Miracle-dz/UniFAR-FTUs huggingface dataset:
Disciplinary differences in blended learning design: a network analytic study

Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016--2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.
Experiences with perinatal death among midwives, obstetricians, and assistant nurses: Results from a qualitative study in Norway / Erfahrungen von Hebammen, Geburtshelfern und Kinderpflegern mit perinatalem Tod: Ergebnisse einer qualitativen Studie in Norwegen

Abstract This study aims to explore experiences of healthcare professionals, including midwives, obstetricians, and assistant nurses, after a perinatal death. A qualitative study design was used, and 20 participants were interviewed. Data were analyzed using content analysis. Three categories emerged from the data: (i) external responses, (ii) internal responses, and (iii) personal responses. Generally, there was a “culture of blame” in response to the perinatal death among the professionals. The lack of opportunity to deal with perinatal death and having an inappropriate way to handle feelings and experiences are the important findings of this study. If not well supported, the concerned healthcare professionals may face serious health challenges in the long term. Hence, supportive interventions need to be designed and evaluated to provide adequate support to the concerned professionals.
Toward point-of-care uropathogen detection using SERS active filters

150 million people worldwide suffer one or more urinary tract infections (UTIs) annually. UTIs are a significant health burden: societal costs of UTI exceed $3.5 billion in the U.S. alone; 5% of sepsis cases arise from a urinary source; and UTIs are a prominent contributor toward antimicrobial resistance (AMR). Current diagnostic frameworks exacerbate this burden by providing inaccurate and delayed diagnosis. Rapid point-of-care bacterial identification will allow for early precision treatment, fundamentally altering the UTI paradigm. Raman spectroscopy has a proven ability to provide rapid bacterial identification but is limited by weak bacterial signal and a susceptibility to background fluorescence. These limitations may be overcome using surface enhanced Raman spectroscopy (SERS), provided close and consistent application of bacteria to the SERS-active surface can be achieved. Physical filtration provides a means of capturing uropathogens, separating them from the background solution and acting as SERS-active surface. This work demonstrates that filters can provide a means of aggregating bacteria, thereby allowing subsequent enhancement of the acquired Raman signal using metallic nanoparticles. 60 bacterial suspensions of common uropathogens were vacuum filtered onto commercial polyvinylidene fluoride membrane filters and Raman signals were enhanced by the addition of silver nanoparticles directly onto the filter surface. SERS spectra were acquired using a commercial Raman spectrometer (Ocean Optics, Inc.). Principal Component – Linear Discriminant Analysis provided discrimination of infected from control samples (accuracy: 88.75%, 95% CI: 79.22-94.59%, p-value <0.05). Amongst infected samples uropathogens were classified with 80% accuracy. This study has demonstrated that combining Raman spectroscopy with membrane filtration and SERS can provide identification of infected samples and rapid bacterial classification.
PESC: A Per System-Call Stack Canary Design for Linux Kernel

Stack canary is the most widely deployed defense technique against stack buffer overflow attacks. However, since its proposition, the design of stack canary has very few improvements during the past 20 years, making it vulnerable to new and sophisticated attacks. For example, the ARM64 Linux kernel is still adopting the same design with StackGuard, using one global canary for the whole kernel. The x86_64 Linux kernel leverages a better design by using a per-task canary for different threads. Unfortunately, both of them are vulnerable to kernel memory leaks. Using the memory leak bugs or hardware side-channel attacks, e.g., Meltdown or Spectre, attackers can easily peek the kernel stack canary value, thus bypassing the protection. To address this issue, we proposed a fine-grained design of the kernel stack canary named PESC, standing for Per-System-Call Canary, which changes the kernel canary value on the system call basis. With PESC, attackers cannot accumulate any knowledge of prior canary across multiple system calls. In other words, PESC is resilient to memory leaks. Our key observation is that before serving a system call, the kernel stack is empty and there are no residual canary values on the stack. As a result, we can directly change the canary value on system call entry without the burden of tracking and updating old canary values on the kernel stack. Moreover, to balance the performance as well as the security, we proposed two PESC designs: one relies on the performance monitor counter register, termed as PESC-PMC, while the other one uses the kernel random number generator, denoted as PESC-RNG. We implemented both PESC-PMC and PESC-RNG on the real-world hardware, using HiKey960 board for ARM64 and Intel i7-7700 for x86_64. The synthetic benchmark and SPEC CPU2006 experimental results show that the performance overhead introduced by PESC-PMC and PESC-RNG on the whole system is less than 1%.
Influential Nodes Identification in Complex Networks via Information Entropy

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.
Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Atrial fibrillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its first line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identification requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classification accuracy of 100% in Group 1 and 84.0–85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identified as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modification.
Characteristics of Nonthermal Dupree Diffusion on Space-Charge Wave in a Kappa Distribution Plasma Column with Turbulent Diffusion

