Jehangir Clinical development Centre

Bird invasions in agricultural regions result in significant crop damage, thereby diminishing food output and impacting farmers' income. Traditional deterrence techniques, such as scarecrows and auditory devices, sometimes prove ineffective owing to their restricted flexibility and absence of automation. Progress in deep learning and computer vision has facilitated the creation of sophisticated bird detection systems that can monitor and identify avian behaviors in real time. This article examines contemporary deep learning methodologies employed for avian identification and their significance in current smart agricultural practices. It emphasizes the accessible datasets, prevalent detection algorithms (including YOLO and Faster R-CNN), assessment measures, and IoT-based hardware integration for practical applications. Finally, the report examines existing problems and proposes future research avenues for developing dependable and cost-effective automated avian monitoring systems

Accurate estimation of the State of Charge (SOC) and State of Health (SOH) is critical for the safe and efficient operation of lithium ion batteries in electric vehicles and energy storage systems. However, measurement inaccuracies and noise degrade the accuracy of the estimate, especially under dynamic load conditions. This paper proposes a Dual Extended Kalman Filter (DEKF)-based approach that adaptively adjusts the Kalman gain based on the measured load current magnitude. The algorithm decouples SOC and SOH, allowing independent tuning of the estimation processes into two interacting filters and real-time correction of the model parameters. The simulation results under Hybrid Pulse Power Characterization(HPPC) and Randomized World Motorcycle Test Cycle(WMTC) driving profiles and noise scenarios demonstrate that the proposed method improves the estimation accuracy by reducing the SOC error below 2 % and the SOH drift below 2 %, even with high sensor noise (up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{5 m V}$</tex> voltage and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{1 \%}$</tex> current errors at the cell level). The proposed DEKF framework is computationally efficient and suitable for real-time on-board implementation.

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early detection through regular screening significantly improves treatment outcomes and survival rates. This paper presents a novel smart sensor network architecture for breast cancer screening and monitoring, leveraging Internet of Things (IoT) technologies, machine learning algorithms, and wearable sensors. Our proposed system integrates multiple sensing modalities including thermography, ultrasound, and bio-impedance measurements to provide continuous, non-invasive monitoring capabilities. The network architecture employs edge computing for real-time data processing and cloud-based analytics for comprehensive health assessment. Experimental results demonstrate 94.2% accuracy in detecting suspicious tissue changes, with a false positive rate of 6.8%. The system also achieves significant improvements in patient comfort and accessibility compared to traditional screening methods. This research contributes to the advancement of personalized healthcare and early cancer detection through innovative sensor network technologies.

A crucial component of the functioning of a three-phase inverter in Permanent Magnet Synchronous Motor (PMSM) drives is the DC-link capacitors. The functioning of the inverter may be affected by an excessive voltage ripple caused by a DC connection capacitor of insufficient size. In contrast, an oversized capacitor can increase the system cost and volume and affect the transient response. Moreover, electrolytic capacitors, commonly used in DC-links, face significant lifetime limitations due to thermal stress from ripple current and voltage fluctuations. This paper presents a comprehensive methodology for selecting and sizing DC-link capacitors in inverter-fed PMSM drives, focusing on minimizing voltage ripple while ensuring compliance with the lifetime requirement for the electrolytic capacitors. Using a closed-loop simulation model, the functionality of the new method with the upgraded DC-link capacitor is confirmed based on system parameters, load profiles, and operational conditions.

The chargers for electric vehicles (EVs) need to withstand 440 V ac for a long duration due to neutral-open faults or incorrect connections. The techniques to withstand overvoltage suggested by the literature are non-recoverable, bulky, and require additional power supplies. This paper proposes a simple, low-cost solution based on a series relay to withstand high voltage conditions. The proposed circuit allows retrofitting the overvoltage withstand functionality with minimal changes in the host system. It consists of a capacitive drop power supply, sensing circuit, and comparator circuit for controlling the relay. The design of the capacitive drop power supply and other subcomponents is meticulously done for a complete over-voltage range. Both configurations of series relay, i.e., Normally Open (NO) and Normally Closed (NC), are studied along with their relative merits/demerits. The obtained design is compared with the solutions from the literature in terms of cost, weight, and area of PCB required for implementation. The experimental validation of both configurations of the proposed circuit is also included.

The proposed research study established and validated a RP-HPLC method for the estimation of Empagliflozin (EMG) in tablet formulation and bulk drug, that is new, sensitive, robust and appropriate. Developed RP-HPLC method utilized a mobile phase of Acetonitrile: 0.05% TFAA in Water (80:20), with a flow rate of 1.0 ml/min. The HPLC system included UV detector, Kromasil C18 column measuring 250 mm by 4.6 mm and having particle size of 5 µm. The detection was done at wavelength of 225 nm. The method produced an appropriate retention time of 3.04 minutes for EMG. The method was validated as per ICH guidelines. A simple and accurate method for assaying EMG in both tablet formulation and bulk drug was developed. This method is less time-consuming, making it suitable for regular use in the industry for analyzing bulk drug and marketed product of EMG.

