Electrical and optical properties of semi-insulating polycrystalline silicon thin films: the role of microstructure and doping

We have investigated the relationship between microstructure and electrical conductivity in semi-insulating polycrystalline silicon (SIPOS) with oxygen concentrations in the 2–35 at.% range and the effect of doping with boron, phosphorus, arsenic and erbium by ion implantation. SIPOS thin films are mixtures of silicon and silicon oxide phases. The chemical and morphological evolution of these phases upon annealing is emphasized. Electrical conductivity measurements are interpreted in terms of a physical model containing few free parameters related to the material microstructure. A direct extension of this model explains also the conductivity increase in SIPOS doped with elements of the third or the fifth group. In the last part of the paper, data of electroluminescence at 1.54 μm in Er-implanted SIPOS due to intra-4f transitions of the Er³⁺ ion are shown and discussed.     

Development of an undergraduate course – Internet-based instrumentation and control

The objective, strategy, and implementation details of a new undergraduate course, Internet-based Instrumentation and Control, are presented. The course has a companion laboratory that is supported by the National Science Foundation and industry. The combination is offered to senior-level undergraduate engineering students interested in sensing, instrumentation, control, and web programming that want to learn more about the integration of these technologies for solving real-world engineering problems. The course will also be offered to gifted high school seniors with similar interests and can serve as a vehicle to attract them to engineering disciplines. Preliminary assessment of the first offering of the course is encouraging and has shown that the course has achieved success in helping students understand concepts and master basic technologies for developing Internet-based automatic systems.     

Preceramic organoboron–silicon polymers

Several boron-containing organosilicon polymers were synthesized from a sodium-coupling reaction of silicon and boron halides with and without alkyl halide in hydrocarbon solvents. The B–Si preceramic polymers were characterized using techniques such as IR, UV, and NMR spectrometry, gel permeation chromatography, elemental analysis, molecular weight measurement, and thermal analyses (TGA, DSC, DTA, and TMA). The chemical structures of the preceramic polymers were postulated based on the analytical results. Black ceramic materials were obtained from the precursor polymers upon thermal degradation at temperatures above 1000°C in an inert atmosphere. The precursor polymers had a ceramic yield of up to 70%. Thermogravimetric analysis of the ceramic material in air at a flow rate of 100 mL/min showed it was stable up to 1000°C with little weight gain or loss. Several methods were used to characterize the ceramic materials: XRD, solid NMR, high-temperature DTA, elemental analysis, and acid digestion. The analyses indicated that the ceramic materials comprised a mixture of silicon carbide (SiC), silicon borides (SiB₄, SiB₆), and amorphous Si–B–C ceramics, with small amounts of silica and free silicon.     

High-Performance Multilayer Capacitor Dielectrics from Chemically Prepared Powders

Advantages of chemically prepared powders for electronic ceramics have been demonstrated for a number of multilayer capacitor (MLC) dielectrics. A cost-efficient precipitation process was developed to produce undoped or doped crystalline barium titanate powder with a narrow particle size distribution close to 0.5 μm. More complex compositions, e.g., barium-neodymium titanate, were amorphous as precipitated but could be crystallized by calcination below 1000°C. Additional compositional modifications, to adjust electrical properties or to lower sintering temperature, were accomplished by doping the surface of the powder particles using a solution coating process. Exceptional fired densities and electrical performance were obtained.     

High-Fat Diet Leads to Reduced Protein O-GlcNAcylation and Mitochondrial Defects Promoting the Development of Alzheimer’s Disease Signatures

The disturbance of protein O-GlcNAcylation is emerging as a possible link between altered brain metabolism and the progression of neurodegeneration. As observed in brains with Alzheimer’s disease (AD), flaws of the cerebral glucose uptake translate into reduced protein O-GlcNAcylation, which promote the formation of pathological hallmarks. A high-fat diet (HFD) is known to foster metabolic dysregulation and insulin resistance in the brain and such effects have been associated with the reduction of cognitive performances. Remarkably, a significant role in HFD-related cognitive decline might be played by aberrant protein O-GlcNAcylation by triggering the development of AD signature and mitochondrial impairment. Our data support the impairment of total protein O-GlcNAcylation profile both in the brain of mice subjected to a 6-week high-fat-diet (HFD) and in our in vitro transposition on SH-SY5Y cells. The reduction of protein O-GlcNAcylation was associated with the development of insulin resistance, induced by overfeeding (i.e., defective insulin signaling and reduced mitochondrial activity), which promoted the dysregulation of the hexosamine biosynthetic pathway (HBP) flux, through the AMPK-driven reduction of GFAT1 activation. Further, we observed that a HFD induced the selective impairment of O-GlcNAcylated-tau and of O-GlcNAcylated-Complex I subunit NDUFB8, thus resulting in tau toxicity and reduced respiratory chain functionality respectively, highlighting the involvement of this posttranslational modification in the neurodegenerative process.     

Improving the performance of Invertible Bloom Lookup Tables

Invertible Bloom Lookup Tables (IBLTs) have been recently introduced as an extension of traditional Bloom filters. IBLTs store key-value pairs. Unlike traditional Bloom filters, IBLTs support both a lookup operation (given a key, return a value) and an operation that lists out all the key-value pairs stored. One issue with IBLTs is that there is a probability that a lookup operation will return “not found” for a key. In this paper, a technique to reduce this probability without affecting the storage requirement and only moderately increasing the search time is presented and evaluated. The results show that it can significantly reduce the probability of not returning a value that is actually stored in the IBLT. The overhead of the modified search procedure, compared to the standard IBLT search procedure, is small and has little impact on the average search time.     

Using visual and text features for direct marketing on multimedia messaging services domain

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.

Unsupervised tissue classification of brain MR images for voxel‐based morphometry analysis

In this article, a fully unsupervised method for brain tissue segmentation of T1‐weighted MRI 3D volumes is proposed. The method uses the Fuzzy C‐Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro‐radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial‐and‐error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro‐Spinal Fluid in an unsupervised way. The method has been tested on the IBSR dataset, on the BrainWeb Phantom, on the BrainWeb SBD dataset, and on the real dataset “University of Palermo Policlinico Hospital” (UPPH), Italy. Sensitivity, Specificity, Dice and F‐Factor scores have been calculated on the IBSR and BrainWeb datasets segmented using the proposed method, the FCM algorithm, and two state‐of‐the‐art brain segmentation software packages (FSL and SPM) to prove the effectiveness of the proposed approach. A qualitative evaluation involving a group of five expert radiologists has been performed segmenting the real dataset using the proposed approach and the comparison algorithms. Finally, a usability analysis on the proposed method and reference methods has been carried out from the same group of expert radiologists. The achieved results show that the segmentations of the proposed method are comparable or better than the reference methods with a better usability and degree of acceptance.