bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pd(II) complexes of acetylcholinesterase reactivator obidoxime
Authors: Nedzhib Ahmed , Stoykova Silviya , Atanasov Vasil , Pantcheva Ivayla , Antonov Liudmil 
Published: January 2015

ABSTRACT
The ability of the acetylcholinesterase reactivator obidoxime (H2L2+) to bind palladium(II) cations was evaluated spectrophotometrically at different reaction conditions (pH, reaction time, metal-to-ligand molar ratio). The results showed that immediately after mixing the reagents, pH 7.4, complex species of composition [PdHL]3+ existed predominantly with a value of conditional stability constant lgβ'=6.52. The reaction was completed within 24 hours affording the formation of species [Pd2L]4+ with significantly increased stability (lgβ'=9.34). The spectral data suggest that obidoxime coordinates metal(II) ions through the oximate functional groups. The in vitro reactivation assay of paraoxon-inhibited rat brain acetylcholinesterase revealed that the new complex species were much less active than the non-coordinated obidoxime. The lack of reactivation ability could be explained by the considerable stability of complexes in solution as well as by the deprotonation of oxime groups essential for recovery of the enzymatic activity.
Salvia officinalis L. extract and its new food antioxidant formulations induce apoptosis through mitochondrial/ caspase pathway in leukemia L1210 cells
Authors: Jantová Soňa , Hudec Roman , Sekretár Stanislav , Kučerák Juraj , Melušová Martina 
Published: January 2015

ABSTRACT
Salvia officinalis, L. (Lamiaceae) is one of the most widespread herbal species used in the area of human health and in the foodprocessing industry. Salvia and its extracts are known to be a rich source of antioxidants. As shown previously, the crude ethanolic extract of salvia (SE) exerts lower anti-oxidative properties in lard compared to the new salvia food formulations No. 1 (SF1; 32% of SE + 68% of the emulsifier Dimodan S-T) and No. 2 (SF2; 32% of SE + 68% of the emulsifier Topcithin 50).
The aim of the present study was to investigate and compare the effects of the SE and its food formulations SF1 and SF2 on the toxicity and/or proliferation of L1210 leukemia cells. We found that SE and both SF1 and SF2 demonstrated different concentration- and time-dependent cytotoxic/antiproliferative cellular effects already within the first 24 h of the treatment. However, SE was nearly 10 times more effective than the new salvia food formulations SF1 and SF2.
We investigated partially also the molecular mechanisms lying behind the action of SE, SF1 and SF2 induced apoptosis in our cell model. We found an apparent involvement of the mitochondrial/caspase-dependent pathway in the described processes. Nevertheless, further investigation is needed before salvia extract and its new antioxidant formulations can be included among the potential food antioxidants with protective properties against cancer.
Chronic alcoholism-mediated metabolic disorders in albino rat testes
Authors: Shayakhmetova Ganna M. , Bondarenko Larysa B. , Matvienko Anatoliy V. , Kovalenko Valentina M. 
Published: January 2015

ABSTRACT
There is good evidence for impairment of spermatogenesis and reductions in sperm counts and testosterone levels in chronic alcoholics. The mechanisms for these effects have not yet been studied in detail. The consequences of chronic alcohol consumption on the structure and/or metabolism of testis cell macromolecules require to be intensively investigated. The present work reports the effects of chronic alcoholism on contents of free amino acids, levels of cytochrome P450 3A2 (CYP3A2) mRNA expression and DNA fragmentation, as well as on contents of different cholesterol fractions and protein thiol groups in rat testes. Wistar albino male rats were divided into two groups: I - control (intact animals), II - chronic alcoholism (15% ethanol self-administration during 150 days). Following 150 days of alcohol consumption, testicular free amino acid content was found to be significantly changed as compared with control. The most profound changes were registered for contents of lysine (-53%) and methionine (+133%). The intensity of DNA fragmentation in alcohol-treated rat testes was considerably increased, on the contrary CYP3A2 mRNA expression in testis cells was inhibited, testicular contents of total and etherified cholesterol increased by 25% and 45% respectively, and protein SH-groups decreased by 13%. Multidirectional changes of the activities of testicular dehydrogenases were detected. We thus obtained complex assessment of chronic alcoholism effects in male gonads, affecting especially amino acid, protein, ATP and NADPH metabolism. Our results demonstrated profound changes in testes on the level of proteome and genome. We suggest that the revealed metabolic disorders can have negative implication on cellular regulation of spermatogenesis under long-term ethanol exposure.
High mortality due to accidental salinomycin intoxication in sheep
Authors: Ashrafihelan Javad , Eisapour Hamed , Erfani Amir Mehdi , Kalantary Amir Ali , Amoli Jamileh Salar , Mozafari Morteza 
Published: January 2015

