Miracle-dz/UniFAR-FTUs huggingface dataset:
Load balancing parameters for geographic routing protocol in wireless sensor networks

Geographical routing protocol has been proposed to reduce the complexity of routing protocols, which are based on topology information in wireless/mobile networks. This kind of protocol is regarded as good working protocol in wireless sensor networks because reduced control overhead in geographic routing protocol is very suitable for each battery-powered sensor node. However, despite of several advantages, original greedy forwarding technique is very likely to cause different type of problems such as congestion. In this paper, we develop new parameters and apply existing possible parameters into original greedy forwarding in order to balance load efficiently. These parameters are derived from key characteristics of wireless sensor networks. Simulation results show that higher packet delivery ratio is achieved by proposed parameters in greedy forwarding.
Fault-Tolerant Partitioning Scheduling Algorithms in Real-Time Multiprocessor Systems

This paper presents the performance analysis of several well-known partitioning scheduling algorithms in real-time and fault-tolerant multiprocessor systems. Both static and dynamic scheduling algorithms are analyzed. Partitioning scheduling algorithms, which are studied, are heuristic algorithms that are formed by combining any of the bin-packing algorithms with any of the schedulability conditions for the rate-monotonic (RM) and earliest-deadline-first (EDF) policies. A tool is developed which enables to experimentally evaluate the performance of the algorithms from the graph of tasks. The results show that among several partitioning algorithms evaluated, the RM-small-task (RMST) algorithm is the best static algorithm and the EDF-best-fit (EDF-BF) is the best dynamic algorithm, for non fault-tolerant systems. For fault-tolerant systems which require about 49% more processors, the results show that the RM-first-fit decreasing utilization (RM-FFDU) is the best static algorithm and the EDF-BF is the best dynamic algorithm. To decrease the number of processors in fault-tolerant systems, the RMST is modified. The results show that the modified RMST decreases the number of required processors between 7% and 78% in comparison with the original RMST, the RM-FFDU and other well-known static partitioning scheduling algorithms
Mundo plural: ¿por qué los individuos hacen lo que hacen?

In a context of hyper-specialization, the article addresses a panoramic view of society and a complex conception of individuals, while not forgetting both the theoretical ambition and the empirical pulse. To this end, it asks: "why do individuals do what they do? ", and proposes to respond with a universal formula for the study of practices that combines a dispositional dimension with a contextualist one. On the one hand, the dispositions and competences of individuals, understood as the internalized product of more or less frequent access to certain contexts of action. On the other hand, present contexts of action and their diversity. From this formula, the article evaluates different approaches to the social: on the one hand, the oblivion of the past, characteristic of pragmatist, interactionist and rational choice sociologies; on the other hand, the neglect of contexts, typical of certain uses of Bourdieu's sociology and certain psychoanalytic theories. Finally, the article reviews the diversity of contemporary research in social sciences (in relation to the scale of observation, the level of social reality studied, the interest in knowledge, the type of object, the time scales, the number of actors studied, etc.), showing that the dispositional-contextualist approach can guide works in such different directions.
Assessing Gibberellins Oxidase Activity by Anion Exchange/Hydrophobic Polymer Monolithic Capillary Liquid Chromatography-Mass Spectrometry

Bioactive gibberellins (GAs) play a key regulatory role in plant growth and development. In the biosynthesis of GAs, GA3-oxidase catalyzes the final step to produce bioactive GAs. Thus, the evaluation of GA3-oxidase activity is critical for elucidating the regulation mechanism of plant growth controlled by GAs. However, assessing catalytic activity of endogenous GA3-oxidase remains challenging. In the current study, we developed a capillary liquid chromatography – mass spectrometry (cLC-MS) method for the sensitive assay of in-vitro recombinant or endogenous GA3-oxidase by analyzing the catalytic substrates and products of GA3-oxidase (GA1, GA4, GA9, GA20). An anion exchange/hydrophobic poly([2-(methacryloyloxy)ethyl]trimethylammonium-co-divinylbenzene-co-ethylene glycol dimethacrylate)(META-co-DVB-co-EDMA) monolithic column was successfully prepared for the separation of all target GAs. The limits of detection (LODs, Signal/Noise = 3) of GAs were in the range of 0.62–0.90 fmol. We determined the kinetic parameters (K m) of recombinant GA3-oxidase in Escherichia coli (E. coli) cell lysates, which is consistent with previous reports. Furthermore, by using isotope labeled substrates, we successfully evaluated the activity of endogenous GA3-oxidase that converts GA9 to GA4 in four types of plant samples, which is, to the best of our knowledge, the first report for the quantification of the activity of endogenous GA3-oxidase in plant. Taken together, the method developed here provides a good solution for the evaluation of endogenous GA3-oxidase activity in plant, which may promote the in-depth study of the growth regulation mechanism governed by GAs in plant physiology.
POINT DISTRIBUTION GENERATION USING HIERARCHICAL DATA STRUCTURES

