Miracle-dz/UniFAR-FTUs huggingface dataset:
Soil biodiversity and soil community composition determine ecosystem multifunctionality

Significance Biological diversity is the foundation for the maintenance of ecosystems. Consequently it is thought that anthropogenic activities that reduce the diversity in ecosystems threaten ecosystem performance. A large proportion of the biodiversity within terrestrial ecosystems is hidden below ground in soils, and the impact of altering its diversity and composition on the performance of ecosystems is still poorly understood. Using a novel experimental system to alter levels of soil biodiversity and community composition, we found that reductions in the abundance and presence of soil organisms results in the decline of multiple ecosystem functions, including plant diversity and nutrient cycling and retention. This suggests that below-ground biodiversity is a key resource for maintaining the functioning of ecosystems. Biodiversity loss has become a global concern as evidence accumulates that it will negatively affect ecosystem services on which society depends. So far, most studies have focused on the ecological consequences of above-ground biodiversity loss; yet a large part of Earth’s biodiversity is literally hidden below ground. Whether reductions of biodiversity in soil communities below ground have consequences for the overall performance of an ecosystem remains unresolved. It is important to investigate this in view of recent observations that soil biodiversity is declining and that soil communities are changing upon land use intensification. We established soil communities differing in composition and diversity and tested their impact on eight ecosystem functions in model grassland communities. We show that soil biodiversity loss and simplification of soil community composition impair multiple ecosystem functions, including plant diversity, decomposition, nutrient retention, and nutrient cycling. The average response of all measured ecosystem functions (ecosystem multifunctionality) exhibited a strong positive linear relationship to indicators of soil biodiversity, suggesting that soil community composition is a key factor in regulating ecosystem functioning. Our results indicate that changes in soil communities and the loss of soil biodiversity threaten ecosystem multifunctionality and sustainability.
Digital field oriented control for dual three-phase induction motor drives

A direct rotor field oriented control (DRFOC) of a dual-three phase induction motor drive is described in this paper. The induction machine has two sets of stator three-phase windings spatially shifted by 30 electrical degrees. The stator windings are fed by a current controlled PWM (CRPWM) six-phase voltage source inverter (VSI). Three key issues are discussed: (1) the machine dynamic model is based on the vector space decomposition theory; (2) the PWM strategy uses the double zero-sequence injection modulation technique which gives good results with low computational and hardware requirements; and (3) to eliminate the inherent asymmetries of the drive power section, a new current control scheme is proposed. Experimental results are presented for a 10 kW dual three-phase induction motor drive prototype.
Understanding cardiac alternans: a piecewise linear modeling framework.

Cardiac alternans is a beat-to-beat alternation in action potential duration (APD) and intracellular calcium (Ca(2+)) cycling seen in cardiac myocytes under rapid pacing that is believed to be a precursor to fibrillation. The cellular mechanisms of these rhythms and the coupling between cellular Ca(2+) and voltage dynamics have been extensively studied leading to the development of a class of physiologically detailed models. These have been shown numerically to reproduce many of the features of myocyte response to pacing, including alternans, and have been analyzed mathematically using various approximation techniques that allow for the formulation of a low dimensional map to describe the evolution of APDs. The seminal work by Shiferaw and Karma is of particular interest in this regard [Shiferaw, Y. and Karma, A. , "Turing instability mediated by voltage and calcium diffusion in paced cardiac cells," Proc. Natl. Acad. Sci. U.S.A. 103, 5670-5675 (2006)]. Here, we establish that the key dynamical behaviors of the Shiferaw-Karma model are arranged around a set of switches. These are shown to be the main elements for organizing the nonlinear behavior of the model. Exploiting this observation, we show that a piecewise linear caricature of the Shiferaw-Karma model, with a set of appropriate switching manifolds, can be constructed that preserves the physiological interpretation of the original model while being amenable to a systematic mathematical analysis. In illustration of this point, we formulate the dynamics of Ca(2+) cycling (in response to pacing) and compute the properties of periodic orbits in terms of a stroboscopic map that can be constructed without approximation. Using this, we show that alternans emerge via a period-doubling instability and track this bifurcation in terms of physiologically important parameters. We also show that when coupled to a spatially extended model for Ca(2+) transport, the model supports spatially varying patterns of alternans. We analyze the onset of this instability with a generalization of the master stability approach to accommodate the nonsmooth nature of our system.
Homology-Based Distributed Coverage Hole Detection in Wireless Sensor Networks

