Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy and Error Analysis of Vector Measurement of Ocean Surface Current by Multi-Aperture Along-Track Interferometric SAR

The ocean surface current vector contains velocity and direction information, and its acquisition method has important research value in marine applications. Multi-aperture along-track interferometric (MA-ATI) SAR is a novel method of current vector detection. However, due to the lack of real experimental data validation, complex marine environment and diverse system parameters have an uncertain impact on the current measurement ability of MA-ATI SAR. Therefore it is necessary to analyze the measurement accuracy and error of this method in theory. Based on the current measurement principle of MA-ATI SAR, this article deduces and establishes the accuracy and error simulation model, and analyzes the influence of radar parameters and marine environment on the velocity and direction results. In the simulation results, the airborne MIMO-SAR radar parameters is more suitable to use MA-ATI method for accurate current measurement, and the accuracy of current measurement is higher for the current with high velocity and direction close to azimuth. It is found that the variation of wind direction will lead to obvious errors in MA-ATI current results, and it is pointed out that accurate current results can be obtained by iteratively correcting the velocity error of sub-apertures using M4S model.
Synchronization in Multiple Neural Networks With Delay and Disconnected Switching Topology via Event-Triggered Impulsive Control Strategy

In this article, an event-triggering impulsive control strategy is proposed, in which the impulsive time sequence can be different from the event-triggered time sequence. The synchronization problems of a multiple neural network with delay (MDNN) and the directed disconnected switching topology are discussed by using this strategy. The considered switching topology is assumed to be directed and sequentially or jointly connected. The combined measurement method is adopted in the event-triggering strategy, so that each delayed neural network only updates the control rules at the moment of its event triggering. First, we prove that the designed event-triggering rules can avoid Zeno behavior. Then, the sufficient conditions of event-based quasi-synchronization (synchronization) for the MDNN can be obtained by using the iterative method. In addition, as an extension, the case of a jointly connected topology and the case of a pure impulsive control protocol are considered, too. Finally, a numerical example is provided to test the results in theory analysis.
Cervical Cancer Screening Access, Outcomes, and Prevalence of Dysplasia in Correctional Facilities: A Systematic Review.

BACKGROUND Incarcerated women often access health care primarily through contact with correctional systems. Cervical cancer screening within the correctional system can address the preventable outcome of cervical dysplasia and cancer in this high-risk population. MATERIALS AND METHODS A search of PubMed, EMBASE, CINAHL, and ClinicalTrials.gov was conducted for articles published between January, 1966 and December, 2018. All studies on a population of jailed or incarcerated females and at least one of the following outcomes: cervical cancer or dysplasia, pap smear screening, knowledge about screening, treatment of cervical dysplasia, and compliance with follow-up were analyzed. RESULTS Forty-two studies met inclusion criteria. All 21 studies with prevalence outcomes described a higher prevalence of cervical dysplasia and cancer in the women involved with corrections, compared to a variety of different sources that served as community control groups. The data on screening outcomes were inconsistent. Follow-up compliance for abnormal results was poor, with a study finding that only 21% of women were rescreened within 6 months of the recommended time period. Knowledge about cervical cancer and screening was evaluated in eight studies and was poor across all studies. CONCLUSION Women involved in correctional systems have a higher prevalence of cervical dysplasia and cancer than women in the general population. Acceptance of screening varies, and no published interventions have been shown to improve screening within the prison system. Treatment and compliance with follow-up recommendations are extremely poor and should be a focus of future research.
Evaluation of the bacterial ocular surface microbiome in clinically normal horses before and after treatment with topical neomycin-polymyxin-bacitracin

Next generation sequencing (NGS) studies have demonstrated a rich and diverse ocular surface-associated microbiota in people that was previously undetected by traditional culture-based methods. The ocular surface microbiome of horses has yet to be investigated using NGS techniques. This study aimed to determine the bacterial composition of the ocular surface microbiome in healthy horses, and to identify whether there are microbial community changes over time and following topical antibiotic use. One eye of 12 horses was treated 3 times daily for 1 week with neomycin-polymyxin-bacitracin ophthalmic ointment. Contralateral eyes served as untreated controls. The inferior conjunctival fornix of both eyes was sampled at baseline prior to initiating treatment (day 0), after 1 week of treatment (day 7), and 4 weeks after concluding treatment (day 35). Genomic DNA was extracted from ocular surface swabs and sequenced using primers that target the V4 region of bacterial 16S rRNA. At baseline, the most abundant phyla identified were Proteobacteria (46.1%), Firmicutes (24.6%), Actinobacteria (12.6%), and Bacteroidetes (11.2%). The most abundant families included Pasteurellaceae (13.7%), Sphingomonadaceae (7.9%), an unclassified Order of Cardiobacteriales (7.7%), and Moraxellaceae (4.8%). Alpha and beta diversity measurements were unchanged in both treatment and control eyes over time. Overall, the major bacterial taxa on the equine ocular surface remained stable over time and following topical antibiotic therapy.
Behavioral Intention to Use Mobile Learning: Evaluating the Role of Self-Efficacy, Subjective Norm, and WhatsApp Use Habit

