Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Critical Success Factors for Enterprise Systems Implementation: A Failure Case Analysis Based on Process Theory

Although Enterprise Systems (ES) have promised major strategic benefits and process improvements from business and technology integration, their implementation has been plagued by a high failure rate and difficulty in realizing the promised benefits. For the purpose of understanding implementation failures, previous studies have focused on identifying critical success factors (CSFs) for information systems implementation. However, there has been little research on how these CSFs actually lead to successful results. In this study, based on process theory, we examined the process of ES implementation by explaining how the factors of ES implementation influence each other and how interaction among them produces results. Based on a failure case, we then developed a process model of ES implementation thus allowing us to explain the process of ES implementation. The proposed model facilitated an understanding of how repeating patterns of ES failure can be reversed. This model can be used for guiding new ES implementation projects.
Performance in Private Equity: Are General Partnership Owners Important?

This study demonstrates that the ownership of private equity (PE) funds influences investment performance. The analysis focuses on the universe of PE investments made by Italian closed-end funds from 1999 to 2005. We find that bank-owned funds generally engage in weaker monitoring of the firms in which they invest, because their representatives hold a plurality of offices on boards of directors of such firms, whether PE-backed or not. This leads to lower revenue growth for portfolio firms, and consequently to lower IRR. On the contrary, corporate-owned funds are able to enforce closer supervision of their investments, leading to better performance.
The effect of verbal context on olfactory neural responses

Odor names refer usually to “source” object categories. For example, the smell of rose is often described with its source category (flower). However, linguistic studies suggest that odors can also be named with labels referring to categories of “practices”. This is the case when rose odor is described with a verbal label referring to its use in fragrance practices (“body lotion,” cosmetic for example). It remains unknown whether naming an odor by its practice category influences olfactory neural responses differently than that observed when named with its source category. The aim of this study was to investigate this question. To this end, functional MRI was used in a within‐subjects design comparing brain responses to four different odors (peach, chocolate, linden blossom, and rose) under two conditions whereby smells were described either (1) with their source category label (food and flower) or (2) with a practice category label (body lotion). Both types of labels induced activations in secondary olfactory areas (orbitofrontal cortex), whereas only the source label condition induced activation in the cingulate cortex and the insula. In summary, our findings offer a new look at olfactory perception by indicating differential brain responses depending on whether odors are named according to their source or practice category. Hum Brain Mapp 35:810–818, 2014. © 2012 Wiley Periodicals, Inc.
Robust fault detection and diagnosis in a class of nonlinear systems using a neural sliding mode observer

This article presents a robust fault detection and diagnosis scheme for any abrupt and incipient class of faults that can affect the state of a class of nonlinear systems. A nonlinear observer which synthesizes sliding mode techniques and neural state space models is proposed for the purpose of online health monitoring. The sliding mode term is utilized to eliminate the effect of system uncertainties on the state observation. The switching gain of the sliding mode is updated via an iterative learning algorithm and an iterative fuzzy model, respectively. Moreover, a bank of neural state space models is adopted to estimate various state faults. Robustness with respect to modeling uncertainties, fault sensitivity, and stability of this neural sliding mode observer-based fault diagnosis scheme are rigorously investigated in theory. Moreover, the proposed fault detection and diagnosis scheme is applied to the model of a fourth-order satellite dynamic system, and the simulation results illustrate the effectiveness of the proposed approach.
Phosphorylation and Methylation of Proteasomal Proteins of the Haloarcheon Haloferax volcanii

Proteasomes are composed of 20S core particles (CPs) of α- and β-type subunits that associate with regulatory particle AAA ATPases such as the proteasome-activating nucleotidase (PAN) complexes of archaea. In this study, the roles and additional sites of post-translational modification of proteasomes were investigated using the archaeon Haloferax volcanii as a model. Indicative of phosphorylation, phosphatase-sensitive isoforms of α1 and α2 were detected by 2-DE immunoblot. To map these and other potential sites of post-translational modification, proteasomes were purified and analyzed by tandem mass spectrometry (MS/MS). Using this approach, several phosphosites were mapped including α1 Thr147, α2 Thr13/Ser14 and PAN-A Ser340. Multiple methylation sites were also mapped to α1, thus, revealing a new type of proteasomal modification. Probing the biological role of α1 and PAN-A phosphorylation by site-directed mutagenesis revealed dominant negative phenotypes for cell viability and/or pigmentation for α1 variants including Thr147Ala, Thr158Ala and Ser58Ala. An H. volcanii Rio1p Ser/Thr kinase homolog was purified and shown to catalyze autophosphorylation and phosphotransfer to α1. The α1 variants in Thr and Ser residues that displayed dominant negative phenotypes were significantly reduced in their ability to accept phosphoryl groups from Rio1p, thus, providing an important link between cell physiology and proteasomal phosphorylation.
Dynamics of the Wind Power Plants at Small Wind Speeds

