Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Innovation in Technology from the Perspective of Entrepreneurship and Invention

This paper reports on a study on perceptions about innovation in technology from the viewpoints of entrepreneurship and invention. The results of the study indicate that perceptions on technological innovation are largely seen through the paradigms of entrepreneurship and invention, and can be classified into four distinct categories. A proposed framework is developed from the study’s recorded responses and presented as an interpretive model to explain the observed outcomes. The goal of this paper is to provide insights about attitudes on innovation for practitioners in technology fields and offer a testable model for researchers to further study and validate the framework presented. Along with the proposed framework, we provide descriptions of the innovation categories based on the responses from the study’s participants.
Strawberry and cranberry polyphenols improve insulin sensitivity in insulin-resistant, non-diabetic adults: a parallel, double-blind, controlled and randomised clinical trial

Abstract Plant-derived foods rich in polyphenols are associated with several cardiometabolic health benefits, such as reduced postprandial hyperglycaemia. However, their impact on whole-body insulin sensitivity using the hyperinsulinaemic-euglycaemic clamp technique remains under-studied. We aimed to determine the effects of strawberry and cranberry polyphenols (SCP) on insulin sensitivity, glucose tolerance, insulin secretion, lipid profile, inflammation and oxidative stress markers in free-living insulin-resistant overweight or obese human subjects (n 41) in a parallel, double-blind, controlled and randomised clinical trial. The experimental group consumed an SCP beverage (333 mg SCP) daily for 6 weeks, whereas the Control group received a flavour-matched Control beverage that contained 0 mg SCP. At the beginning and at the end of the experimental period, insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, and glucose tolerance and insulin secretion by a 2-h oral glucose tolerance test (OGTT). Insulin sensitivity increased in the SCP group as compared with the Control group (+0·9 (sem 0·5)×10−3 v. −0·5 (sem 0·5)×10−3 mg/kg per min per pmol, respectively, P=0·03). Compared with the Control group, the SCP group had a lower first-phase insulin secretion response as measured by C-peptide levels during the first 30 min of the OGTT (P=0·002). No differences were detected between the two groups for lipids and markers of inflammation and oxidative stress. A 6-week dietary intervention with 333 mg of polyphenols from strawberries and cranberries improved insulin sensitivity in overweight and obese non-diabetic, insulin-resistant human subjects but was not effective in improving other cardiometabolic risk factors.
Script-description Pair Extraction from Text Documents of English as Second Language Podcast

One of the best effective way to learn a language is having a conversation with a native speaker. However it is often very expensive way. A good alternative way is using Dialog-Based Computer Assisted Language Learning (DB-CALL) systems. The feedback quality in DB-CALL systems is very important. Therefore, to provide various expressions as feedback information, we propose a method which extracts script and their description sentence pairs from English as a Second Language (ESL) podcast web site. A linear CRFs classifier is used to find the corresponding description sentences and several features are selected according to the characteristics of the ESL text documents. The experimental results show that the performance of our system is acceptable.
Moving Automotive Electronics from Reliability/Durability Testing to Virtual Validation Modeling Using a Physics of Failure CAE App

Quality, Reliability, Durability (QRD) and Safety of vehicular Electrical/Electronics (E/E) systems traditionally have resulted from arduous rounds of Design-Built-Test-Fix (DBTF) Reliability and Durability Growth Testing. Such tests have historically required 12-16 or more weeks of Accelerated Life Testing (ALT), for each round of validation in a new product development program. Challenges have arisen from:  The increasing number of E/E modules in today's vehicle places a high burden on supplier's test labs and budgets.  The large size and mass of electric vehicle power modules results in a lower test acceleration factors which can extend each round of ALT to 5-6 months.  Durability failures tend to occur late in life testing, resulting in the need to: perform a root cause investigation, fix the problem, build new prototype parts and then repeat the test to verify problem resolutions, which severely stress program budgets and schedules. To resolve these challenges, automakers and E/E suppliers are moving to Physics of Failure (PoF) based durability simulations and reliability assessment solutions performed in a Computer Aided Engineering (CAE) Environment. When PoF knowledge is converted into math models and integrated into CAE durability simulations and reliability assessments tools, it can be determined if and when a device will be susceptible to failure mechanisms over its life cycle.
Software design to support discussion in the primary curriculum

