Bennett University

Three-dimensional (3D) parts are widely gaining popularity in industries like automotive, aerospace, medical healthcare, consumer goods, and electronics. The properties and performance of these parts vary depending on the parameters chosen at the design and manufacturing stages. This study experimentally evaluates the impact of topology on energy absorption and structural stability of eight strut-based lattice topologies fabricated using Fused Deposition Modeling (FDM) with Polylactic Acid (PLA). Quasi-static compression tests were conducted in accordance with ASTM D695. Specific Energy Absorption (SEA), crush force efficiency (CFE), and newly defined metrics—Peak-to-Plateau Load Ratio (PPLR) and Structural Efficiency (SE)—were used to determine collapse behavior and load stability. All lattices were manufactured within a narrow relative density range (0.29–0.30) to ensure topology-driven assessment. The Double Octagon lattice showed superior performance, achieving a maximum SEA of 6653.18 J kg⁻¹ , total absorbed energy of 57.55 J, and the highest structural efficiency among the investigated configurations. Substantial energy dissipation and trustworthy collapse mechanisms were shown by the Hex Truss and Kelvin cell; however, the Face Centered and Face Diagonal structures showed comparatively lower energy absorption. The proposed PPLR metric quantifies initial peak load severity relative to sustained collapse response, complementing conventional SEA and CFE measures. The findings highlight the critical role of topology optimization in balancing stiffness, weight, and energy absorption in guiding lightweight crashworthy lattice design.

In present work, tribology tests were enacted to analyze the wear behavior of composite reinforced with Aloe vera, Corn, Eucalyptus and Soybean Fiber and Epoxy resin. Fatigue and creep tests were accomplished to evaluate the strength of prepared composite specimen under fluctuating and static load conditions. Tribological analysis revealed that hybrid composites achieved higher frictional force as compared to single fiber-based composites. Hybrid ACESE composite exhibits maximum frictional force of 14.01 N, 35.78 N and 37.46 N at 10, 30 and 50 N load for 5 m/s sliding speed as compared to other prepared composite specimens. COF of prepared composite samples were calculated at different sliding speeds (3, 5 and 7 m/s) and applied load of 10, 30 and 50 N load. Hybrid ACESE composite shows maximum value of COF at 10, 30 and 50 N load for 3, 5 and 7 m/s sliding speed. Sliding speed (3, 5 and 7 m/s) with respect to applied load (10, 30 and 50 N) shows the variations in specific wear rate (SWR) for all prepared specimens. During tribological analysis, higher frictional force achieved by hybrid ACESE composite exhibits the highest value of interfacial temperature at 5 m/s sliding speed for 10, 30 and 50 N applied load. While the lower value of frictional force achieved by CE composite displays the lowest value of interfacial temperature at 5 m/s sliding speed for 10, 30 and 50 N applied load. The fatigue test illustrated that hybrid ACESE composite attained the highest fatigue number of cycles at 3822, 3217 and 2218 for 25, 50 and 75% UTS. While CE composites resulted in the lowest fatigue number of cycles at 2234, 1826, and 1219 for 25, 50 and 75% UTS among all prepared specimens.

Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often rely on invasive histopathological assessments or imaging-only techniques, which may not fully capture the molecular and morphological intricacies of tumor response. This article presents a novel, noninvasive framework for breast cancer analysis and therapy monitoring that combines two parallel mechanisms: (1) a dual-stream convolutional neural network (CNN) processing high-intensity ultrasound images, and (2) a biomarker-aware CNN stream utilizing patient-specific breast cancer biomarkers, including carbohydrate antigen 15-3, carcinoembryonic antigen, and human epidermal growth factor receptor 2 levels. The imaging stream extracts spatial and morphological features, while the biomarker stream encodes quantitative molecular indicators, enabling a multimodal understanding of tumor characteristics. The outputs from both streams are fused to predict the cancer grade (G1-G3) with high reliability. Experimental evaluation on a cohort of pre- and postchemotherapy patients demonstrated the effectiveness of the proposed approach, achieving an overall grading accuracy of 97.8%, with an area under the curve of 0.981 for malignancy classification. The model also enables quantitative post-therapy analysis, revealing an average tumor response improvement of 41.3% across the test set, as measured by predicted regression in grade and changes in biomarker-imaging correlation. This dual-parallel artificial intelligence strategy offers a promising noninvasive alternative to traditional histopathological and imaging-alone methods, supporting real-time cancer monitoring and personalized treatment evaluation. The integration of high-resolution imaging with biomolecular data significantly enhances diagnostic depth, paving the way for intelligent, patient-specific breast cancer management.

The study delves into the use of sentiment analysis to extract valuable insights from social media, supporting critical decision-making processes. Social media serves as a significant platform for documenting public thoughts and expressing sentiments, making it a rich source for opinion mining. A key application of this technique is predicting election trends by analyzing sentiment patterns on platforms such as Facebook, Instagram, and Twitter. This research examines methodologies to forecast voter preferences and political attitudes by leveraging sentiment insights from these social networks. Furthermore, it provides a comprehensive discussion on the challenges and unresolved issues in utilizing sentiment analysis for election predictions. The study also outlines future directions to enhance the effectiveness of sentiment analysis in political forecasting, establishing a scientific foundation for its advancement in electoral contexts.