Doon University

Abstract Background: Evans index (EI) and bicaudate index (BCI) are two simplified linear indices that are widely used to assess brain ventricle measurement to diagnose hydrocephalus. The establishment of normal values of these indices plays a significant role in making early diagnosis of brain ventricle enlargement and analysing patterns of age- and sex-related changes. This study was done to establish baseline values of EI and BCI in adults attending a tertiary health care center and to evaluate their variation with age and sex. Methodology: A total of 84 patients, comprising 42 males and 42 females aged 16–75 years, were enrolled in this cross-sectional study, who underwent magnetic resonance imaging (MRI) brain for various indications and were found to be normal for age and gender. EI and BCI were measured on magnetic resonance images by using DICOM viewer 2024. Data were analyzed on SSP-22 for sex- and age-wise variation and correlation between indices. Results: Mean EI was 0.25 ± 0.017 in males and 0.24 ± 0.021 in females ( P = 0.045). Mean BCI was 0.11 ± 0.014 in males and 0.09 ± 0.020 in females ( P < 0.001). Both indices showed a progressive increase with age. A notable positive correlation was observed between the EI and BCI ( R = 0.461, P < 0.001), most prominent in males in the age group 16–25 years. Conclusions: EI and BCI both increase with age and are markedly higher in males. Their strong correlation, particularly in young adult males, demonstrates their synergistic role in evaluating age-related and potentially pathological brain ventricle changes.

This paper presents an approximation method of integrable functions using a modified Barbosu operator, aimed at improving the rate of convergence in function approximation on the interval [0,1]. By introducing a suitable adjustment in the weight function, we construct a sequence of positive linear operators that better preserve the function's characteristics and demonstrate superior approximation behaviour, are studied. Theoretical error bounds are established in terms of the first and second order modulus of smoothness using their equivalence with the K-functionals. Finally, numerical experiments using test functions validate the theoretical findings and confirm that the King-type modified Barbosu operator achieve better approximation performance than the usual Kantorovich type Barbosu operator.

Flood frequency and magnitude in the Himalaya are projected to increase over the next century due to accelerating glacier melt and more intense monsoons. Most research has focused on the upstream triggers of floods, such as glacial lake outbursts, avalanches, or intense rainfall. Entrainment of sediment stored within mountain valleys as they travel downstream can transform these floods into debris or hyperconcentrated flows, increasing both their travel distances and downstream impacts. This hazard cascade means the risk to downstream communities is conditioned not just by the trigger events but also the distribution of sediment fill along the flow path. However, controls on the spatial and temporal dynamics of sediment accumulation in mountain valleys are poorly constrained due to a lack of systematic, large-scale datasets that capture both channel sediment and landsliding in steep mountain catchments. Inventories of slow moving landslides, which can also supply sediment to channels, are even more scarce. Therefore, our understanding of how shallow and slow landslides contribute to stores of channel sediment in these catchments is limited.Here, we apply (semi-) automated approaches to generate inventories of channel and hillslope sediment stores for the Alaknanda Basin systematically through time, using a combination of deep learning and remote sensing data. Using these inventories, we develop a framework that explores connectivity between hillslope and channel sediment. We assess whether certain topographic and/or channel characteristics control the spatial and temporal dynamics of sediment supply and fill. We will use these findings to gain a better understanding of the role sediment stores in mountain landscapes that are highly susceptible to sediment-rich flood events.

