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Decisions in the natural world are rarely made in isolation. Each action that an organism selects will affect the future situations in which it finds itself, and those situations will in turn affect the future actions that are available. Achieving real-world goals often requires successfully navigating a sequence of many actions. An efficient and flexible way to achieve such goals is to construct an internal model of the environment, and use it to plan behavior multiple steps into the future. This process is known as multi-step planning, and its neural mechanisms are only beginning to be understood. Here, we review recent advances in our understanding of these mechanisms, many of which take advantage of multi-step decision tasks for humans and animals.

Unlike many other clastic rocks, relating velocity and permeability to porosity for micrite-bearing carbonate rocks has been largely unsuccessful. Recent studies have shown that additional parameters, most notably the distribution and/or proportion of micrite, can be used to parameterize the velocity and permeability behavior. However, there is currently no scale-consistent, 3D methodology for differentiating macroporosity and microporosity from the total porosity measured on bench-top laboratory equipment. Previous studies estimated microporosity and micrite content by combining total porosity measurements conducted on whole 50 mm cores with measurements of phase volumes on 1 mm digital rocks (i.e., scale-inconsistent). As a step forward from those, we imaged dual-porosity carbonate rocks using X-ray microcomputed tomography and then leveraged a recently developed, optimization-based technique, called data-constrained modeling, to map the macroporosity and microporosity distribution of our samples. We evaluate the volumetric proportions of macropores, micropores, and coarse-grained calcite as a function of micrite content — with their respective uncertainties — all measured on the same digital rock and with the same method. Finally, we determine how measurements of the volumetric phase proportions could be extended using standard effective medium models to predict reservoir physical properties. The sensitivity of these models to the proportion of micrite and microporosity within the micrite is evidence that the nonuniqueness among permeability, velocity, and porosity that is commonly observed of micrite-bearing carbonate rocks can be explained by a variation of micrite content and microporosity at a similar porosity.

This research investigates the usage of deep neural networks (DNNs) to optimize encoding masks that, when combined with digital imaging systems, can recover high spatial frequency content that would otherwise be filtered out by the OTF. It has been shown that Fourier ptychographic photography (FPP) can recover high resolution imagery by imaging scenes that have been illuminated by a controlled light source. Instead of a controlled light source, this research utilizes light encoding masks that are optimized by a DNN to allow for decreased blur and increased resolution of recovered imagery. A masks generator is optimized in a generative adversarial network (GAN) where generated masks are used to recover imagery through a FPP phase recovery algorithm. The masks and recovery algorithm are optimized according to a loss function comparing the recovered imagery to the pristine, undegraded imagery as dictated by a Wasserstein critic, a DNN model. This method creates masks optimal for recovering high frequency spatial information of specific imaged object types based on the training dataset used.

The successful application of machine learning algorithms to ground-to-ground, long-range image applications is dependent upon the availability of a training set of imagery that adequately spans the range of relevant degraded environments. As such, NVESD has developed a turbulence simulation algorithm with the intent of generating realistic, long-range, turbulence–degraded imagery. To properly assess the realism of simulated turbulence– degraded imagery, image comparison metrics must be useful in identifying salient aspects of image degradation. The structural SIMilarity (SSIM) index metric has been developed with the idea that the human visual system is responsive to structural information content. Subsequently, the Multi-Scale SSIM (MS-SSIM) index metric was developed to better handle scale-dependence in image degradation, and the Complex Wavelet SSIM (CW-SSIM) index metric was developed in part to mitigate phase shifts which do not contribute to changes in structural information content. In this study, we assess the extent to which SSIM, MS-SSIM and CW-SSIM are able to quantify salient aspects of degradation in simulated long-range imagery and field data with respect to a pristine reference. Additionally, via the MS-SSIM and CW-SSIM metric approaches, we plan to assess the sensitivities of contrast, structure and luminance in NVESD simulated imagery to perturbations in optical turbulence. We then compare these simulated sensitivities to corresponding field data sensitivities with the intent to inform turbulence simulation development efforts.