ABSTRACT
In February 2012, 100% mortality was reported in a herd with 79 local sheep that were kept around of Abhar, Northwest of Iran. The ration for adult sheep was daily mixed (40 kg straw, 25 kg wheat and 2 kg Vit-C premix) and accidentally 1 500 g of salinomycin (Salinomycin 12% Premix; Aras Bazar Laboratories, Iran) had been added to the ration (22388 mg/kg = 22388 ppm) and overnight was fed to herd. At the morning, 78 sheep were founded dead and one of them showed convulsive seizures. Postmortem examination revealed pulmonary congestion and edema, hemorrhages in abomasum, large pale kidney and white streak lines in myocardium. Main histopathologic lesions were extensive subepicardial and intercardiomyofibers hemorrhages, extensive cardiomyolysis and myocarditis in heart, severe hyperemia and extensive acute tubular necrosis (ATN) in kidneys and focal necrosis and retention of bile cholangitis in the liver. In this study, on the basis of the history, observation of the ionophore remnant in the ration, clinical signs, gross and histopathological findings, acute salinomycin intoxication is definitely diagnosed.
Performance Analysis of Robust Image Features Detection Algorithms
Authors: Nikolova I. 
Published: January 2015

Abstract
This paper deals with the challenging task of acquiring stable image features in a sequence of images of the same scene taken under different viewing positions by a digital still camera. Two popular contemporary algorithms for discrete feature detection: SIFT and SURF are regarded. The results of the timing performance analysis of their sequential implementations are presented and discussed. The performance speedup analysis and scalability tests with multi-threading and GPU-based implementations are analyzed
UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays
Authors: Zapryanov G. 
Published: January 2015

Abstract
A new universal demosaicing method suitable for imaging pipelines using a RGB color filter array (CFA) is proposed. The proposed algorithm can reconstruct single-sensor images registered by a digital camera equipped with any RGB CFA currently in use. The algorithm includes a spatio-spectral interpolation model, a new edge-detection technique, and a post processing to reduce the color shifts and artifacts of the reconstructed image. Experimental results over various test CFA patterns and real images demonstrate the efficiency and feasibility of a universal demosaicing algorithm proposed in the paper. Two methods - objective metrics (mean squared error - MSE and peak signal to noise ratio - PSNR), and subjective evaluation are used to analyze the results
Agent-Based Dispatching Enables Autonomous Groupage Traffic
Authors: Gath Max , Edelkamp Stefan , Herzog Otthein 
Published: January 2015

Abstract
The complexity and dynamics in groupage traffic require flexible, efficient, and adaptive planning and control processes. The general problem of allocating orders to vehicles can be mapped into the Vehicle Routing Problem (VRP). However, in practical applications additional requirements complicate the dispatching processes and require a proactive and reactive system behavior. To enable automated dispatching processes, this article presents a multiagent system where the decision making is shifted to autonomous, interacting, intelligent agents. Beside the communication protocols and the agent architecture, the focus is on the individual decision making of the agents which meets the specific requirements in groupage traffic. To evaluate the approach we apply multiagent-based simulation and model several scenarios of real world infrastructures with orders provided by our industrial partner. Moreover, a case study is conducted which covers the autonomous groupage traffic in the current processes of our industrial parter. The results reveal that agent-based dispatching meets the sophisticated requirements of groupage traffic. Furthermore, the decision making supports the combination of pickup and delivery tours efficiently while satisfying logistic request priorities, time windows, and capacity constraints.
Dsmk-Means “Density-Based Split-And-Merge K-Means Clustering Algorithm”
Authors: Aldahdooh Raed T. , Ashour Wesam 
Published: January 2015