In this paper, the operation of the wind power plants at small values of the wind speed is analyzed. The values of the wind turbine’s delivered power are determined based on the experimental data from a wind farm located in Galbiori – Dobrogea, in order to observe, to what extent, its operation is in the optimum area from the point of view of the captured energy. The possibility of capturing the maximum wind energy existent at a certain moment is investigated. The time-evolution of the wind turbine’s delivered power, at small wind speed values is also analyzed.
Context awareness in opportunistic computing

Hybrid wireless mesh networks are infrastructureless networks. These networks can selfconfigure and self-heal and are therefore preferred candidates for the dynamic adaptive networks such as emergency response services, military applications and so on. In addition to that these networks have been recommended as a complementary technology for offloading the ever increasing data traffic from cellular networks. Around the world, wireless mesh networks have been deployed by the public safety sector as a way to establish essential communications for law enforcement personnel and to provide city video surveillance. Most applications in these networks use traditional end-to-end routing protocols however the performance of such routing protocols degrades due to network conditions such as mobility, heavy load and interference. These network conditions can be termed as network context information. In these network conditions if any link fails between a pair of nodes then it can fail the whole communication path. This is because a traditional end-to-end protocol communicates over a multi-hop connection when all the nodes between a source and a destination are connected at the same time. Another paradigm of routing that does not require infrastructure support is opportunistic routing. In this routing mechanism nodes communicate over multi-hops even though connection among them is intermittent. Whenever nodes come in contact with each other they store and forward data. These routing protocols have degraded performance as compared to the traditional end-to-end routing protocol and they are characterized by long delays and lowered packet delivery ratio. These routing protocols can be applied to network situations where end-to-end route is not possible e.g. providing Internet connections in rural areas. This thesis proposes a protocol that is a context aware integration of traditional end-to-end and opportunistic routing. The resultant hybrid protocol can utilize capabilities of both routing mechanisms. Such a hybrid protocol has the capability of dynamically switching between both the routing modes depending upon the network situation. This hybrid protocol can improve the performance of the network compared to the existing end-to-end or opportunistic protocols. To evaluate the performance of the proposed protocol a wide range of network situations from the connected to sparse networks are considered and they show significant protocol performance improvement in most cases. To explore the possible potential of the hybrid solution, two variants of this protocol are investigated i.e. broadcast and unicast approaches with distinct algorithm designs. Both
How to design a powerful family of particle swarm optimizers for inverse modelling

In this paper, we show how to design a powerful set of particle swarm optimizers to be applied in inverse modelling. The design is based on the interpretation of the swarm dynamics as a stochastic damped mass-spring system, the so-called particle swarm optimization (PSO) continuous model. Based on this idea we derived a family of PSO optimizers (GPSO, CC-PSO and CP-PSO) having different exploitation and exploration capabilities. Their convergence is related to the stability of their first (mean trajectories)- and second-order moments (variance and temporal covariance). Good parameter sets are located inside their first stability regions close to the upper border of their respective second stability regions where the attraction from the particles oscillation centre is very weak. In this region of weak attraction, both convergence to the global minimum and exploration of the search space are possible. Based on this idea, we have designed a particle–cloud algorithm where each particle in the swarm has different inertia (damping) and acceleration (rigidity) constants. We explored the performance of these algorithms for different PSO members using different benchmark functions, showing that the cloud algorithms have a very good balance between exploration and exploitation. Also, the cloud design helps to avoid two main drawbacks of the PSO algorithm: the tuning of the PSO parameters and the clamping of the particles velocities. We also present the lime and sand algorithm that changes the time step with iterations. This feature helps to avoid entrapment in local minima when the time step is increased, and enables exploration around the global best when the time step is decreased. All these designs are based on the theoretical analysis of the PSO dynamics. We explain how to use this knowledge to the solution and appraisal of inverse problems. Finally, we briefly introduce the combined use of PSO and model reduction techniques to allow posterior sampling in high dimensional spaces.
Fear of pain in pediatric headache

