Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrospinning of Nanofibers: Reinventing the Wheel?

Electrospinning provides a simple and versatile method for generating ultrathin fibers from a rich variety of materials that include polymers, composites, and ceramics. This article presents an overview of this technique, with focus on progress achieved in the last three years. After a brief description of the setups for electrospinning, we choose to concentrate on the mechanisms and theoretical models that have been developed for electrospinning, as well as the ability to control the diameter, morphology, composition, secondary structure, and spatial alignment of electrospun nanofibers. In addition, we highlight some potential applications associated with the remarkable features of electrospun nanofibers. Our discussion is concluded with some personal perspectives on the future directions in which this wonderful technique could be pursued.
Evaluating OpenEHR for Storing Computable Representations of Electronic Health Record Phenotyping Algorithms

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.
Traffic-Aware Optimal Spectral Access in Wireless Powered Cognitive Radio Networks

Traffic patterns associated with different primary users (PUs) might provide different spectral access and energy harvesting opportunities to secondary users (SUs) in wireless powered cognitive radio networks (WP-CRNs). Since the traffic applications have their own distinctive patterns, spectral access and energy harvesting opportunities are also expected to be distinctive. In this paper, we propose a novel approach to identify the PU traffic patterns and estimate the energy harvested from each traffic pattern so that SU can maximize its capacity accordingly. More specifically, we propose a theoretical framework based on a variational inference algorithm to cluster various traffic patterns and design a threshold-based SU transmission strategy by taking into account the spectral access and energy harvesting opportunities for each traffic pattern, so as to optimize SU transmission. Through simulations, we demonstrate the effectiveness of the proposed scheme in terms of throughput gains and show the transmission thresholds under various traffic applications (patterns). Further, we illustrate the effects of different collision costs on throughput for different traffic applications using real wireless traces.
Blind restoration of blurred images using local patches

In this paper, we propose a blind method that rapidly restores blurred images using local patches. In this method, a portion of the blurred image is used for PSF (point spread function) estimation. In addition, we propose an automatic PSF size calculation algorithm that generates an autocorrelation map (auto map). Experimental results show that our proposed method generates accurate deblurred images, and processing time is significantly lower than that of the conventional deblurring method.
Co-training over Domain-independent and Domain-dependent features for sentiment analysis of an online cancer support community

Sentiment analysis has been widely researched in the domain of online review sites with the aim of getting summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users in online health communities such as cancer support forums, etc. Online health communities act as a medium through which people share their health concerns with fellow members of the community and get social support. Identifying sentiments expressed by members in a health community can be helpful in understanding dynamics of the community such as dominant health issues, emotional impacts of interactions on members, etc. In this work, we perform sentiment classification of user posts in an online cancer support community (Cancer Survivors Network). We use Domain-dependent and Domain-independent sentiment features as the two complementary views of a post and use them for post classification in a semi-supervised setting using the co-training algorithm. Experimental results demonstrate effectiveness of our methods.
Cognitive Radio Networks With Primary Receiver Assisted Interference Avoidance Protocol

In this paper, a novel opportunistic spectrum access protocol, namely the primary receiver assisted interference avoidance (PRA-IA) protocol, is proposed and analyzed in cognitive radio (CR) networks to simultaneously exploit the underutilized positions of the primary network and avoid transmission collisions among secondary transmitters (STs). Particularly, the proposed PRA-IA protocol is comprised of two processing phases, i.e., the qualification phase and the contention phase. The qualification phase is designed to preselect the set of STs (denoted by eligible STs) which are in the “spatial holes” of the active primary receivers to guarantee the primary transmissions. The contention phase, on the other hand, aims to improve the performance of secondary transmissions by further resolving the potential collisions among the eligible STs based on the randomly generated backoff timer. With mathematical tools from stochastic geometry, the transmission probability of active STs, the coverage probability, and thereby the spatial throughput of the CR network under the PRA-IA protocol are characterized and analyzed. Furthermore, simulations are provided to verify the accuracy of the derived analytical results and demonstrate the impacts of key network parameters on network performance. From the numerical results, it is shown that the proposed PRA-IA protocol is superior to the PRA protocol on the spatial throughput tradeoff of the primary and secondary networks.
STONE: Shaping terrorist organizational network efficiency

