Control Process Research Articles

Control system and process optimization for electroless nickel plating

A novel method based on mid-frequency vibration is proposed to eliminate coating defects such as bubbles during electroless nickel plating. An automated control system for the plating, enabling precise and stable measurements and adjustments of critical parameters such as plating solution temperature, pH, and nickel ion concentration, is also established, which significantly improves process efficiency and coating quality. Experimental results indicate that the system is capable of realizing stable operation over extended periods. A nonporous nickel–phosphorus coating with a thickness greater than 200 μm is successfully obtained, with high phosphorus content, robust adhesion, and superior machinability.

The self-relevant spotlight metaphor: Self-relevant targets diminish distractor–response-binding effects

AbstractRecently, it has been proposed that self-relevance of a stimulus enhances executive control and reduces the impact of distractors on current task performance. The present study aimed to test whether the binding between a distractor and a response is influenced by self-relevance, too. We assumed that targets’ self-relevance should increase executive control processes and therefore reduce the influence of distractors on current performance. In a distractor–response-binding (DRB) task, which measures the strength of binding between distractor stimuli and responses, we varied target relevance so that participants responded to targets that either were or were not self-relevant. Our design made it possible to measure DRB effects for both relevance conditions separately. DRB effects were diminished if targets were self-relevant compared to when they were not. These results expand our understanding of the influence of self-relevance on cognitive performance. The influence of self-relevance is not purely perceptual (Sui & Humphreys, 2012, Journal of Experimental Psychology: Human Perception and Performance, 38[5], 1105–1117), but also found in higher-order processes such as executive control. Moreover, whereas for different paradigms binding advantages of self-relevance are assumed (Sui & Humphreys, 2015a, Trends in Cognitive Sciences, 19[12], 719–728; Humphreys & Sui, 2016, Cognitive Neuroscience, 7[1/4], 5–17), this study identifies an important boundary condition, in that distractor–response binding is reduced by target self-relevance.

The Antecedents of Transformer Models

Transformer models of language represent a step change in our ability to account for cognitive phenomena. Although the specific architecture that has garnered recent interest is quite young, many of its components have antecedents in the cognitive science literature. In this article, we start by providing an introduction to large language models aimed at a general psychological audience. We then highlight some of the antecedents, including the importance of scale, instance-based memory models, paradigmatic association and systematicity, positional encodings of serial order, and the learning of control processes. This article offers an exploration of the relationship between transformer models and their precursors, showing how they can be understood as a next phase in our understanding of cognitive processes.

Application of AI to minimize information risk in tax control: Evidence from Bulgaria

The application of artificial intelligence (AI) in tax control is becoming more and more relevant in the conditions of globalization and digitalization of the economy, which creates a need for effective mechanisms for managing information risk. With the growing volume of data and the complexity of the tax processes, the risk of deviations, fraud and inconsistencies increases significantly. This article aims to examine applied AI tools and approaches that can minimize information risk in tax control, as well as to analyze their effectiveness through the prism of practice in Bulgaria. The methodology is based on correlation analysis, which measures the strength and direction of the linear relationship between pairs of variables. A Pearson correlation coefficient was used to evaluate the variable "Artificial Intelligence" and the different forms of tax control. Kendall's tau-b correlation coefficient was used to assess the linear relationship between the variables of collection, processing, verification, and management of tax information related to the application of AI. The main results show that AI has a positive and statistically significant effect on risk management and cybersecurity, suggesting the potential to improve data protection and reliability. However, the verification and revision of tax information needs to significantly impact AI, which points to the need for further development of these technologies for better integration. In conclusion, the application of AI offers significant opportunities to minimize information risk in tax control but requires targeted adaptation and refinement in specific aspects of control processes.

The sliding mode controller with composite reaching law for upper limb rehabilitation robot

Purpose Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs. Design/methodology/approach First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system. Findings The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%. Originality/value The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

Perfusion Process Intensification for Lentivirus Production Using a Novel Scale-Down Model.

