Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter-Organisational Systems, Strategy and Structure: the case for planning environments

Electronic trade and commerce have brought inter organisational co-operation to the forefront of organisational strategies. However, inter-organisational systems (IOS) planning lacks appropriate methodological guidelines, and tends to be practised in an insular manner. This paper examines the development of systems in three interorganisational networks in Ireland in order to ascertain the planning that went into them. The networks studied are representative of three types of structural interdependencies - pooled, reciprocal and sequential. Although it is difficult to identify an activity that can be clearly labelled IOS planning, planning is integral to the development of all networks. The interdependencies influence planning processes and outcomes. For the pooled and reciprocal networks, the planning process gives a focus. For the sequential network, the main planning is done by the hub organisation, and extended to the spokes. Planning interactions across all networks illustrate similarities that reveal that existence of planning environments within which IOS planning takes place. These planning environments are proposed as being an appropriate basis for the development of IOS planning guidelines.
Slow Inactivation Does Not Affect Movement of the Fast Inactivation Gate in Voltage-gated Na+ Channels

Voltage-gated Na+ channels exhibit two forms of inactivation, one form (fast inactivation) takes effect on the order of milliseconds and the other (slow inactivation) on the order of seconds to minutes. While previous studies have suggested that fast and slow inactivation are structurally independent gating processes, little is known about the relationship between the two. In this study, we probed this relationship by examining the effects of slow inactivation on a conformational marker for fast inactivation, the accessibility of a site on the Na+ channel III–IV linker that is believed to form a part of the fast inactivation particle. When cysteine was substituted for phenylalanine at position 1304 in the rat skeletal muscle sodium channel (μl), application of [2-(trimethylammonium)ethyl]methanethiosulfonate (MTS-ET) to the cytoplasmic face of inside-out patches from Xenopus oocytes injected with F1304C RNA dramatically disrupted fast inactivation and displayed voltage-dependent reaction kinetics that closely paralleled the steady state availability (h∞•) curve. Based on this observation, the accessibility of cys1304 was used as a conformational marker to probe the position of the fast inactivation gate during the development of and the recovery from slow inactivation. We found that burial of cys1304 is not altered by the onset of slow inactivation, and that recovery of accessibility of cys1304 is not slowed after long (2–10 s) depolarizations. These results suggest that (a) fast and slow inactivation are structurally distinct processes that are not tightly coupled, (b) fast and slow inactivation are not mutually exclusive processes (i.e., sodium channels may be fast- and slow-inactivated simultaneously), and (c) after long depolarizations, recovery from fast inactivation precedes recovery from slow inactivation.
Analysis of a Urinary Biomarker Panel for Clinical Outcomes Assessment in Cirrhosis

Background Biomarkers are potentially useful in assessment of outcomes in patients with cirrhosis, but information is very limited. Given the large number of biomarkers, adequate choice of which biomarker(s) to investigate first is important. Aim Analysis of potential usefulness of a panel of urinary biomarkers in outcome assessment in cirrhosis. Patients and Methods Fifty-five patients with acute decompensation of cirrhosis were studied: 39 had Acute Kidney Injury (AKI) (Prerenal 12, type-1 HRS (hepatorenal syndrome) 15 and Acute Tubular Necrosis (ATN) 12) and 16 acute decompensation without AKI. Thirty-four patients had Acute-on-chronic liver failure (ACLF). A panel of 12 urinary biomarkers was assessed, using a multiplex assay, for their relationship with ATN, ACLF and mortality. Results Biomarker with best accuracy for ATN diagnosis was NGAL (neutrophil-gelatinase associated lipocalin): 36 [26-125], 104 [58-208] and 1807 [494-3,716] μg/g creatinine in Prerenal-AKI, type-1 HRS and ATN, respectively; p<0.0001 (AUROC 0.957). Other attractive biomarkers for ATN diagnosis were IL-18, albumin, trefoil-factor-3 (TFF-3) and glutathione-S-transferase-π (GST-π) Biomarkers with less accuracy for ATN AUCROC<0.8 were β2-microglobulin, calbindin, cystatin-C, clusterin and KIM-1 (kidney injury molecule-1). For ACLF, the biomarker with the best accuracy was NGAL (ACLF vs. No-ACLF: 165 [67-676] and 32 [19-40] μg/g creatinine; respectively; p<0.0001; AUROC 0.878). Interestingly, other biomarkers with high accuracy for ACLF were osteopontin, albumin, and TFF-3. Biomarkers with best accuracy for prognosis were those associated with ACLF. Conclusions A number of biomarkers appear promising for differential diagnosis between ATN and other types of AKI. The most interesting biomarkers for ACLF and prognosis are NGAL, osteopontin, albumin, and TFF-3. These results support the role of major inflammatory reaction in the pathogenesis of ACLF.
Software Product Line Development by Analogy