The availability of large quantities of labeled data is key to the successful application of artificial intelligence and machine-learning (AIML) algorithms. However, live field data is expensive to collect and the atmospheric conditions that affect image quality are impossible to control. The capability to generate realistic, synthetic training data under operational conditions represents a significant opportunity to operationalize AIML algorithms at a significant cost-savings compared to live field collections. This work seeks to improve upon prior NVESD efforts to simulate the degrading effects of atmospheric turbulence on long-range, ground-to-ground imagery. Specifically, we implement a novel Generative Adversarial Network (GAN) architecture – which we named Noise-GAN – that is capable of detecting and then emulating spatially-varying signals directly from image data. We present results from a proof-of-concept study in which we demonstrate the capability of the Noise-GAN learn the spatial statistics of blur and distortion from turbulence-degraded imagery.

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as value representations, and they play two very different roles. Firstly, they drive choice: the expected values of available options are compared to one another, and the best option is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. Whether these different functions are mediated by different neural representations remains an open question. Here we employ a recently-developed multi-step task for rats that computationally separates learning from choosing. We investigate the role of value representations in the rodent orbitofrontal cortex, a key structure for value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that these representations do not directly drive choice. Instead, they signal expected reward information to a learning process elsewhere in the brain that updates choice mechanisms.

The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research., 22 pages, 5 figures

A key challenge to widespread implementation of silicon photonics is achieving optical switching at ultrafast speed and ultralow power using on-chip silicon modulators. Here we report experiments demonstrating that the ultrafast photo-induced phase transition in VO2 can be harnessed for all-optical in the telecommunications band when small VO2 volumes are integrated within a silicon waveguide. "On"-to-"off" switching speeds in this in-line modulator are less than 1 ps, thus consistent with Tbps speeds, and switching energies near threshold are less than 500 fJ for modulation depths near 6 dB. Early results showing significant reductions in switching energies in hybrid VO2:Si ring resonators will also be presented.

The author recounts his experience as a Fulbright U.S. Scholar in the Department of Special Education at Armenian State Pedagogical University in Yerevan during the fall of 2016. He describes the Fulbright U.S. Scholar Program, especially as it pertains to Armenia, and explores the current state of special education in Armenia, specifically inclusion and its impact on students, families, and faculty. The author describes courses he taught for students majoring in speech–language pathology or the education of students who are deaf or hard-of-hearing as well as guest lectures given in Yerevan and elsewhere in Armenia. In addition, he describes a research project he initiated at an elementary school in Yerevan. He concludes with insights gained from this experience.

This article reviews current developments in state and national policies that affect rural special education. A brief overview of the federal role in rural education is provided, with emphasis on the implications for the provision of special education services in rural communities. A variety of challenges are identified, including (a) the variable contexts of rural communities and the associated problems with the identity of rural education, (b) influences of rural poverty and accompanying decline in economic development in many rural communities, (c) ongoing personnel shortages in rural schools that pose unique dynamics for recruitment and retention of rural educators, (d) the disparities in available resources targeted for education in rural communities in comparison with urban counterparts, and (e) the influences of increased learner diversity on schools in rural America. A number of promising practices that address the challenges are identified, including attention to comprehensive statewide systems of educator identification, preparation, and ongoing support for educators and schools located in rural communities across America. A set of recommendations for policymakers and policy implementers is offered for consideration to advance the improvement of special education programs and services for learners in rural communities.

Cigarette use is a preventable cause of mortality and diseases. The World Health Organization states that Europe and especially Greece has the highest occurrence of smoking among adults. The prevalence of smoking among women in Greece was estimated to be over 30% in 2012. Smoking is a risk factor for many diseases. Studies have demonstrated the association between smoking and laryngeal pathologies as well as changes in voice characteristics. The purpose of this study was to estimate the effect of early smoking habit on young adult female voices and if they perceive any vocal changes using two assessment methods. The Voice Handicap Index and the acoustic analyses of voice measurements were used, with both serving as mini-assessment protocols. Two hundred and ten young females (110 smokers and 100 nonsmokers) attending the Technological Educational Institute of Epirus in the School of Health and Welfare were included. Statistically significant increases for physical and total scores of the Voice Handicap Index were found in the smokers group (P

