Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling Method and Tree-Age Affect Soil Organic C and N Contents in Larch Plantations

We currently have a poor understanding of how different soil sampling methods (pedogenetic horizon versus fixed-depth) influence the evaluation of soil properties. Here, 159 soil profiles were sampled from larch (Larix gmelinii) plantations in northeast China using both the pedogenetic horizon and fixed-depth sampling methods. Analysis of variance was used to determine how sampling method influences the assessment of the spatial variation in the concentration and storage of soil organic C (SOC) and N (SON), as well as how these properties are affected by tree age-group ( 40 years). In both the 20 cm (surface) and 80 cm (whole profile) sampling depths, pedogenetic sampling resulted in 1.2- to 1.4-fold higher SOC and SON concentrations than fixed-depth sampling. Surface soil nutrient storage between the two sampling methods was not significantly different, but was it was 1.2-fold higher (p 40 years age-group than in the <20 years age-group (p < 0.05), while SON was the highest in the 20–40 years age-group (p < 0.05). The total amount of SOC and nutrients in soil is fixed regardless how you sample, it is the different assumptions and different ways of extrapolation from samples to the population that cause sampling by horizon versus fixed depth to lead to different conclusions. Our findings highlight that soil sampling method and tree age-group affect the determination of the spatial variation of SOC and SON and future soil assessments should control for methodological differences.
Fingerprint Mosaicking by Rolling with Sliding

Numerous fingerprint-based verification systems have been widely adopted throughout the modern world, since these systems are convenient to use and relatively superior to other biometric systems in terms of price and performance. One advantage of small sensors (e.g., solid-state sensors) is that they can be used with many applications (e.g., laptops, cellular phones). However, information about the fingerprint is limited due to the small physical size of the sensing area , as shown in Fig.
Working in and With Community: Leading for Partnerships in a Community School

ABSTRACT The community school model is a strategy for addressing educational and place-based inequities. Our case study of a community school in Pennsylvania examines the role of the principal in leading toward authentic partnerships with families and community organizations. Our findings demonstrate how the school principal’s approach to partnerships evolved toward expanded notions of student outcomes, fostering democratic dialogue and productive disagreement, building and sustaining relational trust, and negotiating reciprocity for generativity. We conclude with implications and recommendations for educational leadership practices that cultivate working in and with communities. This study contributes to growing scholarship about leadership in community schools.
Yes we can!? Annotating English modal verbs

This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain. We describe our annotation scheme and discuss problematic cases for modality annotation based on the inter-annotator agreement during the annotation. Furthermore, we present experiments on automatic sense tagging, showing that our annotations do provide a valuable training resource for NLP systems.
The integration of microRNA target data by biclustering techniques opens new roads for signaling networks analysis

Motivation and Objectives MicroRNAs (miRNAs) are key modulators of gene expression. In addition to their recognised role in embryonic and adult cell proliferation and differentiation (Ren et al., 2009), many recent studies on diverse types of human cancer have demonstrated that miRNAs are functionally integrated into those oncogenic pathways that are central to tumorogenesis (Olive et al., 2010). Although microarray profiling and next generation sequencing technologies have allowed researchers to discover much of their structural and functional features as well as many new miRNAs, the current challenge is to understand their specific biological functions and mechanisms through which they are able to ensure cell homeostasis and to control developmental timing and cancer progression. This is not a trivial task because the post-transcriptional regulation of gene expression mediated by miRNAs is rarely resolved by a simple one-to-one interaction between a miRNA and a target gene. It is much more complex, often involving multiple binding of the same miRNA and/or of different miRNAs in a cooperative manner. The combinatorial effects of different miRNAs on the same gene, or on different genes of the same pathway, is an essential part of the mechanism through which they are able to fine-tune signaling pathways (Inui et al., 2010). Indeed, the effect of a miRNA may change depending on which other miRNAs are co-expressed or silenced, which in turn depends on the specific context in which the cell, the tissue or the organism is considered. This makes the interpretation of miRNAs expression profile really difficult and a mere analysis of the list of differentially expressed genes cannot provide enough information to elucidate the multiplicity of potential miRNA:mRNA interactions. In this context, the exploitation of data mining techniques, and in particular of biclustering algorithms, is considered as a useful approach to search the correlations among miRNAs and mRNAs. However, as each miRNA may target hundreds of genes, the selection of the most significant results for further experimental validations still remains a challenging task for many biologists. The proposed method, which is implemented in the system HOCCLUS2, has been designed to analyse data of miRNA:mRNA interactions (derived from expression arrays or from large sets of predictions) in order to detect significant co-regulatory partnerships. In particular, the aim is to provide the biologists with a tool which can support them in two challenging tasks, that is, the detection of actual miRNAs target genes and the identification of the context-specific co-associations of different miRNAs. A further contribution to the considered research consists in the ranking of the extracted biclusters on the basis of the semantic similarity between the target genes, which allows the biologists to easily select the most significant results, from a biological view point. Availability: http://www.di. uniba.it/~ceci/micFiles/ systems/HOCCLUS2/index.html
Faint members of the Chamaeleon I cloud

