Responding to Diversity with More Than Simple Lip-Service

Using contentious topics like those addressed in Joe Limer’s poem “White Hollywood” as catalysts for sparking conversations on complex social issues has potential to raise social consciousness and to support collaborative conversation. Miller’s GREEN APPLE acronym guides teachers and learners in honoring diversity and nurturing social justice. In critical race theory fashion, GREEN APPLE questions enable students of all races and ethnicities to have informed, productive conversations about the forces that have shaped, and continue to shape, the society in which they live

ZINCIAN ILMENITE. ECANDREWSITE FROM A PELITIG SCHIST. DEATH VALLEY. CALIFORNIA, AND THE PARAGENESIS OF (Zn,Fe)TiO3 SOLID SOLUTION lN METAMORPHIC ROCKS

ABSIRACT Two compositionally and petrographicatly distinct populations of ilmenite--ecandrewsite sotd solution, FeTiO3 -ZnTiO3 coexist in a kyanite-bearing schist from the Black Mountains, Death Valley, Califomia. The first population is extremely zinc-rich and has the stoichiometric formula (2n6.17-n.s1Fe6.1s-{.6aMn0.01)Tio3. Single grains span the entire compositional range. The mineral is orange-brown in transmitted light, and occun both as inclusions in gamet and as an abundant phase (*3-5 modal Vo) in the matrix. The second population is opaque in transmitted light occurs exclusively as a matrix phase, and has the stoichiometric formula(Zn, a.1zFeo.ss+.seMno.o1_a.9)TiO3. Single grains aregenerally homogeneous, butthere is compositionalvariationamong grains. The paragenesis of zinc-bearing ilmenite solid-solution in metapelitic rocks is problematic. Thermodynamic calculations and comparison with other reportsd occurrences indicate that ilmenite with greater than a few molvo ZnTiO3 component in metapelitic rocks should be metastable relative to gahnite + quartz + rutile over the range of geologically relevant conditions of regional metamorphism. Keywords: zinc, ilmenite, ecandrewsite, metapelitic rocks, Death Valley, Califomia. SoMMARE Deux membres de la solution solide ilm€nite -ecandrewsite (FeTiO3-ZnTiO3), distincts non seulement en composition mais aussi en aspecn texturalx, coexistent dans un schiste d kyanite provenant des Black Mountains, Death Valley, Califomie. Le premier groupe est riche en zinc et rdpond I la formule stoechiom6trique (Znn.17-a.srFq.1s-n.64Mn0.01)>1.00TiO3. Un seul grain peut contenir l'intervalle complet de compositions. Le min6ral est orange brundtre en lumibre transmise, et se trouve en inclusions dans le grenat et comme phase rdp andue (-3-5Vo par volume) dans la matrice. Les grains du second groupe sont opaques en lumidre transmise, se trouvent seulement dans la matrice, et rdpondent i la formule stoechiomdtrique (Zn6-s.12Fe0.g5-{.saMno.or-0.02b1.00TiO3. Chaque grain est homogbne, en g6n6ral, mais nous d6celons une variation parmi les grains. la paragenbse de la solution solide (Zn,Fe)TiO3 dans les roches mdtap6litiques est 6nigmatique. ks calculs themrodynamiques et une comparaison avec les exemples pris de la littdrature montrent que I'ikndnite ayant plus de quelque pourcents du p6le ZnTiO3 dans les roches p6litiques devrait €tre mdtastable par rapport a l'assemblage gahnite + quartz + rutile dans un intervalle r6aliste de conditions du m6tamorphisme r6gional. (traduit par la R6daction

Bioelectronic DNA detection of human papillomaviruses using eSensor™: a model system for detection of multiple pathogens

BACKGROUND: We used human papillomaviruses (HPV) as a model system to evaluate the utility of a nucleic acid, hybridization-based bioelectronic DNA detection platform (eSensor™) in identifying multiple pathogens. METHODS: Two chips were spotted with capture probes consisting of DNA oligonucleotide sequences specific for HPV types. Electrically conductive signal probes were synthesized to be complementary to a distinct region of the amplified HPV target DNA. A portion of the HPV L1 region that was amplified by using consensus primers served as target DNA. The amplified target was mixed with a cocktail of signal probes and added to a cartridge containing a DNA chip to allow for hybridization with complementary capture probes. RESULTS: Two bioelectric chips were designed and successfully detected 86% of the HPV types contained in clinical samples. CONCLUSIONS: This model system demonstrates the potential of the eSensor platform for rapid and integrated detection of multiple pathogens