The current study concerns fundamental controls on fluid flow in tight carbonate rocks undergoing CO2-injection. Tight carbonates exposed to weak carbonic acid exhibit order of magnitude changes in permeability while maintaining a nearly constant porosity with respect to the porosity of the unreacted sample. This study aims to determine – if not porosity – what are the microstructural changes that control permeability evolution in these rocks? Given the pore-scale nature of chemical reactions, we took a digital rock physics approach. Tight carbonate mudstone was imaged using X-ray micro-computed tomography. We simulated calcite dissolution using a phenomenological numerical model that stands from experimental and microstructural observations under transport-limited reaction conditions. Fluid flow was simulated using the lattice-Boltzmann method, and the pore wall was adaptively eroded at a rate determined by the local surface area and velocity magnitude, which we use in place of solvent flux. We identified preexisting, high-conductivity fluid pathways imprinted in the initial microstructure. Though these pathways comprise a subset of the total connected porosity, they accommodated 80 to 99.4% of the volumetric flux through the digital sample and localized dissolution. Porosity-permeability evolution exhibited two stages: selective widening of narrow pore throats that comprised preferential pathways and development and widening of channels. We quantitatively monitored attributes of the pore geometry, namely porosity, specific surface area, tortuosity, and average hydraulic diameter, which we qualitatively linked to permeability. This study gives a pore-scale perspective on the microstructural origins of laboratory permeability-porosity trends of tight carbonates undergoing transport-limited reaction with CO2-rich fluid.

We demonstrate all-optical, subpicosecond switching in silicon photonics devices, using the femtosecond insulator-to-metal transition in vanadium dioxide as the driving mechanism for an in-waveguide modulator, and very low switching thresholds in a hybrid ring resonator.

Pressure‐solution creep is an important fluid‐mediated deformation mechanism, causing chemo‐mechanical transformations and porosity and permeability changes in rocks. The presence of phyllosilicates, in particular, has previously been hypothesized to further reduce porosity and pore connectivity. Nevertheless, a full characterization of the spatio‐temporal evolution of permeability during this process has yet to be reported. A pure NaCl aggregate and a mixture of NaCl and biotite were deformed through pressure‐solution creep while monitoring their microstructural evolution through computed X‐ray micro‐tomography. The evolution of permeability and fluid velocity of the samples were computed by using the pore geometries from the X‐ray micro‐tomography as input for the Lattice‐Boltzmann modeling. The results indicate that, as deformation proceeds, porosity and permeability decrease in both samples. In the salt ‐biotite sample pressure solution creep causes the formation of a compaction band perpendicular to the direction of loading, forming a barrier for permeability. Along the other two directions, pore connectivity and permeability are retained in the marginal salt layers, making the sample strongly anisotropic. The presence of biotite controls the way pore coordination number evolves and hence, the connectivity of the pathways. Biotite flakes create an enhanced porosity decrease leading to compaction and reduction of pore connectivity. This reduction in porosity affects local stresses and local contact areas, reducing over time the driving force. According to a texture‐porosity process, the reduction in porosity causes salt ions to dissolve in the marginal salt and precipitate within the biotite‐bearing layer, where the bulk volume of salt grains increases over time.

Observations of dunite channels in ophiolites and uranium-series disequilibria in mid-ocean ridge basalt suggest that melt transport in the upper mantle beneath mid-ocean ridges is strongly channelized. We present experimental evidence that spatial variations in mineralogy can also focus melt on the grain-scale. This lithologic melt partitioning, which results from differences in the interfacial energies associated with olivine-melt and orthopyroxene-melt boundaries, may complement other melt focusing mechanisms in the upper mantle such as mechanical shear and pyroxene dissolution. We document here lithologic melt partitioning in olivine/orthopyroxene-basaltic melt samples containing nominal olivine to orthopyroxene ratio of 3 to 2 and melt fractions of 0.02 to 0.20. Experimental samples were imaged using synchrotron-based X-ray micro-computed tomography at a resolution of 700 nm per voxel. By analyzing the local melt fraction distributions associated with olivine and orthopyroxene (opx) grains in each sample, we found that the melt partitioning coefficient, i.e., the ratio of melt fraction around olivine to that around orthopyroxene grains, varies between 1.1 and 1.6. The permeability and electrical conductivity of our digital samples were estimated using numerical models and compared to those of samples containing only olivine and basaltic melt. Our results suggest that lithologic melt partitioning and preferential localization of melt around olivine grains might play a role in melt focusing, potentially enhancing average melt ascent velocities.