This study empirically investigates factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Two baseline models namely the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) with the addition of habit as an exogenous construct were used for this purpose. The data were collected from 171 engineering undergraduates and analyzed based on structural equation modeling. The results suggest (1) Behavioral intention was positively and significantly influenced by mobile learning self-efficacy, attitude, and perceived usefulness; (2) Attitude was positively and significantly influenced by subjective norm, perceived usefulness, and mobile learning self-efficacy; (3) Mobile learning self-efficacy was only influenced by perceived ease of use and (4) Habit of using WhatsApp did not influence perceived usefulness nor perceived ease of use but had a positive and significant relationship with mobile learning self-efficacy. Nonlinear relationships were also observed between (1) Behavioral intention with perceived ease of use, perceived usefulness, and subjective norm (2) Habit with perceived usefulness and mobile learning self-efficacy. The nonlinear findings indicate that the relationships between these constructs, which were previously reported as linear, are prone to saturation and warrants further investigation. Our findings also stipulate a practical reference for higher educational institutions targeting to practice mobile learning for engineering undergraduates.
Delay-throughput tradeoff in satellite data relay networks with prioritized user satellites

The efficient antenna scheduling strategy for data relay satellites (DRSs) is essential to optimize the throughput or delay of the satellite data relay network. However, these two objectives conflict with each other since the user satellites (USs) with higher priorities take up more transmission time of DRSs' antennas for greater throughput but the USs storing more packets cause a severer waiting delay to the whole network. To balance the conflicting metrics for meeting the delay-throughput integrated requirements, we formulate the antenna scheduling as a stochastic non-convex fractional programming, which is challenging to be solved. For the tractability, we equivalently transform the fractional programming to a parametric problem and implement the Lyapunov drift to guarantee the constraint of mean rate stability. By proposing a delay and throughput tradeoff based antenna scheduling algorithm, we further transform the parametric problem to a solvable weight matching problem. Simulation results reveal the feasible region of the preference control parameter for integrated QoS cases and its variation relationship with network delay and throughput.
Adequacy and efficiency of nursing staff in a child-welfare-clinic at Umtata General Hospital, South Africa.

Background: South Africa has a serious shortage of human and financial resources to provide primary healthcare services especially in the historically under-served areas. It is a tedious task to carry out healthcare delivery for the masses without rationalizing human resources in the form of re-allocation and re-deployment of healthcare personnel. This study aimed to establish the level of adequacy and efficiency of nursing staff in the former Transkei region. The study was carried out in the child and family welfare clinic of the Umtata General Hospital. Objective: To assess adequacy and efficiency of nursing staff in a child welfare clinic. Method: This was a retrospective study. The workload of two nurses allocated to the child welfare clinic at Umtata General Hospital South Africa was calculated based on the patient numbers and hours of work per week. Calculations excluded time breaks for tea, lunch and annual leave. This work load was compared to the norms of standard examination time that is 12 minutes per patient obtained from the sub directorate of Natalia Office of the Department of Health. Information regarding number of patients attended to by the nurses and their disease conditions was obtained from retrospective review of hospital records. Results: While each nurse was on duty for 8 hours per day each had only 6 reproductive hours. The two nurses examined only 310 patients giving staff utilization of 2.46% (i.e. 310/12600) and adequacy of staff of 0.0492 or 310/6300. Conclusion: There is under-utilization of the staff in Umtata General Hospital, South Africa. Recommendation: To increase utilization of the nurses a process of sharing and merging with a clinic or a ward with similar type of work is recommended. Keywords: Efficiency, adequacy, utilization, re-allocation, and re-deployment. African Health Sciences 2003; 3(3): 127 - 130
Weight and Height in Children and Adolescents with Attention-Deficit/Hyperactivity Disorder: A Longitudinal Database Study Assessing the Impact of Guanfacine, Stimulants, and No Pharmacotherapy