Abstract. We developed a new algorithm for generating point distributions for meshless solvers. This method uses the quadtree data structure to generate point distributions in two-dimensions. Each leaf node in the quadtree supplies a point to the distribution. The quadtree is constructed and refined according to the desired point density specifications. The generated quadtree is then balanced to achieve a smooth distribution of points. For each point in the distribution, the neighborhood information is also obtained using the quadtree. We used this procedure to generate the point distributions over various configurations. Kinetic meshless method is used to solve the flow over these point distributions. We present and discuss some of the obtained results.
Fault detection using adaptive observer for a class of nonlinear systems: An LMI approach

This paper addresses a new technique for fault detection and estimation in Lipschitz nonlinear systems influenced by bounded disturbances. A robust adaptive nonlinear observer is designed for this purpose, the stability conditions of this robust adaptive observer based on Lyapunov technique are derived using linear matrix inequality approach. A numerical example is provided which the nonlinear terms depend on unmeasured state and simulation results are presented to show the effectiveness of proposed method.
Web Services Enabling Ubiquitous Computing Applications: Lessons Learned by Integrating Ubiquitous e-Learning Applications

Ubiquitous computing is a user-computer interaction paradigm supporting users in everyday tasks carried out away from the desktop. The literature has exploited themes such as natural interfaces (e.g. pen-based), automatic capture and access of everyday experiences and context-aware computing. Regarding context-aware applications, context is any relevant information describing entities in a user-computer interaction (e.g. user location). The design and implementation of high-quality context management software is an area of active research. We have described elsewhere the Context Kernel, a Web Service that manages context information on behalf of applications. In this paper, we present a case study of how the Context Kernel has been used to integrate a suite of e-learning applications — a domain rich in research exploiting the ubiquitous computing themes. We show how the Web Services approach allowed applications to seamlessly exchange context on the Web through a uniform context representation schema. In particular, we discuss how our approach has allowed us to tackle an important problem of e-learning applications: the support to authoring learning object metadata. We also present lessons learned from integrating e-learning applications via Web Services.
Experiments on the loudness-transfer of headphone-based virtual acoustics.

Binaural synthesis is a virtual-acoustics technology based on the convolution of sound signals with impulse responses modeling the propagation paths between sources and listener. The convolution products are typically presented by headphones. The frequency-dependent correction level necessary for a binauralsynthesis system to elicit the reference-scene loudness is referred to as loudnesstransfer function. An ideal binaural-synthesis system provides frequency-independent loudness-transfer functions for every listener. The frequency dependence of a binaural-synthesis system’s inter-individually averaged loudness-transfer function has been shown to depend on the hardware, the implementation, and the degree of individualization. In this contribution, perceptually acquired loudness-transfer functions of binaural-synthesis systems are discussed from an auditory-adapted perspective with regard to listening experiments, especially sound-quality judgments. The results provide quantitative estimates of the accuracy of sound-quality judgments and noise ratings achievable with different headphone-based binaural-synthesis implementations.
A Truncated Prediction Framework for Streaming Over Erasure Channels

We propose a new coding technique for sequential transmission of a stream of Gauss–Markov sources over erasure channels under a zero decoding delay constraint. Our proposed scheme is a combination (hybrid) of predictive coding with truncated memory, and quantization-and-binning. We study the optimality of our proposed scheme using an information theoretic model. In our setup, the encoder observes a stream of source vectors that are spatially independent and identically distributed (i.i.d.) and temporally sampled from a first-order stationary Gauss–Markov process. The channel introduces an erasure burst of a certain maximum length <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>, starting at an arbitrary time, not known to the transmitter. The reconstruction of each source vector at the destination must be with zero delay and satisfy a quadratic distortion constraint with an average distortion of <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. The decoder is not required to reconstruct those source vectors that belong to the period spanning the erasure burst and a recovery window of length <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula> following it. We study the minimum compression rate <inline-formula> <tex-math notation="LaTeX">$R(B,W,D)$ </tex-math></inline-formula> in this setup. As our main result, we establish upper and lower bounds on the compression rate. The upper bound (achievability) is based on our hybrid scheme. It achieves significant gains over baseline schemes such as (leaky) predictive coding, memoryless binning, a separation-based scheme, and a group of pictures-based scheme. The lower bound is established by observing connection to a network source coding problem. The bounds simplify in the high resolution regime, where we provide explicit expressions whenever possible, and identify conditions when the proposed scheme is close to optimal. We finally discuss the interplay between the parameters of our burst erasure channel and the statistical channel models and explain how the bounds in the former model can be used to derive insights into the simulation results involving the latter. In particular, our proposed scheme outperforms the baseline schemes over the i.i. d. erasure channel and the Gilbert–Elliott channel, and achieves performance close to a lower bound in some regimes.
An automatic and efficient foreground object extraction scheme

