Miracle-dz/UniFAR-FTUs huggingface dataset:
The concentration of thiobarbituric acid reactive substances (TBARS) and paraoxonase activity in blood of patients with osteoarthrosis after endoprosthesis implantation

Summary Background The aim of this study was to evaluate the concentration of malondialdehyde (MDA) in erythrocytes and in blood plasma and the activity of blood paraoxonase (PON1) of patients with osteoarthrosis (OA) submitted to endoprosthesis implantation for evaluating oxidative stress. Material/Methods Study was conducted on 55 patients with OA and on 54 total movement-efficient volunteers. The material for the study was venous blood plasma, serum and erythrocytes. Results Increased concentration of MDAe before surgery was observed in the group of men and in patients with a degenerative process affecting hip joints. After an implantation of endoprosthesis, MDAe decreased to the level observed in the control groups. In the study group MDA concentration in plasma was slightly lower before surgery, and after an operation it reached the value of the parameter of the reference groups. Regardless of sex or age, paraoxonase activity was almost twice as high in almost all subgroups as in the reference group. A positive correlation between PON 1 activity and MDAe concentration was demonstrated both before and after surgery in the group of men. Conclusions The increase of PON1 activity in patients’ serum in relation to the control groups indicates a probable pathogenic role of the increased formation of reactive oxygen species in the course of OA and may suggest acute inflammation of the synovial joint. The high level of PON 1 activity after endoprosthesis implantation indicates that surgical treatment may additionally stimulate ROS generation. MDAe concentration indicate more intensive process of lipid peroxidation in the elderly.
An evolutionary study of fan-in and fan-out metrics in OSS

Excessive coupling between object-oriented classes is widely acknowledged as a maintenance problem that can result in a higher propensity for faults in systems and a ‘stored up’ future problem. The aim of this paper is to explore the relationship between ‘fan-in’ and ‘fan-out’ coupling metrics over multiple versions of open-source software. More specifically, we explore the relationship between the two metrics to determine patterns of growth in each over the course of time. The JHawk tool was used to extract the two metrics from five open-source systems. Two questions were posed for each system. First, what are the characteristics of classes exhibiting the highest fan-in values? Second, do fan-in and fan-out increase in corresponding and consistent amounts over time? Results show a wide range of traits in the classes to explain both high and low levels of fan-in and fan-out. We also found evidence of certain ‘key’ classes (with both high fan-in and fan-out) and ‘client’ and ‘server’-type classes with just high fan-out and fan-in, respectively. We provide an explanation of the composition and existence of such classes as well as for disproportionate increases in each of the two metrics over time.
Behavioral parameterized SPICE models of photovoltaic modules

In the present paper, behavioral computer SPICE models of photovoltaic modules are developed. The model parameters are automatically calculated using datasheet data. The temperature dependence of the model parameters is taken into account. The models are built in the form of parameterized blocks. The input data are the short circuit current Isc, the open circuit voltage Voc and two remarkable points of the flat segments of the IV characteristic from the datasheet. The computer realization is performed in the Cadence Capture and Cadence PSpice environment.
Predictive QoS-based admission control for multiclass traffic in cellular wireless networks

We develop the notion of quality of service (QoS) for multimedia traffic in terms of maximum call dropping probabilities independent of system load and a predefined call blocking probability profile for the different traffic classes for wireless networks of arbitrary shape and dimension. We describe two distributed predictive admission control algorithms, independent multiclass one-step prediction (IMOSP-CS and IMOSP-RES), which provide each traffic class with a given call dropping probability and a desired call blocking probability profile. Both algorithms may be seen as extensions of the multimedia one-step prediction (MMOSPRED) algorithm previously reported, which uses prediction of the overload probability in the home and neighbor cells in deciding whether to admit new users into a multiclass cellular system. The two algorithms differ in their approach to handoff call admission. The first algorithm completely shares the bandwidth among the entering handoff users while the second implements a partition-based reservation scheme. In this paper, we additionally impose a call blocking criterion that ensures a system-imposed call priority independent of the traffic in the system and which adapts to changes in the offered load. In comparing these algorithms to each other, we focus on system throughput and class independence. Both algorithms provide appropriate throughput under both homogeneous and heterogeneous traffic loading conditions while maintaining steady call dropping probabilities for each traffic class.
Central Representation of Postingestive Chemosensory Cues in Mice That Lack the Ability to Taste