Abstract Objectives: To assess the impact of long-term pharmacotherapy with guanfacine immediate- or extended-release (GXR), administered alone or as an adjunctive to a stimulant, on weight and height in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). Methods: Data were extracted from U.S. Department of Defense medical records for patients 4–17 years of age at index date (initiation of any study medication following a year without ADHD medications, or diagnosis if unmedicated) with weight/height measurements for the analysis period (January 2009–June 2013) and the previous year (baseline). Longitudinal weight and height z-scores were analyzed using multivariable regression in three cohorts: guanfacine (initial period of guanfacine exposure), first-line stimulant monotherapy (initial period of exposure), and unmedicated. Guanfacine cohort subgroups were based on previous/concurrent stimulant exposure. Results: The weight analyses included 47,910 patients (66.8% male) and the height analyses 41,248 (67.2% male). Mean initial exposure in the weight analyses was 237 days (standard deviation [SD] = 258, median = 142) for guanfacine and 257 days (SD = 284, median = 151) for first-line stimulant monotherapy, and was similar in the height analyses. Modeling indicated that guanfacine monotherapy was not associated with clinically meaningful deviations from normal z-score trajectories for weight (first-line, n = 943; nonfirst-line, n = 796) or height (first-line, n = 741; nonfirst-line, n = 644). In patients receiving guanfacine adjunctive to a stimulant, modeled weight (n = 1657) and height (n = 1343) z-scores followed declining trajectories. In this subgroup, mean standardized weight/height had decreased during previous stimulant monotherapy. For first-line stimulant monotherapy, modeled weight (n = 32,999) and height (n = 28,470) z-scores followed declining trajectories during year 1. In the unmedicated cohort, modeled weight (n = 11,515) and height (n = 10,050) z-scores were stable. Conclusions: Guanfacine monotherapy (first-line or nonfirst-line) was not associated with marked deviations from normal growth in this modeling study of children and adolescents with ADHD. In contrast, growth trajectories followed an initially declining course with stimulants, whether given alone or with adjunctive guanfacine.
Cocoons counting and classification based on image processing

For the count of cocoons, the image is segmented by kmeans method in the first place, then the distance transformation method is adopted to obtain the distance gray image, and the cocoons that are adhered to each other are separated by morphological operation to obtain the image of unconjugated cocoon. Finally, the cocoon counting is realized by traversing the connected domain.For cocoon classification, a fine tuned AlexNet neural network is used to classify cocoons, and batch normalization is used to replace local response normalization in conv1 and conv2.The data in the network is normalized to a normal distribution with variance of 1. This method can not only accelerate the training speed of the network, but also slow down the gradient disappearance problem in the training process, and improve the generalization ability of the network. Finally, higher recognition accuracy rate is obtained than the AlexNet network without improvement.
Towards Intelligent Assistive System for Visually Impaired People: Outdoor Navigation System

Visually impaired people face numerous challenges in their everyday life, especially in outdoor environments. The most widely adopted mobility aids among the visually impaired community are white cane and guide dogs. With the advent of information and communication technologies (ICTs), they are now making use of smartphones and related services, e.g. GPS to assist them in navigation and everyday activities. Nevertheless, they still experience navigation difficulties in unfamiliar outdoor environments. Deep learning-based approaches have shown tremendous performance in various domains, e.g. computer vision, robotics, etc. which makes them an excellent choice for solving the navigation difficulties faced by visually impaired people. In this paper, we propose a learning-based outdoor navigation system to empower visually impaired people in their independent mobility. The approach builds upon two navigation strategies; namely macro- and micro-navigation. Further, we present our unique pedestrian-centric dataset. To evaluate the system performance, both navigation strategies were put to test. The results show the usability of the navigation system.
Building resilience to climate risks through social protection: from individualised models to systemic transformation

This article analyses the role of social protection programmes in contributing to people's resilience to climate risks. Drawing from desk‐based and empirical studies in Ethiopia, Kenya and Uganda, it finds that social transfers make a strong contribution to the capacity of individuals and households to absorb the negative impacts of climate‐related shocks and stresses. They do so through the provision of reliable, national social safety net systems—even when these are not specifically designed to address climate risks. Social protection can also increase the anticipatory capacity of national disaster response systems through scalability mechanisms, or pre‐emptively through linkages to early action and early warning mechanisms. Critical knowledge gaps remain in terms of programmes’ contributions to the adaptive capacity required for long‐term resilience. The findings offer insights beyond social protection on the importance of robust, national administrative systems as a key foundation to support people's resilience to climate risks.
ASAP: An Analytical Strategy for AQFP Placement

