Efficacy Of Method Research Articles

Dynamic estimation of an object's center-of-mass direction: a novel control method for robotic interaction in uncertain environments

To operate in uncertain environments, robots must dynamically interact with and recognize objects. This paper proposes a novel control method to dynamically estimate the center-of-mass of an uncertain object. In this method, a robot finger moves an object, and the moving direction of the finger is dynamically adjusted to estimate the direction of the object's center-of-mass (friction center). The primary advantage of this method is its capacity to rapidly estimate the center-of-mass direction utilizing a single contact. Once the direction of the center-of-mass is determined, this information can be used, for example, for the robot hand to grasp the object securely by surrounding its center-of-mass, ensuring stable handling without unexpected movements. Moreover, determining the center-of-mass can aid in automatically producing training data for machine learning applications. Both simulation and experimental results validate the proposed control method's efficacy, demonstrating its ability to quickly converge to the desired state. This control problem poses a significant challenge as the system has fewer actuators than degrees of freedom, indicating that this controlled system is underactuated. Nonetheless, the proposed control method operated successfully.

Adaptive Arithmetic Coding-Based Encoding Method Toward High-Density DNA Storage.

With the rapid advancement of big data and artificial intelligence technologies, the limitations inherent in traditional storage media for accommodating vast amounts of data have become increasingly evident. DNA storage is an innovative approach harnessing DNA and other biomolecules as storage mediums, endowed with superior characteristics including expansive capacity, remarkable density, minimal energy requirements, and unparalleled longevity. Central to the efficient DNA storage is the process of DNA coding, whereby digital information is converted into sequences of DNA bases. A novel encoding method based on adaptive arithmetic coding (AAC) has been introduced, delineating the encoding process into three distinct phases: compression, error correction, and mapping. Prediction by Partial Matching (PPM)-based AAC in the compression phase serves to compress data and enhance storage density. Subsequently, the error correction phase relies on octal Hamming code to rectify errors and safeguard data integrity. The mapping phase employs a "3-2 code" mapping relationship to ensure adherence to biochemical constraints. The proposed method was verified by encoding different formats of files such as text, pictures, and audio. The results indicated that the average coding density of bases can be up to 3.25 per nucleotide, the GC content (which includes guanine [G] and cytosine [C]) can be stabilized at 50% and the homopolymer length is restricted to no more than 2. Simulation experimental results corroborate the method's efficacy in preserving data integrity during both reading and writing operations, augmenting storage density, and exhibiting robust error correction capabilities.

Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Real-time measurement parameters are crucial for diagnosing faults in aero-engine gas path performance, ensuring engine reliability, and mitigating potential economic losses. Traditional aero-engines performance diagnosis was mainly based on the measurements of steady-state condition and lacked the utilization of data under transient conditions. Gas path diagnosis of aero-engines under transient conditions is crucial for early fault detection and safety of flight within the envelope. The challenge lies in the inconsistent distribution of performance deviations caused by variable operating conditions, especially with complex fault types, which can undermine diagnostic credibility. To improve reliability of gas path diagnosis under transient conditions, an offline reinforcement learning fault diagnosis framework based on a transient aero-engine performance model is proposed. To address the issue of variable operating conditions during transient states, a domain adaptive approach is utilized to reconstruct the measurement baseline and facilitate the transfer of different performance deviation distributions. Additionally, by adding spool acceleration as a measurement parameter, the multi-component fault coupling is solved. Finally, validation with actual operating data simulates fault cases, demonstrating the proposed method's efficacy in quantitatively detecting gradual, sudden, and multiple component faults under transient conditions with high accuracy and efficiency. The method proposed in this study achieves a computational speed improvement by 64% compared to the conventional method, achieving a time of 0.13 seconds, with an average error of less than 0.00389%. Additionally, it demonstrates strong robustness in the presence of noise, with an average error of less than 0.03125%. This proposed method improves real-time fault detection under transient conditions for its higher accuracy and efficiency, and therefore significantly enhance gas path health monitoring and diagnosis capability.