The gustatory nerves of mice lacking P2X2 and P2X3 purinergic receptor subunits (P2X-dblKO) are unresponsive to taste stimulation (Finger et al., 2005). Surprisingly, P2X-dblKO mice show residual behavioral responses to concentrated tastants, presumably via postingestive detection. Therefore, the current study tested whether postingestive signaling is functional in P2X-dblKO mice and if so, whether it activates the primary viscerosensory nucleus of the medulla, the nucleus of the solitary tract (nTS). Like WT animals, P2X-dblKO mice learned to prefer a flavor paired with 150 mm monosodium glutamate (MSG) over a flavor paired with water. This preference shows that, even in the absence of taste sensory input, postingestive cues are detected and associated with a flavor in P2X-dblKO mice. MSG-evoked neuronal activation in the nTS was measured by expression of the immediate early gene c-Fos [c-Fos-like immunoreactivity (Fos-LI)]. In rostral, gustatory nTS, P2X-dblKO animals, unlike WT animals, showed no taste quality-specific labeling of neurons. Furthermore, MSG-evoked Fos-LI was significantly less in P2X-dblKO mice compared with WT animals. In contrast, in more posterior, viscerosensory nTS, MSG-induced Fos-LI was similar in WT and P2X-dblKO mice. Together, these results suggest that P2X-dblKO mice can form preferences based on postingestive cues and that postingestive detection of MSG does not rely on the same purinergic signaling that is crucial for taste.
Experimental Evaluation of Multilayer Perceptrons with Entropic Risk Functionals on Real-World Datasets

We investigate the performance of MLPs with four risk functionals: the classical mean square error (MSE), the cross-entropy (CE), a generalized exponential risk (EXP), and the Shannon entropy of the classifier’s output error (HS). The performance is compared with an SVM with RBF kernel in terms of average balanced and unbalanced error rates, and their generalization, on practical classification tasks. For this purpose we carried out experiments on 35 public real-world datasets. A battery of statistical tests applied to the experimental results showed no significant difference among the classifiers in terms of unbalanced error rates. However, in terms of balanced error rates SVM-RBF performed significantly worse than MLP-CE and MLP-EXP. Regarding generalization, SVM-RBF and MLP-EXP scored as the classification methods with significantly better generalization, both in terms of balanced and unbalanced error rates.
Research on Turn Ratio of Magnetic Valve Type Controlled Reactor Based on Bifurcation Theory

Magnetic valve type controlled reactor (MVCR) is the electrical equipment which shows some strongly nonlinear properties when working and its performance is influenced by its turn ratio. Some irregular phenomenon, for example, the mutation of ultimate dynamic state, the uncertain electromagnetic or mechanical noise, may occur under the condition of the improper value of turn ratio. In order to study the nonlinear behavior, by using the fixed period discrete mapping modeling method, the discrete mapping model of MVCR is established, the performance of the MVCR is analyzed based on the bifurcation theory for nonlinear dynamics and the reasonable value range of turn ratio is obtained. Simulation confirms that the result above is rational. The conclusion in the paper may provide a certain theoretical basis for the designing of controllable reactor in actual engineering.
Moving up the value chain

Survey of the use of business information in UK libraries and information services, based on in-depth interviews with leading business information managers. Key findings are: Expansion of business in India and the Far East is having implications for information infrastructure domestically and in this region. Business information services staffing is very stable, excepting the offshoring dimension. Staff development is increasingly being conducted internally. 20 per cent of those sampled offshore some aspects of their information and research services; another 25 per cent are investigating this option. Knowledge sharing is increasingly recognized as necessary in companies, and most information services are playing their part. There is a distinct trend for information services to move up the value chain through more evaluation and analysis functions. The majority (70%) of business information services are involved in supporting compliance work. Factiva tops the expenditure league. European private company information is still underprovided. Business information services are committed to training users. Social technology tools are not being used much but have great potential. Improving customer services is still the highest strategic priority.
Big Data Processing for Prediction of Traffic Time Based on Vertical Data Arrangement

To predict future traffic conditions in each road with unique spatiotemporal pattern, it is necessary to analyze the conditions based on historical traffic data and select time series forecasting methods which can be predicting next pattern for each road according to the analyzed results. Our goal is to create a new statistical model and a new system for predictive graphs of traffic times based on big data processing tools. First, we suggest a vertical data arrangement, gathering past traffic times in the same time slot for long-term prediction. Second, we analyze each traffic pattern to select time-series variables because a time-series forecasting method for a location and a time will be selected according to the variables that are available. Third, we suggest a spatiotemporal prediction map, which is a two-dimensional map with time and location. Each element in the map represents a time-series forecasting method and an R-squared value as indicator of prediction accuracy. Finally, we introduce a new system including RHive as a middle point between R and Hadoop clusters for generating predicted data efficiently from big historical data.
A Novel Supervised Clustering Based on the Feature Classification Weight

In the d-dimensional feature space, the classification weight is defined against the different contribution of every feature that used to classification on the training sample set. And the classification weight calculates the membership functions which set up unascertained classification. Then a novel supervised clustering algorithm based on above is given. The algorithm is concise in calculation, fast in speed and effective in decreasing the computational complexity dramatically. IRIS data training demonstrates that the algorithm is much better than other clustering methods.
A Stable Marriage Requires Communication

