Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous and controlling reasons underlying achievement goals during task engagement: their relation to intrinsic motivation and cheating

The aim of this study was to investigate the relation of autonomous and controlling reasons underlying an endorsed achievement goal to intrinsic motivation and cheating. The endorsement of the achievement goal was ensured by involving 212 (Mage = 19.24, SD = .97) freshman students in a spatial task and asking them to report their most important achievement goal, as well as the reasons for adopting the goal, during the task. Results from a hierarchical regression analysis revealed that independent of the achievement goal the students adopted, the autonomous reasons for the endorsed goal were positively related to the indices of intrinsic motivation. Furthermore, the autonomous reasons underlying either performance or mastery-avoidance goals were negatively related to cheating. Alternatively, the controlling reasons for the endorsed goal were positively related to pressure and tension. The importance of considering both the ‘what’ and the ‘why’ aspect of achievement motivation are discussed.
Isolation and phylogenetic analysis of zinc resistant Acinetobacter sp . and its potential for bioremediation

1 Department of Microbiology, Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran. 2 Department of Biology, Faculty of Science, Islamic Azad University Islamshahr Branch, Islamshahr, Iran. 3 Department of marine pollution and environment protection, Faculty of marine science and technology, Islamic Aazad University, North Tehran Branch, Tehran, Iran. 4 Molecular Biology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
A Survey on Privacy Preserving Distributed Data Mining Based On Multi-Objective Optimization and Algorithmic Game Theory

Use of technology for data collection and analysis has seen an unprecedented growth in the last couple of decades. Individuals and organizations generate huge amount of data through everyday activities. This data is either centralized for pattern identification or mined in a distributed fashion for efficient knowledge discovery and collaborative computation. This, obviously, has raised serious concerns about privacy issues. The data mining community has responded to this challenge by developing a new breed of algorithms that are privacy preserving. Specifically, cryptographic techniques for secure multi-party function evaluation form the class of privacy preserving data mining algorithms for distributed computation environments. However, these algorithms require all participants in the distributed system to follow a monolithic privacy model and also make strong assumptions about the behavior of participating entities. These conditions do not necessarily hold true in practice. Therefore, most of the existing work in privacy preserving distributed data mining fails to serve the purpose when applied to large real-world distributed data mining applications. We develop a novel framework for privacy preserving distributed data mining that allows personalization of privacy requirements for individuals in a large distributed system and removes certain assumptions regarding participant behavior, thereby making the framework efficient and real-world adaptable.
Measurement and prediction of network demand in the presence of renewable distributed generation

As the amount of renewable generation on the LV and 11kV distribution networks rises, it becomes difficult for distribution network operators to track the underlying demand for electricity. This paper describes a system under development to deduce renewable generator capacity, generation and consumer demand on individual feeders from current measurement data collected at the primary or distribution transformer. This provides DNOs with a low cost system to understand the true impact of generation on their network, better plan for outages and manage fault conditions. It gives them knowledge of the actual demand on their system, the generation connected and prediction of these values for the hours or days ahead using data already available. (4 pages)
Asymmetric transmission of linearly polarized light through dynamic chiral metamaterials in a frequency regime of gigahertz–terahertz

Abstract. In a lossy media, anisotropic chiral metamaterial (MTM) structures with normal incidence asymmetric transmission of linearly polarized electromagnetic (EM) waves are investigated and analyzed in both microwave and terahertz frequency regimes. The proposed lossy structures are used to perform dynamic polarization rotation and consist of square-shaped resonators with gaps on both sides of dielectric substrates with a certain degree of rotation. Asymmetric transmission of a linearly polarized EM wave through the chiral MTMs is realized by experimental and numerical studies. The dynamic structures are adjustable via various parameters to be tuned for any desired frequency regimes. From the obtained results, the suggested structure can be used to design new polarization control devices for desired frequency regimes.
Numerical Investigation of Phase Estimation for 3D Measurement in the Fringe Projection Technology

Phase data modulating the intensity of fringe patters can provide useful information about object surface or shape using an optical fringe projection system. Measuring the phase map of recorded fringe pattern via an appropriate phase retrieval algorithm can lead as to the object shape information. Several physical and numerical parameters can affect the estimation of phase maps. We systematically studied effects of theses parameters on different phase analyses using a recently proposed directional wavelet algorithm (DWA) that is based on 2D continuous wavelet transform (2D-CWT), an advanced multiscale decomposition algorithm. We considered two types of parameters that are often presented in recorded intensity images: local spatial frequencies of data and modulation rate of fringe patterns. In this paper, we propose a study of the phase retrieval problem by comparing two different methods on numerical and experimental data, to improve the performance of the analysis process and then the accuracy of results.
Absorption, Movement and Metabolism of 14C-cyfluthrin in Cotton Leaves