Although the benefits of taking a product line approach to develop similar software systems are well documented, early case studies have revealed significant barriers to adopt this approach. In order to minimize the paradigm shift between conventional software engineering and software product line engineering, this paper presents a new development process where the products of a domain are generated by analogy to an existing product. This paper also discusses the capabilities and limitations of different techniques, currently applied to generalize code, to implement the analogy relation and proposes a new language to overcome the limitations.
From variable weighting to cluster characterization in topographic unsupervised learning

We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and local weight vector for each cluster. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector). Based on the Self-Organizing Map approach, we present two new simultaneously clustering and weighting algorithms: local weighting observation lwo-SOM and local weighting distance lwd-SOM. Both algorithms achieve the same goal by minimizing different cost functions. After learning phase, a selection method with weight vectors is used to prune the irrelevant variables and thus we can characterize the clusters. We illustrate the performance of the proposed approach using different data sets. A number of synthetic and real data are experimented on to show the benefits of the proposed local weighting using self-organizing models.
Evaluation of the sub-pixel performance of anomaly detectors

Anomaly detection in hyperspectral data has received much attention for various applications and is especially important for defense and security applications. Anomaly detection detects pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Most existing methods estimate the spectra of the (local or global) background and then detect anomalies as pixels with a large spectral distance w.r. t. the determined background spectra. Many types of anomaly detectors have been proposed in literature. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test and the background. This paper investigates the sub-pixel detection performance of two classes of anomaly detectors: the family of RX-based detectors and the segmentation-based anomaly detectors. Representative examples of each class are selected and results obtained on three different datacubes are analyzed.
Targeted massively parallel sequencing of maternal plasma DNA permits efficient and unbiased detection of fetal alleles.

BACKGROUND Massively parallel sequencing has recently been used in noninvasive prenatal diagnosis. The current costs of this technology are still relatively expensive, however, and sample throughput is still relatively low when it is used as a molecular diagnostic tool. Rather than nonselectively sequencing the genome, target enrichment provides a logical approach for more efficient and cost-effective massively parallel sequencing because it increases the proportion of informative data from the targeted region(s). Existing applications of targeted sequencing have mainly been qualitative analyses of genomic DNA. In this study, we investigated its applicability in enriching selected genomic regions from plasma DNA and the quantitative performance of this approach. METHODS DNA was extracted from plasma samples collected from 12 pregnant women carrying female fetuses. The SureSelect Target Enrichment System (Agilent Technologies) was used to enrich for exons on chromosome X. Plasma DNA libraries with and without target enrichment were analyzed by massively parallel sequencing. Genomic DNA samples of the mother and fetus for each case were genotyped by microarray. RESULTS For the regions targeted by the enrichment kit, the mean sequence coverage of the enriched samples was 213-fold higher than that of the nonenriched samples. Maternal and fetal DNA molecules were enriched evenly. After target enrichment, the coverage of fetus-specific alleles within the targeted region increased from 3.5% to 95.9%. CONCLUSIONS Targeted sequencing of maternal plasma DNA permits efficient and unbiased detection of fetal alleles at genomic regions of interest and is a powerful method for measuring the proportion of fetal DNA in a maternal plasma sample.
Applying Knowledge Discovery in Database Techniques in Modeling Packet Header Anomaly Intrusion Detection Systems

This paper describes packet header anomaly intrusion detection system modeling. The essence of the discussion in this paper is on applying knowledge discovery in database technique to produce expert production rules which is one of the main components of our model which we call as Protocol based Packet Header Anomaly Detector (PbPHAD) Intrusion Detection System. PbPHAD is designed to detect the anomalous behavior of network traffic packets based on three specific network and transport layer protocols namely UDP, TCP and ICMP to identify the degree of maliciousness from a set of detected anomalous packets identified from the sum of statistically modeled individually rated anomalous field values.
Fast Line, Arc/Circle and Leg Detection from Laser Scan Data in a Player Driver

A feature detection system has been developed for real-time identification of lines, circles and people legs from laser range data. A new method suitable for arc/circle detection is proposed: the Inscribed Angle Variance (IAV). Lines are detected using a recursive line fitting method. The people leg detection is based on geometrical relations. The system was implemented as a plugin driver in Player, a mobile robot server. Real results are presented to verify the effectiveness of the proposed algorithms in indoor environment with moving objects.
A Partial-Order Approach to Array Content Analysis

