Control Set Research Articles

Artificial CT images can enhance variation of case images in diagnostic radiology skills training

ObjectivesWe sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.MethodsArtificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated.ResultsPrimary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones.ConclusionArtificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases.Critical relevance statementArtificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images.Key points• Artificial images, similar to original ones, can be created using generative networks.• Pathological features of artificial images can be adjusted through guiding the network.• Artificial images proved viable to augment the depth and broadening of diagnostic training.Graphical

Finite Control Set Model Predictive Control (FCS-MPC) for Enhancing the Performance of a Single-Phase Inverter in a Renewable Energy System (RES)

A single-phase five-level T-type topology has been investigated in this article. This topology has emerged as a viable option for renewable energy systems (RES) due to its inherent benefits. The finite control set model predictive control (FCS-MPC) strategy has been implemented to this topology in order to improve the performance and overall reliability of the system. This control strategy empowers the inverter to predict future behavior based on a discrete set of control signals, enabling precise modulation and high-speed response to system dynamics. In the realm of RES, integration of FCS-MPC with multilevel inverters (MLIs) holds great potential to enhance energy conversion efficiency, grid integration, and overall system reliability. The article is structured to present an overview of the evolving landscape of power electronic systems, and the advantages of FCS-MPC. This paper provides a comprehensive analysis of the FCS-MPC control strategy applied to the single-phase five-level T-type topology. The study covers various aspects including the theoretical framework, hardware development, and experimental evaluation. It is obvious from the analysis that this inverter topology is reliable. Several redundant states make it fault-tolerant which helps in maintaining the output voltage at the same level even in the fault conditions. Additionally, the results show that the output load voltage is maintained at the same level irrespective of load change. Also, output load voltage has maintained the high-quality sinusoidal characteristics as the total harmonic distortion (THD) is very low. With all these features, this system is suitable within the framework of RES.

Design and FPGA-in-loop based validation of predictive hierarchical control for islanded AC microgrid

This paper proposes a decentralized control approach for the flexible operation of an autonomous AC microgrid (MG). AC MG typically consists of two or more voltage source inverters (VSI), capable of simultaneously regulating the voltage at the point of common coupling (PCC) and meeting the local power demand. The classical linear control techniques attain these functions. However, they have many limitations, such as slow transient response, high sensitivity to the parameter variations, and inability to handle the system nonlinearities. This paper aims to address these issues by presenting an improved finite control set model predictive control (FCS-MPC) approach for inverter-based distributed generation (DG). The proposed scheme tracks the voltage trajectory using cost function (CF) over two-step prediction horizons. The proposed control method is employed for the AC MG having two parallel DGs. Droop control is responsible for the power-sharing between the parallel DGs regardless of impedance mismatch at the distribution level of AC MG. The decentralized secondary control is developed to eliminate the deviation in the voltage and frequency caused due to overlooking the primary control. The proposed control scheme has been validated through extensive simulations and real-time controller hardware in the loop tests using an FPGA ZYBO Z7 board. Moreover, the proposed methodology depicts the enhanced transient response, less computational burden than the classic MPC, and shows robustness to parametric uncertainties in terms of THD than hierarchical linear control. The simulations and experimental results visually represent research outcomes, exhibiting the THD of 0.98 % for different dynamic loads, which is within the limit of IEEE and IEC standards. Furthermore, the proposed controller’s mathematical stability is further supported by analysis based on the Lyapunov stability theory.

