Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of air cooled microchannel heat exchangers to card cage systems

An application of a novel-geometry, air-cooled, microchannel heat exchanger for electronic chips reported by C. Hilbert et al. (1990) is described. These heat exchangers, based on narrow channels and laminar flow, are scaled to cool large packets, mated to high-power pin grid arrays (PGAs) or multichip modules (MCMs), and mounted on printed circuit boards in a card cage. The heat exchanger reported fits within the package footprint and meets chip temperature specifications to 50 W. The system provides cooling for two MCM or PGA boards, each carrying eight 35-W modules using the scaled design. The modules were mounted on a printed wiring board, along with conventional components dissipating an additional 100 W. Chip temperature rises within the packages were kept to 50 degrees C above ambient. The entire card cage contained the two MCM or PGA boards and several other conventionally cooled boards. Fan placement and air flow design were conventional except for the provision of ducting above the high-power packages. The heat exchangers operate at a very low pressure loss, allowing the MCM section to be cooled with two common axial flow fans while generating acoustic noise within the range acceptable for office equipment. <<ETX>>
Diet cost and quality using the Healthy Eating Index-2015 in adults from urban and rural areas of Mexico

Abstract Objective: To assess the association between diet cost and quality by place of residence. Design: We analysed cross-sectional data of the National Health and Nutrition Survey-2012. Diet cost was estimated by linking dietary data, obtained from a 7-d SFFQ, with municipality food prices, which were derived from a national expenditure survey. Diet quality was assessed using the Healthy Eating Index-2015 (HEI-2015). Association between quintiles of diet cost and HEI-2015 was assessed using linear regression analysis. Settings: Mexico. Participants: 2438 adults (18–59 years). Results: Diet cost was positively associated with diet quality (HEI-2015) in urban but not in rural areas. Compared with quintile (Q1) of cost, the increment in diet quality score was 1·17 (95 % CI –0·06, 4·33) for Q2, 2·14 (95 % CI –0·06, 4·33) for Q3, 4·70 (95 % CI 2·62, 6·79) for Q4 and 6·34 (95 % CI 4·20, 8·49) for Q5 (P-trend < 0·001). Individuals in rural v. urban areas on average have higher quality diets at lower cost with higher intakes of whole grains and beans and lower intakes of Na, added sugars and saturated fats. Living in the South, being indigenous and having low socio-economic status were also associated with higher quality diets. Conclusions: Diet cost was positively associated with diet quality, but only in urban areas. Further studies are needed to understand the relation between diet cost and quality in rural areas. To improve overall diet quality in Mexico, strategies that aim to reduce the cost of high-quality diets should consider the heterogeneity by place of residence.
Confirmation of Cause of Death Via Comprehensive Autopsy and Whole Exome Molecular Sequencing in People With Epilepsy and Sudden Unexpected Death

Background Sudden cardiac arrest is the leading mode of death in the United States. Epilepsy affects 1% of Americans; yet epidemiological data show a prevalence of 4% in cases of sudden cardiac arrest. Sudden unexpected death in epilepsy (SUDEP) may share features with sudden cardiac arrest. The objective of this study was to report autopsy and genomic findings in a large cohort of SUDEP cases. Methods and Results Mayo Clinic Sudden Death Registry containing cases (ages 0–90 years) of sudden unexpected and unexplained deaths 1960 to present was queried. Exome sequencing performed on decedent cases. From 13 687 cases of sudden death, 656 (4.8%) had a history of seizures, including 368 confirmed by electroencephalography, 96 classified as SUDEP, 58 as non‐SUDEP, and 214 as unknown (insufficient records). Mean age of death in SUDEP was 37 (±19.7) years; 56 (58.3%) were male; 65% of deaths occurred at night; 54% were found in bed; and 80.6% were prone. Autopsies were obtained in 83 cases; bystander coronary artery disease was frequently reported as cause of death; nonspecific fibrosis was seen in 32.6% of cases, in structurally normal hearts. There were 4 cases of Dravet syndrome with pathogenic variants in SCN1A gene. Using whole exome sequencing in 11 cases, 18 ultrarare nonsynonymous variants were identified in 6 cases including CACNB2, RYR2, CLNB, CACNA1H, and CLCN2. Conclusions This study examined one of the largest single‐center US series of SUDEP cases. Several cases were reclassified as SUDEP, 15% had an ECG when alive, and 11 (11.4%) had blood for whole exome sequencing analysis. The most frequent antemortem genetic finding was pathogenic variants in SCN1A; postmortem whole exome sequencing identified 18 ultrarare variants.
Vehicle-Bridge Interaction Element for Dynamic Analysis