A greenhouse experiment was conducted to study the uptake and metabolism in cotton leaves of Clabelled cyfluthrin, (C22H18Cl2FNO3, active ingredient of Baythroid insecticide). The labelled chemical was sprayed onto the selected portions of leaves which were subsequently studied for the movement (using autoradiography) and recovery of C (using extraction, purification procedures). Autoradiography showed a fairly rapid movement of C in the leaf tissues through vascular tissues. Movement was more efficient when application was made on the mid-rib region. Dissipation of C was also fast and even after 2 days, >60 percent of it was unaccounted. Subsequently, however, the losses were slow and amounted to 70.6 percent after 35 days. Thin layer chromatography/co-chromatography of organic extracts followed by linear scanning revealed that >60 percent of the C was still present as parent compound. Partial hydrolysis of cyfluthrin was found to be the main process involved in degradation that resulted in two major degradation products or metabolites.
The Damselfly Compound Eye in the Stream Habitat: Biological Design for Object Detection in a Dark Complex Habitat

Abstract : Habitats characterized by high spatial variation in absolute light levels and spectral quality present a challenge to animals that rely on visual orientation and visual target discrimination. Insects, in particular, face several difficulties in visual performance related to the small absolute size and simplicity of visual components comprising their compound eyes, including the lack of a focusing mechanism, relatively limited light capture and course spatial resolution. Therefore, an understanding of the morphological and behavioral means by which insects overcome these limitations in order to perform highly demanding visual tasks can provide insight into both ecological specialization and artificial visual system optimization. We investigated optical geometry, perch orientation and microhabitat selection in the Hawaiian damselfly Megalagrion xanthomelas, a sit-and-wait predator that intercepts aerial prey among heterogeneous vegetation bordering streams and wetlands. We found that while the eyes of Megalagrion xanthomelas are roughly spherical, maximum visual acuity appears concentrated in an oblong region shifted below the equatorial frontal plane. This optical geometry corresponds well with typical orientations of damselflies resting on perches and suggests an arrangement maximizing detection of prey or conspecifics traveling along stream margins. We discuss the visual morphology and behavior of Megalagrion xanthomelas in relation to available light environments and in comparison to the visual systems of other species.
The index of small length sequences

Let n ≥ 2 be a fixed integer. Define (x)n to be the unique integer in the range 0 ≤ (x)n < n which is congruent to x modulo n. Given x1,…,xl ∈ ℤ, let ∥(x1,…,xl)∥1 = min{(ux1)n + ⋯ + (uxl)n:u ∈ ℤ, g.
Weighted prediction method with multiple time series using multi-kernel least squares support vector regression

Fault or health trend prediction technique has become one of the effective ways to protect the safe operation of high reliable systems. However complex systems often show complex dynamic behaviors and uncertainty, which lead to hardly establishing their precise physical models. In this case, in order to obtain the satisfactory prediction results, time series analysis methods are often used to perform the prediction in practice [2, 12, 15, 19, 26]. Among the known non-linear time series prediction methods, the effectiveness of statistics theory based methods have been demonstrated, such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), etc. ANN has been applied in many fields due to its universal approximation property. However ANN suffers from local minimum traps, difficulty in determining the hidden layer size and learning rate, poor capacity for generalization, etc. [8, 10, 32] On the contrary, SVR overcomes the problems existing in ANN. SVR aims at the global optimum and exhibits better accuracy in non-linear and non-stationary time series data prediction due to its implementation of the structural risk minimization principle [10, 27, 28]. But complexity of SVR depends not only on the input space dimension, but also on the number of sample data. For large sample data, the quadratic programming (QP) problem is more complex, it will cost a lot of computing time. For this reason, LS-SVR was proposed by Suykens et al. [16, 23] In GUO Y-M, RAN C-B, LI X-L, MA J-Z, ZHANG L. Weighted prediction method with multiple time series using multi-kernel least squares support vector regression. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2013; 15 (2): 188–194.
The Value and Risk of Curated Shopping: Online Consumer’s Choice