We present a survey of the central 100 arcmin 2 of the Chamaeleon I star forming cloud, including objective prism spectroscopy in the H region and deep imaging in the near- infrared. We estimate the expected number of very low mass objects within the survey, taking as a reference the higher mass members identified in previous studies, and assuming different ages and slopes of the initial mass function of the Chamaeleon I population. A new approach is introduced to estimate the con- tribution of background objects to the counts of low luminosity sources. This method takes advantage of the fact that the con- tribution of Chamaeleon I members should be negligible at the faintest magnitudes covered by our survey for any reasonable shape of the initial mass function. K-band source counts indicate the absence of a significant population of very low mass stars, implying that the initial mass function at very low masses, approximated by a power law, has a form (M)d M/M 1 dM or flatter. This conclusion is in qualitative agreement with the discovery of six new emission line objects in the objective prism survey, and with the fact that only 2-3 faint objects are detected in the region of the (J H), (H K) diagram diagnostic of near infrared excesses of cir- cumstellar origin. The masses of the new emission line objects, derived from recent pre-main sequence evolutionary tracks, are found to be near, and possibly below, the hydrogen burning limit, and their ages to be younger than 3 10 6 years. One of them is found to be a bona-fide brown dwarf, and its detection in a deep ROSAT exposure makes it the first, and so far the only, brown dwarf known to emit X-rays (Neuh¨ auser & Comer´ 1998, Science, 282, 83). The near-infrared properties of the H emission objects suggest that, unlike at higher masses, strong H emission near the hydrogen-burning limit is not accompa- nied by infrared excess detectable in the K band. Comparing the numbers of very low mass objects expected from K band counts with the number of new H-emitting members, for which we derive individual masses and ages, we find that the spectro- scopic survey samples the initial mass function completely, or nearly completely, down to the hydrogen-burning limit.
An Autonomic Active Queue Management Mechanism to Improve Multimedia Flow Delivery Quality

With the rapid development of network technology and network services, the users’ requirements of network QoS are also improved. The traditional Active Queue Management(AQM) mechanisms are not adequate to provide QoS guarantee to multimedia video traffics effectively. To provide better quality of service to the multimedia applications in Mobile Ad Hoc Network(MANETs), whose resource is limited and changed dynamically, in this paper, we propose an Active Queue Management mechanism with autonomic features, named Autonomic Active Queue Management (AAQM). AAQM tries to adjust its behavior according to service and network condition information. Simulation under NS2 in MANETs has shown that AAQM is capable of improving the quality of multimedia video streams.
EDL3 is an F-box protein involved in the regulation of abscisic acid signalling in Arabidopsis thaliana

The EID1-like protein 3 (EDL3) shows high similarity to EID1 (Empfindlicher im dunkelroten Licht 1), an F-box protein that functions as a negative regulator in the signalling cascade downstream of the phytochrome A photoreceptor in Arabidopsis thaliana. Analyses revealed a strong and rapid induction of EDL3 gene expression under osmotic stress, high salinity, and upon abscisic acid (ABA) application. Therefore, it was speculated that EDL3 is involved in the regulation of responses controlled by this plant hormone, which not only regulates many aspects of plant development but also integrates responses towards temperature, drought, osmotic, and salt stresses. Physiological data obtained with over-expresser lines and a conditional knock-down mutant demonstrated that EDL3 functions as a positive regulator in ABA-dependent signalling cascades that control seed germination, root growth, greening of etiolated seedlings, and transition to flowering. Results further demonstrate that EDL3 regulates anthocyanin accumulation under drought stress. The observed effects on physiological responses fit to tissue-specific expression patterns obtained with EDL3-promoter:GUS lines. Bimolecular Fluorescence Complementation assays and yeast two-hybrid analyses showed that EDL3 carries a functional F-box domain. Thus, the protein is presumed to act as a component of a ubiquitin ligase complex that specifically directs negatively acting factors in ABA signalling to degradation via the proteasome.
7 Seasonal Climate Prediction for the Australian Sugar Industry Using Data Mining Techniques

