Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the impact of a bowel management protocol on patients and clinicians' compliance in cardiac intensive care: A mixed‐methods approach

BACKGROUND Bowel management protocols standardise care and, potentially, improve the incidence of diarrhoea and constipation in intensive care. However, little research exists reporting compliance with such protocols in intensive care throughout patients' stay. Furthermore, there is a limited exploration of the barriers and enablers to bowel management protocols following their implementation, an important aspect of improving compliance. AIM AND OBJECTIVE To investigate the impact of a bowel management protocol on the incidence of constipation and diarrhoea, levels of compliance, and to explore the enablers and barriers associated with its use in intensive care. METHODS A mixed-methods study was conducted in cardiac intensive care using two phases: (a) a retrospective case review of patients' hospital notes, before and after the protocol implementation, establishing the levels of diarrhoea and constipation and levels of compliance; (b) focus groups involving users of the protocol, 6 months following its implementation, exploring the barriers and enablers in practice. RESULTS AND FINDINGS Fifty-one patients' notes were reviewed during phase one: 30 pre-implementation and 21 post-implementation. Following the protocol implementation, there was a tendency for a higher incidence of constipation and less severe cases of diarrhoea. Overall compliance with the protocol was low (2.3%). However, there was evidence of behavioural change following protocol implementation, including less variation in aperients given and a shorter, less varied time period between starting enteral feed and administering aperients. Several themes emerged from the focus groups: barriers and enablers to the protocol characteristics and dissemination; barriers to bowel assessment; nurse as a barrier; medical involvement and protocol outcomes. CONCLUSIONS The bowel management protocol implementation generated some positive outcomes to bowel care practices. However, compliance was low and until there is improvement, through overcoming the barriers identified, the impact of such protocols in practice will remain largely unknown.
Contouring Control of a 5-DOF Dual-Arm Robot for Machining Based on Equivalent Errors

The purpose of this work is to study the dynamics of a 5-DOF dual-arm robot and its holonomic constraints during grasping a workpiece and to design the contouring controller for machining. With the constraints addressed by utilizing the existing forward kinematics of the left and right arms, the system dynamics can be represented by only the joint space of the prime arm (the left-sided one). On the other hand, the contouring controller is designed based on the method of equivalent errors. For comparison, the numerical results performed by two controllers: (1) an integral sliding mode controller based on the method of equivalent error and (2) a distributed controller, are conducted. The results show that the contouring performance of the proposed controller is much better than those by the distributed controller, verifying the effectiveness of the proposed method.
Gender differences in research areas and topics: An analysis of publications in 285 fields

Although the gender gap in academia has narrowed, females are underrepresented within some fields in the USA. Prior research suggests that the imbalances between science, technology, engineering and mathematics fields may be partly due to greater male interest in things and greater female interest in people, or to off-putting masculine cultures in some disciplines. To seek more detailed insights across all subjects, this article compares practising US male and female researchers between and within 285 narrow Scopus fields inside 26 broad fields from their first-authored articles published in 2017. The comparison is based on publishing fields and the words used in article titles, abstracts, and keywords. The results cannot be fully explained by the people/thing dimensions. Exceptions include greater female interest in veterinary science and cell biology and greater male interest in abstraction, patients, and power/control fields, such as politics and law. These may be due to other factors, such as the ability of a career to provide status or social impact or the availability of alternative careers. As a possible side effect of the partial people/thing relationship, females are more likely to use exploratory and qualitative methods and males are more likely to use quantitative methods. The results suggest that the necessary steps of eliminating explicit and implicit gender bias in academia are insufficient and might be complemented by measures to make fields more attractive to minority genders.
Dynamic Motion Generation in a Hexapod Robot Using Fixed-point Trajectories of a Simple Model with Period Scaling

We report on the study of the dynamic motion generation of a robot with leg trajectories derived from the fixed-point trajectories of the rolling spring-loaded pendulum (R-SLIP) model. The period of leg trajectory is further elongated and shrunk without altering the profile shape; thus, we investigate the effect of linear period scaling, which is widely used in walking gait, on the dynamics of the robot. In other words, this strategy explores the balance of the enforced “clock” and the passive dynamics of the system. The leg clock modulates the R-SLIP model by means of the proportional-derivative control, and the model dynamics are simulated and analyzed. The proposed strategy is also an experimental evaluation of a RHex-style hexapod robot TWIX. The experimental results show that a robot using a clock with period variation in a certain range of the original fixed point can perform stable dynamic motions, yet with a profile that differs from the original profile.
Comparative effect of different dietary inulin sources and probiotics on growth performance and blood characteristics in growing–finishing pigs

