Miracle-dz/UniFAR-FTUs huggingface dataset:
A simulation study of the effects of activation-dependent muscle stiffness on proprioceptive feedback and short-latency reflex

A biologically-inspired model of the Soleus neuromuscular system was used to study the influences of musculotendon mechanical properties on the proprioceptive feedback and on the short-latency reflex. The model structure comprised: i) stochastic descending drive to a motoneuron pool; ii) a Hill-type muscle model; and iii) a muscle spindle model with its Ia sensory afferents establishing a feedback loop with the motoneurons. H-reflex and short-latency stretch reflex were simulated considering their possible dependence on musculotendon mechanical properties and on the muscle activation level. The main results showed an intrinsic relationship between the Ia response to muscle stretch and the relative stiffness between muscle and tendon. In addition, fusimotor activation and synaptic gain could modulate the short-latency reflex, indicating that these mechanisms may help to maintain reflexes functionally active during different motor tasks. These preliminary results match some experimental data and provide insights on the intricate relationship between muscular mechanics and neuronal activity. Moreover, the adopted model of the closed-loop neuromuscular system has shown its potential as a tool for studies of complex mechanisms involved in motor control.
c-Fos immunoreactivity and variation of neuronal units in rat's motor cortex after chronic implants

Recovering of people suffering from spinal cord and brain lesion is a medical challenge. Brain-machine interface (BMI) emerges as a potential candidate, by allowing patients to use their own brain activity to reestablish sensorimotor control of paralyzed body parts. BMI can be divided in two main groups: non-invasive, based in the capture of the neuronal signal over the cranium, and invasive, much more effective in generating high resolution brain-derived motor control signals, despite requiring a brain surgery for implantation of recording microelectrodes. Accordingly, chronic multielectrodes implants define the fundamental component of an invasive BMI. However, it is important to characterize the impact of microwire arrays' implant on the nervous tissue before this technique can be available to human clinical trials. Here we evaluated the expression of immediate early-gene c-fos and inflammatory response (astrogliosis), as well as the quality of the neuronal signal comparing the variation of the total number and the amplitude of the recorded units after long-lasting chronic multielectrode implants. Electrode recordings remained viable for 6 months after implant, and did not alter the general physiology of the implanted tissue, as revealed by normal c-Fos expression in implanted sites. Moreover, there was a small inflammatory response across implanted regions. Our findings suggest that tungsten microwire arrays can be viable candidates to future human BMI interventions.
Correlated X–ray timing and spectral behavior in GX 349+2

We present a detailed and systematic investigation of correlated spectral and timing properties of the Z source GX 349+2, using extensive data (∼221 ks) obtained from the Proportional-Counter-Array on-board RXTE satellite, during January 1998 and September-October 1998. For the first time a detailed comparison between the normal-branch-properties and the flaring-branch-properties of GX 349+2 has been possible, since this study showed the presence of a rare, extended normal branch. Peaked noise properties have been analyzed as functions of position along the Z-track and have been compared to those of Cyg X-2.
Towards Interoperability in Heterogeneous Database Systems

Distributed heterogeneous databases consist of systems which differ physically and logically, containing different data models and data manipulation languages. Although these databases are independently created and administered they must cooperate and interoperate. Users need to access and manipulate data from several databases and applications may require data from a wide variety of independent databases. Therefore, a new system architecture is required to manipulate and manage distinct and multiple databases, in a transparent way, while preserving their autonomy. This report contains an extensive survey on heterogeneous databases, analysing and comparing the different aspects, concepts and approaches related to the topic. It introduces an architecture to support interoperability among heterogeneous database systems. The architecture avoids the use of a centralised structure to assist in the different phases of the interoperability process. It aims to support scalability, and to assure privacy and confidentiality of the data. The proposed architecture allows the databases to decide when to participate in the system, what type of data to share and with which other databases, thereby preserving their autonomy. The report also describes an approach to information discovery in the proposed architecture, without using any centralised structure as repositories and dictionaries, and broadcasting to all databases. It attempts to reduce the number of databases searched and to preserve the privacy of the shared data. The main idea is to visit a database that either contains the requested data or knows about another database that possible contains this data.
The activation loop of PIP5K functions as a membrane sensor essential for lipid substrate processing

