Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of the Denitrification-Decomposition Model to Predict Carbon Dioxide Emissions under Alternative Straw Retention Methods

Straw retention has been shown to reduce carbon dioxide (CO2) emission from agricultural soils. But it remains a big challenge for models to effectively predict CO2 emission fluxes under different straw retention methods. We used maize season data in the Griffith region, Australia, to test whether the denitrification-decomposition (DNDC) model could simulate annual CO2 emission. We also identified driving factors of CO2 emission by correlation analysis and path analysis. We show that the DNDC model was able to simulate CO2 emission under alternative straw retention scenarios. The correlation coefficients between simulated and observed daily values for treatments of straw burn and straw incorporation were 0.74 and 0.82, respectively, in the straw retention period and 0.72 and 0.83, respectively, in the crop growth period. The results also show that simulated values of annual CO2 emission for straw burn and straw incorporation were 3.45 t C ha−1 y−1 and 2.13 t C ha−1 y−1, respectively. In addition the DNDC model was found to be more suitable in simulating CO2 mission fluxes under straw incorporation. Finally the standard multiple regression describing the relationship between CO2 emissions and factors found that soil mean temperature (SMT), daily mean temperature (T mean), and water-filled pore space (WFPS) were significant.
The transformation of education in South Africa since 1994 : a historical-educational survey and evaluation

This study entails a critical investigation into the issue and trends that shaped Black perspectives on education in South Africa in the period between 1652 and 1993. A theoretical-philosophical exposition is presented to establish the fundamental characteristics of education and therefore characteristics valid for every human being under any circumstances at any time. These characteristics were used to eventually appraise the issues and trends underlying the shaping of Black perspectives on education in South Africa. This study has revealed that the shaping of Black perspectives on education in South Africa goes hand in hand with the growing realization among Blacks that education is not merely a political issue but also deeply imbedded in the interconnectedness and/or differences between social realities such as the church, the school itself, teachers' associations, the news media et cetera. This should be accounted for by every citizen in a responsible way.
Tutorial about the implementation of a vehicular high speed communication system

The growing network of mechatronic components within a vehicle, as well as their interaction creating new systems, request special and higher challenges to future communication systems. Besides the software implementation of these innovative systems, the investigation of new physical layers is of essential interest and of special interest in this article. Meanwhile the use of the electrical power supply grid as a communications medium has reached a considerable level of quality. Due to this success, it appears rewarding to open further areas of application for the new and innovative powerline technology, e.g. automotive power line communications. This paper investigates the properties of automotive onboard power supply networks for the purpose of an integrated power and data transmission. It is obvious, that the electrical power supply system within an automobile has very few in common with a residential area's supply grid. This means that new and different approaches must be found. This article introduces a brief tutorial about the realization of a vehicular high speed data communication on powerlines. Besides the investigation of existing harnesses, a prospect of required modifications and network components are presented.
A hybrid approach to concept extraction and recognition-based matching in the domain of human resources

We describe the Convex system for extracting concepts from resumes and subsequently matching the best qualified candidates to jobs. A blend of knowledge-based and speculative concept extraction provides high quality results even outside the scope of the built-in knowledge. A comparison test shows the results found by Convex are significantly better than those found by engines using a keyword or statistical conceptual approach.
Perturb And Simplify: Multi-level Boolean Network Optimizer

In this paper, we discuss the problem of optimizing a multi-level logic combinational Boolean network. Our techniques apply a sequence of local perturbations and modifications of the network which are guided by the automatic test pattern generation ATPG based reasoning. In particular, we propose several new ways in which one or more redundant gates or wires can be added to a network. We show how to identify gates which are good candidates for local functionality change. Furthermore, we discuss the problem of adding and removing two wires, none of which alone is redundant, but when jointly added/removed they do not affect functionality of the network. We also address the problem of efficient redundancy computation which allows to eliminate many unnecessary redundancy tests. We have performed experiments on MCNC benchmarks and compared the results to those of misII and RAMBO. Experimental results are very encouraging.
Working Memory Performance, Learning and Study Strategies and Learning Styles of Dyslexic and Non Dyslexic Adult Learners