Model Predictive Control Design and Hardware in the Loop Validation for an Electric Vehicle Powertrain Based on Induction Motors

Electric vehicles (EV) have gained importance in recent years due to environmental pollution and the future scarcity of fossil resources. They have been the subject of study for many years, where much work has focused on batteries and the electric motor (EM). There are several types of motors in the market but the most widely used are induction motors, especially squirrel cage motors. Induction motors have also been extensively studied and, nowadays, there are several control methods used—for example, those based on vector control, such as field-oriented control (FOC) and direct torque control (DTC). Further, at a higher level, such as the speed loop, several types of controllers, such as proportional integral (PI) and model predictive control (MPC), have been tested. This paper shows a comparison between a Continuous Control Set MPC (CCS-MPC) and a conventional PI controller within the FOC method, both in simulation and hardware in the loop (HIL) tests, to control the speed of an induction motor for an EV powered by lithium-ion batteries. The comparison is composed of experiments based on the speed and quality of response and the controllers’ stability. The results are shown graphically and numerically analyzed using performance metrics such as the integral of the absolute error (IAE), where the MPC shows a 50% improvement over the PI in the speed tracking performance. The efficiency of the MPC in battery consumption is also demonstrated, with 5.07 min more driving time.

Conditional-Extreme-Point Based Fault-Tolerant Predictive Control of Five-Phase PMSM With Low Switching Frequency

The virtual voltage vector (VVV) based fault-tolerant control has been widely concerned, but suffering from the increased switching frequency and the reduced bus voltage utilization at the faulty operation. Also, the harmonic space is uncontrollable. This paper proposes a conditional-extreme-point based fault-tolerant model predictive control for a five-phase permanent magnet synchronous motor. The conditional extreme point is used to predict the optimal reference voltage vector. Firstly, to suppress the harmonics, dual control sets are established without the sacrifice of bus voltage utilization. Then, the unconditional extreme point and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> -axis voltage are predicted to select the appropriate control set, and two adjacent vectors are pre-selected for generating the optimal reference voltage vector. Subsequently, the conditional extreme point of the cost function is calculated to obtain the optimal reference voltage vector. Due to the unemployment of the null vector, the switching frequency is greatly reduced. Finally, the experimental results are presented to verify the proposed control scheme.

Meiotic analysis of induced translocation heterozygotes in Lepidium sativum Linn.

Aim: To assess the cytological effects of Gamma rays on the pollen mother cells and post meiotic products of Lepidium sativum Linn. which resulted in isolation of translocation heterozygote and aneuploid gametes. Methodology: During the experiment, parameters such as Meiotic stages, chromosomal structure, and pollen fertility were studied. Fresh seeds of Garden cress were orderly arranged in plastic and divided into four groups. The fresh seeds of each group were individually irradiated with 60Gy, 150Gy, 300Gy, 450Gy and 600Gy @ 7.247KGyh-1 in the Floriculture Laboratory of National Botanical Research Institute (NBRI), Lucknow (India). The source for gamma ray irradiation was Cobalt-60. The irradiated seeds were sown in triplicates along with their respective controls. Results: Meiotic analysis of pollen mother cells (PMCs) at 300Gy showed translocation heterozygotes, however, in the control sets, the rate was negligible and phenotypically the plants were weak and short heighted. The cytological illustration of chromosomal configuration at diakinesis and metaphase I exhibited the superiority of tetravalents, bivalent and other forms of chromosomal associations such as multivalents. The induced translocation heterozygotes resulted in stickiness at metaphase and anaphase states along with the formation of bridges at anaphase stage. Due to chromosomal anomalies, the pollen fertility was reduced to 37.54±0.25% in contrast to control plants (98.20±0.16%). Interpretation: The current research work would be of paramount interest as translocation heterozygote serves as a source for raising aneuploid offspring having novel genetic combinations. Key words: Chromosomal anomalies, Heterozygotes, Lepidium sativum Linn, Meiosis, Pollen fertility

A Novel Simplified Finite Control Set Repeat Model Predictive Control for Grid-Connected Inverters

A Novel Simplified Finite Control Set Repeat Model Predictive Control for Grid-Connected Inverters

Physiological deviations in the body when working in a heating environment in shunt shielding personal protective equipment

