Central University of Jharkhand

Electric Vehicle Charging Stations (EVCS) serve as a backbone of the transportation system, which significantly contributes to mitigating greenhouse gas emissions and forms a sustainable transportation system. However, the EVCS is heavily dependent on the communication system, which is exposed to diverse cyber-attacks and destabilizes the overall system. Hence, the effective attack detection model is vital for EVCS to ensure dependability and security against cyberattacks. This research proposed the Hybrid Attention-enabled Deep Reinforcement Learning-based Bidirectional Network (HA-DRLBiNet) framework to detect the cyberattacks, including communication delay and False Data Injection (FDI), which enhance the cyberattack resilience in EVCS. Notably, the HA-DRLBiNet model analyzes the data in both forward and backward sequences, which facilitates the identification of intricate attack patterns by sampling multiple data points. Additionally, the developed HA-DRLBiNet technique emphasizes on identifying important learning parameters and assigning adaptive weights, which enable parallel learning and improves the robustness and stability. Moreover, the HA-DRLBiNet approach offers effective measures against cyberattacks by learning the optimal policy and handles high-dimensional data, which improves the detection capability of unseen cyberattack patterns. Eventually, the proposed method achieved the accuracy of 98.56%, the specificity of 98.67% and the sensitivity of 98.51%, for IEEE34 bus system.

This paper is based on the results of petrological and geochemical studies carried out on coal samples taken from Lower Gondwana coal-bearing horizons (Barakar Formation) of Sohagpur coalfield of Burhar-Amalai sub-basin of Madhya Pradesh (India). The study also shows that the coals of this basin are striated in nature and are dominated by ‘striated, striated dim’ and ‘striated bright’ components. Moreover, vitrinite (mean 33.1 vol

Bihar is one of the worst-affected flood-prone states of India due to recurring floods along the Ganga River during the monsoon season. Frequent riverine floods caused substantial economic loss and damage to agriculture and infrastructure. This study aims to assess the severity of flood hazards, vulnerability and risk using the long-term MODIS satellite-derived indices (2001–2025). The indices such as Normalized Difference Water Index (NDWI), modified-NDWI (MNDWI), Spectrum-Photometric Water Index (SPWI) and Enhanced Vegetation Index (EVI) were computed from July–September (monsoon season) to assess the flood inundation mapping. The flood frequency map was generated by aggregating the flood maps at a yearly scale for 25 years. This flood frequency map is converted to flood hazard and integrated with a socio-economic vulnerability (SEV) map to identify flood risk zones, which can assist in flood prevention and management activities. Over the last 25 years, the average flood-affected area is estimated as 6881 km2 in Bihar, accounting for 7.3

Metal-organic frameworks (MOFs) have emerged as a versatile class of porous crystalline materials due to their tunable structures, large surface areas, and diverse functionalities. Among them, copper-based MOFs (Cu-MOFs) are of particular interest because of the redox activity of copper centers, flexible coordination chemistry, and high affinity for various organic linkers. These features make Cu-MOFs promising candidates for a wide range of applications spanning catalysis, energy conversion and storage, and chemical sensing. This review highlights recent advances in the synthesis and design strategies of Cu-MOFs, including conventional solvothermal and hydrothermal routes, microwave- and sonochemical-assisted methods, electrochemical synthesis, and postsynthetic modifications. Particular emphasis is placed on the ability of these synthetic approaches to tune particle size, morphology, porosity, and stability, which, in turn, dictate their functional performance. The catalytic applications of Cu-MOFs are critically examined, with a focus on photocatalysis, electrocatalysis, and heterogeneous catalytic processes for pollutant degradation, hydrogen production, and carbon dioxide reduction. In the context of energy storage, the integration of Cu-MOFs into supercapacitors and batteries is discussed, highlighting their roles in enhancing the capacity, conductivity, and cycling stability. Furthermore, the potential of Cu-MOFs as sensitive and selective sensing platforms for gases, biomolecules, and environmental pollutants is explored, underscoring their versatility beyond catalytic applications. Despite rapid progress, key challenges remain in scaling up the synthesis, ensuring long-term structural stability, and integrating Cu-MOFs into practical devices. Future research directions include the development of cost-effective green synthesis methods, hybrid composite materials with enhanced conductivity, and defect and interface engineering to tailor activity and durability. Overall, Cu-MOFs represent a highly adaptable platform whose continued advancement may significantly impact sustainable energy technologies, environmental remediation, and next-generation sensing systems.

While survival rates among breast cancer patients continue to improve, many women in India experience persistent physical, emotional, and social challenges during treatment. This study aimed to compare the quality of life (QOL) between reproductive-age women (≤ 49 years; youngest participant aged 35 years in the study sample) and older women (≥ 50 years) undergoing chemotherapy for breast cancer, and to identify factors associated with QOL in both groups to inform age-sensitive and supportive care practices. A cross-sectional study was conducted at a tertiary hospital in Bhubaneswar, India, involving 85 breast cancer patients who had completed at least three cycles of chemotherapy. QOL was assessed using the WHOQOL-BREF questionnaire. Association between independent variables and QOL were determined using an independent T-test and ANOVA. Multiple linear regression analysis was used to identify sociodemographic and clinical factors associated with overall QOL. The overall QOL score among participants was 60.8 ± 16.3. Age remained significantly associated with overall QOL after adjusting for sociodemographic and clinical variables. Reproductive-age women reported higher overall QOL (70 ± 10) compared to older women (47 ± 07). In the physical domain, younger women had the highest scores (83 ± 08), whereas older women had the lowest (24 ± 21). Poor overall QOL was significantly associated with older age (β = -28.174, p < 0.001), rural residence (β = -35.69 for environment domain), and advanced cancer stage (β = -38.62 for physical domain). Breast cancer affects QOL differently across age groups. Reproductive-age women demonstrated better physical health and stronger social support, while older women faced greater physical and psychological burden. These findings highlight the need for age-specific interventions, improved access to supportive services, and further longitudinal and mixed-methods research to enhance patient-centered oncology care.