The crosstalk between the nerve and stomatognathic systems plays a more important role in organismal health than previously appreciated with the presence of emerging concept of the “brain-oral axis”. A deeper understanding of the intricate interaction between the nervous system and the stomatognathic system is warranted, considering their significant developmental homology and anatomical proximity, and the more complex innervation of the jawbone compared to other skeletons. In this review, we provide an in-depth look at studies concerning neurodevelopment, craniofacial development, and congenital anomalies that occur when the two systems develop abnormally. It summarizes the cross-regulation between nerves and jawbones and the effects of various states of the jawbone on intrabony nerve distribution. Diseases closely related to both the nervous system and the stomatognathic system are divided into craniofacial diseases caused by neurological illnesses, and neurological diseases caused by an aberrant stomatognathic system. The two-way relationships between common diseases, such as periodontitis and neurodegenerative disorders, and depression and oral diseases were also discussed. This review provides valuable insights into novel strategies for neuro-skeletal tissue engineering and early prevention and treatment of orofacial and neurological diseases.
To ensure that a hardware Trojan remains hidden in a circuit, it is usually necessary to ensure that the trigger signal has a low testability, which has been widely recognized and proven. The most advanced testability-based detection methods are rather slow for large circuits, and the false-positive rate is not as low as that for small circuits. In this paper, a hardware Trojan, through the low testability of the trigger signal and its position characteristics in the circuit, was detected, which greatly improves the detection speed while maintaining a lower false positive rate when being applied to large circuits. First, the Sandia Controllability/Observability Analysis Program (SCOAP) was applied to obtain the 0–1 controllability of the signals in the netlist. Secondly, the controllability value was calculated by the differential amplification model, in order to facilitate K-means clustering to get better results. Then, we calculate the shortest path between each suspicious signal to get the connection between each suspicious signal. Finally, we divide the suspicious signals into several suspicious circuit blocks to screen the real trigger signal. As a result, the false-negative rate of 0% and the highest false-positive rate of 5.02% were obtained on the Trust-Hub benchmarks.
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