Control Limits Research Articles

Planning maintenance when resources are limited: a study of periodic opportunistic replacement

Abstract Accepted by: M. Zied Babai We study an aged-based replacement policy with two control limits. The first triggers opportunistic replacement and the second triggers a guaranteed replacement. The policy is novel because: the instances for component replacement are restricted to instances of time, which we call slots, that arise periodically; and a slot provides an opportunity for replacement with a particular probability. The policy models contexts in which maintenance is periodic, and resources are limited or execution of maintenance is not guaranteed. The policy is important for practice because it is simple and reflects the common reality of time-based maintenance planning. Long-run cost per unit time and average availability are calculated in a renewal-reward framework. Numerical study indicates that, if opportunities are rare, guaranteed replacement is beneficial and opportunities should be taken early in the life of a system. Using the policy, a maintainer can evaluate the cost–benefit of investing more resources to reduce the time between slots. Specific analysis and policy comparisons can be carried out using a web-application developed by the authors.

Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry.

Clinical flow cytometry laboratories require quality control materials for assay development, validation, and performance monitoring, including new reagent lot qualification. However, finding suitable controls for populations with uncommonly expressed antigens or for rare populations, such as mast cells, can be difficult. To that end, we evaluated synthetic abnormal mast cell particles (SAMCP), developed together with, and manufactured by, Slingshot Biosciences. The SAMCP's were designed to phenotypically mimic abnormal neoplastic mast cells: they were customized to have the same light scatter and autofluorescence properties of mast cells, along with surface antigen levels of CD45, CD33, CD117, CD2, CD25, and CD30 consistent with that seen in mast cell disease. We evaluated several performance characteristics of these particles using ARUP's high sensitivity clinical mast cell assay, including limit of detection, off-target activity and FMO controls, precision, scatter properties of the particles utilizing several different cytometer platforms, and particle antigen stability. The phenotype of the SAMCP mimicked abnormal mast cells, and they could be distinguished from normal native mast cells. FMO controls demonstrated specificity of each of the markers, and no off-target binding was detected. The limit of detection of the particles spiked into normal bone marrow was found to be ≤0.003% in a limiting dilution assay. The mast cell particles were found to perform similarly on Becton Dickinson Lyric, Cytek Aurora, and Beckman Coulter Navios and CytoFLEX platforms. Within run and between run precision were less than 10% CV. SAMCP were stable up to 13 days with minimal loss of antigen fluorescence intensity. The SAMCP's were able to successfully mimic neoplastic mast cells based on the results of our high sensitivity mast cell flow cytometry panel. These synthetic cell particles represent an exciting and innovative technology, which can fulfill vital needs in clinical flow cytometry such as serving as standardized control materials for assay development and performance monitoring.

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

The diagnostic accuracy of quality control rules

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfr and only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules.

MAXIMUM EXPONENTIALLY WEIGHTED MOVING AVERAGE WITH MEASUREMENT ERROR (USING COVARIATE METHOD) USING AUXILIARY INFORMATION FOR CEMENT QUALITY CONTROL

The main quality characteristic at XYZ Inc. that should be observed is Compressive Strength. Cement production quality control is carried out on the average and process variability jointly with the Max-EWMA control chart. Measurement error can be found in the Compressive Strength. It can affect the sensitivity of the control chart, so quality control will be carried out by considering the presence of measurement error. Handling measurement errors can be done through three approaches (covariate method, multiple measurements, and linearly increasing variance). This research only focuses on the covariate method. Auxiliary variables also explain variance in the production process, so they are also considered in this research, with Blaine used as an auxiliary variable. Therefore, the control chart that will be formed is the Max-EWMA ME (Covariate) AI. The Max-EWMA and Max-EWMA ME (Covariate) AI control charts show that the XYZ Inc. cement production process based on variability and process averages is simultaneously statistically controlled. The controlled Max-EWMA control chart has an upper control limit of UCL=1.503018, and parameters dan . Max-EWMA ME (Covariate) AI has in-control parameters . The Max-EWMA ME (Covariate) AI control chart is more sensitive than the Max-EWMA control chart. Cement production capabilities based on Compressive Strength have a Cpl and Cpk capability index of 1.54, which means that the cement production process is capable, consistent, and has high accuracy so that the quality has reached the target.

The economic production quantity model for an imperfect production process by control chart design

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Limits of Judicial Control at the Pre-trial Stages of the Criminal Process

The article examines the issues of the limits of judicial control in accordance with Art. 125 of the Code of Criminal Procedure of the Russian Federation at the pre-trial stages of criminal proceedings. The author concludes that the court, verifying the legality and validity of the actions (inaction) and decisions of the investigation body in accordance with Art. 125 of the Code of Criminal Procedure, has no right to invade the investigation process and establish the factual circumstances of the case by these bodies and assess the validity of the internal conviction of officials of these bodies, monitor the correctness of their application of criminal and criminal procedure laws.Otherwise, the court would invade the competence of the investigator, interrogator, prosecutor, limiting their procedural independence in the commission of procedural actions and decision-making and would prejudge issues that may later become the subject of legal proceedings.

Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.

Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

AbstractIn this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location‐scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value‐at‐risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location‐scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real‐world applications.

The Optimal Control Method of Test Power Supply for DC Distribution Network

Background:: The test power supply is one of the key devices in the DC distribution network, which is necessary to supply power with high quality for testing other devices' performance. The double active bridge (DAB) converter is a common circuit topology for test power supply, which has the advantages of high-frequency electrical isolation, bi-directional power flow, and high power density. For the converter, control methods, such as single-phase-shift (SPS) control, and single-current stress or single-return power optimization under extended-phase-shift (EPS) control, have their limitations, which influences efficiency. Objective:: This paper aims to propose a dual-objective optimal control method, which can effectively improve efficiency of the test power supply. Methods:: This paper addresses the limitations of SPS control, and single-current stress or singlereturn power optimization under EPS control of the DAB converter, and proposes a dual-objective optimal control method based on the idea of using objective planning in the full power range under the condition of satisfying soft switching, which effectively improves efficiency of the test power supply. Results:: With the proposed dual-objective optimal control method, the converter achieves a smaller current stress similar to that with the single-current stress optimal control, and the return power is also reduced by 29.51%. Efficiency of the test power supply reaches 86.9%, which is better than 82.5% with SPS control and 85.3% with single-current stress optimization under EPS control. The experimental results fully verify the effectiveness of the proposed control method. Conclusion:: A dual-objective optimal control method is proposed. By using the presented method, current stress and return power are both optimally designed, so the efficiency of the test power supply can be effectively improved.

Performance evaluation of PID and Fuzzy Logic controllers for residential ORC-based cogeneration systems

Cogeneration systems have been identified as a way to reduce primary energy consumption, especially the ones using fossil fuels. Although this technology has already reached maturity on a medium-to-large scale, it is not very well explored on a micro-scale, especially at a residential level where the market is currently filled with carbon-fueled boilers and combi-boilers. For implementation at a residential level, the organic Rankine cycle has been mentioned as a suitable technology because of its simplicity and thermal/ electrical efficiencies. Despite its performance, one of the most important aspects of these applications is the response time, which is mainly connected to the system’s control. Due to its influence, this study aims to provide insights for developing flexible control strategies and analyze two different control methodologies, a standard PID and a Fuzzy Logic controller, considering the operating conditions of an experimental ORC-based micro-cogeneration test rig.Before being implemented in the test rig, both control methodologies were optimized and tested in a virtual environment in order to avoid the exposure of the test rig to unsafe operating conditions. While both controllers show a reasonable response in operating conditions similar to the ones used in the virtual environment, the Fuzzy Logic approach proved to be more adaptable to changes in the system’s dynamics, which are caused by intrinsic (e.g., unpredicted evaporative phenomena) or external disturbances (e.g., a pressure ratio variation), obtaining an overall better performance. Additionally, the limitations of static PID controllers for complex nonlinear processes, like the ORC, were highlighted by their incapacity to stabilize the system when the organic fluid is diverted from the bypass line to the expansion valve. Conversely, it was concluded that the ability to incorporate expert knowledge and heuristics makes the Fuzzy Logic controller well-suited for ORC systems with uncertain, unstable, and fluctuating behavior.

Phase I quality control framework for monitoring organ-at-risk dose

Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling’s T2 chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40’s plan and updating the DVH and control chart, P40 was IC, but P14* (P14’s re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

Independent Phase Control in Gap‐Tuned Metasurfaces for Dual‐Function Switching

AbstractSeeking new ways to actively control the phase using metasurfaces has gained significant attention in recent years, which is the key to achieve controllable wavefronts under external stimuli. Among various active phase control mechanisms, a mechanical method based on gap tuning, termed as gap‐tuned metasurface here, has shown promise in continuous phase adjustment. However, previous studies face limitations in independent and complete phase control at different gap distances, which is crucial in dual‐function switching applications. In this study, a novel type of gap‐tuned metasurfaces are proposed, which can overcome the above limitations through simultaneously manipulating the Pancharatnam–Berry phase and gap‐induced dynamic phase under circularly polarized incidences. Two exemplary gap‐tuned metasurfaces capable of orbital angular momentum (OAM) switching and focusing‐polarity switching are experimentally demonstrated. Furthermore, the special dynamic phase design also exhibits phase conjugate property similar to the Pancharatnam–Berry phase, making circular polarization handedness an alternative route for dual‐function switching. This method provides a straightforward and effective means for the development of active wavefront control devices.

Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ([Formula: see text]) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

Consistency evaluation of multidimensional quality attributes of Atractylodis Macrocephalae Rhizoma by fusion of multi-source information

The quality control of Chinese medicinal decoction pieces is one of the key tasks in the traditional Chinese medicine industry. In this study, multi-source information fusion was employed to fuse the data from near-infrared spectroscopy, electronic tongues, and other tests and establish an overall quality consistency evaluation method for Atractylodis Macrocephalae Rhizoma, which provided methodological support for the overall quality evaluation of Atractylodis Macrocephalae Rhizoma. The near-infrared spectroscopy information was measured in both static and dynamic states for 23 batches of Atractylodis Macrocephalae Rhizoma samples from different sources, and the electronic tongue sensory information, moisture content, and leachate content were measured. The overall quality of Atractylodis Macrocephalae Rhizoma was evaluated by multi-source information fusion. The results showed that the near-infrared spectroscopy information of 16122103, 801000509, 801000352, 701003656, HX21L01, and 160956 was different from that of other batches of Atractylodis Macrocephalae Rhizoma powder in the static state, and 701003298, 16122103, 701003656, 701003107, 801000229, and 18090404 were the different batches in the dynamic state. The moisture content showed no significant difference between batches. The leachate content in the batch 801000509 was different from that in other batches. The electronic tongue sensory information of 150721004, 151237, 160703004, HX21M01, HX21K04, HX21K01, and 601003516 was different from that of other batches. Furthermore, data layer fusion was employed to analyze the overall quality of Atractylodis Macrocephalae Rhizoma. Four batches, 150721004, HX21M01, HX21K04, and HX21K01, showed the parameters exceeding the 95% control limits and differed from the other samples in terms of the overall quality. This study integrated the information of moisture, near-infrared spectroscopy, and other sources to evaluate the quality consistency among 23 batches of Atractylodis Macrocephalae Rhizoma samples, which provides a reference for the quality consistency evaluation of Chinese medicinal decoction pieces.

Imaging in a Pandemic: How Lack of Intravenous Contrast for Computed Tomography Affects Emergency Department Throughput.

During the coronavirus 2019 pandemic, hospitals in the United States experienced a shortage of contrast agent, much of which is manufactured in China. As a result, there was a significantly decreased amount of intravenous (IV) contrast available. We sought to determine the effect of restricting the use of IV contrast on emergency department (ED) length of stay (LOS). We conducted a single-institution, retrospective cohort study on adult patients presenting with abdominal pain to the ED from March 7-July 5, 2022. Of 26,122 patient encounters reviewed, 3,028 (11.6%) included abdominopelvic CT with a complaint including "abdominal pain." We excluded patients with outside imaging and non-ED scans. Routine IV contrast agent was administered to approximately 74.6% of patients between March 7-May 6, 2022, when we altered usage guidelines due to a nationwide shortage. Between May 6-July 5, 2022, 32.8% of patients received IV contrast after institutional recommendations were made to limit contrast use. We compared patient demographics and clinical characteristics between groups with chi-square test for frequency data. We analyzed ED LOS with nonparametric Wilcoxon rank-sum test for continuous measures with focus before and after new ED protocols. We also used statistical process control charts and plotted the 1, 2 and 3 sigma control limits to visualize the variation in ED LOS over time. The charts include the average (mean) of the data and upper and lower control limits, corresponding to the number of standard deviations away from the mean. After use of routine IV contrast was discontinued, ED LOS (229.0 vs 212.5 minutes, P = <0.001) declined by 16.5 minutes (95% confidence interval -10, -22). Intravenous contrast adds significantly to ED LOS. Decreased use of routine IV contrast in the ED accelerates time to CT completion. A policy change to limit IV contrast during a national shortage significantly decreased ED LOS.

Exhalation metabolomics: A new force in revealing the impact of ozone pollution on respiratory health

Near-surface ozone pollution poses a significant threat to respiratory health, with the lung micro-environment playing a crucial role in its effects. This article highlights the potential of exhalation metabolomics as a novel approach to investigate the respiratory health impacts of ozone pollution. By analyzing exhaled breath metabolites, we can gain insights into the biological responses triggered by ozone exposure. This strategy offers a non-invasive, real-time monitoring tool to assess individual susceptibility and health outcomes. Furthermore, exhalation metabolomics has the potential to inform the development of concentration limits for ozone pollution control, providing a scientific basis for effective policy-making and public health interventions.

Research on force-controlled polishing system for robotic arm based on compliance control

This thesis enhances robotic arm control in intricate polishing environments by using impedance control for consistent force regulation between the arm and workpiece. An adaptive impedance control algorithm accommodates contact force variations, boosting system robustness. The PSO-BP variable impedance controller integrates particle swarm optimization and BP neural network for real-time optimization, overcoming limitations of fixed-parameter impedance control on surfaces with curvature changes. Experimental results show a significant reduction in polishing force fluctuations and improved force tracking. These findings support efficient robotic arm utilization in polishing tasks.