Miracle-dz/UniFAR-FTUs huggingface dataset:
Disturbance suppression research in parallel dual permanent synchronous linear motors based on 2DOF PID controller

For dual linear motor cooperative control system, there is only one controlled input signal can be used. Each linear motor of the control system is independent. Therefore, the position loop of control system is easier to be disturbed. This paper presents a 2DOF PID controller to position loop for dual linear motor cooperative control system so that the two groups of parameters can be adjusted independently. The simulation result shows that the system has a strong robust and also has a good anti-disturbance performance and setting value tracking ability which has a good application prospects. The dynamic responsibility of dual linear motor cooperative control system based on two degrees of freedom (2DOF) PID controller is faster than traditional PID controller.
A Fourier-Based Approach to the Angiographic Assessment of Flow Diverter Efficacy in the Treatment of Cerebral Aneurysms

Flow diversion is an emerging endovascular treatment option for cerebral aneurysms. Quantitative assessment of hemodynamic changes induced by flow diversion can aid clinical decision making in the treatment of cerebral aneurysms. In this article, besides summarizing past key research efforts, we propose a novel metric for the angiographic assessment of flow diverter deployments in the treatment of cerebral aneurysms. By analyzing the frequency spectra of signals derived from digital subtraction angiography (DSA) series, the metric aims to quantify the prevalence of frequency components that correspond to the patient-specific heart rate. Indicating the decoupling of aneurysms from healthy blood circulation, our proposed metric could advance clinical guidelines for treatment success prediction. The very promising results of a retrospective feasibility study on 26 DSA series warrant future efforts to study the validity of the proposed metric within a clinical setting.
Space-time bit-interleaved coded modulation with an iterative decoding strategy

A space-time coding approach for wireless communications deploying multiple transmit and multiple receive antennas is presented. The approach is based on the concatenation of a convolutional encoder, a bit interleaver, and a space-time signal constellation mapper that combines multi-level/phase modulation with multiple transmit antennas. The decoding strategy follows an iterative (turbo-like) algorithm where soft information is exchanged between a soft-in soft-out demapper and a soft-in soft-out convolutional decoder. It is applicable with one or more receive antennas, and shows that spectral efficient/reliable communications are possible with few iterations. We address the performance analysis in both block and fast flat Rayleigh fading in order to provide insight into the space-time code construction criteria for the approach that we propose. Finally, simulation results are reported for schemes with 2 bits/s/Hz and 4 bits/s/Hz.
Quantitative Automata under Probabilistic Semantics

Automata with monitor counters, where the transitions do not depend on counter values, and nested weighted automata are two expressive automata-theoretic frameworks for quantitative properties. For a well-studied and wide class of quantitative functions, we establish that automata with monitor counters and nested weighted automata are equivalent. We study for the first time such quantitative automata under probabilistic semantics. We show that several problems that are undecidable for the classical questions of emptiness and universality become decidable under the probabilistic semantics. We present a complete picture of decidability for such automata, and even an almost-complete picture of computational complexity, for the probabilistic questions we consider.
Contesting police governance : respect, authority and belonging in organised violent gangs in Cape Town

Gangs establish their own governance arrangements and in so doing, contest the governance processes of the police. Gangs also govern themselves and the communities in which they live and contest police governance through their own governance practices. This research describes the types of governance practiced by armed and violent gangs, the impact on the manner in which they are policed and how this impacts on and regulates the police. This article also examines the gang problem through a nodal governance lens.
Classification of Petroleum Well Drilling Operations Using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a support vector machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert.
Forma Analysis of Permutation Random Keys

Random keys are a common approach to the representation of permutations in evolutionary algorithms. This paper makes use of forma analysis, a powerful tool for the study of representations and operators used in evolutionary algorithms. Using forma analysis, it shall be argued that this approach, by abstracting away problem structure, can be improved by approaches where the structures they manipulate map more directly onto the problem domain. The arguments presented here are supported by an empirical study of the flowshop sequencing problem
LRRK2 Kinase Activity Is Dependent on LRRK2 GTP Binding Capacity but Independent of LRRK2 GTP Binding