Abstract
Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.
On the Influence of Topological Characteristics on Robustness of Complex Networks
Authors: Kasthurirathna Dharshana , Piraveenan Mahendra , Thedchanamoorthy Gnanakumar 
Published: January 2015

Abstract
In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesised scale-free, small-world and random networks, we look at a number of network measures, including assortativity, modularity, average path length, clustering coefficient, rich club profiles and scale-free exponent (where applicable) of a network, and how each of these influence the robustness of a network under targeted attacks. We use an established robustness coefficient to measure topological robustness, and consider sustained targeted attacks by order of node degree. With respect to scale-free networks, we show that assortativity, modularity and average path length have a positive correlation with network robustness, whereas clustering coefficient has a negative correlation. We did not find any correlation between scale-free exponent and robustness, or rich-club profiles and robustness. The robustness of small-world networks on the other hand, show substantial positive correlations with assortativity, modularity, clustering coefficient and average path length. In comparison, the robustness of Erdos-Renyi random networks did not have any significant correlation with any of the network properties considered. A significant observation is that high clustering decreases topological robustness in scale-free networks, yet it increases topological robustness in small-world networks. Our results highlight the importance of topological characteristics in influencing network robustness, and illustrate design strategies network designers can use to increase the robustness of scale-free and small-world networks under sustained targeted attacks.
A Big-Bang Big-Crunch Optimized General Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making
Authors: Naim Syibrah , Hagras Hani 
Published: January 2015

Abstract
Multi-Criteria Group Decision Making (MCGDM) aims to find a unique agreement from a number of decision makers/users by evaluating the uncertainty in judgments. In this paper, we present a General Type-2 Fuzzy Logic based approach for MCGDM (GFLMCGDM). The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers’ (DMs) judgments and the vagueness of the appraisal. In order to find the optimal parameters of the general type-2 fuzzy sets, we employed the Big Bang-Big Crunch (BB-BC) optimization. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs’ opinions into a unique approval. We present results from an application for the selection of reading lighting level in an intelligent environment. We carried out various experiments in the intelligent apartment (iSpace) located at the University of Essex. We found that the proposed GFL-MCGDM effectively handle the uncertainties between the various decision makers which resulted in producing outputs which better agreed with the users’ decision compared to type 1 and interval type 2 fuzzy based systems.
Mosaic Reasoning for Discoveries
Authors: Stilman Boris 
Published: January 2015

Abstract
We investigate structure of the Primary Language of the human brain as introduced by J. von Neumann in 1957. Two components have been investigated, the algorithm optimizing warfighting, Linguistic Geometry (LG), and the algorithm for inventing new algorithms, the Algorithm of Discovery. The latter is based on multiple thought experiments, which manifest themselves via mental visual streams (“mental movies”). There are Observation, Construction and Validation classes of streams. Several visual streams can run concurrently and exchange information between each other. The streams may initiate additional thought experiments, program them, and execute them in due course. The visual streams are focused employing the algorithm of “a child playing a construction set” that includes a visual model, a construction set, and the Ghost. Mosaic reasoning introduced in this paper is one of the major means to focusing visual streams in a desired direction. It uses analogy with an assembly of a picture of various colorful tiles, components of a construction set. In investigating role of mosaic reasoning in the Algorithm of Discovery, in this paper, I replay a series of four thought experiments related to the discovery of the structure of the molecule of DNA. Only the fourth experiment was successful. This series of experiments reveals how a sequence of failures eventually leads the Algorithm to a discovery. This series permits to expose the key components of the mosaic reasoning, tiles and aggregates, local and global matching rules, and unstructured environment. In particular, it reveals the aggregates and the rules that played critical role in the discovery of the structure of DNA. They include the generator and the plug-in aggregates, the transformation and complementarity matching rules, and the type of unstructured environment. For the first time, the Algorithm of Discovery has been applied to replaying discoveries not related to LG and even to mathematics
Fixed Final Time Optimal Adaptive Control of Linear Discrete-Time Systems in Input-Output form
Authors: Zhao Qiming , Xu Hao , Jagannathan S. 
Published: January 2015

