Veterinary Biological and Research Institute

Purpose of the Study: This study synthesizes previous research on PhysicalSciences faculty's academic roles and information-use habits.It examines how teaching, research, lab supervision, outreach, and administrative tasks generate distinct information needs and influence resource selection.However, since the study is limited to published English-language research from the past three decades , there is a risk of omitting non-English and unpublished works.This limitation could affect comprehensiveness, suggesting that future studies could benefit from a broader scope.

This paper describes the development of an innovative and intelligent framework for identifying personality types in real time using voice samples. The system can automatically categorize a user's speech into one of the sixteen Myers-Briggs Type Indicator (MBTI) categories by combining two cutting-edge AI technologies: OpenAI's Whisper and a specifically optimized RoBERTa model. This approach provides users with instantaneous, realistic feedback as a contemporary, non-invasive replacement for conventional self-report personality assessments. With Whisper offering high-fidelity speech-to-text (ASR) conversion and the RoBERTa model carrying out the following MBTI classification on the transcribed text, the system's modular architecture guarantees robustness. The system reaches a noteworthy peak accuracy of 85.9 %, according to validation. This system, which was created with an easy-to-use web interface in mind, is ready for scalable implementation and has great potential for usage in digital mental wellness, personalized education, and human resources. It represents a substantial advancement in voice-based psychological analytics.

An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across heterogeneous environments serve as primary interfaces for sensing, computation, and actuations. Their physical deployment in unattended scenarios puts them at risk of being targets for resource manipulation. One widely accepted IoT architecture with traditional notions of edge may consider a threat to its centralized knowledge with an unbounded attack surface that includes anything that can remotely connect to the edge from the cloud-like domain. Existing strategies either forget the dynamic risk context of edge nodes or do not achieve a reasonable trade-off between security and resource constraints, essentially degrading the robustness and trustworthiness of solutions intended for real-life scenarios. To address the existing gaps, the work presents a novel Blockchain Integrated Deep Learning Framework for secure IoT edge computing, introducing a hybrid architecture where the transparency of blockchain meets deep learning flexibility. The proposed system incorporates five specialized components: Blockchain-Orchestrated Federated Curriculum Learning (BOFCL), which ensures risk-prioritized training using threat indices derived from blockchain logs; this adaptive sequencing enhances responsiveness to high-risk edge scenarios. Zero-Knowledge Proof Enabled Secure Inference Engine (ZK-SIE) provides verifiable privacy-preserving inference, ensuring model integrity without exposing input data or model internals in process. Blockchain Indexed Adversarial Attack Simulator (BI-AAS) focuses on testing the models in edge environments against attack scenarios drawn from common adversarial profiles and thereby facilitates a model defensive retraining. Energy-Aware Lightweight Consensus with Adaptive Synchronization (ELCAS) avoids overhead by seeking energy-efficient participants for global model synchronization in constrained environments. Trust Indexed Model Provenance and Deployment Ledger (TIMPDL) ensures model lineage tracking and deploy ability in a transparent manner by providing composite trust scores computed from data quality, node reputation, and validation metrics. Altogether, the framework combines the data integrity, adversarial robustness, and trust-aware deployment, shortening training latency, synchronization energy, and privacy leakage. It is a foundational advancement supporting secure decentralized edge intelligence for next-generation IoT ecosystems.

Colorectal cancer (CRC) is the second most common cause of cancer-related mortality worldwide, underscoring the necessity for computer-aided diagnosis (CADx) systems that are interpretable, accurate, and robust. This study presents a practical CADx system that combines Vision Transformers (ViTs) and DeepLabV3 + to accurately identify and segment colorectal lesions in colonoscopy images.The system addresses class balance and real-world complexity with PCA-based dimensionality reduction, data augmentation, and strategic preprocessing using recently curated CKHK-22 dataset comprising more than 14,000 annotated images of CVC-ClinicDB, Kvasir-2, and Hyper-Kvasir. ViT, ResNet-50, DenseNet-201, and VGG-16 were used to quantify classification performance. ViT achieved best-in-class accuracy (97%), F1-score (0.95), and AUC (92%) in test data. The DeepLabV3 + achieved segmentation state-of-the-art for tasks of localisation with 0.88 Dice Coefficient and 0.71 Intersection over Union (IoU), ensuring sharp delineation of areas that are malignant. The CADx system accommodates real-time inference and served through Google Cloud for information that accommodates scalable clinical implementation. The image-level segmentation effectiveness is evidenced by comparison with visual overlay and expert-manually deliminated masks, and its precision is illustrated by computation of precision, recall, F1-score, and AUC. The hybrid strategy not only outperforms traditional CNN strategies but also overcomes important clinical needs such as detection early, balance of highly disparate classes, and clear explanation. The proposed ViT–DeepLabV3 + system establishes a basis for advanced AI support to colorectal diagnosis by utilizing self-attention strategies and learning with different scales of context. The system offers a high-capacity, reproducible computerised colorectal cancer screening and monitoring solution and can be best deployed where resources are scarce, and it can be highly desirable for clinical deployment.

