Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Sector Financial Support for SME Innovativeness: Case Study of Selected CEE countries

The impact of innovation on economic development cannot be underestimated. Innovation has been touted as the anchor on which firms can nick a competitive advantage for themselves. Over the years, public sector interest in innovation has been emphasized due to the impact innovation has made on economic development of some nations. Different public sector support systems are available to firms to access for their innovative activities. Small and Medium Scale Enterprises (SME’s) play vital role in the economies of most countries. Despite their contribution to development, they are constrained by inadequate funds to engage in R&D for innovation. In the light of this, the research sought to find out whether the innovativeness of SME’s from Selected CEE Countries determines the public financial support from the government or the European Union. The logistic regression model was employed using the harmonized European Union Community Innovation Survey 2012-2014 data for the empirical analysis. Our results indicate that SME’s that are engaged in innovative activities attract public financial support from national government and/or the European Union. This is because, the public sector financial support from the government and the EU target firms that are already engaged in innovation or show potential to be innovative.
Relationship between Diet Quality and Socioeconomic and Health-Related Factors in Adolescents by Gender

Adolescence is a key period for consolidating heathy lifestyles and proper eating habits that can last into adulthood. To analyze the diet quality of Spanish adolescents and its association with socioeconomic factors and health behaviors by gender, a cross-sectional study was conducted using data from the DESKcohort project, consisting of a biannual panel survey on health, health behaviors, and associated determinants, collected in secondary education centers. The study population consisted of 7319 students aged 12 to 18 years. Data were collected from October 2019 to March 2020. The dependent variable was diet quality score according to the Spanish adaptation of the Healthy Eating Index (S-HEI). The independent variables included were socioeconomic factors and health behaviors. We conducted linear regression separately by gender. Diet quality score was significantly higher for girls than for boys (68 and 65, respectively, p < 0.001). For both genders, poorer diet quality was associated with a low level of physical activity [−0.9 (95% CI = −1.6:−0.2) in boys, −1.2 (95% CI = −1.9:−0.4) in girls], alcohol use [−2.5 (95% CI = −3.7:−1.3) in boys, −1.0 (95% CI = −1.9:0.0) in girls], poor self-perceived health [−1.1 (95% CI = −2.4:0.2) in boys, −3.5 (95% CI = −4.6:−2.4) in girls], and having attended Intermediate Level Training Cycles [−2.9 (95% CI = −4.3:−1.5) in boys, −1.9 (95% CI = −3.5:−0.3) in girls]. In girls, poorer diet quality also was associated with low mood [−1.1 (95% CI = −1.9:−0.3)]. The variance was 9% in boys and 12% in girls. Our results highlight the need to consider socioeconomic and health-related factors, as well as gender, when conducting interventions to promote healthy eating among adolescents.
Investigating the subcellular localisation and function(s) of dystrophin protein Dp71 isoforms in glioma

The Dp71 protein is the most expressed product of the DMD gene in the nervous system. Mutation in the region codes dystrophin protein (Dp71) linked to cognitive disturbances in Duchenne muscular dystrophy (DMD) patients. There is growing evidence that the gene is contributing to the development of Central Nervous System related cancers. The aim of this study is to characterise the role of the main 4 Dp71 isoforms by investigating its subcellular localisation and putative cellular functions in glioblastoma cells, the most aggressive and common type of glioma. By transfecting the four GFP-Dp71 constructs into a well characterised human glioblastoma cell line – U251-MG. Immunoblotting was used to assess Dp71 expression in human glioblastoma cell line. Moreover, we examined the subcellular localisation and the effect of Dp71 over expression on the nuclear Lamin B1, cell migration immunofluorescence, and scratch assay. A 71 kDa endogenous Dp71 was expressed in all glioblastoma cells and only GFP-Dp71a (99 kDa) isoform was overexpressed in the transfected cells. Lower Lamin B1 fluorescence intensity and abnormal nuclear shape was observed in cells overexpressing GFP-Dp71a. Furthermore, cytoplasmic and nuclear localisation of Dp71 isoform was found in both cytoplasm and nucleus, but higher in the nucleus. Overexpression of Dp71a transfected cells reduced the cell migration and covering the scratched tissue gaps with 5% and 6%, whilst that of the control cells were 29% and 50% at time 24 and 48 hours, respectively compared to the that at time 0. Dp71ab transfected cells showed similar cell migration to the control. We concluded that Dp71 overexpression had a clear effect on the expression of Lamin B1 and cell migration. Additionally, localisation of Dp71a was higher in the nucleus than in the cytoplasm.
Deep Stochastic Conﬁguration Networks with Universal Approximation Property