The ability to predict rainfall with adequate certainty and lead time is beneficial to both industry and public. Periods of high or low seasonal rainfall can have many follow on effects to agriculture, industry, public health and, water supply and management. In order to implement decisions, planning and management strategies to contend with these issues, the ability to predict seasonal rainfall quantities is of great importance (Klopper et al., 2006). Climate conditions are known to influence the cultivation of Sugarcane influencing planting, harvesting and milling (Muchow and Wood, 1996; Everingham et al., 2002; Jones and Everingham, 2005). Unforeseen climate events such as excessive rainfall, can adversely effect the agricultural practices related to Sugarcane cultivation. The Australian Sugarcane harvest period commences in May/June and aims to finish by November/December before the start of the rainy season (Everingham et al., 2002). The risk of excessive rainfall disrupting harvest operations is greatest towards the end of the sugarcane harvest period (Muchow and Wood, 1996; Everingham et al., 2002). Therefore, improved seasonal rainfall prediction during the October-December period is beneficial. Statistical prediction of seasonal rainfall can be performed using a variety of techniques including: regression (Singhrattna et al., 2005), classification methods (Drosdowsky and Chambers, 2001), canonical correlation analysis (Landman and Mason, 1999) and neural networks (Mason, 1998). All statistical models require predictor variables which act as proxies for describing the behaviour of response variables (Hastie et al., 2001). When considering a seasonal forecast model, it is useful to draw predictor variables from a climate data set that is both historically and spatially complete (Washington and Downing, 1999). One of the most temporally and spatially resolute climate parameters is sea surface temperature (SST) data. Consequently, SST data are often used as an empirical measure of the ocean-atmosphere interaction in statistical climate models. However, a vast proportion of potential SST predictors may be redundant. Therefore employing data mining methods for the purpose of feature extraction and data reduction is advantageous. Principal component analysis (PCA) is a commonly used feature extraction method that reduces data dimensionality whilst retaining the majority of the variability (Jolliffe, 1986). As sea surface temperature data sets are large, it is useful to perform PCA data reduction such that the bulk of the variability is contained in a small subset of variables (Wilks, 1995). PCA also referred to as empirical orthogonal function (EOF) analysis is commonly used throughout climate research (Wilks, 1995). PCA is popular because it is available in most
Performance Analysis of DVS Algorithms for Reducing Processor Energy Consumption

Dynamic voltage scaling (DVS) is an energy saving technique, which is achieved by reducing energy dissipation of the core by lowering the supply voltage and operating frequency. DVS technology lies at the heart of advanced digital products ranging from laptops, personal digital assistants, mobile phones and even game consoles. DVS has been widely acknowledged as a powerful technique for trading off for power consumption and delay for processors. In this paper a new DVS algorithm is presented, which achieves better energy savings with deadline guarantees by splitting task between discrete frequency levels and task deferring technique. The proposed DVS algorithm is compared with existing basic RT-DVS algorithms for real-time periodic task sets, analyzing their energy efficiency, and discussing the performance differences quantitatively. The simulation results show that the proposed algorithm closely approaches the theoretical lower bond on energy consumption and reduces the energy consumption by 10-15% over the existing DVS algorithm.
On the Iota-Delta Function: Mathematical Representation of Two-Dimensional Cellular Automata

Even though the patterns appearing in the evolution of two-dimensional cellular automata have been deeply studied, the evolution rules themselves have not received the same amount of attention. In the present paper, the evolution rules of totalistic and outer totalistic two-dimensional cellular automata for a set of neighborhood templates have been expressed in terms of the iota-delta function. Additionally, the idea of iota-delta function bases for the rule space of two-dimensional cellular automata evolution is introduced. By means of suitable bases, every two-dimensional cellular automaton can have its evolution rule described in terms of the iota-delta function. This approach enables investigating the evolution rules as mathematical functions and the evolution itself as a function composition process.
The Firm, the Market, and the Law