Introduction. The article presents results of the study of the human body functional state when using a set of shielding personal protective equipment during modelling of electrical staff occupational activity in a hot environment.&#x0D; Materials and methods. The studies were carried out in the climatic chamber with an air temperature 35.5 ± 0.5 °C and a WGBT index of 30.0 ± 0.8 °C. Studies of the human body functional state were conducted with the participation of twelve volunteers who used a set of shunting shielding personal protective equipment and a control clothing set. The values of body temperature, skin both temperature and humidity, clothing temperature and humidity under dressing were recorded. General heat and local moisture perceptions were evaluated. During the research, the heart rate and metabolic rate were recorded by indirect calorimetry. The values of the average skin &amp; body temperature, heart rate increase, changes in heat storage rate, as well as moisture exchange indicators were calculated. &#x0D; Results. The results of the physiological cost assessment indicate that the use of a shielding personal protective equipment set compared to the control set led to a greater increase in core temperature (by 47.7%), higher values of average body temperature (by 1.3%), heat storage rate (by 24.2%), heart rate (by 24.7%), the metabolic rate (by 20.7%), moisture loss (by 55.6%).&#x0D; Limitations. The study had limitations due to sample size of the included volunteers.&#x0D; Conclusion. The data obtained indicate a significant change in the human body functional state and a critical risk of overheating when using shielding personal protective equipment. The use of a shielding clothing increases the physiological and energy cost of work, which is expressed in a gain in the metabolic rate level, oxygen consumption, and a work category elevation from IIa to IIb.

Species-specific microRNA discovery and target prediction in the soybean cyst nematode

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline’s hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA).

Robust three‐voltage‐vector‐based model‐free predictive current control for permanent magnet synchronous motor with ultra‐local model

AbstractA large current ripple may arise in the finite control set model‐free predictive current control (FCS‐MFPCC) strategy because only one voltage vector can be applied during the whole control period. Therefore, an improved three‐vector‐based model‐free predictive current control (MFPCC) strategy to improve the current tracking performance is proposed by increasing the number of the applied voltage vector. Based on the extreme existence theorem for the function of two variables, the optimal voltage vector can be synthesized. Compared with the one‐vector‐based FCS‐model‐based predicted current control (FCS‐MBPCC) and two‐vector‐based FCS‐MBPCC in the time and frequency domains, the maximum tracking error is decreased by 42.17% and 48.58%, and the total harmonic distortion is decreased by 40.97% and 37.56%, respectively. Besides, compared with the previous three‐vector‐based strategy, the computational time of the proposed strategy is reduced from 57.6 to 45.89 , which is reduced by 20.33%. Furthermore, the switching frequency of the proposed method can be decreased by 70.3% compared with the deadbeat MFPCC. The experimental results in the time and frequency domains demonstrate that the proposed three‐vector‐based MFPCC can not only improve the current tracking performance but also improve robustness against parameter mismatches.

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

Improved hybrid sphere decoding algorithm for long horizon finite control set model predictive control of grid-tied inverter

In this paper, the reference tracking and switching loss minimization problem of grid-tied inverters has been formulated as an integer least square (ILS) problem. It is considered an NP-hard and combinatorial problem due to the binary nature of switching status. A novel modification to the traditional non-recursive sphere decoding algorithm is being proposed in this research to address the implementation of long-horizon control with reduced computational burden. The proposed algorithm demonstrates a significant reduction in computation cost for long horizons resulting in energy minimization. The proposed method exploits the state-space model of the system to predict the sector in the space vector two-dimensional plane in which reference voltage could exist, thereby reducing the search space and time to search for the optimal solution. The proposed algorithm optimizes the utilization of controller resources compared to the conventional approach. The algorithm’s performance has been validated through simulations and Hardware in loop verification, for the performance optimization of a three-phase grid-tied inverter coupled using RL filter.

