Search for: All records

The design and fabrication of 3D printed ATESs within vivoadhesion and application potential, shape design capability, as well as accessible and convenient fabrication and application process. 

As the crisis of confidence and trust in overseas foundries arises, the industry and academic community are paying increasing attention to Printed Circuit Board (PCB) security. PCB, the backbone of any electronic system hardware, always draws attackers’ attention as it carries system and design information. Numerous ways of PCB tampering (e.g., adding/replacing a component, eavesdropping on a trace and bypassing a connection) can lead to more severe problems, such as Intellectual Property (IP) violation, password leaking, the Internet of Things (IoT) attacks or even more. This paper proposes a technique of active self-defense PCB modules with zero performance overhead. Those protection modules will only be activated when the boards are exposed to the attacks. A set of PCBs with proposed protection modules is fabricated and tested to prove the effectiveness and efficiency of the techniques. 

The abrupt weakening of the East Asian summer monsoon (EASM) during Younger Dryas (YD) has been attributed to freshwater discharge into the North Atlantic ocean and resultant Northern Hemisphere cooling. Recent studies have found that sea ice variability in the Nordic Sea during the YD exerted a great influence upon the northern high-latitude climate. However, the influence of sea ice upon EASM evolution during YD event remains unclear. In this paper, we report two precisely-dated speleothem oxygen isotope records from the EASM-dominated region of central China. Our records archive abrupt changes in EASM variability during the YD event. Initially, there was a significant strengthening of the EASM during the mid-YD following the gradually increased Atlantic meridional overturning circulation (AMOC). Later this trend reversed at ∼12.15 ka due to northern high-latitude sea ice fluctuations and a consequent reduction of AMOC. At the YD termination, abrupt intensification of the EASM was synchronous with the rapid decline of sea-ice and recovery of the AMOC indicating that sea ice variability was a significant influence on high latitude climate and EASM variation during the YD. 

Understanding the relationship among elemental compositions, nanolamellar microstructures, and mechanical properties enables the rational design of high-entropy alloys (HEAs). Here, we construct nanolamellar Al_xCoCuFeNi HEAs with alternating high– and low–Al concentration layers and explore their mechanical properties using a combination of molecular dynamic simulation and density functional theory calculation. Our results show that the HEAs with nanolamellar structures exhibit ideal plastic behavior during uniaxial tensile loading, a feature not observed in homogeneous HEAs. This remarkable ideal plasticity is attributed to the unique deformation mechanisms of phase transformation coupled with dislocation nucleation and propagation in the high–Al concentration layers and the confinement and slip-blocking effect of the low–Al concentration layers. Unexpectedly, this ideal plasticity is fully reversible upon unloading, leading to a remarkable shape memory effect. Our work highlights the importance of nanolamellar structures in controlling the mechanical and functional properties of HEAs and presents a fascinating route for the design of HEAs for both functional and structural applications. 

Abstract Adhesive tissue engineering scaffolds (ATESs) have emerged as an innovative alternative means, replacing sutures and bioglues, to secure the implants onto target tissues. Relying on their intrinsic tissue adhesion characteristics, ATES systems enable minimally invasive delivery of various scaffolds. This study investigates development of the first class of 3D bioprinted ATES constructs using functionalized hydrogel bioinks. Two ATES delivery strategies, in situ printing onto the adherend versus printing and then transferring to the target surface, are tested using two bioprinting methods, embedded versus air printing. Dopamine‐modified methacrylated hyaluronic acid (HAMA‐Dopa) and gelatin methacrylate (GelMA) are used as the main bioink components, enabling fabrication of scaffolds with enhanced adhesion and crosslinking properties. Results demonstrate that dopamine modification improved adhesive properties of the HAMA‐Dopa/GelMA constructs under various loading conditions, while maintaining their structural fidelity, stability, mechanical properties, and biocompatibility. While directly printing onto the adherend yields superior adhesive strength, embedded printing followed by transfer to the target tissue demonstrates greater potential for translational applications. Together, these results demonstrate the potential of bioprinted ATESs as off‐the‐shelf medical devices for diverse biomedical applications. 

This paper discusses commonly used reverse engineering methods to illegally recreate printed circuit board (PCB) designs. A solution using transformative electronics is presented to prevent the discussed reverse engineering methods by obfuscating the design. The transformative electronics solution is employed in a specific application that results in a reverse engineered board to be incorrectly recreated, where the signals would be distorted due to added electromagnetic interference (EMI). The non-conductive vias that are part of the obfuscation would allow the inclusion of EMI generators that would not affect the circuit in an original design but would prevent copied designs from working correctly. A machine learning algorithm is being designed to optimize the placement of the EMI sources in an original PCB. 

Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledge aware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on real world datasets involving five domains (product types) shows the effectiveness of the approach.