Homology theory provides new and powerful solutions to address the coverage problems in wireless sensor networks (WSNs). They are based on algebraic objects, such as Čech complex and Rips complex. Čech complex gives accurate information about coverage quality, but requires a precise knowledge of the relative locations of nodes. This assumption is rather strong and hard to implement in practical deployments. Rips complex provides an approximation of Čech complex. It is easier to build and does not require any knowledge of nodes location. This simplicity is at the expense of accuracy. Rips complex cannot always detect all coverage holes. It is then necessary to evaluate its accuracy. This work proposes to use the proportion of the area of undiscovered coverage holes as performance criteria. Investigations show that it depends on the ratio between communication and sensing radii of a sensor. Closed-form expressions for lower and upper bounds of the accuracy are also derived. For those coverage holes that can be discovered by Rips complex, a homology-based distributed algorithm is proposed to detect them. Simulation results are consistent with the proposed analytical lower bound, with a maximum difference of 0.5%. Upper-bound performance depends on the ratio of communication and sensing radii. Simulations also show that the algorithm can localize about 99% coverage holes in about 99% cases.
Evolution of Social Networks: New Patterns and a New Generator

How does a social network evolve? Sociologists have studied this question since 1930s. Some famous sociologists (for example, Scott Feld) concluded that a social network is composed of superposed cliques of different sizes, and a social network evolves in the form of clique superposition. However, sociologists didn't verify the theory in large scale data due to lack of computing ability. Motivated by this challenge, incorporated with the theory, we propose a Clique-superposition model for social networks. This model generates undirected weighted networks which obey earlier reported patterns and the new patterns observed in our study. The main contributions of this study include the following: (a) we discover a number of new patterns in undirected weighted social networks based on three large real world data sets, (b) we study the principle of social network evolution and propose a Clique-superposition model for social networks following our intuition, (c) we conduct extensive experiments to demonstrate that our model can generate networks with observed patterns, old and new.
A flexible layered architecture for accurate digital baseband algorithm development and verification

Many emerging communication technologies significantly increase the complexity of the physical layer and have dramatically increased the number of operating configurations. To ensure maximum performance, designers have to optimize their algorithm implementations, which requires for comprehensive performance testing in all possible operating modes various channel conditions. This paper presents a flexible and affordable framework for baseband algorithm development and performance verification for digital communication systems with an arbitrary number of modules, each operating at a possibly different sampling rate with various latencies. The proposed architecture is scalable to support complex scenarios, such as multiple antenna systems, and is compact enough to be implemented within a single field-programmable gate array.
Haptic Simulation of Multibody Contact Dynamics for Fixture Loading Planning

The paper presents a haptics system for multibody contact dynamics simulation. The purpose of the study is for fixture loading planning, which is to plan the applying force on a workpiece to move it into the fixture. The system incorporates the dynamics simulation engine with the haptics interaction. The dynamics simulation engine uses a time stepping method with Guass-Seidel algorithm for resolving the forces on multiple contacts. The haptics interaction is performed in two modes: no contact mode and contact mode. To reach the high updating rate required for haptics simulation (1 KHz), we use two computers for the simulation, and the data is exchanged through a network connection. Examples show that the system can generate a physically plausible workpiece motion and reaction force for haptics simulation
Dictionary Learning for SAR Images Despeckling

In recent years, dictionaries combined with sparse learning techniques became extremely popular in computer vision. The image denoising approaches can be categorized as spatial domain, transform domain, and dictionary learning based according to the image representation. Using machine learning, sparse representations have become a trend and are used image and vision applications. The general idea of dictionary learning for image denoising by learning a large group of patches from an image dataset such that each patch in the estimated image can be expressed as a linear combination of only few patches from this redundant dictionary.The aim of the present paper is to demonstrate that both SVD and PCA has same task in image denoising provided that they are learned directly from the noisy image.In this paper, we present a result of comparison among four dictionary learning algorithmsK-SVD, and local PCA, hierarchical PCA and global PCA applied on the Synthetic Aperture radar (SAR) despeckling task. The experimental results show that the proposed K-SVD algorithm is provide an adequate results in removing speckle noise from the SAR images.
An efficient GPU-based implementation of recursive linear filters and its application to realistic real-time re-synthesis for interactive virtual worlds

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Resveratrol Selectively Induces DNA Damage, Independent of Smad4 Expression, in Its Efficacy against Human Head and Neck Squamous Cell Carcinoma

