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A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes; users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptive windows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance. [ABSTRACT FROM AUTHOR]

Statistical simulation is a necessary step in integrated circuit design since it provides a realistic picture of the circuit's behavior in the presence of manufacturing process variations. When some of the circuit components lack an accurate analytical model, as is often the case for emerging semiconductor devices or ones working at cryogenic temperatures, an approximation model is necessary. Such models are usually based on a lookup table or artificial neural network individually fitted to measurement data. If the number of devices available for measurement is limited, so is the number of approximation model instances, which renders impossible a reliable statistical circuit simulation. Approximation models using the device's physical parameters as inputs have been reported in the literature but are only useful if the end user knows the statistical distributions of those parameters, which is not always the case. The solution proposed in this work uses a type of artificial neural network called the variational autoencoder that, when exposed to a small sample of I-V curves under process variations, captures their essential features and subsequently generates an arbitrary number of similarly disturbed curves. No knowledge of the underlying physical sources of these variations is required. The proposed generative model trained on as few as 20 instances of a MOSFET is shown to precisely reproduce the period and power consumption distributions of a ring oscillator built with these MOSFETs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is the critical reflection of managerial pressure on the minimization of process variability by management methods based on measurability and performance of companies with the help of technological functions. Methodology: The study analytically describes management efforts to minimize the process variability and capitalize on technological innovations to increase flexibility and efficiency of work processes. Then the study analyses the logistical and personal consequences of process minimization in the context of general managerial principles. The Research questions are: 1. What are the implications of the current efforts of management to minimize its processes? 2. What are the personnel implications of minimizing processes? Results: Identification of technological optimism applied to production management and logistics, and limits and weaknesses of minimization of process variability by mechanical and technological management approaches in order to increase efficiency, regardless of the potential of staff. The consequences and specific challenges of study are particularly important for strategy and management. Research limitations: As it is a conceptual study, it is natural that it works with a certain perspective of the authors, who are motivated by practical experience in companies and the need to talk about the mentioned phenomena in management education. This means that the study only touches on some areas and leaves others out. The study also does not claim to be a comprehensive exhaustion of all related topics, it is a stimulus in what it condescendingly names. The biggest proctor for further work lies in the rapidly evolving digitalization and application of AI in organizations and companies. Further potential lies in deeper analysis of human thought processes and decision making in critical situations. This field (e.g., neuroscience) is also developing its research rapidly. Practical implications: Conceptual study deals with a critical discussion over the current trends of management, which believes in improving the performance of companies through minimization of process variability. However, mechanistic approaches or technological innovations have a significant limit, that manifest themselves in negative impact on workers exposed to increased digital control. Originality: The reduction of motivation, creativity, autonomy, active participation in corporate processes, disruption of corporate culture etc., are only some of them. The resulting consequences reveal the reason for team member burnout, as well as the decrease of creativity, inspiration, and long-term stability of the staff. [ABSTRACT FROM AUTHOR]

To achieve better product quality, the dual‐response surface has been widely used to simultaneously achieve targets for the mean and reduced variance. The general assumption is that the experimental data are normally distributed without censoring. However, reliability data are usually non‐normally distributed and censored. Consequently, the classical dual‐response surface method may not be suitable for handling reliability data. This study proposed a dual‐response surface method based on robust parameter design (RPD) for reliability data. First, a percentile lifetime and an interquartile range (IQR) were used for the location and dispersion effects, respectively. A two‐stage method was then developed to identify the effects of significant factors. During the first stage, the Boruta algorithm was employed for factor effect selection, and during the second stage, the least absolute shrinkage and selection operator (LASSO) dimension reduction method was used to determine the final significant factor effects. Finally, a modified standard particle swarm optimization (SPSO) algorithm was adopted to determine the optimal solution. The proposed method was demonstrated using an industrial thermostat experiment. Compared with other methods, the proposed method performed better over various warranty periods, particularly for the 10th percentile. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-sided Phase II S 2 -chart with probability limits because the surveillance of both an increase and decrease in the process variance plays a decisive role in a continuous quality improvement program. We propose a two-sided average run length unbiased S 2 - chart under the repetitive sampling with probability limits for a fixed in-control average run length and average sample size to eliminate the average run length biasedness. It is well established that the Shewhart-type charts are less sensitive to detect small to moderate changes in the process parameters. Therefore, a repetitive sampling scheme is taken into consideration to improve the S 2 -chart’s ability to detect changes in the process variance. Under the repetitive sampling methodology, the detection ability of the chart is improved by using more than one samples if the first sample does not provide sufficient evidence to decide whether the process is in-control or out-of-control. The proposed chart is compared with the existing charts, such as the equal tailed standard Shewhart S 2 -chart and unequal tailed S 2 -chart under repetitive sampling. Results show that the proposed chart is more efficient than the existing chart. Finally, an illustration has been provided in the favor of the proposed chart with the help of a published dataset. [ABSTRACT FROM AUTHOR]

