Variation In System Performance Research Articles

FSVPy: A python-based package for fluorescent streak velocimetry (FSV)

Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and electrical systems. In this work, we explore the use of fluorescent particle-streak analysis to directly measure the local velocity field of a pressure-driven flow, introducing a new Python package (FSVPy) to perform the analysis. Fluorescent streak velocimetry combines high-speed imaging with highly fluorescent particles to produce images that contain fluorescent streaks, whose length and intensity can be related to the local flow velocity. By capturing images throughout the sample volume, the three-dimensional velocity field can be quantified and reconstructed. We demonstrate this technique by characterizing the channel flow profiles of several non-Newtonian fluids: micellar Cetylpyridinium Chloride solution, Carbopol 940, and Polyethylene Glycol. We then explore more complex flows, where significant acceleration is created due to microscale features encountered within the flow. We demonstrate the ability of FSVPy to process streaks of various shapes and use the variable intensity along the streak to extract position-specific velocity measurements from individual images. Thus, we demonstrate that FSVPy is a flexible tool that can be used to extract local velocimetry measurements from a wide variety of fluids and flow conditions.

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.

Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings.

A virtual experiment for measuring system resilience: A case of chemical process systems

Resilience is an emergent property of a system, which changes with various internal and external factors. Resilience is also a hidden property of a system that cannot be observed. Thus, experiments should be performed for a given system to measure its resilience. However, physical experiments are practically impossible. Inspired by the tensile test for the stress-strain curve in Material Science, this paper proposes a virtual experiment for measuring system resilience and applies it to a chemical process system. The physical parameters of system resilience of a process system are mapped to those of material resilience. A process system is viewed as a 'specimen' in this experiment. The system performance variation caused by disruptions is seen as the displacement of the specimen caused by the applied load. In absorption phase, the decrease speed of system performance is determined by the failure rate of components under disruptive condition. Response time, including fault diagnosis time and resource allocation time, is used to represent adaptation ability. Restoration ability depends on repair rate of components. For simplicity purpose, the proposed method is applied to resilience assessment of a release prevention barrier system used in the Chevron Richmond refinery crude unit and its associated upstream process.

Human-centric production system simulation in mixed reality: An exemplary case of logistic facility design

A production system with manual operations tends to suffer from variations in system performance induced by individual differences of human actions. To re-design the system under such uncertainty can be time-consuming without the support of advanced simulation tools. Hence, this research proposes a novel idea of production system simulation in mixed reality (MR) that facilitates production planning involving human operators. An exemplary case of logistic facility design demonstrates a MR-based planning tool implementing this idea. In this case, a manager and an operator, both wearing a HoloLens, simultaneously collaborate in a facility constructed by overlaying virtual equipment with existing one in real environment. The manager attempts to adjust the production parameters in the facility to reach a new target created by mixed-model production, while the operator accomplishes manual tasks related to material transportation and storage. The planning results show that the MR tool is advantageous over traditional simulation software in terms of planning quality and flexibility. This work validates the feasibility of MR as a new approach to realizing human-centric production system design.

Three Dimensional Fuzzy Reliability for System Performance Evaluation

The research proposed a developed methodology for evaluation the system performance in uncertainty associated with traditional modelling methodology is focused on either load L or resistance R variability, but not both. A two-dimensional (2D) fuzzy set (traditional model), represent with the one dimension for universe of discourse (in x-direction) and the second dimension of his membership degree (in y-direction), is not full sufficient to handle both, load and resistance variation of system performance. The theoretical principle basis of this research is based on development of the three dimensional (3D) of fuzzy set that includes system performance variability in load and resistance from two dimensional. The proposed methodology (traditional model) extends the acceptance level of partial performance of system concept to a 3D-dimantion representation. This representation allows to capturing the changing of preferences of decision makers in load and resistance. The major objective of the research is to proposed the original methodology for evaluate system performance and management that is capable of; (a) addressing uncertainty caused by load and resistance variability and ambiguity; (b) integrating objective and subjective evaluation; and (c) assisting system performance management decision making based on a more detailed certainty evaluation of load and resistance variability. The study proposed two models for fuzzy reliability performance indexes: first traditional model included (I) 2D fuzzy reliability-vulnerability Rv index, (II) 2D fuzzy robustness Ro index; the second developed model (i) 3D fuzzy reliability-vulnerability Rv index, (ii) 3D fuzzy robustness Ro index; and comparing between them. These indexes have the capability of evaluating the operational performance of complex systems. Proposed methodology is illustrated by using the Al-Wathba Water Supply System (WWSS) as a case study.