This paper focuses primarily on the Person Successor Problem (PSP): when a terrorist is removed from a terrorist network, who is most likely to take his place? We leverage the solution to PSP to predict a new terrorist network after removal of a set of terrorists and to answer the question: which set of k (k > 0) terrorists should be removed in order to minimize the lethality of the terrorist network? We propose a theoretical model to study these questions taking into account the fact that terrorists may have different individual capabilities. We develop an algorithm for PSP in which analysts can specify the conditions an individual needs to satisfy in order to replace another person. We test the correctness of our algorithm on a real-world partial network dataset for two terrorist groups: Al-Qaeda and Lashkar-e-Taiba where we have ground truth about who replaced who, as well as a synthetic dataset where experts estimate who replaced who. Building on the solution to PSP, we develop an algorithm to identify which set of k people to remove from a terrorist network to minimize the organization's efficiency (formalized as an objective function in some different ways).
Wave Height Extraction From the First-Order Bragg Peaks in High-Frequency Radars

The conventional second-order Bragg-spectrum-based wave height extraction method is often susceptible to external noise and spatial aliasing. To improve the wave height estimate, we turn to the first-order Bragg peaks and propose a new method which directly estimates the wave height from them. The key point is to confirm and use the unsaturated property of the first-order Bragg spectral power. The quantitative relation between the first-order Bragg peak power and the significant wave height can be established with the help of an in situ wave buoy, and consequently, the wave height is to be read out from the curve via the maximum Bragg peak power on one range cell. The first-order method is validated by a two-month-long data set collected by the OSMAR-S radar at 13 MHz. Compared with the second-order method, the improvement is obvious under low and moderate sea states. The new method opens the way for wider use of the first-order Bragg peaks in wave height extraction by high-frequency radars.
Pulmonary Rehabilitation in Non-small Cell Lung Cancer Patients After Completion of Treatment

ObjectivesThe functional status of patients with non-small cell lung cancer (NSCLC) is often limited not only by cancer itself, but also by the different types of treatment and by comorbidities [eg, chronic obstructive pulmonary disease (COPD), congestive heart failure]. The aim of this study was to investigate the utility of an inpatient multidisciplinary pulmonary rehabilitation (PR) program on pulmonary function and exercise capacity of patients with NSCLC after completion of their treatment. MethodsForty-seven patients with NSCLC underwent a PR program after completing cancer treatment. Pulmonary function tests, arterial blood gases, 6-minute walk test, and dyspnea severity before and after PR were retrospectively analyzed. ResultsAfter undergoing PR, patients exhibited significantly higher forced expiratory volume in the first second (mean increase, 110±240 mL; P=0.007), forced vital capacity (mean increase, 130±290 mL; P=0.001), and 6-minute walk test distance (mean increase, 41 m; P<0.001). A statistically significant improvement in the severity of dyspnea (mean decrease in the Modified Medical Research Council dyspnea scale: 0.26±0.61; P=0.007) was observed. We observed that improvement in pulmonary function and exercise capacity was similar among patients with and without COPD and among patients who underwent thoracic surgery or not. ConclusionsPatients with NSCLC who could accomplish PR program, after multidisciplinary treatment for the main disease, seem to benefit in terms of exercise capacity and pulmonary function. These benefits are independent of concurrent COPD and surgical treatment for lung cancer.
Experimental Study on Burden Descending Behavior in COREX Shaft Furnace with AGD Beams

COREX is an industrially and commercially proven smelting reduction process. It consists of two reactors, the shaft furnace (SF) and the melter gasifier and the SF is placed above the melter gasifier. In the new design of COREX‐3000 in Baosteel, China, a new technique called Areal Gas Distribution (AGD) has been adopted. With the installation of two AGD beams, the cross‐sectional area varies in a complicated manner which will affect the burden descending behaviour. This work uses a cold model to investigate solid particle behaviour in the SF of COREX‐3000. The effect of gas flow, discharging rate and abnormal conditions are characterised in terms of solid flow pattern. The results show that gas flow has a minor effect on burden descending behaviour. Increasing the discharging rate has an effect of reducing the quasi‐stagnant zone. A gas channel is observed downstream of AGD beam and the triangle shaped free area increase with the increase of burden discharging rate. For asymmetric flow, particles descend from the top and move to the discharging outlet and a very big stagnant zone is formed on the opposite side of the discharging outlet. The effect of AGD beams on solid flow is evaluated under normal and abnormal conditions. The investigation reveals that the AGD beams affect the solid flow significantly and lead to poor adaptability of furnace to abnormal operations.
sRNA41 affects ribosome binding sites within polycistronic mRNAs in Methanosarcina mazei Gö1