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. Understanding differences among process variants helps analysts and managers to make informed decisions as to how to standardise or otherwise improve a business process. Existing process variant mining approaches typically fall short in full supporting semantic process variability mining, especially rarely taking activity behaviour relationships and trace context semantic into consideration. Here, we propose a semantic process variant discovery method, aimed at solving the difficulty of distinguishing similar-but-different behaviours directly from event logs. More specifically, we adapt concepts of benchmark logs and trace context tree to formalise context semantic of event log, to classify benchmark logs into several parts, thereby the clustered trace cohorts are mapped to discover the configurable process variants. In the experimental part, some performance metrics of the proposed method are evaluated and calculated by real-world event logs, supporting the usefulness of the proposed method. The experimental results show that the proposed method is able to distinguish similar-but-different behaviours and is superior to the characteristic trace clustering method using conventional neural networks. [ABSTRACT FROM AUTHOR]

A configurable process Change Mining approach can detect changes from a collection of event logs and provide details on the unexpected behavior of all process variants of a configurable process. The strength of Change Mining lies in its ability to serve both conformance checking and enhancement purposes; users can simultaneously detect changes and ensure process conformance using a single, integrated framework. In prior research, a configurable process Change Mining algorithm has been introduced. Combined with our proposed preprocessing and change log generation methods, this algorithm forms a complete framework for detecting and recording changes in a collection of event logs. Testing the framework on synthetic data revealed limitations in detecting changes in different types of variable fragments. Consequently, it is recommended that the preprocessing approach be enhanced by applying a filtering algorithm based on sliding and adaptive windows. Our improved approach has been tested on various types of variable fragments to demonstrate its efficacy in enhancing Change Mining performance.

In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the S 2 -DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the S 2 -DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the S 2 -DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new S 2 -DGWMA chart. [ABSTRACT FROM AUTHOR]

Vertically Stacked Nanosheet TFET (VNS-TFET) can break the subthreshold swing limit of MOSFETs and achieve higher layout efficiency. Due to the scaled-down device size, VNS-TEFT becomes vulnerable to process variability during fabrication. In this paper, the statistical impedance field method (sIFM) is used to investigate the effects of process variability, such as random doping fluctuations (RDF), work function variation (WFV), and oxide thickness variation (OTV), on VNS-TEFT. The standard deviation of the threshold voltage (σVth) is used to measure the effects of doping concentration, gate metal grain-related parameters and device parameters on the device process variability. The TCAD simulation results show that choosing an appropriate doping concentration for the source region can effectively reduce the effects of RDF. As the average grain size increases, the effect on WFV and OTV increases, but RDF has no effect. In addition, using a physical gate oxide with higher-κ in the VNS-TFET can effectively suppress WFV. Finally, it can be seen that the RDF is most sensitive to the size variation of the VNS-TFET. [ABSTRACT FROM AUTHOR]

This work analyzes a set of Full Adder circuits considering variability effects, comparing delay and energy consumption when operating at nominal voltage and near-threshold voltage. The decoupling cell technique was adopted for reducing process variability effects. It is evaluated traditional adders and XOR-based adders in different scenarios, from traditional nominal conditions to circuits with decoupling cells addition. Mirror Adder still is the best alternative to performance optimization at nominal voltage operation. The near-threshold operation can reduce the power by 97% on average for the evaluated circuits, with penalties of 6.4 times on delay and increasing up to 2 times the process variability sensitivity. XOR-based FAs are less sensitive to process variability effects on power, independently of the voltage operation. Moreover, the decoupling cell technique brought benefits to all evaluated adders about the mitigation of process variability effects on power, since this technique introduces a reduction in the power consumption of the circuits. So, the use of decoupling cells demonstrates to be a good option to achieve greater robustness to variability impact regarding the circuits power consumption. • Process Variability affects the expected behavior of circuits at nanometer nodes. • FinFET devices show better control of short-channel effects and radiation robustness. • Even so, process variability is a considerable challenge. arithmetic blocks. • This work proposes adoption of XOR-based Full Adders, together with the decoupling cells mitigation technique, to reduce the process variability deviation. • Decoupling cells brought benefits to all evaluated adders about the mitigation of process variability effects on power. [ABSTRACT FROM AUTHOR]