Understanding geographical variations in health system performance: a population-based study on preventable childhood hospitalisations

ObjectiveTo investigate interdistrict variations in childhood ambulatory sensitive hospitalisation (ASH) over the years.DesignObservational population-based study over 2008–2018 using the Primary Health Organisation Enrolment Collection (PHO) and the National Minimum Dataset...

Regional Variation in National Healthcare Expenditure and Health System Performance in Central Cities and Suburbs in Japan.

The increasing national healthcare expenditure (NHE) with the aging rate is a significant social problem in Japan, and efficient distribution and use of NHE is an urgent issue. It is assumed that comparisons in subregions would be important to explore the regional variation in NHE and health system performance in targeted municipalities of the metropolitan area of Tokyo (central cities) and the neighboring municipalities of Chiba Prefecture (suburbs). This study aimed to clarify the differences of the socioeconomic factors affecting NHE and the health system performances between subregions. A multiple regression analysis was performed to extract the factors affecting the total medical expenses of NHE (Total), comprising the medical expenses of inpatients (MEI), medical expenses of outpatients (MEO), and consultation rates of inpatients (CRI) and outpatients (CRO). Using the stepwise method, dependent variables were selected from three categories: health service, socioeconomic, and lifestyle. Then, health system performance analysis was performed, and the differences between regions were clarified using the Mann–Whitney U test. The test was applied to 18 indicators, classified into five dimensions referred to in the OECD indicators: health status, risk factors for health, access to care, quality of care, and health system capacity and resources. In the central cities, the number of persons per household was the primary factor affecting Total, MEI, MEO, and CRO, and the number of persons per household and the percentage of the entirely unemployed persons primarily affected CRI. In the suburbs, the ratio of the population aged 65–74 and the number of hospital beds were significantly positively related to Total, MEI, and CRI, but the number of workers employed in primary industries was negatively related to Total and MEI. The ratio of the population aged 65–74 was significantly positively related to MEO and CRO. Regarding health system performance, while risk factors for health was high in the central cities, the others, including access to care, quality of care, and health system capacity and resources, were superior in the suburbs, suggesting that the health system might be well developed to compensate for the risks. In the suburbs, while risk factors for health were lower than those in the central cities, access to care, quality of care, and health system capacity and resources were also lower, suggesting that the healthcare system might be poorer. These results indicate a need to prioritize mitigating healthcare disparities in the central cities and promoting the health of the elderly in the suburbs by expanding the suburbs’ healthcare systems and resources. This study clarified that the determinants of NHE and health system performance are drastically varied among subregional levels and suggested the importance of precise regional moderation of the healthcare system.

Stability and thermal analysis of a single-phase natural circulation looped parabolic trough receiver

The sustainability and reliability of solar parabolic trough technology can be enhanced by introducing a natural circulation loop. The configuration that suits better is horizontal heater horizontal cooler, which is the most efficient but least stable loop. Hence, as an effort towards the evolution of natural circulation looped collector, 3D computational analysis of a 70 mm uniform diameter square loop (Dowtherm A as the heat transfer fluid) is done with uniform and non-uniform heat flux (SolTrace based) as well as different instability restraining techniques. Prior to the analyses, the stability maps were generated and validated, which predicts high level of instability. Later, the validated computational model is used for parametric analyses, which includes heat flux pattern, loop tilt, use of orifice, and combination of tilt and orifice. Since the loop geometry is highly unstable (ratio of loop length to diameter is 57), the stable operation is observed only with high tilt (θ = 45°), low beta ratio (β = 0.25) and combinations of tilt and orifice (β (0.50) + θ (15°) and β (0.50) + θ (30°)). However, the latter method is suggested for trough application as the former two versions are impractical. Further, the heat load analysis with uniform and non-uniform flux designated insignificant variation in system performance. Since the loop is effective with restraining techniques and its performance is on par with the conventional trough system (predicted by the analytical model), an effort towards implementation of natural circulation loop in parabolic trough technology is prolific.