Cognitive models are a fundamental tool in computational neuroscience, embodying in software precise hypotheses about the algorithms by which the brain gives rise to behavior. The development of such models is often a hypothesis-first process, drawing on inspiration from the literature and the creativity of the individual researcher to construct a model, and afterwards testing the model against experimental data. Here, we adopt a complementary approach, in which richly characterizing and summarizing the patterns present in a dataset reveals an appropriate cognitive model, without recourse to an a priori hypothesis. We apply this approach to a large behavioral dataset from rats performing a dynamic reward learning task. The revealed model suggests that behavior in this task can be understood as a mixture of three components with different timescales: a quick-learning reward-seeking component, a slower-learning perseverative component, and a very slow “gambler’s fallacy” component.

Estimates of melt content beneath fast-spreading mid-ocean ridges inferred from magnetotelluric tomography (MT) vary between 0.01 and 0.10. Much of this variation may stem from a lack of understanding of how the grain-scale melt geometry influences the bulk electrical conductivity of a partially molten rock, especially at low melt fraction. We compute bulk electrical conductivity of olivine–basalt aggregates over 0.02 to 0.20 melt fraction by simulating electric current in experimentally obtained partially molten geometries. Olivine–basalt aggregates were synthesized by hot-pressing San Carlos olivine and high-alumina basalt in a solid–medium piston-cylinder apparatus. Run conditions for experimental charges were 1.5 GPa and 1350 °C. Upon completion, charges were quenched and cored. Samples were imaged using synchrotron X-ray micro-computed tomography (μ-CT). The resulting high-resolution, 3-dimensional (3-D) image of the melt distribution constitutes a digital rock sample, on which numerical simulations were conducted to estimate material properties. To compute bulk electrical conductivity, we simulated a direct current measurement by solving the current continuity equation, assuming electrical conductivities for olivine and melt. An application of Ohm's Law yields the bulk electrical conductivity of the partially molten region. The bulk electrical conductivity values for nominally dry materials follow a power-law relationship σ bulk = C σ melt ϕ m with fit parameters m = 1.3 ± 0.3 and C = 0.66 ± 0.06 . Laminar fluid flow simulations were conducted on the same partially molten geometries to obtain permeability, and the respective pathways for electrical current and fluid flow over the same melt geometry were compared. Our results indicate that the pathways for flow fluid are different from those for electric current. Electrical tortuosity is lower than fluid flow tortuosity. The simulation results are compared to existing experimental data, and the potential influence of volatiles and melt films on electrical conductivity of partially molten rocks is discussed.

We demonstrate a hybrid silicon–vanadium dioxide (Si-VO2) electro-optic modulator that enables direct probing of both the electrically triggered semiconductor-to-metal phase transition in VO2 and the reverse transition from metal to semiconductor. By using a two-terminal in-plane VO2 electrical switch atop a single-mode silicon waveguide, the phase change can be initiated electrically and probed optically, separating the excitation and measurement processes and simplifying the analysis of the metal-to-semiconductor dynamics. We demonstrate a record switch-on time for high-speed electrical semiconductor-to-metal transition, with switching times less than 2 ns, and quantify the slower inverse transition, which is dominated by thermal dissipation and relaxation of the metallic rutile lattice to the monoclinic semiconducting phase. By limiting the current through the VO2 to reduce Joule heating, we enable inverse relaxation times as fast as 3 ns.

