Bareilly College, Bareilly (BCB) is an institution located in the metropolis of Bareilly in the Indian state of Uttar Pradesh. The college is affiliated to the M. J. P. Rohilkhand University, and has been affiliated to the Agra University and Allahabad University before the establishment of Rohilkhand University. It was established during the reign British Empire in the year 1837..
Ensuring people's safety in public places is a significant challenge for administrations today. The importance of automated crowd-monitoring systems has recently expanded beyond their role in addressing security concerns in densely populated areas. These systems have become increasingly vital for safeguarding human lives by helping to mitigate the spread of lethal infectious viruses, such as H3N2, SARS-CoV-2, Influenza, and COVID-19. Artificial intelligence (AI) has added a new dimension to this effort by addressing novel and real-world human safety challenges through automated crowd-monitoring frameworks. The proposed AI framework for crowd surveillance (AIFCS) employs a deep C2DN network to count people and issue warning signals for images exceeding a specified crowd threshold. Four datasets, including three publicly available ones (Mall, Beijing-BRT, and SmartCity) and one self-constructed dataset (Indiana), were used to evaluate the alarm-based congestion monitoring efficiency. The people-counting results for highly crowded frame detection accuracy on the Mall, Beijing-BRT, SmartCity, and Indiana datasets were 98.21%, 86.23%, 75.0%, and 87.01%, respectively. The proposed AIFCS framework ensures real-time predictions across diverse sequences to prevent overcrowding in public places.
The rapid proliferation of interconnected devices within the Internet of Things (IoT) continues to generate vast amounts of sensitive, context-rich data, raising significant concerns regarding data confidentiality, verifiability, trust management, and systemic resilience. Traditional IoT network architectures typically rely on centralised third-party entities. This reliance creates single points of failure and elevates the risk of unauthorised data access. To address these limitations, this paper proposes a confidential and verifiable IoT network based on a decentralised security architecture that integrates blockchain with proxy re-encryption. The framework uses threshold cryptography and zero-knowledge proofs to enable privacy-preserving transformations of ciphertext across consensus nodes. This design protects sensitive data while preserving transaction verifiability and integrity. As a result, the system effectively counters threats such as node collusion, Sybil attacks, and metadata leakage. Comprehensive simulations and performance evaluations underscore that the presented model substantially diminishes dependence on centralised proxies while delivering enhanced scalability, robust security, and increased trustworthiness, making it particularly well-suited for practical implementation in confidential IoT environments.
The present work explored the environment friendly all-inorganic Cs-based perovskite CsGeI3-xBrx as prime light active materials in the purposed PSC device. The purposed solar cell heterostructure simulated in SCAPS-1D at room temperature for the cell configuration FTO/ZnO/Graded CsGeI3-xBrx/Cu2O/Au. Further, linear and parabolic grading performed along the depth of absorber CsGeI3-xBrx by changing the composition (x) of Br from 0 to 3 to enhance the PV performance of the purposed device. The impact of composition (x) variation from 0 to 3, thickness variation from 0.5 to 1.0 mu m and bowing factor variation from 0 to 1 on the output parameters obtained under the linear and parabolic grading of the purposed PSC device was extensively investigated and comprehensively analyzed. The effects of series resistance, back contact metal work function and the overall device operating temperature were also extensively studied. The various hole and electron transport layers (HTLs and ETLs) are explored and investigated under both linear and parabolic grading conditions for selecting the appropriate and suitable one. The present detailed investigation and comprehensive analysis of the linear and parabolic graded outcome revealed the superior PV performance. The exceptionally impressive PCE similar to 33.42 % at zero series resistance and room temperature condition delivered by the purposed device along with excellent PV parameters V-OC similar to 1.33 V, J(SC)similar to 29.302 mA/cm(2), FF similar to 85.34 % for 1 mu m thick CsGeI3-xBrx under the parabolic graded condition.
Oxytocics are drugs that stimulate uterine motility. Inspite of promoting the contractility of the uterus they hasten labor and are therefore useful clinically in the management of labor, particularly if they are selective for the uterus. Although oxytocics are occasionally used to induce labor at term or to complete a threatened abortion and to control hemorrhage during and following abortion, their principal use in obstetrics is to control postpartum hemorrhage. Oxytocics are now used more or less routinely in the management of labor. They are administered in small dose to support the process of involution. during which uterus returns to its normal, nonpregnant condition. In case of delayed involution, which usually is associated with uterine atony, the stimulation of uterus by oxytocics is definitely helpful. Compounds exhibiting pronounced oxytocic activity are quite varied in their chemical structure. The naturally occurring compounds include the hormones of posterior pituitary, oxytocin and vasopressin, the ergot alkaloid spartine. The synthetic substances comprise modified analogs of oxytocin and ergonovine, model compounds based on the structure of ergot alkaloids, certain derivatives of quinolizidine and some aminomethyl derivatives, besides there are number of botanical drugs and synthetic substances have been found to possess oxytocic activity.
The health of livestock, particularly cows, plays a critical role in the agricultural and dairy industries. Early detection of health issues can improve productivity, reduce economic losses, and ensure animal welfare. This paper presents an improved XGBoost algorithm, with finetuned model which aims for predicting health status of cows (healthy or unhealthy) using sensor data. The proposed algorithm is analyzed against state-of-the-art over accuracy, precision, F1 score and recall, utilized an online considering real time values taken from…. dataset with 9 parameters namely Milk Production, Walking Capacity, Sleeping Duration, Eating Duration, lying down duration, Rumen fill, Breed, Heart Rate, and Body Condition Score with feature selection, while considering 13 parameters without feature selection. The comparative evaluation of these algorithms reveals that the improved finetunes XGBoost outperforms the others in accuracy by achieving a predictive accuracy of 99%. Results demonstrate the potential of combining sensor technology with advanced machine learning techniques to enhance real-time health monitoring systems for livestock, thereby promoting proactive care and improved operational efficiency.
