Real-time Stability Research Articles

A Model Study to Assess Fibrillation and Product Stability to Support Peptide Drug Design.

The fibrillation of therapeutic peptides can present significant quality concerns and poses challenges for manufacturing and storage. A fundamental understanding of the mechanisms of fibrillation is critical for the rational design of fibrillation-resistant peptide drugs and can accelerate product development by guiding the selection of solution-stable candidates and formulations. The studies reported here investigated the effects of structural modifications on the fibrillation of a 29-residue peptide (PepA) and two sequence modified variants (PepB, PepC). The C-terminus of PepA was amidated, whereas both PepB and PepC retained the carboxylate, and Ser16 in PepA and PepB was substituted with a helix-stabilizing residue, α-aminoisobutyric acid (Aib), in PepC. In thermal denaturation studies by far-UV CD spectroscopy and fibrillation kinetic studies by fluorescence and turbidity measurements, PepA and PepB showed heat-induced conformational changes and were found to form fibrils, whereas PepC did not fibrillate and showed only minor changes in the CD signal. Pulsed hydrogen-deuterium exchange mass spectrometry (HDX-MS) showed a high degree of protection from HD exchange in mature PepA fibrils and its proteolytic fragments, indicating that most of the sequence had been incorporated into the fibril structure and occurred nearly simultaneously throughout the sequence. The effects of the net peptide charge and formulation pH on fibrillation kinetics were investigated. In real-time stability studies of two formulations of PepA at pH's 7.4 and 8.0, analytical methods detected significant changes in the stability of the formulations at different time points during the study, which were not observed during accelerated studies. Additionally, PepA samples were withdrawn from real-time stability and subjected to additional stress (40 °C, continuous shaking) to induce fibrillation; an approach that successfully amplified oligomers or prefibrillar species previously undetected in a thioflavin T assay. Taken together, these studies present an approach to differentiate and characterize fibrillation risk in structurally related peptides under accelerated and real-time conditions, providing a model for rapid, iterative structural design to optimize the stability of therapeutic peptides.

High-speed laser centerline extraction and heterogeneous measurement architectures for low-cost hardware

Due to its real-time efficiency, stability, and reliability, structured light 3D measurement is widely used in measuring surface quality and manufacturing accuracy of industrial products. With the structured light measurement system's slow acquisition speed and low efficiency, it is difficult to meet the needs of online real-time detection, resulting in sparse 3D contour data and loss of details. To improve the algorithm's running speed and balance hardware resource consumption, a structured light extraction algorithm adapted to hardware acceleration is proposed, which executes the three low-coupling computational modules at eight-pixel parallel speed and in a pipelined manner. In order to further reduce the overall computational efficiency and deployment cost of high-speed structured light measurement tasks, an FPGA camera architecture with parallel acquisition and processing is designed, and the FPGA camera is cooperated with the CPU to form a more efficient heterogeneous measurement system. For samples of different materials and shapes, the standard deviation of sub-pixel coordinates extracted is less than 0.65. With this architecture, it is possible to process video stream with one million pixels at 500FPS, and the power consumption is 2.566w. Experimental results of measuring blocks and steel pipes show that the measurement error is below 0.15 mm. The proposed algorithm and heterogeneous processing measurement architecture are applicable in engineering, especially for high-speed real-time 3D measurement.

Randomized singular value decomposition based Real-time Wide Area Voltage Stability (WAVS) Assessment

Phasor Measurement Units (PMUs) are the advanced measuring devices which provide time-stamped measurements of voltage magnitudes as well as phase angles in Wide Area Monitoring Systems. These synchrophasors are exploited in this paper to develop a model-free voltage stability assessment method using randomized singular value decomposition (rSVD). Stage-1 of the proposal carries out an off-line study to (a) identify voltage weak buses to equip them with PMUs and (b) estimate the voltage stability monitor’s threshold based on the largest singular value (LSV). Stage-2 proposes a temporal change detection technique to identify and isolate the disturbance samples from steady state measurement window to maintain linearity of PMU data matrix. Finally, Stage-3 proposes the use of rSVD technique in an online environment to assess the WAVS. This technique performs SVD of low dimensional PMU data matrix to make the approach computationally lighter. The performance of the proposed scheme is demonstrated on the IEEE 14-bus system and the IEEE 118-bus system. The viability of the real-time implementation of the proposed scheme is also verified by way of its Real-Time Digital Simulation on the IEEE 14-bus system.

