Hyperthermia as an Antineoplastic Treatment Modality

Preclinical evaluation of hyperthermia for treating tumerous cancers is discussed

Facies Modelling of Mishrif Formation in Selected Wells of Tuba Oil Field, Southern Iraq

The current study includes building a 3D geological facies model of the Mishrif Formation (Cenomanian-Early Turonian) in Tuba oilfield, southern Iraq. Microfacies study and core samples examination reveals the occurrence of six facies associations within Mishrif succession represented by; Basin, deep marine, rudist biostrome, shoal, back- shoal, and lagoon. Each reservoir unit is characterized by distinct facies distribution that controls their quality. High reservoir quality is predominantly developed in rudistid facies that are productive from units MB1 and MB2. The 3D facies model shows that these units have greater continuity and thickness along Tuba anticline and control the structural and stratigraphic trapping. Units MA and Mishrif have lower reservoir quality due to the dominance of mud-dominated facies. The unit CR2 consists of non-reservoir facies, and can be captured along the oilfield structure

A mirrorless spinwave resonator

Optical resonance is central to a wide range of optical devices and techniques. In an optical cavity, the round-trip length and mirror reflectivity can be chosen to optimize the circulating optical power, linewidth, and free-spectral range (FSR) for a given application. In this paper we show how an atomic spinwave system, with no physical mirrors, can behave in a manner that is analogous to an optical cavity. We demonstrate this similarity by characterising the build-up and decay of the resonance in the time domain, and measuring the effective optical linewidth and FSR in the frequency domain. Our spinwave is generated in a 20 cm long Rb gas cell, yet it facilitates an effective FSR of 83 kHz, which would require a round-trip path of 3.6 km in a free-space optical cavity. Furthermore, the spinwave coupling is controllable enabling dynamic tuning of the effective cavity parameters.Comment: 13 pages, 4 figure

Comparing unsupervised layers in neural networks for financial time series prediction

In this study, we propose and compare neural network models that use unsupervised layers for the prediction of financial time series. We compare the novel FL-RBM and FL-SMIA-RMB models that integrate a Restricted Boltzmann Machine (RBM) and the self-organizing layer of the Selforganized Multi-Layer Network using the Immune Algorithm (SMIA) with the FL-SMIA network and a standard MLP. We aim to investigate the performance of unsupervised learning in comparison to purely supervised and other mixed models. The FL-RBM model combines the products of raw input features (the Functional Link, FL), with the Restricted Boltzmann Machine RBM as a self-organizing first hidden layer, while the FL-SMIA model uses the Immune Algorithm on the first layer. The FLSMIA- RBM model, combines both self-organizing layers with a back-propagation network. The results show that the FL-SMIA model outperforms the FL-RBM, the FL-SMIA-RBM and the MLP as measured by Annualized Return (AR) in one-day-ahead prediction on exchange rates time series. In terms of volatility, the FL-SMIA and MLP perform similarly

Evaluation of brick infill walls under in-plane and out-of-plane loading

In this paper, the in-plane (IP) and out-of-plane (OP) interaction of masonry infill walls with various length-to-height ratios and vertical forces from dead and live loads are studied. For this purpose, calibrated numerical simulation for IP and OP behaviors of infilled frames has been exploited. In this method, first, the vertical loads are applied, then increasing IP displacement is imposed at the top of the models and finally OP demands are applied to the walls up to their failure. Two different methods of applying OP loading are studied: increasing static uniform pressure on the wall, and increasing dynamic acceleration. Three levels of IP displacement demands are considered: at the first reduction of tangential stiffness for IP force-displacement response, at the maximum IP strength, and at the displacement related to 20 % reduction of IP strength. The results obviously show that up to the point of the maximum IP strength capacity, the OP behavior of the considered models slightly enhanced due to the effects of improved arching actions originated from the development of IP compressive diagonal struts. Moreover, slight differences exist between the static and dynamic loadings in OP direction, hence proving the accuracy of the equivalent static loading in determining OP capacity for the studied infilled frames. Comparing the results of masonry infilled frames with those of the corresponding masonry walls indicate that the IP displacements negatively affect the OP strength in the latter, even at small IP displacement demands; however, the rate of OP strength reduction in larger IP drift ratios is lower compared to that of the corresponding infilled frame

The FL-SMIA Network: A Novel Architecture for Time Series Prediction

In this paper we propose the FL-SMIA model, a novel neural network model that combines the principles of the Functional Link Neural Network (FLNN) with the Self-organizing Multilayer Neural Network using the Immune Algorithm (SMIA). We describe the FL-SMIA architecture and operation and evaluate its predictive performance on different financial time series in comparison to other neural network models. The FL-SMIA model combines the higher-order inputs of the tensor-product FLNN, i.e. the products of raw input features, with the self-organizing hidden layer of SMIA that dynamically grows and adapts to the input vectors. The FL-SMIA has two advantages over other models. First, it can dynamically adapt to growing amounts of data with a model that grows increasingly complex. Second, it keeps an explicit representation of the patterns it recognises in the data. Experimental results show that the FL-SMIA improves performance, as measured by annualised return in five-days-ahead and one-day-ahead prediction tasks for share prices and exchange rates, over the SMIA networks alone and over standard multilayer perceptrons. It performs on the same level as the FLNN, sometimes better but not significantly so. The result that FLNN and FL-SMIA outperform other multilayer models indicates that particularly the higher-order features contribute to the improved performance and motivate further research into mixed neural network architectures for financial time series prediction