The Gale-Shapley algorithm for the Stable Marriage Problem is known to take $\Theta(n^2)$ steps to find a stable marriage in the worst case, but only $\Theta(n \log n)$ steps in the average case (with $n$ women and $n$ men). In 1976, Knuth asked whether the worst-case running time can be improved in a model of computation that does not require sequential access to the whole input. A partial negative answer was given by Ng and Hirschberg, who showed that $\Theta(n^2)$ queries are required in a model that allows certain natural random-access queries to the participants' preferences. A significantly more general - albeit slightly weaker - lower bound follows from Segal's general analysis of communication complexity, namely that $\Omega(n^2)$ Boolean queries are required in order to find a stable marriage, regardless of the set of allowed Boolean queries. Using a reduction to the communication complexity of the disjointness problem, we give a far simpler, yet significantly more powerful argument showing that $\Omega(n^2)$ Boolean queries of any type are indeed required for finding a stable - or even an approximately stable - marriage. Notably, unlike Segal's lower bound, our lower bound generalizes also to (A) randomized algorithms, (B) allowing arbitrary separate preprocessing of the women's preferences profile and of the men's preferences profile, (C) several variants of the basic problem, such as whether a given pair is married in every/some stable marriage, and (D) determining whether a proposed marriage is stable or far from stable. In order to analyze "approximately stable" marriages, we introduce the notion of "distance to stability" and provide an efficient algorithm for its computation.
Insight: its relationship with cognitive function, brain volume and symptoms in schizophrenia

Background. Lack of insight is frequently observed in schizophrenia. Relationships have been noted between poor insight, clinical symptoms and cognitive impairments but the findings are inconsistent. There have been some recent attempts to relate poor insight to neuro-anatomical measures. Method. We assessed insight, positive and negative symptoms of schizophrenia, cognitive performance, and whole brain volumes in a sample of 78 DSM-IV male schizophrenics and 36 normal male comparison subjects matched for age and IQ. Subjects underwent a dual-echo MRI brain scan to establish grey, white and whole brain volumes. Results. Poor Wisconsin Card Sorting Test performance inversely correlated with insight in schizophrenic patients, as did the symptoms alogia, anhedonia, avolition/apathy, affective flattening, inappropriate affect, thought disorder and delusions. The presence of inappropriate affect, delusions and thought disorder, showed the most significant impact on insight levels. There were no significant correlations between whole brain, white and grey matter volume and degree of insight. Conclusions. The results suggest that poor insight is significantly related to schizophrenic psychopathology, and confirm that there is a relationship between insight and executive performance. Awareness of illness is not related to any global brain measures, suggesting future investigations should pay attention to more specific cortical regions such as the frontal cortex.
Phase Retrieval with Application to Optical Imaging: A contemporary overview

This review article provides a contemporary overview of phase retrieval in optical imaging, linking the relevant optical physics to the signal processing methods and algorithms. Our goal is to describe the current state of the art in this area, identify challenges, and suggest future directions and areas where signal processing methods can have a large impact on optical imaging and on the world of imaging at large with applications in a variety of fields ranging from biology and chemistry to physics and engineering.
Low-Dose Micro-CT Imaging for Vascular Segmentation and Analysis Using Sparse-View Acquisitions

The aim of this study is to investigate whether reliable and accurate 3D geometrical models of the murine aortic arch can be constructed from sparse-view data in vivo micro-CT acquisitions. This would considerably reduce acquisition time and X-ray dose. In vivo contrast-enhanced micro-CT datasets were reconstructed using a conventional filtered back projection algorithm (FDK), the image space reconstruction algorithm (ISRA) and total variation regularized ISRA (ISRA-TV). The reconstructed images were then semi-automatically segmented. Segmentations of high- and low-dose protocols were compared and evaluated based on voxel classification, 3D model diameters and centerline differences. FDK reconstruction does not lead to accurate segmentation in the case of low-view acquisitions. ISRA manages accurate segmentation with 1024 or more projection views. ISRA-TV needs a minimum of 256 views. These results indicate that accurate vascular models can be obtained from micro-CT scans with 8 times less X-ray dose and acquisition time, as long as regularized iterative reconstruction is used.
Performance analyses of optical burst-switching networks

This paper provides a scalable framework for analysis and performance evaluation of optical burst-switching (OBS) networks. In particular, a new reduced load fixed point approximation model to evaluate blocking probabilities in OBS networks is introduced. The model is versatile enough to cover known OBS reservation policies such as just-enough-time, just-in-time, burst segmentation, and route-dependent priorities. The accuracy of the model is confirmed by simulation and the various policies are compared.
MICROBIAL AND OTHER RELATED CHANGES IN A NIGER DELTA RIVER SEDIMENT RECEIVING INDUSTRIAL EFFLUENTS.