ABSTRACT The study was focused on assessment of the effect of an extract of long-chain inulin (LCI) and dried tubers of Jerusalem artichoke (JA) and a multispecies probiotic preparation as well as a combination thereof on growth performance and blood parameters of fattening pigs. In total, 144 pigs (initial body weight 30.0 ± 0.5 kg) were used in a 98-d experiment. The six dietary treatments consisted of the control diet (Con), diet Con supplemented with probiotics (ConP) and four diets supplemented with LCI or JA alone or with probiotics (diets LCIP and JAP). Throughout the fattening period, there was a beneficial effect of the probiotic supplementation to the inulin-containing diets and the average daily gain (ADG) was increased by supplementation of probiotics in combination with inulin sources (p < 0.05). At the end of the fattening period, ADG and feed conversion ratio (FCR) were higher after supplementation of LCI only (p < 0.05). Compared with group ConP, in groups LCI and JA, the ADG and FCR were improved (p < 0.05). Only in the first fattening stage, the addition of the prebiotics and/or probiotics had an impact on the level of white blood cells and some biochemical indices in pigs. In younger animals, probiotic or LCI supplementation increased the IgG level (p < 0.05). There was also an interaction between the probiotics and JA resulting in increased IgG and IgA concentrations (p < 0.05). In the finishing period, LCI addition increased the IgM level (p < 0.05), whereas JA addition increased IgG and IgM levels as well (p < 0.05). In conclusion, both dietary sources of inulin and probiotic supplementation can improve the fattening performance and health status of growing pigs.
A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

SDN has been regarded as the next-generation network paradigm as it decouples complex network management from packet forwarding, which significantly simplifies the operation of switches in the data plane. The good programmability of SDN infrastructure also improves network feasibility. To alleviate the burden of the explosive growth in network traffic, in this article we propose a non-supervised deep learning based routing strategy running in the SDN controller. In our proposal, we utilize the CNNs as our deep learning architecture, and the controller runs the CNNs to choose the best path combination for packet forwarding in switches. More importantly, in our proposal, the controller collects the network traffic trace and periodically trains the CNNs to adapt them to the changing traffic patterns. Simulation results demonstrate that our proposal is able to retain learning from previous experiences and outperform conventional routing protocols.
Theoretical Results of QoS-Guaranteed Resource Scaling for Cloud-Based MapReduce

Quality of Service (QoS) is a new issue in cloud-based MapReduce, which is a popular computation model for parallel and distributed processing of big data. QoS guarantee is challenging in a dynamical computation environment due to the fact that a fixed resource allocation may become under-provisioning, which leads to QoS violation, or over-provisioning, which increases unnecessary resource cost. This requires runtime resource scaling to adapt environmental changes for QoS guarantee. Aiming to guarantee the QoS, which is referred as to hard deadline in this work, this paper develops a theory to determine how and when resource is scaled up/down for cloud-based MapReduce. The theory employs a nonlinear transformation to define the problem in a reverse resource space, simplifying the theoretical analysis significantly. Then, theoretical results are presented in three theorems on sufficient conditions for guaranteeing the QoS of cloud-based MapReduce. The superiority and applications of the theory are demonstrated through case studies.
Sliding Window Optimized Information Entropy Analysis Method for Intrusion Detection on In-Vehicle Networks

With the considerable growth of cybersecurity risks in modern automobiles, cybersecurity issues in the in-vehicle network environment have attracted significant attention from security researchers in recent years. Enhancing the cybersecurity ability of in-vehicle networks while considering the computing resource and cost constraints become an urgent issue. To address this problem, a novel information entropy-based method is proposed in this paper, which uses a fixed number of messages as sliding windows. By improving the sliding window strategy and optimizing the decision conditions, the detection accuracy is increased and the false positive rate is reduced. Experimental results demonstrate that the proposed method can provide real-time response to attacks with a considerably improved detection precision for intrusion detection in the in-vehicle network environment.
Detection of Spam Reviews and Spammers in E-Commerce Sites

