Miracle-dz/UniFAR-FTUs huggingface dataset:
THE INFLUENCE OF KNOWLEDGE MANAGEMENT EFFECTIVENESS ON ADMINISTRATIVE INNOVATION AMONG MALAYSIAN MANUFACTURING FIRMS

The purpose this study is to examine the direct effect of knowledge management effectiveness on administrative innovation. Knowledge management effectiveness is conceptualised as comprising of knowledge acquisition effectiveness, knowledge sharing effectiveness, and knowledge application effectiveness. Three hypothesised relationships were tested using a sample of 171 large manufacturing firms in Malaysia. The results derived from the regression analyses showed that of the three dimensions of knowledge management effectiveness (knowledge acquisition effectiveness, knowledge sharing effectiveness, and knowledge application effectiveness), only knowledge acquisition effectiveness was found to have a significant positive effect on administrative innovation. The implications of the study as well as suggestions for future studies are also discussed.
An Improved Rao-Blackwellized Particle Filter for SLAM

Simultaneous localization and map building (SLAM) is one of the fundamental problems in robot navigation, and FastSLAM algorithms based on Rao-Blackwellized particle filters (RBPF) have become popular tools to solve the SLAM problems. For solving the potential limitations, which are the derivation of the Jacobian matrices, and particles impoverishment in SLAM algorithms, this paper proposes an improved algorithm based on unscented Kalman filter (UKF) for landmark feature estimate and particles resampling strategy to overcome the above- mentioned drawbacks. Experimental results demonstrate the effectiveness of the proposed algorithm.
A Search for Stars of Very Low Metal Abundance. V. Photoelectric UBV Photometry of Metal-weak Candidates from the Northern HK Survey

We report photoelectric UBV data for 268 metal-poor candidates chosen from the northern HK objective-prism/interference-filter survey of Beers and colleagues. Over 40% of the stars have been observed on more than one night, and most have at least several sets of photometric measurements. Reddening estimates, preliminary spectroscopic measurements of abundance, and type classifications are reported.
Novel Coprinopsis cinerea Polyesterase That Hydrolyzes Cutin and Suberin

ABSTRACT Three cutinase gene-like genes from the basidiomycete Coprinopsis cinerea (Coprinus cinereus) found with a similarity search were cloned and expressed in Trichoderma reesei under the control of an inducible cbh1 promoter. The selected transformants of all three polyesterase constructs showed activity with p-nitrophenylbutyrate, used as a model substrate. The most promising transformant of the cutinase CC1G_09668. 1 gene construct was cultivated in a laboratory fermentor, with a production yield of 1.4 g liter−l purified protein. The expressed cutinase (CcCUT1) was purified to homogeneity by immobilized metal affinity chromatography exploiting a C-terminal His tag. The N terminus of the enzyme was found to be blocked. The molecular mass of the purified enzyme was determined to be around 18.8 kDa by mass spectrometry. CcCUT1 had higher activity on shorter (C2 to C10) fatty acid esters of p-nitrophenol than on longer ones, and it also exhibited lipase activity. CcCUT1 had optimal activity between pH 7 and 8 but retained activity over a wide pH range. The enzyme retained 80% of its activity after 20 h of incubation at 50°C, but residual activity decreased sharply at 60°C. Microscopic analyses and determination of released hydrolysis products showed that the enzyme was able to depolymerize apple cutin and birch outer bark suberin.
Patento-scientometric indicators for the selection of projects by investment funds

Purpose – This paper aims to assess a method that applies scientometric and patentometric indicators in the selection process of projects by seed capital funds. There is increasing interest in technology-based enterprises, for their capacity to contribute to economic and social development. Nevertheless, in practice, there is some difficulty in assessing non-financial criteria associated with technology for the purposes of choosing investment opportunities. Design/methodology/approach – The literature has presented various methods to instrumentalize the process of evaluation and selection of investment projects. This study focuses on an enterprise that received an investment by the largest seed capital fund in Brazil, to assess to what extent scientific and technological indicators can contribute to understanding the market potential of the firm’s technology. Findings – The results show that the use of scientometric and patentometric indicators favors the process of judging non-financial criteria, in part.
Analysis and Effects of Cytosolic Free Calcium Increases in Response to Elicitors in Nicotiana plumbaginifolia Cells Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.005579.

