Miracle-dz/UniFAR-FTUs huggingface dataset:
HOspitalised Pneumonia Extended (HOPE) Study to reduce the long-term effects of childhood pneumonia: protocol for a multicentre, double-blind, parallel, superiority randomised controlled trial

Introduction Early childhood pneumonia is a common problem globally with long-term complications that include bronchiectasis and chronic obstructive pulmonary disease. It is biologically plausible that these long-term effects may be minimised in young children at increased risk of such sequelae if any residual lower airway infection and inflammation in their developing lungs can be treated successfully by longer antibiotic courses. In contrast, shortened antibiotic treatments are being promoted because of concerns over inducing antimicrobial resistance. Nevertheless, the optimal treatment duration remains unknown. Outcomes from randomised controlled trials (RCTs) on paediatric pneumonia have focused on short-term (usually <2 weeks) results. Indeed, no long-term RCT-generated outcome data are available currently. We hypothesise that a longer antibiotic course, compared with the standard treatment course, reduces the risk of chronic respiratory symptoms/signs or bronchiectasis 24 months after the original pneumonia episode. Methods and analysis This multicentre, parallel, double-blind, placebo-controlled randomised trial involving seven hospitals in six cities from three different countries commenced in May 2016. Three-hundred-and-fourteen eligible Australian Indigenous, New Zealand Māori/Pacific and Malaysian children (aged 0.25 to 5 years) hospitalised for community-acquired, chest X-ray (CXR)-proven pneumonia are being recruited. Following intravenous antibiotics and 3 days of amoxicillin-clavulanate, they are randomised (stratified by site and age group, allocation-concealed) to receive either: (i) amoxicillin-clavulanate (80 mg/kg/day (maximum 980 mg of amoxicillin) in two-divided doses or (ii) placebo (equal volume and dosing frequency) for 8 days. Clinical data, nasopharyngeal swab, bloods and CXR are collected. The primary outcome is the proportion of children without chronic respiratory symptom/signs of bronchiectasis at 24 months. The main secondary outcomes are ‘clinical cure’ at 4 weeks, time-to-next respiratory-related hospitalisation and antibiotic resistance of nasopharyngeal respiratory bacteria. Ethics and dissemination The Human Research Ethics Committees of all the recruiting institutions (Darwin: Northern Territory Department of Health and Menzies School of Health Research; Auckland: Starship Children’s and KidsFirst Hospitals; East Malaysia: Likas Hospital and Sarawak General Hospital; Kuala Lumpur: University of Malaya Research Ethics Committee; and Klang: Malaysian Department of Health) have approved the research protocol version 7 (13 August 2018). The RCT and other results will be submitted for publication. Trial registration ACTRN12616000046404.
Role for the shoot apical meristem in the specification of juvenile leaf identity in Arabidopsis

Significance Developmental transitions during shoot development in plants are regulated by factors originating outside and within the shoot apical meristem (SAM). The best-known example of this is the vegetative-to-reproductive transition, which is initiated by a leaf-derived signal that transforms the vegetative SAM into a developmentally stable inflorescence meristem. Although the juvenile-to-adult vegetative transition (vegetative phase change) is also thought to be regulated by factors exogenous and internal to the SAM, how this process is coordinated spatially remains unknown. Here we demonstrate that the SAM specifies leaf identity early in development, but that leaves become more important determinants of shoot identity as the shoot ages. We also reveal a role for the plant aging pathway in the regulation of meristem size. The extent to which the shoot apical meristem (SAM) controls developmental decisions, rather than interpreting them, is a longstanding issue in plant development. Previous work suggests that vegetative phase change is regulated by signals intrinsic and extrinsic to the SAM, but the relative importance of these signals for this process is unknown. We investigated this question by examining the effect of meristem-deficient mutations on vegetative phase change and on the expression of key regulators of this process, miR156 and its targets, SPL transcription factors. We found that the precocious phenotypes of meristem-deficient mutants are a consequence of reduced miR156 accumulation. Tissue-specific manipulation of miR156 levels revealed that the SAM functions as an essential pool of miR156 early in shoot development, but that its effect on leaf identity declines with age. We also found that SPL genes control meristem size by repressing WUSCHEL expression via a novel genetic pathway.
Garbage Collector Scheduling in Dynamic, Multiprocessor Real-Time Systems

