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SummaryThe coleoid cephalopods (cuttlefish, octopus, and squid) are a group of soft-bodied marine mollusks that exhibit an array of interesting biological phenomena, including dynamic camouflage, complex social behaviors, prehensile regenerating arms, and large brains capable of learning, memory, and problem-solving [1–10]. The dwarf cuttlefish,Sepia bandensis, is a promising model cephalopod species due to its small size, substantial egg production, short generation time, and dynamic social and camouflage behaviors [11]. Cuttlefish dynamically camouflage to their surroundings by changing the color, pattern and texture of their skin. Camouflage is optically-driven, and is achieved by expanding and contracting hundreds of thousands of pigment-filled saccules (chromatophores) in the skin, which are controlled by motor neurons emanating from the brain. We generated a dwarf cuttlefish brain atlas using magnetic resonance imaging (MRI), deep learning, and histology, and we built an interactive web tool (cuttlebase.org) to host the data. Guided by observations in other cephalopods [12–20], we identified 32 brain lobes, including two large optic lobes (75% the total volume of the brain), chromatophore lobes whose motor neurons directly innervate the chromatophores of the color-changing skin, and a vertical lobe that has been implicated in learning and memory. This brain atlas provides a valuable tool for exploring the neural basis of cuttlefish behavior.

Foreign exchange is one of the macroeconomic variables which affect the financial performance of firms. Fluctuations in the foreign exchange rate have an effect on the operating cashflows, investment decisions, profits, sales and a firm’s market value. Foreign exchange rate level is determined by various factors which include the balance of payment, terms of trade, inflation and government borrowing. Given the importance of multinational firms to the economy of Kenya, it is crucial to determine the effect of determinants of foreign exchange on their performance. The objective of the study was to determine the effect foreign exchange determinants on the financial performance of multinational companies in Kenya. The independent variables included balance of payment, government debt, terms of trade and inflation. Measurement of financial performance was done using return on equity and return on assets. The study was anchored by the balance of payments theory, Keynesian theory of public debt, theory of international trade and the quantity theory of money. The study sampled four multinational companies operating in the nation of Kenya. The period covered by the study was 2002-2017. Data collected for the study was from both the central bank publications and financial statements of multinational companies. Study data was evaluated using panel data. The fixed effects model was used for the study. The study established that the balance of payments had a negative as well as statistically significant impact on return on assets. The study also established that government debt had a positive and significant effect on return on assets. Terms of trade and inflation were found to have positive but statistically insignificant effects on return on assets. The balance of payment and inflation had a negative and statistically significant effect on the return on equity. The terms of trade was found to have a positive but statistically insignificant effect on the return on equity. Government debt had a positive and statistically significant effect on return on equity.

The forwarding rules, used by the legacy and SDN network devices to perform routing/forwarding decisions, are generally stored in Ternary Content Addressable Memory (TCAM) modules, which offer constant look-up times, but have limited capacity, due to their high capital and operational costs, high power consumption and high silicon footprint. To counter this limitation, some commercial switches offer both, hardware and software flow table implementations, termed hybrid flow table architecture in this paper. The software-based tables are stored in non-TCAM memory modules, which offer higher capacity, but with slower lookup times. In addition, these memory modules are limited in terms of how many requests they can serve per time unit. Thus, exceeding this threshold will lead to packet loss in the network. This paper proposes a novel placement algorithm, which dynamically decides whether a new flow rule should be placed in a hardware (expensive) or a software (cheap) table. The placement decisions are based on a number of criteria with the goal to increase the utilization of the software-based table, without introducing performance degradation in the network in terms of significant delay and packet loss. The performance of the placement algorithm was evaluated through experimental measurements in a testbed, which comprises a hybrid SDN switch, a server performing traffic generation and a server hosting the SDN controller. The results indicate that, by limiting the maximum allowed processing capacity of the software table, the number of accommodated flows is significantly increased, while bounding any excessive delays and avoiding packet loss.

Over the course of the 2009 Federal Fiscal Year the United States (U.S.) Department of Homeland Security (DHS), in partnership with the Departments of Defense (DoD) and Energy (DOE), is continuing existing programs and introducing new programs designed to maintain a highly qualified, enduring workforce capable of performing the technical nuclear forensics mission. These student and university programs are designed to recruit the best and brightest students, develop university faculty and research capabilities, and engage the national laboratories in fields of study with application in nuclear forensics. This comprehensive effort constitutes the National Nuclear Forensics Expertise Development Program.