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding–blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis. [Display omitted] • A stochastic process model is developed for a continuous feeding–blending unit. • The impact of process variability on the performance of an MPC is investigated. • A case study is carried out for a realistic setup of a CDC line in closed-loop. [ABSTRACT FROM AUTHOR]

Reviews for products and services written by previous consumers have become an influential input to the purchase decision of customers. Many service businesses monitor the reviews closely for feedback as well as detecting service flaws, and they have become part of the performance review for service managers with rewards tied to improvement in the aggregate rating. Many empirical papers have documented a bias in the aggregate ratings, arising because of customers' inherent self-selection in their choices and bounded rationality in evaluating previous reviews. Although there is a vast empirical literature analyzing reviews, theoretical models that try to isolate and explain the bias in ratings are relatively few. Assuming consumers simply substitute the average rating that they see as a proxy for quality, we give a precise characterization of the self-selection bias on ratings of an assortment of products when consumers confound ex ante innate preferences for a product or service with ex post experience and service quality and do not separate the two. We develop a parsimonious choice model for consumer purchase decisions and show that the mechanism leads to an upward bias, which is more pronounced for niche products. Based on our theoretical characterization, we study the effect on pricing and assortment decisions of the firm when potential customers purchase based on the biased ratings. Our results give insights into how quality, prices, and customer feedback are intricately tied together for service firms. This paper was accepted by David Simchi-Levi, operations management. [ABSTRACT FROM AUTHOR]

• Trace metal availability can impact culture processes & protein quality. • Optimal trace metal composition is important in chemically defined media. • Role of Mn, Cu, Zn in central carbon metabolism is reviewed. • Trace metals impact on CHO cell culture and recombinant protein quality reviewed. Replacement of serum and increasing use of chemically defined media demands optimisation of trace metal components for biomanufacturing applications. Trace metal availability can impact culture performance, productivity and product quality. Several trace metals are cofactors of metabolic and other enzymes, and thus their availability regulates cellular metabolism. Additionally, they can also affect the availability of other trace metals and stability of some medium components. Such factors also need to be considered while formulating trace metal concentrations in the culture medium. Due to their very low concentrations, these components are susceptible to substantial variability arising from contaminants from other raw material and leaching from process equipment and can contribute to process variability. Understanding the role and impact of trace metals will help develop strategies to achieve targeted process parameters and increase process robustness vis-à-vis any lot-to-lot variability in trace metal concentration in culture medium. This review describes the role of trace metals, particularly manganese, copper and zinc, in central carbon metabolism to aid in understanding the basis of metal-mediated effects on culture performance and provides a comprehensive review of the reported impact of trace metals on CHO cell culture performance and recombinant protein quality. [ABSTRACT FROM AUTHOR]

The need of higher quality products from the customers and the tendency of industries to quality improvement for maintaining their competitive position over the long run are the main motives of using improvement methodologies. However, any use of improvement methodology besides its additional costs will also change the total variability of the process. Changes in total variability will affect the optimal value of tolerance as well as the quality of the product. Therefore, one of the main concerns of the producers is to find an effective way to manufacture their goods in a manner that reduces the production costs and gains customers’ satisfaction. To meet these goals, this study introduces an algorithm to propose a riskless approach of improvement that results in the maximum amount of net savings. It is shown that using appropriate improvement strategies simultaneous with a proper selection of tolerance have an important impact to enhance productivity and quality. [ABSTRACT FROM AUTHOR]

Control charting techniques are widely used in the manufacturing industry. One of the common charts that are used to monitor process variability is the S control chart. Finite horizon process monitoring has received great attention in the last decade. In the current literature, no attempt has been made to monitor the process variability in a finite horizon process. To fill this gap in research, this paper proposes two one-sided modified S charts for monitoring the standard deviation in a finite horizon process. The performance of the proposed charts is evaluated in terms of the truncated average run length and truncated standard deviation of the run-length criteria. The numerical performances of the proposed charts are shown with the selection of numerous process shifts. The effect of the sample sizes, the number of inspections and the process shifts are studied. [ABSTRACT FROM AUTHOR]

