gebhart/patrepeval huggingface dataset:
Test Controller Design, Implementation, and Performance for a Magnetic Suspension Continuous Flow Ventricular Assist Device (1999)

A new continuous flow ventricular assist device using full magnetic suspension has been designed, constructed, and tested. The magnetic suspension centers the centrifugal pump impeller within the clearance passages in the pump, thus avoiding any form of contact. The noncontact operation is designed to give very high expected mechanical reliability, large clearances, low hemolysis, and a relatively small size compared to current pulsatile devices. A unique configuration of magnetic actuators on the inlet side and exit sides of the impeller provides full 5 axis control and suspension of the impeller. The bearing system is divided into segments which allow for 3 displacement axes and 2 angular control axes. The controller chosen for the first suspension tests consists of a decentralized set of 5 proportional integral derivative (PID) controllers. This document describes both the controller and an overview of some results pertaining to the magnetic bearing performance. The pump has been successfully operated in both water and blood under design conditions suitable for use as a ventricular assist device.
Growth of Epitaxial KNbO<sub>3</sub>Thick Films by Hydrothermal Method and Their Characterization (2009)

KNbO3 films were grown at 240 °C by the hydrothermal method using KOH and Nb2O5 as source materials. Film thickness increased with reaction time up to 3 h, however decreased for longer reaction times. A 16-µm-thick epitaxially grown KNbO3 film with {100}pc orientation were successfully grown on (100)cSrRuO3∥SrTiO3 substrate for 3 h. The relative dielectric constant and dielectric loss at 100 kHz were 415 and 8%, respectively. Clear hysteresis loops originating from ferroelectricity were observed and the remanent polarization was 20 µC/cm2 at the maximum applied electric field of 220 kV/cm. The effective longitudinal piezoelectric constant, d33eff, obtained using a laser Doppler velocimeter, was 86 pm/V.
Valorization of Biomass: Deriving More Value from Waste (2012)

Most of the carbon-based compounds currently manufactured by the chemical industry are derived from petroleum. The rising cost and dwindling supply of oil have been focusing attention on possible routes to making chemicals, fuels, and solvents from biomass instead. In this context, many recent studies have assessed the relative merits of applying different dedicated crops to chemical production. Here, we highlight the opportunities for diverting existing residual biomass--the by-products of present agricultural and food-processing streams--to this end.
Modeling Mid-air Gestures With Spherical Coordinates (2015)

Generally, touchless mid-air gestural interaction use some form of Cartesian coordinate system within the input space. Most implementations map the input space in 3-D and map it to the 2-D of the monitor for output. In our previous work we showed that modeling the interaction space produces better interaction [1]. In this study, we use the Myo armband to show that a modeled interaction space also benefits devices that use spherical coordinates.
Security and Privacy Issues in Deep Learning (2018)

To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion attack. Poisoning attacks compromise the training process by corrupting the data with malicious examples, while evasion attacks use adversarial examples to disrupt entire classification process. Defenses proposed against such attacks include techniques to recognize and remove malicious data, train a model to be insensitive to such data, and mask the model's structure and parameters to render attacks more challenging to implement. Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications. To maintain data privacy, several solutions that combine existing data-privacy techniques have been proposed, including differential privacy and modern cryptography techniques. In this paper, we describe the notions of some of methods, e.g., homomorphic encryption, and review their advantages and challenges when implemented in deep-learning models.
Model selection for automated reconstruction from multiple views (2002)

We describe progress in automatically fitting a plane plus modelled perturbation surface model to represent architectural scenes. There are two areas of novelty. The first is a method of fitting parametrized models in which the cost function is based on a combination of disparity and gradient extrema, both computed over multiple views. The second is the use of an evaluation criteria for model selection, learnt from training examples. We demonstrate the method on reconstructions of several college scenes from multiple images.
评有关“后工业社会”的两本书 (1982)

这两本书的主题都是“后工业社会”。在不同程度上它们显然和贝尔有关。对这两部书来说,贝尔著作中最重要和最有争议的主题是:(1) 从以商品生产为主的经济向劳务经济过渡;(2) 职业结构中的相应变化不只是从蓝领阶层到白领阶层的变化,同时还包括在非手工业工人中以其相当完备的规模与重大意义而发展起来的专业和技术阶层;(3) 既是革新和政策制订的根源又是专业和技术阶层成长的社会经济基础的理论知识的重要性得到加强;(4)出现了一个新的统治阶层,他们垄断了理论知识和技术情报。尽管这四点远远未能概括贝尔
Algorithms, Algorithms, Algorithms (2020)

