Publications by authors named "B C Tripathy"

Heavy metal pollution has become a significant concern as the world continues to industrialize, urbanize, and modernize. Heavy metal pollutants impede the growth and metabolism of plants. The bioaccumulation of heavy metals in plants may create chlorophyll antagonism, oxidative stress, underdeveloped plant growth, and reduced photosynthetic system.

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Background: Hypospadias is one of the common congenital anomalies of male genitalia. Although over 300 different operative techniques have been described, post-operative complications are still common, of which glans dehiscence (GD) is the most severe complication requiring redo urethroplasty. Some surgeons use the vascular flap to cover the glanular part of the neourethra to prevent GD, but there are controversies regarding its usefulness.

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Background: Ureteropelvic junction obstruction (UPJO) is the most common cause of antenatal hydronephrosis. Although majority of them improve with time, none of the existing diagnostic modalities can accurately predict which hydronephrotic kidney is at the risk of progressive renal damage and will benefit from early surgery. Postural variations in the anteroposterior pelvic diameter (APPD) of the hydronephrotic kidney in children during follow-up postnatal ultrasonography (USG) reflect the intrapelvic tension, which might help in predicting the need of surgery amongst these patients.

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Due to their large size and obligate nature, Cymothoid isopods inflict a high degree of tissue damage to fish. Still, they are understudied at an ecosystem level despite their global presence and ecological role. In this work, we collected fish host-isopod parasite data, along with their life history and ecological traits, from the northern part of the east coast of India and investigated patterns in host specialisation and preference of isopod parasites using a trait-based network perspective.

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Article Synopsis
  • The COVID-19 pandemic prompted the need for advanced early detection and diagnosis methods, leading to the integration of IoT devices in healthcare and the use of AI to analyze vast amounts of IoT data for disease prediction.
  • To address challenges in feature analysis of complex IoT data, the study introduces the optimal iterative COVID-19 classification network (OICC-Net), which combines machine learning optimization techniques and deep learning methods for accurate classification.
  • Using a combination of the RFI-PS-BWO algorithm and an iterative deep convolution learning method, the OICC-Net achieved impressive performance metrics, including a 99.97% F1-score and perfect sensitivity, specificity, and precision rates in classifying COVID-19 related
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