The nonthermal diffusion effects on the dispersion equations of ion-acoustic space-charge wave (SCW) in a nonthermal plasma column composed of nonthermal turbulent electrons and cold ions are investigated based on the analysis of normal modes and the separation of variables. It is found that the real portion of the wave frequency of the SCW in a Maxwellian plasma is greater than that in a nonthermal plasma. It is also found that the magnitude of the damping rate of the SCW decreases with an increase of the spectral index of the nonthermal plasma. It is also shown that the magnitude of the scaled damping rate increases with an increase of the Dupree diffusion coefficient. Moreover, the influence of the nonthermal character of the nonthermal plasma on the damping rate is found to be more significant in turbulent plasmas with higher diffusion coefficient. The variations of the wave frequency and the growth rate due to the characteristics of nonthermal diffusion are also discussed.
A decentralized and secure blockchain platform for open fair data trading

As the value of data has received considerable attention, data trading shows broad market prospects. The existing data trading methods, including private trades and centralized trades, have high risks regarding transaction security and data protection. To solve this problem, we propose a decentralized trading solution for open fair data trading by deploying the smart contract on the blockchain network. The data for sale are encrypted and stored on the distributed storage platform but not directly on the blockchain network. Because the trading content is the decryption key of the data, the proposed new method can alleviate the storage pressure of the blockchain by reducing the transaction cost. We conduct a security analysis which shows that our scheme achieves secure, practical, open, and fair trading. We implement our trading contract with solidity and test it on the Ethereum's test network, and extensive experiments demonstrate desirable feasibility of our proposal.
Computer Vision and Image Understanding

Increasing the depth of Convolutional Neural Networks (CNNs) has been recognized to provide better generalization performance. However, in the case of 3D CNNs, stacking layers increases the number of learnable parameters linearly, making it more prone to learn redundant features. In this paper, we propose a novel 3D CNN structure that learns shared 2D triplanar features viewed from the three orthogonal planes, which we term S3PNet . Due to the reduced dimension of the convolutions, the proposed S3PNet is able to learn 3D representations with substantially fewer learnable parameters. Experimental evaluations show that the combination of 2D representations on the different orthogonal views learned through the S3PNet is sufficient and effective for 3D representation, with the results outperforming current methods based on fully 3D CNNs. We support this with extensive evaluations on widely used 3D data sources in computer vision: CAD models, LiDAR point clouds, RGB-D images, and 3D Computed Tomography scans. Experiments further demonstrate that S3PNet has better generalization capability for smaller training sets, and learns more of kernels with less redundancy compared to kernels learned from 3D CNNs.
DEPRESSION AMONGST PARENTS OF CHILDREN WITH INTELLECTUAL DISABILITY

Background: Parental stress is the major factor that increases a risk of unwillingly placing children with disabilities. The aim of our study is to assess the frequency of depression amongst parents of children with intellectual disability. Methods: This was a cross sectional study conducted for 10 months in various hospitals and rehabilitation centers in Karachi, Pakistan. Participants were inducted using purposive sampling technique. Parents of intellectually challenged children up to 18 years of age were included and a self-administered questionnaire was utilized for data collection both in English and Urdu. PHQ 8 was used to assess the severity of depression amongst parents. For all purposes, p-value of <0.05 was considered statistically significant. Permission was sought from the ethical review board. Results: High proportion of parents interviewed suffered significantly from moderate to severe depression [223 (66.2%)]. Gender comparison revealed [186 (75.9%)] females had moderate to severe depression compared to [136 (40.3%)] males (p-value=0.000). Some factors associated with the child such as severity of disability also had an impact [197 (69.3%)], parents having children with Cerebral Palsy suffered moderate to severe depression as opposed to [26 (49%)] parents of children with Down’s Syndrome (p-value=0.03). The most common way to relieve stress was praying practiced by [176 (52.2%)] the parents. Conclusion: Amongst the parents of children with intellectual disability (ID), a significantly high proportion was suffering from depression. From the child’s aspect, age and severity of disability had an impact. Most common strategy identified to overcome depression was praying.
Assessing semantic similarity between concepts: A weighted‐feature‐based approach

Traditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and TFIDF, we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.
Extraction Technic for Built-up Area Classification in Landsat 8 Imagery

Built-up areas play a significant type of land use associated with the urbanization. Classifying the built-up area by using satellite image, is also a high demand for a relevant organization to investigate the urban sprawl. This study aims to develop an index used to classify the built-up areas accurately. In order to achieve the aim of the study, two objectives were intensively studied to compare the results from several indices used to appropriately classify the built-up areas from satellite data of Landsat 8. The second objective is to develop a built-up area index which is suitable for classifying the built-up areas. This study considers the two study areas, Bangkachao and Bangkok International Airport, which encounter the rapid urbanization. This study employs a satellite image of Landsat 8 OLI (Operational Land Imager (OLI). The Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) were intensively examined to present a BUI map correspondingly. On the other hand, Modified Normalized Difference Water Index (MNDWI) was tested to separate the water areas and wetlands from the built-up areas distinctively. Furthermore, the Modified Built-Up Index (MBUI) were developed based on the integration between the MNDWI and BUI map. As a result, it is clearly shown that the MBUI provides more accurate results of built-up area classification than the BUI. Also, the MBUI presents 82-83% accuracy of both study areas, which are higher than the BUI map. It is to say that MBUI can be employed to classify the built-up areas of the study areas accurately.
Responses to Interactive Comments by Paolo De Luca on “Spatial Dependency in Nonstationary GEV Modelling of Extreme Precipitation over Great Britain”