Process intensification has become an important strategy to lower production costs and increase manufacturing capacities for biopharmaceutical products. In particular for the production of viral vectors like lentiviruses (LVs), the transition from (fed-)batch to perfusion processes is a key strategy to meet the increasing demands for cell and gene therapy applications. However, perfusion processes are associated with higher medium consumption. Therefore, it is necessary to develop appropriate small-scale models to reduce development costs. In this work, we present the use of the acoustic wave separation technology in combination with the Ambr 250 high throughput bioreactor system for intensified perfusion process development using stable LV producer cells. The intensified perfusion process developed in the Ambr 250 model, performed at a harvest rate of 3 vessel volumes per day (VVD) and high cell densities, resulted in a 1.4-fold higher cell-specific functional virus yield and 2.8-fold higher volumetric virus yield compared to the control process at a harvest rate of 1 VVD. The findings were verified at bench scale after optimizing the bioreactor set-up, resulting in a 1.4-fold higher cell-specific functional virus yield and 3.1-fold higher volumetric virus yield.

Evaluating manufacturing plants readiness for distributed ledger technology: a network framework for ambiguity levels

The utilisation of blockchain technology has gained significant traction within contemporary supply chains owing to its ability to enhance transparency, security, and traceability. Manufacturing plants, as pivotal components of the supply chain, stand to benefit from improved tracking and transparency of goods movement, real-time visibility, quality control processes, and adherence to industry standards through blockchain implementation. Nonetheless, without a comprehensive assessment of manufacturing plants’ readiness to embrace blockchain technology, the anticipated benefits may give way to unforeseen challenges. In this study, a novel network framework is offered to evaluate manufacturing plants’ readiness for adopting distributed ledger technology, specifically blockchain, under varying levels of ambiguity, including high (fuzzy) and low (scenario) ambiguity. This framework is distinguished by its ability to address uncertainty in evaluations, incorporating both scenario-based and fuzzy programming approaches. Furthermore, the framework treats evaluation criteria as interconnected entities, fostering a network perspective rather than a black-box approach. The proposed framework is then validated through a case study involving five manufacturing plants and twenty-four evaluation criteria. Our findings underscore the pivotal role of uncertainty considerations in ranking manufacturing plants, with the fifth plant emerging as the frontrunner across both fuzzy and scenario-based assessments in most instances.

Intelligent-based approach for parameters identification and control of fractional order systems

The optimization algorithms for identification and control processes take more and more place in the industrial domain. However, determining a control law for fractional systems remains challenging for researchers today. This paper presents a simple method for using a fractional PID (FOPID) corrector to regulate a class of fractional systems that show a stable step response. The procedure is based on an algorithmic identification to assimilate the fractional system into a simple model. Optimization methods were used. Using techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) to ascertain the parameters of the basic model, a first-order system with delay. Then, the FOPID controller will be calculated using Ziegler–Nichols methods and optimization algorithms to stabilize the real process. Better performance Overshoot and Settling time as well as minimal Energy control effort are benefits of the proposed design. To confirm that the fractional regulator is robust which is optimized by the best-chosen method (PSO), the parameters of the chosen process will be randomly varied. Through two numerical simulations, the efficiency of the suggested method has been confirmed.

Event‐Triggered Adaptive Neural Network Control for 4WS4WD Wheeled Mobile Robot

ABSTRACTIn this article, an event‐triggered adaptive neural network controller based on threshold band is designed for a four wheels independently steered and four wheels independently driven (4WS4WD) mobile robot. The 4WS4WD mobile robot is attracting attention for its excellent motion performance such as manipulation versatility and posture flexibility. However, its control difficulty is increased due to its characteristics of being controlled by eight motors for steering and driving respectively. Also, since the robot itself has limited computing and communication resources, real‐time control cannot be guaranteed. To copy with that, the kinematic and dynamics models are first introduced for the 4WS4WD mobile robot. Second, an adaptive neural network controller with low‐frequency learning rate is utilized to control the mobile robot since there are unknown perturbations in the model. It can maintain system stability while handing unidentified model perturbations. The stability of the controller is demonstrated by Lyapunov stability analysis. An event‐triggered based on threshold band is suggested to lessen the amount of computation in the control process. Finally, the simulation outcomes further demonstrate how the suggested approach can greatly lessen the computational and communication cost while maintaining control performance.

Attention and the forward testing effect.