Past research has shown that working memory is a good predictor of learning performance. The working memory processes determine an individuals’ learning ability and capability. The current study was conducted to examine the: (a) differences in the working memory performance of dyslexic students in postsecondary institutions, (b) differences in dyslexic students’ study strategies and learning styles, (c) differences in the working memory profiles of non-dyslexic university students based on their disciplines (science versus humanities), (d) differences between non-dyslexic science and humanities students in their study strategies and learning styles, (e) relationship between working memory and study skills and (f) hypothesised memory models that best fit the actual data gathered using structured equation modelling technique. Two separate studies were performed to address these aims. For Study 1, a group of 26 dyslexic individuals along with a group of 32 typical non-dyslexic students were assessed for their working memory and study skills performances. A significant difference in working memory was found between the two groups. The dyslexic group showed weaker performance in the verbal working memory tasks which concurs with previous findings. The result also provides support that weakness in the verbal working memory of dyslexic individuals still exist and persist into adulthood. Significant differences in the students’ study skills were also identified. Dyslexic students reported to be more anxious and concerned about their academic tasks, lack in concentration and attention, less effective in selecting important materials during reading, using less test taking and time management strategies. Significant relationships were found between working memory component and selected study skills. Study 2 was conducted to investigate working memory differences and study skills of nondyslexic students based on their disciplines. A sample of 168 university learners consisted of 82 sciences and 86 humanities students were recruited. Analysis of data revealed that students from the sciences disciplines show significantly weaker performance in the verbal short-term memory and verbal working memory tasks. Results from both studies showed similarity in the working memory profiles of dyslexic and science students. Findings in both of the studies with regards to the working memory models and learning and study skills are discussed with practical implications and recommendations for future research.
WR-3 Band Waveguides and Filters Fabricated Using SU8 Photoresist Micromachining Technology

This paper demonstrates a two-layer SU8 photoresist micromachining technology that has similar performance to conventionally machined metal. The technology is demonstrated in the WR-3 band (220-325 GHz). Three different WR-3 band circuits, namely a WR-3 band straight through waveguide, a bandpass filter and a dual-band filter are demonstrated. For the measurements, a conventionally precision machined metal block was used for the WR-3 band waveguide and the bandpass filter to achieve good calibration and accurate interconnection with standard waveguide flanges; whereas, for the dual-band filter, two back-to-back right-angle bends are added in order to achieve accurate, reliable waveguide interconnection without using the metal block. A measured average insertion loss of 0.03 dB/mm has been achieved for the 14.97 mm long straight through waveguide. This is comparable to the loss of around 0.02 dB/mm for a standard metal waveguide at this frequency. The fifth-order waveguide filter exhibits an 8% 3 dB bandwidth at a central frequency of around 300 GHz. The minimum passband insertion loss was measured to be around 1 dB and the return loss was better than 10 dB throughout the passband. The filter results showed a notable improvement over those obtained from the separate SU8 layer technique that was also used to make the same devices for comparison. To further demonstrate the advantages of the new two-layer SU8 micromachining technique, the dual-band filter included isolated regions in the waveguide channels that would have not been possible for micromachining using the previous separate single layer technique. The performance of the micromachined dual band filter was excellent in terms of very low insertion losses on both passbands.
Functions Required for Extracellular Quinolone Signaling by Pseudomonas aeruginosa

ABSTRACT A set of 30 mutants exhibiting reduced production of the phenazine poison pyocyanin were isolated following transposon mutagenesis of Pseudomonas aeruginosa PAO1. The mutants could be subdivided into those with defects in the primary phenazine biosynthetic pathway and those with more pleiotropic defects. The largest set of pleiotropic mutations blocked the production of the extracellular Pseudomonas quinolone signal (PQS), a molecule required for the synthesis of secondary metabolites and extracellular enzymes. Most of these pqs mutations affected genes which appear to encode PQS biosynthetic functions, although a transcriptional regulator and an apparent response effector were also represented. Two of the genes required for PQS synthesis (phnA and phnB) had previously been assumed to encode phenazine biosynthetic functions. The transcription of one of the genes required for PQS synthesis (PA2587/pqsH) was regulated by the LasI/R quorum-sensing system, thereby linking quorum sensing and PQS regulation. Others of the pleiotropic phenazine-minus mutations appear to inactivate novel components of the quorum-sensing regulatory network, including one regulator (np20) previously shown to be required for virulence in neutropenic mice.
Organizing for Innovation Ambidexterity in Emerging Markets

