Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary Protein Intake, Breast Feeding and Growth in Human Milk Fed Preterm Infants

Protein intakes of preterm infants are frequently below recommendations, but few studies report accurate intakes due to the difficulty of analysing human milk clinically. This observational analysis from a randomised trial of infants born <31 weeks’ gestation, investigating two levels of protein fortification, reports protein intakes compared with requirements and determines the association of direct breastfeeding on growth. Ninety-two infants (median gestational age 28 weeks, Interquartile range (IQR) 26–29; mean birth weight 1040 g, SD 300 g) were studied. Infants born weighing <1000 g were underfed protein compared with recommendations (median (IQR) intake of 3.0 (2.0–3.7) g/kg/day in week 2 versus recommendation of 4–4.5 g/kg/day), while those born weighing ≥1000 g met recommended protein intakes after the first week of life (median (IQR) intake of 3.7 (3.0–4.0) g/kg/day in week 2 versus recommendation of 3.5–4.5 g/kg/day). A moderate, negative correlation between the mean number of breast feeds and change in rate of weight gain (r = −0.37, p = 0.001) was found. Protein intakes of infants <1000 g did not meet recommendations and all infants were underfed protein and energy in the first week of life. Current protein fortification is inadequate for infants born <1000 g. Exploratory analysis showed faltering rate weight gain associated with increasing number of breast feeds and these results warrant confirmation.
A potential problem with extended EOF analysis of standing wave fields

Abstract The extended empirical orthogonal function (EEOF) analysis of signals containing a standing wave component is considered. It is shown using a simple analytical model that for a single time‐lag EEOF analysis, two EEOF patterns describe the standing wave, and that their associated eigenvalues become degenerate if the lag chosen is close to a zero of the sample autocovariance function of the standing wave time series. This degeneracy strongly affects the relative magnitudes of the two sectors of each EEOF pattern and the structure of the associated spatial pattern. A set of EEOF analysis of varying lags is performed on a tropical Pacific sea level pressure (SLP) dataset, known to be dominated by standing variance, and the predictions of the analytical model are verified. As well, it is seen that at degeneracy there is a reduction in the quality of the spatial patterns produced by the EEOF analysis.
Attention Based Residual Network for Micro-Gesture Recognition

Finger micro-gesture recognition is increasingly become an important part of human-computer interaction (HCI) in applications of augmented reality (AR) and virtual reality (VR) technologies. To push the boundary of microgesture recognition, a novel Holoscopic 3D Micro-Gesture Database (HoMG) was established for research purpose. HoMG has an image subset and a video subset. This paper is to demonstrate the result achieved on the image subset for Holoscopic Micro-Gesture Recognition Challenge 2018 (HoMGR 2018). The proposed method utilized the state-of-the-art residual network with an attention-involved design. In every block of the network, an attention branch is added to the output of the last convolution layer. The attention branch is designed to spotlight the finger micro-gesture and reduce the noise introduced from the wrist and background. With an extensive analysis on HoMG, the proposed model achieved a recognition accuracy of 80.5% on the validation set and 82.1% on the testing set.
‘If labels for GM food were present, would consumers trust them?’ Insights from a consumer survey in Uganda

Abstract Food labelling is costly. Food labelling is often demanded with the introduction of new food products such as genetically modified (GM) food. If consumers do not have trust in the label, scarce resources are wasted. This paper investigates factors affecting the trust in food labels among Ugandan consumers. The results suggest that older, less-educated individuals of smaller household sizes and with trust in government institutions have more trust in food labels. Other factors were also found to be important. The government has to consider those differences in consumer trust when designing a GM labelling policy.
Comparative Thermal and Structural Characterization of Sintered Nano-Silver and High-Lead Solder Die Attachments During Power Cycling