We present a parametric abstract domain for array content analysis. The method maintains invariants for contiguous regions of the array, similar to the methods of Gopan, Reps and Sagiv, and of Halbwachs and Peron. However, it introduces a novel concept of an array content graph, avoiding the need for an up-front factorial partitioning step. The resulting analysis can be used with arbitrary numeric relational abstract domains; we evaluate the domain on a range of array manipulating program fragments.
Performance evaluation of fair and capacity scheduling in Hadoop YARN

Big Data research can be divided broadly into the scheduling of jobs and controlling the rate at which jobs are generating and running. Hadoop YARN provides better resource management schemes to schedule jobs by having a focus on the reduction of total time required to complete the jobs. This paper provides a study of scheduling algorithms in Hadoop YARN and evaluates the performance of two scheduling algorithm, fair scheduling and capacity scheduling using Yarn Scheduler Load Simulator (SLS). The result of this evaluation can be used further to enhance the capabilities of scheduling algorithm in different type of data sets.
Glacier flow estimation by SAR image correlation

The Shirase Glacier is the largest in the Lutzow Holm Bay and is well known as the fastest-flowing ice stream in Antarctica. Although several attempts have been made to measure ice flow velocities of the glacier using ground measurements and remotely sensed data, continuous measurement over a long period has been impossible due to severe environmental conditions and the lack of datasets from continuous observation. This report of our preliminary ice-flow estimations of the Shirase Glacier correlates the amplitude images with the available synthetic aperture radar (SAR) data. We propose to determine ice-flow velocities using this image correlation. We apply our technique to the SAR data obtained by observing the Shirase Glacier to determine and analyze the ice flow velocities in the summary
The association of maternal age with infant mortality, child anthropometric failure, diarrhoea and anaemia for first births: evidence from 55 low- and middle-income countries

Objective To examine the association between maternal age at first birth and infant mortality, stunting, underweight, wasting, diarrhoea and anaemia in children in low- and middle-income countries. Design Cross-sectional analysis of nationally representative household samples. A modified Poisson regression model is used to estimate unadjusted and adjusted RR ratios. Setting Low- and middle-income countries. Population First births to women aged 12–35 where this birth occurred 12–60 months prior to interview. The sample for analysing infant mortality is comprised of 176 583 children in 55 low- and middle-income countries across 118 Demographic and Health Surveys conducted between 1990 and 2008. Main outcome measures Infant mortality in children under 12 months and stunting, underweight, wasting, diarrhoea and anaemia in children under 5 years. Results The investigation reveals two salient findings. First, in the sample of women who had their first birth between the ages of 12 and 35, the risk of poor child health outcome is lowest for women who have their first birth between the ages of 27 and 29. Second, the results indicate that both biological and social mechanisms play a role in explaining why children of young mothers have poorer outcomes. Conclusions The first-born children of adolescent mothers are the most vulnerable to infant mortality and poor child health outcomes. Additionally, first time mothers up to the age of 27 have a higher risk of having a child who has stunting, diarrhoea and moderate or severe anaemia. Maternal and child health programs should take account of this increased risk even for mothers in their early 20s. Increasing the age at first birth in developing countries may have large benefits in terms of child health.
Photolithography with transparent reflective photomasks

A new type of photomask was fabricated by casting a prepolymer of a transparent, elastomeric polymer (polydimethylsiloxane, PDMS) against a Si(100) master whose surface has been patterned with V-shaped trenches or pyramidal pits using anisotropic etching. The PDMS replica, when placed in contact with a film of photoresist and illuminated, acts as a photomask. The sidewalls of the trenches and pits in the silicon master meet with the plateaus in dihedral angles of 54°; as a result, the PDMS replica selectively blocks the incident light in regions where it has sloping features by total internal reflection, and acts as a reflective contact mask for photolithography. The feasibility of this new type of photomask has been demonstrated by the fabrication of micropatterns in photoresist (and in an underlying silicon substrate) with smaller feature sizes and higher complexities than those present on the original chrome mask used in patterning the silicon master. The patterns produced using these elastomeric photo.
The Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Project: rationale and planned nationwide studies of genetic predictors, physical exercise, and individualized pharmacological treatment