This paper puts forward a framework for the integration of pedagogy and software design to support educationally valuable discussion within the primary curriculum. In order to illustrate and to evaluate this framework, two educational programs were designed to be used in conjunction with a series of lessons to coach the use of ‘exploratory talk’ in small group work. Evaluation of the first item of software, in the area of citizenship, focused upon the difference that off-computer lessons in exploratory talk made to the way it was used. Evaluation of the second program, in the area of science, focused on the effect of the overall approach on learning outcomes. The results of these two illustrative studies support the value of the proposed framework. The first shows that combining software design with the off-computer coaching of exploratory talk can enhance the quality of interactions at the computer. The second shows that, with this pedagogical framework, computers can be used to stimulate collaborative learning and to direct it towards curriculum goals.
KIOM-4 Protects against Oxidative Stress-Induced Mitochondrial Damage in Pancreatic β-cells via Its Antioxidant Effects

The protective effect of KIOM-4, a mixture of plant extracts, was examined against streptozotocin (STZ)-induced mitochondrial oxidative stress in rat pancreatic β-cells (RINm5F). KIOM-4 scavenged superoxide and hydroxyl radicals generated by xanthine/xanthine oxidase and Fenton reaction (FeSO4/H2O2), respectively, in a cell-free chemical system. In addition, a marked increase in mitochondrial reactive oxygen species (ROS) was observed in STZ-induced diabetic cells; this increase was attenuated by KIOM-4 treatment. Mitochondrial manganese superoxide dismutase (Mn SOD) activity and protein expression were down-regulated by STZ treatment and up-regulated by KIOM-4 treatment. In addition, NF-E2 related factor 2 (Nrf2), a transcription factor for Mn SOD, was up-regulated by KIOM-4. KIOM-4 prevented STZ-induced mitochondrial lipid peroxidation, protein carbonyl and DNA modification. Moreover, KIOM-4 treatment restored the loss of mitochondrial membrane potential (Δψ) that was induced by STZ treatment, and inhibited the translocation of cytochrome c from the mitochondria to the cytosol. In addition, KIOM-4 treatment elevated the level of ATP, succinate dehydrogenase activity and insulin level, which were reduced by STZ treatment. These results suggest that KIOM-4 exhibits a protective effect through its antioxidant effect and the attenuation of mitochondrial dysfunction in STZ-induced diabetic cells.
HDTreeV : a Multidimensional Visualization Tool

As access to high-performance computing has increased over the years, the scientific community has in turn sought to analyze increasingly complex data. However, discovering and understanding complex relationships in multidimensional data can be a daunting task. Traditionally, exploratory data analysis has been used to discover patterns and garner substantive understanding of data by emphasizing the use of graphical representation, but methods for visualizing data beyond two or three dimensions are rarely used because of the inherent limitations of Cartesian representation where variables are mapped to spatial dimensions. In order to confront the task of visualizing multidimensional data, it is necessary to draw from principles of human visual perception and cognition. It is well-known that humans are incredibly adept at categorizing the natural world by reducing large degrees of freedom down to “oak trees” or “maple trees”, for instance. The overall shape of the trees is the result of high-order relationships and interactions among individual components, yet even in the face of such complexity, a young child can make these distinctions. Lindenmayer systems use simple procedural algorithms to render realistic flora that can be categorized just as easily as natural flora. Knowing this, the goal of the current project is to exploit the human ability to easily categorize nature by assigning variables in a multidimensional data set to features of binary trees. A tool, HDTreeV (High Dimensional Tree Visualization), has been created to provide the user a way to go through a process of categorizing and remapping, in order to ascertain multidimensional relationships in the data. Contact: Jeffrey R. Spies Dept. of Psychology University of Notre Dame Notre Dame, IN 46556 Tel: 1-574-631-7675 Fax: 1-574-631-8883 Email: jspies@nd.edu
A Brief Comparison of News Filtering Software

