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Saltwater intrusion is a significant problem in the coastal estuaries of India. Moreover, monitoring and forecasting salinity intrusion is crucial for sustainable water resources management. This research aims to apply and compare the performance of hybrid metaheuristic algorithms for predicting saltwater intrusion in the east Upputeru estuary of India. To attain this goal, extreme learning machines (ELM) coupled with three metaheuristic algorithms, such as artificial bee colony (ABC), particle swarm optimization (PSO), and invasive weed optimization (IWO) algorithms use only groundwater salinity data and location attributes. Additionally, various levels wavelets transform (WT) were employed to improve the prediction results along with the ELM features. The input data is composed of 184 data points in each year of 2017, 2018, 2020, and 2021. Yearly salinity was measured during the pre-monsoon consideration from March to May. The coefficient of determination (R2) and Root Mean Squared Error (RMSE) is used to evaluate the performances of the prediction models. The research results indicated that the hybrid model, such as WT-IWO-ELM, achieved high performance for salinity forecasting with R2= 0.99 and RMSE = 0.65 for the testing period. The prediction results revealed that the hybrid soft computing models with WT outperformed other models regarding prediction performance. Moreover, it was concluded that choosing of appropriate level of WT has a significant impact on model performance.

ABSTRACTSurgical simulators not only allow planning and training of complex procedures, but also offer the ability to generate structured data for algorithm development, which may be applied in image-guided computer assisted interventions. While there have been efforts on either developing training platforms for surgeons or data generation engines, these two features, to our knowledge, have not been offered together. We present our developments of a cost-effective and synergistic framework, named Asynchronous Multibody Framework Plus (AMBF+), which generates data for downstream algorithm development simultaneously with users practicing their surgical skills. AMBF+ offers stereoscopic display on a virtual reality (VR) device and haptic feedback for immersive surgical simulation. It can also generate diverse data such as object poses and segmentation maps. AMBF+ is designed with a flexible plugin setup that allows for unobtrusive extension for simulation of different surgical procedures. We show one use case of AMBF+ as a virtual drilling simulator for lateral skull-base surgery, where users can actively modify the patient anatomy using a virtual surgical drill. We further demonstrate how the data generated can be used for validating and training downstream computer vision algorithms.

The present study explores the green synthesis of silver nanoparticles (AgNPs) using Urginea indica(Roxb.) Kunth bulb extract, emphasizes its eco-friendly and cost-effective nature. Characterization techniques, including UV-Vis Spectrophotometer, FTIR spectroscopy, XRD, and TEM, confirmed the successful synthesis, revealing spherical AgNPs with a size range of 9-30 nm. In antimicrobial activity, these nanoparticles exhibited significant growth inhibition activity against bacteria like Pseudomonas aeruginosa(22.66 ± 3.05 mm), S. aureus(15.33 ± 0.57 mm), E. coli(14 ± 1 mm), and fungi like Candida albicans(35.6 mm), with a notable zone of inhibition. The phytochemical analysis of the bulb extract reveals that it has various bioactive compounds, such as phenols, flavonoids, saponins, glycosides, terpenoids, and steroids, likely contributing to the reduction and stabilization of AgNPs. The green synthesis process was used for its simplicity, cleanliness, and lack of pollutants. The study shows how natural resources can be used to develop nanoparticles to fight against increasing drug resistance problems.

Morphing materials are typically either very compliant to achieve large shape changes or very stiff but with small shape changes that require large actuation forces. Interestingly, fish fins overcome these limitations: fish fins do not contain muscles, yet they can change the shape of their fins with high precision and speed while producing large hydrodynamic forces without collapsing. Here, we present a ‘stiff’ morphing beam inspired from the individual rays in natural fish fins. These synthetic rays are made of acrylic (PMMA) outer beams (‘hemitrichs’) connected with rubber ligaments which are 3–4 orders of magnitude more compliant. Combinations of experiments and models of these synthetic rays show strong nonlinear geometrical effects: the ligaments are ‘mechanically invisible’ at small deformations, but they delay buckling and improve the stability of the ray at large deformations. We use the models and experiments to explore designs with variable ligament densities, and we generate design guidelines for optimum morphing shape (captured using the first moment of curvature), that capture the trade-offs between morphing compliance (ease of morphing the structure) and flexural stiffness. The design guidelines proposed here can help the development of stiff morphing bioinspired structures for a variety of applications in aerospace, biomedicine or robotics.

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