13.5 mm ${\times }\,\,$ 13.5 mm sintered nano-silver attachments for power devices onto AlN substrates were prepared at 250 °C and a pressure of 10 MPa for 5 min and compared with Pb5Sn solder joint die attachments under constant current power cycling with an initial temperature swing of 50 °C–175 °C. Both effective thermal resistance and microstructural evolution of the samples were monitored using transient thermal impedance measurement and nondestructive X-ray computed tomography at regular power cycling intervals. The results showed a gradual increase in the effective thermal resistance of the Pb5Sn solder joints from 0.047 to 0.133 K/W from zero to 41k power cycles, followed by a rapid escalation to 0.5018 K/W at 52k cycles. This was accompanied with the formation and development of oblique cracks within the Pb5Sn die attachments until delamination occurred at the solder/device and solder/substrate interfaces. By contrast, the effective thermal resistance of the sintered Ag joints remained almost constant at 0.040 K/W up to 116k power cycles. This was explained in terms of thermally induced continuation of densification of the sintered structure and the formation and development of networked vertical cracks within the sintered Ag die attachments, some of which further extended into the Cu tracks of the AlN substrate.
Energy Harvesting and Magneto-Inductive Communications With Molecular Magnets on Vibrating Graphene and Biomedical Applications in the Kilohertz to Terahertz Band

Magneto-inductive (MI) Terahertz (THz) wireless channels provide significant theoretical performances for MI communications (MIC) and wireless power transmission (WPT) in nanoscale networks. Energy harvesting (EH) and signal generation are critical for autonomous operation in challenging mediums including biomedical channels. State of the art electromagnetic vibrational devices have millimeter dimensions while targeting low frequency EH without any real-time communications. In this paper, graphene resonators are combined with single molecule magnets (SMMs) to realize nanoscale EH, MIC, and WPT with novel modulation methods achieving simultaneous wireless information and PT. The unique advantages of graphene including atomic thickness, ultra-low weight, high strain, and resonance frequencies in the Kilohertz to THz band are combined with high and stable magnetic moments of Terbium (III) bis (phthalocyanine) SMMs. Numerical analyses provide tens of nanowatts powers and efficiencies of 10 $^{4}$ W/ $\text{m}^{3}$ in acoustic and ultrasound frequencies comparable with vibrational EH devices while millimeter wave carrier generation is numerically analyzed. Proposed model and communication theoretical analysis present a practical framework for challenging applications in the near future by promising simple mechanical design. Applications include nanoscale biomedical tagging including human cells, sensing and communication for diagnosis and treatment, EH and modulation for autonomous nano-robotics, and magnetic particle imaging.
PLFA Profiling of Microbial Community Structure Influencing Ecosystem Restoration in Chronosequence Iron Mine Overburden Spoil

Phospholipid fatty acid analysis (culture-independent approach) provides a set of molecular markers for microbial taxa and consistently used to discriminate microbial communities of different origin and land uses. PLFA profiling provides an approach for microbial community assessment and their changes regulating ecosystem restoration. PLFAs are synthesized during microbial growth, rapidly degraded following cell death and hence can reliably reflect living microbial communities involved in ecosystem structure and function. Relative distribution of 75 PLFAs across the sites revealed significant differences in microbial community structure in seven different iron mine overburden spoil with variation in Shannon diversity index from 2.4905 (IB4) to 2.8490 (IB2) and Pielous evenness index from 0.7005 (IB0) to 0.7556 (IB4). The ratio of gram-positive to gram-negative exhibited a decline trend from IB0 (2.129) to IB25 (1.137) over time. Fungal to bacterial ratio exhibited an increasing trend from IB0 (0.0233) to IB25 (0.0640) and was found to be higher in NF soil (0.1116), revealing the sign of ecosystem restoration over time. F:B ratio showed positive correlation with pH (r = 0.872, p0.001), moisture (r = 0.888, p0.001). Based on the relative distribution of PLFAs, the principal component analysis and cluster analysis can able to discriminate different mine overburdens into independent clusters. Redundancy analysis can contribute for soil quality assessment based on the shift in 75 PLFAs. Thus, PLFA profiling evaluated the broad scale patterns of distribution of microbial community structure influencing soil quality and can be used for monitoring the restoration of iron mine spoil over time.
Suicidal and Aggressive Ideation Associated with Feelings of Embitterment