A number of workers are currently providing or researching mechanisms to lter Internet messages. The most common target of these e orts are directed at Usenet News, a system for computer-mediated communication with world-wide distribution. Inspired by a compilation of references to message ltering systems provided by Doug Oard 1 (oard@glue.umd.edu) this paper compares a few systems using a number of informal, but no less informative, measures.
Sparse signal recovery using sparse random projections

The problem of estimating a high-dimensional signal based on an incomplete set of noisy observations has broad applications. In remote sensing, network traffic measurement, and computational biology, the observation process makes it difficult or costly to obtain sample sizes larger than the ambient signal dimension. Signal recovery is in general intractable when the dimension of the signal is much larger than the number of observations. However, efficient recovery methods have been developed by imposing a sparsity constraint on the signal. There are different ways to impose sparsity, which has given rise to a diverse set of problems in sparse approximation, subset selection in regression, and graphical model selection. This thesis makes several contributions. First, we examine the role of sparsity in the measurement matrix, representing the linear observation process through which we sample the signal. We develop a fast algorithm for approximation of compressible signals based on sparse random projections, where the signal is assumed to be well-approximated by a sparse vector in an orthonormal transform. We propose a novel distributed algorithm based on sparse random projections that enables refinable approximation in large-scale sensor networks. Furthermore, we analyze the information-theoretic limits of the sparse recovery problem, and study the effect of using dense versus sparse measurement matrices. Our analysis reveals that there is a fundamental limit on how sparse we can make the measurements before the number of observations required for recovery increases significantly. Finally, we develop a general framework for deriving information-theoretic lower bounds for sparse recovery. We use these methods to obtain sharp characterizations of the fundamental limits of sparse signal recovery and sparse graphical model selection.
THE EFFECT OF DEFENSIVE STRATEGIES ON THE PHYSIOLOGICAL RESPONSES AND TIME-MOTION CHARACTERISTICS IN SMALL-SIDED GAMES

The purpose of this study was to investigate the effects of alterations in the defensive strategies on physiological responses and time-motion characteristics during 3 × 4 min small-sided games (SSGs) in young soccer players. Eighteen young soccer players (age 19.6±0.5 years, body height 178.3±4.6 cm, body mass 71.9±7.5 kg and VO2max 49.51±2.44 ml·kg-1·min-1) voluntarily participated in SSGs with different defensive strategies (FP: free play, MM: man-marking, DMP: double-man pressure). Heart rate (HR) and total distance covered in different speed zones were monitored during all SSGs, whereas the session-rating of perceived exertion (session-RPE, CR-10) and venous blood lactate (La-) were determined at the end of the last bout of each SSG. The results demonstrated that DMP produced significantly higher La(F=23.82, p<.05, η2=0.58), HR (F=10.10; p<.05; η2=0.37), %HRmax (F=81.11; p<.05; η2=0.82), and session-RPE (F=215.63, p<.05, η2=0.92) responses compared to FP and MM. Furthermore, significant differences were found between MM and FP condition. In addition, during the DMP, players covered greater distances in the high-intensity running zone (>18 km·h-1) (F=13.67; p<.05; η2=0.44) compared to MM and FP. The findings of this study revealed that the alterations in the defensive strategies brought about different physiological responses and time-motion characteristics during the course of SSGs. Therefore, the recommendation for coaches is to choose DMP or MM defensive strategies if they target higher physiological responses and time-motion characteristics during the SSGs.
Global Analysis of Fission Yeast Mating Genes Reveals New Autophagy Factors