The microbial and other related changes of Okpoka- Woji River serving as a sink for effluents of industries located in its vicinity within the TransAmadi Industrial area were investigated. Sediment samples were collected from six sampling stations located along the channel for the assessment of total heterotrophic and hydrocarbon-utilizing bacteria, total heterotrophic and hydrocarbon-utilizing fungi, pH, electrical conductivity, redox potential, total organic carbon, oil and grease and total petroleum hydrocarbon. The mean values for the total heterotrophic and hydrocarbon- utilizing bacteria ranged from 3.4 x 10 5 to 7.84 x 10 5 cfu/g and 1.58 x 10 5 to 4.57 x 10 5 cfu/g respectively. The mean values of the total heterotrophic and hydrocarbon – utilizing fungal counts ranged from 1.98 x 10 4 to 8.83 x 10 4 cfu/g and 5.08 x 10 3 to 9.93 x 10 3 cfu/g respectively. The pH mean values ranged from 4.87 to 6.17 while the redox potential mean values ranged from 57.70 to 125.7mV. The electrical conductivity and total organic carbon mean values ranged from 138.02 to 3113µS/cm and 3.58 to 6.32% respectively. The mean values for the oil and grease and total petroleum hydrocarbon ranged from 3791.72 to 21,537.30mg/Kg and 3037.60 to 17,461.63mg/Kg respectively. The bacterial genera isolated from the samples included Nocardia , Pseudomonas . and Staphylococcus. The fungal isolates included Rhizopus , Aspergillus , Fusarium , Mucor and Candida. The study showed that the river is being polluted by the activities of the industries operating in the area. of
Object-Based Techniques for Image Retrieval

To overcome the drawback of using only low-level features for the description of image content and to fill the gap between the perceptual property and semantic meaning, this chapter presents an object-based scheme and some object level techniques for image retrieval. According to a multi-layer description model, images are analyzed in different levels for progressive understanding, and this procedure helps to gain comprehensive representations of the objects in images. The main propulsion of the chapter includes a multi-layer description model that describes the image content with a hierarchical structure; an efficient region-based scheme for meaningful information extraction; a combined feature set to represent the image at a visual perception level; an iterative training-and-testing procedure for object region recognition; a decision function for reflecting common contents in object description and a combined feature and object matching process, as well as a selfadaptive relevance feedback that could work with or without memory. Object-Based Techniques for Image Retrieval 155 Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. With the proposed techniques, a prototype retrieval system has been implemented. Real retrieval experiments have been conducted; results show that the object-based scheme is quite efficient and the performance of object level techniques have been confirmed.
Sensor network traffic load prediction with Markov random field theory

Following recent advances in wireless communications and computing technology, sensor networks are widely deployed in different fields for both monitoring and control purposes. In this work, we focus on using Markov random field (MRF) theory to model traffic intensity of the three types of sensor networks. Shortest path routing is adopted in the three typical lattice network models. Then, the influences, which affect the traffic distribution dynamically in real situations, are modelled by adding the Gaussian noise to the traffic load distribution in the MATLAB simulation. Given measurements of real-time samples of traffic, we are able to predict the traffic at each sensor node for specific network models by a MRF smoothing algorithm.
A Systematic Literature Survey on Network Attacks, Classification and Madels For Anomaly- Based Network Intrusion Detection Systems

The importance of network security has rown tremendously and a number of devices have been introduced to improve the security of a network. Network intrusion detection systems (NIDS) are among the most widely deployed such system. Popular NIDS use a collection of signatures of known security threats and viruses, which are used to scan each packet's payload. Most IDSs lack the capability to detect novel or previously unknown attacks. A special type of IDSs, called Anomaly Detection Systems, develop models based on normal system or network behavior, with the goal of de- tecting both known and unknown attacks. Anomaly detection systems face many problems including high rate of false alarm, ability to work in online mode, and scalability. This paper presents a selective survey of incremental approaches for detecting anomaly in normal system and network traffic.
The Influence of Chord Duration Modeling on Chord and Local Key Extraction

In this paper, we investigate the effect of different types of chord duration modeling on the performance of a simultaneous chord and local key extraction system. Two hypotheses are examined, (1) whether the introduction of multiple states per key-chord combination makes any difference as it changes the prior duration from a geometric distribution to a negative binomial distribution, and (2) whether making the prior mean duration of a key-chord combination a function of the chord has a positive effect. We found that the introduction of multiple states per key-chord has no influence on neither key nor chord extraction performance, but making the mean duration a function of the chord interpreted in its key leads to an increase in the key estimation capabilities.