Cell suspensions obtained from Nicotiana plumbaginifolia plants stably expressing the apoaequorin gene were used to analyze changes in cytosolic free calcium concentrations ([Ca2+]cyt) in response to elicitors of plant defenses, particularly cryptogein and oligogalacturonides. The calcium signatures differ in lag time, peak time, intensity, and duration. The intensities of both signatures depend on elicitor concentration and extracellular calcium concentration. Cryptogein signature is characterized by a long-sustained [Ca2+]cyt increase that should be responsible for sustained mitogen-activated protein kinase activation, microtubule depolymerization, defense gene activation, and cell death. The [Ca2+]cyt increase in elicitor-treated cells first results from a calcium influx, which in turns leads to calcium release from internal stores and additional Ca2+ influx. H2O2 resulting from the calcium-dependent activation of the NADPH oxidase also participates in [Ca2+]cyt increase and may activate calcium channels from the plasma membrane. Competition assays with different elicitins demonstrate that [Ca2+]cyt increase is mediated by cryptogein–receptor interaction.
An Empirical Study on Machine Learning Models for Wind Power Predictions

Wind power prediction is of great importance in the utilization of renewable wind power. A lot of research has been done attempting to improve the accuracy of wind power predictions and has achieved desirable performance. However, there is no complete performance evaluation of machine learning methods. This paper presents an extensive empirical study of machine learning methods for wind power predictions. Nine various models are considered in this study which also includes the application and evaluation of deep learning techniques. The experimental data consists of seven datasets based on wind farms in Ontario, Canada. The results indicate that SVM, followed by ANN, has the best overall performance, and that k-NN method is suitable for longer ahead predictions. Despite the findings that deep learning fails to give improvement in basic predictions, it shows the potential for more abstract prediction tasks, such as spatial correlation predictions.
Under Consideration for Publication in Theory and Practice of Logic Programming Adding Plural Arguments to Curry Programs

Functional logic languages combine lazy (demand-driven) evaluation strategies from functional programming with non-deterministic computations from logic programming. To provide a strategy-independent semantics, most languages are based on the call-time choice semantics where parameters are passed as values. From an implementation point of view, the call-time choice semantics fits well with sharing performed by lazy languages. On the other hand, there are also situations where it is intended to pass non-deterministic arguments as sets of values in order to exploit the power of non-deterministic programming. This alternative parameter passing model is known under the name " plural " arguments. In this paper, we show how both mechanisms can be integrated in a single language. In particular, we present a novel technique to implement plural arguments in a call-time choice language so that existing implementations of contemporary functional logic languages can be easily re-used to implement plural parameter passing. 1 Motivation Functional logic languages support the most important features of functional and logic programming in a single language (see (Antoy and Hanus 2010; Hanus 2013b) for recent surveys). They provide higher-order functions and demand-driven evaluation from functional programming as well as logic programming features like non-deterministic search and computing with partial information (logic variables). This combination led to new new techniques to implement programming tools, like partial evaluators (Alpuente et al. 1998) or test case generators (Fischer and Kuchen 2007; Runciman et al. 2008). The execution model of contemporary functional logic languages, like Curry (Hanus (ed.) 2012) or TOY (López-Fraguas and Sánchez-Hernández 1999), is based on (some variant of) needed narrowing (Antoy et al. 2000) which subsumes demand-driven term rewriting, used to evaluate functional programs, and unification and resolution applied in logic programming. Needed narrowing is an optimal evaluation strategy for large classes of programs. Moreover, operations in functional logic programs can be also
Automatic Seamless Image Mosaicing: An Approach Based on Quad-tree Technique

In real-life situations, the field of view of the imaging devices is usually smaller than the scene to be imaged, due to the inherent limitations. Even if the whole scene is captured in a single exposure, it could result in an image having poor resolution. In such circumstances, it will never be possible to capture the scene as a single image for further processing or human inspection. The only solution to such a problem is to capture the scene by splitting the scene into adjacently placed multiple frames. This process warrants a post imaging operation that requires cohering of these small images to create a single image. In this paper, a new approach is used to create a single large image by adopting a quad-tree approach, where the image is decomposed into sub images to find the corresponding points in the sub images and thereby reducing the search space. The proposed algorithm reduces the search space to a greater extent for finding correspondence between the split images where the complexity reduces to the order of ) 1 ) 3 ( (log 2 4  n
The Chemical Industry: Regulation, Innovation, and Competitiveness

The effect of environmental regulation on innovation by firms and on competitiveness by countries has been a source of theorizing and debate for more than half a century. In particular, the chemical industry, which arose in Europe and the US at the end of the 19 century together with the second industrial revolution, has been accused of causing numerous large-scale accidents, which have had significant impact on the environment. Indirectly, these accidents can be seen as responsible for the gradual growth of environmental consciousness and, beginning in the 1970s, the development of environmental regulations, first in the industrialized countries and later in developing economies.
Optimal Dividends Under a Ruin Probability Constraint

ABSTRACT We consider a classical surplus process modified by the payment of dividends when the insurer's surplus exceeds a threshold. We use a probabilistic argument to obtain general expressions for the expected present value of dividend payments, and show how these expressions can be applied for certain individual claim amount distributions. We then consider the question of maximising the expected present value of dividend payments subject to a constraint on the insurer's ruin probability.
A Multi-objective Discrete PSO Algorithm Based on Enhanced Search