Here we provide an overview of the rationale and methods of a series of planned population based studies within the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Project. The project aims to support and evaluate ongoing political and administrative efforts to implement nationwide guidelines for maintaining metabolic control in newly diagnosed type 2 diabetes (T2D) patients to prevent diabetic complications and improve quality of life. The DD2 is designed as a prospective cohort study (collection of epidemiological data) supplemented by randomized clinical intervention trials (on physical exercise and individualized pharmacological treatment) and the establishment of a biobank comprised of material from a large number of newly diagnosed T2D patients. Inclusion of the majority of newly diagnosed T2D patients as they are diagnosed at their general practitioner or diabetes hospital outpatient clinics and entered into the DD2 cohort will establish a nationwide database comprising a large number of future incident cases of T2D in Denmark. These cases will form the project cohort of the DD2. Within the first 6 months of diagnosis, all patients will be invited to contribute to a biobank of DNA, plasma, urine, and tissue sampling. The DNA biobank will enable future studies of the effect of pharmacological treatment and outcome in subsets of patients with specific genetic risk profiles covering disease etiology and specific drug kinetics and metabolism. We will also perform two clinical intervention trials examining: the effectiveness of physical exercise on diabetes-related outcomes and the impact of trial outcomes on individualized pharmacological treatment. Moreover, the DD2 will serve as a platform for testing and developing new antidiabetic drugs. All together, we expect this study to contribute to substantially improved diabetes care in T2D patients locally and abroad.
The Scale of Functional Specialization within Human Prefrontal Cortex

At what scale is it possible to observe consistent functional specialization within human prefrontal cortex (PFC), reproducible from one individual to the next? Some studies suggest gross functional divisions between large regions of PFC, but it is not known whether PFC exhibits specialization at the fine-grained scale known to differentiate posterior cortical functions. We used fMRI to confirm a three-way segregation of function between three regions of medial anterior PFC, each centered on coordinates within 15 mm of the other two. Naive participants performed three tasks based on earlier studies, and we investigated activity at regions defined by previous results. In each task, signal was significantly greater at the predicted region than the other two, just millimeters away. These results indicate reproducible functional specialization within PFC, at a much finer scale than previously demonstrated. Furthermore, these findings suggest that divergent results from previous studies may reflect the recruitment of functionally distinct regions and that “reverse inference” should be undertaken with caution.
Mathematical structure and formulas for the minC combination of belief functions

Principal ideas of the minC combination are recalled. A mathematical structure of generalized frames of discernment is formalized. A generalized schema for a computation of the minC combination is presented. Conflicting belief masses redistribution among non-conflicting focal elements is overviewed. Final general formulas for computation of the minC combination are presented. Some examples of computation of the minC combination follow. Related works and approaches are referred in the end.
Effective Subspace Clustering with Dimension Pairing in the Presence of High Levels of Noise

Attempts at clustering large and high dimensional data have been made with a focus on scalability. While still inefficient for more complex problems, the effectiveness is also questionable because data becomes very sparse in a high dimensional space. If clusters exist in the data, they tend to remain hidden in some unidentified sub-spaces. So far, the few solutions to this problem have not been able to handle high levels of noise and are often inefficient. Most clustering solutions also require fine-tuning of parameter settings, which are difficult or impossible to set in advance especially by a novice user. We propose a new method, MAXCLUS, which first identifies sub-spaces where clusters could be located then pinpoints the clusters in each sub-space. MAXCLUS has proved robust even with high levels of noise and is very efficient and accurate with little or no parameter adjustment on a wide range of problems outperforming existing approaches.
Data driven model based least squares image reconstruction for radio astronomy

Image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user specified constraints to the reconstruction process that may produce an undesirable bias to the solution. We propose a data driven model based least squares reconstruction method based on the Karhunen-Loève transform. We show that this constraint stems from intrinsic physical properties of the measurement process and demonstrate the improvement of the method over unregularized least squares reconstruction using actual data from the Low Frequency Array (LOFAR), a phased array radio telescope in the Netherlands.
Analyzing impact of outliers' detection and removal from the test sample in Blind Source Extraction using Multivariate Calibration Techniques

Blind Source Extraction (BSE) may be an essential but a challenging task where multiple sources are convolved and/or time delayed. In this article we discuss the performance of Multivariate Calibration Techniques that comprise of Classical Least Square (CLS), Inverse Linear Regression (ILS), Principal Component Regression (PCR) and Partial Least Square Regression (PLS) in achieving this task in robust speech recognition systems with varying Signal-to-Noise Ratios (SNR). We specifically analyze two methods for identifying and removing outliers from the sample, namely; Outlier Sample Removal (OSR) and Descriptor Selection (DS) for Classical Least Square and Factor Based Regression respectively, which results in higher correlation among predicted and the expected results. Our experiments suggest that factor based methods produce much reliable results than Classical Least Square Regression. However, Classical Least Square is much more immune to white noise as compared to Factor Based Regressions. Our results prove that successful detection and removal of outliers from the Sample Under Test (SUT) may result in as low as 37% and 56% improvement in prediction with Classical Least Square and Principal Component Regression respectively.