Formulation and evaluation of fluconazole emulgels for potential treatment of vaginal candidiasis

Localised treatment of vaginal candidiasis could improve the therapeutic outcomes of patients with vaginal candidiasis as well as reduce drug dosage and dosing interval. The aim of the research project was to develop fluconazole emulgel formulations, and evaluate their physicochemical, drug release, antifungal, safety, and stability profile, for potential treatment of vaginal candidiasis. Xanthan gum and HPMC E5LV-based fluconazole emulgels were prepared using the spontaneous emulsification method and their physicochemical properties, drug content, drug release profile, anti-fungal activity against Candida albicans, in vivo vaginal biocompatibility and stability profile were evaluated using standard protocols. The fluconazole emulgels exhibited satisfactory properties: pH: 5.2–5.4; spreadability: 1.6–2.5 cm; apparent viscosity: 85–314 cP; zone of inhibition against Candida albicans: 22–38 mm; drug content: 91–102 %, and vaginal biocompatibility. All the studied fluconazole emulgels exhibited controlled fluconazole release over 6 h and their drug release kinetics fitted well with Korsmeyer-Peppas model. HPMC-based emulgels exhibited unsatisfactory real-time stability profile. To our knowledge, this is the first report where xanthan gum and HPMC E5LV-based fluconazole emulgels have been studied for possible treatment of vaginal candidiasis. Xanthan gum-based fluconazole emulgels are promising drug formulations that could reduce the drug dosage and dosing frequency. In addition, they could serve as alternative dosage forms to Flucos® gel.

Requirements for the information on reference standards submitted in the dossier for biologicals

SCIENTIFIC RELEVANCE. The use of appropriately certified reference standards (RSs) is a necessary element of analytical procedure standardisation that ensures comparability of results across different laboratories assessing the quality of medicines, which is important for the quality control of biologicals. Marketing authorisation involves providing information on RSs, but no specific requirements are given for the relevant section of the dossier. Therefore, it is necessary to develop the requirements for the appropriate guidelines.AIM. This study aimed to develop specific requirements for the materials that should be included in the Reference Standards section of the dossier for a biological product.DISCUSSION. The authors analysed national and international regulatory frameworks governing RSs for biologicals, including documents by the World Health Organisation (WHO), the Eurasian Economic Union, the International Organisation for Standardisation (ISO), and the Russian Federal Agency for Technical Regulation and Metrology. Additionally, the authors analysed the experience of RS certification and use at the Scientific Centre for Expert Evaluation of Medicinal Products. This article covers specific aspects of biological RS certification, considering the differences between measurement techniques and analytical procedures. The authors formulated the key requirements for the documents submitted in the dossier section on RSs for biologicals (biological reference materials). The methods (analytical procedures) used for RS certification should be described in accordance with the requirements for standard operating procedures. Adequate validation studies should establish the necessary validation characteristics of these analytical procedures. Additionally, this article presents the requirements developed to certify primary RSs for structure confirmation of proteins in novel biologicals. The values of certified characteristics and critical quality indicators should be established, and the RS production technology should be described and in compliance with WHO recommendations and/or Good Manufacturing Practice (GMP) standards. The basic certification stages are common to all types of RSs. The RS shelf life should be established in real-time stability studies, whereas accelerated stability testing may be useful for studying degradation mechanisms and the effects of short-term deviations from the specified storage conditions. In accordance with WHO recommendations, the Reference Standards section of the registration dossier should specify changes to the RS that require regulatory approval.CONCLUSIONS. The authors believe that the requirements for the materials in the Reference Standards section of the dossier for a biological product, as systematised in this study for inclusion in the relevant guidelines, will provide a unified approach to RS development, certification, and documentation for applicants, developers, and competent authority experts and will streamline RS consideration during marketing authorisation.