Several noncoding RNAs potentially involved in nitrogen (N)‐regulation have been detected in Methanosarcina mazei, however, targets have been identified only for one of them. Here, we report on the function of sRNA41, highly expressed under N‐sufficiency. Comprising 120 nucleotides, sRNA41 shows high sequence and structural conservation within draft genomes of numerous Methanosarcina species. In silico target prediction revealed several potential targets, including genes of two homologous operons encoding for acetyl‐CoA‐decarbonylase/synthase complexes (ACDS) representing highly probable target candidates. A highly conserved single stranded region of sRNA41 was predicted to mask six independent ribosome binding sites of these two polycistronic mRNAs and was verified in vitro by microscale thermophoresis. Proteome analysis of the respective sRNA41‐deletion mutant showed increased protein expression of both ACDS complexes in the absence of sRNA41, whereas no effect on transcript levels was detected, arguing for sRNA41‐mediated post‐transcriptional fine‐tuning of ACDS expression. We hypothesize that the physiological advantage of downregulating sRNA41 under N‐limiting conditions is the resulting increase of ACDS protein levels. This provides sufficient amounts of amino acids for nitrogenase synthesis as well as reducing equivalents and energy for N2‐fixation, thus linking the carbon and N‐metabolism.
Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth

In order to estimate fog density correctly and to remove fog from foggy images appropriately, a surrogate model for optical depth is presented in this paper. We comprehensively investigate various fog-relevant features and propose a novel feature based on the hue, saturation, and value color space, which correlate well with the perception of fog density. We use a surrogate-based method to learn a refined polynomial regression model for optical depth with informative fog-relevant features, such as dark-channel, saturation-value, and chroma, which are selected on the basis of sensitivity analysis. Based on the obtained accurate surrogate model for optical depth, an effective method for fog density estimation and image defogging is proposed. The effectiveness of our proposed method is verified quantitatively and qualitatively by the experimental results on both synthetic and real-world foggy images.
Distributed community detection in web-scale networks

Partitioning large networks into smaller sub-networks (communities) is an important tool to analyze the structure of complex linked systems. In recent years, many in-memory community detection algorithms have been proposed for graphs with millions of edges. Analyzing massive graphs with billions of edges is impossible for existing algorithms. In this contribution, we show how to find community partitions of networks with billions of edges. Our approach is based on an ensemble learning scheme for community detection that provides a way to identify high quality partitions from an ensemble of partitions with lower quality. We present a pre-processing procedure for community detection algorithms that significantly decreases the problem size. After reducing the problem size, traditional non-distributed community detection algorithms can be applied. We implemented a weak but highly scalable label propagation algorithm on top of the distributed-computing framework Apache Hadoop. The evaluation of our implementation on a 50-node Hadoop cluster and with evaluation datasets up to 3.3 billion edges shows very good results with respect to clustering quality as well as scalability. For a smaller 260 million edge network, we show that our preprocessing can improve the results of the popular Louvain modularity clustering algorithm.
Approximation algorithms for Vietoris-Rips and Čech filtrations

Persistent Homology is a tool to analyze and visualize the shape of data from a topological viewpoint. It computes persistence, which summarizes the evolution of topological and geometric information about metric spaces over multiple scales of distances. While computing persistence is quite efficient for low-dimensional topological features, it becomes overwhelmingly expensive for medium to high-dimensional features. In this thesis, we attack this computational problem from several different angles: • We present efficient techniques to approximate the persistence of metric spaces. Three of our methods are tailored towards general point clouds in Euclidean spaces. We make use of high dimensional lattice geometry to reduce the cost of the approximations. In particular, we discover several properties of the Permutahedral lattice, whose Voronoi cell is well-known for its combinatorial properties. The last method is suitable for point clouds with low intrinsic dimension, where we exploit the structural properties of the point set to tame the complexity. In some cases, we achieve a reduction in size complexity by trading off the quality of the approximation. Two of our methods work particularly well in conjunction with dimension-reduction techniques: we arrive at the first approximation schemes whose complexities are only polynomial in the size of the point cloud, and independent of the ambient dimension. • On the other hand, we provide a lower bound result: we construct a point cloud that requires super-polynomial complexity for a high-quality approximation of the persistence. Together with our approximation schemes, we show that polynomial complexity is achievable for rough approximations, but impossible for sufficiently fine approximations. • For some metric spaces, the intrinsic dimension is low in small neighborhoods of the input points, but much higher for large scales of distances. We develop a concept of local intrinsic dimension to capture this property. We also present several applications of this concept, including an approximation method for persistence. This thesis is written in English.
Refinement Checking Parameterised Quorum Systems