Assessing the Effectiveness of MoSCoW Prioritization in Software Development: A Holistic Analysis across Methodologies

Effective software Requirement Prioritization plays a pivotal role in the success of the Software Development process, ultimately contributing to the successful delivery of high-quality products. Among the various methods for Requirement Prioritization, the MoSCoW method has gained widespread adoption due to its ease of use. However, its overall effectiveness remains a subject of inquiry. This paper presents a rigorous assessment of the MoSCoW Requirement Prioritization technique, drawing insights from software developers who engage in the Prioritization process. Our evaluation encompasses a distinct perspective: that of the developers tasked with Prioritization. The feedback solicited from developers encapsulates a diverse set of criteria, shedding light on the method's efficacy. Additionally, we perform sentiment analysis on the user experience of the Prioritization task to corroborate the method's accuracy and efficiency. Our study unfolds through a practical exercise involving the Prioritization of a predefined set of requirements using MoSCoW principles. A mixed method approach is employed for the purpose of assessing the effectiveness of MoSCoW. The findings of our quantitative research underscore the method's limitations, indicating that it may not be as effective and precise as previously believed. Furthermore, through qualitative analysis, we are able to highlight the complexities and challenges associated with MoSCoW-based Prioritization. The insights gained from this analysis prompt contemplation regarding the potential introduction of an evolved Requirement Prioritization method, while leveraging MoSCoW as a foundational framework. This research aims to inform the ongoing evolution of Requirement Prioritization methodologies, ultimately enhancing the efficiency and accuracy of Software Development processes.

Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing.

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.

Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition.

The concept of controllability within complex networks is pivotal in determining the minimal set of driver vertices required for the exertion of external signals, thereby enabling control over the entire network's vertices. Target controllability further refines this concept by focusing on a subset of vertices within the network as the specific targets for control, both of which are known to be NP-hard problems. Crucially, the effectiveness of the driver set in achieving control of the network is contingent upon satisfying a specific rank condition, as introduced by Kalman. On the other hand, structural controllability provides a complementary approach to understanding network control, emphasizing the identification of driver vertices based on the network's structural properties. However, in structural controllability approaches, the Kalman condition may not always be satisfied. In this study, we address the challenge of target controllability by proposing a feed-forward greedy algorithm designed to efficiently handle large networks while meeting the Kalman controllability rank condition. We further enhance our method's efficacy by integrating it with Barabasi et al.'s structural controllability approach. This integration allows for a more comprehensive control strategy, leveraging both the dynamical requirements specified by Kalman's rank condition and the structural properties of the network. Empirical evaluation across various network topologies demonstrates the superior performance of our algorithms compared to existing methods, consistently requiring fewer driver vertices for effective control. Additionally, our method's application to protein-protein interaction networks associated with breast cancer reveals potential drug repurposing candidates, underscoring its biomedical relevance. This study highlights the importance of addressing both structural and dynamical aspects of network controllability for advancing control strategies in complex systems. The source code is available for free at:Https://github.com/fatemeKhezry/targetControllability.

Utilizing a fully digital approach for oral squamous cell carcinoma treatment and zygomatic implant-based rehabilitation for maxillary defects.

This clinical report details the functional and aesthetic rehabilitation of a patient with a severe maxillary defect secondary to subtotal maxillectomy for oral squamous cell carcinoma (OSCCM) using a maxillary prosthesis anchored by four zygomatic implants. The procedure involved meticulous subtotal maxillectomy and defect repair with zygomatic implant support, incorporating advanced digital surgical methods, including 3D reconstruction, computer-guided surgery, and photogrammetry (Icam4D). A 3D finite element analysis (FEA) was conducted to assess the method's efficacy in analyzing stress distribution around the zygomatic implants. The patient expressed high satisfaction with the prosthesis's functionality, aesthetics, speech, and swallowing capabilities, underscoring the value of zygomatic implant-supported maxillofacial prosthetics. This synergy of advanced planning, surgical precision, and biomechanical analysis marks a significant advancement in maxillofacial prosthetics.

Novel nanoliter spray enhanced microwave plasma ionization mass spectrometry for the simultaneous detection of heavy metals and organic plasticizers in soil: A case study in a lead-acid battery industrial park

Soil pollution is predominantly attributed to the presence of heavy metal elements and organic compounds; However, current detection methodologies are restricted to the identification of only one of these two sources at a time. A novel analytical approach, known as nanoliter spray enhanced microwave plasma ionization mass spectrometry (Nano-Spray-EMPI-MS), has been developed to facilitate the simultaneous detection of both heavy metals and organic pollutants in soil samples. This technique is characterized by its requirement for minimal sample volumes, thereby allowing for efficient and rapid analysis. The research concentrated on the simultaneous analysis of five heavy metals (Pb, Zn, Cu, Cr, and Ni) and three major phthalates (PAEs), specifically DEHP, DBP, and DMP. The detection and quantification limits for the heavy metals were established to be between 0.16-0.57 and 0.53–1.88 μg L−1, respectively, while the limits for the PAEs ranged from 0.02 to 0.05 and 0.07–0.16 μg L−1. Validation of the method's efficacy in soil detection demonstrated recovery rates of 90.9 %–105.7 % for heavy metals and 89.4 %–97.2 % for PAEs. The application of this method analyzing soil samples collected from an area adjacent to a lead-acid battery industrial park in China revealed varying levels of contamination by both heavy metals and PAEs. Notably, Lead contamination was found to be the most pronounced, with a peak concentration of 862.5 mg kg−1 and a correspondingly high pollution index. These findings are significant for evaluating local ecological risks, pinpointing sources of pollution, and formulating effective pollution management strategies in the region.