Virtual testbed for development and evaluation of power system digital twins and their applications

Digital twins (DTs) show considerable promise for development of applications and solutions across a diverse range of power system services. Ensuring these applications function as intended without causing disruptions or unexpected issues requires comprehensive testing prior to practical deployment. However, current approaches to power system digital twin (PSDT) application development and testing experience significant obstacles, including limitations and risks associated with accessing real-world power systems, models and data. This article introduces a virtual testbed based on the concept of a virtual physical twin (VPT), that replaces the need for access to a physical twin (PT), as a platform designed to remove the heavy reliance on online data flows and provide a close to real-world environment. A testbed enables simultaneous development and testing of DTs and their related applications in a realistic, interactive, and safe setting without impact on the operation of the actual power system. A complete workflow for PSDT testbed implementations is proposed, with all necessary steps from concept to the eventual connection to the physical system. To validate the practical implementation steps, utility, and value of the testbed, a real-time transient stability analysis with two artificial intelligence algorithms is provided. The proposed testbed is expandable and adaptable, promoting the development of advanced and intelligent PSDTs. The key benefit of the testbed is that it enables the development of DT and its application closely in a realistic environment without affecting the operation of PT.

RT-CBCH: Real-Time VPN Traffic Service Identification Based on Sampled Data in High-Speed Networks

Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffic. Therefore, VPN traffic service identification is critical in network management. The existing identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes enormous resources, limiting the real-time processing of traffic identification. This paper proposes a real-time VPN traffic service identification method named RT-CBCH. We construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). Experimental results validate the real-time capability, stability and accuracy of our method. At the sampling ratio of 1/256, it takes only 23.63 seconds to process the mixed traffic of 900-second traffic generated on a 10 Gbps link and our collected V2Ray traffic, which is increasingly common in VPN traffic. Under different sampling ratios, the identification results remain respectable, with an overall accuracy of about 90% for application service and over 99% for V2Ray proxy service. Furthermore, comparisons with similar work illustrate the high accuracy and low resource consumption of RT-CBCH. Experimental results show that our method can stably implement real-time VPN traffic service identification from sampled data in high-speed networks.

Research on risk prediction algorithm for operation and maintenance data based on grey theory

This paper proposes a risk prediction algorithm for power operation and maintenance data networks based on the grey theory, aiming at the real-time, healthy, and high stability requirements of power operation and maintenance data networks in work. By combining the entropy weight method to assign weight to risk indicators, the risk prediction of data networks in dynamic networks is achieved. By using two indicators of risk value and risk dispersion, the overall system is predicted for risk. Through experimental comparison, the prediction algorithm based on grey theory designed in this paper has a smaller error than existing prediction methods, which more scientifically realizes the risk prediction mechanism, ensures the stability of power operation and maintenance data, and ensures the reliable operation of the power grid.

Structural Insights into SARS-CoV-2 Nonstructural Protein 1 Interaction with Human Cyclophilin and FKBP1 to Regulate Interferon Production.

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 coronavirus and the perpetual rise of new variants warrant investigation of the molecular and structural details of the infection process and modulation of the host defense by viral proteins. This Letter reports the combined experimental and computational approaches to provide key insights into the structural and functional basis of Nsp1's association with different cyclophilins and FKBPs in regulating COVID-19 infection. We demonstrated the real-time stability and functional dynamics of the Nsp1-CypA/FKBP1A complex and investigated the repurposing of potential inhibitors that could block these interactions. Overall, we provided insights into the inhibitory role Nsp1 in downstream interferon production, a key aspect for host defense that prevents the SARS-CoV-2 or related family of corona virus infection.