Firms struggle to be ambidextrous in the sense of being able to successfully manage both new and incremental innovation activities simultaneously. Applying the knowledge-based view, we examine the important moderating influences of supplier involvement and foreignness on the relationship between innovation ambidexterity and performance. We test our hypotheses at the business-unit level of analysis in the emerging market of Brazil. We examine two types of innovation ambidexterity: the balanced dimension and the combined dimension. We found that firms possessing greater supplier involvement reap higher performance benefits from the combined dimension of innovation ambidexterity. Last, foreign subsidiaries also achieved higher levels of performance than domestic firms from the combined dimension of innovation ambidexterity.
Multilabel Classification by BCH Code and Random Forests

Multilabel classification deals with problems in which an instance can belong to multiple classes. This paper uses error correcting codes for multilabel classification. BCH code and Random Forests learner are used to form the proposed method. Thus, the advantage of the error-correcting properties of BCH is merged with the good performance of the random forests learner to enhance the multilabel classification results. Three experiments are conducted on three common benchmark datasets. The results are compared against those of several exiting approaches. The proposed method does well against its counterparts for the three datasets of varying characteristics. positive relevance classification is associated with the instance. Zhang and Zhou (3) report a multi-label lazy learning approach which is derived from the traditional k-Nearest Neighbour (kNN) and named ML-kNN. For each unseen instance, its k nearest neighbors in the training set are identified. Then, based on statistical information gained from the label sets of these neighboring instances, maximum a posteriori principle is used to determine the label set for the unseen instance. Zhang and Zhou (4) present a neural network-based algorithm that is Backpropagation for Multi-Label Learning named BP-MLL. It is based on the backpropagation algorithm but uses a specific error function that captures the characteristics of multi-label learning. The labels belonging to an instance are ranked higher than those not belonging to that instance. Tsoumakas and Vlahavas (5) propose RAndom K- labELsets (RAKEL) which is an ensemble method for multilabel classification based on random projections of the label space. An ensemble of Label Powerset (LP) classifiers is trained on smaller size of label subset randomly selected from the training data. The RAKEL takes into account label correlations by using single- label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. It therefore tackles difficulty of learning due to a large number of classes associated with only a few examples. Several other important works can be also found in (6-11). The main motivation behind the work reported in this paper is our desire to improve the performance of the multilabel classification methods. This paper explores the use of error correcting code for multilabel classification. It uses the Bose, Ray-Chaudhuri, Hocquenghem (BCH) code and Random Forests learner to form a method that can deal with multilabel classification problems improving the performance of several popular exiting methods. The description of the theoretical framework as well as the proposed method is given in the following sections.
An Approach to Analyzing Temporal Properties in UML Class Models

The Unified Modeling Language (UML) Class Models are widely used for modeling the static structure of object-oriented software systems. Temporal properties of such systems can expressed using TOCL, a temporal extension to the Object Constraint Language (OCL). Verification and validation of temporal properties expressed in TOCL is non-trivial and there are no automated tools that can aid such analysis. Existing approaches rely on transforming the UML models to another language that supports automated analysis. Such transformation is complex and can introduce errors. Towards this end, we propose an approach for directly analyzing temporal properties expressed in TOCL. We present a case study based on the Steam Boiler Control System to demonstrate the applicability of the approach.
Contribution to control strategies for converter connected to unbalanced grid

The paper deals with developed and experimentally tested effective synchronization and power control techniques for grid-connected converter. A new FLL (Frequency Locked Loop) technique is developed that incorporates advantages of both the cascaded delayed signal cancellation and dual second order generalized integrator schemes. Experimental results are evaluated in terms of quality of the positive voltage sequence, frequency and phase angle identification for the grid voltage disturbed by harmonic, subharmonic, and negative sequence components as well as for amplitude, frequency, and phase angle jumps in the voltage. A novel power control technique for grid-connected converter under unbalanced voltage conditions is referred as well. The current positive and negative sequences during grid voltage sags are controlled to assure a proper exchange of active and reactive powers without substantial power ripples, and especially, without current harmonics.
A TimeStamp based Multi-version STM Protocol that satisfies Opacity and Multi-Version Permissiveness

Software Transactional Memory Systems (STM) are a promising alternative to lock based systems for concurrency control in shared memory systems. In multiversion STM systems, each write on a transaction object produces a new version of that object. The advantage obtained by storing multiple versions is that one can ensure that read operations do not fail. Opacity is a commonly used correctness criterion for STM systems. Multi-Version permissive STM system never aborts a read-only transaction. Although many multi-version STM systems have been proposed, to the best of our knowledge none of them have been formally proved to satisfy opacity. In this paper we present a time-stamp based multiversion STM system that satisfies opacity and mv-permissiveness. We formally prove the correctness of the proposed STM system. We also present garbage collection procedure which deletes unwanted versions of the transaction objects and formally prove it correctness.
Organizational Silence A Predictor of Organizational Commitment in Higher Education Institution