The pupillary direct and consensual reflex is an important non-invasive quick assessment of the neurological state of the eye. Currently, there is no cheap and affordable recording tool for screening and documentation of a relative afferent pupillary defect. We describe how to construct a frugal, do-it-yourself handheld scotopic binocular pupillometer device called Pupilmate. Two simple Wi-Fi-enabled board cameras fitted on both sides of the back of a rectangular cardboard box separated by a central paper divider, one side of the rectangular box was left open. An infrared red (IR) light source in the form of light-emitting diodes (LEDs) was fitted around each of the cameras. Two white light LEDs were fitted on either side of the box controlled by a two-way switch. A simultaneous video stream of both pupils could then be tuned in on any chosen device using the internet. Each pupil is manually illuminated for a desired time allowing recording of direct as well as consensual reflexes, which are objectively observed. Pupil reaction was easily recorded and repeatable using Pupilmate. It served as a good medium for initial screening in an outpatient department (OPD) setting, outreach programs, clinics, etc.

In today's digital era, the spread of electronic gadgets has given consumers with a collection of choices, leading to challenges in making informed purchasing decisions. Despite the plenty of customer feedback available across various online platforms, comprehensively analyzing this feedback to extract meaningful insights remains a difficult task. Additionally, consumer sentiments towards electronic gadgets have considerable influence over purchasing decisions and brand perception, necessitating a systematic analysis to better understand sentiment dynamics in this domain. This study aims to address these challenges by conducting a comprehensive analysis of customer feedback regarding electronic gadgets, focusing on sentiment dynamics and their impact on consumer behavior. However, the study faces several limitations, including dependence on online sources for data collection, potential language bias towards English-language feedback, limited geographic scope Particularly focused on some specific areas, and subjectivity in sentiment analysis interpretation. In conclusion, this study endeavors to provide valuable insights into consumer sentiments towards electronic gadgets, thereby empowering consumers, aiding manufacturers and marketers in decision-making, and enhancing overall consumer experience in the electronics industry.

Even though Convolutional Neural Networks (CNNs) have greatly improved face-related algorithms, it is still difficult to keep both accuracy and efficiency in real-world applications. The most cutting-edge approaches use deeper networks to improve performance, but the increased computing complexity and number of parameters make them impractical for usage in mobile applications. To tackle these issues, this article presents a model for object detection that combines Deeplabv3+ with Swin transformer, which incorporates GLTB and Swin-Conv-Dspp (SCD). To start with, in order to lessen the impact of the hole phenomena and the loss of fine-grained data, we employ the SCD component, which is capable of efficiently extracting feature information from objects at various sizes. Secondly, in order to properly address the issue of challenging object recognition due to occlusion, the study builds a GLTB with a spatial pyramid pooling shuffle module. This module allows for the extraction of important detail information from the few noticeable pixels of the blocked objects. Crocodile search algorithm (CSA) enhances classification accuracy by properly selecting the model's fine-tuning. On a benchmark dataset known as WFLW, the study experimentally validates the suggested model. Compared to other light models, the experimental findings show that it delivers higher performance with significantly fewer parameters and reduced computing complexity.

Abstract Influenza viruses have become a major threat to humans, animals and agriculture posing a huge serious economic loss. In this connection, a rapid, high sensitive and cost effective detection method is the need of the day to keep a regular surveillance and to control the spreading of the viruses. Newcastle disease virus (NDV) is most prevalent virus in avian species causing disastrous losses in worldwide poultry industries. Despite of the availability of several vaccines, eradication of the disease still remains a challenge due to the inability of the conventional vaccines to differentiate the infected from vaccinated animals (DIVA).This led to the need for the development of marker vaccines which in conjunction with diagnostic tools that are able to differentiate the infected animals from the vaccinated animals. The limitations possessed by conventional diagnostic tools has paved the way to the implementation of biosensors for its rapid, cost effective, on site detecting features. This research work advocates, the development of a bio sensing strategy for the detection of anti-bodies developed when the chickens exposed to Newcastle disease virus (NDV) and Classical Swine Fever (CSF) using peptide-functionalized polydiacetylene (PEP-PDA) based on the unique color variation (bio-chromism) of the PDA. The PDA vesicle is formed from the ratio of 10, 12-pentacosadionic acid (PCDA), 1, 2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) using a peptide of NDV and CSF attached to the surface of the vesicle. The specificity, sensitivity and proficiency of the method is evaluated. The developed method is very cost effective, advantageous than existing methods.