This paper develops a randomized approach for incrementally building deep neural networks, where a supervisory mechanism is proposed to constrain the random assignment of the weights and biases, and all the hidden layers have direct links to the output layer. A fundamental result on the universal approximation property is established for such a class of randomized leaner models, namely deep stochastic conﬁguration networks (DeepSCNs). A learning algorithm is presented to implement DeepSCNs with either speciﬁc architecture or self-organization. The read-out weights attached with all direct links from each hidden layer to the output layer are evaluated by the least squares method. Given a set of training examples, DeepSCNs can speedily produce a learning representation, that is, a collection of random basis functions with the cascaded inputs together with the read-out weights. An empirical demonstration on a function approximation and a case study are carried out to demonstrate some properties of the proposed deep learner model.
A spatial agent-based model for preemptive evacuation decisions during typhoon

Natural disasters continue to cause tremendous damage to human lives and properties. The Philippines, due to its geographic location, is considered a natural disaster-prone country experiencing an average of 20 tropical cyclones annually. Understanding what factors significantly affect decision making during crucial evacuation stages could help in making decisions on how to prepare for disasters, how to act appropriately and strategically respond during and after a calamity. In this work, an agent-based model for preemptive evacuation decisions during typhoon is presented. In the model, civilians are represented by households and their evacuation decisions were based from calculated perceived risk. Also, rescuer and shelter manager agents were included as facilitators during the preemptive evacuation process. National and municipal census data were employed in the model, particularly for the demographics of household agents. Further, geospatial data of a village in a typhoon-susceptible municipality was used to represent the environment. The decision to evacuate or not to evacuate depends on the agent's perceived risk which also depends on three decision factors: characteristics of the decision maker (CDM); capacity related factors (CRF); and hazard related factors (HRF). Finally, the number of households who decided to evacuate or opted to stay as influenced by the model's decision factors were determined during simulations. Sensitivity analysis using linear regression shows that all parameters used in the model are significant in the evacuation decision of household agents.
‘Information Compression via the Matching and Unification of Patterns’ (ICMUP) as a foundation for AI

This paper describes how ‘Information Compression via the Matching and Unification of Patterns’ (ICMUP) provides a foundation for the SP-multiple-alignment concept—a concept which has been shown in other publications to be a powerful vehicle for modelling diverse aspects of intelligence and the representation and processing of diverse kinds of AI-related knowledge. It also shows with examples how the SP-multiple-alignment construct may function as a generalisation of six other variants of ICMUP—a generalisation that appears to be largely responsible for the AI-related strengths and potential of the SP-multiple-alignment construct. Each of those six variants is described in a separate section, and in each case, there is a demonstration of how that variant may be modelled via SP-multiple-alignment.
Irreversible Infarction Lesion Size at Early MR Imaging and Neonatal Rat Model: Relationship between Hypoxic-Ischemic Brain Injury in the

BACKGROUND AND PURPOSE: By using a neonatal rat hypoxia-ischemia (HI) model, we studied the relationship between lesion volume—measured by diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) at an early time point—and irreversible infarct volume. We also evaluated the optimal apparent diffusion coefficient (ADC) threshold that provides the best correlation with irreversible infarct size. MATERIALS AND METHODS: Twenty-three neonatal rats underwent right common carotid artery ligation and hypoxia. MR imaging was performed 1–2 hours post-HI by using DWI and T2WI and at day 4 post-HI by using T2WI. Lesion volumes relative to whole brain (%LV) were measured on ADC maps by using different relative ADC thresholds 60%–80% of mean contralateral ADC and T2WI. Pearson correlation and multiple linear regression analysis were used to study the relationships between ln(%LV) at MR imaging and %LV at histopathology. RESULTS: At 1–2 hours post-HI, all lesion volume measurements on DWI were significantly correlated with the infarct volume on histopathology, with the best correlation attained at the 80% ADC threshold ( r (cid:1) 0.738; P (cid:2) . 001). The estimated regression formula was %LV on histopathology (cid:1) 20.60 (cid:3) 3.33 ln(%LV on 80% ADC threshold) (adjusted R 2 (cid:1) 0.523; P (cid:2) . 001). Lesion volume at 1–2 hours post-HI tended to underestimate the final infarct volume. CONCLUSION: Early post-HI MR imaging by using DWI correlates moderately well with the size of irreversible infarct, especially when measured by using a relative ADC threshold of 80% mean contralateral ADC.
Effect of Priority and Delay of Prone Positioning and Chest Physiotherapy on Respiratory Outcomes in Patients with COVID-19: A Randomized Clinical Trial

