Central University of South Bihar (CUSB) is one of the sixteen newly established Central Universities by the Government of India under the Central Universities Act, 2009 (Section 25 of 2009). The university is located at Panchanpur, Gaya, India. On 27 February 2014, Lok Sabha Speaker Meira Kumar laid the foundation stone for the permanent campus in Gaya. When completed, it will be spread in a 300-acre campus at Panchanpur. Currently further construction is ongoing. Dr. C. P. Thakur is now the newly appointed Chancellor by President of India. CUSB is NAAC Accredited 'A' grade university.
The present study is carried out in four cities of Indo-Gangetic region of Bihar, India. The cities have been reported to have serious issues of air pollution, mainly due to population growth, urban development, and vehicle traffic increase. Ʃ16 PAH levels were higher in winter (49.3–275 ng/m³; median: 105 ng/m³) than in summer (29.9–176 ng/m³; median: 73.0 ng/m³). Three-ring PAH were the most abundant, making up about 54.0 The study was conducted in four urban cities of Bihar, India, using a passive air sampler, where detectable concentrations of priority PAH pollutants were found. Ʃ16 PAH levels were higher in winter than in summer, and 3-ring PAH were most abundant, followed by 4-ring and 2-ring compounds. Fugacity modelling and flux estimation were used to understand the air–soil exchange of these compounds. The ILCR study indicates a substantial cancer risk from PAH exposure, particularly in the winter season.
This study investigates past and future trends of extreme precipitation indices: Consecutive Wet Days (CWD), Consecutive Dry Days (CDD), and Total Precipitation across four major smart cities of the Gangetic Plain: Delhi, Lucknow, Patna, and Kolkata. Climate Hazards Infrared Precipitation with Stations (CHIRPS) data has been used as the past observational data for calibration and validation of the model. Future projections were analysed using statistically downscaled outputs from three CMIP6 global climate models (CanESM5, MPIESM1-2, and NorESM2) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The non-parametric Sen’s slope estimator was applied to quantify trend magnitudes, while statistical significance was assessed using the Mann–Kendall test. Past records revealed mixed patterns in CWD and CDD, with a general increase in Total Precipitation in Delhi, Lucknow, and Patna, and a decline in Kolkata. Future projections indicate marked scenario-dependent variations, with SSP5-8.5 consistently showing stronger and statistically significant changes compared to SSP2-4.5. Under SSP5-8.5, CWD increases are most prominent in Delhi (0.039 days/year in CanESM5, 0.117 days/year in NorESM2) and Kolkata (0.241 days/year in CanESM5), suggesting longer wet spells. CDD is projected to decline in most cities, particularly in Lucknow (-0.596 days/year) and Patna (-0.166 days/year) under CanESM5 and MPIESM1-2, respectively, indicating shorter dry periods. Total precipitation shows a substantial increase, with the highest trends projected over Delhi (10.19 mm/year), Lucknow (8.77 mm/year), and Kolkata (8.18 mm/year) in the CanESM5 model. The findings point toward a future shift toward wetter conditions, with prolonged wet spells and higher annual rainfall totals, increasing the potential for urban flooding and waterlogging. These results underscore the need for targeted climate adaptation strategies, improved stormwater management, and enhanced resilience planning to address the increasing risks of extreme precipitation events in rapidly growing urban areas of the Gangetic Plain.
Sustainable food packaging materials derived from renewable sources are in increasing demand in the food industry. This has generated remarkable interest in incorporating bioactive compounds to fabricate antimicrobial, disease-resistant, and eco-friendly food packaging films. This review highlights a wide range of biodegradable polymeric materials, including agro- based (soy protein, zein, and cellulose), animal based (gelatin and chitosan), synthetic (polylactic acid [PLA], polyethylene adipate [PEA], and polycaprolactone [PCL]), and a few others derived from microbial sources (polyhydroxyalkanoates [PHA] and polyhydroxybutyrates [PHBs]), that can be used to develop biodegradable films. The focus is on strategies for incorporating aromatic compounds (e.g., essential oils and phenolics) into these polymeric matrices to enhance antimicrobial activity and extend food shelf life. This review summarizes the usage of different bio-based polymers, mostly derived from renewable resources, in the development of film materials by addition of aromatic functionalized compounds. Additionally, the antioxidant and antimicrobial mechanism of functionalized compounds are described in detail. Through a comprehensive analysis of current research and emerging technologies, this review provides insights into the potential of aromatic compound-based biopolymer systems to develop sustainable active food packaging materials that achieve desired functional performance while minimizing environmental effects. Though the wide spread usage of bio-based packaging materials are desired but several issues with respect to material performance such as high hydrophilicity, migration of chemicals from packaging system and less shelf-life are the key challenges. Also from future prospect point of view, the acceptance of bio-based packaging material by consumer and Government’s directive to increase usage of bio-based materials will boost its application.
The Great Mica Belt of Jharkhand, India, is an intensively disturbed mining region due to long-term mica extraction, which increases anthropogenic pressures on environmental components. Despite the environmental fragility of this region, systematic baseline information on rainwater chemistry and trace metal deposition has been lacking, particularly in comparison to other mining-impacted regions where such studies are more extensively documented. Addressing this gap is crucial for understanding atmospheric deposition processes in mining-dominated landscapes of eastern India. Unlike previous studies that predominantly focus on coal-mining, urban, or industrial regions, this study specifically investigates a mica-mining dominated environment, which has distinct mineralogical composition and emission characteristics influencing rainwater chemistry. This study aims to evaluate the chemical composition and trace metal concentrations of monsoonal rainwater collected at Koderma and Tisri within the Great Mica Belt. A total of 60 rain water samples were analyzed for physicochemical parameters (pH, EC, TDS), major and minor ions (i.e., Ca2⁺, Mg2⁺, Na⁺, K⁺, Cl⁻, HCO₃⁻, SO₄2⁻, NO₃⁻, NH₄⁺ and F⁻), and metals. Source contributions were evaluated using enrichment factor (EF), non-sea-salt fraction (nssf), and neutralization factor analyses. Rainwater pH ranged from 5.38 to 7.26, indicating acidic to alkaline conditions, with 9
Deepfake technology has been widely adopted and has changed the face of digital media, offering unprecedented opportunities for creative expression and entertainment while simultaneously posing a significant threat to information integrity. As synthetic media becomes increasingly indistinguishable from authentic content, the risk of misinformation and digital forgery has grown, necessitating more sophisticated detection frameworks. Deep convolutional neural networks have revolutionized facial analysis, yet achieving a balance between high-precision classification and computational efficiency remains a significant challenge. We propose Attentive ResNet101, a specialized architecture that integrates a Convolutional Block Attention Module to refine feature localization and highlight anomalous facial artifacts. By strategically applying both channel and spatial attention mechanisms, the model prioritizes high-frequency details essential for deepfake detection. We conduct a rigorous comparative analysis against established benchmarks, including ResNet50, MobileNetV2, and EfficientNetB0. Our experimental results demonstrate that the Attentive ResNet101 outperforms all baseline models, achieving a superior accuracy of 96.53