Leucine rich repeat kinase 2 (LRRK2) is a Parkinson's disease (PD) gene that encodes a large multidomain protein including both a GTPase and a kinase domain. GTPases often regulate kinases within signal transduction cascades, where GTPases act as molecular switches cycling between a GTP bound “on” state and a GDP bound “off” state. It has been proposed that LRRK2 kinase activity may be increased upon GTP binding at the LRRK2 Ras of complex proteins (ROC) GTPase domain. Here we extensively test this hypothesis by measuring LRRK2 phosphorylation activity under influence of GDP, GTP or non-hydrolyzable GTP analogues GTPγS or GMPPCP. We show that autophosphorylation and lrrktide phosphorylation activity of recombinant LRRK2 protein is unaltered by guanine nucleotides, when co-incubated with LRRK2 during phosphorylation reactions. Also phosphorylation activity of LRRK2 is unchanged when the LRRK2 guanine nucleotide binding pocket is previously saturated with various nucleotides, in contrast to the greatly reduced activity measured for the guanine nucleotide binding site mutant T1348N. Interestingly, when nucleotides were incubated with cell lysates prior to purification of LRRK2, kinase activity was slightly enhanced by GTPγS or GMPPCP compared to GDP, pointing to an upstream guanine nucleotide binding protein that may activate LRRK2 in a GTP-dependent manner. Using metabolic labeling, we also found that cellular phosphorylation of LRRK2 was not significantly modulated by nucleotides, although labeling is significantly reduced by guanine nucleotide binding site mutants. We conclude that while kinase activity of LRRK2 requires an intact ROC-GTPase domain, it is independent of GDP or GTP binding to ROC.
Biochemical investigation of a human pathogenic mutation in the nuclear ATP5E gene using yeast as a model

F1F0-ATP synthase is a key enzyme of the mitochondrial energetic metabolism responsible for the production of most cellular ATP in humans. Mayr et al. (2010) recently described a patient with a homozygote (Y12C) mutation in the nuclear gene ATP5E encoding the ε-subunit of ATP synthase. To better define how it affects ATP synthase, we have modeled this mutation in the yeast Saccharomyces cerevisiae. A yeast equivalent of this mutation (Y11C) had no significant effect on the growth of yeast on non-fermentable carbon sources (glycerol/ethanol or lactate), conditions under which the activity of the mitochondrial energy transducing system is absolutely essential. In addition, similar to what was observed in patient, this mutation in yeast has a minimal effect on the ATPase/synthase activities. On the contrary, this mutation which has been shown to have a strong impact on the assembly of the ATP synthase complex in humans, shows no significant impact on the assembly/stability of this complex in yeast, suggesting that biogenesis of this complex differs significantly.
Communicating remote Gaussian sources over Gaussian multiple access channels

We study a multiple-terminal joint source-channel coding problem, where two remote correlated Gaussian sources are transmitted over a Gaussian multiple-access channel with two transmitters. Each transmitter observes one of the sources contaminated in Gaussian noise. The receiver wishes to reconstruct both sources. We derive necessary conditions and sufficient conditions for the receiver to be able to reconstruct the sources with given expected squared-error distortions. These conditions establish the optimality of uncoded transmission below some signal-to-noise ratio (SNR) threshold, and they also establish the high-SNR asymptotics. To achieve the latter, a coding scheme is proposed that superimposes analog uncoded transmission and digital combined source-channel Gaussian vector quantization.
The new conditionality : the politics of poverty reduction strategies

* 1. Poverty, politics and states of partnership - Jeremy Gould * 2. Tanzania: Merging in the Circle - Jeremy Gould & Julia Ojanen * 3. Vietnam: Dealing with donors - Irene Norlund, Tran Ngoc Ca and Nguyen Dinh Tuyen * 4. Honduras: Transforming the concessional state - Maaria Seppanen * 5. The politics of consultation - Jeremy Gould
A Set of Globally Stable N-PID Regulators for Robotic Manipulators

This paper deals with the position control of robot manipulators with uncertain and varying-time payload. Proposed is a set of novel N-PID regulators consisting of a linear combination of the proportional control mode, derivative control mode, nonlinear control mode shaped by a nonlinear function of position errors, linear integral control mode driven by differential feedback, and nonlinear integral control mode driven by a nonlinear function of position errors. By using Lyapunov’s direct method and LaSalle’s invariance principle, the simple explicit conditions on the regulator gains to ensure global asymptotic stability are provided. The theoretical analysis and simulation results show that: an attractive feature of our scheme is that N-PID regulators with asymptotic stable integral actions have the faster convergence, better flexibility and stronger robustness with respect to uncertain and varying-time payload, and then the optimum response can be achieved by a set of control parameters in the whole control domain, even under the case that the payload is changed abruptly.
Limit Theorems for Degree of Coverage and Lifetime in Large Sensor Networks