Purpose: Alterations in Smad4 signaling and its loss cause genomic instability and head and neck squamous cell carcinoma (HNSCC), suggesting that agents that target both Smad4-dependent and -independent pathways could control HNSCC. Experimental Design: Resveratrol efficacy was evaluated against the HNSCC cells FaDu, Cal27, Det562, and Cal27-Smad4 for viability, DNA damage, cell-cycle progression, and apoptosis, as well as γ-H2AX expression, and focus formation (γ-H2AX and Brca1). Resveratrol efficacy was also examined in nude mice for FaDu xenograft growth. Xenografts were analyzed for γ-H2AX and cleaved caspase-3. Results: Resveratrol (5–50 μmol/L) suppressed viability and induced DNA damage in FaDu and Cal27 cells but not in normal human epidermal keratinocytes and human foreskin fibroblasts, showing its selectivity toward HNSCC cells; however, Det562 cells were resistant to resveratrol even at 100 μmol/L. Cal27 cells stably transfected with Smad4 showed similar resveratrol effects as parental Cal27, indicating that a lack of resveratrol effect in Det562 cells was independent of Smad4 status in these cells. Furthermore, resveratrol caused S-phase arrest and apoptotic death of FaDu and Cal27 cells together with induction of Brca1 and γ-H2AX foci. Resveratrol (50 mg/kg body weight) treatment also inhibited FaDu tumor growth in nude mice, and γ-H2AX and cleaved caspase-3 were strongly increased in xenografts from resveratrol-treated mice compared with controls. Conclusion: Our findings for the first time showed antiproliferative, DNA damaging, and apoptotic effects of resveratrol in HNSCC cells independent of Smad4 status, both in vitro and in vivo, suggesting that more studies are needed to establish its potential usefulness against HNSCC. Clin Cancer Res; 17(16); 5402–11. ©2011 AACR.
First-order Convex Optimization Methods for Signal and Image Processing

In this thesis we investigate the use of first-order convex optimization methods applied to problems in signal and image processing. First we make a general introduction to convex optimization, first-order methods and their iteration complexity. Then we look at different techniques, which can be used with first-order methods such as smoothing, Lagrange multipliers and proximal gradient methods. We continue by presenting different applications of convex optimization and notable convex formulations with an emphasis on inverse problems and sparse signal processing. We also describe the multiple-description problem. We finally present the contributions of the thesis. The remaining parts of the thesis consist of five research papers. The first paper addresses non-smooth first-order convex optimization and the trade-off between accuracy and smoothness of the approximating smooth function. The second and third papers concern discrete linear inverse problems and reliable numerical reconstruction software. The last two papers present a convex optimization formulation of the multiple-description problem and a method to solve it in the case of large-scale instances.
An Ecologically-inspired Parallel Approach Applied to the Protein Structure Reconstruction from Contact Maps

The Protein Folding Problem (PFP) is considered one of the most important open challenges in Biology and Bioinformatics. This paper describes the application of a parallel ecology-inspired algorithm (pECO) to a hard problem related to the PFP: the protein structure reconstruction from Contact Maps. The fitness function proposed includes information not only about the free-energy of the conformation, but also similarity measures commonly used in classification systems. Experiments were done to evaluate the adequacy of the proposed approach. Results show that the combination of concurrent evolutionary approaches take advantage of both the coevolution effect and the different search strategies. Furthermore, it is observed that parallel processing was not only justified but also essential for obtaining results in reasonable processing time.
Zero Variance Importance Sampling Driven Potential Tracing Algorithm for Global Illumination

This paper presents a novel Monte Carlo strategy for solving the global ilumination problem. The unusual Monte Carlo approach is different from the one, which is utilized widely in prevalent global illumination algorithms at present, and breaks a new Monte Carlo path for settling the problem. In this way, plenty of unbiased estimators can be employed to enrich the solutions so as to lead to simple error control, and also various variance reduction techniques can be applied conveniently to speed up the estimation at little additional cost. Especially, an implementation of a theoretically zero variance importance sampling driven potential algorithm on premise of the new scheme has been proposed and carried out. Results having been obtained and comparisons with traditional algorithms show that this new framework and new algorithm is very promising.
THE EFFECT OF DIFFERENT TEACHING STYLES ON CRITICAL THINKING AND ACHIEVEMENT GOALS OF PROSPECTIVE TEACHERS

Learning environment enriched with diverse teaching styles can be effective in fostering critical thinking (CT) and achievement goals (AG) for preparing prospective teachers to cope with challenges of twenty-first century. To examine the effect of diverse teaching styles on prospective teachers’ CT and AG; guided discovery, convergent discovery and divergent production styles (Mosston & Ashworth, 2002) were used in teaching of badminton during fourteen weeks. Voluntary participants were forty prospective classroom teachers (treatment group n=20; comparison group n=20) from a Teacher Education Program at a public university in central Turkey. California Critical Thinking Skills Test-Form 2000 (CCTST), The California Critical Thinking Disposition Inventory (CCTDI) and Trichotomous Achievement Goal Scale (TAGS) were administered as pre-post test. The results showed that diverse teaching styles had positive effect on CT skills, CT dispositions, AG of performance approach and performance avoidance, but negative effect on mastery AG of prospective teachers
Exploring potential immune system markers for predicting frailty syndrome in older adults: different statistical approaches

Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Exploring potential immune system markers for predicting frailty syndrome in older adults: different statistical approaches
Results from an Evaluation of a Dialogue Manager