The objective of this study is to develop an element that is both accurate and efficient for modeling the vehicle-bridge interaction (VBI) in analysis of railway bridges carrying high-speed trains, which may consist of a number of cars in connection. In this study, a train is modeled as a series of sprung masses lumped at the bogie positions and a bridge with track irregularities by beam elements. Two sets of equations of motion that are coupled can be written, one for the bridge and the other for each of the sprung masses. To resolve the problem of coupling, the sprung mass equation is first discretized using Newmark's finite difference formulas and then condensed to that of the bridge element in contact. The element derived is referred to as the vehicle-bridge interaction element, which has the same number of degrees of freedom (DOF) as the parent element, while possessing the properties of symmetry and bandedness in element matrices. For this reason, conventional assembly procedures can be employed to .
Diversion of Attention Leads to Conflict between Concurrently Attended Stimuli, Not Delayed Orienting to the Object of Interest

Abstract The control processes that guide attention to a visual-search target can result in the selection of an irrelevant object with similar features (a distractor). Once attention is captured by such a distractor, search for a subsequent target is momentarily impaired if the two stimuli appear at different locations. The textbook explanation for this impairment is based on the notion of an indivisible focus of attention that moves to the distractor, illuminates a nontarget that subsequently appears at that location, and then moves to the target once the nontarget is rejected. Here, we show that such delayed orienting to the target does not underlie the behavioral cost of distraction. Observers identified a color-defined target appearing within the second of two stimulus arrays. The first array contained irrelevant items, including one that shared the target's color. ERPs were examined to test two predictions stemming from the textbook serial-orienting hypothesis. Namely, when the target and distractor appear at different locations, (1) the target should elicit delayed selection activity relative to same-location trials, and (2) the nontarget search item appearing at the distractor location should elicit selection activity that precedes selection activity tied to the target. Here, the posterior contralateral N2 component was used to track selection of each of these search-array items and the previous distractor. The results supported neither prediction above, thereby disconfirming the serial-orienting hypothesis. Overall, the results show that the behavioral costs of distraction are caused by perceptual and postperceptual competition between concurrently attended target and nontarget stimuli.
Uterine Fibroid Symptom and Quality of Life questionnaire (UFS-QOL NL) in the Dutch population: a validation study

Objective Uterine fibroids can cause a variety of symptoms in women, from heavy menstrual bleeding and dysmenorrhea to bulk symptoms. The Uterine Fibroid Symptom and health-related Quality Of Life questionnaire (UFS-QOL) is a patient-reported outcome measure developed for assessing fibroid-related symptoms in a standardised way. Our aim was to translate and validate the UFS-QOL in Dutch. Design Validation study. Setting Patients were recruited by a gynaecologist at the outpatient clinic. Participants Women with uterine fibroids. Methods The UFS-QOL was translated into Dutch (UFS-QOL NL) and validated through testing construct validity (comprising of structural validity and hypotheses testing), reliability, responsiveness and interpretability, assessing floor and ceiling effects and minimal important change. An option to answer ‘not applicable’ was added to the translated questionnaire. Results 191 women with uterine fibroids completed the UFS-QOL NL at baseline, after 2 weeks and after 3 months. The questionnaire retained the same factor structure after translation (Comparative Fit Index 0.94–0.95; Tucker-Lewis fit Index 0.93–0.95; Root Mean Square Error of Approximation 0.10–0.11) and correlations to other questionnaires (RAND 36, Hospital Anxiety and Depression Scale and Golombok Rust Inventory of Sexual Satisfaction) were generally moderate, as hypothesised (Pearson’s r 0.3–0.7). We found a sufficient reliability with intraclass correlation coefficients of approximately 0.8–0.9 for all subscales. Responsiveness was sufficient when testing hypotheses comparing women who had surgery with those who did not. Cronbach’s alpha was higher than 0.7 for all subscales, indicating sufficient internal consistency and there were no concerns about floor or ceiling effects. Minimal important change could not be calculated due to low correlation between the different subscales and the anchor question. Conclusions The results support the measurement properties of the Dutch UFS-QOL for assessing fibroid-related symptoms and health-related quality of life in Dutch women with uterine fibroids.
Maximum Likelihood Detection for OFDM signals with strong nonlinear distortion effects

