Explore the vast range of titles available.

The I21 beamline at Diamond Light Source is dedicated to advanced resonant inelastic X‐ray scattering (RIXS) for probing charge, orbital, spin and lattice excitations in materials across condensed matter physics, applied sciences and chemistry. Both the beamline and the RIXS spectrometer employ divergent variable‐line‐spacing gratings covering a broad energy range of 280–3000 eV. A combined energy resolution of ∼35 meV (16 meV) is readily achieved at 930 eV (530 eV) owing to the optimized optics and the mechanics. Considerable efforts have been paid to the design of the entire beamline, particularly the implementation of the collection mirrors, to maximize the X‐ray photon throughput. The continuous rotation of the spectrometer over 150° under ultra high vacuum and a cryogenic manipulator with six degrees of freedom allow accurate mappings of low‐energy excitations from solid state materials in momentum space. Most importantly, the facility features a unique combination of the high energy resolution and the high photon throughput vital for advanced RIXS applications. Together with its stability and user friendliness, I21 has become one of the most sought after RIXS beamlines in the world. The design of the resonant inelastic X‐ray scattering beamline at Diamond Light Source, I21, is presented. X‐ray commissioning results are shown and compared with the optical simulations.

Developing tools for precise quantification of brain metabolites using magnetic resonance spectroscopy (MRS) is an active area of research with broad application in non-invasive neurodegenerative disease studies. The tools are mainly developed based on black box (data-driven), or basis sets approaches. In this study, we offer a multi-stage framework that integrates data-driven and basis sets methods. We first use truncated Hankel singular value decomposition (HSVD) to decompose free induction decay (FID) signals into single tone FIDs, as the data-driven stage. Subsequently, single tone FIDs are clustered into basis sets while using initialized K-means with prior knowledge of the metabolites, as the basis set stage. The generated basis sets are fitted with the magnetic resonance (MR) spectra while using a linear constrained least square, and then the metabolite concentration is calculated. Prior to using our proposed multi-stage approach, a sequence of preprocessing blocks: water peak removal, phase correction, and baseline correction (developed in house) are used.

Magnetic Resonance Imaging (MRI) plays a significant role in the current characterization and diagnosis of multiple sclerosis (MS) in radiological imaging. However, early detection of MS lesions from MRI still remains a challenging problem. In the present work, an information theoretic approach to cluster the voxels in MS lesions for automatic segmentation of lesions of various sizes in multi-contrast (T1, T2, PD-weighted) MR images, is applied. For accurate detection of MS lesions of various sizes, the skull-stripped brain data are rescaled and histogram manipulated prior to mapping the multi-contrast data to pseudo-color images. For automated segmentation of multiple sclerosis (MS) lesions in multi-contrast MRI, the improved jump method (IJM) clustering method has been enhanced via edge suppression for improved segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions if present. From this preliminary clustering, a pseudo-color to grayscale conversion is designed to equalize the intensities of the normal brain tissues, leaving the MS lesions as outliers. Binary discrete and 8-bit fuzzy labels are then assigned to segment the MS lesions throughout the full brain. For validation of the proposed method, three brains, with mild, moderate and severe hyperintense MS lesions labeled as ground truth, were selected. The MS lesions of mild, moderate and severe categories were detected with a sensitivity of 80%, and 96%, and 94%, and with the corresponding Dice similarity coefficient (DSC) of 0.5175, 0.8739, and 0.8266 respectively. The MS lesions can also be clearly visualized in a transparent pseudo-color computer rendered 3D brain.

The narrow subcarrier spacing and wide bandwidth arrangement in the LTE downlink produce a vulnerability to sample clock mismatch between the transmitting and receiving data converters. Without high precision sampling clock frequencies, a high level of inter-carrier interference (ICI) is introduced, yielding undesirable performance. In this article, a method to jointly estimate and correct sampling frequency mismatch is proposed. The proposed method uses information already known to the receiver, operates strictly in the time domain and does not require the aid of pilot symbols or other frequency domain information. The method allows clocks with lower precision to be used with minimal performance degradation. Results are presented using MATLAB simulation as well as an FPGA hardware implementation.

Many common modalities of medical images acquire high-resolution and multispectral images, which are subsequently processed, visualized, and transmitted by subsampling. These subsampled images compromise resolution for processing ability, thus risking loss of significant diagnostic information. A hybrid multiresolution vector quantizer (HMVQ) has been developed exploiting the statistical characteristics of the features in a multiresolution wavelet-transformed domain. The global codebook generated by HMVQ, using a combination of multiresolution vector quantization and residual scalar encoding, retains edge information better and avoids significant blurring observed in reconstructed medical images by other well-known encoding schemes at low bit rates. Two specific image modalities, namely, X-ray radiographic and magnetic resonance imaging (MRI), have been considered as examples. The ability of HMVQ in reconstructing high-fidelity images at low bit rates makes it particularly desirable for medical image encoding and fast transmission of 3D medical images generated from multiview stereo pairs for visual communications.

Attribution theory in the classroom suggests that teachers search for causes to which they might attribute student behavior and or academic performance. Using a consensual qualitative research (CQR) and phenomenological approach, this research examined how teachers’ beliefs, expectations, and perceptions influenced their classroom practices. Using a multi-modal data collection process, involving interviews, classroom observations, review of teacher artifacts, and collection of demographic and Likert-type scale questionnaires, nine general education teachers from three elementary schools in one school district shared their perspective on working with students, including those from diverse backgrounds. Six themes emerged from the data and were titled: (a) Connection, (b) Teacher Approach, (c) Structured Support, (d) Student Self-Regulation, (e) Perception of Student Desire for Learning, and (f) Family Support of Student. Findings from this study may help improve teacher training and provide guidance for ongoing professional development. Additionally, these results may help school district promote policies that support modification of district policies on curriculum development and disciplinary actions.

Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through resting-state studies have been used to obtain information about the spontaneous activations inside the brain. One of the approaches for analysis and interpretation of resting-state fMRI data require spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Clustering is often used to generate functional parcellation. However, major clustering algorithms, when used for fMRI data, have their limitations. Among commonly used parcellation schemes, a tradeoff exists between intra-cluster functional similarity and alignment with anatomical regions. Approach: In this work, we present a clustering algorithm for resting state and task fMRI data which is developed to obtain brain parcellations that show high structural and functional homogeneity. The clustering is performed by multistage binary k-means clustering algorithm designed specifically for the 4D fMRI data. The results from this multistage k-means algorithm show that by modifying and combining different algorithms, we can take advantage of the strengths of different techniques while overcoming their limitations. Results: The clustering output for resting state fMRI data using the multistage k-means approach is shown to be better than simple k-means or functional atlas in terms of spatial and functional homogeneity. The clusters also correspond to commonly identifiable brain networks. For task fMRI, the clustering output can identify primary and secondary activation regions and provide information about the varying hemodynamic response across different brain regions. Conclusion: The multistage k-means approach can provide functional parcellations of the brain using resting state fMRI data. The method is model-free and is data driven which can be applied to both resting state and task fMRI.

University-Industry Partnership in Semiconductor EngineeringA critical component of the US industrial base is the development, production, and deploymentof semiconductor devices. This industry relies on a high number of specially trained engineers toaccomplish these missions. As semiconductor technologies have continued to advance, severedemands have been placed on educational institutions to properly prepare students for the rigorsof employment. To maintain exceptional student development, strong partnerships betweenindustry and academia are a necessity.We describe a long-standing and successful university-industry partnership in semiconductordevice engineering with a primary focus on product and test engineering. The partnership, nowin its 15th year, relies on a symbiotic relationship that has evolved over the years to reflectindustrial trends and advancing university capabilities. The success of the partnership is due to amultifaceted approach with an emphasis on frequent communication between the company andthe university. Bi-directional on-site visits by all participants (faculty, students, alumni, and otherindustry engineers) strengthen the initiative and clearly communicate the nature of the industrialenvironment and work expectations to the students. Mentoring of students by working engineersprovides the necessary one-on-one guidance as critical employment path decisions are made.Visits by industry representatives to the university for recruitment and technical talks providepositive visibility to the company. This interaction feeds the core component of the program,student internships. These internships, for which students can obtain course credit, are done atboth the undergraduate and graduate level and provide a nearly seamless pathway from school tofull-time employment. For graduate students, the industrial internship provides the material forthe graduate thesis.Corporate and federal funding of these initiatives has been a driving force to expedite studentprogress and strengthen educational tools. The semiconductor device engineering program,initiated by visionary alumni, is currently supported by both industry sponsored scholarships andfellowships, as well as a four-year award from the National Science Foundations’ Scholarships inSTEM program. These scholarships have allowed many students to stay in school and morequickly finish their degrees. Corporate support of university research and educational labfacilities has provided a tool set that replicates many of the systems that a student will see oncethey begin their careers at the company. Prior experience reduces the need for on the job trainingand gives these students a competitive advantage in the internship and hiring process.The full paper will provide a detailed description of the key success factors of the long-standingindustry university collaboration. It will provide insights into benefits of the collaboration asexperienced by students, university faculty, industry project managers, and human resources.

It is well-known that involving students in activities and courses within their major early in their academic careers has a positive impact on student retention. We have developed several programs targeted at involving freshmen and pre-freshmen students in Electrical and Computer Engineering (ECE) projects. Teams of 4 to 5 students were formed, with at least one ECE freshman, a high school student (or recent graduate), a junior or senior level ECE student, and a community college student. Students were paid as interns for a six-week summer session. An industry or community mentor and an ECE faculty member were assigned to each team. Projects included: re-engineering an adaptive bicycle to enable use by a physically disabled child; designing a fall detector to automatically detect a fall in an elderly person; and, implementing smart sensors to measure energy and water use in a residential environment. Students were required to give weekly presentations to the faculty members and other teams in a formal setting. In assessing the success of the program in general and of each team’s progress, several factors were determined to be significant. The presence of a strong peer role model and an active industry mentor influenced the level of involvement of each team member and the progress each team made toward achieving their project goals. This paper describes a program in the Electrical and Computer Engineering (ECE) Department at Texas Tech University that provides research and design opportunities for freshmen and pre-freshmen engineering students. The goal of the program was to increase recruitment and retention of students in ECE by exposing them to engineering through paid internships that focused on projects with social or community significance. The program was designed to address several key issues present when involving freshmen, high school, and community college students in engineering research and design. Among these: many students do not have a clear knowledge of what engineers do or of the engineering problem solving approach; faculty tend to be overwhelmed with the amount of time required to supervise very inexperienced students who have not amassed any technical knowledge or skills; and finally, students, particularly those from economically disadvantaged backgrounds, typically work and do not have the time to commit to summer or extended hour programs. These issues were addressed in various ways as the program was developed. Pedagogical Background The authors’ motivations in developing this program were to increase engineering enrollment by bringing new students into the field and to improve student retention by exciting these students about engineering careers. Respondents to an ACT survey in 2004 reported by Habley1 rated 24 institutional and 20 student characteristics for contribution

Re: Marie Nesbitt's letter, Make Shared Custody Automatic in Most Cases, Dec. 30. Thank you for hitting the nail on the head. I could not have said it better myself.