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with very low energy dissipation. Each AQFP cell is driven by AC-power to serve as both power supply and clock signal. The clock signals trigger the data flow from one clock phase to the next clock phase, and the delay for each output in the same phase has to be equal. At the same time, the signal current attenuates as the wire becomes longer. When a wire exceeds a maximum length, the weak current causes incorrect data. Thus, rows of buffers have to be inserted as repeaters to satisfy both delay synchronization and wirelength constraint. These inserted buffers significantly increase the power consumption and also the total delay of AQFP circuits. In this paper, we propose an analytical strategy for AQFP placement (ASAP) to provide effective placement results that greatly reduce the number of additional inserted buffers. ASAP includes two main characteristics: 1) a new wire-length function for analytical global placement and 2) detailed placement including fixed-order Lagrangian relaxation and cell balancing algorithm. Experimental results show the efficiency of ASAP framework and a 53% reduction of buffers over the state-of-the-art method.
Full-Band Quantum Transport of Heterojunction Electron Devices With Empirical Pseudopotentials

This article presents the methodology, implementation, and application of a full-band quantum transport model based on the nonequilibrium Green’s function formalism and the empirical pseudopotentials. In particular, this article reports the treatment of heterojunctions between lattice-matched semiconductors, comprising a gradual transition region described according to a virtual crystal approximation. Our approach entails several numerical techniques to make the full-band quantum transport method computationally affordable and thus enable robust and efficient self-consistent device simulations. Then, we employ our simulation scheme for the analysis of some exemplary devices based on quantum tunneling, such as an Esaki tunneling diode, as well as n- and p-type heterojunction tunnel FETs. In particular, we investigate the influence on the current–voltage characteristics of the width of the heterojunction transition region. We observe that a gradual transition region mainly affects the device characteristics by lengthening the tunneling path at the heterojunction, which has a different impact on device current depending on the external bias conditions.
Self-Supervised Pose Adaptation for Cross-Domain Image Animation

Image animation is to animate a still image of the object of interest using poses extracted from another video sequence. Through training on a large-scale video dataset, most existing approaches aim to explore disentangled appearance and pose representations of training frames. Then, the desired output with a specific appearance and pose can be synthesized via recombining learned representations. However, in some real-world applications, test images may lack the corresponding video ground-truth or follow a different distribution than the distribution of the training video frames (i.e., different domains), which largely limit the performance of existing methods. In this paper, we propose domain-independent pose representations that are compatible with and accessible by still images from a different domain. Specifically, we devise a two-stage self-supervised pose adaptation framework for general image animation tasks. A domain-independent pose adaptation generative adversarial network (DIPA-GAN) and a shuffle-patch generative adversarial network (Shuffle-patch GAN) are proposed to penalize the rationality of the synthesized frame's pose and appearance, respectively. Finally, experiments evaluated on various image animation tasks, which include same/cross-domain moving objects, facial expression transfer and human pose retargeting, demonstrate the superiority of the proposed framework over prior literature. Impact Statement—Image animation is a popular technology in video production. Benefiting from the rapid development of artificial intelligence (AI), recent image animation algorithms have been widely used in real-world applications, such as virtual AI news anchor, virtual try-on, and face swapping. However, most existing methods are designed for specific cases. To animate a new portrait, users are asked to collect hundreds of images of the same person and train a new model. The technology proposed in this paper overcomes these training limitations and generalizes image animations. In the challenging cross-domain facial expression transfer task, the user study demonstrated that our technology achieved more than 20% increase in animation success rate. The proposed technology could benefit users in a wide variety of industries including movie production, virtual reality, social media and online retail.
Bridging the.id Domain Purchase Intention with Website Channels: Factors Analysis Approach

Domain is a unique name used to identify the IP address of a computer server. It had a function to facilitate visitors in accessing and remembering the addresses of the website. Organized by PANDI, Indonesia has.id as country code Top Level Domain (ccTLD). Unfortunately, the.id domain in Indonesia penetration was relatively low, with a ratio of 1:855 and the high gap among registrars. This research identified and analyzed factors influencing.id domain purchasing through website channels to fix that situation effectively. This research aimed to determine which factors influence users in purchasing a.id domain through website channels by using a combined research model from Website Quality, Theory of Planned Behavior, Brand Equity Website, and Social Website Interactivity. By involving 143 respondents in an online questionnaire, this research employed 11 hypotheses, nine of them were accepted while two others were rejected. It was using Partial List Square-Structural Equation Modeling (PLS-SEM). They proved factors that influence the.id domains purchasing through the website were: trust, brand experience, and brand choice. They were supported by website quality and social website interactivity while attitude and social response did not.