All-Domain Sensor Network Orchestration from Seabed-to-Space

NPS NRP Project PosterThe DoD seeks to conduct all-domain operations, requiring Intelligence Surveillance and Reconnaissance (ISR) across all domains of conflict. For the Navy, this uniquely includes the deep seabed, undersea, sea surface, air, space and cyberspace operations. All-Domain ISR encompasses and integrates information from all domains of the maritime environment, sensors and sources from seabed-to-space, to provide commanders with the most complete picture of adversary activities. This capability supports the Navy approach to Distributed Maritime Operations (DMO), an operational concept that enables widely dispersed naval units to perform sensing, command and control and weapon activities such that the distributed platforms act as a coherent whole. All-domain ISR requires a network to enable widely dispersed sensors to exchange and combine sensor data (the fusion of data) to provide a complete understanding of the operational picture, and to provide targeting information for long-range engagement required by DMO. This research studies the diverse sensor access time horizons, sensor mode options, observation feasibilities, and relative contribution of all-domain sensors (seabed-to-space) which pose a significant mathematical and computational challenge to achieve all-domain ISR. Furthermore, the delays from sensing to fusion across such a wide range of sensors can diminish the contribution of some combinations of sensing modes. The study also evaluates the distribution of fusion nodes across an all-domain network to improve the delivery of information across the network.Naval Information Warfare Center Pacific (NIWC Pacific)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Machine Learning (ML) for Signal Detection

NPS NRP Project PosterResearch has shown that machine learning holds promise as a technique to improve the identification and classification of signals of interest. This study proposes the use of machine learning and generative adversarial networks (GANs) to classify received signals based on their down-converted (but not demodulated) in-phase and quadrature (I&Q) samples and evaluate their probability of being of interest. The approach will use a generative adversarial network (GAN) to train a discriminator neural network that will determine the likelihood that a received signal is of interest. The discriminator can then be used to identify signals of interest as they are received.Naval Special Warfare Command (NAVSPECWARCOM)N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

All-Domain Sensor Network Orchestration from Seabed-to-Space

NPS NRP Executive SummaryThe DoD seeks to conduct all-domain operations, requiring Intelligence Surveillance and Reconnaissance (ISR) across all domains of conflict. For the Navy, this uniquely includes the deep seabed, undersea, sea surface, air, space and cyberspace operations. All-Domain ISR encompasses and integrates information from all domains of the maritime environment, sensors and sources from seabed-to-space, to provide commanders with the most complete picture of adversary activities. This capability supports the Navy approach to Distributed Maritime Operations (DMO), an operational concept that enables widely dispersed naval units to perform sensing, command and control and weapon activities such that the distributed platforms act as a coherent whole. All-domain ISR requires a network to enable widely dispersed sensors to exchange and combine sensor data (the fusion of data) to provide a complete understanding of the operational picture, and to provide targeting information for long-range engagement required by DMO. This research studies the diverse sensor access time horizons, sensor mode options, observation feasibilities, and relative contribution of all-domain sensors (seabed-to-space) which pose a significant mathematical and computational challenge to achieve all-domain ISR. Furthermore, the delays from sensing to fusion across such a wide range of sensors can diminish the contribution of some combinations of sensing modes. The study also evaluates the distribution of fusion nodes across an all-domain network to improve the delivery of information across the network.Naval Information Warfare Center Pacific (NIWC Pacific)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

All-Domain Sensor Network Orchestration from Seabed-to-Space

NPS NRP Technical ReportThe DoD seeks to conduct all-domain operations, requiring Intelligence Surveillance and Reconnaissance (ISR) across all domains of conflict. For the Navy, this uniquely includes the deep seabed, undersea, sea surface, air, space and cyberspace operations. All-Domain ISR encompasses and integrates information from all domains of the maritime environment, sensors and sources from seabed-to-space, to provide commanders with the most complete picture of adversary activities. This capability supports the Navy approach to Distributed Maritime Operations (DMO), an operational concept that enables widely dispersed naval units to perform sensing, command and control and weapon activities such that the distributed platforms act as a coherent whole. All-domain ISR requires a network to enable widely dispersed sensors to exchange and combine sensor data (the fusion of data) to provide a complete understanding of the operational picture, and to provide targeting information for long-range engagement required by DMO. This research studies the diverse sensor access time horizons, sensor mode options, observation feasibilities, and relative contribution of all-domain sensors (seabed-to-space) which pose a significant mathematical and computational challenge to achieve all-domain ISR. Furthermore, the delays from sensing to fusion across such a wide range of sensors can diminish the contribution of some combinations of sensing modes. The study also evaluates the distribution of fusion nodes across an all-domain network to improve the delivery of information across the network.Naval Information Warfare Center Pacific (NIWC Pacific)ASN(RDA) - Research, Development, and AcquisitionThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Machine Learning (ML) for Signal Detection