This paper proposes a new multi-objective discrete particle swarm optimization algorithm based on enhanced search (MODPSO-ES). Combined with the global search strategy based on elitist seed repopulation, a new velocity update rule for particles is presented to enhance the exploitation and exploration ability of MODPSO-ES. The performance of MODPSO-ES is verified on four benchmark problems. Empirical studies demonstrate that MODPSO-ES is highly competitive in both approximating the Pareto-optimal front and maintaining the diversity of the solutions on the front.
Robust Training of Multilayer Neural Networks Using Parameterized Online Quasi-Newton Algorithm

This paper describes a novel robust training algorithm based on quasi-Newton process in which online and batch error functions are associated by a weighting coefficient parameter. The parameter is adjusted to ensure that the algorithm gradually changes from online to batch. That is, the transition from the online method to the batch one is parameterized in the proposed algorithm in the same concept as the improved online quasi-Newton algorithm introduced in [9][10]. Furthermore, an analogy between the proposed and Langevin algorithms is considered. Langevin algorithm is a gradient-based continuous optimization method incorporating Simulated Annealing concept. The proposed algorithm is employed for robust neural network training purpose. Neural network training for some benchmark problems with high-nonlinearity is presented to demonstrate the validity of proposed algorithm. The proposed algorithm achieves more accurate and robust training results than the other quasi-Newton based training algorithms.
Implementation of Spatial Transformation Rules for Goal-Directed Reaching via Gain Modulation in Monkey Parietal and Premotor Cortex

Planning goal-directed movements requires the combination of visuospatial with abstract contextual information. Our sensory environment constrains possible movements to a certain extent. However, contextual information guides proper choice of action in a given situation and allows flexible mapping of sensory instruction cues onto different motor actions. We used anti-reach tasks to test the hypothesis that spatial motor-goal representations in cortical sensorimotor areas are gain modulated by the behavioral context to achieve flexible remapping of spatial cue information onto arbitrary motor goals. We found that gain modulation of neuronal reach goal representations is commonly induced by the behavioral context in individual neurons of both, the parietal reach region (PRR) and the dorsal premotor cortex (PMd). In addition, PRR showed stronger directional selectivity during the planning of a reach toward a directly cued goal (pro-reach) compared with an inferred target (anti-reach). PMd, however, showed stronger overall activity during reaches toward inferred targets compared with directly cued targets. Based on our experimental evidence, we suggest that gain modulation is the computational mechanism underlying the integration of spatial and contextual information for flexible, rule-driven stimulus–response mapping, and thereby forms an important basis of goal-directed behavior. Complementary contextual effects in PRR versus PMd are consistent with the idea that posterior parietal cortex preferentially represents sensory-driven, “automatic” motor goals, whereas frontal sensorimotor areas are stronger engaged in the representation of rule-based, “inferred” motor goals.
Regular expressions on the web

On the web, static pages fade into the past; web sites use server and client-side scripting techniques to improve the user experience. For client-side scripting, JavaScript is by far the most commonly used dynamic language. In these scripts, regular expressions are widely used for several purposes, e.g., for text filtering and form validation. In this paper, we take a closer look at regular expressions on the web. We investigate historical data and determine the trends of the use of regular expressions over time on various web sites. Then, we also take a snapshot of the current status. We find that although regular expressions are being used more and more often, their use is highly repetitive: on the most popular web sites only 4% of the regular expressions are unique. Based on this result, we show that web browsers can cut down regular expression compilation time to the third by applying caching techniques.
Friedman and Divisia Monetary Measures

This paper explores the relationship between Milton Friedman’s work and the work on Divisia monetary aggregation, originated by William A. Barnett. The paradoxes associated with Milton Friedman’s work are largely resolved by replacing the official simple-sum monetary aggregates with monetary aggregates consistent with economic index number theory, such as Divisia monetary aggregates. Demand function stability becomes no more of a problem for money than for any other good or service. Money becomes relevant to monetary policy in all macroeconomic traditions, including New Keynesian economics, real business cycle theory, and monetarist economomics. Research and data on Divisia monetary aggregates are available for over 40 countries throughout the world from the online library within the Center for Financial Stability’s (CFS) program, Advances in Monetary and Financial Measurement. This paper supports adopting the standards of monetary data competency advocated by the CFS and the International Monetary Fund (2008, pp. 183-184).
A Prospective, Longitudinal Study of the Effect of Remission on Cortical Thickness and Hippocampal Volume in Patients with Treatment-Resistant Depression