In this paper, we investigate the fundamental limits of sensor network lifetime that any algorithm can achieve. In our settings, n nodes are deployed as a Poisson point process with density lambda in a region of size S and each sensor node can cover a unit-area disk. For any k and lambda, let V(k, lambda) be the random variable (r.v.) of the size of the region that is covered by at most k - 1 nodes. Under these assumptions, we first show that for any function omega satisfying 1 Gtomega (k) Lt k1/2 the r.v. V(k, k- omega(k)k1/2) converges almost surely to S. In contrast, if the intensity is set to lambda = k + omega(k)k1/2 we obtain that V(k, k +omega(k)k 1/2) converges almost surely to 0. These limit theorems extend the results of Zhang and Hou in [21], [22] where the authors worked with fixed degree of coverage (k = O(1)) and lambda = log S+O(k) log log S. Assume that each sensor has the same lifetime T. As consequences of our analytical results, we derive randomized algorithms (working with high probability) that can maintain constantly high degrees of coverage while prolonging the lifetime of the network.
Application of multi-core parallel computing in FPGA placement

As the sizes of FPGA device grow, the long run-time of the placement is becoming a great challenge for the FPGA design flow. Simulated annealing is the best-known method applied to this problem due to the good quality of result (QoR), but its computation time seems not satisfactory. In this paper, we propose a parallel placement algorithm named MPP-SA (Multi-core Parallel Placement algorithm based on Simulated Annealing). Our goal is to provide a fast placement algorithm with high QoR. MPP-SA has the same annealing schedule as the traditional simulated annealing, but it uses the parallel approach to move blocks concurrently by multiple threads that are run on different cores of the same processor. To ensure the correctness of the results, MPP-SA also uses synchronization technology and lock mechanism, which brings some overheads. However, experiment results show that these overheads have not seriously affected the performance of our algorithm, especial for large circuits. Compared with the placement algorithm of T_VPlace in VPR5.0, MPP-SA is able to decrease the run-time of 5 different size benchmark circuits by an average of 32%-42% without losing QoR.
Fast Autonomous Growing Neural Gas

This paper aims to address the ability of self-organizing neural network models to manage real-time applications. Specifically, we introduce fAGNG (fast Autonomous Growing Neural Gas), a modified learning algorithm for the incremental model Growing Neural Gas (GNG) network. The Growing Neural Gas network with its attributes of growth, flexibility, rapid adaptation, and excellent quality of representation of the input space makes it a suitable model for real time applications. However, under time constraints GNG fails to produce the optimal topological map for any input data set. In contrast to existing algorithms the proposed fAGNG algorithm introduces multiple neurons per iteration. The number of neurons inserted and input data generated is controlled autonomous and dynamically based on a priory learnt model. Comparative experiments using topological preservation measures are carried out to demonstrate the effectiveness of the new algorithm to represent linear and non-linear input spaces under time restrictions.
An Agent Based Architecture for VM Software Tracking in Cloud Federations

Cloud Computing is the reference model for IT specialists offering high quality services in a pay-per-use fashion. Architectures and protocols for Cloud Federations have been designed to meet emerging requirements of Cloud infrastructure. In such a context IT providers can mutually rent virtual resources and exchange metadata in order to get more opportunity. In this paper we define VST - Virtual machine Software Tracking - as the process of extracting detailed information about software running in virtual machines in a transparent manner. Coupling VST data with those of resource consumption can help IaaS providers to be more precise when assigning resources to VMs. Moreover, sharing VST information across the cloud federation can help IaaS providers to get decision about VM placement in time. For this aim we provide an agent based architecture for collecting and sharing VST data in a Cloud Federation, together with a skeleton implementation by which the architecture has been tested.
Industry-Science Connections in Agriculture: Do Public Science Collaborations and Knowledge Flows Contribute to Firm-Level Agricultural Research Productivity?

Prior research shows long-run productivity growth in agriculture is associated with increases in the stock of public scientific knowledge and private patented inventions. However, private inventions may be a function of the stock of public knowledge. In this paper, we examine the possibility that public knowledge contributes to productivity through its relationship with private sector invention. Our analysis identifies connections between the stock of public knowledge and private firm R&D and examines whether the degree of “connectedness” to public science is associated with greater firm-level research productivity in agriculture. Bibliographic information identifies the nature and degree to which firms use public agricultural science through citations and collaborations on scientific papers. Fixed effects models show that greater citations and collaborations with university researchers are associated with greater private agricultural research productivity.
Tipping the Noxa/Mcl-1 Balance Overcomes ABT-737 Resistance in Chronic Lymphocytic Leukemia