Online shopping is attractive to consumers because they can look for and compare products faster and easier. However, as the number of products available online rapidly increases, consumers are required to spend more time and efforts searching for relevant product information. As a result, curated shopping, which recommends a limited number of products carefully chosen by professional shopping curators, is becoming more popular among online consumers. In this study, we empirically investigate how the consumers’ perceived risk, perceived value of curated shopping, and personal characteristics affect their decision to use curated shopping. Our results show that the perceived convenience, the efficiency of curated shopping, and the degree of shopping fatigue are positive factors that increase the intention to use curated shopping. On the other hand, the perceived financial risk is shown to be a significant negative factor. Furthermore, we found that previous experiences of curated shopping moderate the aforementioned relationships. The academic and practical implications of these findings are discussed.
Mechanisms of exclusion: attitudes toward allocation of social rights to out-group population

Abstract This study examines the ways in which perceived socioeconomic threat, perceived threat to national identity, and prejudice (as reflected in negative stereotypes and desire for social distance) prompt objection to allocation of rights to out-groups. The paper presents a simultaneous test of three theoretical explanations and delineates the complex interrelationships among all concepts, using data from a representative sample of Israelis in structural equation models. This study finds that: (1) perception of socioeconomic threat is positively associated with perception of threat to national identity; (2) the two types of threat do not directly affect attitudes toward allocation of rights, but instead tend to increase negative stereotyping of the out-group; (3) negative stereotypes are likely to contribute to a desire for social distance from the out-group; and (4) the attribution of negative characteristics and a sense of social distance tend to increase objection to allocation of social rights to the out-group.
Māori-medium Education: Current Issues and Challenges

This paper summarises the key issues and challenges that have emerged from a recent major report by the authors on Māori-medium education in Aotearoa/New Zealand. The discussion is situated within a wider international analysis of bilingual/immersion programmes, including heritage language programmes for indigenous peoples. Key issues explored in the paper include the negotiation of, and occasional tension between, the wider goals of indigenous Māori language revitalisation and the successful achievement of bilingualism and biliteracy in Māori-medium educational contexts. Issues to do with current pedagogy, staffing and resourcing of Māori-medium programmes are also examined. The paper concludes with suggestions for the ongoing development and extension of Māorimedium education.
A Quantile Analysis of Default Risk for Speculative and Emerging Companies

Using quantile regression, this article examines default risk of emerging and speculative companies in Australia and the United States as compared to established investment entities. We use two datasets for each of the two countries, one speculative and one established. In the US we compare companies from the S&P 500 to those on the Speculative Grade Liquidity Ratings list (Moody's Investor Services, 2010). For Australia, we compare entities from the S&P/ASX 200 to those on the S&P/ASX Emerging Companies Index (EMCOX). We also divide the datasets into GFC and Pre-GFC periods to examine default risk over different economic circumstances. Quantile Regression splits the data into parts or quantiles, thus allowing default risk to be examined at different risk levels. This is especially useful in measuring extreme risk quantiles, when corporate failures are most likely. We apply Monte Carlo simulation to asset returns to calculate Distance to Default using a Merton structural credit model approach. In both countries, the analysis finds substantially higher default risk for speculative as compared to established companies. The spread between speculative company and established company default risk is found to remain constant in Australia through different economic circumstances, but to increase in the US during the GFC as compared to pre-GFC. These findings are important to lenders in understanding, and providing for, default risk for companies of different grades through varying economic cycles.Classification-JEL:
Marine fisheries management in Cameroon : overview and perspectives ' for improvement of the survey system and data collection

Since 1982, a datacollection system has been in place in Cameroon to obtain statistics for the small-scale manne fisheries sector. Surveys have been conducted in an attempt to determine the impact of fishing effort on the resources, and data collected refer mostly to catch and effort. Although such data may be used for analysis of resource dynamics, they are generally inappropriate for analysing fishing activity in terms of the dynamics of exploitation. The latter analysis is necessary in order to address questions on the developmental capacity of the fisheries sector. With such a collection system, the main sources of uncertainty come from a lack of knowledge of the socioeconomic dynamics of fishing communities, including the dynamics of the fishing fleet and technology. Data on those factors are needed to evaluate the impact of resource state and of the socioeconomics of the operation on the fishing mortality. More information is therefore needed and a more systemic approach to data collection has been taken. This new approach and its implementation are discussed, taking into consideration catch-effort data as well as the new surveys on fishing units and socioeconomic dynamics. These last two include information on fìshing strategies (through surveying a fixed set of fishing units), and processing and trade of the fisheries products (through a sample of processors). Some results obtained from these data are presented, and their usefulness for fisheries development and management is discussed.
Energy-Efficient, Learning-Inspired Channel Decision and Access Technique for Cognitive Radio-based Wireless Sensor Networks

