Structural characterization of the disulfide folding intermediates of bovine alpha-lactalbumin

Specific three- and two-disulfide intermediates that accumulate transiently during reduction of the disulfide bonds of Ca(2+)-bound bovine alpha-lactalbumin have been trapped, isolated, and characterized. The three-disulfide intermediate was shown to lack the Cys6-120 disulfide bond, confirming the observations of others. The newly-recognized two-disulfide form has been shown to lack the Cys6-120 and Cys28-111 native disulfide bonds. The remaining native disulfide bonds in the two partially reduced derivatives of alpha-lactalbumin are stable only when the proteins are in a Ca(2+)-bound state. Otherwise, they adopt an equilibrium between molten globule and unfolded conformations, and rapid thiol-disulfide interchange occurs, at a rate as high as when the proteins are fully unfolded in 8 M urea, to generate distinct mixtures of rearranged products. Urea gradient electrophoresis, circular dichroism, fluorescence, and ANS binding have been combined to give a detailed structural picture of alpha-lactalbumin, its derivatives with native and with nonnative disulfide bonds, and the fully reduced protein. The native structure of alpha-lactalbumin appears to be split by selective disulfide bond cleavage into at least one subdomain, which retains the Ca(2+)-binding site. The alpha-lactalbumin molten globule state is shown largely to result from nonspecific hydrophobic collapse, to be devoid of cooperative or specific tertiary interactions, and not to be stabilized substantially by the native or rearranged disulfide bonds.     

Common pediatric dental problems

Physicians who provide primary care for children have a unique position to provide diagnostic, triage, educational, and preventive dental care for patients. Several papers have been published regarding primary pediatricians' participation in the preventive dental health care of their patients. One publication, a survey of physicians in Alabama focusing on physicians' overall awareness of dental issues, concluded that most physicians believe they have a role in the oral health of their patients, yet most were not aware of the American Academy of Pediatric Dentistry's recommendations. Most physicians report that they routinely perform oral examinations during physical examinations of children and deliver preventive, oral information by the age of 6 months or earlier; however, most recommend that infants' first visit should be at 3 years of age, not at the time of first-tooth eruption as the authors recommend. Furthermore, many primary care physicians do not talk about oral health during prenatal counseling. Many physicians understand the preventive advantages of fluoride, yet most do not prescribe vitamin combinations that contain fluoride. If an understanding of the aforementioned issues of dental care, as well as aspects of preventive care in infants and children, become more uniform among primary care physicians, the prevention-based practice of pediatric dentistry will become much more successful, and children and adults will enjoy better dental health.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k-covered with a predefined minimum spatial resolution. Since the problem is NP-complete, and even NP-hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.     

Effect of frequency and environment on fatigue-crack propagation of SA533B-1 steel

The effects of a decrease in the frequency of cyclic loading on the fatigue-crack propagation characteristics of SA533B-1 steel in various environments were investigated. Frequency levels of 10 Hz, 1.0 Hz and 0.1 Hz were employed in laboratory air, distilled water and a 3.5% NaCl solution. As the loading frequency was decreased, statistically significant increases in the fatigue-crack growth rates (da/dN) for the distilled water and salt water environments, as compared to those measured in laboratory air, were observed. These increases in growth rates were limited to certain ranges of stress intensity range (ΔK) values depending upon the frequency level being tested. A hydrogen embrittlement mechanism is proposed to explain the increase in growth rates based upon a fractographic analysis.     

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.     

Predicting Chicago's Land Use Pattern

The planning of community facilities requires accurate quantitative estimates of the area, type, intensity, and location of land uses required by the growth of urban activities. The distribution of these activities and the intensity of their use of land are highly related to accessibility as measured by distance from the CBD. This paper outlines a procedure whereby area-wide estimates of future population and economic activity can be distributed to small subareas so that measures of local land requirements may be obtained.     

Data Processing for City Planning

In city planning, data-processing machines permit the rapid handling and summarization of data in housing, renewal, land use, transportation, and basic urban research. Electronic computers. specifically, are exciting because they offer a means of solving complex city planning problems, especially in forecasting land use and travel demand.

For most planning operations punched card data processing is superior to other processes. The functions of these machines are described herein and three examples of data processing are elaborated on. In future years, planning techniques may have to be altered considerably to take advantage of the potentials of data processing.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

Ink‐jet printing: The route to production of full‐color P‐OLED displays

Abstract— Ink‐jet‐printing technology for displays has been making rapid progress and is now nearing production. This paper will go through the developments that have been carried out at CDT, Ltd., to achieve this status. The ink‐jet head and machine development that underpin the capability of the RGB printing process to manufacture P‐OLEDs at high speed with high reliability and with the film‐forming properties to achieve the appropriate device performance will be described. The important contribution made by the availability of flexible metrological tools will be discussed.