A two-layer architecture for economically optimal process control and operation

A two-layer architecture for dynamic real-time optimization (or nonlinear modelpredictive control (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on two time-scales. On the upper layer, a rigorous optimization problem is solved with an economic objective function at a slow time-scale, which captures slow trends in process uncertainties. On the lower layer, a fast neighboring-extremal controller is tracking the trajectory in order to deal with fast disturbances acting on the process. Compared to a single-layer architecture, the two-layer architecture is able to address control systems with complex models leading to high computational load, since the rigorous optimization problem can be solved at a slower rate than the process sampling time. Furthermore, solving a new rigorous optimization problem is not necessary at each sampling time if the process has rather slow dynamics compared to the disturbance dynamics. The two-layer control strategy is illustrated with a simulated case study of an industrial polymerization process.     

Development of a Cloud Trust Label: A Delphi Approach

The purpose of this research is to identify the potential information components of an online, real-time trust label, which is proposed as a communication mechanism to encourage trust in cloud service providers and cloud computing products. An online Delphi process was used with 28 cloud computing experts (including vendors, software providers, and legal and business representatives). The proposed label contains 81 information components, covering the cloud service provider (e.g. physical location, legal jurisdiction), the cloud service itself (e.g. data location, security, backup, certification), and a historical service-level summary (e.g. uptime data, support response times). The potential benefits of such a label to encourage trustworthiness perceptions and trust behaviors in the cloud computing environment are explored. Limitations of the study are highlighted, and further research studies are suggested to test the concept of the label and to refine the components of the label itself.     

Modeling the glucose regulatory system in extreme preterm infants

Background

Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.

Methods

A metabolic system model is adapted from adult critical care to the unique physiological case of the neonate. Integral-based fitting methods were used to identify time-varying insulin sensitivity and non-insulin mediated glucose uptake profiles. The clinically important predictive ability of the model was assessed by assuming insulin sensitivity was constant over prediction intervals of 1, 2 and 4 h forward and comparing model-simulated versus actual clinical glucose values for all recorded interventions. The clinical data included 1091 glucose measurements over 3567 total patient hours, along with all associated insulin and nutritional infusion data, for N = 25 total cases. Ethics approval was obtained from the Upper South A Regional Ethics Committee for this study.

Results

The identified model had a median absolute percentage error of 2.4% [IQR: 0.9-4.8%] between model-fitted and clinical glucose values. Median absolute prediction errors at 1-, 2- and 4-h intervals were 5.2% [IQR: 2.5-10.3%], 9.4% [IQR: 4.5-18.4%] and 13.6% [IQR: 6.3-27.6%] respectively.

Conclusions

The model accurately captures and predicts the fundamental dynamic behaviors of the neonatal metabolism well enough for effective clinical decision support in glycemic control. The adaptation from adult to a neonatal case is based on the data from the literature. Low prediction errors and very low fitting errors indicate that the fundamental dynamics of glucose metabolism in both premature neonates and critical care adults can be described by similar mathematical models.     

Innovations in Individual Feature History Management—The Significance of Feature-based Temporal Model

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO’s temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO’s temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure.

Observations of Aggressive Children During Peer Provocation and With a Best Friend.

Observational methods were used to examine aggressive children's peer relations in 2 contexts: when being teased by a peer and when interacting with a best friend. Because aggressive children may have more difficulty than nonaggressive children in both peer contexts, the authors also examined whether relations between behaviors across contexts varied as a function of aggression. Results indicated that aggression was related to children's behavior when provoked. Children's behavior when provoked was associated with fewer positive and more negative interactions with their best friend, particularly for aggressive children. Results are discussed with respect to social norms in middle childhood and informing interventions for aggressive children. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Diffusible Signal Factor Family of Bacterial Cell?Cell Signals

Bacterial cell-to-cell signals of the diffusible signal factor (DSF) family are cis-2-unsaturated fatty acids that differ in their chain length and branching patterns. Signaling involving DSF family members occurs in diverse bacteria to include plant and human pathogens. In the majority of these organisms, the perception of DSF is linked to turnover of the second messenger cyclic di-GMP by one of two “core” pathways. Additional “accessory” signal transduction pathways can also be found, but are not widely conserved. DSF signaling acts to regulate diverse functions to include biofilm formation and architecture, antibiotic resistance, and the production of virulence factors in pathogens. DSF family signals can also participate in interspecies signaling with other bacteria and interkingdom signaling with the yeast Candida albicans. Such interactions may have importance in modifying microbial behavior during polymicrobial infections.     

Design and optimization of electrochemical microreactors for continuous electrosynthesis

The study focuses on the design and construction, as well as the theoretical and experimental optimization of electrochemical filter press microreactors for the electrosynthesis of molecules with a high added value. The main characteristics of these devices are firstly a high-specific electrochemical area to increase conversion and selectivity, and secondly the shape and size of the microchannels designed for a uniform residence time distribution of the fluid. A heat exchanger is integrated into the microstructured electrode to rapidly remove (or supply) the heat required in exo- or endothermic reactions. The microreactors designed are used to perform-specific electrosynthesis reactions such as thermodynamically unfavorable reactions (continuous NADH regeneration), or reactions with high enthalpy changes.     

Bayes Linear Estimators

A class of linear estimators, called Bayes linear estimators, is developed by finding, among all linear estimators, ones which have least average total mean squared error, averaged over parameter points. Ridge, generalized ridge, restricted least squares, subset least squares, least squares, best, and generalized inverse linear estimators are all either Bayes linear estimators or limits of Bayes linear estimators. Results on Bayes linear estimators are extended to affine estimators. “Bootstrapping” procedures, in which the data are recycled in the guise of prior information, are discussed.     

Prosocial rule breaking on health information security at healthcare organisations in South Korea

Regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), establish standards to protect patients' medical records from security breaches. Insiders' prosocial misbehaviour within healthcare organisations can cause significant damage to relevant stakeholders. Such behaviour without malicious intention needs to be better understood and carefully managed from the perspective of prosocial behaviour. For this study, a research model was developed that includes the factors influencing student nurses' intention to disclose patient health information. The model was empirically tested with nursing students in South Korea with a scenario-based experiment. We find that both altruistic (impact on others) and egoistic (impact on the self) motivations are significantly important in raising situational empathy. On the other hand, an egoistic motivation (impact on the self) significantly affects people's perception of their responsibility, which mediates the relationship between situational empathy and prosocial intention to disclose. The implications of our findings are discussed.