Background: COVID-19, caused by the SARS-CoV-2 virus, is a novel coronavirus that has led to a global pandemic. Physiotherapy is among the beneficial treatments for this disease. This study investigated the impact of the priority and delay of prone positioning and chest physiotherapy (CPT) on patients with COVID-19. Methods: A randomized clinical trial was conducted involving 26 COVID-19 patients who were randomly assigned to two groups: one group prioritizing prone positioning over chest physiotherapy (comprising seven men and six women, with an average age of 67.46±5.91) and the other group prioritizing CPT over prone positioning (comprising eight men and five women, with an average age of 68.38±5.85). Respiratory outcomes, including SpO2, PaO2, FiO2, FEV1, and FVC, were measured using pulse oximetry, arterial blood gas analysis (ABG), and spirometry). FEV1/FVC and SpO2/FiO2 ratios were evaluated both before and after the intervention Results: The demographic characteristics of the patients and the baseline respiratory outcomes between the two groups did not exhibit significant differences (P>0.05). A significant difference was observed in respiratory outcomes within each group before
Effects of input discretization, model complexity, and calibration strategy on model performance in a data‐scarce glacierized catchment in Central Asia

Glacierized high‐mountainous catchments are often the water towers for downstream region, and modeling these remote areas are often the only available tool for the assessment of water resources availability. Nevertheless, data scarcity affects different aspects of hydrological modeling in such mountainous glacierized basins. On the example of poorly gauged glacierized catchment in Central Asia, we examined the effects of input discretization, model complexity, and calibration strategy on model performance. The study was conducted with the GSM‐Socont model driven with climatic input from the corrected High Asia Reanalysis data set of two different discretizations. We analyze the effects of the use of long‐term glacier volume loss, snow cover images, and interior runoff as an additional calibration data. In glacierized catchments with winter accumulation type, where the transformation of precipitation into runoff is mainly controlled by snow and glacier melt processes, the spatial discretization of precipitation tends to have less impact on simulated runoff than a correct prediction of the integral precipitation volume. Increasing model complexity by using spatially distributed input or semidistributed parameters values does not increase model performance in the Gunt catchment, as the more complex model tends to be more sensitive to errors in the input data set. In our case, better model performance and quantification of the flow components can be achieved by additional calibration data, rather than by using a more distributed model parameters. However, a semidistributed model better predicts the spatial patterns of snow accumulation and provides more plausible runoff predictions at the interior sites.
Remote Sensing of Environment

) is an essential variable for understanding key physical and biological processes. Blended and interpolated L4 SST products o ﬀ er major advantages over alternative SST data sources due to their spatial and temporal completeness, yet their ability to discriminate upwelling-induced steep temperature transitions in coastal waters remains largely unassessed. Here we analysed the performance of eleven L4 GHRSST-compliant products in estimating in situ water temperatures recorded by a large network of shallow subtidal and intertidal temperature loggers deployed in shores covering regimes with a wide range of upwelling intensities. Results indicate that while most products perform satisfactorily for most of the year, performance is severely a ﬀ ected during the upwelling season in locations with strong upwelling. We show that upwelling negatively impacts all four metrics used to assess dataset performance (average bias, correlation, centred root-mean-square error and normalized standard deviation), leading to a considerable overestimation of coastal water temperatures (with average bias exceeding 2 °C in some cases). We also show that while the use of L3 data (i. e., prior to blending and interpolation) leads to an increase in performance compared to L4 GHRSST-compliant products, the gain is probably not substantial enough to o ﬀ set issues related with their spatial and temporal inconsistency along coastlines. Our results suggest that the use of L4 GHRSST-compliant products can lead to a misrepresentation of the thermal ﬁ ngerprint of upwelling, and thus should be limited (or even avoided) in locations dominated by its e ﬀ ects. Conversely, the use of L4 GHRSST-compliant products on locations with little to no upwelling appears to be warranted. The mismatch between in situ and remotely-sensed sea water temperatures here reported also highlights the need for implementation of long-term monitoring networks of in situ temperature loggers.
Journal of Experimental Psychology: General Constant Curvature Segments as Building Blocks of 2D Shape Representation

How the visual system represents shape, and how shape representations might be computed by neural mechanisms, are fundamental and unanswered questions. Here, we investigated the hypothesis that 2-dimensional (2D) contour shapes are encoded structurally, as sets of connected constant curvature segments. We report 3 experiments investigating constant curvature segments as fundamental units of contour shape representations in human perception. Our results showed better performance in a path detection paradigm for constant curvature targets, as compared with locally matched targets that lacked this global regularity (Experiment 1), and that participants can learn to segment contours into constant curvature parts with different curvature values, but not into similarly different parts with linearly increasing curvatures (Experiment 2). We propose a neurally plausible model of contour shape representation based on constant curvature, built from oriented units known to exist in early cortical areas, and we confirmed the model’s prediction that changes to the angular extent of a segment will be easier to detect than changes to relative curvature (Experiment 3). Together, these findings suggest the human visual system is specially adapted to detect and encode regions of constant curvature and support the notion that constant curvature segments are the building blocks from which abstract contour shape representations are composed.
Original article Development and initial validation of a composite disease activity score for systemic juvenile idiopathic arthritis

