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Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.

Additive manufacturing, often known as three-dimensional (3D) printing, is a process in which a part is built layer-by-layer and is a promising approach for creating components close to their final (net) shape. This process is challenging the dominance of conventional manufacturing processes for products with high complexity and low material waste . Titanium alloys made by additive manufacturing have been used in applications in various industries. However, the intrinsic high cooling rates and high thermal gradient of the fusion-based metal additive manufacturing process often leads to a very fine microstructure and a tendency towards almost exclusively columnar grains, particularly in titanium-based alloys . (Columnar grains in additively manufactured titanium components can result in anisotropic mechanical properties and are therefore undesirable .) Attempts to optimize the processing parameters of additive manufacturing have shown that it is difficult to alter the conditions to promote equiaxed growth of titanium grains . In contrast with other common engineering alloys such as aluminium, there is no commercial grain refiner for titanium that is able to effectively refine the microstructure. To address this challenge, here we report on the development of titanium-copper alloys that have a high constitutional supercooling capacity as a result of partitioning of the alloying element during solidification, which can override the negative effect of a high thermal gradient in the laser-melted region during additive manufacturing. Without any special process control or additional treatment, our as-printed titanium-copper alloy specimens have a fully equiaxed fine-grained microstructure. They also display promising mechanical properties, such as high yield strength and uniform elongation, compared to conventional alloys under similar processing conditions, owing to the formation of an ultrafine eutectoid microstructure that appears as a result of exploiting the high cooling rates and multiple thermal cycles of the manufacturing process. We anticipate that this approach will be applicable to other eutectoid-forming alloy systems, and that it will have applications in the aerospace and biomedical industries.

Quality control (QC) is a systematic approach to ensuring that products and services meet customer requirements. It is an essential part of manufacturing and industry, as it helps to improve product quality, customer satisfaction, and profitability. Quality practitioners generally apply control charts to monitor the industrial process, among many other statistical process control tools, and to detect changes. New developments in control charting schemes for high-quality monitoring are the need of the hour. In this paper, we have enhanced the performance of the mixed homogeneously weighted moving average (HWMA)-cumulative sum (CUSUM) control chart by using the auxiliary information-based (AIB) regression estimator and named it MHC.sub.AIB . The proposed MHC.sub.AIB chart provided an unbiased and more efficient estimator of the process location. The various measures of the run length are used to judge the performance of the proposed MHC.sub.AIB and to compare it with existing AIB charts like CUSUM.sub.AIB, EWMA.sub.AIB, MEC.sub.AIB (mixed AIB EWMA-CUSUM), and HWMA.sub.AIB . The Run length (RL) based performance comparisons indicate that the MHC.sub.AIB chart performs relatively better in monitoring small to moderate shifts over its competitor's charts. It is shown that the chart's performance improves with the increase in correlation between the study variable and the auxiliary variable. An illustrative application of the proposed MHC.sub.AIB chart is also provided to show its implementation in practical situations.

Control charts are popular tools in the statistical process control toolkit and the exponentially weighted moving average (EWMA) chart is one of its essential component for efficient process monitoring. In the present study, a new Bayesian Modified-EWMA chart is proposed for the monitoring of the location parameter in a process. Four various loss functions and a conjugate prior distribution are used in this study. The average run length is used as a performance evaluation tool for the proposed chart and its counterparts. The results advocate that the proposed chart performs very well for the monitoring of small to moderate shifts in the process and beats the existing counterparts. The significance of the proposed scheme has proved through two real-life examples: (1) For the monitoring of the reaming process which is used in the mechanical industry. (2) For the monitoring of golf ball performance in the sports industry.

Control charts are an amazing and essential statistical process control (SPC) instrument that is commonly used in monitoring systems to detect a specific defect in the procedure. The mixed Tukey modified exponentially weighted moving average - moving average control chart (MMEM-TCC) with motivation detection ability for fewer shifts in the process mean under symmetric and non-symmetric distributions is proposed in this paper. Average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) were used as efficiency criteria in the Monte Carlo simulation, and their efficiency was compared to existing control charts. Furthermore, the expected ARL (EARL) is a method for evaluating the performance of control charts beyond a specific range of shift sizes. The distinguishing feature of the proposed chart is that it performs efficiently in detecting small to moderate shifts. There are applications for PM 2.5 and PM 10 data that demonstrate the performance of the proposed chart.

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. Graphical abstract

Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

The book presents basic structures, concepts and algorithms in the area of multilayer optimizing control of industrial systems, as well as the results of the research that was carried out by the authors over the last two decades. The methodologies and control algorithms are thoroughly illustrated by numerous simulation examples. Also, the applications to several case study examples are presented. These include ethylene distillation column, vaporizer pilot scale plant, styrene distillation line consisting of three columns and industrial furnace pilot scale plant. A temporal decomposition is applied to the Integrated Wastewater System case study to derive multilayer dynamic optimizing controller with repetitive robust model predictive control mechanism distributed over the layers operating in different time scales.

Ultrathin two-dimensional (2D) semiconducting layered materials offer great potential for extending Moore's law of the number of transistors in an integrated circuit . One key challenge with 2D semiconductors is to avoid the formation of charge scattering and trap sites from adjacent dielectrics. An insulating van der Waals layer of hexagonal boron nitride (hBN) provides an excellent interface dielectric, efficiently reducing charge scattering . Recent studies have shown the growth of single-crystal hBN films on molten gold surfaces or bulk copper foils . However, the use of molten gold is not favoured by industry, owing to its high cost, cross-contamination and potential issues of process control and scalability. Copper foils might be suitable for roll-to-roll processes, but are unlikely to be compatible with advanced microelectronic fabrication on wafers. Thus, a reliable way of growing single-crystal hBN films directly on wafers would contribute to the broad adoption of 2D layered materials in industry. Previous attempts to grow hBN monolayers on Cu (111) metals have failed to achieve mono-orientation, resulting in unwanted grain boundaries when the layers merge into films . Growing single-crystal hBN on such high-symmetry surface planes as Cu (111) is widely believed to be impossible, even in theory. Nonetheless, here we report the successful epitaxial growth of single-crystal hBN monolayers on a Cu (111) thin film across a two-inch c-plane sapphire wafer. This surprising result is corroborated by our first-principles calculations, suggesting that the epitaxial growth is enhanced by lateral docking of hBN to Cu (111) steps, ensuring the mono-orientation of hBN monolayers. The obtained single-crystal hBN, incorporated as an interface layer between molybdenum disulfide and hafnium dioxide in a bottom-gate configuration, enhanced the electrical performance of transistors. This reliable approach to producing wafer-scale single-crystal hBN paves the way to future 2D electronics.

This work is aimed at developing methods for increasing the production of heavy crude oil while optimizing energy costs. Various methods have been studied for recovering heavy oil from deep reservoirs. Based on the developed methods, a number of dynamic models have been obtained that describe the behavior of the temperature field in the tubing. Estimations of thermal deformation are carried out. On the basis of dynamic models, fundamentally new devices are obtained and registered in the prescribed manner, providing a subsystem for automated process control systems.