Dynamic output feedback robust MPC hybrid fault-tolerant control for discrete uncertain systems: a switching control strategy

To guarantee the control performance of the discrete system under unmeasurable state and partial actuator failure, a robust dynamic output feedback predictive fault-tolerant control method derived from switching strategy is developed. A new iterative error switching model is established, which both ensures tracking performance and lays the foundation for the development of the next switching controller. Based on this model, output feedback switching fault-tolerant controller is developed. Compared with conventional fault-tolerant control methods, the designed controller can be used under the case of unmeasurable states, and it is more fault tolerant and less conservative. According to the Lyapunov stability theory, the robust stability analysis is carried out under normal and fault conditions, respectively. Then, depending on the findings of the stability analysis, sufficient conditions to guarantee the stability of the closed-loop normal and faulty system are derived. Finally, the suggested method's efficacy is confirmed using the injection moulding process as an example.

Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection.

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models-long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)-were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method's efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management.

Acoustic leak localization for water distribution network through time-delay-based deep learning approach

Water leakage within water distribution networks (WDNs) presents significant challenges, encompassing infrastructure damage, economic losses, and public health risks. Traditional methods for leak localization based on acoustic signals encounter inherent limitations due to environmental noise and signal distortions. In response to this crucial issue, this study introduces an innovative approach that utilizes deep learning-based techniques to estimate time delay for leak localization. The research findings reveal that while the Res1D-CNN model demonstrates inferior performance compared to the GCC-SCOT and BCC under high signal-to-noise ratio (SNR) conditions, it exhibits robust capabilities and higher accuracy in low SNR scenarios. The proposed method's efficacy was empirically validated through field measurements. This advancement in acoustic leak localization holds the potential to significantly improve fault diagnosis and maintenance systems, thereby enabling efficient management of WDNs.

Efficient 3D robotic mapping and navigation method in complex construction environments

AbstractRecent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

Indirect measurement of bridge surface roughness using vibration responses of a two-axle moving vehicle based on physics-constrained generative adversarial network

This study addresses the challenge of indirectly measuring bridge surface roughness through the vibration responses of a moving vehicle, which is crucial for pavement maintenance and bridge safety assessment. A physics-constrained generative adversarial network (PC-GAN) was proposed for the probabilistic estimation of surface roughness. The method consists of two steps: initially, a GAN informed by physics-based knowledge extracts combined information of bridge vibration deflection and surface roughness from vehicle accelerations. Subsequently, a feed-forward network isolates the bridge surface roughness from the combined data. Numerical examples validate the PC-GAN method, demonstrating sustained high accuracy under challenging conditions, including ISO 8608 level C road roughness, vehicle speeds up to 8 m s-1, 10 % deviation in vehicle parameters, 10 % environmental noise, and 10 % vehicle damping ratio. Laboratory tests further confirmed the method's efficacy, with the successful detection of artificial barriers on the bridge surface and a mean relative error of 3.33 % in height estimation. The PC-GAN method is demonstrated to be a robust tool for estimating bridge surface roughness under various numerical and laboratory conditions. These findings provide valuable insights for the rapid inspection of bridge pavement conditions using vibration responses from moving test vehicles.

Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals

Pediatric Sleep Apnea–Hypopnea (SAH) presents a significant health challenge, particularly in diagnostic contexts, where conventional Polysomnography (PSG) testing, although effective, can be distressing for children. Addressing this, our research proposes a less invasive method to assess pediatric SAH severity by analyzing blood oxygen saturation (SpO2) signals. We adopted two advanced deep learning architectures, namely ResNet-based and attention-augmented hybrid CNN-BiGRU models, to process SpO2 signals in a one-dimensional (1D) format for Apnea–Hypopnea Index (AHI) estimation in pediatric subjects. Employing the CHAT dataset, which includes 844 SpO2 signals, the data was partitioned into training (60%), testing (30%), and validation (10%) sets. A predefined validation subset was randomly selected to ensure the models' robustness via a threefold cross-validation approach. Comparative analysis revealed that while the ResNet model attained an average accuracy of 72.9% across four SAH severity categories with a kappa score of 0.57, the CNN-BiGRU-Attention model demonstrated superior performance, achieving an average accuracy of 75.95% and a kappa score of 0.63. This distinction underscores our method's efficacy in both estimating AHI and categorizing SAH severity levels with notable precision. Further, to evaluate diagnostic capabilities, the models were benchmarked against common AHI thresholds (1, 5, and 10 events/hour) in each test fold, affirming their effectiveness in identifying pediatric SAH. This study marks a significant advance in the field, offering a non-invasive, child-friendly alternative for pediatric SAH diagnosis. Although challenges persist in accurately estimating AHI, particularly in severe cases, our findings represent a critical stride towards improving diagnostic processes in pediatric SAH.