A nuclear magnetic resonance study reveals the molecular basis of activation and regulation of the lipid kinase PIP5K by membrane lipids. Phosphatidylinositol 4-phosphate 5-kinase (PIP5K), a representative member of the phosphatidylinositol phosphate kinase (PIPK) family, is a major enzyme that biosynthesizes the signaling molecule PI(4,5)P2 (phosphatidylinositol 4,5-bisphosphate) in eukaryotic cells. The stringent specificity toward lipid substrates and the high sensitivity to the membrane environment strongly suggest a membrane-sensing mechanism, but the underlying structural basis is still largely unknown. We present a nuclear magnetic resonance (NMR) study on a peptide commensurate with a PIP5K’s activation loop, which has been reported to be a determinant of lipid substrate specificity and subcellular localization of PIP5K. Although the activation loop is severely disordered in the crystal structure of PIP5K, the NMR experiments showed that the largely unstructured peptide folded into an amphipathic helix upon its association with the 1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) micellar surface. Systematic mutagenesis and functional assays further demonstrated the crucial roles of the amphipathic helix and its hydrophobic surface in kinase activity and membrane-sensing function, supporting a working model in which the activation loop is a critical structural module conferring a membrane-sensing mechanism on PIP5K. The activation loop, surprisingly functioning as a membrane sensor, represents a new paradigm of kinase regulation by the activation loop through protein-membrane interaction, which also lays a foundation on the regulation of PIP5K (and other PIPKs) by membrane lipids for future studies.
Local maximum edge cooccurance patterns for image indexing and retrieval

Current research work proposes a new feature descriptor, the local maximum edge co occurrence patterns (LMECoP) for feature extraction for retrieval of images from large database. The LMECoP, firstly collects the local maximum edge information between the referenced pixel and its possible neighbors, then the binary patterns are formed from the extracted maximum edge information. Further the proposed method collects the cooccurrence matrix on the maximum edge information response and to improve the performance of method, the amalgamation of color information and texture information as features is also anticipated in this paper. For color based feature, we convert the RGB color space in to HSV color space and standard histograms are collected for each color space (H, S and V). These color features (HSV histogram) and texture feature (LMECoP) are integrated for the generation final feature vector. The evaluation of LMECoP is performed by tested on Corel-5K database based on average retrieval precision (ARP). The results after investigation it is apparent that the LMECoP outperforms the other existing methods in terms of ARP on Corel-5K database.
Voltage-mode first-order allpass filter using single-ended OTAs and grounded capacitor

This paper presents a new electronically tunable voltage-mode first-order allpass filter using only three single-ended operational transconductance amplifiers (OTAs) and one grounded capacitor, which is highly suitable for integrated circuit implementation. The pole frequency of the filter can be tuned electronically by adjusting the transconductance value of the OTA. No component-matching conditions are also required for realizing allpass response. The characteristics of the proposed circuit are simulated using PSPICE simulations to confirm the theory.
A Social Relations Model of Everyday Talk and Relational Satisfaction in Stepfamilies

This study examined the intrapersonal and interpersonal mechanisms underlying reported frequencies of everyday talk and relational satisfaction in stepfamilies. Participants included a parent, stepparent, and child from 114 stepfamilies (N =342) from the Midwest and Southwest regions of the United States. Social relations model analyses revealed that everyday talk and relational satisfaction vary across stepfamily relationships as a function primarily of actor and relationship effects. Stepparents’ reports of everyday talk with the parent (i.e., their spouse) varied primarily as a function of actor effects, whereas reports of both children's and parents’ satisfaction with the stepparent varied primarily as a function of relationship effects. Dyadic reciprocity emerged in the stepparent/stepchild relationship for reports of both everyday talk and satisfaction. Finally, stepparents engaging in everyday talk more frequently with stepchildren were more likely to be satisfied with stepchildren, and were more likely to have stepchildren reporting satisfaction with them, than stepparents engaging less frequently in everyday talk.
Applying Simple Combining Techniques with Artificial Neural Networks to Some Standard Time Series Classification Problems

In this work we use connectionist techniques to solve some problems of pattern classification involving time series. We build our classifiers using two different artificial neural networks paradigms: the self organizing maps and the multilayer perceptrons. We compare the results achieved using the single classifier and an ensemble of classifiers. The conclusions are quite hopeful, achieving results with a similar or better accuracy compared with those of previous works.
Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables

Companies employ international diffusion models to assess the local market potential and local diffusion speed to support their decision making on market entry. After their entry into a country, they use the model forecasts for their performance controlling. To this end, empirical applications of international diffusion models aim to link differential diffusion patterns across countries to various exogenous drivers. In the literature, macro- and socioeconomic variables like population characteristics, culture, economic development, etc. have been linked to differential penetration developments across countries. But as companies cannot influence these drivers, their marketing decisions that shape national diffusion patterns are ignored. Is this reasonable? What then, is the role of marketing instruments in an international diffusion context? We address this issue and compare the influence of these prominent exogenous drivers of international diffusion with that of industry and marketing-mix variables. To account for all of these factors and simultaneously accommodate the influence of varying cross-country interactions, we develop a more flexible yet parsimonious model of international diffusion. Finally, to avoid technical issues in implementing spatially dependent error terms we introduce the test concept of Moran's I to international diffusion model. We demonstrate that the lead-lag effect in conjunction with spatial neighborhood effects controls most of the spatial autocorrelation. Using this combined approach we find that --- for cellulars --- industry and marketing-mix variables explain international diffusion patterns better than macro- and socioeconomic drivers.
Accurate photometry of extended spherically symmetric sources

We present a new method to derive reliable photometry of extended spherically symmetric sources from HST images (WFPC2, ACS/WFC and NICMOS/NIC2 cameras), extending existing studies of point sources and marginally resolved sources. We develop a new approach to accurately determine intrinsic sizes of extended spherically symmetric sources, such as star clusters in galaxies beyond the Local Group (at distances <20 Mpc), and provide a detailed cookbook to perform aperture photometry on such sources, by determining size-dependent aperture corrections (ACs) and taking sky oversubtraction as a function of source size into account. In an extensive Appendix, we provide the parameters of polynomial relations between the FWHM of various input profiles and those obtained by fitting a Gaussian profile (which we have used for reasons of computational robustness, although the exact model profile used is irrelevant), and between the intrinsic and measured FWHM of the cluster and the derived AC. Both relations are given for a number of physically relevant cluster light profiles, intrinsic and observational parameters. AC relations are provided for a wide range of apertures. Depending on the size of the source and the annuli used for the photometry, the absolute magnitude of such extended objects can be underestimated by up to 3 mag, corresponding to an error in mass of a factor of 15. We carefully compare our results to those from the more widely used DeltaMag method, and find an improvement of a factor of 3−40 in both the size determination and the AC.
Governance factors affecting internal auditors' ethical decision making: an exploratory study

Purpose – The purpose of this paper is to explore the ethical decision making of internal auditors and the impact of corporate governance mechanisms thereon. It also aims to explore whether ethical decision making is influenced by years of experience in internal auditing.Design/methodology/approach – A total of 66 internal auditors were presented with five ethical dilemmas. For each scenario, a key element of corporate governance was manipulated to assess its impact on ethical decision making. These were audit committee support; management integrity regarding accounting policies; management integrity regarding pressure on internal audit; external auditor characteristics; and organisational code of conduct.Findings – Participants were generally sensitive to ethical dilemmas but were not always confident that their peers would act ethically. A higher quality external audit function was positively associated with internal auditors' ethical decision making. However, the strength of other governance mechanisms.
Enterprise resource planning systems induced stress: a comparative empirical analysis with young and elderly SAP users

In this research study we investigate whether and how ERP system characteristics cause its users to experience stress. In order to do so, we analyze a research model explaining enterprise resource planning systems induced stress with an empirical study in two organizations (N=227). The results reveal that usefulness, complexity, reliability, and pace of change are important ERP sys- tem characteristics leading to the perception of stressors and exhaustion. Fur- thermore, our comparative empirical analysis with young and elderly ERP users indicate that the elderly ones perceive ERP characteristics more negatively and are more stressed and exhausted than the younger users.
A Critical Review on Test Case Prioritization and Optimization using Soft Computing Techniques

Test case prioritization involves scheduling test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also that detects the most severe faults at the earliest in its testing life cycle. Regression Testing is an usual and a very costly activity to be performed, often in a time and resource constrained environment. Thus we use techniques like Test Case Selection and Prioritization, to select and prioritize a subset from the complete test suite, fulfilling some chosen criteria. Present paper gives the approach into existing single objective test cases prioritization and optimization using techniques such as Genetic Algorithms, Ant Colony Optimization. Key Word: Test Case Prioritization, Regression Testing, Genetic Algorithm (GA), Prioritization Factors, Ant Colony Optimization
Realistic Social Networks for Simulation using Network Rewiring