We consider garbage collection (GC) in dynamic, multiprocessor real-time systems. We consider the time-based, concurrent GC approach and focus on real-time scheduling to obtain mutator timing assurances, despite memory allocation and garbage collection. We present a scheduling algorithm called GCMUA. The algorithm considers mutator activities that are subject to time/utility function time constraints, stochastic execution-time and memory demands, and overloads. We establish that GCMUA probabilistically lower bounds each mutator activity's accrued utility, lower bounds the system-wide total accrued utility, and upper bounds the timing assurances' sensitivity to variations in mutator execution-time and memory demand estimates. Our simulation experiments validate our analytical results and confirm GCMUA's effectiveness.
The User-Subjective Approach to Personal Information Management Systems Design : Evidence and Implementations

Personal Information Management (PIM) is an activity in which an individual stores his\her personal information items in order to retrieve them later on. In a former article, we suggested the user-subjective approach, a theoretical approach proposing design principles with which PIM systems can systematically use subjective attributes of information items. In this consecutive paper, we report on a study that tested the approach by exploring the use of subjective attributes (project, importance and context) in current PIM systems, and its dependence on design characteristics. Participants were 84 personal computer users. Tools included a questionnaire (N=84), a semi-structured interview that was transcribed and analyzed (N=20), and screen captures taken from this sub-sample. Results indicate that participants tended to use subjective attributes when the design encouraged them to, however, when the design discouraged such use, they either found their own alternative ways to use them or refrained from using them altogether. This constitutes evidence in support of the user-subjective approach as it implies that current PIM systems do not allow for sufficient use of subjective attributes. The article also introduces seven novel system design schemes, suggested by the authors, which demonstrate how the user-subjective principles can be implemented. Personal Information Management (PIM) is an activity in which an individual stores his\her personal information items in order to retrieve them later on. PIM is performed in a physical environment (e.g. in an office), with mobile devices (such as mobile phones and PDAs), and by using personal computers. The information items used in personal computers include documents, emails, Web Favorites, tasks and contacts. Following Soergel’s (1985) model, Barreau (1995) lists five PIM activities: acquisition, organization and storage,
Simplified Sinusoidal Pulse Width Modulation of Cross-switched Multilevel Inverter

Multilevel inverters (MLIs) are playing a major role in every sector of power industries, which is growing significantly over the years. Although multilevel inverters are highly applicable, they have some stern disadvantages that need to be dealt with. Some of the most common issues of MLIs include high usage of electric components, highly complex modulation techniques, presence of harmonics and so on. By keeping all these issues into mind, this paper investigates a single phase cross-switched MLI using abridged electric components. Furthermore, it has described a novel and simplified method of implementing sinusoidal pulse width modulation (SPWM) technique. The investigated MLI and the proposed modulation technique is validated by following both simulation and hardware approach. The simulation is performed using MATLAB Simulink software while the hardware prototype of the whole topology is built using TMS320F28335 digital signal processor (DSP) board.
A High Conversion Non-Isolated Bidirectional DC-DC converter with Low Stress for Micro-Grid Applications

This paper tackles a common ground non-isolated bidirectional DC-DC converter for applications of micro-grids. The suggested converter consists of four main switches with their body diodes, three inductors, and four capacitors. Simple structure, vast voltage gain scope, low voltage tension in the switches and joint common ground among the input and the output are the merits of the suggested converter. Furthermore, the suggested converter provides high step-up/step-down voltage gain. Regarding the simplicity in this configuration, the control concept of the suggested converter is a straightforward conception. Doing so, this paper deals with steady-state analyses in both directions, voltage stress across the power switches, and a watchful survey in comparison between other bidirectional converters. Finally, the simulation results are concluded to validate the effectiveness of the analyses in the suggested converter.
Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare

Healthcare domain is a very prominent research field with rapid technological advancement and increasing data day by day. In order to deal with large volume of healthcare data we need Big Data Analytics which is an emerging approach in Healthcare domain. Millions of patients seek treatments around the globe with various procedure. Analyzing the trends in treatment of patients for diagnosis of a particular disease will help in making informed and efficient decisions to improve the overall quality of healthcare. Machine Learning is a very promising approach which helps in early diagnosis of disease and might help the practitioners in decision making for diagnosis. This paper aims at building a classifier model using WEKA tool to predict diabetes disease by employing Naive Bayes, Support Vector Machine, Random Forest and Simple CART algorithm. The research hopes to recommend the best algorithm based on efficient performance result for the prediction of diabetes disease. Experimental results of each algorithm used on the dataset was evaluated. It is observed that Support Vector Machine performed best in prediction of the disease having maximum accuracy.
Towards Energy-Efficient Heterogeneous Multicore Architectures for Edge Computing

