Engineered Elastomer Substrates for Guided Assembly of Complex 3D Mesostructures by Spatially Nonuniform Compressive Buckling

Approaches capable of creating 3D mesostructures in advanced materials (device‐grade semiconductors, electroactive polymers, etc.) are of increasing interest in modern materials research. A versatile set of approaches exploits transformation of planar precursors into 3D architectures through the action of compressive forces associated with release of prestrain in a supporting elastomer substrate. Although a diverse set of 3D structures can be realized in nearly any class of material in this way, all previously reported demonstrations lack the ability to vary the degree of compression imparted to different regions of the 2D precursor, thus constraining the diversity of 3D geometries. This paper presents a set of ideas in materials and mechanics in which elastomeric substrates with engineered distributions of thickness yield desired strain distributions for targeted control over resultant 3D mesostructures geometries. This approach is compatible with a broad range of advanced functional materials from device‐grade semiconductors to commercially available thin films, over length scales from tens of micrometers to several millimeters. A wide range of 3D structures can be produced in this way, some of which have direct relevance to applications in tunable optics and stretchable electronics.     

Subnanometer Analysis and Modeling of MBE Grown InP Based MODFETs

InGaAs/InAlAs double-doped double-strained modulation-doped field-effect transistors OD-SMODFETs)1 were grown by solid source molecular beam epitaxy. The structures were characterized using high resolution x-ray diffraction, Hall effect, and cross-sectional scanning tunneling microscopy. A record two-dimensional electron gas (2DEG) sheet density of 8.5 × 10¹²/cm² and 8.1 × 10¹²/cm² for 300 and 77K, respectively, was achieved. The mobility was 6500 and 12000 cm²/ Vs for 300 and 77K, respectively. To the author’s knowledge,² the previous record 2DEG result was 6.58 × 10¹²/cm². The electron mobility was limited by alloy scattering and interface roughness caused by the presence of “clustering.” Using cross-sectional scanning tunneling microscopy to verify the presence of these clusters, we have the first images of the lattice matched InAlAs (spacer)-InGaAs (quantum well) interface. These images reveal clusters that have approximate spherical or cylindrical shapes with equivalent cubic dimensions ranging from 25 to 45Å.     

Distributed detection of replica node attacks with group deployment knowledge in wireless sensor networks

Several protocols have been proposed to mitigate the threat against wireless sensor networks due to an attacker finding vulnerable nodes, compromising them, and using these nodes to eavesdrop or undermine the operation of the network. A more dangerous threat that has received less attention, however, is that of replica node attacks, in which the attacker compromises a node, extracts its keying materials, and produces a large number of replicas to be spread throughout the network. Such attack enables the attacker to leverage the compromise of a single node to create widespread effects on the network. To defend against these attacks, we propose distributed detection schemes to identify and revoke replicas. Our schemes are based on the assumption that nodes are deployed in groups, which is realistic for many deployment scenarios. By taking advantage of group deployment knowledge, the proposed schemes perform replica detection in a distributed, efficient, and secure manner. Through analysis and simulation experiments, we show that our schemes achieve effective and robust replica detection capability with substantially lower communication, computational, and storage overheads than prior work in the literature.     

Entropy-based measures for quantifying sleep-stage transition dynamics: relationship to sleep fragmentation and daytime sleepiness

We present two novel entropy-based measures that quantify sleep-stage transition dynamics (sleep structure) from polysomnogram derived hypnograms: Walsh spectral entropy (WSE) and Haar spectral entropy (HSE). These measures quantify patterns of temporal regularity of a categorical time series without requiring numerical encoding (scaling) of the (categorical) sleep stages. Additionally, we show that conditional entropy (CE) is well suited for quantifying predictability of the hypnogram. The relationship of those measures with traditional sleep fragmentation indices (arousal index, total sleep time, and sleep efficiency) is explored for a 394 participant sample of the Cleveland Family Study, an epidemiologic study in which standardized single-night polysomnogram data were collected. The new entropy-based sleep structure measures (WSE, HSE, and CE) are positively correlated (moderate to weak) with the traditional sleep fragmentation indices. Because the sleep structure measures developed in this paper provide direct information related to temporal patterns of sleep that is not contained in traditional sleep fragmentation measures, the correlation between these new alternative sleep structure measures and the traditional sleep fragmentation measures is less important. Our goal is not to develop alternative measures that correlate highly with traditional measures of sleep fragmentation, but rather to provide methods to quantify sleep structure by examining other (e.g., dynamic sleep-stage transition) properties of the hypnogram. Additionally, the relationship of the new entropy-based and traditional measures with daytime sleepiness as quantified by the Epworth sleepiness scale (ESS) is investigated. Multiple linear regression analysis shows that WSE has a stronger relationship with ESS than the traditional measures, even after both are adjusted for common confounders (age, race, gender, and body mass index). This further suggests that the entropy-based measures, especially WSE, are capturing additional temporal patterns of sleep not captured in the traditional sleep fragmentation measures, and have a relationship with daytime sleepiness.     

A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.     

The Effect of Nanoparticle Shape on the Photocarrier Dynamics and Photovoltaic Device Performance of Poly(3‐hexylthiophene):CdSe Nanoparticle Bulk Heterojunction Solar Cells

The charge separation and transport dynamics in CdSe nanoparticle:poly(3‐hexylthiophene) (P3HT) blends are reported as a function of the shape of the CdSe‐nanoparticle electron acceptor (dot, rod, and tetrapod). For optimization of organic photovoltaic device performance it is crucial to understand the role of various nanostructures in the generation and transport of charge carriers. The sample processing conditions are carefully controlled to eliminate any processing‐related effects on the carrier generation and on device performance with the aim of keeping the conjugated polymer phase constant and only varying the shape of the inorganic nanoparticle acceptor phase. The electrodeless, flash photolysis time‐resolved microwave conductivity (FP‐TRMC) technique is used and the results are compared to the efficiency of photovoltaic devices that incorporate the same active layer. It is observed that in nanorods and tetrapods blended with P3HT, the high aspect ratios provide a pathway for the electrons to move away from the dissociation site even in the absence of an applied electric field, resulting in enhanced carrier lifetimes that correlate to increased efficiencies in devices. The processing conditions that yield optimum performance in high aspect ratio CdSe nanoparticles blended with P3HT result in poorly performing quantum dot CdSe:P3HT devices, indicating that the latter devices are inherently limited by the absence of the dimensionality that allows for efficient, prolonged charge separation at the polymer:CdSe interface.     

Single frequency inverse obstacle scattering: a sparsity constrained linear sampling method approach

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.