Personalization has emerged as a crucial element for boosting user satisfaction in the swiftly changing realm of digital entertainment platforms. This project introduces a sentiment-aware movie recommendation system designed to offer highly customized suggestions to movie enthusiasts, particularly targeting audiences such as Cinemaniacs and Film Junkies. The system merges collaborative filtering techniques with natural language processing (NLP) methods to conduct sentiment analysis of user-generated reviews and ratings. By integrating user preferences such as genre selection and viewing history with sentiment insights derived from textual movie reviews, the platform transcends traditional numeric ratings to capture more nuanced indicators of user satisfaction and dissatisfaction. The recommendation engine was enhanced with genre-specific filtering, actor-based matching, and predictive modelling to anticipate interest in upcoming movies and web series releases. Sentiment analysis is driven by a fine-tuned BERT model, which improves the accuracy of emotional tone detection in reviews. In addition, the system offers trailer predictions and releases alerts to keep users informed and engaged with emerging content. This multilayered architecture aims to deliver a more intelligent and emotionally responsive user experience, ultimately fostering deeper engagement by aligning recommendations with both behavioral and emotional user patterns.

While recommending a human papillomavirus (HPV) single-dose vaccination schedule in 2022, the World Health Organization highlighted the need for long-term follow-up studies to monitor waning of protection. We report on vaccine efficacy against HPV infections in 1-, 2-, and 3-dose schedules and protection against cervical precancers at a median follow-up of 12 years postvaccination. This randomized multicenter study in India was originally designed to vaccinate unmarried girls aged 10-18 years with either 2 or 3 doses of quadrivalent HPV vaccine. A ministerial decree to halt vaccination in trials resulted in the creation of cohorts receiving different doses, including just a single dose. Cohorts were assessed for incident and persistent infections by genotyping cervical samples collected yearly for 4 consecutive years after participants were married. Cervical screening with an HPV test was initiated at age 25 years for married participants. Age- and site-matched unvaccinated married women were recruited to be compared with vaccinated cohorts. Vaccine efficacy was assessed using proportional incidence ratios. The number of participants in the 1-, 2- (at 0 and 6 months), and 3-dose cohorts was 4949, 4980, and 4348, respectively. Of the recipients, 71%-82% in the different cohorts were eligible to provide samples for genotyping. Vaccine efficacy against persistent HPV 16 and 18 infection was 92.0% (95% confidence interval [CI] = 87.0% to 95.0%) in 3022 recipients of the single dose; and comparable with that observed in the 2-dose arm (94.8%, 95% CI = 90.0% to 97.3%) and the 3-dose arm (95.3%, 95% CI = 90.9% to 97.5%). No high-grade precancer associated with HPV 16 and 18 was detected among vaccinated participants compared with 8 precancers detected among the unvaccinated women. This observational cohort study has established that a single dose of HPV vaccine provides high protective efficacy against persistent HPV 16 and 18 infections and associated neoplasia 15 years postvaccination.

ABSTRACT AZD1222 (ChAdOx1 nCoV-19) is a replication-deficient adenoviral vectored coronavirus disease-19 (COVID-19) vaccine that is manufactured as SII-ChAdOx1 nCoV-19 by the Serum Institute of India Pvt Ltd following technology transfer from Oxford University/AstraZeneca. The non-inferiority of SII-ChAdOx1 nCoV-19 with AZD1222 was previously demonstrated in an observer-blind, phase 2/3 immuno-bridging study (trial registration: CTRI/2020/08/027170). In this analysis of immunogenicity and safety data 6 months post first vaccination (Day 180), 1,601 participants were randomized 3:1 to SII-ChAdOx1 nCoV-19 or AZD1222 (immunogenicity/reactogenicity cohort n = 401) and 3:1 to SII-ChAdOx1 nCoV-19 or placebo (safety cohort n = 1,200). Immunogenicity was measured by anti-severe acute respiratory syndrome coronavirus 2 spike (anti-S) binding immunoglobulin G and neutralizing antibody (nAb) titers. A decline in anti-S titers was observed in both vaccine groups, albeit with a greater decline in SII-ChAdOx1 nCoV-19 vaccinees (geometric mean titer [GMT] ratio [95% confidence interval (CI) of SII-ChAdOx1 nCoV-19 to AZD1222]: 0.60 [0.41–0.87]). Consistent similar decreases in nAb titers were observed between vaccine groups (GMT ratio [95% CI]: 0.88 [0.44–1.73]). No cases of severe COVID-19 were reported following vaccination, while one case was observed in the placebo group. No causally related serious adverse events were reported through 180 days. No thromboembolic or autoimmune adverse events of special interest were reported. Collectively, these data illustrate that SII-ChAdOx1 nCoV-19 maintained a high level of immunogenicity 6 months post-vaccination. SII-ChAdOx1 nCoV-19 was safe and well tolerated.