In cell culture processes cell growth and metabolism drive changes in the chemical environment of the culture. These environmental changes elicit reactor control actions, cell growth response, and are sensed by cell signaling pathways that influence metabolism. The interplay of these forces shapes the culture dynamics through different stages of cell cultivation and the outcome greatly affects process productivity, product quality, and robustness. Developing a systems model that describes the interactions of those major players in the cell culture system can lead to better process understanding and enhance process robustness. Here we report the construction of a hybrid mechanistic-empirical bioprocess model which integrates a mechanistic metabolic model with subcomponent models for cell growth, signaling regulation, and the bioreactor environment for in silico exploration of process scenarios. Model parameters were optimized by fitting to a dataset of cell culture manufacturing process which exhibits variability in metabolism and productivity. The model fitting process was broken into multiple steps to mitigate the substantial numerical challenges related to the first-principles model components. The optimized model captured the dynamics of metabolism and the variability of the process runs with different kinetic profiles and productivity. The variability of the process was attributed in part to the metabolic state of cell inoculum. The model was then used to identify potential mitigation strategies to reduce process variability by altering the initial process conditions as well as to explore the effect of changing CO 2 removal capacity in different bioreactor scales on process performance. By incorporating a mechanistic model of cell metabolism and appropriately fitting it to a large dataset, the hybrid model can describe the different metabolic phases in culture and the variability in manufacturing runs. This approach of employing a hybrid model has the potential to greatly facilitate process development and reactor scaling. • Hybrid mechanistic-empirical model developed and fit to manufacturing-scale bioprocess data. • Interaction of metabolism, cell signaling, and the reactor environment reproduced the variability in lactate production. • In silico bioprocess simulation identified key process changes to reduce variability which may drive higher productivity. [ABSTRACT FROM AUTHOR]

Introduction: The Covid-19 pandemic has measured the response, adaptation and research capacity of chemical and pharmaceutical industry. Worldwide, pharmaceutical companies launched the race for a vaccine development. However, some micro, small, medium and large enterprises have adapted to meet the increase in disinfection products demand. A process requires quality assurance from the design phase, thereby reducing the number of defective products in the operation phase and adapting to market requirements. Objective: To design a production process for a hydroalcoholic gel, which meets quality specifications using artisanal alcohol as raw material. Materials and Methods: A procedure was used that considers product quality from conceptual design. It uses the guide to the expression of uncertainty in measurement (GUM) to determine the uncertainty of operational parameters. Results and Discussion: In the applied case study, carbopol concentration of 0.34 to 0.45 and pH of 5.07 to 6.16 were determined as the operational range, thus the quality attribute is within the specification limits and a sigma level higher than three is achieved, resulting in a productivity superior than 95 %. [ABSTRACT FROM AUTHOR]

In the present article, we propose a generally weighted moving average control chart based on a three-parameter logarithmic transformation for monitoring shifts in the process variability (regarded as S2-GWMA chart). Monte Carlo simulations are used to evaluate its performance in terms of the run-length distribution. A comparison study versus the S2-EMWA, S2-CUSUM, CS-EWMA, S2-HEWMA and GWMAV charts indicates that the proposed chart is more effective than its competitors in detecting small to moderate upward shifts while it performs similarly with the other charts for large shifts. Finally, a real example is presented to display the application of the S2-GWMA chart. [ABSTRACT FROM AUTHOR]