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
Alteração da composição corporal de futebolistas profissionais de um clube participante do Brasileiro da série D 2010 (2010)

Change of the body composition of professional soccer players of the club participant in theBrazilian Championship Series D 2010 Introduction: During the soccer game are performed different actions, with variations in different aspects: intensity, interval (characterized by the passive phase of the game), directions etc.Because it is a sport played on a large field, playing long time and decided to be at high intensities, the soccer goes about 11,5 km; this distance that's growing each season, by increasing the intensity of the game, advances in technology and sports physiology.Objective: This study examined changes in anthropometric variables of a professional soccer club participating in the Brazilian Championship Series D 2010, in three stages: pre-preparatory, pre-competitive and competitive.Materials and Methods:The following data were collected from 28 athletesage, weight, height and skin folds.The adopted protocol Jackson and Pollock (1978) -7 folds -to calculate body density and like this determine the percentage of fat and fat-free mass.Results: The 28 athletes had a mean age of 24.8 4.1 years and mean height of 178.5 6.6 cm.Body weight showed no statistical differences between the moments.In contrast,% fat and fat-free mass showed statistical changes.Discussion: The data in this study coincided with those published previously.Conclusion: There was a statistically significant evolution of income, ie, gain of FFM and loss of FM.
FPGA-BASED HARDWARE ACCELERATOR FOR K-NEAREST NEIGHBOR CLASSIFICATION FOR MACHINE LEARNING, An (2017)

Machine learning has become the cornerstones of the information technology.Machine learning is a type of artificial intelligence (AI), which enables a program (or a computer) to learn, without being explicitly programmed and without human intervention.Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering).Among many classification algorithms, K-nearest neighbor (K-NN) classifier is one of the most commonly used machine learning algorithms.This algorithm is typically used for pattern recognition, data mining, image recognition, text categorization, and predictive analysis.Many machine learning algorithms, including K-NN classification are compute and data intensive, requiring significant processing power.For instance, the K-NN consists of many complex and iterative computations, which include computing the distance measure between the training dataset and the testing dataset, element by element, and simultaneously sorting these measurements.In this research work, our main objective is to investigate and provide an efficient hardware architecture to accelerate the K-NN algorithm, while addressing the associated requirements and constraints.Our hardware architecture is designed and developed on an FPGA-based (Field Programmable Gate Array-based) development platform.We perform experiments to evaluate our hardware architecture, in terms of speed-performance, space, and accuracy.Our hardware architecture is also evaluated with its software counter-part running on the same development platform.Experiments are performed using three different benchmark datasets with varying data sizes. IVWe introduce unique techniques, including pre-fetching and burst transfers, to reduce the external memory access latency issue that is common in embedded platforms.We also address various issues with the proposed hardware support for K-NN algorithm in the existing literature.This investigation demonstrates that machine learning algorithms can indeed benefit from hardware support.Our proposed hardware architecture is generic and parameterized.Our hardware design is scalable to support varying data sizes.Our experimental results and analysis illustrate that our proposed hardware is significantly faster, for instance, our hardware architecture executed up to 127 times faster than its software counter-part.
Machine Learning of Lifestyle Data for Diabetes (2016)

Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data.\nWe believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations.\nThe most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective.\nA lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works.\nGlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management.
Similarity-regulation of OS-EM for accelerated SPECT reconstruction (2016)