Many fault-tolerant algorithms are based on decisions made by a quorum of nodes. Since the algorithms are utilised in safety critical applications such as distributed databases, it is necessary to make sure that they operate reliably under every possible scenario. We introduce a generic compositional formalism, based on parameterised labelled transition systems, which allows us to express safety properties of parameterised quorum systems. We prove that any parameterised verification task expressible in the formalism collapses into finitely many finite state refinement checking problems. The technique is implemented in a tool, which performs the verification completely automatically. As an example, we prove the leader election phase of the Raft consensus algorithm correct for an arbitrary number of terms and for a cluster of any size.
BAN-GZKP: Optimal Zero Knowledge Proof Based Scheme for Wireless Body Area Networks

In this paper, we propose BAN-GZKP that optimizes the best to date secure lightweight and energy efficient authentication scheme, BANZKP, designed for WBAN networks. BANZKP is vulnerable to several security attacks such as the replay attack, DDoS attacks at sink and redundancy information crack. Also BANZKP needs an end-to-end authentication which is not compliant with the human body postural mobility. Our scheme, BAN-GZKP, improves both the security and postural mobility resilience of BANZKP. In order to fix the security vulnerabilities of BANZKP, BAN-GZKP uses a novel random key allocation. Moreover, BAN-GZKP uses a hop-by-hop authentication scheme which makes it tolerant to postural mobility. We further prove the reliability of our scheme to various attacks including those to which BANZKP is vulnerable. Furthermore, via extensive simulations we prove that our scheme, BAN-GZKP, outperforms BANZKP in terms of reliability to human body postural mobility for various network parameters (end-to-end delay, number of packets exchanged in the network, number of transmissions). We compared both schemes using representative convergecast strategies with various transmission rates and human postural mobility. When our BAN-GZKP scheme is used the percentage of packets received increases by 34.06%, the end-to-end-delay reduces by 36.02% and the number of transmissions reduces by 8.75% with respect to the case when BANZKP is used. Moreover, BAN-GZKP uses only a three-phase authentication which is optimal in the class of ZKP protocols. Finally, it is important to mention that BAN-GZKP has no additional cost in terms memory, computational complexity or energy consumption compared to BANZKP.
Defining indoor heat thresholds for health in the UK

Introduction: It has been recognised that as outdoor ambient temperatures increase past a particular threshold, so do mortality/morbidity rates. However, similar thresholds for indoor temperatures have not yet been identified. Due to a warming climate, the non-sustainability of air conditioning as a solution, and the desire for more energy-efficient airtight homes, thresholds for indoor temperature should be defined as a public health issue. Aims: The aim of this paper is to outline the need for indoor heat thresholds and to establish if they can be identified. Our objectives include: describing how indoor temperature is measured; highlighting threshold measurements and indices; describing adaptation to heat; summary of the risk of susceptible groups to heat; reviewing the current evidence on the link between sleep, heat and health; exploring current heat and health warning systems and thresholds; exploring the built environment and the risk of overheating; and identifying the gaps in current knowledge and research. Methods: A global literature search of key databases was conducted using a pre-defined set of keywords to retrieve peer-reviewed and grey literature. The paper will apply the findings to the context of the UK. Results: A summary of 96 articles, reports, government documents and textbooks were analysed and a gap analysis was conducted. Evidence on the effects of indoor heat on health implies that buildings are modifiers of the effect of climate on health outcomes. Personal exposure and place-based heat studies showed the most significant correlations between indoor heat and health outcomes. However, the data are sparse and inconclusive in terms of identifying evidence-based definitions for thresholds. Further research needs to be conducted in order to provide an evidence base for threshold determination. Conclusions: Indoor and outdoor heat are related but are different in terms of language and measurement. Future collaboration between the health and building sectors is needed to develop a common language and an index for indoor heat and health thresholds in a changing climate.
Estimation error stability of the SDDRE based filter

The asymptotic stability of the estimation error of the State Dependent Differential Riccati Equation (SDDRE) based observer of nonlinear deterministic time-varying systems is proved. Boundedness of all matrices and measured quantities, and uniform observability and controllability along the observer's trajectories are sufficient for the estimation error convergence to zero. The stability-convergence is demonstrated by simulation of Van der Pol equation with nonlinear measurement.