Background: Magnetic resonance imaging studies have provided evidence of structural modifications in cortical-limbic regions in major depressive disorder. To date, however, few studies have tracked structural changes in patients during treatment. This prospective, longitudinal imaging study investigated associations between brain structure and clinical responsiveness in a sample of patients with treatment-resistant major depressive disorder during an approximate 1-year follow-up period. Methods: FreeSurfer software was used to extract volume or cortical thickness values from 6 regions of interest (hippocampus, rostral middle frontal gyrus, orbitofrontal cortex, rostral and caudal anterior cingulate cortices, and inferior temporal gyrus) in patients (n = 26) and matched healthy controls (n = 28). Regions of interest were selected based on previous evidence of potential associations between morphometric characteristics in these regions and treatment response or remission. Analyses were conducted to compare volume and cortical thickness in patients and controls at baseline imaging, determine whether patients’ brain structure at treatment initiation was associated with response over follow-up, and compare longitudinal changes in volume and cortical thickness in patients who achieved sustained 6-month remission (Montgomery-Åsberg Depression Rating Scale Score ≤12) with nonremitters. Results: Patients and controls showed no structural differences at baseline. Among patients, thicker right caudal anterior cingulate cortex at baseline was associated with greater symptom improvement over follow-up. Remitters and nonremitters showed subtle changes in volume and thickness over time in opposing directions, with increased hippocampal volume and cortical thickness in the rostral middle frontal gyrus, orbitofrontal cortex, and inferior temporal gyrus in remitters, and decreased volume or thickness in these regions in nonremitters. Conclusions: The results suggest that longitudinal structural trajectories may differ in major depressive disorder patients according to their clinical response to treatment.
Analysing Engineering Tasks Using a Hybrid Machine Vision and Knowledge Based System Application

We propose a novel application that automatically analyses video sequences arising from the conceptual stage of design engineering tasks. It is capable of handling cognitive activities, i.e. procedures which take place in the engineer’s mind. A hybrid machine vision and knowledge based system framework is employed to efficiently identify stages of the design process and detect mistakes. Activity analysis is performed by examination of the temporal relationships between the engineer’s actions. Experimental results captured in a complex, real life scenario showed that our system was able to correctly characterise input sequences with high accuracy rate.
SID: a graphical browser

Humans have powerful image processing systems which can assist in managing large amount of information. Program visualization can be used to exploit this capability to make complex systems manageable. In addition to being a tool for managing complexity, program visualization can support the maintenance of conceptual integrity, by providing a framework for viewing the program as a whole.This paper presents SID, a program visualization tool. The program view is determined by a control panel, which is accessed by the user and a knowledge based assistant called the Gue. SID is a prototype system for viewing C++ programs.
Estimation of skylight value in hazy outdoor images

The visibility of outdoor images is reduced by the turbid medium of the atmosphere. The reduction of visibility is due to the substantial presence of particles in the atmosphere that scatters and absorbs light. The atmospheric scattering model is usually used to describe and restore the visibility in outdoor images. This model is mainly based on the depth of the scene and the skylight. In this research, we suggest new method to precisely estimate the skylight value. It is a fast method because it is based on linear calculation. In addition, it can be applied to different types of outdoor images.
Providing action plans helps people complete tasks

People complete tasks more quickly when they have concrete plans. However, task lists place the burden of developing plans on the user. This paper investigates whether crowds can provide people with action plans. A betweensubjects experiment found that people who received action plans from the crowd completed more tasks than a control group that did not. We next describe two techniques for scaling this approach. First, we created a social variant where participants both used the task lists and provided action plans for others. In a between-subjects experiment, participants using this social approach completed more tasks than the control condition. Second, we developed a natural language processing technique that uses WordNet and synonym detection to group similar tasks and reuse action plans across them. Running this technique on a corpus of 2872 tasks with two human auditors of the results found that 69.4% tasks had at least one match. This suggests automatically providing action plans is quite scalable. We have incorporated these principles into a system, the TaskGenies Community. Author
Classification of Stock Index movement using k-Nearest Neighbours (k-NN) algorithm

Many research studies are undertaken to predict the stock price values, but not many aim at estimating the predictability of the direction of stock market movement. The advent of modern data mining tools and sophisticated database technologies has enabled researchers to handle the huge amount of data generated by the dynamic stock market with ease. In this paper, the predictability of stock index movement of the popular Indian Stock Market indices BSE7SENSEX and NSE7NIFTY are investigated with the data mining tool of k7Nearest Neighbours algorithm (k7NN) by forecasting the daily movement of the indices. To evaluate the efficiency of the classification technique, the performance of k7NN algorithm is compared with that of the Logistic Regression model. The analysis is applied to the BSE7SENSEX and NSE7NIFTY for the period from January 2006 to May 2011.