Accurate prediction of rainfall is of paramount importance for India, given its significant reliance on monsoondriven agriculture and economic stability. This study conducts a systematic evaluation of various Global Climate Models (GCMs) integrated with a Long Short-Term Memory (LSTM) deep learning framework for the comparative assessment of Indian monsoon rainfall variability. The analysis employs long-term datasets sourced from the Climatic Research Unit (CRU) and the India Meteorological Department (IMD). Precipitation data were processed and utilized as input to the LSTM model to comparatively evaluate the rainfall representation capabilities of several GCMs, including BCC-CSM1.1, CanESM2, CNRM-CM5, INMCM4, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, and NorESM1-M models. Model performance was assessed using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The findings indicated variability in model performance, with the NorESM1-M model exhibiting the lowest RMSE on the CRU dataset. Although the R2 values are relatively low, this study underscores the complexities involved in modeling the nonlinear dynamics of the Indian monsoon. The novelty lies in providing a unified comparative evaluation framework for multiple GCM outputs using a consistent LSTM architecture. The results provide valuable insights into the comparative performance of GCMs and may support future improvements in rainfall variability assessment with potential applications in agricultural planning, water resource management, and climate resilience strategies in India.
Biological desalination is emerging as a promising alternative to conventional technology. In the current study, Chlorella kessleri was employed as a model microalga to evaluate its potential for salinity reduction and its effects on biomass production and quality. The microalgae were cultivated in BG11 medium added with different NaCl concentrations (2.5, 5, 10, 15, 20, and 30 g L-1). The impact of salinity was assessed using growth performance, growth inhibition, sedimentation rate, biochemical composition, chlorophyll a content, and desalination efficiency. Results show that the highest biomass was achieved in BG11 medium, whilst the lowest was in 30 g L- 1 NaCl. The highest lipid and carbohydrate contents of 25.57% and 32.26%, respectively, were observed in 20 g L- 1 NaCl, which were 37.87% and 44.24%, respectively, higher than the control. The protein yield in the control was 23.42, 35.83, 50.12%, and 58.02% higher than at 10, 15, 20, and 30 g L- 1 NaCl, respectively. Overall, C. kessleri grown in 10 g L- 1 NaCl demonstrates strong potential for integrated desalination, biomass generation, and biochemical production, with a desalination efficiency of 39.88% and NaCl removal of 4 g L-1. The CO2 fixation rates at 10, 5, and 2.5 g L- 1 NaCl were relatively similar (0.13-0.15 g CO2 L- 1 d-1), indicating that C. kessleri maintained stable carbon assimilation at lower salinity. The findings were further supported by Fourier transform infrared (FTIR). Overall, results show that C. kessleri has promising potential as a green, environmentally friendly desalination technology, offering an alternative to conventional approaches and opening new opportunities for industrial water treatment.
Lead halide perovskite solar cells (LHPSCs) have achieved outstanding power conversion efficiencies, yet their long-term operational stability remains the primary barrier to commercialisation. Device degradation arises from both intrinsic factors like ion migration, light-induced phase segregation, and crystallographic instabilities, and extrinsic stressors such as moisture, oxygen, thermal stress, ultraviolet radiation, and electrical bias. These mechanisms collectively accelerate perovskite decomposition, interfacial deterioration, and electrode corrosion, ultimately reducing device efficiency and operational lifespan. To systematically address these challenges, this review critically examines engineering strategies across multiple structural and functional levels. Bulk compositional modification and defect passivation improve intrinsic material stability and suppress ion migration. Surface and interface passivation enhances energy-level alignment while reducing non-radiative recombination losses. Charge transport layer optimisation ensures effective carrier extraction and improved thermal resilience, while electrode engineering mitigates metal diffusion and chemically induced deterioration. Finally, we outline future directions centred on stability-by-design principles, integrating advanced materials and interface engineering, robust encapsulation, operando characterisation, and artificial intelligence and machine learning-driven optimisation to overcome key degradation pathways and enable long-lasting, high-performance LHPSCs.
The green synthesis of silver nanoparticle formation using medicinal plants, rich in bioactive compounds, aids in overcoming challenges associated with physical and chemical approaches of nanoparticle synthesis. The current work investigated the biogenic synthesis of Tn-AgNPs using aqueous extracts of Trapa natans stems, a non-conventional medicinal plant, rich in bioactive compounds. The formation of T. natans (Tn)-AgNPs was confirmed by the colour shift and by recording the surface plasmon resonance (SPR) absorption peaks in the range of 420–440 nm in the UV–visible spectrum. Identification of functional groups involved in reduction and stabilisation was conducted using the FTIR spectrum. Transmission electron microscopy (TEM) revealed the formation of short silver nanorods with a length ranging from 160 to 180 nm and a width of approximately 15–30 nm. X-ray diffraction examinations revealed a highly crystalline structure of the resultant Tn-AgNPs. The well diffusion assay demonstrated antibacterial efficacy of Tn-AgNPs with increasing concentration against laboratory reference strain Pseudomonas aeruginosa (ATCC 27853). Additionally, the growth kinetics assay demonstrated significant reductions in the growth absorbance after 24 h. Subsequently, these nanoparticles are also biocompatible up to 20 ppm, as revealed by anti-haemolysis activity. This work aligns with SDG3 and highlights the potential of Tn-AgNPs as an alternative antibacterial agent.
This study proposes BlockPark, a novel blockchain-driven auction system designed for efficient parking allocation and profit enhancement in shared parking environments. BlockPark integrates an auction mechanism with blockchain technology to ensure that parking demanders secure parking spaces at a true market value through competitive bidding. Each parking slot is divided into multiple time-based units, allowing flexible allocation of spaces among multiple providers. The highest bidder wins the auction, while the final price is determined by the second-highest bid, following the standard second-price auction mechanism. Additionally, BlockPark intelligently allocates spaces so that a winning demander receiving parking units from multiple providers does not need to relocate their vehicle. The system enhances profitability by optimizing space utilization and auction dynamics, while blockchain ensures immutability, transparency, and decentralization in the reservation process. It also encourages private parking owners to share their unused spaces without security or trust concerns. The system’s performance, evaluated through simulations under varying provider–demander ratios, demonstrates superior allocation efficiency and profitability compared to state-of-the-art model, while its Ethereum-based implementation and gas cost analysis demonstrate the functional feasibility and blockchain execution overhead of the proposed system. Overall, the proposed system promotes sustainable urban mobility by optimizing parking resource utilization and leveraging advanced information technologies for community benefit.