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis.

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

An Inverse Optimization Approach to Measuring Clinical Pathway Concordance

Clinical pathways outline standardized processes in the delivery of care for a specific disease. Patient journeys through the healthcare system, however, can deviate substantially from these pathways. Given the positive benefits of clinical pathways, it is important to measure the concordance of patient pathways so that variations in health system performance or bottlenecks in the delivery of care can be detected, monitored, and acted upon. This paper proposes the first data-driven inverse optimization approach to measuring pathway concordance in any problem context. Our specific application considers clinical pathway concordance for stage III colon cancer. We develop a novel concordance metric and demonstrate using real patient data from Ontario, Canada that it has a statistically significant association with survival. Our methodological approach considers a patient’s journey as a walk in a directed graph, where the costs on the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc costs: reference pathways developed by a provincial cancer agency (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Thus, our inverse optimization framework extends existing models by including data points of both varying “primacy” and “alignment.” Data primacy is addressed through a two-stage approach to imputing the cost vector, whereas data alignment is addressed by a hybrid objective function that aims to minimize and maximize suboptimality error for different subsets of input data.This paper was accepted by Chung Piaw Teo, Management Science Special Section on Data-Driven Prescriptive Analytics.

Performance, environment, and cost‐benefit analysis of a split air conditioning unit usingHC‐290 andHCFC‐22

AbstractA widely used hydrochlorofluorocarbon (HCFC) refrigerant HCFC‐22 in the split air conditioner (AC) is being phased out in all countries under the Montreal Protocol. Propane (HC‐290) is a favorable substitute for HCFC‐22. The performance, environment impact and cost‐benefit analysis of a split AC unit operated with HCFC‐22 and HC‐290 has been carried out experimentally under different test conditions prescribed by IS 1391. The results showed that the variation in system performance was more significant for HCFC‐22 than for that of HC‐290 while varying the refrigerant charge. The experienced optimum charges that represent the maximum coefficient of performance (COP) was varied with the working environment and it was realized that, generally the optimum charge for HC‐290 was 50% lesser than that of HCFC‐22. The COP of the AC unit with HC‐290 was observed to be 5% more than that of HCFC‐22. However, the system capacity diminished by 7.8%. The operation of a split AC unit with HC‐290 produced up to 15.9% lesser CO2emission than that of HCFC‐22 under all the test conditions. The use of HC‐290 in an existing HCFC‐22 split AC system can save up to 12.22% of the life time total cost. Finally, it was inferred that the replacement of HCFC‐22 with HC‐290 in the split AC unit showed dominance in all aspects such as performance, emission, and life time total cost.

Single and simultaneous effects of naphthalene and salinity on anaerobic digestion: Response surface methodology, microbial community analysis and potential functions prediction

Polycyclic aromatic hydrocarbons (PAHs) are a persistent and prevalent class of pollutants in petroleum-contaminated saline environment, which pose potential harm to organisms. Researches on anaerobic biodegradation of PAHs are gradually emerging, but the response of anaerobic microorganisms to salinity changes and the co-effects of salinity and PAHs in anaerobic digestion (AD) system have seldom been reported. Thus, we investigated the variations of AD system performance and anaerobic microbial community caused by different concentrations of naphthalene (Nap) or/and NaCl based on Box-Behnken Design (0 mg/L ≤ Nap ≤150 mg/L, 0 g/L ≤ NaCl ≤25 g/L). The promoted efficiencies of acidogenesis and methanogenesis were found in presence of moderate NaCl or Nap, but high salinity (NaCl >4.4 g/L) weakened AD performance. Moreover, the high salinity (NaCl >4.4 g/L) and Nap resulted in reduced microbial Ca2+ Mg2+- ATPase activity, poor EPS secretion and the highest difference of the microbial operational taxonomic units (OTUs), and synergistically inhibited AD process. Microbiological analysis revealed that the relative abundance of Clostridium and acetoclastic Methanosaeta was increased by 2.01 times and 2.17 times in single Nap treated group compared to control. With the simultaneous addition of NaCl and Nap, Proteiniphilum and hydrogenotrophic methanogens (Methanobacterium, Methanofollis, and Methanolinea) occupied the major abundance. Potential functions prediction indicated that high salinity could disrupt the co-metabolism between carbohydrate metabolism and Nap degradation. This study provides basis for anaerobic bioremediation of PAHs-polluted saline environment.