Purpose: Chronic lymphocytic leukemia (CLL) cells in lymph nodes (LN), from which relapses are postulated to originate, display an antiapoptotic profile in contrast to CLL cells from peripheral blood (PB). The BH3 mimetic ABT-737 antagonizes the antiapoptotic proteins Bcl-XL and Bcl-2 but not Mcl-1 or Bfl-1. Previously, it was shown that CD40-stimulated CLL cells were resistant to ABT-737. We aimed to define which antiapoptotic proteins determine resistance to ABT-737 in CLL and whether combination of known antileukemia drugs and ABT-737 was able to induce apoptosis of CD40-stimulated CLL cells. Experimental Design: To mimic the LN microenvironment, PB lymphocytes of CLL patients were cultured on feeder cells expressing CD40L and treated with ABT-737 with or without various drugs. In addition, we carried out overexpression or knockdown of pro- and antiapoptotic proteins in immortalized primary B cells. Results: Upon CD40 stimulation patient-specific variations in ABT-737 sensitivity correlated with differences in levels of Mcl-1 and its antagonist Noxa. Knockdown of Noxa, as well as Mcl-1 overexpression, corroborated the importance of the Noxa/Mcl-1 ratio in determining the response to ABT-737. Inhibition of NF-κB resulted in increased Noxa levels and enhanced sensitivity to ABT-737. Interestingly, increasing the Noxa/Mcl-1 ratio, by decreasing Mcl-1 (dasatinib and roscovitine) or increasing Noxa levels (fludarabine and bortezomib), resulted in synergy with ABT-737. Conclusions: Thus, the Noxa/Mcl-1 balance determines sensitivity to ABT-737 in CD40-stimulated CLL cells. These data provide a rationale to investigate the combination of drugs which enhance the Noxa/Mcl-1 balance with ABT-737 to eradicate CLL in chemoresistant niches. Clin Cancer Res; 18(2); 487–98. ©2011 AACR.
Population Structure in Genetic Association Studies

Standard genetic association tests using case-control data are based on certain assumptions about the population from which study subjects were sampled. Two types of departure from these assumptions have been studied: population stratification and cryptic relatedness. Both types of departure have been called population structure. Each can lead to erroneous inferences due to differences between a test statistic’s actual null distribution and the nominal one valid only for populations without structure. The differences can reflect either confounding bias or variance distortion. For each type of structure, adjusted test statistics have been proposed whose actual null distributions, in the presence of the structure, equal the nominal ones appropriate for unstructured populations. This paper reviews models for population stratificationand cryptic relatedness, and uses them to examine the effects of each on the Armitage trend test for case-control data. Specifically, population stratification can cause confounding bias but not variance distortion, while cryptic relatedness can cause variance distortion but not confounding bias. Consequently the adjusted statistics developed for population stratification (e.g. the latent variable methods of Pritchard et al. (1999, 2001); Satten et al. (2001); Schork et al. (2001); Wang et al. (2005)), address potential confounding bias but not variance distortion. Conversely, the adjusted statistics developed for cryptic relatedness (e.g. the Genomic Control (GC) methods of Devlin and Roeder (1999), Setakis et al. (2006) and Zheng et al. (2006)) address variance distortion but not confounding bias. These differences may explain the anomalous behavior of adjusted statistics when applied to populations with structure of a type that differs from the one for which the method was designed. They indicate that care is needed to specify the nature of the underlying structure anticipated for a given population, and to use appropriate methods to adjust for it.
Simplified clique detection for collusion-resistant reputation management scheme in P2P networks

Reputation management systems have been proposed to provide helpful information for reliable peer selection in an environment where honest peers coexist with malicious peers. The reputation management systems generally seek to generate an accurate assessment in the face of various factors including but not limited to potentially adversarial environments. Thus, performance of the reputation management systems mainly depends on an effectiveness of detection of various malicious attacks. In this paper, we focus on a detection of colluders who usually form a clique among various malicious attacks since only heuristic-based approaches which are still vulnerable to sophisticated colluding attacks have been proposed due to a NP-completeness of the detection of colluders forming a clique. For collusion-resistant reputation management, we introduce a simplified clique detection that can be applied to the reputation management system so that the colluders forming a clique can be easily detected. Then, we introduce a way to calculate a collusion probability-weighted reputation to reduce falsely cumulated reputation by malicious collusions. Through simulations, we show that our approach is enough to detect colluders forming a clique and shows better performance than heuristic-based approach in terms of authentic file download and reputation management.