Morphological and Biochemical Changes in Moth Bean during Drought Stress

The current investigation on the repercussions of drought stress on the growth parameters of the Moth bean (Vigna aconitifolia) has been carried out in a semi-field condition. Two sets of local moth bean cultivars (RMO-40: fifteen in each) were prepared, and one was subjected to terminal drought stress. The rest was maintained with proper watering as a control set. The treated plants were thoroughly evaluated based on leaf length, root length, and relative leaf water content in comparison with the control ones. The findings of this study reveal a significant impact of drought stress on moth bean's growth and physiological performance. Under drought conditions, treated plants exhibited considerable reductions in leaf length and root length compared to their well-watered counterparts. Additionally, the relative leaf water content declined under drought stress, indicating decreased water uptake and retention within the plants under stress. These outcomes underscore the vulnerability of moth bean to drought stress, emphasizing the urgency of developing efficient water management strategies and drought-resistant varieties to safeguard food security in regions where this legume serves as a staple crop. This research highlights the importance of understanding the adverse effects of drought stress on moth bean's growth parameters for sustainable agriculture and food production.

Controllability of periodic linear systems, the Poincaré sphere, and quasi-affine systems

AbstractFor periodic linear control systems with bounded control range, an autonomized system is introduced by adding the phase to the state of the system. Here, a unique control set (i.e., a maximal set of approximate controllability) with nonvoid interior exists. It is determined by the spectral subspaces of the homogeneous part which is a periodic linear differential equation. Using the Poincaré sphere, one obtains a compactification of the state space allowing us to describe the behavior “near infinity” of the original control system. Furthermore, an application to quasi-affine systems yields a unique control set with nonvoid interior.

A Two-Step Continuous-Control-Set MPC for Modular Multilevel Converters Operating With Variable Output Voltage and Frequency

This paper presents a new enhanced single-stage two-step Continuous Control Set Model Predictive Control (CCS-MPC) algorithm, for Modular Multilevel Converters (MMCs) operating with variable output frequency at the AC port. The proposed two-step CCS-MPC allows the implementation of feed-forward compensation of the one-step ahead perturbations at the AC side, using an algorithm based on the Moore-Penrose pseudoinverse matrix. Limitations of the current and voltages in the MMC clusters are implemented to avoid overmodulation and overcurrents, being the solution of the constrained CCS-MPC online solved using the active-set method. Without losing generality, the experimental validation of the single-stage CCS-MPC is performed using an 18-cell MMC prototype, driving a 7.5 kW cage induction machine operating in a wide speed range. A dSPACE MicroLabBox platform is used to control the MMC-based drive by implementing the CCS-MPC algorithm, the modulation stage and the active-set method. It is experimentally demonstrated that the compensation of the low-frequency perturbations allows a considerable reduction in the required circulating currents at the low-frequency operation of the drive. Reduction in the capacitor voltage ripple, improved efficiency, and a smaller Total Harmonic Distortion (THD) in the output currents are also achieved with the proposed single-stage two-step CCS-MPC.

The Effects of Sawdust of Coconut (Cocos nucifera) to Seed Germination of Papaya (Carica papaya)

The documentation of the effects of sawdust of coconut (Cocos nucifera) on the seed germination of papaya (Carica papaya) was assessed using quantitative descriptive method of research. There were three experimental set-ups and a control set- up to test the effectivity of Cocos nuciferasawduston seed germination of Carica papaya.The following set-ups were used: Set-up A, using 25% concentration of Cocos nuciferasawdust, Set-up B, using 50% concentration, Set-up C, using 75% concentration and Set-up D, served as the control set-up, using 100% pure soil.The results revealed thatit is possible to use Cocos nucifera (coconut) sawdust as an alternative source of fertilizer.Particularly,25%, 50% and 75% concentration of Cocos nuciferasawdust show considerable effect on Carica papayaseed germination using the one-way ANOVA. The results further showed that Carica papaya seeds germinate faster and taller in Set-up B which contains 50% concentration of Cocos nuciferasawdustwhich gains a mean height of 8.45 cm. using the Duncan’s Multiple Range Test (DMRT).This concentration can help in the rapid germination of the plants. This means, that an equal amount of sawdust and soil is good for the germination of Carica papaya (Papaya) seeds.