Abstract. This paper presents a study on extreme precipitation using both stationary and non-stationary Generalized Extreme Value (GEV) models over a large number of samples distributed over Great Britain (GB) for the last century, aiming to gain insights in the spatial dependency of the GEV distribution. Not only L-Moments (LM) and Maximum Likelihood (ML) estimation methods but a Bayesian Markov-Chain Monte Carlo (B-MCMC) method are incorporated into the GEV models to characterize the uncertainty in the nonstationary risk-based assessment. The samples are generated using a toolbox of spatial random sampling for grid-based data analysis (SRS-GDA). The results show that a markedly large proportion (70 %) of the samples are favour nonstationary assumption GEV models as far as the annual maximum daily rainfall (AMDR) is concerned. The most frequent AMDR, as represented by the location parameter tend to be increasing over the time for more than half of the samples and in contrast, only 8 % have a downward trend. A spatially clustering pattern is also clearly present. For rarer (with 0.1 probability) AMDR, they are shown to have a tendency of becoming more extreme over time, for more than half of the samples. For the three methods, the LM method with stationary GEV maintain best results but for AMDR values with higher probability (5-year return level); the B-MCMC method with nonstationary GEV, however, outperform other combinations by a large margin for more extreme events (50-year return level). The findings suggest that an overhaul of the current engineering design storm practice may be needed in view of environmental change impact on natural processes.
Gain-Enhanced Metamaterial Absorber-Loaded Monopole Antenna for Reduced Radar Cross-Section and Back Radiation

This paper proposes a gain-enhanced metamaterial (MM) absorber-loaded monopole antenna that reduces both radar cross-section and back radiation. To demonstrate the proposed idea, we designed a wire monopole antenna and an MM absorber. The MM absorber comprised lumped elements of subwavelength unit cells and achieved 90% absorbance bandwidth from 2.42–2.65 GHz. For low-profile configurations, the MM absorber was loaded parallel to and 10 mm from the monopole antenna, corresponding to 0.09 λ0 at 2.7 GHz. The monopole antenna resonated at 2.7 GHz with a 3.71 dBi peak gain and 2.65 GHz and 6.46 dBi peak gain, before and after loading the MM absorber, respectively. Therefore, including the MM absorber increased peak gain by 2.7 dB and reduced back radiation by 15 dB. The proposed antenna radar cross-section was reduced by 2 dB compared with a monopole antenna with an artificial magnetic conductor.
The impact of homogenized cross-section correction mechanisms in OSCAR-4 as applied to SAFARI-1 research reactor

The nodal solver OSCAR-4 reactor analysis system contains a series of approximations (nonlinear extensions) which aim to correct the full-core nodal calculation (mostly via corrections to the homogenised cross-sections) for typical errors induced during coarse-mesh homogenisation and group condensation. These schemes are intended to correct the nodal diffusion result as compared to an idealised full-core transport solution, which is in practice seldom performed or even practical to attempt. These schemes were developed for PWRs and the application and relevance of these schemes to highly heterogeneous research reactor designs have not as yet been fully quantified. This work focuses on the analysis of the cross-section re-homogenisation correction scheme. The purpose of this work is to perform an evaluation of this non-linear model as implemented in OSCAR-4, specifically with respect to the newly proposed OSCAR-4 SAFARI-1 core model. The new model is based in part on nodal cross-sections generated from the Monte Carlo based Serpent code. Serpent is a consistent reference transport solution against which the capability of the re-homogenisation scheme is measured. The SAFARI-1 mini-core model results show that the scheme is applicable in some cases, like the fuel-follower. In the full-core model, the environmental error due to infinite lattice approximation was 252 pcm, 2.30 % in average assembly power and 4.59 % in maximum power. The scheme reduced these to 88 pcm, 1.69 % and 3.52 % respectively. The scheme should therefore be applied selectively in the full-core to maximise it’s capability. The work will support both the verification and validation of SAFARI-1 reactor models.
IODVA1, a guanidinobenzimidazole derivative, targets Rac activity and Ras-driven cancer models

We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D and 3D cell proliferation, cell spreading and ruffle and lamellipodia formation through downregulation of Rac activity. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets Rac signaling to induce death of Ras-transformed cells. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.