E-services have grown tremendously in the last ten years, as people prefer online shopping. This is because they no more have time to go to real markets, to bargain with sellers or to negotiate. In recent years, online reviews have become the most significant means of customers’ opinions. However, driven by the desire for profit or publicity, fraudsters have produced deceptive (spam) reviews affecting the organization’s business. Filtering out this spam reviews ensures trust and credibility among the users. The present research proposes a systematic pproach for detection of spam reviews and spammers using a fuzzy based approach. Along with review text mining, customer loyalty based on purchasing behavior is considered as input to the fuzzy inference system. After filtration of spam reviews and spammers based on spam degree assigned to the reviews the new overall score for the product is calculated.
Improved oral bioavailability of magnolol by using a binary mixed micelle system

Abstract The aim of this study was to prepare two novel magnolol (MO)-loaded binary mixed micelles (MO-M) using biocompatible copolymers of Soluplus (SOL) and Solutol® HS15 (HS15), SOL and d-alpha-tocopheryl polyethylene glycol 1000 succinate (TPGS), to improve magnolol’s poor solubility and its oral bioavailability. The organic solvent evaporation method was used to obtain two MO-M by optimization; one was prepared by using SOL and HS15 (MO-H), and the other was prepared by using SOL and TPGS (MO-T). The entrapment efficiency (EE%) and drug loading (DL%) of MO-T were 94.61 ± 0.91% and 4.03 ± 0.19%, respectively, and the MO-H has higher EE% and DL% (98.37 ± 1.23%, 4.12 ± 0.16%). TEM results showed that the morphology of MO-M was homogeneous and was spherical in shape. The dilution stability of MO-M did not undergo significant changes. Permeability of MO-M across a Caco-2 cell monolayer was enhanced in Caco-2 cell transport models. The pharmacokinetics study showed that the relative oral bioavailability of MO-T and MO-H increased by 2.39- and 2.98-fold, respectively, compared to that of raw MO. This indicated that MO-H and MO-T could promote absorption of MO in the gastrointestinal tract. Collectively, the mixed micelles demonstrated greater efficacy as a drug delivery system. The development of these novel mixed micelles is valuable for resolving the poor solubility and bioavailability of drugs.
An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading

Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P –V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.
Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real time. Empirical mode decomposition is used to decompose the nonstationary PQEs into the monocomponent mode of oscillation, known as intrinsic mode functions (IMFs). The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs. An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine, least-square support vector machine, extreme learning machine, and bidirectional extreme learning machine. The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform associated with HT and WBELM method, and tunable-Q wavelet transform along with HT and WBELM method. The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system. Finally, a hardware prototype is developed based on the digital signal processor to verify the cogency of the proposed method in real time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.
Grid Map Guided Indoor 3D Reconstruction for Mobile Robots with RGB-D Sensors

This paper presents a novel automatic indoor three-dimensional (3D) scene reconstruction approach which is guided by a beforehand two-dimensional (2D) grid map. The proposed system collects only a few images with a RGB-D sensor (Kinect v2) at poses precalculated by the grid map to reconstruct a complete model of the indoor environment. To remove noises result from mirror reflection, a wall detection based cloud filtering method is proposed as preprocessing for the point clouds. Then, robot location information provided by a laser scanner is utilized as an initial guess of the point cloud registration which uses Iterative Closest Point (ICP). Finally, to maintain global consistency and improve the overall accuracy, a graph optimization strategy is applied. Experimental results are provided to show the effectiveness of the proposed approach.
Low-Profile Wideband Dual-Circularly Polarized Metasurface Antenna Array With Large Beamwidth

One low-profile wideband dual-circularly polarized metasurface antenna (MSA) with large beamwidths is proposed. The proposed MSA is constructed by an array of corner-truncated polarization-rotation metasurface (PRMS) cells, and excited by a novel dual-feeding structure through a cross-like slot, which are specifically designed to simultaneously realize wideband impedance matching for the right- and left-handed circular polarization (RHCP, LHCP). Furthermore, benefiting from wideband and widebeam PR property and good polarization independence of the PRMS cells, the proposed MSA exhibits good RHCP and LHCP performances in a low profile of 0.069 λ0, including large axial-ratio bandwidths ARBWs of over 12.4%, large AR beamwidths over 80°, and a high polarization isolation of over 15 dB. In addition, combined with a sequential phase network, the proposed MSA is extended to construct a 2 × 2 dual-circularly polarized antenna array with large ARBWs of over 22.4% and high gains of over 11 dBi.
Improved Shrinkage-to-Tapering Estimation for High-Dimensional Covariance Matrices