This paper presents a method to differentiate the foreground objects from the background of a color image. Firstly a color image of any size is input for processing. The algorithm converts it to a grayscale image. Next we apply canny edge detector to find the boundary of the foreground object. We concentrate to find the maximum distance between each boundary pixel column wise and row wise and we fill the region that is bound by the edges. Thus we are able to extract the grayscale values of pixels that are in the bounded region and convert the grayscale image back to original color image containing only the foreground object.
On Linear Network Coding

Many recent papers study methods, bounds and limitations for linear network coding. We examine prior definitions of linearity in network coding and show their implications in terms of code design and performance. We demonstrate how codes with one notion of linearity can be used to build, in a distributed manner, codes with another notion of linearity. One such reduction provides a simple construction for convolutional multicast network codes. We also provide examples of networks for which convolutional network code design is significantly less complex than for any block code. Finally, we introduce the new class of filter-bank network codes of which all previous definitions of linear network codes are special cases. This work is motivated by discussions with Dr. Ralf Koetter.
Design of Low Cost Force Sensor System for Human Machine Interaction – Force Feedback Joystick

—The paper describes a low cost force sensor system ready to be evaluated in force-feedback joystick designed for a medical haptics application. It is based on low-cost parts from home appliance scale with strain gauge technology, and of-the-shelf IC instrumentation amplifier. The development process, electronic circuitry and designed software are presented. Experimental results give a good ground to believe that this approach can provide sufficient quality in further development of human-machine interaction algorithms.
Neuro-fuzzy inference system (ASuPFuNIS) model for intervention time series prediction of electricity prices

This paper presents an approach to time series prediction based on Asymmetric Subsethood-Product Fuzzy Neural Inference System (ASuPFuNIS). The standard time series techniques have standard averaging where a fixed weight is added to the past values. In this paper we present a novel neuro-fuzzy inference system based on asymmetric subsethood with intervention based transfer function based time series model for accurate prediction of time series. The design of the model is described, and the scheme is evaluated by application to real-world problem of cost of electricity prices over a period of seven year in Ontario, Canada. We also study the various statistical properties of the data.
The teaching of english vocabulary to third age adults in a public nursing home of Pereira

This qualitative research study aimed at the integration of the Theory of Andragogy and games as the main technique to teach and foster English vocabulary language learning with third age adults at a state retirement home in the city of Pereira. The project provided a twelve-session English language instruction as an option to the current problematic that third age adults have in regards to their inclusion in bilingual education processes within the context of the city. Additionally, the study pretended to enhance the interest and strived to show a different view point towards the English Language Teaching community perception about third age adults‟ bilingual education. In that sense, it brought new opportunities for further research on the field. The research project, included the participation of a group of third-age adults, males and females starting from the age of 60 (Ley 1276 de 2009). 6 elders were selected through a purposive typical sampling process in order to collect data. For this reason, throughout the English vocabulary instruction, teachers‟ journals, observations, one survey conducted at the beginning of the study, and an interview at the end of the lessons, were implemented as data collection methods. The data was analyzed through grounded theory. After the analysis, data revealed that through the implementation of games, third age adults learned English vocabulary entailed with their daily lives, having a transition from isolated words to simple language structures, consolidated relations between words and meanings, and developed their skills in terms of listening, writing and speaking, focusing on the pronunciation of the vocabulary; besides, they developed and implemented four (4) strategies in line with their learning style that facilitated them to increase and foster their foreign language learning.
Performance characteristic of novel P-I-D digital control switching power supply

This paper presents the performance of novel digital P-I-D control switching power supply with fast proportional control, which is compared with conventional digital control. The transient response is improved, while the transient response of proposed method is improved even with P-I-D parameters that are same to the conventional method. It is confirmed that it has the superior transient response, which is verified with 800W prototype.
Dry-reagent double-monoclonal assay for cystatin C.

