National Institute of Technology Srinagar (NIT Srinagar or NITSRI) is a public technical university located in Srinagar, Jammu and Kashmir, India. It is one of the 31 National Institutes of Technology (NITs) and as such is directly under the control of the Ministry of Education (MoE). It was established in 1960 as one of several Regional Engineering Colleges established as part of the Second Five Year Plan (1956–61) by the Government of India. It is governed by the National Institutes of Technology Act, 2007 which has declared it as Institute of National Importance.NIT Srinagar admits its undergraduate students through Joint Entrance Examination (Mains), previously AIEEE. It has 12 academic departments covering Engineering, Applied Sciences, Humanities and Social Sciences programs. Also, the medium of instruction is English.
In-situ composites are vital for achieving superior tribological properties due to their distinct advantages. This study focuses on developing ZA-27 based composites reinforced with titanium carbide (TiC) particles. The abrasive wear performance of the ZA-27/TiC composites was systematically evaluated. The findings revealed that increasing the TiC content significantly enhanced the wear resistance of the matrix alloy. Among the test samples, the ZA-27/10
Natural bamboo is widely acknowledged as a sustainable construction material because of its renewable origin. However, its application in structural systems has historically been limited by inherent factors such as geometric irregularity, variability in mechanical behaviour, and relatively small cross-sectional dimensions. To address these an innovative engineered bamboo composite has been developed, in which bamboo fibres are chemically integrated through the use of phenol-formaldehyde resin. A series of experimental investigations was performed to assess the mechanical performance of the phenol - formaldehyde-bonded bamboo composite (PFBC). The PFBC exhibited a density ranging from 1,124 kg/m & sup3; to 1,254 kg/m & sup3;, with a mean value of 1157 kg/m & sup3;. The mean compressive strength parallel-to-fibre was 50 MPa, while the mean tensile strength parallel-to-fibre reached 37 MPa. The mean flexural strength was 65 MPa, and the shear strengths were 6.9 MPa (parallel-to-fibre) and 8.7 MPa (perpendicular-to-fibre). The experimental results clearly demonstrate that PFBC exhibits superior mechanical performance with minimal dispersion, satisfying the strength requirements that are typically associated with structural components. The results demonstrate that full-volume PF resin impregnation forms a continuous fibre-matrix interphase that enables stable strength with low statistical dispersion, allowing for the derivation of characteristic design values and confirming the feasibility of PFBC for structural load-bearing applications.
Crash injury severity prediction in heterogeneous traffic remains challenging due to complex behavioural–vehicle interactions and limited interpretability of data-driven models. This study proposes a mathematically formulated Random Forest (RF) framework for crash severity classification along NH-44, India, integrating rigorous preprocessing, matrix-structured model representation, and perturbation-based sensitivity evaluation. A key contribution of this work is its move beyond isolated predictors to model interpretable driver–vehicle interactions, validated through Gini-importance and perturbation sensitivity. The model demonstrates high multi-class predictive performance, achieving 88.7
Deep learning algorithms, particularly convolutional neural networks, have led to advancements in various fields, including biometric authentication. Deep convolutional neural networks conduct feature extraction and classification by training the entire system in an end-to-end framework, obviating the requirement for manual feature extraction. In this paper, a multialgorithm face biometric is proposed to be developed leveraging the power of transfer learning in convolutional neural networks. Transfer learning is used to offset the dataset size limitation and save on computing resources. Face, images are first subjected to preprocessing such as augmentation and resizing. Then, features are extracted using pretrained networks: AlexNet, VGG-16 and InceptionV3. The dimensions of features are reduced using a combination of principal component analysis and linear discriminant analysis, with classification done using linear and quadratic kernels of support vector machines. The effectiveness of the proposed approach was evaluated on VIdTIMIT face dataset.
This study incorporated fly ash (FA), an industrial byproduct, into an epoxy-polyester (EP) hybrid matrix to develop composites with enhanced mechanical and thermal properties. Composites were fabricated and systematically studied with varying fly ash contents. The composite with 6 wt% FA (EPS6) exhibited the highest tensile strength of 37.09 MPa, which was 72% higher than that of pure EP. Furthermore, the maximum flexural strength at 6 wt% FA was 85.62 MPa, 6% higher than that of pure EP (80.72 MPa). Impact strength improved at low FA contents (2 wt%), but decreased with increasing FA contents due to decreased matrix deformability and particle cohesion. Thermogravimetric analysis revealed that the introduction of FA enhanced the thermal stability. For EPS6 (234 degrees C), at 5wt% mass loss the thermal stability enhancement was 17% over EP (200 degrees C). The enhancement is due to restricted polymer chain movement. Morphological studies revealed that lower filler content resulted in a more uniform particle distribution, which was closely related to improved interfacial adhesion and stress transfer efficiency. These results demonstrate that the optimized FA addition (6 wt%) not only significantly improves mechanical strength and thermal stability, but also provides a cost-effective and environmentally friendly method for producing high-performance hybrid composites suitable for structural and industrial applications.
