Studies of the shellside performance of shell-and-tube heat exchangers

Accurate prediction of shellside pressure drop in a baffled shell-and-tube heat exchanger is very difficult because of the complicated shellside geometry. Ideally, all the shellside fluid should be alternately deflected across the tube bundle as it traverses from inlet to outlet. In practice, up to 60% of the shellside fluid may bypass the tube bundle or leak through the baffles. This short-circuiting of the main flow reduces the efficiency of the exchanger. Of the various shellside methods, it is shown that only the multi-stream methods, which attempt to obtain the shellside flow distribution, predict the pressure drop with any degree of accuracy, the various predictions ranging from -30% to +70%, generally overpredicting. It is shown that the inaccuracies are mainly due to the manner in which baffle leakage is modelled. The present multi-stream methods do not allow for interactions of the various flowstreams, and yet it is shown that three main effects are identified, a) there is a strong interaction between the main cross flow and the baffle leakage streams, enhancing the crossflow pressure drop, b) there is a further short-circuit not considered previously i.e. leakage in the window, and c) the crossflow does not penetrate as far, on average, as previously supposed. Models are developed for each of these three effects, along with a new windowflow pressure drop model, and it is shown that the effect of baffle leakage in the window is the most significant. These models developed to allow for various interactions, lead to an improved multi-stream method, named the "STREAM-INTERACTION" method. The overall method is shown to be consistently more accurate than previous methods, with virtually all the available shellside data being predicted to within ±30% and over 60% being within ±20%. The method is, thus, strongly recommended for use as a design method.

Work stressors, strains and hospital unit performance

Hospital employees who work in an environment with zero tolerance to error, face several stressors that may result in psychological, physiological, and behavioural strains, and subsequently, in suboptimal performance. This thesis includes two studies which investigate the stressor-to-strain-to-performance relationships in hospitals. The first study is a cross-sectional, multi-group investigation based on secondary data from 65,142 respondents in 172 acute/specialist UK NHS trusts. This model proposes that senior management leadership predicts social support and job design which, in turn, moderate stressors-to-strains across team structure. The results confirm the model's robustness. Regression analysis provides support for main effects and minimal support for moderation hypotheses. Therefore, based on its conclusions and inherent limitations, study one lays the framework for study two. The second study is a cross-sectional, multilevel investigation of the strain-reducing effects of social environment on externally-rated unit-level performance based on primary data from 1,137 employees in 136 units, in a hospital in Malta. The term "social environment" refers to the prediction of the moderator variables, which is to say, social support and decision latitude/control, by transformational leadership and team climate across hospital units. This study demonstrates that transformational leadership is positively associated with social support, whereas team climate is positively associated with both moderators. At the same time, it identifies a number of moderating effects which social support and decision latitude/control, both separately and together, had on specific stressor-to-strain relationships. The results show significant mediated stressor-to-strain-to-performance relationships. Furthermore, at the higher level, unit-level performance is positively associated with shared unit-level team climate and with unit-level vision, the latter being one of the five sub-dimension of transformational leadership. At the same time, performance is also positively related to both transformational leadership and team climate when the two constructs are tested together. Few studies have linked the buffering effects of the social environment in occupational stress with performance. Therefore, this research strives to make a significant contribution to the occupational stress and performance literature with a focus on hospital practice. Indeed, the study highlights the wide-ranging and far-reaching implications that these findings provide for theory, management, and practice.

T-cell epitope prediction and immune complex simulation using molecular dynamics:state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics.

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Speech and non-speech stimuli in the prediction of early reading skills:the influence of processing demands and response-type

Purpose: Both phonological (speech) and auditory (non-speech) stimuli have been shown to predict early reading skills. However, previous studies have failed to control for the level of processing required by tasks administered across the two levels of stimuli. For example, phonological tasks typically tap explicit awareness e.g., phoneme deletion, while auditory tasks usually measure implicit awareness e.g., frequency discrimination. Therefore, the stronger predictive power of speech tasks may be due to their higher processing demands, rather than the nature of the stimuli. Method: The present study uses novel tasks that control for level of processing (isolation, repetition and deletion) across speech (phonemes and nonwords) and non-speech (tones) stimuli. 800 beginning readers at the onset of literacy tuition (mean age 4 years and 7 months) were assessed on the above tasks as well as word reading and letter-knowledge in the first part of a three time-point longitudinal study. Results: Time 1 results reveal a significantly higher association between letter-sound knowledge and all of the speech compared to non-speech tasks. Performance was better for phoneme than tone stimuli, and worse for deletion than isolation and repetition across all stimuli. Conclusions: Results are consistent with phonological accounts of reading and suggest that level of processing required by the task is less important than stimuli type in predicting the earliest stage of reading.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