BACKGROUND Cystatin C is a low molecular weight cysteine proteinase inhibitor whose plasma or serum concentrations have been shown to be better correlated with glomerular filtration rate than serum creatinine concentrations. Routine assays for cystatin C are based on use of polyclonal antibodies and immunoturbidimetric and nephelometric designs. This study aimed to develop a double-monoclonal immunoassay for cystatin C. METHODS We tested functionality of 42 2-site antibody combinations involving 7 monoclonal antibodies with recombinant and plasma cystatin C. We developed a heterogeneous assay using 2 antibodies selected to give the best analytical performance. The assay used a dilution step and was based on a dry-reagent, all-in-one immunoassay concept with time-resolved fluorometry. The assay was performed on an automated immunoanalyzer in single wells that contained all the required assay components. We used heparin-derived plasma samples for methodological evaluation of the assay. RESULTS From a relative epitope map involving 7 cystatin C-specific antibodies, we selected a pair of antibodies for a 2-site sandwich-type dry-reagent assay. Total assay time was 15 min, and 10 microL of a 100-fold diluted sample was used. The analytical detection limit (background + 3SD) and functional detection limit (CV 20%) were 0.01 mg/L and 0.02 mg/L, respectively. Within-run and total assay imprecision were <4.7% and <5.6% (at 0.84-3.2 mg/L), respectively, and plasma recoveries of added cystatin C were 94%-110%. Regression analysis with the Roche particle-enhanced immunoturbidimetric method yielded the following (SD): slope, 1.391 (0.029); y-intercept, -0.152 (0.045) mg/L; S(y logical or, bar belowx)=0.294 mg/L (n=131). CONCLUSIONS The developed assay enables rapid and reliable measurement of cystatin C.
Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals With Preservation of Rare Vectors

In this paper, we address the problem of redundancy-reduction of high-dimensional noisy signals that may contain anomaly (rare) vectors, which we wish to preserve. For example, when applying redundancy reduction techniques to hyperspectral images, it is essential to preserve anomaly pixels for target detection purposes. Since rare-vectors contribute weakly to the -norm of the signal as compared to the noise, -based criteria are unsatisfactory for obtaining a good representation of these vectors. The proposed approach combines and norms for both signal-subspace and rank determination and considers two aspects: One aspect deals with signal-subspace estimation aiming to minimize the maximum of data-residual -norms, denoted as , for a given rank conjecture. The other determines whether the rank conjecture is valid for the obtained signal-subspace by applying Extreme Value Theory results to model the distribution of the noise -norm. These two operations are performed alternately using a suboptimal greedy algorithm, which makes the proposed approach practically plausible. The algorithm was applied on both synthetically simulated data and on a real hyperspectral image producing better results than common -based methods.
GROUP-THEORETICAL PROPERTIES OF NILPOTENT MODULAR CATEGORIES

We characterize a natural class of modular categories of prime power Frobenius-Perron dimension as representation categories of twisted dou- bles of finite p-groups. We also show that a nilpotent braided fusion category C admits an analogue of the Sylow decomposition. If the simple objects of C have integral Frobenius-Perron dimensions then C is group-theoretical in the sense of (ENO). As a consequence, we obtain that semisimple quasi-Hopf algebras of prime power dimension are group-theoretical. Our arguments are based on a reconstruction of twisted group doubles from Lagrangian subcategories of modular categories (this is reminiscent to the characterization of doubles of quasi-Lie bialgebras in terms of Manin pairs given in (Dr)).
Design and implementation of hybrid ring coupler for 800MHz–1200 MHz

A novel wide band hybrid ring coupler is proposed in this paper. The change in length of transmission line is calculated and it is converted in to equivalent impedance. Impedance of the circuit is varied using variable capacitor. The device is to be fabricated and tested for frequency range 800-1200MHz. The circuit shows improved isolation and return loss. The circuit is reduced by using Koch fractal curve method. The results for both conventional and the reduced Koch hybrid coupler are simulated and the results are compared.
Latent structure of the proposed ICD-11 post-traumatic stress disorder symptoms: Implications for the diagnostic algorithm

Background The latent structure of the proposed ICD-11 post-traumatic stress disorder (PTSD) symptoms has not been explored. Aims To investigate the latent structure of the proposed ICD-11 PTSD symptoms. Method Confirmatory factor analyses using data from structured clinical interviews administered to injury patients (n = 613) 6 years post-trauma. Measures of disability and psychological quality of life (QoL) were also administered. Results Although the three-factor model implied by the ICD-11 diagnostic criteria fit the data well, a two-factor model provided equivalent, if not superior, fit. Whereas diagnostic criteria based on this two-factor model resulted in an increase in PTSD point prevalence (5.1% v. 3.4%; z = 2.32, P<0.05), they identified individuals with similar levels of disability (P = 0.933) and QoL (P = 0.591) to those identified by the ICD-11 criteria. Conclusions Consistent with theorised reciprocal relationships between re-experiencing and avoidance in PTSD, these findings support an alternative diagnostic algorithm requiring at least two of any of the four re-experiencing/avoidance symptoms and at least one of the two hyperarousal symptoms.