Objective The current study provides the first measure of pain-related fear for pediatric headache patients. Methods From a large pediatric headache clinic, a cross-sectional cohort of 206 children and adolescents completed measures of pain-related fear, anxiety sensitivity, catastrophizing, pain acceptance, functional disability, and school functioning. Results The two-factor solution of the Fear of Pain Questionnaire (FOPQ) was confirmed from the originally derived structure with pediatric headache patients. Simultaneously regressing FOPQ subscales fear of pain and activity avoidance on theorized construct validity measures demonstrated that fear of pain was more closely linked with anxiety sensitivity and pain catastrophizing while activity avoidance had a strong negative association with pain acceptance (activity engagement and pain willingness). Pain-related fear was not significantly associated with pain level. After controlling for demographic factors and pain, fear of pain and activity avoidance accounted for an additional 26% of the variance in functional disability and school functioning outcomes, with activity avoidance accounting for much of this relationship. Conclusions Although typically considered an influential construct among musculoskeletal patients, pain-related fear is also an important factor influencing functioning among pediatric headache patients, with the dimension of activity avoidance particularly salient.
Modelling Climate Change Impact on Phosphorus Load in Swedish Rivers

Climate change impact on phosphorus load is modelled and compared for two Swedish rivers with different characteristics. The modelling was based on the ECHAM4/OPYC3 B2 scenario, downscaled with the RCA3 model and complementary scaling procedure. Hydrology and phosphorus concentrations were simulated for the time period 1961–2100, using the ICECREAM model for arable leaching, and HBV-NP model for integrated catchment analysis including all sources, erosion and major turnover processes at the matchment scale. The results show 10% increased load, and 25% reduced load, respectively, for the rivers. In both catchments, phosphorus leaching from crops was found to increase; however, this load was only a minor fraction in one river. For the other river, a suggested plan of measures for reducing load was found to be less effective in a future climate. Finally, the study concludes that climate change impact studies are based on a chain of assumptions and uncertainties which should be emphasised in future research.
Scalable Normalized Cut with Improved Spectral Rotation

Many spectral clustering algorithms have been proposed and successfully applied to many highdimensional applications. However, there are still two problems that need to be solved: 1) existing methods for obtaining the final clustering assignments may deviate from the true discrete solution, and 2) most of these methods usually have very high computational complexity. In this paper, we propose a Scalable Normalized Cut method for clustering of large scale data. In the new method, an efficient method is used to construct a small representation matrix and then clustering is performed on the representation matrix. In the clustering process, an improved spectral rotation method is proposed to obtain the solution of the final clustering assignments. A series of experimental were conducted on 14 benchmark data sets and the experimental results show the superior performance of the new method.
Stereotactic body radiation therapy for primary and metastatic liver tumors.

OBJECTIVES The full potential of stereotactic body radiation therapy (SBRT), in the treatment of unresectable intrahepatic malignancies, has yet to be realized as our experience is still limited. Thus, we evaluated SBRT outcomes for primary and metastatic liver tumors, with the goal of identifying factors that may aid in optimization of therapy. METHODS From 2005 to 2010, 62 patients with 106 primary and metastatic liver tumors were treated with SBRT to a median biologic effective dose (BED) of 100 Gy (42.6-180). The majority of patients received either three (47%) or five fractions (48%). Median gross tumor volume (GTV) was 8.8 cm(3) (0.2-222.4). RESULTS With a median follow-up of 18 months (0.46-46.8), freedom from local progression (FFLP) was observed in 97 of 106 treated tumors, with 1- and 2-year FFLP rates of 93% and 82%. Median overall survival (OS) for all patients was 25.2 months, with 1- and 2-year OS of 81% and 52%. Neither BED nor GTV significantly predicted for FFLP. Local failure was associated with a higher risk of death [hazard ratio (HR) = 5.1, P = . 0007]. One Child-Pugh Class B patient developed radiation-induced liver disease. There were no other significant toxicities. CONCLUSIONS SBRT provides excellent local control for both primary and metastatic liver lesions with minimal toxicity. Future studies should focus on appropriate selection of patients and on careful assessment of liver function to maximize both the safety and efficacy of treatment.
Are Microcredit Borrowers in Bangladesh Over-Indebted?

Microcredit programs in Bangladesh have experienced spectacular growth in recent years, with a growing number of borrowers availing credit from multiple microcredit agencies. There is a growing concern that if there are not sufficient returns to borrowing from microfinance institutions (MFIS), some borrowers might be taking loans that they will not be able to repay. A household may be considered over-indebted, for example, if its debt liability exceeds 40 percent of its income or assets. Using a long panel of household survey data from Bangladesh, the paper finds that some 26 percent of microcredit borrowers are over-indebted on this measure versus 22 percent of non-microcredit borrowers. Econometric analysis suggests that both MFI competition and multiple borrowing raise indebtedness. However, repeated borrowing, while it affects short-term liability adversely, does affect the long-term debt-asset ratio favorably. That is, repeated borrowing helps increase assets more than debt over time. Microcredit borrowers in Bangladesh are thus not necessarily over-indebted. But when borrowing is seen as protection against shocks such as floods even at the cost of being indebted, MFIs may offer micro-insurance schemes to safeguard borrowers against economic shocks.
Genetic Risk Factors for Arterial Ischemic Stroke in Children: A Possible MTHFR and eNOS Gene-Gene Interplay?