Identification of mould varieties infecting maize kernels based on Raman hyperspectral imaging technique combined with multi-channel residual module convolutional neural network

Maize kernels are easily infected with different kinds of moulds in nature which has terrible negative effects on kernels. In this research, a series of multi-channel residual modules (MCRMs) were introduced to convolutional neural network (CNN) to identify the mould varieties combined with Raman hyperspectral imaging technique. Specifically, Raman hyperspectral images of maize kernels infected with different moulds were acquired and their characteristics were summarized and prepared for further analysis. Traditional machine learning (TML) models and deep learning (DL) models were both established and compared. For TML, three kinds of modeling methods combined with four kinds of variables selection methods were applied to establish the mould varieties identification model. For DL, five different residual units were explored to obtain the optimal MCRM-CNN architecture. The results showed that MCRM-CNN was more capable of mining hidden information compared with TML methods. Finally, MCRM-CNN-SVM provided the optimal model to identify the moulds varieties with accuracy in the testing set of 100% by taking the variables extracted by MCRM-CNN as the inputs of SVM. The residual unit was composed of three stacking residual blocks with different kernels of 3, 5, 7 without the shortcut connection (MNR) and the recall rates of A. niger, A. flavus, A. glaucus, A. parasiticus and control set were all 100%, respectively. Consequently, the proposed method expanded the nondestructive online detection for identifying mould varieties infecting maize kernels by combining RHSI and DL.

Building a Natural Language Processing Artificial Intelligence to Predict Suicide-Related Events Based on Patient Portal Message Data

ObjectiveTo develop a natural language processing artificial intelligence model trained on text from patient portal messages to predict 30-day suicide-related events (SRE). Patients and MethodsPatient portal messages sent by patients between January 1, 2013, and October 31, 2017 were screened for an associated SRE within 30 days. For both patient portal messages associated with a 30-day SRE and a randomized control set, we automatically extracted several features: (1) frequencies of keywords; (2) message metadata; and (3) message sentiment. ResultsA total of 840 patient portal messages were included in our final analysis, including 420 messages with and without an associated 30-day SRE. Patient messages with an associated 30-day SRE had a mean sentiment score that was less than those without an SRE (P<.001). Messages with an associated 30-day SRE had greater word counts (P=.002) and more use of ellipses (P=.02), but less use of exclamation marks (P=.04) and question marks (P=.007) compared with messages without a 30-day SRE. The neural network machine learning model had the highest area under the receiver operating curve at 0.710, with a sensitivity of 56.0% and a specificity of 69.0%. ConclusionA natural language processing artificial intelligence model trained on a subset of patient portal message data was able to predict 30-day SRE at a level comparable to commonly used suicide assessment tools. Predictors that conveyed the overall tone of a patient message, such as the sentiment score, were more highly weighted by machine learning models in predicting 30-day SRE than the frequencies of individual words.

B-322 Assessment of MassTrak™ Immunosuppressant Calibrator and Control Sets: Confidence in Your Calibrators

Abstract Background The analytical performance of the Waters MassTrak Immunosuppressant Calibrator and Quality Control Sets (IVD) were evaluated using a laboratory developed LC-MS/MS method, to analyze all four immunosuppressants in a single run. Methods The MassTrak Immunosuppressant Calibrator and Quality Control Sets were used to quantify cyclosporine, everolimus, sirolimus and tacrolimus in human whole blood. Samples (50 µL) were treated with zinc sulfate and acetonitrile containing internal standards. A water/methanol/ammonium acetate gradient was used with a Waters C18 HSS SB Column on a Waters ACQUITYTM UPLTM I-Class FL and Xevo TQ-S micro Mass Spectrometer with an injection-to-injection time of less than 2 min. Results No system carryover was observed from high concentration samples. Precise quantification (≤20% CV, ≤15% bias) at concentrations equal to or lower than the lowest concentration calibrator was demonstrated. Total precision and repeatability (3 pools, 5 replicates, 5 days; n = 25) were determined to be ≤7.1% CV. The method was linear over the measuring interval of 19.3–1500 ng/mL (cyclosporine) and 0.77–39 ng/mL (everolimus, sirolimus and tacrolimus). Addition of high concentrations of several endogenous materials did not affect quantification. External quality assurance samples for all drugs met the scheme acceptance criteria (cyclosporine: n = 39, range 0–2658 ng/mL, mean % bias +1.0%; everolimus: n = 35, range 0–21.9 ng/mL, mean % bias +0.9%; sirolimus: n = 34, range 0–74.2 ng/mL, mean % bias −7.5%; tacrolimus: n = 40, range 0–23.0 ng/mL, mean % bias −0.5%). Conclusion Performance of the Waters MassTrak Immunosuppressant Calibrator and Quality Control Sets, with the developed method described provide good method performance. The accuracy data demonstrates confidence in the MassTrak Immunosuppressant Calibrator Set, providing harmonization across laboratories. This method is an example of an application using the instrumentation, software and consumables described in this document. This method has not been cleared by any regulatory entity for diagnostic purposes. The end user is responsible for completion of the method development and validation. MassTrak Immunosuppressant Calibrator and Quality Control Sets are not available for sale in all countries.