Ordered subsets expectation maximization (OS-EM) is widely used to accelerate image reconstruction in single photon emission computed tomography (SPECT). Speedup of OS-EM over maximum likelihood expectation maximization (ML-EM) is close to the number of subsets used. Although a high number of subsets can shorten reconstruction times significantly, it can also cause severe image artifacts such as improper erasure of reconstructed activity if projections contain few counts. We recently showed that such artifacts can be prevented by using a count-regulated OS-EM (CR-OS-EM) algorithm which automatically adapts the number of subsets for each voxel based on the estimated number of counts that the voxel contributed to the projections. While CR-OS-EM reached high speed-up over ML-EM in high-activity regions of images, speed in low-activity regions could still be very slow. In this work we propose similarity-regulated OS-EM (SR-OS-EM) as a much faster alternative to CR-OS-EM. SR-OS-EM also automatically and locally adapts the number of subsets, but it uses a different criterion for subset regulation: the number of subsets that is used for updating an individual voxel depends on how similar the reconstruction algorithm would update the estimated activity in that voxel with different subsets. Reconstructions of an image quality phantom and in vivo scans show that SR-OS-EM retains all of the favorable properties of CR-OS-EM, while reconstruction speed can be up to an order of magnitude higher in low-activity regions. Moreover our results suggest that SR-OS-EM can be operated with identical reconstruction parameters (including the number of iterations) for a wide range of count levels, which can be an additional advantage from a user perspective since users would only have to post-filter an image to present it at an appropriate noise level.
Mitochondria Protection after Acute Ischemia Prevents Prolonged Upregulation of IL-1β and IL-18 and Arrests CKD (2016)

The innate immune system has been implicated in both AKI and CKD. Damaged mitochondria release danger molecules, such as reactive oxygen species, DNA, and cardiolipin, which can cause NLRP3 inflammasome activation and upregulation of IL-18 and IL-1<i>β</i> It is not known if mitochondrial damage persists long after ischemia to sustain chronic inflammasome activation. We conducted a 9-month study in Sprague-Dawley rats after 45 minutes of bilateral renal ischemia. We detected glomerular and peritubular capillary rarefaction, macrophage infiltration, and fibrosis at 1 month. Transmission electron microscopy revealed mitochondrial degeneration, mitophagy, and deformed foot processes in podocytes. These changes progressed over the study period, with a persistent increase in renal cortical expression of IL-18, IL-1<i>β</i>, and TGF-<i>β</i>, despite a gradual decline in TNF-<i>α</i> expression and macrophage infiltration. Treatment with a mitoprotective agent (SS-31; elamipretide) for 6 weeks, starting 1 month after ischemia, preserved mitochondrial integrity, ameliorated expression levels of all inflammatory markers, restored glomerular capillaries and podocyte structure, and arrested glomerulosclerosis and interstitial fibrosis. Further, helium ion microscopy vividly demonstrated the restoration of podocyte structure by SS-31. The protection by SS-31 was sustained for ≥6 months after treatment ended, with normalization of IL-18 and IL-1<i>β</i> expression. These results support a role for mitochondrial damage in inflammasome activation and CKD and suggest mitochondrial protection as a novel therapeutic approach that can arrest the progression of CKD. Notably, SS-31 is effective when given long after AKI and provides persistent protection after termination of drug treatment.
A case report of multiple pulmonary chondromatous hamartomas (1981)

53才, 男性に発症した稀な多発性肺過誤腫の手術例を報告した.左上下葉にそれぞれ径3.2cm, 1crhのほぼ円形の腫瘤を認め, 上葉部分切除および下葉切除を行なった.組織学的には軟骨性過誤腫であった.多発性肺過誤腫の症例は, 胸部レ線上, 転移性肺腫瘍との鑑別が極めて困難で, 報告例の多くは転移性肺腫瘍との診断で開胸術を施行している.本症例は, われわれの知る限りでは, 諸外国を含めて31例目の報告例である.
Practical Synthesis of Iboxamycin, a Potent Antibiotic Candidate, in Amounts Suitable for Studies in Animal Infection Models (2021)

A gram-scale synthesis of iboxamycin, an antibiotic candidate bearing a fused bicyclic amino acid residue, is presented. A pivotal transformation in the route involves an intramolecular hydrosilylation–oxidation sequence to set the ring-fusion stereocenters of the bicyclic scaffold. Other notable features of the synthesis include a high-yielding, highly diastereoselective alkylation of a pseudoephenamine amide, a convergent sp3–sp2 Negishi coupling, and a one-pot transacetalization–reduction reaction to form the target compound’s oxepane ring. Implementation of this synthetic strategy has provided ample quantities of iboxamycin to allow for its in vivo profiling in murine models of infection.
Spectral Efficiency of Mixed-ADC Massive MIMO (2018)

We study the spectral efficiency (SE) of a mixed-analog-to-digital converter (ADC) massive multiple-input multiple-output (MIMO) system in which K single-antenna users communicate with a base station equipped with M antennas connected to N high-resolution ADCs and M - N 1-b ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N ≪ M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and 1-b quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining and numerical results are presented for zero-forcing detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.
Perspectives on the use of new diagnostic tools in the treatment of chronic lymphocytic leukemia (2005)