Application of a novel local and automatic PCA algorithm for diffraction pattern denoising in TEM-ASTAR analysis in microelectronics

This paper introduces a novel denoising method for TEM-ASTAR™ Diffraction Pattern (DP) datasets, termed LAT–PCA (Local Automatic Thresholding – Principal Component Analysis). This approach enhances the established PCA algorithm by partitioning the 4D dataset (a 2D map of 2D DPs) into localized windows. Within these windows, PCA identifies a basis where the physical signal predominantly resides in the higher-order principal components. By thresholding lower-order components, the method effectively reduces noise while retaining the essential features of the DPs. The locality of the approach, focusing on small windows, enhances computational efficiency and aligns with the localized nature of the crystallographic grain signals in ASTAR. The automatic aspect of the method employs a theoretical pure noise distribution, i.e. a Marchenko-Pastur Distribution, to set a threshold, beyond which the components are mostly noise.The LAT–PCA method offers significant reductions in acquisition and post-processing times. With denoised data, selecting the correct parameters for accurate phase maps and grain orientations becomes more straightforward, facilitating robust quantitative grain analysis. Experiments performed on a silicon-germanium-carbon sample validate the method's efficacy. The sample was analyzed with varying acquisition times to produce a high signal-to-noise ratio reference dataset and a lower ratio test dataset. The LAT–PCA algorithm's denoising results on the test dataset were benchmarked against the reference, demonstrating considerable improvements and reliability.In summary, LAT–PCA is an effective, automatic solution for denoising TEM DP datasets. Its adaptability to different noise levels and local processing capability makes it a valuable tool for enhancing dataset quality and reducing the time required for data acquisition and analysis. This method can minimize acquisition time, conserve microscope usage, and reduce sample drift and deterioration, leading to more accurate characterizations with fewer deformation artifacts.

Method for Extracting Inland Unmanned Vessel Routes Based on AIS Data

This study introduces a novel approach to enhance inland navigation safety and optimize unmanned vessel routes using AIS data. The trajectory partition algorithm categorizes Yangtze River trajectory data to formulate round-trip routes. A turning point identification algorithm contributes to recognizing crucial turning points, followed by clustering using the HDBSCAN algorithm. Cluster centroids extracted from these clusters serve as essential waypoints for route planning. The Akima interpolation polynomial is applied judiciously to interpolate waypoints, culminating in meticulous route generation. Validation employs a dataset of 5,480,049 dynamic trajectory points from the Yangtze River, demonstrating the method's efficacy. Results indicate mean squared errors of 0.77% and 6.21%, symmetrical mean absolute percentage errors of 5.3% and 7.3%, and correlation coefficients of 99.62% and 97.14% with actual routes, respectively. This method adeptly extracts inland vessel round-trip routes, aligning seamlessly with stringent criteria for accuracy, effectiveness, and applicability.

Improvements on speed, stability and field of view in adaptive optics OCT for anterior retinal imaging using a pyramid wavefront sensor.

We present improvements on the adaptive optics (AO) correction method using a pyramid wavefront sensor (P-WFS) and introduce a novel approach for closed-loop focus shifting in retinal imaging. The method's efficacy is validated through in vivo adaptive optics optical coherence tomography (AO-OCT) imaging in both, healthy individuals and patients with diabetic retinopathy. In both study groups, a stable focusing on the anterior retinal layers is achieved. We further report on an improvement in AO loop speed that can be used to expand the imaging area of AO-OCT in the slow scanning direction, largely independent of the eye's isoplanatic patch. Our representative AO-OCT data reveal microstructural details of the neurosensory retina such as vessel walls and microglia cells that are visualized in single volume data and over an extended field of view. The excellent performance of the P-WFS based AO-OCT imaging in patients suggests good clinical applicability of this technology.