Memory retrieval affects subsequent memory in both positive (e.g., the testing effect) and negative (e.g., retrieval-induced forgetting [RIF]) ways, and can be contrasted with other forms of memory modification (e.g., study-based encoding). Divided attention substantially impairs study-based encoding but has a modest effect on retrieval. What of the subsequent learning consequences of retrieval? Earlier studies indicate that certain positive effects (i.e., the testing effect) are not reduced by distraction, whereas negative effects (i.e., RIF) are eliminated. The present study assessed an indirect (positive) effect of retrieval-the forward testing effect (FTE), in which prior retrieval or retrieval attempts enhance subsequent learning. Two experiments examined the role of attention in both the standard FTE and the pretesting effect. In Experiment 1, participants learned three study lists through retrieval practice or restudy, followed by a fourth study list. Prior retrieval practice enhanced subsequent new learning more than restudy (i.e., the standard FTE), and to a similar degree under full attention (FA) and divided attention (DA). In Experiment 2, participants learned cue-target word pairs by either studying the pair or guessing the target when shown a cue (i.e., pretesting) followed by the correct pair. Pretesting enhanced memory more than just studying to a similar degree under FA and DA. In sum, both forms of the FTE were unaffected by distraction, indicating that these positive consequences of retrieval are not based on controlled processes but instead appear to be relatively obligatory consequences of retrieval (or retrieval attempts). These results also have relevance for specific accounts of the standard FTE and the pretesting effect.

NCBI RefSeq: reference sequence standards through 25years of curation and annotation.

Reference sequences and annotations serve as the foundation for many lines of research today, from organism and sequence identification to providing a core description of the genes, transcripts and proteins found in an organism's genome. Interpretation of data including transcriptomics, proteomics, sequence variation and comparative analyses based on reference gene annotations informs our understanding of gene function and possible disease mechanisms, leading to new biomedical discoveries. The Reference Sequence (RefSeq) resource created at the National Center for Biotechnology Information (NCBI) leverages both automatic processes and expert curation to create a robust set of reference sequences of genomic, transcript and protein data spanning the tree of life. RefSeq continues to refine its annotation and quality control processes and utilize better quality genomes resulting from advances in sequencing technologies as well as RNA-Seq data to produce high-quality annotated genomes, ortholog predictions across more organisms and other products that are easily accessible through multiple NCBI resources. This report summarizes the current status of the eukaryotic, prokaryotic and viral RefSeq resources, with a focus on eukaryotic annotation, the increase in taxonomic representation and the effect it will have on comparative genomics. The RefSeq resource is publicly accessible at https://www.ncbi.nlm.nih.gov/refseq.

Novel RP-HPLC method for simultaneous determination of dapagliflozin and teneligliptin in tablet formulation and identification of degradation products by LC-MS/MS

Abstract Background Diabetes mellitus affects millions globally, necessitating effective management strategies. Glenmark Pharmaceutical Limited introduced a fixed-dose combination of teneligliptin and dapagliflozin in 2022 to address this need. However, existing methods for their simultaneous detection are limited, lacking forced degradation studies essential for assessing drug stability. Results A stability-indicating RP-HPLC method was developed for the simultaneous determination of dapagliflozin and teneligliptin in pharmaceutical formulations. The separation was efficiently achieved employing a Zorbax Eclipse Plus C18 column (150 mm × 4.6 mm, 5 µm). The mobile phase comprised a mixture of 10 mM ammonium acetate buffer in water, methanol, and acetonitrile in a suitable proportion and delivered at a flow rate of 0.6 mL/min. Detection of the isocratic eluents was performed at 224 nm using a photodiode array (PDA) detector. Validation against various stress conditions, as per ICH guidelines, revealed significant degradation of teneligliptin primarily under acidic, basic, and oxidative stress. Moreover, liquid chromatography tandem mass spectrometry (LC-MS/MS)-based characterization was conducted for the primary degradation products of teneligliptin generated under acidic, basic, and oxidative conditions. Conclusions The developed RP-HPLC method with a stability-indicating approach provides an efficient means for the simultaneous determination of dapagliflozin and teneligliptin in pharmaceutical formulations. The significant degradation was observed under various stress conditions and also successfully separated the degradation products by this method. The proposed method directly applied for the characterization of degradation products and based on LC-MS/MS data the structure of degradation products was generated and also degradation pathways under various stress conditions were predicted. The proposed method ensured accurate and precise results in quality control process in pharmaceutical industry, and adherence to ICH validation guidelines affirms its reliability in assessing the stability of these compounds.