Objective. To develop a composite disease activity score for systemic JIA (sJIA) and to provide preliminary evidence of its validity. Methods. The systemic Juvenile Arthritis Disease Activity Score (sJADAS) was constructed by adding to the four items of the original JADAS a ﬁfth item that aimed to quantify the activity of systemic features. Validation analyses were conducted on patients with deﬁnite or probable/possible sJIA enrolled at ﬁrst visit or at the time of a ﬂare, who had active systemic manifestations, which should include fever. Patients were reassessed 2weeks to 3months after baseline. Three versions were examined, including ESR, CRP or no acute-phase reactant. Results. A total of 163 patients were included at 30 centres in 10 countries. The sJADAS was found to be feasible and to possess face and content validity, good construct validity, satisfactory internal consistency (Cronbach’s alpha 0.64–0.65), fair ability to discriminate between patients with different disease activity states and between those whose parents were satisﬁed or not satisﬁed with illness outcome ( P < 0.0001 for both), and strong responsiveness to change over time (standardized response mean 2.04–2.58). Overall, these properties were found to be better than those of the original JADAS and of DAS for RA and of Puchot score for adult-onset Still’s disease. Conclusion. The sJADAS showed good measurement properties and is therefore a valid instrument for the assessment of disease activity in children with sJIA. The performance of the new tool should be further examined in other patient cohorts that are evaluated prospectively.
Trafﬁc Rate Network Tomography via Moment Generating Function Matching

—Network tomography aims at estimating source-destination trafﬁc rates from link trafﬁc measurements. This inverse problem was formulated by Vardi in 1996 for independent Poisson trafﬁc over networks operating under deterministic as well as random routing regimes. Vardi used a second-order moment matching approach to estimate the rates where a solution for the resulting linear matrix equation was obtained using an iterative minimum I-divergence procedure. Vardi’s second-order moment matching approach was recently extended to higher order cumulant matching approach with the goal of improving the rank of the system of linear equations. In this paper we go one step further and develop a moment generating function matching approach for rate estimation, and seek a least squares as well as an iterative minimum I-divergence solution of the resulting linear equations. We also specialize this approach to a characteristic function matching approach which exhibits some advantages. These follow from the fact that the characteristic function matching approach results in fewer conﬂicting equations involving the empirical estimates. We demonstrate that the new approach outperforms the cumulant matching approach while being conceptually simpler.
Maturity Rationing

We propose a model of maturity rationing. When there is asymmetric information about default probabilities, firms may be unable to obtain financing for long-term projects. This is because asymmetric information worsens with the maturity of the project, such that lending markets for long maturities can break down (maturity rationing). Worse, firms whose first-best projects cannot get financed may react by adopting second-best projects of shorter maturities. This worsens the pool of financed projects, further amplifying rationing: a rationing spiral can emerge. Maturity rationing is stronger during recessions and, through its knock-on effects, may amplify the business cycle.
Rationing despite screening: A motif based on price-discrimination

The paper revisits Stiglitz and Weiss (1981)’ seminal Credit Rationing result in more detail. It has often been argued that rationing is irrelevant since a) it only pertains to specific assumptions about the quality of investment projects and b) it is eliminated by offering screening contracts to borrowers. The paper argues the opposite. It shows that Credit Rationing holds for a wide range of assumptions about the quality of investment projects and it explains that if a bank has market power then screening contracts may not only fail to eliminate rationing they may even create it.
How Much Insurance in Bewley Models ?

We assess the degree of consumption insurance implicit in a plausibly calibrated life-cycle version of the standard incomplete-markets model (“Bewley model”), and we compare it to the empirical estimates of Blundell, Pistaferri & Preston (2008) (BPP thereafter). We find that households in Bewley models have access to less consumption smoothing against permanent shocks than what is measured in the data. BPP estimate that 36% of permanent earnings shocks are insurable (i.e., do not translate into consumption growth), while the model’s counterpart of the BPP estimator varies between 7% and 22%, depending on the tightness of debt limits. The BPP estimator of the true insurance coefficient has, in general, a downward bias which grows as borrowing limits become tighter. In the model, the life-cycle profile of the insurance coefficient is sharply increasing, while BPP document that in the data it is roughly flat. Allowing for a plausible degree of “advance information” about future earnings does not reconcile the model-data gap. If earnings shocks display mean reversion, even with very high autocorrelation, then the average degree of consumption smoothing in the model agrees with the BPP empirical estimate, but its life-cycle profile remains steeply increasing and therefore at odds with the data.