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Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes. 

ABSTRACT As modern agriculture faces increasing demands for efficiency and automation, this study presents a novel, untethered soft gripper system designed for autonomous and efficient harvesting. At the core of this innovation is a piston‐driven, pneumatically actuated gripper embedded with flexible tactile sensors, enabling operation without an external air source. The system integrates a compact motorized syringe, forming a closed‐loop fluid circuit that provides precise pressure control for adaptive grasping. The pneumatic actuation mechanism regulates air pressure from −30 to 180 kPa, allowing the gripper to perform delicate and adaptive handling, particularly suited for tree fruits and other fragile crops. A key feature of the system is its intelligent control mechanism, which seamlessly combines pneumatic and electrical systems to enhance autonomy and versatility in agricultural applications. The integration of size recognition and adaptive grasping, enabled by force feedback from embedded tactile sensors, ensures safe, efficient, and damage‐free harvesting. Demonstrating exceptional potential for autonomous agricultural operations, the untethered soft gripper system offers enhanced independence, maneuverability, and adaptability across diverse harvesting environments. Its ability to optimize crop handling while minimizing damage highlights its significance as a pioneering solution for the future of automated agriculture. 

Over the past seven years, our team has disseminated low-cost hands-on learning hardware and associated worksheets in fluid mechanics and heat transfer to provide engineering students with an interactive learning experience. Previous studies have shown (1-5) the efficacy of teaching students with an active learning approach versus a more traditional lecture setup, with a number of approaches already available, such as simple active discussion, think-pair-share, flipped classrooms, etc. Our approach is differentiated by the inclusion of hardware to add both a visual aid and an opportunity for hands-on experimentation while keep the costs low enough for a classroom setting. Learning with a hands-on, interactive approach is supported by social cognitive theory (SCT) (6-7) and information processing theory (8). Unlike earlier views of learning theory, which simply posit that the key to learning is repetition, social cognitive theory considers the agency of the student and the social aspects of learning. The primary assumption of SCT is that students are active participants in the learning process, acquiring and displaying knowledge, skills, and behaviors that align with their goals through a process called triadic reciprocal causation, illustrated in figure 1. 

The mechanical properties of woven jute fiber-reinforced PLA polymer laminates additively manufactured through Laminated Object Manufacturing (LOM) technology are simulated using the finite element method in this work. Woven jute fiber reinforcements are used to strengthen bio-thermoplastic PLA polymers in creating highly biodegradable composite structures that can serve as one of the environmentally friendly alternatives for synthetic composites. A LOM 3D printer prototype was designed and built by the authors. All woven jute/PLA biocomposite laminated specimens made using the built prototype in this study had their tensile and flexural properties measured using ASTM test standards. These laminated structures were modeled using the ANSYS Mechanical Composite PrepPost (ACP) module, and then both testing processes were simulated using the experimentally measured input values. The FEA simulation results indicated a close match with experimental results, with a maximum difference of 9.18%. This study served as an exemplary case study using the FEA method to predict the mechanical behaviors of biocomposite laminate materials made through a novel manufacturing process. 

The mechanical properties of woven natural fiber reinforced polymers additively manufactured through Laminated Object Manufacturing (LOM) technology are investigated in this paper. The benefits of both the material and manufacturing process were combined into a sustainable practice, as a potential alternative to traditional synthetic composite materials made from nonrenewable crude oil with limited end-of-life alternatives. Woven jute fiber reinforcements are used to strengthen both synthetic and bio- thermoplastic polymers in creating highly biodegradable composite structures. Such materials, as one of the prospective alternatives for synthetic composites, can be used in many engineering fields such as automobile panels, construction materials, and commodity and recreational products including sports and musical instruments. A LOM 3D printer prototype was designed and built by the authors. All woven jute/polymer biocomposite test specimens made using the built prototype in this study had their mechanical (both tensile and flexural) properties assessed using ASTM test standards and then compared to similar values measured from pure polymer specimens. Improved mechanical characteristics were identified and analyzed. Finally, SEM imaging was performed to identify the polymer infusion and fibermatrix bonding conditions. 

Despite the significant benefits of the widespread adoption of smart home Internet of Things (IoT) devices, these devices are known to be vulnerable to active and passive attacks. Existing literature has demonstrated the ability to infer the activities of these devices by analyzing their network traffic. In this study, we introduce a packet-based signature generation and detection system that can identify specific events associated with IoT devices by extracting simple features from raw encrypted network traffic. Unlike existing techniques that depend on specific time windows, our approach automatically determines the optimal number of packets to generate unique signatures, making it more resilient to network jitters. We evaluate the effectiveness, uniqueness, and correctness of our signatures by training and testing our system using four public datasets and an emulated dataset with varying network delays, verifying known signatures and discovering new ones. Our system achieved an average recall and precision of 98-99% and 98-100%, respectively, demonstrating the effectiveness and feasibility of using packet-level signatures to detect IoT device activities.