Purpose: Current asphalt pavement life cycle assessment implementation limitations are in part linked to data availability. On top of this, there is currently a trend towards the creation of open-source accessible data for pavement LCA. Therefore, the objective of this study is to undertake a comprehensive assessment of open-source data for the "cradle-to-laid" LCA of asphalt pavements to better understand its potential risks. Methods: This study assesses the variability and uncertainty associated with open-access life cycle inventory data, collated from multiple open-source case study scenario results, project reports and inventories in order to assess its reliability. Six impact categories were considered. The collected data was applied to three different case study scenarios for a pavement surface course, which include a conventional hot-mix, a half-warm mixture and a half-warm mixture with 50% reclaimed asphalt pavement. Results and discussion: The results of this study indicated the variabilities and uncertainties found at both the process level and those propagated to the final LCA results. The results found that fine aggregate production, truck transportation and construction machinery were the most troublesome processes for LCA uncertainty, given their large variability and low impact on the final LCA results. Meanwhile, the binders and cement production and plant operations, being the largest contributors to the environmental impacts (33, 23, 32% average, respectively), also provided large result uncertainties (12, 31, 21% average, respectively) across all impact categories for the cradle-to-laid assessment. Overall, total acidification potential and particulate matter formation potential were the two impact categories most likely to increase due to source variabilities (483.0 and 335.3%, respectively), whilst eutrophication potential was the most likely to decrease (61.7%) for the case studies assessed. Conclusions: The results of this work show that open source data could be used for the LCA of asphalt mixtures and pavements. Nonetheless, justified data sources should be used and users must be aware of the potential uncertainty areas within the system, where this paper has aimed to contribute to the identification and understanding of these uncertainties in order for users minimise them in their work and help identify targets for LCI development. Future work should address the variabilities between open-source and commercial tools, the exploration of the use, maintenance and end-of-life stages and the better quantification of binder, RAP and low-temperature manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper presents a TCAD-based methodology to enable Design-Technology Co-Optimization (DTCO) of advanced semiconductor memories. After reviewing the DTCO approach to semiconductor devices scaling, we introduce a multi-stage simulation flow to study the device-to-circuit performance of advanced memory technologies in presence of statistical and process variability. We present a DRAM example to highlight the DTCO enablement for both memory and periphery. Our analysis demonstrates how the evaluation of different possible technology improvements and design combinations can be carried out to maximize the benefits of continuous technology scaling for a given set of manufacturing equipment.

Seventy-five blocks of low-moisture part-skim mozzarella cheese were procured from an industrial cheese plant, and the relationships between the physicochemical and functional properties were evaluated during refrigerated storage. In total, cheeses were obtained from 1 cheese vat on 7 different production dates, at 2 to 4 monthly intervals, over a 1.5-yr period; all cheeses were made using a standard recipe. The cheeses were held at 4°C for 0, 1, 2, 4, 8, 16, or 32 d and assayed for composition, primary proteolysis, serum distribution, texture profile analysis, heat-induced changes in viscoelastic behavior, cheese extensibility, and melt characteristics. The results demonstrated a substantial increase in serum uptake by the calcium-phosphate para-casein matrix between 1 and 16 d of storage with a concomitant improvement in the functional performance of the cheese. Extending the storage time to 32 d resulted in further changes in the functional quality, concurrent with ongoing increases in protein hydration and primary proteolysis. Differences in the measured characteristics between the cheeses obtained on different sampling occasions were evident. Principal component analysis separated the cheeses based on their variance in functional performance, which was found to be correlated mainly with the calcium content of the cheese. The results indicate that the manufacturing process should be tightly controlled to minimize variation in calcium content and enhance the quality consistency of the cheese. [ABSTRACT FROM AUTHOR]

Mining occurs in highly variable, spatially dynamic environments. Existing methods of planning and operations management are insufficient to adequately account for variability. Managing variability within the value chain aims to maximise business value. This paper presents a methodology for managing variability. This includes: assessing the impact of variability on a deterministic mine schedule; applying guiding principles based on Theory of Constraints to stabilise and exploit the value chain; developing a risk approach to minimise costs of plan delivery; and establishing tools for critical path planning, reconciling performance against plan and establishing corrective actions to deliver plan outcomes. A case study is presented from the Olympic Dam mine. Conclusions include: operations contain significant variability that if not managed will fail to deliver the mine plan; the proposed methodology can be applied to reduce variability and robustly deliver the best-value mine plan; and this methodology has broad application across the mining industry. [ABSTRACT FROM AUTHOR]

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Bias Temperature Instability (BTI) is one of the most crucial reliability issues in modern CMOS technology. It leads to shift in device parameters, which eventually affect circuit performance. SRAM is a widely used circuit which occupies a considerable area in microprocessors. Hence it is important to understand the impact of BTI on performance/stability of SRAM. As device degradation due to BTI depends on gate activity, SRAM performance strongly depends on the data stored in the cell. In this paper, the gate activity is incorporated by activity factor 'α' which takes into account the various data patterns stored in the cell. Although many studies have reported the impact of BTI on SRAM performance, none focused on the worst degradation scenario. Our analysis with varying activity factor 'α' provides an opportunity to identify the data pattern for worst case degradation. Shift in threshold voltage due to BTI is modelled according to continuous and non-continuous applied gate bias, using physics-based compact model. Process variability is incorporated using Monte Carlo (MC) simulations and worst-case degradation at distribution tail is identified. In this paper, we consolidate various SRAM performance metrics from literature over the last three decades and demonstrate the impact of BTI and process variability with activity factor 'α' on these metrics. [ABSTRACT FROM AUTHOR]