Determination of Particle Size for Optimum Biogas Production from Ouagadougou Municipal Organic Solid Waste

Anaerobic digestion’s contribution to sustainable development is well established. It is a sustainable production process that enables energy to be saved and produced and efficient pollution control processes to be implemented, thereby contributing to the sustainable development of our societies. Optimizing biogas yields from the anaerobic digestion of municipal organic waste is crucial for maximum energy recovery and has become an important topic of interest. Substrate particle size is a key process parameter in biogas production and precedes other pretreatment methods for most organic materials. This study aims to evaluate the impact of particle size and incubation period on biomethane production from municipal solid waste. Sampling of municipal solid waste was carried out in waste pre-collection in the city of Ouagadougou, Burkina Faso. Waste characterization showed lignocellulolytic green waste (grass, dead leaves), waste composed of fruit and leafy vegetables and leftover food waste. TableCurve 3D v4.0 software was used to develop an optimal mathematical model to correlate particle size and biomethane productivity to describe optimal production parameters. Particle sizes ranging from 2000 to 63 µm high biogas production values, specifically 385.33 and 201.25 L·kg−1 of MSV. PCA analysis clearly showed a high correlation between particle size and biogas production, with optimum production recorded for size 250 µm with a biomethane production value of 187.53 L·kg−1 of MSV. The average relative errors and RMSE for CH4 content were improved by 24.31% and 44.97%, respectively. The data calculated with the developed mathematical model and the existing experimental data were compared and permutated to validate the model. This work enabled the identification of a mathematical model that describes the correlations between the input parameters of an experiment and the monitored parameters, as well as the definition of the particle size that allows for the optimal production of biomethane.

Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics.

Identification of fractional-order transfer functionsand nonzero initial conditions using exponentiallymodulated signals

Abstract A new methodology that uses exponentially modulated signals with arbitrary excitation waveforms for the identification of fractional order transfer functions is proposed. In contrast to previous approaches where initial conditions were not considered and the system was required to be at rest for the identification procedure, the current contribution extends the formulation to the case where the system has non-zero initial conditions, dispensing with the need to place it at a resting state. This generalization is important in feedback instrumentation and metrology applications where the measurement or control process may not be disrupted to perform identification. Moreover, the procedure has a broader scope of applications because it structurally contemplates the case when the model presents derivatives in the input. Full identification of the system parameters as well as the fractional exponents associated with the model dynamics are achieved through a grid search procedure with resolution adjustable by the user. Two simulation examples are presented to illustrate the effectiveness of the proposed approach. The first example is concerned with the effect of measurement noise at the observed system output, whereas the second involves the identification of the impedance of a three-dimensional RC network model. These types of RC networks
have dynamics capturing complex phenomena with emergent responses and are ideal for emulating the complex dynamics encountered across physical sciences and in particular interdisciplinary subject areas such as biomedical engineering.

Research of Options for Constructing Information Management Systems Based on Network Models of Queuing Systems

The use of information management systems (IMSs) for the management of technical facilities is currently one of the directions for further improvement and increase in the effectiveness of the use of technical facilities in solving their target tasks. The existing modern IMSs are a set of hardware and software tools designed for collecting, processing and storing information and management. In the presence of a large amount of information and contradictory factors affecting the quality of management, making informed and timely decisions in the management process is impossible without the use of IMSs. The IMSs currently being developed are, for the most part, specialized systems and are designed to solve specific tasks. In this regard, the development and design of IMSs should be carried out taking into account the relationship with the target indicators and features of the management facilities, the results of a comprehensive analysis of information about the IMS elements in the process of functioning, and structural and algorithmic parameters that affect performance indicators. The use of mathematical models for the study of options for the construction of an IMS is the basis for the design and development of devices and subsystems of an IMS. The IMS models currently being developed make it possible to conduct research for single-stage management processes with the presence of similar service facilities in the system. At the same time, modern technical facilities and control systems are complex complexes with cyclically repeating control processes of various types of means. As a rule, such complexes have a set of parallel operating devices (control channels) that provide control of different types of objects at various stages of information processing. In this case, the structure of the IMS must be represented as a multiphase multichannel technical system in which the process of simultaneous management of several objects of various types takes place. In this regard, the purpose of the article is to develop a mathematical model of an IMS with two phases of management and the presence of an arbitrary number of serviced different types of management facilities. The basis of the model is a multiphase CFR network model with a limited waiting time for an application in the service queue. The study on the model allows choosing an option for building an IMS, in particular, choosing the optimal number of control channels for various types of objects according to the criterion of optimality and restrictions on the cost and time of management. An algorithm for selecting an option for building an IMS has been developed, and an example of calculating the number of control channels for managing three types of objects is given.

Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS)

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

Adaptive Sliding Mode Control for AUV based on Backstepping and Neural Networks

Abstract Automatic Underwater Vehicle (AUV) inevitably suffers from various interference issues in the marine environment. Due to the limitations of underwater measurement methods and tools, as well as the complexity of AUV's control parameters in the underwater environment, dynamic measurement errors are easily cascaded and amplified, leading to the failure of the control system. In response to this phenomenon, this paper focuses on overcoming the influence of internal and external unknown factors in the tracking and control process of AUV trajectories. The internal factors are mainly the uncertainties generated by the model mismatch problem, and the external factors are mainly from the dynamic ocean currents. The AUV disturbances mainly affect the kinematics and dynamics levels directly or indirectly. In order to achieve the control of internal and external dynamic disturbances, we have designed a control system that employs backstepping (BS) and a sliding mode controller (SMC) with RBF neural networks to achieve the cascaded control of kinematics and dynamics. Comparison of multiple sets of simulations shows that our proposed algorithm has excellent anti-disturbance performance for dynamic conditions with low measurement accuracy.

Superior target genes and pathways for RNAi-mediated pest control revealed by genome-wide analysis in the beetle Tribolium castaneum.

An increasing human population, the emergence of resistances against pesticides and their potential impact on the environment call for the development of new eco-friendly pest control strategies. RNA interference (RNAi)-based pesticides have emerged as a new option with the first products entering the market. Essentially, double-stranded RNAs targeting essential genes of pests are either expressed in the plants or sprayed on their surface. Upon feeding, pests mount an RNAi response and die. However, it has remained unclear whether RNAi-based insecticides should target the same pathways as classic pesticides or whether the different mode-of-action would favor other processes. Moreover, there is no consensus on the best genes to be targeted. We performed a genome-wide screen in the red flour beetle to identify 905 RNAi target genes. Based on a validation screen and clustering, we identified the 192 most effective target genes in that species. The transfer to oral application in other beetle pests revealed a list of 34 superior target genes, which are an excellent starting point for application in other pests. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses of our genome-wide dataset revealed that genes with high efficacy belonged mainly to basic cellular processes such as gene expression and protein homeostasis - processes not targeted by classic insecticides. Our work revealed the best target genes and target processes for RNAi-based pest control and we propose a procedure to transfer our short list of superior target genes to other pests. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A novel automated pipeline to assess MR spectroscopy quality control: Comparing current standards and manual assessment.

The absence of a consensus data quality control (DQC) process inhibits the widespread adoption of MR spectroscopy. Poor DQC can lead to unreliable clinical diagnosis and irreproducible research conclusions. Currently, manual visual assessment or the standard quantitative metrics of signal-to-noise, linewidth, and model fit are used as classifiers, but these measures may not be sufficient. To supplement standard metrics, this paper proposes a novel automated DQC pipeline named Visual Evaluative Control Technology Of Resonance Spectroscopy (VECTORS). Manual DQC ratings were conducted on 7180 spectra obtained from 110 young adults using short-echo chemical shift imaging at 3Tesla. Four reviewers conducted manual ratings on the presence of artifacts and location of metabolites. The ratings were labor intensive, taking over 180hours. VECTORS was developed to quantify their DQC criteria, detecting artifacts that present as duplicate peaks, vertical shifts, and glutamine+glutamate and myoinositol peak shapes. Run on the same data using a standard laptop, VECTORS only took 2hours. The manual ratings were not monotonic to the standard quantitative metrics. VECTORS correctly flagged spectra that the manual ratings missed. VECTORS accurately flagged an additional 126 poor DQ spectra that consensus cutoffs of the standard quantitative metrics deemed good DQ. Standard quantitative metrics may not account for all DQC artifacts as they are not monotonic to the manual ratings. However, manual ratings are labor intensive, subjective, and irreproducible. VECTORS addresses these issues and should be used in conjunction with standard quantitative metrics.