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Abstract Microrobotic systems offer significant potential for precision medicine by enabling minimally invasive interventions in complex fluidic environments. However, effective operation in these settings requires actua...

Abstract Quantum Machine Learning (QML) is emerging as a promising technology for tackling complex computational challenges, although its practical implementation faces significant obstacles owed to the inherent noise in...

Abstract This study presents a novel hybrid methodology combining machine learning (ML) with conventional reservoir simulation to optimize waterflooding in the geologically complex Bahariya Formation, Western Desert, Egy...

Abstract Advances in AI have transformed the traditional Internet of Things (IoT) into smarter, more versatile next-generation devices. Enhanced connectivity among sensors, actuators, and appliances boosts data availabil...

Abstract Fine particulate matter (PM2.5) is a significant air pollutant in the Indo Gangetic Basin (IGB), where levels frequently exceed national and WHO air quality standards. Ground observations from 183 CPCB automatic...

Abstract The design of multi-principal element alloys (MPEAs) is challenged by data scarcity and the complexity of optimizing multiple mechanical properties simultaneously. To address these challenges, we investigate an ...

Abstract Considering crop yield prediction as critical to optimizing agricultural practices and food security, this question is critical to U.S. agricultural planning and regional food security; relevant research on corn...

Abstract Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of Integrated Computational Materials Engi...

Abstract We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compositional structure of KAN and is...

Abstract Accurate prediction of compressive strength is important for the efficient design of sustainable concrete incorporating palm oil fuel ash (POFA). This study developed a hybrid artificial neural network model opt...

Abstract The stability of surrounding rock is a critical challenge in underground engineering. Accurate prediction of Excavation Damaged Zone Thickness (EDZT) in surrounding rock is vital for designing safe underground s...

Abstract Optimizing surgical decisions in breast cancer is critical. Choosing between mastectomy and breast-conserving surgery (BCS) is complex due to heterogeneous pre-operative clinical factors. We developed BrCaM (Bre...

Abstract Substantial efforts have been made to modernize and decarbonize the waste collection industry. Smart sensor-based waste collection (SWC) systems have been developed to optimize collection routes based on the act...