinical manifestations of this heightened immune response include itching, swelling, and respiratory impairment, often accompanied by releasing mediators like histamine. The pathophysiological mechanisms of allergy disorders are intricate, arising from a complex interplay between genetic and environmental factors. While clinical presentations may vary, all allergy conditions share a common foundation in the dysregulated immune response to allergens.ResultThe current aim of this study was to identify innovative anti-allergic agents capable of inhibiting histamine and effectively mitigating allergic reactions by utilizing the computer-aided drug design approach by discovery studio (DS) 2022 v 23.1.1 package. The overarching aim was identifying potential drug candidates targeting the active site within the histamine H1 receptor complex; therefore, a collection of 4000 small druggable compounds was curated from ZINC, PubChem, and DRUG BANK databases sources. Four compounds appeared as promising candidates after assessing docking scores and binding energies. Notably, Compound ID 34154, recognized as tymazoline, showed the highest affinity for the H1 receptor of 3RZE, suggesting it may be the most promising choice for more research. Further chemoinformatic and ADMET (absorption, distribution, metabolism, excretion, and toxicity) analyses were conducted to assess the drug-like qualities of this chosen molecule. In addition, bioisosteric substitution techniques were employed to enhance tymazoline’s ADMET characteristics.ConclusionTymazoline shows strong binding affinity with 3RZE and verified all the drug-likeness criteria to inhibit the allergic disorders. Furthermore, molecular dynamics (MD) studies corroborated tymazoline’s potential as an anti-allergic agent, demonstrating contact between the ligand and the receptor that is well defined and stable.

Rapid response systems, antibiotic stewardship and medication reconciliation: a scoping review on implementation factors, activities and outcomes

IntroductionMany patient safety practices are only partly established in routine clinical care, despite extensive quality improvement efforts. Implementation science can offer insights into how patient safety practices can be successfully...

Advanced autopilot design with extremum-seeking control for aircraft control

Abstract The aim of this research is to enhance adaptive autopilots for the effective management of aircraft systems, control maintenance, and the rejection of external disturbances. To achieve this objective, we propose the design of an autopilot integrated with the extremum-seeking control (ESC) algorithm. Although autopilots proficiently manage the lateral and longitudinal modes of aircraft control, they lack filtering or adaptive capabilities, thereby exposing the system to significant external threats. To mitigate these risks, the ESC method is employed. This adaptive approach can operate in a disturbance rejection manner by adjusting parameters for unknown inputs and restoring the system to its original controlled response. ESC represents a versatile control method suitable for effective application in simulations or experimental models. Through the incorporation of this method, the pitch attitude hold autopilot, altitude hold autopilot, and yaw autopilot acquire advanced disturbance rejection capabilities with adaptive ESC features. The novelty of the proposed method lies in providing advanced disturbance rejection properties to conventional autopilots, thereby rendering them innovative and superior disturbance rejection controllers. The newly developed autopilots are capable of eliminating severe disturbances from the system response, including ramp, sinusoidal, and step disturbances. The integration of autopilots with ESC offers significant advantages, such as superior disturbance rejection properties for the aircraft unmanned aerial vehicle (UAV) system. The proposed method successfully eliminates severe disturbances, as demonstrated in simulation results, surpassing previous methods in effectiveness. Furthermore, the Autopilot-ESC method enhances aircraft operation even under disturbances, minimizing energy consumption and ensuring stability and control. This novel method reduces operator workload and ensures reliable and efficient autonomous flight capabilities. Additionally, the adaptability of the Autopilot-ESC to changing environmental conditions make it well-suited in aircraft UAVs. This upgraded version of autopilot surpasses other robust controllers, such as Linear Quadratic Gaussian (LQG) regulator and Model Predictive Control (MPC), as it can effectively address ramp, sinusoidal, and step disturbances, which LQG and MPC cannot handle.