Nowadays, the wireless communication systems are in great demand. New multimedia services such as data and video need reliable high-speed data rates to guarantee the desired throughput to the mobile users. The block transmission techniques are commonly used in these systems since they fulfil the most part of the requirements. Among these techniques, multicarrier schemes like Orthogonal Frequency Division Multiplexing (OFDM) are widely used since they can deal with strong frequency selective channels and also present high spectral efficiency. However, OFDM signals are very prone to nonlinear distortion effects due to their high envelope fluctuations. Therefore, several techniques were proposed to efficiently reduce these fluctuations. The most promising technique is employing a nonlinear operation to reduce the amplitude peaks followed by a frequency-domain filtering operation to reduce the spectral broadening. In typical OFDM implementations, the resulting nonlinear distortion component is treated as a noise-like term that leads to performance degradation. Under these conditions, to achieve optimum performance should be employed a ML (Maximum Likelihood) receiver that considers all the information associated to nonlinear components. This thesis focus on the study of maximum likelihood receivers for OFDM signals with strong nonlinear distortion effects. It is shown that, contrarily to the one could expect, the nonlinear distortion does not necessary imply a performance degradation because the ratio minimum distance between bit energy of nonlinearly distorted signals is higher than without nonlinear distortion. In fact, the performance of the optimum receiver can be better than with conventional linear transmitters. To prove this, the asymptotic performance of the optimum receiver is evaluated both theoretically and by simulation. In addition, since the optimum receiver presents very high complexity, four less complex sub-optimal receivers are proposed and evaluated, being also studied the impact of employing iterative clipping and filtering techniques and diversity schemes in their BER performance.
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs

We propose a numerical method based on physics-informed Random Projection Neural Networks for the solution of Initial Value Problems (IVPs) of Ordinary Differential Equations (ODEs) with a focus on stiff problems. We address an Extreme Learning Machine with a single hidden layer with radial basis functions having as widths uniformly distributed random variables, while the values of the weights between the input and the hidden layer are set equal to one. The numerical solution of the IVPs is obtained by constructing a system of nonlinear algebraic equations, which is solved with respect to the output weights by the Gauss-Newton method, using a simple adaptive scheme for adjusting the time interval of integration. To assess its performance, we apply the proposed method for the solution of four benchmark stiff IVPs, namely the Prothero-Robinson, van der Pol, ROBER and HIRES problems. Our method is compared with an adaptive Runge-Kutta method based on the Dormand-Prince pair, and a variable-step variable-order multistep solver based on numerical differentiation formulas, as implemented in the \texttt{ode45} and \texttt{ode15s} MATLAB functions, respectively. We show that the proposed scheme yields good approximation accuracy, thus outperforming \texttt{ode45} and \texttt{ode15s}, especially in the cases where steep gradients arise. Furthermore, the computational times of our approach are comparable with those of the two MATLAB solvers for practical purposes.
P‐17: Body Voltage Modulation for High Performance a‐IGZO TFT and its Application on New Inverter Structure

