Poetry of social conscience, poetry of transition: Ann Hawkshaw's 'Introductory Stanzas' and 'The Mother to her Starving Child'

Through close readings of Ann Hawkshaw's poetry in the context of industrial Manchester in the 1840s, this article highlights the interaction of form and content in poetry that makes use of the idea of the past to question or complicate the politics of the present.

”He looked at me ... My God. His eyes were eyes to die for.” : A Feminist Theological Reading of Carol Ann Duffy’s The World’s Wife

Most approaches to Duffy’s work have been a feminist reading of poetry, focusing on the portrayal of women within the theoretical framework of feminism. However, little attention has been paid to the religious elements in Duffy’s work, something that Duffy herself has recognized. This essay will therefore focus on the centrality of religion in Duffy’s work, and will argue that her poems constitute an arena where religion is redefined and female experience and theology are reconciled. The poems under focus, “Delilah”, “Salome”, “Pilate’s wife”, “Pope Joan”, “Mrs Lazarous” and “Queen Herod” are examined in two separate sections: their portrayal of love and sexuality, and their portrayal of motherhood respectively, within the theoretical framework of feminist theology.

Ann Levine Wolff Scrapbook

Ann (Anna Eva) Levine was born on November 12, 1890 to William and Sarah Ida Levine, and died on April 3, 1890. This scrapbook contains family memories, photographs, and an exam book in holograph for a history class in 1936 at Colby College.

Use of Pyrosequencing and Denaturing Gradient Gel Electrophoresis to Examine the Effects of Probiotics and Essential Oil Blends on Digestive Microflora in Broilers Under Mixed Eimeria Infection

A protective digestive microflora helps prevent and reduce broiler infection and colonization by enteropathogens. In the current experiment, broilers fed diets supplemented with probiotics and essential oil (EO) blends were infected with a standard mixed Eimeria spp. to determine effects of performance enhancers on ileal and cecal microbial communities (MCs). Eight treatment groups included four controls (uninfected-unmedicated [UU], unmedicated-infected, the antibiotic BMD plus the ionophore Coban as positive control, and the ionophore as negative control), and four treatments (probiotics BC-30 and Calsporin; and EO, Crina Poultry Plus, and Crina PoultryAF). Day-old broilers were raised to 14 days in floor pens on used litter and then were moved to Petersime batteries and inoculated at 15 days with mixed Eimeria spp. Ileal and cecal samples were collected at 14 days and 7 days postinfection. Digesta DNA was subjected to pyrosequencing for sequencing of individual cecal bacteria and denaturing gradient gel electrophoresis (DGGE) for determination of changes in ileal and cecal MC according to percentage similarity coefficient (%SC). Pyrosequencing is very sensitive detecting shifts in individual bacterial sequences, whereas DGGE is able to detect gross shifts in entire MC. These combined techniques offer versatility toward identifying feed additive and mild Eimeria infection modulation of broiler MC. Pyrosequencing detected 147 bacterial species sequences. Additionally, pyrosequencing revealed the presence of relatively low levels of the potential human enteropathogens Campylobacter sp. and four Shigella spp. as well as the potential poultry pathogen Clostridiun perfringens. Pre- and postinfection changes in ileal (56%SC) and cecal (78.5%SC) DGGE profiles resulted from the coccidia infection and with increased broiler age. Probiotics and EO changed MC from those seen in UU ilea and ceca. Results potentially reflect the performance enhancement above expectations in comparison to broilers not given the probiotics or the specific EO blends as feed supplements.

Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Matriz de contabilidad social (MCS) 2002 de Costa Rica, y los fundamentos metodológicos de su construcción

Incluye Bibliografía

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Niobium Effects on the Recrystallization Behavior on a Full Austenitic 15Cr-15Ni Stainless Steel Investigated Using Adaptive Neural Networks (ANN)

A new series of austenitic stainless steels-Nb stabilized, without Mo additions, non-susceptible to delta ferrite formation and devoid of intemetallic phases (sigma and chi), without deformation induced martensite is being developed, aiming at high temperature applications as well as for corrosive environments. The base steel composition is a 15Cr-15Ni with normal additions of Nb of 0.5, 1.0 and 2 wt%. Mechanical properties, oxidation and corrosion resistance already have been invetigated in previous papers. In this paper, the effects of Nb on the SFE, strain hardening and recrystallization resistance are evaluated with the help of Adaptive Neural Networks (ANN).

Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.