In this article, the author describes three trends currently impacting the education of students who are deaf or hard-of-hearing, including service delivery, students who are deaf or hard of hearing with additional disabilities, and evidence-based practice. Implications of these trends are discussed as they relate primarily to teachers of students who are deaf or hard-of-hearing, but speech-language pathologists as well. The article concludes with recommendations for improving practices used with this population.

The classic dichotomy between habitual and goal-directed behavior is often mapped onto a dichotomy between model-free and model-based reinforcement learning (RL) algorithms, putatively implemented in segregated neuronal circuits. Despite significant heuristic value in motivating experimental investigations, several lines of evidence suggest that this mapping is in need of modification and/or realignment. First, whereas habitual and goal-directed behaviors have been shown to depend on cleanly separable neural circuitry, recent data suggest that model-based and model-free representations in the brain are largely overlapping. Second, habitual behaviors need not involve representations of expected reinforcement (i.e., need not involve RL, model-free, or otherwise) but may be based instead on simple stimulus–response associations. Finally, goal-directed decisions may not reflect a single model-based algorithm but rather a continuum of “model-basedness.” These lines of evidence thus suggest a possible reconceptualization of the distinction between model-free versus model-based RL—one in which both contribute to a single goal-directed system that is value-based, as opposed to distinct, habitual mechanisms that are value-free. In this chapter, we discuss new models that have extended the RL approach to modeling habitual and goal-directed behavior and assess how these have clarified our understanding of the underlying neural circuitry.

All-optical modulators are likely to play an important role in future chip-scale information processing systems. In this work, through simulations, we investigate the potential of a recently reported vanadium dioxide (VO2) embedded silicon waveguide structure for ultrafast all-optical signal modulation. With a VO2 length of only 200 nm, finite-differencetime- domain simulations suggest broadband (200 nm) operation with a modulation greater than 12 dB and an insertion loss of less than 3 dB. Predicted performance metrics, including modulation speed, modulation depth, optical bandwidth, insertion loss, device footprint, and energy consumption of the proposed Si-VO2 all-optical modulator are benchmarked against those of current state-of-the-art all-optical modulators with in-plane optical excitation.

This article reflects on the author’s experience supervising a public school program for students who are deaf or hard-of-hearing, specifically addressing national, regional, and local trends affecting it. These trends included teacher efficacy, changes in educational service delivery, advances in technology, the selection of the listening and spoken language model, the needs of university teacher education programs, and telepractice. Furthermore, the author describes how the program responded to these trends, which ultimately resulted in positive educational outcomes for the students being served.

Melt percolation in mantle rocks is currently poorly constrained, especially at low melt fractions. At mid-ocean ridges, for example, geochemical and geophysical observations produce divergent estimates of how much melt is present in the mantle and how quickly it moves. Accurate estimates of permeability and grain-scale melt distribution in mantle rock are necessary to reconcile these observations. We present three-dimensional (3-D), 700 nm-resolution images of olivine–basalt aggregates, containing nominal melt fractions ( ϕ n ) between 0.02 and 0.20. Samples were prepared from a powdered mixture of San Carlos olivine and high-alumina basalt and hot-pressed in a solid-medium piston–cylinder apparatus at 1350 °C and 1.5 GPa. Images were obtained using synchrotron X-ray microtomography (SXμT) from the Advance Photon Source at Argonne National Laboratory. Stokes flow simulations, conducted using the digital melt volume as the numerical domain, determine that the permeabilities of experimental charges range from 2 × 10 − 16 to 5 × 10 − 13 m 2 for ϕ n = 0.02 to 0.20, respectively. The simulation results are well represented by the power-law relation between permeability (k) and melt fraction (ϕ), k = ϕ n d 2 / C , where n = 2.6 ± 0.2 , and assuming a grain size of 35 μm in the experiments, C = 58 − 22 + 36 . These results place important new constraints on rates of melt migration and melt extraction within partially molten regions of the mantle.