Spacetime-localized response in quantum critical spin systems: Insights from holography

According to the AdS/CFT correspondence, certain quantum many-body systems in d-dimensions are equivalent to gravitational theories in (d+1)-dimensional asymptotically anti–de Sitter (AdS) spacetimes. When a massless particle is sent from the AdS boundary to the bulk curved spacetime, it reaches another point of the boundary after a time lag. In the dual quantum system, it should appear as if quasiparticles have been transferred between two separated points. We theoretically demonstrate that this phenomenon, which we call “spacetime-localized response”, is actually observed in the dynamics of the one-dimensional transverse-field Ising model near the quantum critical point. This result suggests that, if we can realize a holographic spin system in a laboratory, the experimental probing of the emergent extra dimension is possible by applying a designed stimulus to a quantum many-body system, which is holographically equivalent to sending a massless particle through the higher-dimensional curved bulk geometry. We also discuss possible experimental realizations using Rydberg atoms in an optical tweezers array. Published by the American Physical Society 2024

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann–Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

The Factors Affecting the Stability of IOP Homeostasis.

Shear-induced nitric oxide (NO) production by Schlemm's canal (SC) endothelial cells provides a fast, IOP-sensitive feedback signal that normally contributes to IOP homeostasis. Our goal was to analyze the response of this homeostatic system under constant flow perfusion (as occurs in vivo) vs. constant pressure perfusion (as typical for laboratory perfusions). A mathematical model of aqueous humor dynamics, including shear-mediated NO signaling, was formulated and analyzed for stability. The model includes Goldmann's equation, accounting for proximal and distal outflow resistance, and describes how elevated IOP causes narrowing of SC lumen that increases the shear stress on SC cells. Elevated shear stress stimulates NO production, which acts to reduce outflow resistance and relax trabecular meshwork cells to decrease trabecular meshwork stiffness, affecting the SC luminal caliber. During constant flow perfusion, the outflow system is typically stable, returning to baseline IOP after a perturbation. In contrast, during constant pressure perfusion, the outflow system can become unstable and exhibit a time-dependent change in outflow resistance that diverges from baseline. The stability of shear mediated IOP homeostasis is predicted to differ critically between constant flow vs. constant pressure perfusion. Because outflow facility is typically measured at a constant pressure in the laboratory, this instability may contribute to the characteristic time-dependent increase in outflow facility, known as washout, observed in many nonhuman species. Studies of IOP homeostasis should consider how the outflow system may respond differently under constant pressure vs. constant flow perfusion.

Functional and compositional responses of stream microphytobenthic communities to multiple stressors increase and release in a mesocosm experiment

Field observations form the basis of the majority of studies on microphytobenthic algal communities in freshwater ecosystems. Controlled mesocosm experiments data are comparatively uncommon. The few experimental mesocosm studies that have been conducted provide valuable insights into how multiple stressors affect the community structures and photosynthesis-related traits of benthic microalgae. The recovery process after the stressors have subsided, however, has received less attention in mesocosm studies. To close this gap, here we present the results of a riparian mesocosm experiment designed to investigate the effects of reduced flow velocity, increased salinity and increased temperature on microphytobenthic communities. We used a full factorial design with a semi-randomised distribution of treatments consisting of two levels of each stressor (2 × 2 × 2 treatments), with eight replicates making a total of 64 circular mesocosms, allowing a nuanced examination of their individual and combined influences. We aimed to elucidate the responses of microalgae communities seeded from stream water to the applied environmental stressors. Our results showed significant effects of reduced flow velocity and increased temperature on microphytobenthic communities. Recovery after stressor treatment led to a convergence in community composition, with priority effects (hypothesized to reflect competition for substrate between resident and newly arriving immigrant taxa) slowing down community shifts and biomass increase. Our study contributes to the growing body of literature on the ecological dynamics of microphytobenthos and emphasises the importance of rigorous experiments to validate hypotheses. These results encourage further investigation into the nuanced interactions between microphytobenthos and their environment and shed light on the complexity of ecological responses in benthic systems.