Enhancing performance and reducing wearing variability for wearable technology system–body interfaces using shape memory materials

Advancements in e-textiles, sensors, and actuators have propelled wearable technologies toward wide-spread market use, however the physical interface between these technologies and the human body has remained a functional challenge. Prior research has found that system–body interface challenges produce wearing variability, or variation in system placement, orientation, and tightness in relation to a body both between use trials and between users, resulting in large variation and deterioration in system performance. We break down the mechanics of common system–body interface challenges through a summary of design principles critical to any system interfacing with the human body. Additionally, we present an active interface based on shape memory materials that dimensionally adapts to its user’s dimensions. An experimental investigation of these active system interfaces considers the impact of design variables often overlooked in the design process. Recommendations are provided to optimize interfaces for the requirements for a given wearable technology. Additionally, we illuminate methods to reduce wearing variability for a range of users to produce consistent system–body interaction across a user population. Through these active interfaces, we advance a broad range of wearable technologies, including wearable sensing, motion tracking, haptics, and wearable robotic devices.

A Novel Augmented Fractional-Order Fuzzy Controller for Enhanced Robustness in Nonlinear and Uncertain Systems with Optimal Actuator Exertion

An appropriate balance between the controller’s performance and its robustness is a complex design challenge. In order to enhance the control system performance, vigorous and rapid variations in controller output are often employed; however, this poses a critical challenge for practical implementations of the control schemes, as it may affect the actuator badly and may reduce its lifetime. To handle this issue, the current work presents an innovative control structure that aims to strike an apt balance between the control output variability while maintaining the desired control performance. The proposed control scheme exploits the abilities of fuzzy logic to cope with uncertainties in the system and along with the use of fractional calculus to enhance the control performance. Further, to evaluate the performance of the proposed improved fractional-order fuzzy controller (IFOFC), extensive simulation studies have been carried out on a multi-input–multi-output nonlinear system for a wide variety of test scenarios. An exhaustive comparative study with an inline state-of-art controller, i.e., fractional-order fuzzy PID (FOFPID) controller has also been carried out on two interesting performance measures. These performance measures include integral of time-weighted absolute error (ITAE) and another measure which corresponds to undesirable variations in the controller output, i.e., integral of the absolute change of torque (IACT). Based on the detailed simulation studies, it was found that the proposed control structure provides a fair balance between controller output aggression and control performance and also completely outperformed the FOFPID controller, even under large parametric variations.

Geographic variation in health system performance in rural areas of New South Wales, Australia.

(i) To quantify geographic variation in selected health system performance indicators across local government areas of rural New South Wales and (ii) to compare relationships between sociodemographic factors and health system performance indicators across the regions. Ecological study. Rural New South Wales communities. Eighty-nine local government areas in rural areas comprising 47 inner regional, 33 outer regional, 6 remote and 3 very remote areas. Deaths from avoidable causes, public hospital admissions for potentially preventable conditions, screening program participation, immunisation coverage. The largest geographic variation between rural areas of New South Wales was seen for avoidable mortality and potentially preventable hospital admissions. The average annual avoidable age-standardised mortality rate (2013-2017) ranged from 78.1 per 100000 population to 493.7 per 100000 population and the age-standardised rate of potentially preventable hospitalisations (2016-2017) ranged from 1491 to 5797 per 100000 population. Approximately three quarters of local government areas had bowel and breast cancer screening participation rates equivalent to or better than the overall New South Wales rate; however, only 34% of local government areas met the New South Wales rate for cervical cancer screening. The least variation was seen for immunisation coverage; Byron had the lowest immunisation coverage for all 3 ages. The most common explanations for variation between rural local government areas in New South Wales were remoteness and socioeconomic characteristics. The analysis of health system performance indicators reveals differences among New South Wales rural local government areas. The results highlight specific areas that might benefit from targeted intervention to improve inequities particularly for avoidable mortality and potentially preventable hospitalisations.

