Ramesh Kumar

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.

Background India’s 243 million adolescents (21% of the population) face nutritional deficiencies, sexual and reproductive health concerns, mental health disorders, and rising non-communicable diseases (NCDs). Despite policies like Rashtriya Kishor Swasthya Karyakram (RKSK), major implementation gaps remain across public health facilities. Methods This scoping review, guided by Arksey and O’Malley’s framework and PRISMA-ScR guidelines, assessed adolescent health services at Health and Wellness Centers, Primary Healthcare Centers, and Sub-Health Centers from 2015 to 2025. Three electronic databases were searched, with standardized data extraction, expert consultation and thematic analysis mapped RKSK’s four pillars. Study quality was appraised using the MMAT (2018 version). Results Out of 1,371 records, only 11 met the inclusion criteria. Just 12.5% of facilities provided dedicated adolescent services. A study by Prasad et al. in 2024 reported that only 12.5% of assessed facilities provided dedicated adolescent-specific services and 6% of HWCs offered adolescent care. Barriers comprised lack of privacy, staff shortages, provider bias against unmarried youth (25%), supply chain issues, weak referral systems, poor intersectoral coordination, cultural stigma, and the need for parental consent requirements. Opportunities exist through digital health (78% smartphone use among adolescents), Stakeholder consultation participants estimated adolescent smartphone access at about 78% in the Sikar district. Public-Private Partnerships (PPP), community engagement, and innovative service models. Conclusions Despite strong policy frameworks, persistent gaps are evident, with malnutrition (27.4% stunted growth) and rising obesity rates highlighting the urgency. Strengthening services demands political commitment, adequate financing, and culturally sensitive approaches to transform adolescent health and leverage India’s demographic potential.

The identification of biomarkers and therapeutic targets is essential to the success of precision medicine. However, to reliably identify them, one must employ a combination of analytical and computational methods. This article describes the importance of high-quality study design, strict adherence to preanalytical and quality assurance practices and high levels of quality assurance as well as the performance characteristics of multiple types of chromatography/mass spectrometry to achieve the best possible results for sensitive, selective biomarker profiling in various biological fluids such as plasma, serum, urine, cerebrospinal fluid (CSF), and tissues. In addition, it explores the key workflows used in preparing biological samples (i.e., solid-phase extraction/specialised physical or chemical extractions/derivatisation), software/data processing pipelines for peak analysis (i.e., XCMS/MZmine/OpenMS/MS-DIAL) and processes for the identification of compounds by combining spectral libraries (e.g., Human Metabolome Database [HMDB], National Institute of Standards and Technology [NIST] and FiehnLib) with in silico tools (e.g., SIRIUS, CSI: FingerID, CANOPUS). Finally, it discusses several ways in which artificial intelligence/machine learning can be applied to the field of mass spectrometry for peak detection and provides a comprehensive review of the requirements for regulatory-grade validation and the workflow for targeted/multiplexed/quantitative liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC–MS) using stable isotope-labelled internal standards.