This paper describes a 160 MHz 500 mW StrongARM microprocessor designed for lowpower, low-cost applications. The chip implements the ARM V4 instruction set 1 and is bus compatible with earlier implementations. The pin interface runs at 3.3 V but the internal power supplies can vary from 1.5 to 2.2 V, providing various options to balance performance and power dissipation. At 160 MHz internal clock speed with a nominal Vdd of 1.65 V, it delivers 185 Dhrystone 2.1 MIPS while dissipating less than 450 mW. The range of operating points runs from 100 MHz at 1.65 V dissipating less than 300 mW to 200 MHz at 2.0 V for less than 900 mW. An on-chip PLL provides the internal clock based on a 3.68 MHz clock input. The chip contains 2.5 million transistors, 90% of which are in the two 16 kB caches. It is fabricated in a 0.35-m three-metal CMOS process with 0.35 V thresholds and 0.25 m effective channel lengths. The chip measures 7.8 mm ϫ 6.4 mm and is packaged in a 144-pin plastic thin quad flat pack (TQFP) package.

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This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness.

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We show how the switching-on of an electron transport through a system of two parallel quantum dots embedded in a short quantum wire in a photon cavity can trigger coupled Rabi and collective electron-photon oscillations. We select the initial state of the system to be an eigenstate of the closed system containing two Coulomb interacting electrons with possibly few photons of a single cavity mode. The many-level quantum dots are described by a continuous potential. The Coulomb interaction and the para-and dia-magnetic electron-photon interactions are treated by exact diagonalization in a truncated Fock-space. To identify the collective modes the results are compared for an open and a closed system with respect to the coupling to external electron reservoirs, or leads. We demonstrate that the vacuum Rabi oscillations can be seen in transport quantities as the current in and out of the system. Introduction.-Fine-tuning of the electron-photon interaction has opened up new possibilities in semiconductor physics. The transport of electrons through quantum dots assisted by up to four photons in the teraherz frequency range has been observed [1], and double quantum dots have been used to detect single-photons from shot-noise in electron transport through a quantum point contact [2]. The properties and control of atomic or electronic systems in photonic cavities is a common theme in the research effort of many teams working on various aspects of quantum cavity electrodynamics and related fields [3][4][5][6][7][8][9][10]. The non-local single-photon transport properties of two sets of double quantum dots within a photon cavity has recently been modeled [11], and also a pump-probe scheme for electron-photon dynamics in a hybrid conductor-cavity system with one electron reservoir [12]. Many tasks in quantum information processing might be served by mixed photon-electronics circuits. In order to model such systems we need to combine methods and tools that have traditionally been used and developed in the fields of time-dependent electron transport and quantum optics. In this publication we show how time-dependent electron transport through a nanoscale system embedded in a photon cavity could be used to detect vacuum Rabi-oscillations in it. In order to do so we use a generalized master equation (GME) formalism for time-dependent electron transport, that was initially developed for quantum optics systems [13,14].

A series of poly(N-isopropylacrylamide-co-acrylic acid) (PNIPAAm-co-PAA) random copolymers were synthesized through free radical copolymerization in MeOH. The incorporation of the acrylic acid units into PNIPAAm tended to enhance the glass transition temperature (T g ), due to strong intermolecular hydrogen bonding between the amide groups of PNIPAAm and the carboxyl groups of PAA, as observed using 1 H nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopic analyses. The lower critical solution temperature (LCST) increased upon increasing the pH of the aqueous solution containing PNIPAAm-co-PAA because the COOH groups of the PAA segment dissociated into COO − groups, enhancing the solubility of the copolymer. In addition, high-pressure differential scanning calorimetry revealed that the LCSTs of all the aqueous solutions of the copolymers decreased upon increasing the pressure of CO 2 , suggesting that CO 2 molecules had displaced H 2 O molecules around the polar CONH and COOH groups in PNIPAAm-co-PAA, thereby promoting the hydrophobicity of the copolymers in the aqueous solution. In addition, the values of T g of a film sample increased upon treatment with supercritical CO 2 , implying that intermolecular interactions in the copolymer had been enhanced after such treatment.

Research on self-determination theory emphasizes the importance of the internalization of motivation as a crucial factor for determining the quality of motivation. Hence, intrinsic motivation is deemed as an important predictor of learning. Research on epistemic beliefs, on the other hand, focuses on the nature of knowledge, and learning with more sophisticated epistemic beliefs associated with more adaptive outcomes. While learning and achievement are multiply determined, a more comprehensive theoretical model that takes into account both motivational quality and epistemic beliefs is needed. Hence, this study aims to examine the role of intrinsic and instrumental motivation alongside epistemic beliefs in predicting students’ achievement in science. Data were drawn from the PISA 2015 survey. We focused on four of the top-performing societies. Two were Eastern societies – Singapore and Hong Kong, and the other two were Western societies: Canada and Finland. We found both common and specific patterns among the four societies. Regarding the common patterns, we found that intrinsic motivation and epistemic beliefs had direct positive effects on science achievement. As for the regionally-specific findings, instrumental motivation positively predicted achievement only in Western societies (i.e., Finland and Canada), but not in Eastern societies (i.e., Singapore and Hong Kong). The interaction effect between motivation and epistemic beliefs also demonstrated different patterns across the four societies. Implications for the role of motivation and epistemic beliefs in optimizing student learning and achievement are discussed.