A key challenge in secondary education and transition is ensuring that students with disabilities are prepared to access and participate in postsecondary education. Electronic performance support systems offer potential for addressing needs of secondary students who are at risk for failure or who encounter challenges in school due to high-incidence disabilities. The purpose of this study was to investigate the effects of training and implementation of an electronic performance support systems on targeted IEP goals for ninth and twelfth grade students with disabilities. A series of multiple probe designs was used to examine the effectiveness of tool usage across four ninth grade students and four twelfth grade students as well as to investigate tool usage across settings (training, academic, and transition) for each student. This study demonstrated an improvement in target behaviors when the intervention was introduced in the training setting for ninth grade and twelfth grade students with high-incidence disabilities. In addition, each student showed some improvement in the target behavior when the intervention was implemented across settings. The authors discuss limitations along with implications for future research and practice.

Special education teachers and related service providers were interviewed for their perspectives on Greek special education policies and practices and how these influenced their job preparation and duties. Specifically, they were asked about the impact of the following on their jobs: Greek law related to the education of students with disabilities; university teacher training programs; job experience; teaching strategies; and changing teacher roles. They were also asked for suggestions to improve special education practices. Their perspectives are summarized and recommendations are provided.

We demonstrate a silicon waveguide modulator with an in-line vanadium dioxide segment as a platform for optical switching. A modulation depth greater than 10 dB is achieved with a device footprint of only 0.28 μm2.

Planning can be defined as a process of action selection that leverages an internal model of the environment. Such models provide information about the likely outcomes that will follow each selected action, and their use is a key function underlying complex adaptive behavior. However, the neural mechanisms supporting this ability remain poorly understood. In the present work, we adapt for rodents recent advances from work on human planning, presenting for the first time a task for animals which produces many trials of planned behavior per session, allowing the experimental toolkit available for use in trial-by-trial tasks for rodents to be applied to the study of planning. We take advantage of one part of this toolkit to address a perennially controversial issue in planning research: the role of the dorsal hippocampus. Although prospective representations in the hippocampus have been proposed to support model-based planning, intact planning in hippocampally damaged animals has been observed in a number of assays. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide the first causal evidence directly linking dorsal hippocampus with planning behavior. The results reported, and the methods introduced, open the door to new and more detailed investigations of the neural mechanisms of planning, in the hippocampus and throughout the brain.

Recent years have seen a surge of research into the neuroscience of planning. Much of this work has taken advantage of a two-step sequential decision task developed by Daw et al. (2011), which gives the ability to diagnose whether or not subjects’ behavior is the result of planning. Here, we present simulations which suggest that the techniques most commonly used to analyze data from this task may be confounded in important ways. We introduce a new analysis technique, which suffers from fewer of these issues. This technique also presents a richer view of behavior, making it useful for characterizing patterns in behavior in a theory-neutral manner. This allows it to provide an important check on the assumptions of more theory-driven analysis such as agent-based model-fitting.

Habits form a crucial component of behavior. In recent years, key computational models have conceptualized habits as arising from model-free reinforcement learning mechanisms, which typically select between available actions based on the future value expected to result from each. Traditionally, however, habits have been understood as behaviors that can be triggered directly by a stimulus, without requiring the animal to evaluate expected outcomes. Here, we develop a computational model instantiating this traditional view, in which habits develop through the direct strengthening of recently taken actions rather than through the encoding of outcomes. We demonstrate that this model accounts for key behavioral manifestations of habits, including insensitivity to outcome devaluation and contingency degradation, as well as the effects of reinforcement schedule on the rate of habit formation. The model also explains the prevalent observation of perseveration in repeated-choice tasks as an additional behavioral manifestation of the habit system. We suggest that mapping habitual behaviors onto value-free mechanisms provides a parsimonious account of existing behavioral and neural data. This mapping may provide a new foundation for building robust and comprehensive models of the interaction of habits with other, more goal-directed types of behaviors and help to better guide research into the neural mechanisms underlying control of instrumental behavior more generally. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Small-footprint, low-power devices that can modulate optical signals at THz speeds would transform next-generation onchip photonics. We describe a hybrid silicon–vanadium dioxide (Si-VO2) electro-optic ring resonator modulator as a candidate platform for achieving this performance benchmark. Vanadium dioxide (VO2) is a strongly correlated material exhibiting a semiconductor-to-metal transition (SMT) accompanied by large changes in electrical and optical properties. While VO2 can be switched optically on a sub-picosecond time scale, the ultimate electrical switching speed remains to be determined. In a 5 μm radius Si-VO2 ring resonator, we achieve 1.5 dB modulation in response to a 10 ns square voltage pulse of 2.5 V. In the steady state regime, we report a modulation depth of 10 dB. The larger modulation depth at longer timescales is attributed to a Joule heating contribution. Experimental results, corroborated by FDTD simulations, reveal the relationship between the portion of a VO2 patch undergoing the SMT and the resulting effects on the Si-VO2 device performance. This work indicates that with further reduction of VO2 patch sizes and increase in resonator Q factor, there is promise for the Si-VO2 ring resonator electro-optic modulator as a competitive option for on-chip photonics technology.