A safety-guaranteed game-theoretical velocity planning for autonomous vehicles on sharp curve roads

In this paper, a safety-guaranteed game-theoretical velocity planning framework in a hierarchical manner is proposed to generate safe, ride comfort, and travel efficiency-balanced velocity for autonomous vehicles (AVs). In the upper layer, a bang-bang decision-making method is utilized to determine which planning mode to be implemented based on acceleration and jerk constraints, including a comfort mode, an efficiency mode, and a game mode. In the lower layer, asymmetric jerk limits based on comfort characteristics sensibility analysis and safe velocity simultaneously considering longitudinal and lateral stability are firstly developed to maintain ride comfort and driving safety, respectively on curve roads, especially sharp curves where vehicle stability may be not fully considered in most researches. Based on these, a non-cooperative game-theoretical velocity planning method is presented to solve the conflict between comfort mode and efficiency mode by optimizing his own objective based on the other’s action. Finally, for the sake of solving efficiency and accuracy, a chaos optimization-based algorithm (COA) is designed to solve for the Stackelberg equilibrium solution of the bilevel game optimization problem. Three experimental tests are carried out to comprehensively demonstrate the effectiveness, robustness, and real time of the proposed framework. The results show that the proposed method can provide the great performance of ride comfort, travel efficiency, and longitudinal-lateral stability in real time in the velocity planning process.

FORMULATION OF MEMORY SUPPORT TARGETED NANOSTRUCTURED LIPID CARRIERS (NLCS) LOADED WITH KELULUT HONEY EXTRACT PRODUCED WEST KALIMANTAN

Objective: The purposes of this study were to find active compounds of the secondary metabolites, screen and determine the comparison of solid lipid, liquid lipid, and surfactant to produce the optimal NLCs formulation, analyze characteristic optimal NLCs loaded honey extract, and analyze the effectiveness of the optimal NLCs formulation as memory support in vitro. The Design-Expert software used for optimal NLCs kelulut honey extract formulation. Methods: The research method begins with the extraction process using ethanol solvent's maceration method. The formulation of NLCs begins with screening honey extracts for various solid lipids, liquid lipids, and surfactants. Next, the formulation design uses a D-optimal mixture design to obtain 14 run variations of glyceryl monostearate: tocopherol oil: tween 20 and evaluation using transmittance response, pH, and entrapment efficiency. The data on the response is entered into the software to obtain the optimal NLCs honey extract formula. The optimal NLCs honey extract were evaluated for transmittance, pH, entrapment efficiency, particle size, transmission electron microscope (TEM), Fourier transform Infrared Spectroscopy (FTIR), and in vitro activity as memory support. Results: Based on the evaluation, the water content of honey extract was 5.74 %±0.144; metals present in honey are Pb (0.003 mg/kg) and Cd (0.175 mg/kg). Phenolic and Total Flavonoids are 25.91 mg GAE/g±0.15 extract and 182.36 mg GAE/g extract±0.28. The optimal NLCs obtained combined glyceryl monostearate: tocopherol oil: tween 20 with a 0.5: 5.54: 23.96 composition ratio. The optimal NLCs kelulut honey extract formulation for transmittance value was 94.58%±1.54, pH was 6.59±0.15, and Entrapment efficiency was 99.89 %±0.09. Determination for particle size was 327 nm±0.57, and TEM and FTIR provided details on their structure. Evaluation for memory support in vitro, IC50 NLCs optimal formula 61.99±0.34; honey without extract 72.59±0.79; honey extract 38.55±0.24; and NLCs of base optimal formula without extract 829.81±0.93. The real-time stability shows optimal NLCs honey extract stable in real-time stability and freeze-thaw. Conclusion: NLCs from honey extract can be formulated from optimal NLCs using Design-Expert software. NLCs from honey extract has physical characteristics according to requirements and is stable. In vitro antioxidant studies revealed that the optimal formulation NLCs loaded honey extract had higher activity memory support with IC50 61.99±0.34.

Addressing the challenge of process stability control in wire DED-LB/M process

The wire DED-LB/M AM process is fast, cost-effective and it creates high-quality, dense parts. However, its industrial adoption is limited since process stability needs complex monitoring devices and tools. This work proposes a twofold strategy to resolve this issue; A fast-running, physics-based simulation tool calculates the temperature field during the building of single walls, while, an affordable vision-based monitoring system captures the melt pool dimensions that are correlated to the temperature and reveal heat accumulation. Therefore, real-time process stability control is enabled. An infrared thermal camera is used to validate the model and calibrate the monitoring system.