Abstract
In this paper, the fixed final time adaptive optimal regulation of discrete-time linear systems with unknown system dynamics is addressed. First, by transforming the linear systems into the input/output form, the adaptive optimal control design depends only on the measured outputs and past inputs instead of state measurements. Next, due to the time-varying nature of finite-horizon, a novel online adaptive estimator is proposed by utilizing an online approximator to relax the requirement on the system dynamics. An additional error term corresponding to the terminal constraint is defined and minimized overtime. No policy/value iteration is performed by the novel parameter update law which is updated once a sampling interval. The proposed control design yields an online and forward-in-time solution which enjoys great practical advantages. Stability of the system is demonstrated by Lyapunov analysis while simulation results verify the effectiveness of the propose approach
An Nlp-Based Approach for Improving Human-Robot Interaction
Authors: Kilicaslan Yilmaz , Tuna Gurkan 
Published: January 2015

Abstract
This study aims to explore the possibility of improving human-robot interaction (HRI) by exploiting natural language resources and using natural language processing (NLP) methods. The theoretical basis of the study rests on the claim that effective and efficient human robot interaction requires linguistic and ontological agreement. A further claim is that the required ontology is implicitly present in the lexical and grammatical structure of natural language. The paper offers some NLP techniques to uncover (fragments of) the ontology hidden in natural language and to generate semantic representations of natural language sentences using that ontology. The paper also presents the implementation details of an NLP module capable of parsing English and Turkish along with an overview of the architecture of a robotic interface that makes use of this module for expressing the spatial motions of objects observed by a robot
Computationally Inexpensive Appearance Based Terrain Learning in Unknown Environments
Authors: Mishra Prabhakar , Viswanathan Anirudh 
Published: January 2015

Abstract
This paper describes a computationally inexpensive approach to learning and identification of maneuverable terrain to aid autonomous navigation. We adopt a monocular vision based framework, using a single consumer grade camera as the primary sensor, and model the terrain as a Mixture of Gaussians. Self-supervised learning is used to identify navigable terrain in the perception space. Training data is obtained using pre-filtered pixels, which correspond to near-range traversable terrain. The scheme allows for on-line, and in-motion update of the terrain model. The pipeline architecture used in the proposed algorithm is made amenable to real-time implementation by restricting computations to bit-shifts and accumulate operations. Color based clustering using dominant terrain texture is then performed in perception sub-space. Model initialization and update follows at the coarse scale of an octave image pyramid, and is back projected onto the original fine scale. We present results of terrain learning, tested in heterogeneous environments, including urban road, suburban parks, and indoors. Our scheme provides orders of magnitude improvement in time complexity, when compared to existing approaches reported in literature
Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction
Authors: Arabgol Samira , Sang Ko Hoo 
Published: January 2015

Abstract
Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.
A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images
Authors: Karimi Behnam , Krzyżak Adam 
Published: January 2015

Abstract
In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.
The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks
Authors: Chu Joseph Lin , Krzyźak Adam 
Published: January 2015

Abstract
Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images
Quality Parameter Assessment on iris Images
Authors: Makinana Sisanda , Malumedzha Tendani , Nelwamondo Fulufhelo V. 
Published: January 2015

Abstract
Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. Poor quality images may affect the recognition as they have few sufficient feature information. Moreover, existing quality measures focuses on parameters or factors than feature information. In this paper, iris quality assessment research is extended by analysing the effect of entropy, contrast, area ratio, occlusion, blur, dilation and sharpness of an iris image which determines the iris size, amount of information and clearness of the features. A weighting method based on principal component analysis (PCA) is proposed to determine the influence each parameter has on the quality score. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Collection (IC) and University of Beira Interior (UBIRIS) databases are used. A conclusion is drawn that the combination of blur, dilation and sharpness parameters have the most influence in the quality of the image as they weighed more than other parameters