We develop a new and simple method to adjust the threshold voltage of a‐IGZO TFT by capping different metals on device back channel. A threshold voltage shifts from −7.4V to +5.5V can be obtained. The field‐effect mobility is 2 to 3 times larger than the one before metal capping. An n‐type a‐IGZO inverter includes one enhancement‐mode TFT (capped by titanium) and one depletion‐mode TFT (capped by calcium/aluminum dual layer) is fabricated. The voltage gain is 38 when the bias voltage is 20 volts.
Effects of Different Levels of Irrigation Water Salinity and Leaching on Yield and Yield Components of Wheat in an Arid Region

Soil salinity is a major environmental factor limiting the productivity of agricultural lands. To determine the effects of irrigation water salinity and leaching on soil salinity and consequently wheat yield, a field experiment was conducted on a silty clay soil, a typical soil of Rudasht region, Isfahan province, Iran, with three irrigation water salinity levels of 2, 8, and 12 dS∕m with/without leaching levels of 4, 19, and 32% with two different irrigation water managements, using factorial design with four replications for each treatment. The results showed that as the irrigation water salinity and consequently soil salinity increases, the yield components such as grain yield, straw yield, 1,000-grain weight, crop height, spike length, and leaf area index decreases significantly. Leaching caused the yield components to increase significantly. An increase in seed protein percentage was noted as the salinity of irrigation water increased. The interaction effects of irrigation management and leaching on .
Efficient sensor for robust low-power design in nano-CMOS technologies

Conventional design methods add pessimistic safety margins to mitigate increased variability in scaled technologies that incur high power and performance losses. An efficient sensor design is presented that can significantly reduce design margins yet provide robust circuit operation. The proposed design uses the delay of the master latch of a conventional flip-flop to predetect timing failures and enable low-power error-free operation. HSPICE simulation of a 45 nm 16×16 carry save multiplier indicates a 37% reduction in the total power consumption for the proposed design compared to a conventional worst case design when subjected to high statistical variability. Similarly, gate level simulations show a 26% reduction in the total power consumption of a 32 nm 32-bit carry select adder when subjected to temperature variations.
Robust Registration of Multi-modal Medical Images Using Huber’s Criterion

Registration of multi-modal medical images is an essential pre-processing step, for example, for fusion or image guided-interventions. However, the alignment process is prone to high variability in tissue appearance between modalities, in addition to local intensity variations and artefacts. This work introduces a robust multi-modal registration approach that mitigates the undesirable effect of such variability. Robustness is achieved using Huber’s loss function for the data fidelity and regularization terms. We propose a novel approach using Huber’s criterion, which enables a jointly convex estimation of the motions and the associated scale parameters. We formulate the problem as a complex 2D transformation estimation and investigate a robust total-variation smoothing, as well as a dictionary learning-based data fidelity term. Experiments are conducted using two datasets of multi-contrast MR brain images.
Optimum Design of 3-Db Branch-Line Couplers Using Microstrip Lines

A computer-aided design is described that makes it possible to reduce the internal impedance levels of branch-line couplers so that they may be physically constructed by microstrip lines, where the Fletcher-Powell search method has been used to optimize the design. Because microstrip lines are severely restricted in their usable impedance range, the 3-dB couplers presented here should be useful for numerous balanced-type components such as balanced mixers. The validity of the design has been experimentally verified in the microwave and millimeter-wave region.
Fault Feature Extraction Method of Rotating Machinery Based on DeepLabV3+ Semantic Segmentation

The requirements for the health status of rotating machinery equipment continue to increase, and traditional fault diagnosis methods cannot efficiently and intelligently process the collected large-capacity rotating machinery fault test data. In the paper, we combine the advantages of time-frequency representation and deep learning semantic segmentation, and propose the Fault Feature Extraction Method of Rotating Machinery Based on DeepLabV3+ Semantic Segmentation. First, the continuous wavelet transform is used to represent the fault vibration signals in different health states in time-frequency, so as to fully represent the time-varying and non-stationary fault characteristic information of different faults and the time-varying frequency characteristic information. Then in order to efficiently and intelligently identify these features and use them as segmentation targets, a DeepLabV3+ deep learning semantic segmentation model is constructed to accurately extract information of different fault categories. Finally, the model is trained and tested with the rolling bearing failure data, and the influence of the number of training samples on the performance of the model is analyzed. The accuracy of the background and fault features are 87.20% and 92.21%, respectively. The total accuracy of the DeepLabV3+ model is 89.5%, the Kappa coefficient is 0.7895, and the loss is 0.1109. The results prove that the proposed method can accurately extract fault features from a large number of time-frequency representations.
Sequential Policy Network-based Optimal Passive Equalizer Design for an Arbitrary Channel of High Bandwidth Memory using Advantage Actor Critic

