Ramesh Kumar

Abstract: Recent advancements in physics-informed neural networks (PINNs) and their variants have garnered substantial focus from researchers due

These days, wireless communication is facing the problems of short battery lifetime and lower capacity globally for Beyond 5G (B5G) communication. These problems can be addressed by proposing the adv...

Focusing solely on performance marketing risks neglecting crucial brand building, a vital component for long-term success. While digital marketing offers new avenues, foundational principles like segmentation and authentic offerings remain paramount. Brands must adapt to evolving consumer decision-making, leveraging online reviews and user-generated content to build trust and credibility.

School of Health Sciences and Technology, UPES, Dehradun 248007, Uttarakhand, India Author to whom correspondence should be addressed.

High penetration of converter-based wind generation reduces system inertia. It poses challenges to frequency stability in modern distribution networks, particularly in doubly fed induction generator (DFIG)-based wind-energy-conversion systems (WECSs), where frequency regulation is coupled with point-of-common-coupling (PCC) voltage and power factor (PF) dynamics. This study presents a multi-objective comparative evaluation of proportional–integral (PI), proportional–integral–derivative (PID), fractional-order PID (FOPID), and adaptive neuro-fuzzy inference system (ANFIS) controllers for a DFIG-based WECS connected to a radial distribution feeder. Controller parameters are tuned using multi-objective optimisation, considering frequency deviation, overshoot, settling time, disturbance robustness, control smoothness, and computational cost, while maintaining PCC voltage and PF within acceptable limits. MATLAB/Simulink simulations are conducted under turbulent wind conditions, load variations, voltage disturbances, and measurement noise. The results indicate that conventional PI and PID controllers exhibit limited performance under low-inertia conditions, whereas FOPID improves damping and voltage/PF behaviour. ANFIS achieves the best overall performance, providing reduced frequency deviation, faster settling time (below 3 s), improved disturbance rejection, and significantly lower integral absolute error (up to ~90%) compared to PI control. These findings offer practical guidance for selecting and tuning controllers to enhance frequency-centric stability in wind-integrated distribution networks.

Yttrium gallium oxide, Y3GaO6, (YGO) phosphors doped with varying concentrations of Sm3+ ions have been synthesized using a solution combustion approach. Various spectroscopic and structural analyses were performed on the prepared phosphors to investigate their structural and optical features. The crystal structure

To evaluate crowd-management strategies used at mass public gatherings and to identify the evidence gaps and priorities that reduce the risk of stampedes.

Background Globally, anxiety is prevalent during pregnancy and is associated with poor outcomes, yet it is often under-documented, underdiagnosed, and untreated during antenatal care in low- and middle-income countries, and its associated factors remain poorly understood. This study aimed to estimate the prevalence of anxiety among pregnant women in their second and third trimesters and identify associated factors. Methods A community-based cross-sectional study was conducted at four primary health centres in a rural western Indian district. Pregnant women in their second or third trimester without prior mental illness were included. Anxiety was assessed using the PRAQ-R2 tool. Data on sociodemographic characteristics, obstetric history, and various psychosocial factors were collected using the Epicollect5 application and analysed with SPSS v25. Results Among 213 participants, 84% had pregnancy-related anxiety (60.1% mild, 22.5% moderate, and 1.4% severe). The most common PRAQ-R2 item contributing to anxiety was fear of giving birth, followed by worries about bearing an ill child, and concerns about self-appearance. Univariate analysis showed significant association with parity (crude odds ratio [COR] = 4.98, 95% confidence interval [95% CI] = 2.42-10.22), gravida (1.96, 1.04-3.71), history of abortion/intrauterine death (3.47, 1.67-7.20), negative comments or behaviour from family regarding increased appetite (1.99, 1.045-3.79), worried about specific concerns (9.42, 2.20-40.38), difficulty in reaching a healthcare facility (2.17, 1.09-4.31) and past traumatic events (1.92, 1.01-3.64), were significant. In multivariate logistic regression, parity (adjusted odds ratio, AOR = 7.83, 95% CI = 1.68-36.63), history of abortion/intrauterine death (5.08, 1.30-19.83) and worried about specific concerns during pregnancy (9.09, 1.93-42.88) remained independently associated with antenatal anxiety. Conclusion Anxiety was highly prevalent and influenced by a complex interplay of clinical, sociodemographic, and psychosocial factors. The high prevalence in rural settings raises serious public health concerns, suggesting the need for improved screening, early identification, and timely intervention during routine antenatal care to prevent adverse pregnancy outcomes.

Abdominal abnormalities are deviations from the normal structure or function of organs and tissues within the abdominal cavity. Effective detection and diagnosis of these abnormalities, such as tumors, infections, cysts, structural anomalies, and inflammations, are essential for proper treatment and management. Abnormalities can arise from various factors, including disease, injury, and congenital conditions, making accurate detection crucial for timely medical intervention. Hence, this paper presents the Pyramid Xception Network (PyX-Net) framework, an advanced method for detecting abdominal abnormalities using Computed Tomography images. The methodology starts with acquiring abdominal CT images from a medical database, followed by image enhancement through Anisotropic Diffusion. Organ segmentation is then performed using a Conditional Generative Adversarial Network. After segmenting the organs, features are extracted using the Gray-Level Co-occurrence Matrix and wavelet texture analysis. The final detection of abnormalities is achieved with the PyX-Net framework, which integrates Pyramid Network (PyramidNet) and the Xception model. PyX-Net achieved an accuracy of 91.269%, a True Positive Rate of 92.237%, and a True Negative Rate of 91.142% with a K-group of 9.