Application of Adaptive Learning Networks to Quantitative Flaw Definition

Adaptive Learning Networks (ALNs) are algebraic, nonlinear multinomials whose structure and coefficients are learned from empirical data. Over the past several years, their application to quantitative NDE problems has become widespread. The major advantage of the ALN approach is that only a modest data base of experiments is needed, from which the ALN models can be trained. In this work, ALNs are used as a nonlinear, empirical inversion procedure for various defect geometries. Measurements from a sparselypopulated ultrasonic transducer array are input to the ALNs which estimate the defect characteristics. The defects considered are (1) elliptical cracks, (2) irregular-shaped voids, and (3) surface-breaking semielliptical cracks. The models are synthesized from theoretically-generated, forward-scattering data, then evaluated on actual experimental data recorded from titanium and carbon steel samples. The advantage of using theoretical data to train the models is that ultrasonic responses can be generated quickly and inexpensively in a digital computer, thereby avoiding, or greatly minimizing, the expense of calibration sample fabrication. The size and orientation estimates for the experimental evaluation are in excellent agreement with the true defect characteristics

China Maritime Report No. 19: The PLA Airborne Corps in a Joint Island Landing Campaign

The People’s Liberation Army (PLA) Airborne Corps would likely play an important role in a cross-strait invasion through operations behind enemy lines. During the landing campaign, the Corps would conduct paradrops or landing operations onto Taiwan, facilitated by PLA Air Force (PLAAF) aircraft. Once on island, airborne forces would seize and hold terrain and conduct a variety of operations to support the broader invasion. In recent years, the Corps has reorganized to improve its capability for mechanized maneuver and assault, leveraging the PLAAF’s larger inventories of transport aircraft, particularly the Y-20; improved the sophistication of its training at home; and gleaned insights from abroad via training with foreign militaries. Nevertheless, it is uncertain to what extent the Corps is able to overcome key challenges relevant to a cross-strait campaign. These include ensuring effective integration with similar ground force and marine units; carrying out operations in complex or degraded environments; transcending the Corps’ lack of relevant combat experience; and obtaining adequate air support.https://digital-commons.usnwc.edu/cmsi-maritime-reports/1018/thumbnail.jp

Application of Adaptive Learning Networks to Quantitative Flaw Definition

Adaptive Learning Networks (ALNs) are algebraic, nonlinear multinomials whose structure and coefficients are learned from empirical data. Over the past several years, their application to quantitative NDE problems has become widespread. The major advantage of the ALN approach is that only a modest data base of experiments is needed, from which the ALN models can be trained. In this work, ALNs are used as a nonlinear, empirical inversion procedure for various defect geometries. Measurements from a sparselypopulated ultrasonic transducer array are input to the ALNs which estimate the defect characteristics. The defects considered are (1) elliptical cracks, (2) irregular-shaped voids, and (3) surface-breaking semielliptical cracks. The models are synthesized from theoretically-generated, forward-scattering data, then evaluated on actual experimental data recorded from titanium and carbon steel samples. The advantage of using theoretical data to train the models is that ultrasonic responses can be generated quickly and inexpensively in a digital computer, thereby avoiding, or greatly minimizing, the expense of calibration sample fabrication. The size and orientation estimates for the experimental evaluation are in excellent agreement with the true defect characteristics.</p

Application of Adaptive Learning Networks to Quantitative Flaw Definition

Adaptive Learning Networks (ALNs) are algebraic, nonlinear multinomials whose structure and coefficients are learned from empirical data. Over the past several years, their application to quantitative NDE problems has become widespread. The major advantage of the ALN approach is that only a modest data base of experiments is needed, from which the ALN models can be trained. In this work, ALNs are used as a nonlinear, empirical inversion procedure for various defect geometries. Measurements from a sparselypopulated ultrasonic transducer array are input to the ALNs which estimate the defect characteristics. The defects considered are (1) elliptical cracks, (2) irregular-shaped voids, and (3) surface-breaking semielliptical cracks. The models are synthesized from theoretically-generated, forward-scattering data, then evaluated on actual experimental data recorded from titanium and carbon steel samples. The advantage of using theoretical data to train the models is that ultrasonic responses can be generated quickly and inexpensively in a digital computer, thereby avoiding, or greatly minimizing, the expense of calibration sample fabrication. The size and orientation estimates for the experimental evaluation are in excellent agreement with the true defect characteristics.</p

Domestic Factors Could Accelerate the Evolution of China's Nuclear Posture

This research brief describes work done for RAND Project AIR FORCE documented in China’s Evolving Nuclear Deterrent: Major Drivers and Issues for the United States, by Eric Heginbotham, Michael S. Chase, Jacob L. Heim, Bonny Lin, Mark R. Cozad, Lyle J. Morris, Christopher P. Twomey, Forrest E. Morgan, Michael Nixon, Cristina L. Garafola, and Samuel K. Berkowitz, RR-1628-AF, 2017The article of record as published may be found at https://www.rand.org/t/RB995

China's Evolving Nuclear Deterrent: Major Drivers and Issues for the United States

The article of record as published may be found at https://www.rand.org/RR162

Cell-penetrating peptides enhance peptide vaccine accumulation and persistence in lymph nodes to drive immunogenicity

Peptide-based cancer vaccines are widely investigated in the clinic but exhibit modest immunogenicity. One approach that has been explored to enhance peptide vaccine potency is covalent conjugation of antigens with cell-penetrating peptides (CPPs), linear cationic and amphiphilic peptide sequences designed to promote intracellular delivery of associated cargos. Antigen-CPPs have been reported to exhibit enhanced immunogenicity compared to free peptides, but their mechanisms of action in vivo are poorly understood. We tested eight previously described CPPs conjugated to antigens from multiple syngeneic murine tumor models and found that linkage to CPPs enhanced peptide vaccine potency in vivo by as much as 25-fold. Linkage of antigens to CPPs did not impact dendritic cell activation but did promote uptake of linked antigens by dendritic cells both in vitro and in vivo. However, T cell priming in vivo required Batf3 -dependent dendritic cells, suggesting that antigens delivered by CPP peptides were predominantly presented via the process of cross-presentation and not through CPP-mediated cytosolic delivery of peptide to the classical MHC class I antigen processing pathway. Unexpectedly, we observed that many CPPs significantly enhanced antigen accumulation in draining lymph nodes. This effect was associated with the ability of CPPs to bind to lymph-trafficking lipoproteins and protection of CPP-antigens from proteolytic degradation in serum. These two effects resulted in prolonged presentation of CPP-peptides in draining lymph nodes, leading to robust T cell priming and expansion. Thus, CPPs can act through multiple unappreciated mechanisms to enhance T cell priming that can be exploited for cancer vaccines with enhanced potency. </jats:p