Rajarajeswari Medical College and Hospital

Side-Channel Attacks (SCA) represent a critical and increasingly significant class of security vulnerabilities in modern computing and electronic systems, exploiting indirect information leakage rather than traditional algorithmic weaknesses. Unlike conventional cryptanalytic attacks, which target the mathematical structure of cryptographic algorithms, SCAs leverage unintentional physical or logical emissions produced by devices during computation. These emissions may include timing information, power consumption patterns, electromagnetic radiation, acoustic signals, and even subtle variations in system temperature. The underlying principle is that any physical instantiation of a computational process inherently produces observable side effects correlated with internal data, including secret keys or sensitive algorithmic states. By monitoring these side effects, attackers can infer confidential information, effectively bypassing the theoretical security guarantees of cryptographic primitives.

Background: Tumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. Methods: We leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. Results: Binary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). Conclusion: Cross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

In the 21st century, few forces have reshaped the world of work as profoundly as technology and artificial intelligence (AI). What once seemed the domain of science fiction—machines learning from data, robots performing complex surgeries, or algorithms writing poetry—has now become everyday reality. From the factory floor to the executive boardroom, technological advancements are transforming how tasks are performed, how decisions are made, and how value is created. but also enhancing human capabilities, enabling better insights, faster communication, and more personalized customer experiences. real-time analysis, and predictive modeling to stay competitive in fast-evolving markets. Human resource departments are using AI to streamline recruitment, identify employee engagement trends, and even predict potential attrition. Marketing teams are leveraging algorithms to target customers with pinpoint accuracy, while finance departments utilize AI tools to detect fraud, analyze investment opportunities, and manage risk more efficiently.

Facial expression recognition (FER) in uncontrolled, in-the-wild settings is a challenging task due to variations in pose, illumination, occlusion, and background clutter. The state-of-the-art methods on FER employ fixed size pooling in attention networks, resulting in suboptimal robustness in unconstrained environments. In this work, a novel Multi-Scale Attention Network (MSAN) is integrated with the YOLOv8 backbone architecture for expression classification. The YOLOv8 backbone provides efficient feature extraction with a lightweight, detection-oriented architecture, enabling real-time processing and strong spatial feature representation. The Convolutional Block Attention Mechanism (CBAM) is employed in the proposed work. The standard convolutional blocks in YOLOv8 backbone are replaced with MSAN, wherein the fixed-size pooling in the spatial attention mechanism of CBAM is replaced with a Pyramid Pooling Mechanism (PPM). The PPM employs adaptive average pooling at multiple resolutions to capture multi-scale contextual information, which is subsequently fused into a unified feature representation. This enables the spatial attention mechanism in MSAN to capture both global facial context and local region-level cues simultaneously, leading to superior robustness to scale variations, partial occlusions, and diverse expression variations. The work is evaluated on benchmark wild datasets including RAF-DB, FER2013, and AffectNet, with all experiments performed using 5-fold cross-validation. Experimental results demonstrate that the multi-scale attention network with PPM in YOLOv8 backbone achieved improvement in classification accuracy and robustness when compared with the state-of-the-art methods for expression classification in the wild. These findings highlight the effectiveness of integrating MSAN into lightweight detection-based backbones for real-world FER applications.

Embryonal rhabdomyosarcoma accounts for 4.5% of the childhood cancers. Rhabdomyosarcoma is the most common malignant soft tissue sarcoma in children and has the following subtypes: embryonal, alveolar, pleomorphic and spindle cell/sclerosing. About 17% of rhabdomyosarcomas occur in genito-urinary tract. A 2-year-old male child presented with complaints of excessive crying while passing urine and dribbling of urine for 1 day. Patient underwent cystoscopic biopsy and histopathological examination was done after taking written consent by the parents following which showed a cyst arising from bladder neck extending into right lateral wall of prostate. Partial excision of the cystic lesion was done. Gross examination of the biopsy revealed multiple grey-white to grey-brown soft tissue bits largest measuring 2cms in length and smallest measuring 0.2cm in length. Microscopy revealed a malignant neoplasm composed of poorly differentiated tumor cells arranged in sheets. Individual cells are primitive ovoid to spindle shaped with moderate pleomorphism and hyperchromatic nuclei with minimal cytoplasm admixed with myxoid areas. Few scattered Rhabdomyoblast like elongated cells with cytoplasmic eosinophilia and sparse atypical mitosis noted. Cambium layer was seen. Margin status were not assessed as the specimen was received in piece meal. Differential diagnosis of small round blue cell tumor was considered. Immunohistochemistry showed positivity for smooth muscle Actin, Desmin, Myogenin and MyoD1 suggesting Embryonal Rhabdomyosarcoma. Embryonal Rhabdomyosarcoma is the most common subtype of pediatric rhabdomyosarcoma. Age, Stage at diagnosis and treatment received are the predictors of the survival of Embryonal RMS.

At its core, labor market dynamics reflect a complex interplay between economic forces, institutional structures, and individual behaviors. Understanding these dynamics is essential not only for economists and policymakers but also for workers, employers, and anyone interested in the broader economic system. This introduction provides an overview of the key concepts and theories that are fundamental to understanding labor market behavior concepts in labor market dynamics is the theory the number outcomes, including employment levels, participation, the labor market does not always behave according to these simplistic social norms. According to this theory, the more skilled or educated an individual is, the more valuable they become in the labor market, as they can contribute more to the production process Investments in education and training, for example, are seen as ways to increase human capital. The notion of equilibrium is particularly useful as a benchmark for understanding how labor markets are likely to behave under different conditions.

In recent decades, the world has experienced rapid technological advancement, industrialization, and a significant shift toward digitalization. While these trends have contributed to economic growth and innovation, they have also intensified environmental degradation, resource depletion, and electronic waste. To address these challenges, the concept of a circular economy has emerged as a transformative alternative to the traditional linear economic model of “take, make, dispose.” While initially focused on tangible goods and materials, the circular economy model is now expanding to intangible assets, including software. Applying circular economy principles to software development is a novel and promising avenue, aligning the digital realm with sustainability goals. At its core, the circular economy is about designing out waste and maximizing value. One of the foundational principles of circular economy in the software context is design for longevity. This involves writing code that is modular, maintainable, and easy to update or refactor.