In recent years, the edge computing paradigm has been attracting much attention in the Internet-of-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.
An Accurate Modeling Technology Based on Depth Information of Cultural Relics

With the development of three-dimensional modeling technology, the requirements of digital display for cultural relics increase. This paper analyzes the inherent problems of traditional three-dimensional reconstruction techniques, that is, will generate a large number of irrelevant feature points in the process of extracting images by using traditional RGB cameras, especially for bronze artifacts, the existence of irrelevant feature points will directly affect the matching accuracy, which will cause errors in the generation of object trajectories and affect the final reconstruction effect. A method to optimize feature point matching through depth information for target extraction is proposed, thus achieving an accurate solution for accurate reconstruction. In this paper, the depth camera Kinect is used to collect images of the cultural objects with difficulty in recognition. By aligning the depth images with the RGB images, each feature point of each RGB image has depth information so that when character matching and trajectory calculation are performed, the precise and effective feature points can be screened out, that is, the feature points completely on the target object, which can significantly improve the accuracy of the matching and reduce the number of iterations in the process of Bundle Adjustment, thus greatly shortening the period of feature extraction and improving the efficiency of modeling.
Integration Design of Millimeter-Wave Filtering Patch Antenna Array With SIW Four-Way Anti-Phase Filtering Power Divider

In this paper, the integration design of a millimeter-wave filtering patch antenna array fed by a substrate integrated waveguide (SIW) four-way anti-phase filtering power divider is proposed. The multilayer four-way anti-phase filtering power divider handily implemented using the intrinsic field distribution of TE<sub>20</sub>-mode in SIW is proposed for miniaturization. The signal of the lower substrate integrated waveguide cavity (SIWC) bandpass filter is directly coupled to the upper TE<sub>20</sub>-mode SIW through a coupling slot. The intrinsic field distribution of TE<sub>20</sub>-mode SIW is used to generate anti-phase signals. This filtering power divider can be utilized as the feeding structure of a millimeter-wave filtering antenna array. To verify the design concept, a 28-GHz SIW-fed <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> filtering patch antenna array with three-layer substrates is designed and fabricated. The measured results show a fractional bandwidth of 5.03% ranging from 27.15 to 28.55 GHz, a peak gain of 11.1 dBi, cross-polarization levels lower than −20 dB, symmetric radiation patterns in both <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane and <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane, and high selectivity.
Digital communications

"This is a remarkably comprehensive treatment of the field, covering in considerable detail modulation, coding (both source and channel), encryption, multiple access and spread spectrum. It can serve both as an excellent introduction for the graduate student with some background in probability theory or as a valuable reference for the practicing ommunication system engineer. For both communities, the treatment is clear and well presented. "
Performance Enhancement of Okamura Model using Correction Factor

Radio propagation models are the emerging technologies and are essential for wireless network design, deployment and management. The models are concerned with respect to site and keep varying on their operation's frequencies, terrain conditions, mobile terminals and other dynamic factors. Using parameters of signals coverages, data rates to attain, bit error rate and gain rate from an antenna, the characteristic of a radio channel can be predicted. For designing mobile and fixed radio systems the path loss modelling plays a very important role. A standardization process needs to be introduced for prediction of a radio coverage area. To reduce signal attenuation, suitable path loss model to be introduced. Based on the above parameters, okamura path loss model has been implemented before setting up the wireless system. Channels base station antenna height gain has been selected as one of the parameter and results are discussed. The above work implemented using Matlab tool.
Proposal of Combination of NOMA and Beamforming Methods for Downlink Multi-users systems

In this paper, a downlink multi-users (MUs) system, where the MUs are clustered into several clusters based on the located regions, is taken into consideration. A combinition of beamforming and no-orthogonal multiple access (NOMA) methods is proposed and the base station (BS) communicates instantaneously with all users in multiple clusters by the proposed method. The close-form expressions of outage probability and ergodic capacity are derived to evaluate our proposed method. The derived analytical results are verified by Monte Carlo simulations and the proposed method is compared with conventional orthogonal multiple access (OMA) method. The results indicate that our proposed method can improve the performance of downlink MUs systems more than the OMA method.
Multi-timescale biological learning algorithms train spiking neuronal network motor control