B-338 A Clinical Research LC-MS/MS Method for the Analysis of Immunosuppressant Drugs in Whole Blood Using Capitainer B Devices

Abstract Background Traditional laboratory analysis of the immunosuppressant drugs cyclosporine, everolimus, sirolimus and tacrolimus is well-established in clinical research. However there remains a need for individuals to undergo an invasive, time-consuming and disruptive process under the supervision of trained staff in order to collect a sufficient volume of whole blood for laboratory analysis. A reliable, remote sampling method may find utility in a clinical research setting. Here we describe the use of Capitainer® B qDBS devices to obtain analytically sensitive, precise and accurate data for cyclosporine, everolimus, sirolimus and tacrolimus analysis using small sample volumes. The Waters ACQUITY™ UPLC I-Class with Xevo™ TQ Absolute mass spectrometer was used to analyze these samples. Methods An in-house laboratory developed LC-MS/MS method to analyze all four immunosuppressants in a single ruin was developed. Waters MassTrak Immunosuppressant Calibrator and Control Sets (IVD) and whole blood External Quality Assurance samples (LGC, Bury, UK) were used in conjunction with Capitainer® B qDBS devices to assess the performance of the method. Samples were collected using Capitainer® B qDBS devices, and the sample (10 µL) extracted using solvent containing internal standards. A water/methanol/ammonium acetate gradient was used with a Waters C18 HSS SB column on a Waters ACQUITY™ UPLC™ I-Class and Xevo TQ Absolute Mass Spectrometer operating in positive electrospray ionization mode with run time of less than 2 min. Results Analytical sensitivity of the lowest calibrator at 1 ng/mL for everolimus, sirolimus and tacrolimus and 25 ng/mL for cyclosporine was demonstrated with S/N (PtP) &amp;gt; 10 across five analytical runs. We successfully demonstrated linearity of cyclosporine from 25–1500 ng/mL and everolimus, sirolimus and tacrolimus from 1–30 ng/mL, with r2 &amp;gt; 0.995 over five analytical runs. Total precision and repeatability across the four immunosuppressants (2, 8 and 22 ng/mL for everolimus, tacrolimus and tacrolimus; 150, 400 and 900 ng/mL for cyclosporine) with five replicates over five analytical runs (n = 25) was ≤15% CV. External quality assurance samples for all drugs met the scheme acceptance criteria, with mean bias ≤15% CV. Conclusion Using Capitainer® B qDBS devices and sample small volumes (10 µL) of whole blood, an in-house laboratory method was used to meet validation goals for analytical sensitivity, linearity, precision and accuracy for cyclosporine, everolimus, sirolimus and tacrolimus. Furthermore, the advantages conferred by volumetric absorptive microsampling, notably removing the requirement for travel and a venous blood draw and facilitating home sampling, render this technique applicable to clinical research. For Research Use Only. Not for Use in Diagnostic Procedures.