Evaluating resilience and enhancing strategies for old urban communities amidst epidemic challenges

Epidemic outbreaks pose significant risks to sustainable urban development, underscoring the critical need to bolster the resilience of old communities, key to urban epidemic prevention. This study introduces a resilience evaluation system tailored for these communities, employs a cloud model-based fuzzy comprehensive evaluation (FCE) method, and selects Fuzhou City for a case study. It evaluates the resilience of three communities, Qixingjing Xincun (A), the Provincial Forestry Department's dormitory (B), and Wufeng Lanting community (C) across five dimensions. By employing system dynamics (SD) model, the study examines the interplay and impact mechanisms of resilience indicators, predicts trends and future developments, and proposes strategies to enhance resilience. Results indicate that: (1) community B exhibits the highest resilience, followed by A and C, validating the FCE method's efficacy; (2) resilience across all dimensions is on the rise, with no signs of plateauing observed; (3) enhancements in resilience are notably impactful in facility (A3), spatial (A1), environmental (A2) and resident resilience (A5) are extremely close to each other in terms of their impacts, while governance resilience (A4) exhibits minimal changes. Offering insightful contributions to the resilience study of old urban communities, this study serves as a guide for future practice in improving public safety and promoting urban development, and insights into universal urban challenges and resilience strategies.

Overcoming strong retention barriers of A- and V-series nerve agents on silica sorbents

The present study explores the potential of silica solid phase extraction for gas chromatography mass spectrometric analyses of A- and V-series nerve agents. Owing to the presence of basic amidine and amine moieties, these analyte undergo strong ionic interactions with inherently acidic silica surfaces, producing poor recoveries. Subtle optimizations in the elution composition empowered the analytes to overcome the retention barriers from sorbent surfaces. Acetone containing 10 % (v/v) NH4OH effectively minimized strong analyte-sorbent interactions allowing good to excellent recoveries. Recoveries for A-series agents ranged from 88 to 96 %. VX, which is reported to be poorly recoverable from such sorbent matrices offered best data so far, reaching up to 74 % under optimized conditions. The method detection limits for the selected analytes in mass spectrometric analysis ranged from 47 to 171 ng/ml. Strong affinities of analytes towards silica sorbent were further exploited to expand the scope of analysis and establish the method's efficacy for a wide range of organic matrices. The applicability of the method to the real world applications was also validated in blind spiking exercises in diverse organic liquid samples received in 48th, 50th and 52nd proficiency tests conducted by the Organization for the Prohibition of Chemical Weapons (OPCW).

A VAMS‐based LC–MS/MS method for precise cenobamate quantification in epilepsy (patients)

AbstractObjectiveCenobamate (CNB), a recently approved antiseizure medication by the European Medicines Agency (EMA), serves as an adjunctive therapy for focal‐onset seizures in adult patients unresponsive to at least two other treatments. Administered in polytherapy, CNB can potentially interact with co‐administered drugs in epilepsy patients, necessitating dose adjustments and the need for effective therapeutic drug monitoring (TDM).MethodsIn this study, we introduce a novel LC–MS/MS method for precise CNB quantification using Volumetric Absorptive Microsampling (VAMS), following validation according to ICH guidelines M10. VAMS samples are efficiently extracted with 200 μL of methanol, with chromatographic separation achieved using an Acquity UPLC HSS PFP column. The method's efficacy was confirmed through its application to real samples from adult CNB‐treated patients.ResultsOur results demonstrate that the method exhibits linearity within the range of 0.05–30 mg/L, with intra‐ and inter‐run precision ranging from 1% to 8% and accuracy from 1% to 10% based on 30 μL of sample. Furthermore, CNB stability in VAMS is confirmed for up to 15 days at 25°C and −20°C. Importantly, no significant difference was observed between CNB concentrations in VAMS samples and those in plasma obtained from venous blood.SignificanceThis VAMS‐based LC–MS/MS method presents a robust alternative for TDM in CNB‐treated patients. Future investigations should explore CNB concentrations in capillary blood and assess their correlation with plasma levels to further enhance its clinical utility.Plain Language SummaryCenobamate is an antiepileptic drug and used for treatment of focal‐onset seizures in adult patients (≥18 age). TDM can prevent drug interactions and minimize drug toxicity. The aim of this work is to evaluate volumetric absorptive microsampling (VAMS) from capillary blood as an alternative strategy for TDM in patients treated with the newly antiepileptic drug. Our method is suitable for TDM, and this study suggests that VAMS allows monitoring of cenobamate concentration and can offer valuable support for personalized therapy in refractory epilepsy.