Recently, considerable progress has been made in the identification of molecular and cellular markers that may predict the tendency for disease progression in patients with chronic lymphocytic leukemia (CLL) or detect minimal residual disease after therapy. These developments have created uncertainty for clinicians who hope to incorporate the use of these markers and new disease-assessment tools into standard clinical practice. However, clinical trials are required to determine whether poor-prognosis leukemia-cell markers, such as expression of unmutated immunoglobulin genes or the zeta-associated protein of 70 kDa (ZAP-70), can be used as the basis for determining the time or type of therapy. Pending the outcome of such trials, treatment decisions outside the context of a clinical trial still should be based on guidelines established by the most recent National Cancer Institute-sponsored Working Group.
Papain hydrolysates of casein: molecular weight profile and encapsulation in lipospheres (2004)

Abstract Some reaction parameters were tested in the hydrolysis of casein by papain, in order to prepare hydrolysates with high oligopeptide contents, for either dietetic or pharmaceutical purposes. Five casein hydrolysates were prepared and then fractionated by size‐exclusion HPLC. The rapid correct fraction area method was used for quantifying peptides and free amino acids. Among the five reaction conditions tested, three produced similar peptide profiles. However, the use of a temperature of 37°C and an E:S ratio of 2% is probably the most economical condition for use in large‐scale manufacture. With the aim of masking the bitterness of these preparations, a new method, based on the encapsulation in lipospheres, was used. Also, second derivative spectrophotometry was used for the first time to measure the extent of encapsulation of protein hydrolysates, which changed from 50% to 83%. Moreover, the efficiency of this system was evaluated by analysing other parameters, which showed a reduction of hydrophobicity and bitterness of all samples, as well as good chemical stability during 60 days of storage under refrigeration. The electron microscopical analysis of liposheres showed an average size around 5.0 ± 1.0 µm. Copyright © 2004 Society of Chemical Industry
Kinetics of the Reduction of CuO/Bentonite by Methane (CH<sub>4</sub>) during Chemical Looping Combustion (2012)

Chemical looping combustion (CLC) is a process that uses an oxygen-carrier metal, instead of air or pure oxygen, to provide oxygen for combustion. The products of CLC of methane are CO2 and H2O. After condensation of H2O, a concentrated CO2 gas stream is produced and ready for sequestration. An important issue for the CLC process is the selection of metal oxide as an oxygen carrier, because it must retain its reactivity through many cycles. In this study, isothermal thermogravimetric analysis is used to evaluate the rates of reduction of CuO impregnated in bentonite with methane (CH4) over the range 1023–1173 K for 20%, 50%, and 100% CH4 over 10 reduction cycles. The mechanism and reactivity of the CuO oxygen carrier were evaluated by 10 different rate models. The results indicate that the transformation kinetics described by the Johnson–Mehl–Avrami (JMA) model was the best fit. The Avrami exponent n ranges from 1.55 to 2.16. The average value of 1.77 indicates that the crystallization mechanism is mainly two-dimensional diffusion-controlled. The activation energy was estimated to be 37.3 ± 1.3 kJ/mol. No deactivation was observed over 10 cycles at any CH4 concentration. In the first 10 reaction cycles, the reaction rates increased slightly with the increasing number of cycles. Moreover, the rate–time and rate–conversion curves for all the temperatures show that the maximum rate occurred at t > 0. This was confirmed by the outlet gas measurements. The experimental results suggested that the CuO/bentonite oxygen carrier is a promising candidate for the CLC system burning methane.
Thermal Conductivity and Mechanical Properties of Magnesium Oxide Reinforced Polyamide-66 Composites (2015)

Magnesium oxide (MgO) reinforced polyamide-66 (PA66) composites were prepared through melt-compounding method in order to determine the possibility of using MgO particle as conductive filler in the polymer-based composite. The effects of MgO filler content on the thermal conductivity and mechanical properties for the PA66/MgO composites were investigated. The results showed that the addition of MgO filler to the PA66 matrix led to a large increase in thermal conductivity of the PA66/MgO composites. Tensile strengths of the PA66/MgO composites were slightly decreased as MgO filler loading increased. However, flexural strength and flexural modulus were improved with increasing filler loading. Notched Izod impact strengths were dramatically lowered by the addition of MgO filler.