Transfer Learning for Transient Stability Predictions in Modern Power Systems Under Enduring Topological Changes

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Extremely Randomized Trees Approach to Voltage Stability Assessment of Power Distribution System

In modern power grids, ensuring operational stability necessitates real-time assessment of static voltage stability. This paper presents an advanced ensemble machine learning approach to estimate voltage stability in power distribution systems using data from phasor measurement units (PMUs). The methodology integrates multiple machine learning techniques into a unified predictive model to minimize bias and variance. A critical feature of this approach is the inclusion of a feature pre-processing step to eliminate redundant and irrelevant features, enhancing computational efficiency through dimensionality reduction. Additionally, a minority oversampling method for regression is utilized to address data disparity, ensuring robust model performance. Various ensemble models were evaluated using mean square error metrics, with the Bagging model ExtraTrees outperforming other algorithms. The proposed method's efficacy is validated on a 30-bus IEEE system, highlighting its potential for real-time applications. This research offers a robust solution for maintaining grid reliability and preventing power outages by providing accurate, real-time voltage stability assessments.

AC/DC Hybrid Power System Damping Control Based on Estimated Model Predictive Control Considering the Real-Time LCC-HVDC Stability

High Voltage Direct Current (HVDC) can quickly vary its power to stabilize an AC power system when the latter is subject to a disturbance. Typically, linear control is used to design that control strategy. However, due to the uncertainties of the faults and the system model, it is necessary to estimate the linear model of the post-fault power system using real-time measurements. This leads to two main problems: Firstly, after the fault, the HVDC will quickly evolve to steady-state operating conditions and, therefore, result in an unobservable control matrix. Secondly, the HVDC power should not exceed its stable range, which is limited by the post-fault system. To address the above issues, the main novelties are as follows: First, this paper proposes a novel system identification method to handle that case when there is no control input excitation. Second, this paper presents an AC/DC hybrid power system control strategy that fully considers the real-time stability constraints of the HVDC. More specifically, this paper proposes to estimate the system matrix and the control matrix separately. Based on the linearized model of the AC/DC hybrid system, the control matrix is calculated using the sensitivities of the active powers of the generators with respect to those of the HVDC. Then, the system matrix is estimated in real-time using measures of the power angle and frequency. Finally, by processing the local voltage and current measurements, the associated Thevenin equivalent parameters on the system side of the HVDC connecting point are estimated in real-time and the upper bound of the DC power is determined. According to the linearized model and the HVDC power constraints, a control strategy is proposed using the model predictive control method. Simulation results reveal the effectiveness of the proposed strategy.

Hyper Spherical Search (HSS) Algorithm Based Optimization and Real-Time Stability Study of Tidal Energy Conversion System

Hyper Spherical Search (HSS) Algorithm Based Optimization and Real-Time Stability Study of Tidal Energy Conversion System

Research on Edge-Computing-Based High Concurrency and Availability “Cloud, Edge, and End Collaboration” Substation Operation Support System and Applications

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability “cloud-side-end collaboration” based on edge computing. This paper proposes a high concurrency and availability “cloud, edge and end collaboration” architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a “cloud-side-end cooperative” substation operation support system architecture with “high real-time, high concurrency, high security and high stability” is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application.

Research on molten pool flow characteristic in all-position LP-LMH welding

ABSTRACTThe flow characteristics of keyhole and molten pool impact the real-time process stability and seam formation severely in all-position low-power laser MAG hybrid (LP-LMH) welding. To probe into the molten pool’s real-time dynamics at various spatial welding positions, the high-speed camera images of metal transfer and molten pool were sensed. Meanwhile, a novel spatial flow model of molten pool was developed, and the functions of spatial position and gravity on the molten pool flow were investigated. The results proved that as the spatial position increases, the location of where droplet striking molten pool changes accordingly, further disturbing the keyhole’s stability. Meanwhile, the spatial position seriously affects the molten pool’s flow. The driving forces of molten pool flow at various spatial welding positions are basically the same, and gravity affects the overall movement of molten pool, thereby affecting the vortexes and heat transmission inside the molten pool, as reflected in weld penetration.