Biological learning operates at multiple interlocking timescales, from long evolutionary stretches down to the relatively short time span of an individual’s life. While each process has been simulated individually as a basic learning algorithm in the context of spiking neuronal networks (SNNs), the integration of the two has remained limited. In this study, we first train SNNs separately using individual model learning using spike-timing dependent reinforcement learning (STDP-RL) and evolutionary (EVOL) learning algorithms to solve the CartPole reinforcement learning (RL) control problem. We then develop an interleaved algorithm inspired by biological evolution that combines the EVOL and STDP-RL learning in sequence. We use the NEURON simulator with NetPyNE to create an SNN interfaced with the CartPole environment from OpenAI’s Gym. In CartPole, the goal is to balance a vertical pole by moving left/right on a 1-D plane. Our SNN contains multiple populations of neurons organized in three layers: sensory layer, association/hidden layer, and motor layer, where neurons are connected by excitatory (AMPA/NMDA) and inhibitory (GABA) synapses. Association and motor layers contain one excitatory (E) population and two inhibitory (I) populations with different synaptic time constants. Each neuron is an event-based integrate-and-fire model with plastic connections between excitatory neurons. In our SNN, the environment activates sensory neurons tuned to specific features of the game state. We split the motor population into subsets representing each movement choice. The subset with more spiking over an interval determines the action. During STDP-RL, we supply intermediary evaluations (reward/punishment) of each action by judging the effectiveness of a move (e.g., moving the CartPole to a balanced position). During EVOL, updates consist of adding together many random perturbations of the connection weights. Each set of random perturbations is weighted by the total episodic reward it achieves when applied independently. We evaluate the performance of each algorithm after training and through the creation of sensory/motor action maps that delineate the network’s transformation of sensory inputs into higher-order representations and eventual motor decisions. Both EVOL and STDP-RL training produce SNNs capable of moving the cart left and right and keeping the pole vertical. Compared to the STDP-RL and EVOL algorithms operating on their own, our interleaved training paradigm produced enhanced robustness in performance, with different strategies revealed through analysis of the sensory/motor mappings. Analysis of synaptic weight
Flow and changes in appearance

An important, largely unexplored area of computer image generation is the simulation of weathering and its effects on appearance. Weathering results from the interaction of the environment with the materials in the world. The flow of water is one of the most pervasive and important natural forces involved in the weathering of materials, producing a distinctive set of patterns of washes and stains. This paper presents an intuitive phenomenological model for the flow of water over surfaces that is capable of generating such changes in appearance.We model the flow as a particle system, each particle representing a "drop" of water. Themotion of the water particles is controlled by parameters such as gravity, friction, wind, roughness, and constraints that force the particles to maintain contact with the surface. The chemical interaction of the water with the surface materials is governed by a set of coupled differential equations describing both the rate of absorption of water by the surface and the rate of solubility and sedimentation of deposits on the surface. To illustrate the power of this simple model, we show examples of flows over complex geometries made from different materials; the resulting patterns are striking and very difficult to achieve using traditional texturing techniques.
A Compact Low-Loss Multilayer SIW Diplexer at K/Ka-Band

This contribution deals with a compact substrate integrated waveguide (SIW) diplexer for terminal array antennas at K-/Ka-band. The diplexer connects the transmit (Tx) and the receive (Rx) frontend with an antenna element. It is comprised of a bifurcation and two second order bandpass filters with low insertion loss. The Rx-filter has additionally two transmission zeros in the Tx-band. The structure is implemented in multilayer printed circuit board (PCB) technology. This yields a compact design which can be integrated in an array with half-wave element spacing. The simulated diplexer performance is validated by measurements. The insertion loss in the Rx-band remains below 0.7 dB, while the Tx-/Rx-isolation exceeds 40 dB in a 1.2 GHz band at Tx.
Combining Deep Learning with Traditional Features for Classification and Segmentation of Pathological Images of Breast Cancer

The automatic classification of pathological images of breast cancer has important clinical value. In order to improve the accuracy and efficiency of cancer detection, we implement two classifications in this paper. (1) Train deep convolutional neural network (CNN) models based on AlexNet and GoogLeNet network structures. (2) Take the idea of transfer learning to complete the training of classification models. We use CNN to extract image features, then select distinguished features to simplify feature set, and combine them with texture features of images. At last, a Support Vector Machine (SVM) is used for feature learning and classification.