NPS NRP Executive SummaryResearch has shown that machine learning holds promise as a technique to improve the identification and classification of signals of interest. This study proposes the use of machine learning and generative adversarial networks (GANs) to classify received signals based on their down-converted (but not demodulated) in-phase and quadrature (I&Q) samples and evaluate their probability of being of interest. The approach will use a generative adversarial network (GAN) to train a discriminator neural network that will determine the likelihood that a received signal is of interest. The discriminator can then be used to identify signals of interest as they are received.Naval Special Warfare Command (NAVSPECWARCOM)N2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Automated Data Analysis for Network Optimization / Threat Detection in Network Architectures

NPS NRP Executive SummaryReport Type: Final ReportProject Summary: The Marine Corps Network Efficiency Lab (MCNEL) is tasked with analyzing very large network traffic archives collected from operations in order to improve future network design, operations, and security. Until this time, MCNEL has used conventional single node packet analyzers, which have proven to be very limiting. Conventional single node packet analyzers are unable to monitor network traffic at scale. In this research, elements of the Apache Hadoop ecosystem, including HBase, Spark, and MapReduce were employed to conduct network traffic analysis on a large collection of network traffic thereby establishing a prototype for network analysis at very large scale in computer clusters. The MCNEL clusters could be organic or in the cloud, perhaps using govCloud cloud computing assets. Initially, limited analysis was conducted directly on packet capture next generation (pcapng) files on the Hadoop Distributed File System (HDFS) using MapReduce. To allow for repeated analysis on the same dataset without reading all source files in their entirety for every calculation, network traffic archives were parsed, and relevant meta- data was bulk loaded into HBase, a Not Only Structured Query Language (NoSQL) database employing the HDFS for parallelization on computer clusters. This NoSQL database was then accessed via Apache Spark where pertinent data is loaded into dataframes, and additional analysis on the network traffic takes place. This research demonstrates the viability of custom, modular, automated analytics, employing open- source software to enable parallelization, to conduct traffic analysis at scale.IMEFMarine Corps Network Efficiency Lab (MCNEL)NPS-18-M034-BApproved for public release; distribution is unlimited

The biodiversity of freshwater Crustaceans revealed by taxonomy and mitochondrial DNA barcodes

Cytochrome oxidase subunit I (COI) barcode sequences in this file were obtained from specimens collected by plankton net in western Lake Erie in 2012 & 2013, along with later specimens collected at various locations and times, including some collected in Belize in 2015. Methods and other details about these sequences are described in a paper by the same authors in a submitted publication (2021: URL to be given here when published). The right columns below contain additional notes on lengths of sequences, GenBank accession ID (when obtained), and annotation as to whether the sequence represents a new barcode for its genus or species taxon. According to our experience, a DNA identity of \u3e96.5% with previous GenBank barcodes is a reliable range for determining a species level barcode for that morpho species; a DNA identity of 90.5% to 96.5% with previous barcodes is sufficient to identify genus. DNA identities within these ranges are considered to be barcode confirmations. Conversely, DNA identities outside of these ranges are considered to be new barcodes for that species or genus, respectively. Contradictions with previous GenBank sequences are discussed in the manuscript. The submitted manuscript includes the highest percentage identity to a previous sequence in GenBank as determined by BLASTN in June2021. The FASTA file name given here begins with a Ram Lab ID number-location and date of collection with format varying somewhat between various collections/collectors but generally including several (usually three) location letters (e.g., BHL stands for Blue Heron Lagoon) and the date usually in a 6-character format of MMDDYY, and optionally a sample number for that date either preceding the location letters or following the date. Collection location abbreviations include the following: All sequences starting with PM, Toledo Harbor in western Lake Erie; LMUSK, Lake Muskoday, Belle Isle, Detroit; SCL, Saint Clair River; BHL, Blue Heron Lagoon, Belle Isle; LE, LakeErie; LSC, Lake St.Clair; MMLE; Metzgers Marsh, LakeErie; MM, Metzgers Marsh; LP, Leonard Preserve, Manchester, Michigan; HR, Huron River Drive, Ypsilanti, Michigan; LCL, Little Cedar Lake, Orion, MI; HLE, Harbor Lake Erie; LHLE, Lorain Harbor Lake Erie; BZEB1P, Cenote in Shipstern Reserve, Corozal, Belize, Central America