Sarala Birla University (SBU) is a private university located in the Birla Knowledge City in the Ara, about 10 kilometres (6.2 mi) from Ranchi on the Ranchi-Purulia highway, in the Namkum block of Ranchi, Jharkhand, India. The university was established by Bharat Arogya and Gyan Mandir through the Sarla Birla University Act, 2017 which was passed by the Jharkhand Legislative Assembly on 3 February 2017 following a letter of intent in November 2016. It is named after Sarala Birla..
For over five decades, sodium alginate (SA) has been widely employed in pharmaceutical formulations due to its environmentally friendly, biodegradable, and biocompatible nature. Primarily sourced from brown seaweed, this natural polymer plays a crucial role in enhancing drug delivery control. It serves as a versatile excipient in numerous dosage forms such as capsules, tablets, liposomes, and microspheres. SA ionotropic gelation with calcium ions allows drug and enzyme encapsulation for controlled release. Structurally, SA consists of α-L-guluronic acid and β-D-mannuronic acid monomers linked via glycosidic bonds, enabling the formation of strong, porous hydrogels capable of carrying bioactive substances. These hydrogels have been extensively utilized in ocular and oral drug delivery systems for prolonged therapeutic effect. This review highlights the structural and physicochemical characteristics of SA, its pharmaceutical and biomedical applications, food industry relevance, and recent innovations, while also discussing its potential as a multifunctional material for future drug delivery systems.
One of the main concerns raised with ChatGPT misuse is the potential for academic dishonesty; many people think that students will copy the work of others if they use ChatGPT for writing tasks. The most obvious effect of ChatGPT is that it can deliver answers in a blink of an eye, which can severely limit a student’s capacity for critical thinking, creative thinking, and coming up with new ideas. This research focuses on quantifying the negative aspects of ChatGPT among students. The researchers proposed six hypotheses in this study. To accomplish this, survey-based research was conducted among college and university students, with a sample size of 316. The findings of the research indicate that five hypotheses were supported by the data, and only one hypothesis was not supported. Afterwards, the students’ attitudes and behavioural intentions are evaluated. This research will be useful for students and education policymakers.
Seasonal assessment of cropping patterns enhances agricultural productivity by analyzing land usability, crop health, and opportunities for crop intensification. This research seeks to determine Jharkhand's cropping pattern using a statistically derived threshold-based method applied to multiple vegetation indices. Firstly, the growing period in different seasons was identified from the MODIS (Moderate Resolution Imaging Spectroradiometer) Enhanced Vegetation Index (EVI) time-series datasets. Then the Normalized Difference Vegetation Index (NDVI), Green Normalized Vegetation Index (GNDVI), and Soil Adjusted Vegetation Index (SAVI) were generated from the high-resolution Sentinel-2 images. The threshold values of these vegetation indices were identified with the help of 5983 sample points collected over three cropping seasons, i.e., the Kharif, Rabi, and Zaid, through visual interpretation of the Sentinel-2 images and high-resolution Google Earth Images. The upper thresholds varied from 0.59 to 0.74 for NDVI, 0.57-0.61 for GNDVI, and 0.44-0.55 for SAVI over the three growing seasons. The lower thresholds varied from 0.32 to 0.47 for NDVI and 0.28-0.36 for GNDVI over the three seasons. For SAVI, the lower threshold remained the same, with a value of 0.23 in all seasons. A classified image by random forest classification (accuracy: 89.52%) was used to validate the outcomes. The SAVI was observed to outperform the other two indices, with a correlation coefficient of 0.98 (p < 0.05, confidence interval 0.92-0.99). The non-parametric Friedman's test (statistic value 6.75; p > 0.05) and the Wilcoxon signed rank test (p = 0.195 for NDVI and GNDVI, and p = 0.843 for SAVI) also supported this result.
This research aims to identify the factors influencing climate change awareness among Generation Z (Gen Z) travellers in the urban hospitality and tourism sectors in India. It is a significant addition to the expanding conversation of sustainability transitions in tourism cities. The study, which integrates the Value-Belief-Norm (VBN) model and the Theory of Planned Behaviour (TPB), surveyed 381 university students and examined factors, such as digital literacy, environmental beliefs, pro-environmental behaviour, personal values, green practices, social media influence, and climate-aware attitudes. Subsequently, these factors were combined into two variables: Sustainable Development Concerns for Climate Awareness (SDCCA) and Sustainable Tourism Concerns for Climate Awareness (STCCA). The current research extends the existing theoretical framework of urban tourism by providing empirical evidence that youth's awareness of the climate crisis leads to the adoption of sustainable behavioural patterns in tourism destinations. Practically, it is an excellent source of insight for city tourism planners, municipal authorities, and hospitality operators to start value-oriented, culturally grounded sustainability intervention programs that are in line with SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), thus unlocking the benefits of climate-resilient tourism city futures.