Educational teams often are faced with the task of developing and implementing Behavioral Intervention Plans (BIPs) for students who present challenging and/or disruptive behaviors. This article describes the steps used to develop and implement a self-monitoring BIP that incorporated an innovative software system, iKidTools™. An authentic case study is described and easy-to-follow steps for carrying out similar interventions in inclusive settings are provided.

This article reports findings from a follow-up study of teacher education students who utilized multimedia cases in coursework in preparation for teaching students with emotional/behavioral disorders. The purpose of the study was to examine the extent to which knowledge, skills, and attitudes gained through multimedia case-based instruction were maintained following coursework with the cases and transferred to discussions of classroom practice. Research subjects included 16 students who agreed to participate in an online follow-up support group during the two semesters after their use of multimedia cases in their teacher education courses. The independent variable was testing condition (postinstruction versus follow-up Semester 1 versus follow up Semester 2). Dependent variables included the breadth of concept (nodes), the interconnectedness of concept (links), and the quality of content contained in concept maps completed at preinstruction, postinstruction, and two follow-up points in time. Results indicated that conceptual change occurred between preinstruction and the first follow-up phase, and these changes in conceptual knowledge were maintained during the follow-up period while participants were employed in schools. Qualitative data provide a rich understanding of the study findings by relating transfer of knowledge, skills, and attitudes to the prior case instruction and scaffolds embedded in the cases.

Optical phase change materials (O-PCMs) are being explored for a variety of photonic applications due to the extraordinarily large changes in optical properties that occur during electronic and/or structural phase transitions. Here, recent work integrating O-PCMs in integrated silicon photonic devices is presented. Conceptually proposed and experimentally realized thermo-optic, electro-optic, and all-optical Si/O-PCM devices are described and perspectives on the potential for Si/O-PCM electro-optic and all-optical modulators are outlined.

Concept maps are commonly used in a variety of educational settings as a learning aid or instructional tool. Additionally, their potential as a research tool has been recognized. This article defines features of concept maps, describes the use of pre- and postconcept maps as a research tool, and offers a protocol for employing concept maps as an effective research tool. To illustrate the viability of concept maps as a research tool, specific steps and examples are provided from a study that used concept maps to investigate the conceptual change of pre-service and in-service teachers after participation in special and general education courses using multimedia case-based instruction. Support for concept maps as a research tool to evaluate learning and growth in knowledge are provided with specific procedures for creating a concept map quality scoring system.

Interactive, multimedia cases with technology supports present new ways of teaching and learning in teacher education. In this mixed-methods, naturalistic study, the authors investigate how and what participants learn from multimedia cases and, in particular, how instructional implementation affects learning outcomes from multimedia cases. Multimedia cases with technology supports were implemented in 20 different higher education courses with varying University of Missouri—Columbia instructional modes involving 251 pre-service and practicing teacher education students from four different universities. Results indicate that how multimedia cases are integrated into courses makes a difference in case-based learning outcomes and that these differences are evidenced in both quantitative and qualitative data. Overall, significant learning occurred for all instructional implementation groups with one exception—limiting use of cases to context for additional course assignments was not effective. Implications for teacher education are explored.