We give the results from an evaluation of the dialogue manager of a developing, Slovenian and Croatian spoken dialogue system for weather-information retrieval. The results are based on two Wizard-of-Oz experiments and the application of the PARADISE evaluation framework. The only difference between both experiment settings is in the dialogue-management manner, i.e., while in the first experiment dialogue management was performed by a human, the wizard, in the second experiment it was performed by the newly-implemented dialogue-manager component.
Consumer preferences and willingness to pay for character-marked cabinets from Alaska birch

Alaska birch lumber has a higher occurrence of defects (knots, bark pockets, flecks, spalting, etc.) when compared to competing hardwoods. These defects are a disadvantage when birch lumber is graded under standard National Hardwood Lumber Association (NHLA) grading rules. This paper examines whether defects and other character markings found in birch lumber may be an advantage for certain applications. Contingent valuation techniques are used to determine the willingness of consumers to pay a price premium for kitchen cabinet doors made from Alaska birch lumber containing a variety of character features. Results show that consumers prefer cabinet doors with knots and color variation, and that in general, doors with more character marks are preferred to those with less. In addition, consumers were willing to pay price premiums of between $13 and $43 for their favorite door. Further, results indicated that women tend to prefer cabinet doors with fewer or no character marks, while men prefer doors with higher levels of these features.
Degree of Centrality within the motor network for Parkinson’s Disease

REFERENCES [1] Wu, T. (2012), ‘Basal ganglia circuits changes in Parkinson's disease patients’, Neuroscience Letters, vol. 524, no. 1 pp. 55–59. [2] Zhang, J. (2015), ‘Abnormal functional connectivity density in Parkinson's disease’, Behavioural Brain Research, vol. 280, pp. 113-118. [3] Craddock, R. (2016), ‘Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI’, GigaScience Database. [4] Ashburner, J. (2007), ‘A fast diffeomorphic image registration algorithm’, Neuroimage, Vol. 38, no. 1 pp. 95-113. [5] Yeo, B. (2011), ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity’, Journal of neurophysiology, Vol. 106, no. 3 pp. 1125-65. [6] Krauth, A. (2010), ‘A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data’ Neuroimage, vol 49, no. 3 pp. 2053-2062. [7] Keuken, M. (2014), ‘Quantifying inter-individual anatomical variability in the subcortex using 7T structural MRI’, Neuroimage, vol. 94, pp. 40-46. [8] Diedrichsen, J. (2011) ‘Imaging the deep cerebellar nuclei: A probabilistic atlas and normalization procedure’, Neuroimage, vol. 54, no. 3, pp. 1786-94.
Architecture and application partitioning for reconfigurable system design

This paper presents a Genetic Algorithm (GA) based approach for Hardware/Software partitioning targeting an architecture composed of a processor and a dynamically reconfigurable datapath (FPGA). From an acyclic task graph and a set of Area-Time implementation trade offs points for each task, our GA performs HW/SW partitioning and scheduling such that the global application execution time is minimized. The efficiency of our GA is established through its application to a AC-3 decoder function and its performance is compared with a greedy algorithm.
The benefits of human resources certification

This study examines whether Human Resources (HR) certification is beneficial to HR professionals and organizations and explores the antecedents of HR certification. We surveyed 422 HR professionals from local chapters of the Society of Human Resources Management. The results indicated certified HR professionals received more pay (even after controlling for level of education and years of HR experience) and had higher occupational commitment than uncertified HR professionals. We also found that when organizations provided support for certification, HR professionals were more likely to obtain certification and had higher organizational commitment. Because we did not manipulate any variables, we were unable to make causal inferences. All of the instruments were self-reported measures (mono-method bias). Because we relied on a convenience sample, our sample might not be representative of the HR profession. HR certification may have some positive benefits for professionals such as more pay and employees who are more committed to their occupation. Organizations may want to support their employees’ HR certification efforts because of its positive relationship to organizational commitment. A certified HR professional may invest mental energy, time, and even his or her own financial resources to become and remain certified.
Congestion Control Using OpenFlow in Software Defined Data Center Networks

this paper studies congestion control issue in data center networks and proposes a potential solution based on OpenFlow protocol. A main feature of the emerging data center networks is their performance in hosting different cloud applications and services. Since congestion management is necessary to effectively utilize numerous data center applications, in this paper we present an efficient method to control the congestion in the software defined data center networks based on OpenFlow protocol. In our proposed method, congestion in the network links is recognized by centrally checking the port statistics of the OpenFlow enabled switches and after that some of the flows in any congested link are rerouted through paths with more free resources by the OpenFlow controller. We executed our proposed method by Mininet, and the experimental results demonstrate the efficiency of the proposed method in decreasing the congestion and enhancing the performance of the network. Keywords— OpenFlow; data center; congestion control; fattree; software-defined network.