Cognitive radio-based wireless sensor network is the next-generation sensor network paradigm. Important to this emerging sensor network is the need to reduce energy consumption, paving way for ‘green’ communication among sensor nodes. Therefore, in this paper, we have proposed an energy-efficient, learning-inspired, adaptive and dynamic channel decision and access technique for cognitive radio-based wireless sensor networks. Using intelligent learning technique based on the previous experience, the cognitive radio-based wireless sensor network agent decides which available channel to access based on the energy-efficiency achievable by transmitting using the channel. From simulation results, we found that as the channel packet availability increases, the energyefficiency of the channel increase. This lends credence to the fact that the proposed learning-inspired algorithm is significantly energy-efficient for cognitive radio-based wireless sensor networks.
Identification of Nonlinear Site Response Using the H/V Spectral Ratio Method

The horizontal-to-vertical spectral ratio has become increasingly popular in studies of site effect and determination of the predominant period of a site. In this study, this method is extended to identify nonlinear soil responses. To establish this phenomenon, borehole array records that already showed nonlinear site responses using spectral ratios between surface and borehole station pairs were analyzed. Moreover, in this study, the horizontal-to-vertical spectral ratio method was applied to weak and strong motion records from the same dataset. The results demonstrate that nonlinear site responses can be evaluated using horizontal-to-vertical spectral ratios of surface recordings at a single station.
Reasoning with conditionals Guy Politzer

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises.
Radio selection of the most distant galaxy clusters

We show that the most distant X-ray detected cluster known to date, ClJ1001 at z=2.506, hosts a strong overdensity of radio sources. Six of them are individually detected (within 10") in deep 0.75" resolution VLA 3GHz imaging, with S(3GHz)>8uJy. Of the six, AGN likely affects the radio emission in two galaxies while star formation is the dominant source powering the remaining four. We searched for cluster candidates over the full COSMOS 2-square degree field using radio-detected 3GHz sources and looking for peaks in Sigma5 density maps. ClJ1001 is the strongest overdensity by far with >10sigma, with a simple z_phot>1.5 preselection. A cruder photometric rejection of z 2.5. Samples of hundreds such high-redshift clusters could potentially constrain cosmological parameters and test cluster and galaxy formation models.
Sustaining forest landscape connectivity under different land cover change scenarios.

Managing forest landscapes to sustain functional connectivity is considered one of the key strategies to counteract the negative effects of climate and human-induced changes in forest species pools. With this objective, we evaluated whether a robust network of forest connecting elements can be identified so that it remains efficient when facing different types of potential land cover changes that may affect forest habitat networks and ecological fluxes. For this purpose we considered changes both in the forested areas and in the non-forest intervening landscape matrix. We combined some of the most recent developments in graph theory with models of land cover permeability and least-cost analysis through the forest landscape. We focused on a case of study covering the habitat of a forestdwelling bird (nuthatch, Sitta europaea) in the region of Galicia (NW Spain). Seven land-use change scenarios were analysed for their effects on connecting forest elements (patches and links): one was the simplest case in which the landscape is represented as a binary forest/non-forest pattern (and where matrix heterogeneity is disregarded), four scenarios in which forest lands were converted to other cover types (to scrubland due to wildfires, to extensive and intensive agriculture, and to urban areas), and two scenarios that only involved changes in the non-forested matrix (renaturalization and intensification). Our results show that while the network of connecting elements for the species was very robust to the conversion of the forest habitat patches to different cover types, the different change scenarios in the landscape matrix could more significantly weaken its long-term validity and effectiveness. This is particularly the case when most of the key connectivity providers for the nuthatch are located outside the protected areas or public forests in Galicia, where biodiversity-friendly measures might be more easily implemented. We discuss how the methodology can be applied to a wide range of forest landscape management situations, where both the conservation of the forest critical areas and an adequate management of the landscape matrix between them are of concern to achieve the sustainability of the ecological flows and ecosystem services at the wider forest landscape scale.