Autobiographical Elements in the Poetry of Seamus Heaney

The present research paper aims to reflect upon the various autobiographical elements projected throughout the poetry of Seamus Heaney No poet or for that matter artist of any sort can be isolated from the very circumstances prevailing around her him Nor do her his poetic creations can be abandoned completely from the events of her his life In fact Heaney s poetic faculty collected food for the real poetic production from his own life experiences which were in close proximity to him since his childhood to his mature year

Advancements in Neuroradiology via Artificial Intelligence and Machine Learning

Neuroradiology is significantly showing the broad impact in field of Artificial intelligence research and also in Machine learning. Neuro-radiology includes methods such as neuro-imaging which simply diagnose and characterize disorders of the CNS and PNS. Artificial Intelligence (AI) is one of the main attribute in the field of computer science generally focusing on creating "algorithms" which can be used to solve any arbitrary desired problem. AI has several applications in the field of Neuroradiolody and one of the most common and influencing application is machine learning. Machine learning is a data science approach that allows computers to learn without being programmed with specific rules. Some of the factors which shows neuroradiological impact on AI research are; (a) neuroimaging comprising rich, multicontrast, multidimensional, and multimodality data which fit themselves well to machine learning tasks; (b) consideration of well-established neuroimaging public datasets of various neural diseases such as Alzheimer disease, Parkinson disease, tumors, different forms of sclerosis etc. (c) quantitative neuroimaging research history which proves clinical practices. Another major application is Deep learning which is useful in management of information content of digital pictures that a human reader can only identify and use partially. Except this various limitations also come in the picture such as adoption in neuroradiology practice etc. Till now several research has been done which connects the concepts of Neuroradiology and Artificial intelligence and yet more to be done so as to overcome the limitations of AI in Neuroradiology

Analytical Estimation of Radar Cross Section of Dipole Array with Parallel Feed

The scattering characteristics of a phased array depends on its design parameters including its feed. In this paper, the radar cross section of dipole array with parallel-feed network is calculated. The scattered field is obtained by in terms of reflection and transmission coefficients at each component level of the array system. The mutual coupling effect is considered. Scattering till second level of couplers in feed network is taken into account. The analysis presented can be useful in low RCS phased array design

Mutual Coupling in Phased Arrays: A Review

The mutual coupling between antenna elements affects the antenna parameters like terminal impedances, reflection coefficients and hence the antenna array performance in terms of radiation characteristics, output signal-to-interference noise ratio (SINR), and radar cross section (RCS). This coupling effect is also known to directly or indirectly influence the steady state and transient response, the resolution capability, interference rejection, and direction-of-arrival (DOA) estimation competence of the array. Researchers have proposed several techniques and designs for optimal performance of phased array in a given signal environment, counteracting the coupling effect. This paper presents a comprehensive review of the methods that model and mitigate the mutual coupling effect for different types of arrays. The parameters that get affected due to the presence of coupling thereby degrading the array performance are discussed. The techniques for optimization of the antenna characteristics in the presence of coupling are also included

First record of valid species of torpedo electric ray, Torpedo polleni (Bleeker, 1865) (Torpediniformes: Torpedinidae) from Indian waters

1338-1343A species of torpedo electric ray, Torpedo polleni (Bleeker, 1865) has been reported for the first time from Indian waters. Six specimens of T. polleni measuring 147-397 mm TL were collected from shrimp trawl by-catches at Visakhapatnam, central eastern coast of India. The present paper provides description and comparison of morphometric data of T. polleni with closely resembling species of genus Torpedo thus helping in clearing taxonomic ambiguities. The present study suggests that T. polleni (Bleeker, 1865) with 6‑8 knob-like papillae on the posterior margin of spiracle, the central papilla being larger, distance between eye and spiracle less than eye diameter, nasal curtain short and wide, its length more than half the length of inter narial width, first dorsal fin originating entirely above the pelvic fin base and base ending little beyond the pelvic fin, dorsal surface showing ornate appearance with close set of brownish-black spots, few spots very closely set or joined together forming vermiculations and dark brown lines is a valid species

Studies on some aspects of biology of Uranoscopus cognatus Cantor, 1849 (Pisces: Uranoscopidae) off Visakhapatnam, central eastern coast of India

85-92Present study aims at generating baseline data on biology of the most common species, Uranoscopus cognatus that includes length frequency distribution, Length-Weight Relationship (LWR), population parameters and various aspects of reproductive biology. These studies are based on 618 specimens of length range 51-189 mm TL collected during the period January 2015 to December 2016. The present study contributes to an improved understanding of biology of fish communities and the possible impact of fishing on the long-term sustainability of exploited ecosystems

Radar Cross Section (RCS) of a Series-Fed Dipole Array including Mutual Coupling Effect

Abstract attached as PD

Development and validation of radiomic signature for predicting overall survival in advanced-stage cervical cancer

BackgroundThe role of artificial intelligence and radiomics in prediction model development in cancer has been increasing every passing day. Cervical cancer is the 4th most common cancer in women worldwide, contributing to 6.5% of all cancer types. The treatment outcome of cervical cancer patients varies and individualized prediction of disease outcome is of paramount importance.PurposeThe purpose of this study is to develop and validate the digital signature for 5-year overall survival prediction in cervical cancer using robust CT radiomic and clinical features.Materials and MethodsPretreatment clinical features and CT radiomic features of 68 patients, who were treated with chemoradiation therapy in our hospital, were used in this study. Radiomic features were extracted using an in-house developed python script and pyradiomic package. Clinical features were selected by the recursive feature elimination technique. Whereas radiomic feature selection was performed using a multi-step process i.e., step-1: only robust radiomic features were selected based on our previous study, step-2: a hierarchical clustering was performed to eliminate feature redundancy, and step-3: recursive feature elimination was performed to select the best features for prediction model development. Four machine algorithms i.e., Logistic regression (LR), Random Forest (RF), Support vector classifier (SVC), and Gradient boosting classifier (GBC), were used to develop 24 models (six models using each algorithm) using clinical, radiomic and combined features. Models were compared based on the prediction score in the internal validation.ResultsThe average prediction accuracy was found to be 0.65 (95% CI: 0.60–0.70), 0.72 (95% CI: 0.63–0.81), and 0.77 (95% CI: 0.72–0.82) for clinical, radiomic, and combined models developed using four prediction algorithms respectively. The average prediction accuracy was found to be 0.69 (95% CI: 0.62–0.76), 0.79 (95% CI: 0.72–0.86), 0.71 (95% CI: 0.62–0.80), and 0.72 (95% CI: 0.66–0.78) for LR, RF, SVC and GBC models developed on three datasets respectively.ConclusionOur study shows the promising predictive performance of a robust radiomic signature to predict 5-year overall survival in cervical cancer patients