Endometriosis and the neoplastic process

Endometriosis is a frequent disorder that commonly presents with infertility and pelvic pain. Although the precise aetiology of endometriosis is unclear, it is generally considered to involve multiple genetic, environmental, immunological, angiogenic and endocrine processes. Genetic factors have been implicated in endometriosis but the susceptibility genes remain largely unknown. Although endometriosis is a benign disorder, recent studies of endometriosis suggest endometriosis could be viewed as a neoplastic process. Evidence to support this hypothesis includes the increased susceptibility to develop ovarian clear-cell and endometrioid cancers in the presence of endometriosis, and molecular similarities between endometriosis and cancer. In this article we discuss (i) the evidence suggesting that endometriosis might be viewed as a neoplastic process, and (ii) the implications of this hypothesis for elucidating the pathogenesis of endometriosis and developing novel methods of diagnostic classification and individualised treatments.     

Techniques based on genetic algorithms for large deflection analysis of beams

A couple of non-convex search strategies, based on the genetic algorithm, are suggested and numerically explored in the context of large-deflection analysis of planar, elastic beams. The first of these strategies is based on the stationarity of the energy functional in the equilibrium state and may therefore be considered weak. The second approach, on the other hand, attempts to directly solve the governing differential equation within an optimisation framework and such a solution may be thought of as strong. Several numerical illustrations and verifications with ‘exact’ solutions, if available, are provided For communication     

Analog CMOS circuit design and characterization for optical coherence tomography signal processing

We have developed a custom analog CMOS circuit to perform the signal processing for an optical coherence tomography imaging system. The circuit is realized in a 1.5 /spl mu/m low-noise analog CMOS technology. The circuitry extracts the Doppler frequency from the signal and electrically mixes this with the original signal to provide a filtered A-scan. The circuitry was used to produce a two-dimensional image of an onion.     

Optimal Design of Level 1 Redundant Water Distribution Networks Considering Nodal Storage

A water distribution network (WDN) is designed to meet time-varying demands with sufficient pressure, taking into consideration an appropriate demand during peak hours. Therefore, a network has inherent redundancy in the sense that under abnormal conditions such as those arising due to pipe breaks or pump failures, deficiency in supply during peak hours can be met through additional supply during off-peak periods. However, this necessitates a storage facility at the consumer end of the network, which is normally available in the form of a sump or an overhead tank in developing countries. Such a storage enables the consumer to store water during the off-peak period and then use it during the peak period. Reliability of a WDN is assessed herein considering nodal storage, and an iterative method is proposed for the optimal design of Level 1 redundant WDNs, i.e., networks that can sustain a single pipe failure without affecting consumer services either in part or in full. The method is illustrated through an example and the designs of a network with and without storage are compared. Provision of a nodal storage is found to reduce the total cost of the network.     

Photocatalytic degradation of dyes and organic contaminants in water using nanocrystalline anatase and rutile TiO2

Nanocrystalline TiO₂ was synthesized by controlled hydrolysis of titanium tetraisopropoxide. The anatase phase was converted to rutile phase by thermal treatment at 1023 K for 11 h. The catalysts were characterized by X-ray diffraction (XRD), diffuse reflectance spectroscopy (DRS), Fourier-transform infrared absorption spectrophotometry (FT-IR) and N₂ adsorption (BET) at 77 K. This study compare the photocatalytic activity of the anatase and rutile phases of nanocrystalline TiO₂ for the degradation of acetophenone, nitrobenzene, methylene blue and malachite green present in aqueous solutions. The initial rate of degradation was calculated to compare the photocatalytic activity of anatase and rutile nanocrystalline TiO₂ for the degradation of different substances under ultraviolet light irradiation. The higher photocatalytic activity was obtained in anatase phase TiO₂ for the degradation of all substances as compared with rutile phase. It is concluded that the higher photocatalytic activity in anatase TiO₂ is due to parameters like band-gap, number of hydroxyl groups, surface area and porosity of the catalyst.     

A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks

The engineering properties of rocks play a significant role in planning and designing of mining and civil engineering projects. A laboratory database of mechanical and engineering properties of rocks is always required for site characterization and mineral exploitation. Due to discontinuous and variable nature of rock masses, it is difficult to obtain all physicomechanical properties of rocks precisely. Prediction of unconfined compressive strength from seismic wave velocities (Compressional wave, Shear wave) and density of rock using generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference systems (ANFIS) can be appropriate and alternate methods to minimize the time and cost of tests. GRNN and ANFIS models were trained with 41 data sets using conjugate gradient descent algorithms and hybrid learning algorithm, respectively. Performance of both the models was examined with 15 testing data sets. In the present study, obtained network performance indices such as correlation coefficient, mean absolute percentage error, root mean square error and variance account for indicate high performance of predictive capability of GRNN system and closer to actual data over the ANFIS.     

A design fix to supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks

In the article ‘Supervisory control for fault-tolerant scheduling of real-time multiprocessor systems with aperiodic tasks’, Park and Cho presented a systematic way of computing a largest fault-tolerant and schedulable language that provides information on whether the scheduler (i.e., supervisor) should accept or reject a newly arrived aperiodic task. The computation of such a language is mainly dependent on the task execution model presented in their paper. However, the task execution model is unable to capture the situation when the fault of a processor occurs even before the task has arrived. Consequently, a task execution model that does not capture this fact may possibly be assigned for execution on a faulty processor. This problem has been illustrated with an appropriate example. Then, the task execution model of Park and Cho has been modified to strengthen the requirement that none of the tasks are assigned for execution on a faulty processor.     

Magnetic sensor-based simultaneous state and parameter estimation using a nonlinear observer

This paper presents an observer design technique for a newly developed non-intrusive position estimation system based on magnetic sensors. Typically, the magnetic field of an object as a function of position needs to be represented by a highly nonlinear measurement equation. Previous results on observer design for nonlinear systems have mostly assumed that the measurement equation is linear, even if the process dynamics are nonlinear. Hence, a new nonlinear observer design method for a Wiener system composed of a linear process model together with a nonlinear measurement equation is developed in this paper. First, the design of a two degree-of-freedom nonlinear observer is proposed that relies on a Lure system representation of the observer error dynamics. To improve the performance in the presence of parametric uncertainty in the measurement model, the nonlinear observer is augmented to estimate both the state and unknown parameters simultaneously. A rigorous nonlinear observability analysis is also presented to show that a dual sensor configuration is a sufficient and necessary condition for simultaneous state and parameter estimation. Finally, the developed observer design technique is applied to non-intrusive position estimation of the piston inside a pneumatic cylinder. Experimental results show that both position and unknown parameters can be reliably estimated in this application.     

One-step synthesis of composite electrolytes of Eu-doped ceria and alkali metal carbonates

Europium-doped ceria (EDC, Ce_0.9Eu_0.1O_2−δ)/alkaline carbonate (LNC, (Li,Na)₂CO₃) composite ceramics prepared through a one-step citrate-based route were analyzed by powder X-ray diffraction, infrared and laser Raman spectroscopies as well as scanning and transmission electron microscopy. The electrochemical behavior of the electrolyte material was studied by impedance spectroscopy in air, CO₂ and N₂ + H₂ (90/10 vol%, respectively) gas mixtures, in the temperature range 300–600 °C. The sub micrometric and even nanosized ceramic particles appeared as merged inside the mixed carbonates, with modest grain to grain necking. The EDC/LNC composite electrolytes showed a conductivity of 0.27 S cm⁻¹ at 600 °C in air, amongst the best ever reported, exceeding the usual requirements for fuel cell applications.