Cell population heterogeneities and their changes in mammalian cell culture processes are still not well characterized. In this study, the formation and dynamics of cell population heterogeneities were investigated with flow cytometry and stably integrated fluorescent markers based on the lentiviral gene ontology (LeGO) vector system. To achieve this, antibody-producing CHO cells were transduced with different LeGO vectors to stably express single or multiple fluorescent proteins. This enables the tracking of the transduced populations and is discussed in two case studies from the field of bioprocess engineering: In case study I, cells were co-transduced to express red, green, and blue fluorescent proteins and the development of sub-populations and expression heterogeneities were investigated in high passage cultivations (total 130 days). The formation of a fast-growing and more productive population was observed with a simultaneous increase in cell density and product titer. In case study II, different preculture growth phases and their influence on the population dynamics were investigated in mixed batch cultures with flow cytometry (offline and automated). Four cell line derivatives, each expressing a different fluorescent protein, were generated and cultivated for different time intervals, corresponding to different growth phases. Mixed cultures were inoculated from them, and changes in the composition of the cell populations were observed during the first 48 h of cultivation with reduced process productivity. In summary, we showed how the dynamics of population heterogeneities can be characterized. This represents a novel approach to investigate the dynamics of cell population heterogeneities under near-physiological conditions with changing productivity in mammalian cell culture processes. [ABSTRACT FROM AUTHOR]

Virtually all manufacturing processes are subject to variability, an inherent characteristic of most production processes. No two parts can ever be exactly the same in terms of their dimensions. For machining processes such as drilling, milling, and lathing, overall variability is caused in part by machine tools, tooling, fixtures and workpiece material. Since variability, which can be accumulated from tolerance stacking, can result in defective parts the number of parts produced in a batch is limited. When there are too many parts in a batch, the likelihood of producing all acceptable parts in a batch decreases due to the increased tolerances. On the other hand, too small a batch size incurs an increase of manufacturing costs due to frequent setups and tool replacements, whereas the likelihood of acceptable parts increases. To address this challenge, we present a stochastic model for determining the optimal batch size where we consider part-to-part variation in terms of tool wear, which tends to be proportional to batch size. In this paper, a mathematical model is constructed based on the assumption that the process used for producing preceding parts affects the state of subsequent parts in a probabilistic manner. [ABSTRACT FROM AUTHOR]

Variation in CMOS device parameters due to reliability issues like Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI), etc., has posed serious performance concerns in various digital and analog/mixed-signal circuit designs, which include neuromorphic circuits. CMOS-based neuromorphic circuits, also known as silicon neurons (SiNs), are designed to emulate the electrophysiological behavior of biological neurons. Neuromorphic systems are used to learn and adapt in various applications like image processing and classification, wearable/implantable systems, robotics, etc. that require high performance. It is therefore, important to analyze neuromorphic circuits performance over their lifetime. This is achieved by identifying the specific transistors affecting circuit performance and to come up with workable ways to solve these problems. In this work, we have investigated the combined effect of NBTI and HCI on two types of Integrate-and-Fire (I&F) SiNs, Axon-Hillock (AH) and Simplified Leaky Integrate-and-Fire (SLIF) circuits, using key performance parameters. Novel reliability-aware AH and SLIF circuits are proposed to mitigate the reliability issues. The proposed reliability-aware designs show negligible deviations in the performance parameters after degradation. The time-zero process variability analysis is also carried out for the proposed reliability-aware SiNs. The power consumption of existing and proposed reliability-aware neuron circuits is analyzed and compared. • This work analyzes impact of reliability (BTI and HCI) and time-zero variability on key performance parameters of two spike-event neuron circuits, Axon-Hillock (AH) and Simplified Leaky Integrate and Fire (SLIF) silicon neuron circuits. • The study identifies transistors responsible for performance degradation in both conventional AH and conventional SLIF circuits. • Reliability-aware silicon neuron circuits are proposed for minimizing the effects of degradation and to dissipate power more efficiently than conventional neuron circuits. • The proposed reliability-aware design, consisting of diode-connected transistors, is most effective in lowering transistor degradation. • The study demonstrates the importance of considering reliability in neuromorphic circuit designs, enabling the development of resilient and accurate neuromorphic systems. [ABSTRACT FROM AUTHOR]

The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them.