Macroautophagy (autophagy) is crucial for cell survival during starvation and plays important roles in animal development and human diseases. Molecular understanding of autophagy has mainly come from the budding yeast Saccharomyces cerevisiae, and it remains unclear to what extent the mechanisms are the same in other organisms. Here, through screening the mating phenotype of a genome-wide deletion collection of the fission yeast Schizosaccharomyces pombe, we obtained a comprehensive catalog of autophagy genes in this highly tractable organism, including genes encoding three heretofore unidentified core Atg proteins, Atg10, Atg14, and Atg16, and two novel factors, Ctl1 and Fsc1. We systematically examined the subcellular localization of fission yeast autophagy factors for the first time and characterized the phenotypes of their mutants, thereby uncovering both similarities and differences between the two yeasts. Unlike budding yeast, all three Atg18/WIPI proteins in fission yeast are essential for autophagy, and we found that they play different roles, with Atg18a uniquely required for the targeting of the Atg12–Atg5·Atg16 complex. Our investigation of the two novel factors revealed unforeseen autophagy mechanisms. The choline transporter-like protein Ctl1 interacts with Atg9 and is required for autophagosome formation. The fasciclin domain protein Fsc1 localizes to the vacuole membrane and is required for autophagosome-vacuole fusion but not other vacuolar fusion events. Our study sheds new light on the evolutionary diversity of the autophagy machinery and establishes the fission yeast as a useful model for dissecting the mechanisms of autophagy.
The Importance of Political Influence in Explaining Project Management Success Factors

This paper argues that the political power and actions required of project managers to achieve project management success provide a consistent explanation for the emergence of project management success factors deemed critical by project managers in project success factor studies. This represents an important insight for project managers and researchers in understanding the value and nature of project management success factors. For the purposes of the discussion the success factor study by Parr, Shanks, & Darke (1999), which examined critical success factors associated with ERP implementation projects, is used as an example of success factor studies. Two questions are considered: Can the critical project management success factors identified by project managers in project success factor studies such as Parr et al. (1999) be explained in terms of the project manager’s power and their actions of bargaining and negotiation? What is the nature of the association between project manager power and the success factors as operationalised within a project?
A multiple-scale simulation of variations in atmospheric carbon dioxide using a coupled biosphere-atmospheric model

[1] Variations of atmospheric CO2 at regional scales are becoming increasingly important in understanding regional carbon budgets, yet the processes that drive them remain relatively unexplored. A simulation was conducted to test a coupled biosphere-atmospheric model (SiB2-RAMS), by comparing with measurements made at the WLEF-TV tower in Wisconsin, and to investigate some of the mechanisms leading to CO2 variability, both on local and regional scales. The simulation was run for a 5-day period from 26 to 30 July 1997. Multiple nested grids were employed, which enabled mesoscale features to be simulated and which resolved small-scale features in the vicinity of the WLEF tower. In many respects the model was successful at simulating observed meteorological variables and CO2 fluxes and concentrations. The two most significant deficiencies were that excessive nighttime cooling occurred on two of the nights and that late afternoon uptake of CO2 was larger than observed. Results of the simulation suggest that in addition to biological processes causing variations in CO2 concentrations at the WLEF site other factors, such as small nearby lakes, turbulence induced by vertical wind shear, boundary layer thermals, and clouds, also had significant impacts. These factors add to the difficulty of interpreting CO2 measurements. Regional-scale patterns of CO2 variability caused by meteorological processes were also identified. Katabatic winds had a significant effect by causing respired CO2 to pool in valleys and along the shores of the Great Lakes during the night. Furthermore, a large diurnal cycle of CO2 concentration occurred over the lakes, which appeared to be mainly due to the combined action of katabatic winds, ambient winds, and the lake breeze circulation. These results suggest that meteorological processes associated with the complex terrain in this region leads to substantial CO2 advection. Therefore meteorological as well as biological processes are likely to be important causes of regional-scale CO2 variability in the Great Lakes region. A sensitivity test conducted to examine the differences between using a turbulent kinetic energy based subgrid-scale scheme versus a deformation-type subgrid-scale scheme showed advantages and disadvantages to both approaches. Our results suggest that continuous records of CO2 variability measured over heterogeneous continental regions must be interpreted with caution because of the impact of mesoscale circulations on the concentration time series.
Exploiting sorts in expansion-based proof procedures