In this paper, an improved shrinkage-to-tapering oracle approximating approach for estimating high-dimensional covariance matrices is proposed. Since the oracle shrinkage coefficient of shrinkage-to-tapering oracle (STO) estimator is greater than one for some tapering parameter, the optimal shrinkage coefficient is obtained by thresholding the oracle coefficients. The corresponding normalized mean-squared error (MSE) is also obtained. Moreover, an improved shrinkage-to-tapering estimator is proposed by plugging the unbiased and consistent estimators of some functions of unknown covariance matrix into the optimal coefficient and corresponding normalized MSE. Compared with the STO approximating approach using iteration to approximate the oracle coefficient, a closed-form formula of the estimated coefficient and the normalized MSE are derived for given tapering parameter. Numerical simulations and an application to adaptive beamforming show the comparable performance of the proposed estimator.
One-step fabricated keratin nanoparticles as pH and redox-responsive drug nanocarriers

Abstract Keratin is a promising material for building drug carriers due to their biocompatibility, reduction sensitivity, and biodegradability. Herein, we aim to develop acid and glutathione (GSH) dual-responsive keratin-based drug carriers with a one-step strategy. Keratin/DOX complexes were first prepared by means of a simple mixing approach, followed by desolvation and crosslinking to prepare keratin-based drug loaded nanoparticles (KDNPs). The as-prepared KDNPs showed negative-charged surface and globular morphology with the diameter of ca. 129 nm. The drug-loading rate was as high as 14% as a result of electrostatic attraction. Drug delivery profile showed that KDNPs performed pH and GSH dual responsiveness as well as charge reversibility in the simulated tumor microenvironment. MTT assay demonstrated that KDNPs exhibited enhanced inhibitory efficiency against A549 cells. Moreover, CLSM observation suggested that these KDNPs were internalized through endocytosis pathway. All of these results demonstrated that keratin based drug carriers had potential of tumor therapy.
Human Cases of Rift Valley Fever in South Africa, 2018

Abstract Major Rift Valley fever (RVF) epidemics in South Africa occur at irregular intervals, usually spanning several decades, with human cases rarely reported in the absence of widespread outbreaks in livestock. This report describes four cases of RVF in farm workers associated with an isolated outbreak on a sheep farm in the Free State Province of South Africa, in 2018. In contrast to the last major RVF epidemic in South Africa in 2010–2011, where detection of human cases served as an alert for an ongoing outbreak in livestock, the current isolated outbreak was first detected in livestock, and human cases recognized following subsequent epidemiological investigation. This highlights the importance of early recognition of livestock cases in reducing risk and impact of a subsequent RVF epidemic in humans. People working with animals should be aware of transmission routes and take precautions to minimize risk of infection.
Flexible New Opto-Microwave Design Approach for Radio-Over-Fiber Applications: A Case Study of Low-Cost 60-GHz VCSEL-Based IF-RoF Link

This paper introduces and defines a novel complete set of optical-microwave (OM) quantities to analyze radio-over-fiber (RoF) systems. Our approach defines the equivalent opto-microwave power, gain, noise figure (NF), 1-dB compression point and third-harmonic intermodulation product (OM-P1dB, OM-IP3), spurious-free dynamic range (SFDR), and error-vector magnitude (EVM) expressed in the OM domain. These figures of merits are defined at the component level for each device of the RoF link with the practical illustration of the transmission of IEEE802.15. 3c 60-GHz signals along an intermediate-frequency RoF scheme. A behavioral model is developed to implement those quantities into a microwave simulator and is exploited to turn the EVM measurements into a means of providing an original extraction method of devices properties. Such a technique is applied on the evaluation of state-of-the-art 850-nm GaAs vertical cavity surface emitting laser that demonstrates advanced performances with an output OM-P1dB of −4.17 dBm, output OM-IP3 of 2.43 dBm, OM-NF of 34.3 dB, and OM-SFDR of 101.5 $\text {dB}\cdot \text {Hz}^{2/3}$ . The method is applied further to extract the characteristics of various photodetectors such as a commercial photodiode with an integrated transimpedance amplifier revealing an input OM-IP1dB of −41 dBm. The technique will be crucial for the optimization of further RoF links and devices.
Parallel Semi-Supervised Multi-Ant Colonies Clustering Ensemble Based on MapReduce Methodology

Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. MapReduce is a parallel programming model for processing big data using large numbers of distributed computers (nodes). In this paper, we propose a novel semi-supervised multi-ant colonies consensus clustering algorithm and implement the parallelization of this algorithm using MapReduce on Hadoop platform. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the process of the multi-ant colonies ensemble. In addition, it enhances the computational efficiency for big data by adopting a MapReduce Framework. Experimental results demonstrate the effectiveness of the proposed method.