Purpose- The purpose of this paper is to answer the question : how the organizational silence is related with organizational commitment and how it affects the employees. The focus is particularly on the higher education institutions in the federal territory. Design/ Methodology/Approach- The scientific data which has been collected from the teachers of higher education in the form of survey questionnaire on which statistical techniques were implied. The sample size was consist of 80 teachers of public educational institutions out of which 55 were received and 44 were stand correct. The approach adopted in this study is exploratory in nature in order to explore relationship between organizational silence and organizational commitment. Findings- In our findings any relationship of the dimensions of organizational silence in context of reasons why employees remain silent is not found with organization commitment. We believe that this must be related to the chosen organization. The correlation is found positive between organization silence and organizational commitment but regression analysis shows in significance of organizational silence with organizational commitment Research limitations/ Implications- The sample used is relatively small due to lack of time and resources. The research is based upon higher education sector only. Practical implications- Managers will find the study useful in evaluating the environment of organization for employees which feel silence behavior, including the level of organizational commitment for the employees. Originality/Value -The study is focused on the different dimensions of organizational silence which include in the scale and correlates to something that is lacking in the literature. The paper will give birth to the debate and discussion of different dimensions of organizational silence in with the relationship of organizational commitment, and contribute or plugin new experimental fact findings. It also focus on the increasing importance of the organizational silence which is prevailing in the organizations of the current era. Keywords: Organizational silence; Organizational commitment; Higher education sector; Federal territory
URJC&UNED at ImageCLEF 2013 Photo Annotation Task

In this work we describe the URJC&UNED participation in the ImageCLEF 2013 Photo Annotation Task. We use visual informa- tion to find similar images and textual information extracted from the training set to label the test images. We propose two additional visual features apart from the provided by the organization and a method to expand the textual information available. The new visual features pro- posed define the images in terms of color and texture, and the textual method uses WordNet to obtain synonyms and hyperonyms of the tex- tual information provided. The score of each concept is obtained by using a co-ocurrence matrix that matches concepts and textual information of the training images. The experimental results show that the proposal is able to obtain competitive results in all the performance measures used.
PTAT: An efficient and precise tool for collecting detailed TLB miss traces

It is well known that the TLB performance impacts the memory system performance, which is critical for overall system performance. Similar to multi-level caches, multilevel TLBs have become an important leverage for boosting data access performance. Applications have increasingly large working sets. Servers targeting such applications have thus been built with ever larger main memory capacities, but there has been no commensurate growth in TLB sizes. Designing high performance and energy efficient memory hierarchies require insight into the behavior of current designs: when do they work well, and when do they fall short of expectations. Profiling the TLB misses is the prerequisite for memory system optimization. Both designing efficient TLB architecture and TLB-friendly applications require analysis of TLB miss behavior. Although researchers have extensively studied TLB behavior, current approaches have some issues in either efficiency or precision.
Magnetoencephalographic imaging of resting-state functional connectivity predicts postsurgical neurological outcome in brain gliomas.

BACKGROUND The removal of brain tumors in perieloquent or eloquent cortex risks causing new neurological deficits in patients. The assessment of the functionality of perilesional tissue is essential to avoid postoperative neurological morbidity. OBJECTIVE To evaluate preoperative magnetoencephalography-based functional connectivity as a predictor of short- and medium-term neurological outcome after removal of gliomas in perieloquent and eloquent areas. METHODS Resting-state whole-brain magnetoencephalography recordings were obtained from 79 consecutive subjects with focal brain gliomas near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering. The mean imaginary coherence between voxels in and around brain tumors was compared with contralesional voxels and used as an index of their functional connectivity with the rest of the brain. The connectivity values of the tissue resected during surgery were correlated with the early (1 week postoperatively) and medium-term (6 months postoperatively) neurological morbidity. RESULTS Patients undergoing resection of tumors with decreased functional connectivity had a 29% rate of a new neurological deficit 1 week after surgery and a 0% rate at 6-month follow-up. Patients undergoing resection of tumors with increased functional connectivity had a 60% rate of a new deficit at 1 week and a 25% rate at 6 months. CONCLUSION Magnetoencephalography connectivity analysis gives a valuable preoperative evaluation of the functionality of the tissue surrounding tumors in perieloquent and eloquent areas. These data may be used to optimize preoperative patient counseling and surgical strategy.