In order to investigate the influence of genetic factors in childhood stroke, we compared the distributions of mutations/ polymorphisms affecting hemostasis and/or endothelial function (factor V [FV] Leiden, factor II [FII] G20210A, methylenetetrahydrofolate reductase [MTHFR] C677T, angiotensin-converting enzyme [ACE] insertion/deletion [ID], and endothelial nitric oxide synthase [eNOS] G894T) among children with stroke and controls. A total number of 26 children with arterial ischemic stroke and a control group of 50 healthy children were included in the study. No statistically significant differences in allelic and genotypic distribution were detected in comparisons between groups. However, when combined genotypes were analyzed, statistical significance was observed for the association of MTHFR CT and eNOS TT gene variants. The results of our study suggest that this genotype combination represents a risk factor of 7.2 (P = .017) for arterial ischemic stroke in children.
Metabolic syndrome as a predictor of all-cause and cardiovascular mortality in type 2 diabetes: the Casale Monferrato Study.

OBJECTIVE The aim of this study was to assess in an 11-year survival follow-up of a population-based cohort of type 2 diabetes the predictive role of World Health Organization-defined metabolic syndrome, independent of conventional cardiovascular risk factors. RESEARCH DESIGN AND METHODS During the follow-up (1991-2001), 1,565 patients were regularly examined with centralized measurements of HbA(1c). The independent role of the metabolic syndrome as a predictor of all-cause and cardiovascular mortality was assessed with multivariate Cox proportional hazards modeling. RESULTS At baseline, the prevalence of the metabolic syndrome was 75.6% (95% CI 73.6-77.9). Results are based on 685 deaths (520 with the metabolic syndrome and 165 without it) in 10,890.2 person-years of observations. With respect to subjects without the metabolic syndrome, those with the metabolic syndrome had a similar hazard ratio (HR) of cardiovascular mortality after adjustment for age, sex, smoking, total cholesterol level, and coronary heart disease. In contrast, relative to subjects with diabetes only, the HR of subjects with only one component of the syndrome was 2.92 (1.16-7.33), independent of other risk factors. CONCLUSIONS We found that 1) the prevalence of the metabolic syndrome in a population-based cohort of type 2 diabetes is high (75.6%); 2) the metabolic syndrome is not a predictor of 11-year all-cause and cardiovascular mortality; and 3) more than twofold higher cardiovascular risk, independent of conventional risk factors, is evident in diabetic subjects with only one component of the syndrome compared with those with diabetes only. Categorizing type 2 diabetic subjects as having or not having the metabolic syndrome does not provide further prediction compared with the knowledge of its single components.
The Bright and Dark Sides of High-redshift Starburst Galaxies from Herschel and Subaru Observations

We present rest-frame optical spectra from the FMOS-COSMOS survey of 12 z ∼ 1.6 Herschel starburst galaxies, with star formation rate (SFR) elevated by ×8, on average, above the star-forming main sequence (MS). Comparing the Hα to IR luminosity ratio and the Balmer decrement, we find that the optically thin regions of the sources contain on average only ∼10% of the total SFR, whereas ∼90% come from an extremely obscured component that is revealed only by far-IR observations and is optically thick even in Hα. We measure the [N ii]6583/Hα ratio, suggesting that the less obscured regions have a metal content similar to that of the MS population at the same stellar masses and redshifts. However, our objects appear to be metal-rich outliers from the metallicity–SFR anticorrelation observed at fixed stellar mass for the MS population. The [S ii]6732/[S ii]6717 ratio from the average spectrum indicates an electron density ne ∼ 1100 cm−3 , larger than what was estimated for MS galaxies but only at the 1. 5σ level. Our results provide supporting evidence that high-z MS outliers are analogous of local ULIRGs and are consistent with a major-merger origin for the starburst event.
An effort to select a preferable metaheuristic model for knowledge discovery in data mining

Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm intelligence in different fields viz. sensor network, robotic management, microwave filter design, data mining applications etc. In this paper, an effort has been made to make a comparison on the performance of fuzzy logic with different membership function, artificial neural network and swarm intelligence models. The models with minimum errors have been given preference for selection towards the decision making of information. The same methods can also be cross-checked by residual analysis and a new unknown data set to verify the earlier proposed observation. Initially the statistical techniques have been applied on the data set to select the preferable models but it has been observed that statistical technique errors are large. Therefore, under the fuzzy logic, different membership functions have been used to select the preferable membership function for fuzzy logic for the available data. In this paper further effort has also been made to change the expression of the computing fitness function of the path in artificial bee colony to achieve the best result.