Real time detection of multiple stable MHD eigenmode growth rates towards kink/tearing modes avoidance in DIII-D tokamak plasmas

Real time detection of time evolving growth rates of multiple stable magnetohydrodynamic (MHD) eigenmodes has been achieved in DIII-D tokamak experiments via multi-mode three-dimensional (3D) active MHD spectroscopy. The measured evolution of the multi-modes’ growth rates is in good accordance with the variation of the plasma β N . Using experimental equilibria, resistive MARS-F simulations found the two least stable modes to have comparable growth rates to those experimentally measured. Real time and offline calculations of the modes’ growth rates show comparable results and indicate that cleaner system input and output signals will improve the accuracy of the real time stability detection. Moreover, the shortest real time updating time window of multi-mode eigenvalues can be about 2 ms in DIII-D experiments. This real time monitoring of stable, macroscopic kink and tearing modes thus provides an effective tool for avoidance of the most common causes of tokamak disruption.

648. Validation of a Synthetic Multiplexed External Control for Monitoring the Performance and Detection of Multiple Respiratory Viral and Bacterial Pathogens for a Point of Care Nucleic Acid Testing Panel

Abstract Background Advancements in nucleic acid testing has enabled point of care testing solutions to provide clinicians with greatly improved time to diagnosis and patient care. However, complex technologies and regulatory requirements demand close monitoring of assay performance to accurately identify shifts, trends, and random errors to ensure accurate patient reporting. To support these requirements, MMQCI has developed a synthetic, easy-to-use, external control that simultaneously monitors the accurate identification of 15 respiratory pathogens (11 viral, 4 bacterial targets) detected with the BIOFIRE® SPOTFIRE® Respiratory (R) Panel. Methods The multiplex control contains genome segments of all pathogens detected by the SPOTFIRE R Panel. Sequences were designed in silico, ligated into MMQCI vectors, and transformed to create stable frozen clone stocks. In vitro RNA transcripts were generated, quantified, and formulated in a proprietary matrix to stabilize and carry the RNA through the entire test process. Validation studies were conducted to demonstrate precision across multiple reagents, operators and sites. Failure-mode, shipping and stability studies were conducted to confirm robustness and reliability. Three unique lots of the SPOTFIRE RSP Positive and Negative controls were manufactured to include variability in key components and tested across 6 different sites, incorporating multiple reagent lots, operators and instruments. A total of 361 runs were performed across the positive and negative controls using the SPOTFIRE R Panel. Results Of the 361 total runs across the 6 sites, 351 were valid and used for analysis. A total of 171/172 positive controls resulted in correct calls (99.3%), and all 179 negative controls were called correctly (100%). A regression analysis was performed to confirm real-time stability for 12 months when stored at temperatures ranging from 2oC to 25oC.Table 1.Summary of Reproducibility Test Results: External and Internal Site Conclusion Validation studies demonstrated reproducibility, and overall precision and robustness of SPOTFIRE RSP Positive and Negative Controls. The SPOTFIRE RSP Positive and Negative Controls provide a stable, ease to use solution for training, verification and routine monitoring of the performance of the BIOFIRE® SPOTFIRE® Respiratory Solution. Disclosures Nithya Sivaji, MS, Maine Molecular Quality Controls Inc.: employer Emma Farrell, n/a, Maine Molecular Quality Controls Inc.: employer Tania R. Spenlinhauer, PhD, Maine Molecular Quality Controls Inc.: employer Joan Gordon, n/a, Maine Molecular Quality Controls, Inc: President|Maine Molecular Quality Controls, Inc: Ownership Interest Monica Cronin, n/a, bioMerieux: Stocks/Bonds Brad Graham, n/a, bioMerieiuux: Employee Jeffrey Bastar, n/a, bioMerieux: Employee|bioMerieux: Stocks/Bonds