Increase of secondary metabolites in sweet basil (Ocimum basilicum L.) leaves by exposure to N2O5 with plasma technology

Exposure to N2O5 generated by plasma technology activates immunity in Arabidopsis through tryptophan metabolites. However, little is known about the effects of N2O5 exposure on other plant species. Sweet basil synthesizes many valuable secondary metabolites in its leaves. Therefore, metabolomic analyses were performed at three different exposure levels [9.7 (Ex1), 19.4 (Ex2) and 29.1 (Ex3) μmol] to assess the effects of N2O5 on basil leaves. As a result, cinnamaldehyde and phenolic acids increased with increasing doses. Certain flavonoids, columbianetin, and caryophyllene oxide increased with lower Ex1 exposure, cineole and methyl eugenol increased with moderate Ex2 exposure and l-glutathione GSH also increased with higher Ex3 exposure. Furthermore, gene expression analysis by quantitative RT-PCR showed that certain genes involved in the syntheses of secondary metabolites and jasmonic acid were significantly up-regulated early after N2O5 exposure. These results suggest that N2O5 exposure increases several valuable secondary metabolites in sweet basil leaves via plant defense responses in a controllable system.

Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.

Tunable colors from responsive 2D materials

AbstractOne of the long‐sought‐after goals in responsive material development is to generate and tune colors for advanced and emerging applications such as dynamic displays, light‐emitting diodes, lasers, smart windows, chromic fabrics, high‐security encryption, and visual sensors. Benefiting from the atomically thin nature as well as strong optical interaction, two‐dimensional (2D) materials can serve as color‐generating centers for both chemical pigment colors and physical interference colors in solution, gels, films, and matrix interface systems, to offer new promises for color science and applications. Concerning color tunability, 2D material systems have been demonstrated as one of the ideal responsive materials to achieve the desired goals, including the change of composition, layer thickness, strain, magic angle, and thermally/photically/chemically responsive, magnetically‐responsive, electrically‐responsive, mechanically‐responsive. This makes it an attractive prospect for device applications such as optoelectronics, displays, and nanomedicine. However, to the best of our knowledge, no comprehensive review has been reported specifically on color‐centered aspects of 2D materials. In this review, we highlight experimental approaches and related applications for tuning pigment colors, reflective structural colors, and transmissive interference colors, and we refine the challenges and propose opportunities in future studies for the further development of color science in the 2D material system. Eventually, it is anticipated that this review will serve as a resource and source of inspiration for scientists, as well as open up new avenues for the advancement of color science and related fields in responsive 2D material systems.

Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications

With the advancement of network technology, there has been an increase in the volume of data being transmitted across networks. Due to the bandwidth limitation of communication channels, data often need to be quantized or event-triggered mechanisms are introduced to conserve communication resources. On the other hand, network uncertainty can lead to data loss and destroy data integrity. This paper investigates the identification of finite impulse response (FIR) systems under the framework of stochastic noise and the combined effects of the event-triggered mechanism and uncertain communications. The study provides a reference for the application of remote system identification under transmission-constrained and packet loss scenarios. First, a congruential summation-triggered communication scheme (CSTCS) is introduced to lower the communication rate. Then, parameter estimation algorithms are designed for scenarios with known and unknown packet loss probabilities, respectively, and their strong convergence is proved. Furthermore, an approximate expression for the convergence rate is obtained by data fitting under the condition of uncertain packet loss probability, treating the trade-off between convergence performance and communication resource usage as a constrained optimization problem. Finally, the rationality and correctness of the algorithm are verified by numerical simulations.

Tunable linear feedback control of urban drainage systems using models defined purely from data.

Real-time and model-predictive control promises to make urban drainage systems (UDS) adaptive, coordinated, and dynamically optimal. Though early implementations are promising, existing control algorithms have drawbacks in computational expense, trust, system-level coordination, and labor cost. Linear feedback control has distinct advantages in computational expense, interpretation, and coordination. However, current methods for building linear feedback controllers require calibrated software models. Here we present an automated method for generating tunable linear feedback controllers that require only system response data. The controller design consists of three main steps: (1) estimating the network connectivity using tools for causal inference, (2) identifying a linear, time-invariant (LTI) dynamical system which approximates the network, and (3) designing and tuning a feedback controller based on the LTI urban drainage system approximation. The flooding safety, erosion prevention, and water treatment performance of the method are evaluated across 190 design storms on a separated sewer model. Strong results suggest that the system knowledge required for generating effective, safe, and tunable controllers for UDS is surprisingly basic. This method allows near-turnkey synthesis of controllers solely from sensor data or reduction of process-based models.

Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication

Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.