In silico prediction of peptide-MHC binding affinity using SVRMHC

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

A CFD study of biomass pyrolysis in a downer reactor equipped with a novel gas-solid separator - I:hydrodynamic performance

This study presents the first part of a CFD study on the performance of a downer reactor for biomass pyrolysis. The reactor was equipped with a novel gas-solid separation method, developed by the co-authors from the ICFAR (Canada). The separator, which was designed to allow for fast separation of clean pyrolysis gas, consisted of a cone deflector and a gas exit pipe installed inside the downer reactor. A multi-fluid model (Eulerian-Eulerian) with constitutive relations adopted from the kinetic theory of granular flow was used to simulate the multiphase flow. The effects of the various parameters including operation conditions, separator geometry and particle properties on the overall hydrodynamics and separation efficiency were investigated. The model prediction of the separator efficiency was compared with experimental measurements. The results revealed distinct hydrodynamic features around the cone separator, allowing for up to 100% separation efficiency. The developed model provided a platform for the second part of the study, where the biomass pyrolysis is simulated and the product quality as a function of operating conditions is analyzed. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.

Class I T-cell epitope prediction:improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding

Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Threat-evoked anxiety disrupts spatial working memory performance:an attentional account

It is proposed that threat-evoked anxiety and spatial Working Memory (WM) rely on a common visuospatial attention mechanism. A prediction of this hypothesis is that spatial but not verbal WM should be disrupted in conditions of threat anxiety. Participants performed verbal and spatial n-back WM tasks in the presence or absence of threat of shock (shocks were not delivered). The presence of anxiety was assessed via heart rate recordings and self-report. Both measures clearly distinguished between WM blocks associated with threat of shock (Threat) and blocks, in which threat was absent (Safety). Performance on the spatial WM task was impaired in Threat relative to Safety. Furthermore, the more anxiety participants reported and the higher their heart rate in Threat compared to Safety, the more impaired was their spatial WM performance. This effect was not observed for verbal WM. The results indicate selective disruption of spatial WM performance by threat-evoked anxiety, interpreted in terms of more overlap in visuospatial attention between anxiety and spatial WM vs. anxiety and verbal WM.

Analytical BER performance in differential n-PSK coherent transmission system influenced by equalization enhanced phase noise

Long-haul high speed optical transmission systems are significantly distorted by the interplay between the electronic chromatic dispersion (CD) equalization and the local oscillator (LO) laser phase noise, which leads to an effect of equalization enhanced phase noise (EEPN). The EEPN degrades the performance of optical communication systems severely with the increment of fiber dispersion, LO laser linewidth, symbol rate, and modulation format. In this paper, we present an analytical model for evaluating the performance of bit-error-rate (BER) versus signal-to-noise ratio (SNR) in the n-level phase shift keying (n-PSK) coherent transmission system employing differential carrier phase estimation (CPE), where the influence of EEPN is considered. Theoretical results based on this model have been investigated for the differential quadrature phase shift keying (DQPSK), the differential 8-PSK (D8PSK), and the differential 16-PSK (D16PSK) coherent transmission systems. The influence of EEPN on the BER performance in term of the fiber dispersion, the LO phase noise, the symbol rate, and the modulation format are analyzed in detail. The BER behaviors based on this analytical model achieve a good agreement with previously reported BER floors influenced by EEPN. Further simulations have also been carried out in the differential CPE considering EEPN. The results indicate that this analytical model can give an accurate prediction for the DQPSK system, and a leading-order approximation for the D8PSK and the D16PSK systems.

The B-Terminal Busy Probability Prediction

In the teletraffic engineering of all the telecommunication networks, parameters characterizing the terminal traffic are used. One of the most important of them is the probability of finding the called (B-terminal) busy. This parameter is studied in some of the first and last papers in Teletraffic Theory. We propose a solution in this topic in the case of (virtual) channel systems, such as PSTN and GSM. We propose a detailed conceptual traffic model and, based on it, an analytical macro-state model of the system in stationary state, with: Bernoulli– Poisson–Pascal input flow; repeated calls; limited number of homogeneous terminals; losses due to abandoned and interrupted dialling, blocked and interrupted switching, not available intent terminal, blocked and abandoned ringing and abandoned conversation. Proposed in this paper approach may help in determination of many network traffic characteristics at session level, in performance evaluation of the next generation mobile networks.

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8. mm and 2.5. mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data. © 2014 The Society of Manufacturing Engineers.