Background The fast-paced acceptance of artificial intelligence (AI) technologies in various sectors of society, such as health, the judiciary, and education, has had several socio-ethical, legal, and human rights consequences. While AI technologies allow for more efficiency and specialization in decision-making processes, the use of such technologies in very sensitive spheres of social life brings with it great potential dangers, such as bias, discrimination, and violation of privacy. Methods The analysis was performed using Biblioshiny, which is the web interface of the Bibliometric R-package and VOSviewer, to ascertain trends in publication rates, citation impact, keyword development, concentration of journals, institutional affiliations, and inter-country contributions. A co-word and cluster analysis was also performed to show the current dominant and emerging themes of the literature and the thematic evolution from 2018 to 2025. The (theo–context–characteristics–methodology TCCM () framework was applied to analyze the theoretical, contextual, characteristic, and methodological gaps in the literature. Results Most of the heavier hits and influential research are found in only a small number of journals and institutions, mainly located in the United States, United Kingdom, and Australia. There is a predominance of the themes of AI ethics, governance, and data protection. New themes, such as AI governance and algorithmic accountability, have become apparent in recent years. Conclusions This study contributes to the research in the area in that it provides a comprehensive overview of the developments in AI ethics and human rights and also notes the major gaps in this area. A future agenda for research using the TCCM framework is presented, whereby a call is made for the integration of formal ethical theories into the sphere, a move to empirical methodologies, and enhancement of the geographic and sectoral spread of research.

Iron (Fe) contamination in groundwater is a gradual yet significant concern driven by industrial, urban, and agricultural activities, resulting in undesirable organoleptic effects in drinking water. Surface immobilization of bacterial strains onto biochar offers a promising strategy for enhancing adsorption-based remediation. This study investigates the adsorption behaviour of Fe(II) ions using a bacterial-biochar immobilized adsorbent derived from rice husk and Bacillus subtilis, isolated from iron-rich soil samples near handpumps located in Haridwar, Uttarakhand, India. The goal was to evaluate the impact on the adsorption capacity for Fe (II) through various sorption and kinetic models, along with material modified properties. Rice husk biochar was immobilized with Bacillus subtilis and characterized using SEM, BET surface area analysis, and FTIR spectroscopy. Batch adsorption experiments were conducted across Fe(II) concentrations of 10-40mg/L. Three Sorption kinetics were modeled for fitting the data and similarly three Isotherm behaviors were assessed, supported by regression analyses. The bacterial-biochar immobilized adsorbent achieved 79.3% Fe(II) removal, outperforming pristine biochar. BET surface areas of 67.76 and 91.84 m2/g correlated with enhanced adsorption. The FTIR revealed functional groups (alkene: C-H stretching, alcohol: O-H bending, conjugated alkenes: C = C stretching) and metal carbonate structures, provided an insight of active bio sorption sites. A decline in adsorption at higher concentrations, indicated optimal performance at 10-15mg/L concentration of Fe (II) ions. Kinetic (pseudo-first-order and Weber-Morris intraparticle diffusion) and isotherm (Freundlich and Redlich-Peterson) models validated the presence of heterogeneous adsorption sites. Surface immobilization of Bacillus subtilis onto rice husk biochar significantly enhances Fe(II) adsorption. These findings highlight the potential of bacterial-biochar immobilized adsorbent as a basis for valorization of the green biotechnology usage in groundwater remediation applications.

Phytochemicals from plants have provided humans with numerous health benefits. Still, the special properties of the BBB (Blood-Brain Barrier) impede their access to the targeted tissue, and therefore, prevent their therapeutic use in treating brain disorders. In response to the increasing number of fatal diseases, scientists and medical experts have created strict new therapies. The concerning rise in the incidence of fatal neurodegenerative illnesses has brought attention to the need for efficient treatment alternatives. However, the conventional medications that are used to treat them already have disadvantages that make them less than optimal. The most soughtafter alternative to traditional medications is now traditional herbal remedies, which try to offset the disadvantages of synthetic treatments. Their accessibility, cost, and, most of all, their safety and effectiveness have had a major impact on their success against some of the most serious kinds of neurological illness as well as consumer dependence. Clinical and laboratory data indicate that they offer the potential to treat stroke, Alzheimer's, Parkinson's, and other illnesses. For the treatment of neurological illnesses, researchers are considering nanosystems based on herbs to enhance the transport and bioavailability of phytochemical compounds. Neurodegenerative diseases are the primary research targets for this investigation into polyherbalism, herbalmedicinal drugs, and herbal-based nanosystems. This chapter explores phytochemicals and their potential medicinal uses.