Background: Mental disorders can be associated with suicidal or aggressive ideation and behavior, especially in the context of embitterment. The aim of this study is to investigate the types, prevalence, and dangerousness of aggressive and suicidal ideations associated with embitterment. Methods: When therapists from the department of behavioral medicine detected signs of embitterment, aggression, or suicidal thoughts in their patients, they routinely filled out a questionnaire on aggressive ideation, assessed the embitterment, and contacted a senior psychiatrist. Additionally, patients answered an embitterment scale. Results: There were 127 patients (3.84% of all patients) with suicidal and/or aggressive ideation. They had an increased score of 2.93 (SD 0.74) on the embitterment scale, associated with personal vilification (62.7%), breach of trust (30.2%), public humiliation (25.4%), death/loss (5.6%), or attacks by another person (14.3%). We found that 83.5% of the patients harbored aggressive ideations; in 94.1% of this group, these were directed against the person who had caused the problem, 88.3% wanted to inflict severe damage, 38.8% to harm another person, 31.5% showed suicidal ideation, and 3.2% had fantasies of murder-suicide. Only 34.3% of the patients reported spontaneously about their current aggressive ideation. The limitations of the study are that the data come from an inpatient sample and patients were identified according to clinical judgement. Conclusion: Aggressive ideation is regularly associated with embitterment. This deserves the attention of therapists for the prevention of aggressive acts.
Risk and Prevalence of Personality Disorders in Sexual Offenders

3 Introduction.4 Personality Disorder Prevalence in Sex Offenders.4 Factors Related to Recidivism.7 Study Overview.9 Method.9 Participants.9 Materials.10 Results.13 Personality Disorder Prevalence.13 Personality Disorder and Offender Type.14 Personality Disorder and Recidivism.15 Personality Disorder, Recidivism, and Static-99 Risk Levels.15 Discussion.16 Limitations.21 Implications.22 References.25 Table 1.30 Table 2.31 Table 3.32 RISK AND PREVALENCE OF PERSONALITY DISORDERS IN SEXUAL OFFENDERS 3
Defensive Points-To Analysis: Effective Soundness via Laziness

We present a defensive may-point-to analysis approach, which offers soundness even in the presence of arbitrary opaque code: all non-empty points-to sets computed are guaranteed to be over-approximations of the sets of values arising at run time. A key design tenet of the analysis is laziness: the analysis computes points-to relationships only for variables or objects that are guaranteed to never escape into opaque code. This means that the analysis misses some valid inferences, yet it also never wastes work to compute sets of values that are not "complete", i.e., that may be missing elements due to opaque code. Laziness enables great efficiency, allowing a highly precise points-to analysis (such as a 5-call-site-sensitive, flow-sensitive analysis). Despite its conservative nature, our analysis yields sound, actionable results for a large subset of the program code, achieving (under worst-case assumptions) 34-74% of the program coverage of an unsound state-of-the-art analysis for real-world programs.
A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians

GoCARB is a computer vision-based smartphone system designed for individuals with Type 1 Diabetes to estimate plated meals’ carbohydrate (CHO) content. We aimed to compare the accuracy of GoCARB in estimating CHO with the estimations of six experienced dietitians. GoCARB was used to estimate the CHO content of 54 Central European plated meals, with each of them containing three different weighed food items. Ground truth was calculated using the USDA food composition database. Dietitians were asked to visually estimate the CHO content based on meal photographs. GoCARB and dietitians achieved comparable accuracies. The mean absolute error of the dietitians was 14.9 (SD 10.12) g of CHO versus 14.8 (SD 9.73) g of CHO for the GoCARB (p = 0.93). No differences were found between the estimations of dietitians and GoCARB, regardless the meal size. The larger the size of the meal, the greater were the estimation errors made by both. Moreover, the higher the CHO content of a food category was, the more challenging its accurate estimation. GoCARB had difficulty in estimating rice, pasta, potatoes, and mashed potatoes, while dietitians had problems with pasta, chips, rice, and polenta. GoCARB may offer diabetic patients the option of an easy, accurate, and almost real-time estimation of the CHO content of plated meals, and thus enhance diabetes self-management.
Late Ediacaran trackways produced by bilaterian animals with paired appendages

Ediacaran trackways suggest the evolution of early legged bilaterian animals 551 to 541 million years ago. Ediacaran trace fossils provide key paleontological evidence for the evolution of early animals and their behaviors. Thus far, however, this fossil record has been limited to simple surface trails and relatively shallow burrows. We report possible trackways, preserved in association with burrows, from the terminal Ediacaran Shibantan Member (ca. 551 to ca. 541 million years ago) in the Yangtze Gorges area of South China. These trace fossils represent the earliest known trackways. They consist of two rows of imprints arranged in poorly organized series or repeated groups. These trackways may have been produced by bilaterian animals with paired appendages, although the phylum-level phylogenetic affinity of the trace makers remains unknown. It is possible that the trackways and associated burrows were produced by the same trace maker, indicating a complex behavior involving both walking and burrowing. Together, these trackways and burrows mark the arrival of a new era characterized by an increasing geobiological footprint of bilaterian animals.
Stock market random forest-text mining system mining critical indicators of stock market movements

Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy.
Data-Driven Quality Assessment of Noisy Nonlinear Sensor and Measurement Systems

This paper considers the problem of determining and comparing the quality of nonlinear sensor systems concerning a measurement task in a data-driven way. Due to various noise sources and nonlinear characteristics, physical sensor and measurement systems, in general, exhibit an intractable random input-to-output behavior. In practice, this makes it impossible to describe the exact stochastic system model analytically. Nevertheless, such a description is required if one wishes to formulate efficient processing algorithms and to draw rigorous conclusions about the fundamental performance limits of the sensor system. After determining the mean and covariance of a set of user-defined statistics at the sensor output in a calibrated environment, the unknown probabilistic model of the physical measurement system can be approximated by an equivalent model within the exponential family. Such an approximation features a mathematically tractable model description and is guaranteed to be conservative in the sense that it exhibits a lower Fisher information than the exact data-generating model. By considering measurement tasks with nonlinear amplifiers and capacitive sensors, we here outline how to use the presented data-driven model replacement strategy to compare the parameter uncertainty level which is achievable with different sensor layouts.
Longitudinal Model Predictive Control with comfortable speed planner

Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.
Skewing the odds: Optimal strategies in capacity-constrainedcontests

We study contests where, subject only to a capacity constraint on mean performance, contestants, facing a rank-dependent payoff function, choose arbitrary performance distributions. In the case of symmetric capacity, we derive closed-form solutions for equilibrium performance distributions and analyze the effect of contest structure on equilibrium behavior. We show that equilibrium performance distributions are never dispersionmaximizing and are always right-skewed when the contest is selective. When contestants’ capacities are private information, contests serve as a selection mechanism. We show, contrary to the ruin-and-risk-taking intuition, that weaker contestants do not always gamble on high-risk strategies and that, when the capacities of weak and strong contestants are sufficiently different, the contest mechanism produces perfect selection efficiency. Finally, we examine the effect of various modifications of the contest mechanism and apply our results to actual contests, such as student examinations and competition between mutual funds for star rankings.
Training Augmentation with Adversarial Examples for Robust Speech Recognition

This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters. We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against noise and channel variation. Further improvement is obtained when combining adversarial examples with teacher/student training, leading to a 23% relative word error rate reduction on Aurora-4.
Factors Affecting Rural Patients' Primary Compliance with e-Prescription: A Developing Country Perspective

Abstract Background: The electronic prescription system has emerged to reduce the ambiguity and misunderstanding associated with handwritten prescriptions. The opportunities and challenges of e-prescription system, its impact on reducing medication error, and improving patient's safety have been widely studied. However, not enough studies were conducted to explore and quantify the factors that affect rural patients' compliance with e-prescription, especially from the perspective of Asian developing countries where most of the world's population resides. Objective: The objective of this study is to explore and assess the factors that affect rural patients' primary compliance with e-prescription in Bangladesh. Methods: Data were collected from 95 randomly selected rural patients who received e-prescription through a field survey with a structured questionnaire from Bheramara subdistrict, Bangladesh, during June and July 2016. Logistic regression analysis was performed to test the research hypotheses. Results: The study found patients' gender as the most significantly influential factor (regression coefficient [Coef.] = 2.02, odds ratio [OR] = 7.51, p < 0.05) followed by visiting frequency (Coef. = 0.99, OR = 2.70, p < 0.05); education (Coef. = 0.92, OR = 2.51, p < 0.05); and distance to healthcare facility (Coef. = 0.82, OR = 2.26, p < 0.01). However, patients' age, monthly family expenditure, and use of cell phone were found insignificant. The model explains 59.40% deviance (R2 = 0.5940) in the response variable with its constructs. And the “Hosmer–Lemeshow” goodness-of-fit score (0.99) is also above the standard threshold (0.05), which indicates the data fit well with the model. Conclusions: The findings of this study are expected to be helpful for e-health service providers to gain a better understanding of the factors that influence their patients to comply with e-prescriptions.