Few other economists have been read and cited as often as R.H. Coase has been, even though, as he admits, "most economists have a different way of looking at economic problems and do not share my conception of the nature of our subject. " Coase's particular interest has been that part of economic theory that deals with firms, industries, and markets—what is known as price theory or microeconomics. He has always urged his fellow economists to examine the foundations on which their theory exists, and this volume collects some of his classic articles probing those very foundations. "The Nature of the Firm" (1937) introduced the then-revolutionary concept of transaction costs into economic theory. "The Problem of Social Cost" (1960) further developed this concept, emphasizing the effect of the law on the working of the economic system. The remaining papers and new introductory essay clarify and extend Coarse's arguments and address his critics. "These essays bear rereading. Coase's careful attention to actual institutions not only offers deep insight into economics but also provides the best argument for Coase's methodological position. The clarity of the exposition and the elegance of the style also make them a pleasure to read and a model worthy of emulation. "—Lewis A. Kornhauser, Journal of Economic Literature Ronald H. Coase was awarded the Nobel Prize in Economic Science in 1991.
Efficient face segmentation based on face attention model and seeded region merging

This paper presents an efficient face segmentation approach based on face attention model and seeded region merging. A face attention model that jointly exploits the information of skin color and eyepsilas position is first constructed to obtain a facial saliency map, which indicates the position of possible faces and is used to determine seed regions. Then a seeded region merging algorithm based on regional facial saliency is proposed to generate a sequence of regions, and the region with the highest regional facial saliency is selected to represent each face. Experimental results on a variety of images demonstrate the good segmentation performance of the proposed face segmentation algorithm.
Multispectral remote sensing of landscape level foliar moisture: techniques and applications for forest ecosystem monitoring

Broad-scale monitoring of varying moisture levels of leaves has ramifications for understanding fire poten- tial, biogeochemistry, and ecosystem dynamics. Five different shortwave infrared (SWIR)-derived spectral indices, prin- cipal components analysis (PCA), and the tasseled cap transformation (TCT), derived from Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, were used to quantify landscape-level foliar moisture in an ecosystem dominated by Pinus ponderosa P. & C. Lawson. Landsat TM data demonstrated stronger correlations with in situ calculations of foliar moisture than did ASTER data. The second principal component correlated strongly with ground data (r 2 = 0.765). The Landsat-derived TCT wetness component was significantly correlated with ground data (r 2 = 0.638) as well as a normalized difference NIR/SWIR ratio (r 2 = 0.834). The spectral indices and TCT are more practical for ecosystem moisture monitoring than PCA because of the empirical nature of PCA. Based on these results, we recommend modifications to existing methods of satellite-based fire susceptibility monitoring to account for primary effects of vegetation curing and temporal variation in ground fuels. Resume : La detection a grande echelle des variations de teneur en eau des feuilles peut etre utile pour comprendre le potentiel calorifique, la biogeochimie et la dynamique des ecosystemes. Cinq indices spectraux differents, derives de l'infrarouge ondes courtes (IROC), l'analyse en composante principale (ACP), l'espace indiciel transforme (TCT) de- rive des donnees satellitaires du capteur TM de Landsat (TM) et du radiometre spatiale de pointe pour l'etude de la re-
The Unification and Assessment of Multi-Objective Clustering Results of Categorical Datasets with H-Confidence Metric

Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall; ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical datasets and compared them against single objective clustering result in terms of purity and distance measure of k-modes clustering. Our clustering results have been assessed to find the most natural clustering. Our results get hold of existing classes decided by human experts.
An adaptive steganography technique for videos using Steganoflage

Steganography is define as the process of hiding information in a multimedia carrier. Its ultimate objectives are undetectability and robustness of the hidden data. It is recognized as adaptive steganography as the data is embed in to the specific Region of Interest (ROI) of the cover image for the purpose of safety of the inserted data. The assurance of both imperceptibility and robustness requirements are the main objectives of developing an image-hiding technique. The focus is on embedding the data in the skin region of a video frame. Thus focus is on the skin detection algorithm to extract the skin region. This is act as the region of interest for embedding the secret message. The video frames are then converted to YCbCr colour space to perform embedding. The frame having least MSE is selected to embed a secret data. The secret data is then inserted into the Cb component of YCbCr of a frame having least MSE. After embedding secret data Steganoflage video is created by transforming the data into RGB colour space.
The GoldenBricks assembly: A standardized one-shot cloning technique for complete cassette assembly

The BioBricks are a collection of parts that can be assembled one by one in a standardized way. Golden Gate [1] is a technique that is capable of assembling several dozens of parts simultaneously. Taking inspiration from both techniques, we developed a new method we call GoldenBricks, merging the advantages of both technique. GoldenBricks make the assembly of a fully working eukaryotic or prokaryotic cassette possible from standard biological parts, while preserving the compatibility with the RFC 10 BioBricks format. The assembly process in itself takes a single day.