It has long been recognized that formulating a problem in a many-sorted logic can make automated reasoning more efficient, essentially by dealing implicitly via typing with what would otherwise need explicit inference. We point out here that the advantages of a many-sorted formulation for automated reasoning can go further. Certain problems that appear to fall outside one of the known decidable subsets may not do so when considered with the additional refinement that a many-sorted formulation offers. We motivate this observation by considering theories such as metric spaces, and give some practical applications in verification.
Identification of repurposed small molecule drugs for chordoma therapy

Chordoma is a rare, slow growing malignant tumor arising from remnants of the fetal notochord. Surgery is the first choice for chordoma treatment, followed by radiotherapy, although postoperative complications remain significant. Recurrence of the disease occurs frequently due to the anatomy of the tumor location and violation of the tumor margins at the initial surgery. Currently, there are no effective drugs available for patients with chordoma. Due to the rarity of the disease, there is limited opportunity to test agents in clinical trials and no concerted effort to develop agents for chordoma in the pharmaceutical industry. To rapidly and efficiently identify small molecules that inhibit chordoma cell growth, we screened the NCGC Pharmaceutical Collection (NPC) containing approximately 2800 clinically approved and investigational drugs at 15 different concentrations in chordoma cell lines, U-CH1 and U-CH2. We identified a group of drugs including bortezomib, 17-AAG, digitoxin, staurosporine, digoxin, rubitecan, and trimetrexate that inhibited chordoma cell growth, with potencies from 10 to 370 nM in U-CH1 cells, but less potently in U-CH2 cells. Most of these drugs also induced caspase 3/7 activity with a similar rank order as the cytotoxic effect on U-CH1 cells. Cantharidin, digoxin, digitoxin, staurosporine, and bortezomib showed similar inhibitory effect on cell lines and 3 primary chordoma cell cultures. The combination treatment of bortezomib with topoisomerase I and II inhibitors increased the therapeutic potency in U-CH2 and patient-derived primary cultures. Our results provide information useful for repurposing currently approved drugs for chordoma and potential approach of combination therapy.
A comparison study: Support vector machines for binary classification in machine learning

This paper focuses on the fundamental interpretation of various kinds of SVM which achieve comparable performance in dealing with different data sets of two classes in machine learning. Based on the fundamental of original SVM, L2RL2SVM, C-SVC, L1RL2SVM and ν-SVC can linearly handle the large-scale high-dimension data well. When the training data can not be separated by linear classifiers, KSVM is a crucial technique to overcome this shortage in a supervised-learning framework. In a semi-supervised learning framework, LapSVM, is the state-of-the-art technique which better handles both generalization of the classifier and out-of-sample problem. Experiments, on four common datasets of two classes with eleven algorithms, show that accuracies, utilizing different algorithms in different data sets, are different and prior geometrical information from training data is necessary to improve the accuracy when the machine learns to select a optimum model either in a supervised-learning framework or in a semi-supervised-learning framework. By analyzing the results of the experiments, we found that the distributional characteristics of each data set can be observed.
EFFICIENT TRANSMISSION LINE MODELING SENSITIVITY ANALYSIS EXPLOITING RUBBER CELLS

The adjoint variable method is applied for the first time to perform sensitivity analysis with transmission line modelingexploiting rubber cells. Rubber cells allow for the conformal modelingof off-grid boundaries in the transmission line modeling computational domain usingmodified tensor properties. The scatteringmatrix of the rubber cell is analytically dependent on the dimensions of the modeled discontinuities. Usingthis property, an exact adjoint system is derived. The original and adjoint systems supply the necessary field information for the rubber cell based sensitivity calculations. Our technique is illustrated through sensitivity analysis of waveguide filters. The estimated sensitivities are used for fast gradient-based optimization and tolerance analysis.