The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross- Country Analysis

Background: More than one year after the beginning of the international spread of COVID- 19, the reasons explaining its apparently lower reported burden in Africa is still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa. Methods: Data were collected from publicly available and well-known online sources. Cumulative numbers of COVID-19 cases and deaths per one million population reported by African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. Covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution and radiation variables, and use of social media. Collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package. Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per one million population, GINI index, global health security (GHS) index and mean body mass index (BMI) were significantly associated (P<0.05) with COVID-19 cases per one million population. No association was found with the median life expectancy, proportion of rural population and BCG coverage rate. On the other hand, diabetes prevalence, number of nurses and GHS index were found to be significantly associated with COVID-19 deaths per one million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with BCG coverage or communicable disease burden. Conclusions: Low health system capacity, together with some clinical and socio-economic factors were predictors of the reported burden of COVID-19 in Africa. Our results emphasise the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Stackelberg Game Approach for Robust Optimization With Fuzzy Variables

In this article, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation-entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent biobjective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers’ preference for objectives, the optimization framework based on Stackelberg game is established. A leader–follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

Direct laser writing of a titanium dioxide-laden retinal cone phantom for adaptive optics-optical coherence tomography

The spatial resolution of adaptive optics-optical coherence tomography (AO-OCT) enables visualization of retinal components (e.g., photoreceptors), which can advance clinical diagnosis of ocular diseases. Currently, however, variability in AO-OCT system performance suggests a need for standardized physical models, or “phantoms”, that replicate the opto-structural properties of retinal components. Here we employ direct laser writing – a two-photon polymerization-based additive manufacturing technology – using a light scattering titanium (IV) dioxide nanoparticle-laden photomaterial to create novel retinal cone outer segment (OS) phantoms. Fabrication results demonstrate effective mimicry of cone OS diameter and spacing at four retinal eccentricities, while AO-OCT experiments demonstrate cone spacings &gt;4 µm can be resolved. These results suggest a promising methodology for biomimetic photoreceptor phantom development for AO-OCT.

Waste activated sludge treatment in an anaerobic dynamic membrane bioreactor at varying hydraulic retention time: Performance monitoring and microbial community analysis

In this study, the variations in system performance and microbial community composition when treating waste activated sludge without prior chemical or physical pretreatment in anaerobic systems is examined. Two lab-scale digesters, namely anaerobic dynamic membrane bioreactor (AnDMBR) and continuously stirred tank reactor (CSTR), were operated and maintained under mesophilic conditions at varying hydraulic retention times (10-25 days). Decreasing the hydraulic retention time (HRT) affected the methane yield (MY) and methane production rate (MPR) proportionally in both systems. The AnDMBR's maximum MY of 252 mL CH4/g-VS and MPR of 0.73 L/L/d were 25% and 34% higher than those obtained using the CSTR, respectively. Moreover, a decrease in HRT was accompanied by accumulation of butyric acid in the CSTR, which caused further performance deterioration. In both systems, AnDMBR's dynamic membrane acted as an added layer preventing methanogen wash-out and retaining the essential microorganisms for stable sludge treatment. Dominance in the microbial composition shifted from a strict acetoclastic to a mixed acetoclastic and hydrogenotrophic methanogenic community and that a decrease in the ration of Bacteroidetes to Firmicutes was associated with the increased MY.

Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems

Abstract Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.