St. Vincent Pallotti College of Engineering and Technology (SVPCET) is an engineering college located at Gavsi Manapur, Wardha road, Nagpur, Maharashtra, India. It has been founded in 2004 by The Nagpur Pallottine Society. When the college started, it was affiliated to Rashtrasant Tukadoji Maharaj Nagpur University (RTMNU). The University Grants Commission (UGC) conferred autonomous status on St. Vincent Pallotti College of Engineering & Technology on 7 September 2021. In 2019, it was given grade "A" by the National Assessment and Accreditation Council (NAAC).
The rapid usage of recycled aggregate concrete and nano-modified binders necessitates prediction frameworks that can handle tightly correlated mechanical, durability, and functional properties with limited experimental data samples. Traditional empirical formulations and single-output learning models cannot represent nonlinear, multiscale recycled aggregate, nano-admixture, curing history, and microstructure interactions. These limits limit material optimization reliability and prevent the design of durable and intelligent concrete systems for sustainable infrastructure sets. Next generation artificial intelligence frameworks using graph neural networks, capsule networks, neural ordinary differential equations, and neural architecture search predict nano-modified recycled aggregate concrete’s compressive, tensile, flexural, freeze–thaw, chloride penetration, and self-sensing electrical behavior. Microstructural interaction models and physical limitations ensure material believability across varied compositions and curing regimes. A quantitative analysis of over 100 experimental mix configurations indicates significant accuracy gains over multi-output baselines. EvoConcreteNet predicted flexural strength with a R² of 0.95, while GraphSenseNet achieved a coefficient of determination of 0.96 for compressive strength with mean absolute errors < 2.5 MPa. CapsuleRACNet achieved a R² above 0.97 for electrical resistance estimate, while ContinuousConcreteODE reduced freeze-thaw cycle prediction errors by over 40
Guaranteeing transparent and trustworthy AI behavior in safety–critical systems—autonomous vehicles, diagnostic healthcare, and industrial control—has now reached an urgent level of necessity. This paper puts forth a hybrid neuro-symbolic learning architecture that marries the statistical prowess of neural representation with the logical clarity of symbolic reasoning.
Blockchain adoption across industries has led to the emergence of multiple independent blockchain platforms, creating challenges for cross-chain data exchange and system interoperability. This paper addresses this challenge by examining interoperability frameworks that enable communication between heterogeneous blockchain networks. We adopt a platform-oriented analysis to study widely used blockchain ecosystems, focusing on the mechanisms they employ for cross-chain communication and asset transfer. To demonstrate practical applicability, we present a conceptual supply chain scenario that illustrates how interoperable architectures enable interactions among multiple blockchain entities. Finally, we identify key open research challenges, including data management, cross-chain query processing, privacy, governance, scalability, and protocol standardisation. The findings highlight both the capabilities and limitations of existing interoperability solutions and outline directions for future research.
Solar distillation offers a sustainable solution for potable water production, yet the influence of geometric alignment on multi-face systems remains a critical but often overlooked design parameter. This work experimentally isolates the influence of azimuth orientation on the thermal and distillation performance of a fixed pentagonal pyramid solar still under the tropical summer climate of Nagpur, India. Four identical units, constructed with galvanized iron basins, internal matte black coating, thirty millimeters of polyurethane insulation, and four-millimeter-thick clear float glass, were tested simultaneously with a constant basin water depth of two centimeters. The systems were oriented toward the East (90°), West (270°), South-East (135°), and South (180°) to examine azimuth-dependent solar interception over a campaign of at least three clear sky days per configuration. The experimental results, analyzed using a validated energy balance model, determined that the south-facing orientation achieved the highest daily freshwater production of 3.6 to 3.7 L/m²·day−1 and a thermal efficiency of 38 to 40%. The South-East orientation followed with a yield of 3.3 to 3.4 L/m²·day−1, representing a performance decrease of approximately 8 to 9% compared to the optimal south orientation. Conversely, the east-facing unit produced 2.7 to 2.9 L/m²·day−1, while the west-facing unit yielded 2.6 to 2.7 L/m²·day−1, both showing time-shifted productivity peaks and lower cumulative output relative to the south-aligned configuration. These findings validate azimuth alignment as a zero-cost optimization parameter, establishing south orientation as the superior strategy and South-East as a viable alternative for fixed desalination installations.
The prediction of crop yield is one of the major steps in managing crops amid the prevailing climatic changes. In this study, a holistic machine learning technique based on the usage of gradient boosting, time-based features, and statistical analysis is proposed. The sample consists of $\mathbf{3, 1 5 8}$ records collected between 2004 and 2019 in 11 Indian regions covering 13 different crop categories. The prediction of the yield is performed with the help of the CatBoost model that achieves the $\mathbf{R}^{\mathbf{2}}$ of 0.0954 and RMSE value of 54,555.76. While traditional climatic variables are inferior to the farmer management factors, the latter are superior, showing higher $\mathbf{R}^{\mathbf{2}}$ scores - yields (lagged) - 10.83%, irrigation type - 10.91%, and crop variety -10.61%. The advanced diagnostic measures used include 3D climate maps, residual land plots, and time consistency testing.
