Agra College is one of the oldest institutions in India. Pandit Gangadhar Shastri, a noted Sanskrit scholar, founded the college in 1823, long before the first university in India was established in 1857 so all the matters were directly reported to the parliament of England. The institute was a government college until 1883, after which a board of trustees and a Committee of Management took over administration.In 1882, the college became an aided institution and affiliated itself with Calcutta University. Subsequently, its affiliation changed to the University of Allahabad in 1889 and then to Agra University in 1927. Agra University is now Dr. B.R. Ambedkar University. Today it is a grant-in-aid institution and also runs a few self-financing courses. It was recognized by the U.G.C. under Section 2(f) and 12(B) of the University Grants Commission in 1972. B.R..
The Poisson distribution is a fundamental concept in probability theory and statistics. It is widely used to model the occurrence of rare events across diverse fields, including physics, biology, finance, and medicine. It offers a way to calculate the likelihood of a certain number of events occurring within a specified time period, assuming the events happen independently of one another. The Poisson distribution is commonly used to predict the outcome of random, independent events. In heavy ion-induced nuclear reactions where compound nuclei are formed under conditions of high spin and moderate excitation energy, the investigation of one or multiple neutron emissions is of particular interest. When heavy-ion reactions occur, a random number of neutrons may be released as the target becomes excited by partial or complete absorption of projectile energy, leading to the formation of a highly excited composite system. These neutrons are statistically evaporated to de-excite the compound nucleus (CN). The Poisson distribution function can thus be applied to describe the decay probability of an excited nucleus, particularly for predicting the emission of a specific number of neutrons. In this process, the excess of excitation energy over the binding energy of the released neutrons acts as the continuous variable, and the number of released neutrons serves as the discrete variable. This framework helps us quantify the likelihood of observing a given number of neutrons emitted from an excited nucleus formed in a typical nuclear reaction. In this work, the measured cross-section data for multiple neutron evaporation channels in systems such as 12C+165Ho, 16O+169Tm, 12C+128Te, 16O+148Nd, 19F+169Tm, 16O+174Yb and 12C+175Lu reactions have been analyzed using the Poisson distribution. This approach allows all open channels to be treated simultaneously, offering a simplified yet physically meaningful alternative to more elaborate statistical models.
Background: Hypertrophic cardiomyopathy (HCM) is one of the leading congenital causes of sudden cardiac death/sudden cardiac arrest (SCD/SCA) among young competitive athletes. HCM is genetically inherited in an autosomal dominant manner, and genetic mutations in any one of the 12 myocardial contractile filament protein-encoding genes may be attributed to this heterogeneous disorder. Aims and Objectives: Therefore, our objective was to investigate the prevalence of the MYBPC3 d25bp variant among the athletic population in India, and the presence of HCM-associated pathological left ventricular hypertrophy (LVH) among the MYBPC3 d25bp genotype carrier athletes. Materials and Methods: Oral mouthwash samples were collected from a total of 149 elite athletes (age range: 18–36 years) and screened for MYBPC3 d25bp genotype. We performed resting and cardiac stress 12-channel electrocardiography and echocardiography evaluation on heterozygous MYBPC3 d25bp carrier athletes for HCM phenotype diagnosis. Results: We found 4.7% (7/149; 4 male and 3 female) prevalence of deleterious MYBPC3 d25bp heterozygous genotype among our sample athletes. Based on left ventricular mass index (LVMI) and relative wall thickness (RWT), we observed a mild form of concentric LVH among male (LVMI: 127.2 g/m 2 ; RWT: 0.59) and female (LVMI: 98.1 g/m 2 ; RWT: 0.52) carrier athletes. Conclusion: Based on these results, we recommend long-term clinical follow-ups for athletes carrying MYBPC3 d25bp heterozygous genotype coupled with concentric LVH, during their training and competition to differentiate between physiological training-associated cardiac adaptations and pathological left ventricular parameters and mitigate the potential risk of SCD/SCA. Furthermore, preparticipation screening for HCM and other cardiac anomalies should include genetic testing as well.
In the present scenario, cybersecurity is a major concern as technology is increasing day by day, so it has become a major threat. One of the major threats is an intrusion attack. An attacker may attempt to enter the system via this attack via unauthorized means to perform any malicious activity or to find loopholes that become the reason for this. So, there is a need to deal with these loopholes. Hence, one of the sophisticated deep learning models has been covered in this work so that the system can be protected from intrusion attacks. In this methodology, we have used a multiclass classifier, Deep Neural Network (DNN) model, to detect the intrusion attack. We have analyzed and validated the said model on the benchmark dataset named NSL-KDD and observed that the DNN model is giving the best accuracy among existing models. This dataset contains more than 40 features from which we have extracted the useful features and applied a deep learning model that has the capability for feature introspection and can easily perform feature engineering, along with that model can be trained by using a large size of dataset, which ensures a high accuracy rate. There are many approaches given regarding to detect intrusion attacks, but they have focused on different network systems. Here, we have developed a model to detect as well as classify intrusion attacks that is more generic and can be implemented in every network system. The results those are obtained here provide a decent accuracy, as can be seen in the result section, where we have calculated the accuracy to be 99
The complexity of nuclear interactions between heavy targets and projectiles can be effectively elucidated using the optical model. The phenomenological optical model usually adopts a Woods-Saxon form, and its geometrical parameters and depths, known as optical model potential (OMP) parameters, can significantly affect the reaction and scattering probabilities of interacting partners. These potentials are typically used to provide particle transmission coefficients for Hauser-Feshbach studies to estimate theoretical excitation functions. To examine the impact of variations in ejectile-residual nucleus OMP parameters on theoretical excitation functions, certain selected reaction systems have been chosen for this work. Experimental excitation-function data have been taken from existing literature, and theoretical excitation functions have been predicted using the PACE4 for comparative analysis. Because of inherent ambiguities and the nonuniqueness of OMP parameters, different combinations of parameter statistics have been proposed by various authors for different mass ranges and incident energies. In this study, different OMP parameter systematics have been utilized for the estimation of theoretical excitation functions for xn/pxn channels of the considered reaction systems having different target mass numbers and incident energy ranges of 4-7MeV/nucleon. Results obtained strongly suggest that the appropriate choice of potential parameter systematics is essential for heavy-ion studies.