The value of p = 0.6 means that approximately half of the original links are retained, leaving many triangles, and hence producing the required clustering coefficient. The value of π = 5 exaggerates the existing high-degree bias of the Kawachi process, therefore producing “scale free” behaviour, even when p = 0.6. Figure (i) shows a network produced by the extended rewiring process. Networks like this resemble real social networks, and are suitable for use in social and organisational simulations.
Efficient Querying of Transformed XML Documents

An application using XML for data representation requires the transformation of XML data if the application accesses XML data of other applications, or of a global database using another XML format. The common approach transforms entire XML documents from one format into another e.g. by using an XSLT stylesheet. The application can then work locally on a copy of the original document transformed in the application-specific format. Different from the common approach, we use an XSLT stylesheet in order to transform a given XPath query such that we retrieve and transform only that part of the XML document which is sufficient to answer the given query. Among other things, our approach avoids problems of replication, saves processing time and in distributed scenarios, transportation costs. Experimental results of a prototype prove that our approach is scalable and efficient.
Evaluating Time Series Models in Short and Long-Term Forecasting of Canadian Air Passenger Data

This paper uses six time series models to forecast seasonally unadjusted monthly data of Canadian enplaned/deplaned air passengers, for the domestic, transborder and international sectors. We find that forecasting performance of the models varies widely across series and forecast horizons. Our forecasting results are compared with forecasts published by Transport Canada’s Aviation Forecasting Division.
A Lightweight Virtual Machine Image Deduplication Backup Approach in Cloud Environment

As most clouds are based on virtualization technology, more and more virtual machine images are created within data centers. Depending on the need of disaster recovery, the storage space used for backup would easily sprawl to a TB or PB level with the growth of images. Unfortunately, different images have a large amount of same data segments. Those duplicated data segments will lead to serious waste of storage resource. Although there is a lot of work focus on deduplication storage and could achieve a good result in removing duplicate copies, they are not very suitable for virtual machine image deduplication in a cloud environment. Because huge resource usage of deduplication operations could lead to serious performance interference to the hosting virtual machines. This paper propose a local deduplication method which can speed up the operation progress of virtual machine image deduplication and reduce the operation time. The method is based on an improved k-means clustering algorithm, which could classify the metadata of backup image to reduce the search space of index lookup and improve the index lookup performance. Experiments show that our approach is robust and effective. It can significantly reduce the performance interference to hosting virtual machine with an acceptable increase in disk space usage.
Adaptive Reduced-Rank Constrained Constant Modulus Algorithms Based on Joint Iterative Optimization of Filters for Beamforming

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints. The proposed scheme consists of a bank of full-rank adaptive filters that forms the transformation matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. For each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving the performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. A detailed complexity study and a convexity analysis are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.
Pedestrian detection via PCA filters based convolutional channel features

In this paper, we propose a kind of image representation, named PCA filters based convolutional channel features (PCA-CCF) for pedestrian detection. The motivation is to use the convolutional network architecture with orthogonal PCA filters to enhance the state-of-the-art aggregate channel features (ACF). In PCA-CCF, the convolutional operation improves the feature robustness to pedestrian local deformation. The learned PCA filters reduce the correlations among features of each channel, and therefore, improve feature discrimination capability. With the proposed PCA-CCF features and cascaded AdaBoost classifiers, we develop a coarse-to-fine pedestrian detection approach. Experiments show that such approach achieves 3.04%, 17.87% and 6.28% performance gain on the INRIA, Caltech Reasonable and Caltech Overall pedestrian datasets, respectively.
Customer credit quality assessments using data mining methods for banking industries

Personal credit scoring on credit cards has been a critical issue in the banking industry. The bank with the most accurate estimation of its customer credit quality will be the most profitable. The study aims to compare quality prediction models from data mining methods, and improve traditional models by using boosting and genetic algorithms (GA). The predicting models used are instant-based classifiers (such as k-nearest neighbors), Bayesian networks, decision trees, decision tables, logistic regressions, radial basis function neural networks, and support vector machines. Three boosting (or ensemble) algorithms used for performance enhancement are AdaBoost, LogitBoost, and MultiBoost. The mentioned algorithms are optimized by GA for input features. Empirical results indicated that GA substantially improves the performance of underlying classifiers. Considering robustness and reliability, combining GA with ensemble classifiers is better than traditional models. Especially, integrating GA with LogitBoost (C4.5) is the most effective and compact model for credit quality evaluations. Key words: Decision support system, credit risk assessment, genetic algorithm (GA), ensemble classifier, data mining.