Search

Inevitability to reduce the size, cost and complexity of multilevel inverter (MLI) to evolve simplified RSC (Reduced switch count) MLIs has created diversified effects such as limited switching redundancies which further restricted modularity and flexibility of implementing PWM schemes. Among the PWM schemes of RSC-MLIs, modified reduced carrier (MRC) PWM with unified switching logic (USL) is the simplest and claims to be superior with the state of art in terms of implementation, line harmonic performance, imposing least controller computational burden (of 6.5 µS to realise a three-phase seven-level RSC-MLI) by adapting minimal carrier constraints. However, this is valid for sophisticated digital controllers such as dSPACE MicroLabBox, but not for low-end cost-effective controllers such as dSPACE1104 (imposing a computational burden of nearly 25 µS to control a single-phase RSC-MLI). Thus, this paper proposes an improved USL which adopts no carrier constraints and operates with less computational burden over the conventional USL. The proposed USL gives flexibility to the user to implement a part of the switching logic with analogue circuits and thereby avoid the requirement of high-end digital controller. The scalability and superiority of the proposed USL is demonstrated for symmetrical and asymmetrical configurations of popular cascaded T-type RSC-MLI. Efficacy of the proposed USL is demonstrated with dSPACE 1104 controller and dSPACE FPGA R&D Micro-lab Box. [ABSTRACT FROM AUTHOR]

The study on the reproductive performance of Deccani sheep in Telangana region of Andhra Pradesh indicated that the age (days) at first mating were 423.41±3.15 and 303.33± 2.19 for rams and ewes, respectively. The mean value of age (days) at first lambing was 468.89 ± 2.60, which was significantly (P< 0.01) lower in Central Telangana. Lambing interval was significantly (P<0.05) lower in Central Telangana and the sheep flocks showed overall mean lambing interval 292.58± 2.56 days. The overall mean lambing percentage in Deccani sheep was 80.62± 0.62 and was significantly (P< 0.05) higher in Northern Telangana whereas the overall mean twinning per cent was 0.15 ± 0.05. It was noticed that none of the farmer pursued weaning in the study area of Telangana region of Andhra Pradesh. [ABSTRACT FROM AUTHOR]

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)