In this paper, we proposed a sequential policy network-based passive equalizer (PEQ) design method for an arbitrary channel of high bandwidth memory (HBM) using advantage actor critic (A2C) algorithm, considering signal integrity (SI) for the first time. PEQ design must consider the circuit parameters and placement for improving the performance. However, optimizing PEQ is complicated because various design parameters are coupled. Conventional optimization methods such as genetic algorithm (GA) repeat the optimization process for the changed conditions. In contrast, the proposed method suggests the improved solution based on the trained sequential policy network with flexibility for unseen conditions. For verification, we conducted electromagnetic (EM) simulation with optimized PEQs by GA, random search (RS) and the proposed method. Experimental results demonstrate that the proposed method outperformed the GA and RS by 4.4 % and 6.4 % respectively in terms of the eye-height.
Tens of gigabytes per second JSON-to-Arrow conversion with FPGA accelerators

JSON is a popular data interchange format for many web, cloud, and IoT systems due to its simplicity, human readability, and widespread support. However, applications must first parse and convert the data to a native in-memory format before being able to perform useful computations. Many big data applications with high performance requirements convert JSON data to Apache Arrow RecordBatches, the latter being a widely-used columnar in-memory format for large tabular data sets used in data analytics. In this paper, we analyze the performance characteristics of such applications and show that JSON parsing represents a bottleneck in the system. Various strategies are explored to speed up JSON parsing on CPU and GPU as much as possible. Due to performance limitation of the CPU and GPU implementations, we furthermore present an FPGA accelerated implementation. We explain how hardware components that can parse variable-sized and nested structures can be combined to produce JSON parsers for any type of JSON document. Several fully integrated FPGA-accelerated JSON parser implementations are presented using the Intel Arria 10 GX and Xilinx VU37P devices, and compared to the performance of their respective host systems; an Intel Xeon and an IBM POWER9 system. Result show the accelerators achieve an end-to-end throughput close to 7 GB/s with the Arria 10 GX using PCIe, and close to 20 GB/s with the VU37P using OpenCAPI 3. Depending on the complexity of the JSON data to parse, the bandwidth is limited by the host-to-accelerator interface or available FPGA resources. Overall, this provides a throughput increase of up to 6x, compared to the baseline application. Also, we observe a full system energy efficiency improvement of up to 59x more JSON data parsed per joule.
Essays on product return management and closed loop-supply chain network design

ix CHAPTER 1. GENERAL INTRODUCTION . 1 1.1 Motivation . 1 1.2 Thesis Structure and Overview . 3 CHAPTER 2. OPTIMAL PRICING AND RETURN POLICIES FOR REFURBISHED PRODUCTS CONSIDERING TACTICAL CONSUMERS . 6 2.
Operational Performance Metrics Based on Mixed Model Manufacturing Systems Model

Mixed model manufacturing systems are being increasingly used to meet high product variety in market’s demand by providing a broader variety of products. The increase of product variety adds complexity to production processes, thus leading to a negative effect on their performance. It is of great interest, therefore, to effectively model mixed model manufacturing systems. A useful model should be able to provide additional information about interesting aspects of the modeled system. In this paper, we extend a manufacturing systems model by including additional elements. Furthermore, we present a set of operational performance metrics based on the elements of the model and their relationships. Of particular interest is the concept of stable production mix introduced here. We conclude the paper with a case study illustrating the modeling approach, and the application and usefulness of the presented metrics.