Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromosome-Encoded Extended-Spectrum Class A β-Lactamase MIN-1 from Minibacterium massiliensis

ABSTRACT Minibacterium massiliensis strain CIP107820 is a recently discovered waterborne Gram-negative rod isolated from hospital water samples. It harbors a chromosomally located gene encoding an Ambler class A extended-spectrum β-lactamase termed MIN-1, sharing 56%, 54%, and 51% amino acid identities with β-lactamases LUT-1, KPC-2, and CTX-M-2, respectively. β-Lactamase MIN-1 hydrolyzes penicillins, narrow-spectrum cephalosporins, cefotaxime, and, less efficiently, cefepime, while ceftazidime and carbapenems are very poor substrates, and cephamycins and aztreonam are not hydrolyzed.
Case Study : Attentional Cues and Infant Learning

The study of cognitive development hinges, largely, on the analysis of infant looking. But analyses of eye gaze data require the adoption of linking hypotheses: assumptions about the relationship between observed eye movements and underlying cognitive processes. We develop a general framework for constructing, testing, and comparing these hypotheses, and thus for producing new insights into early cognitive development. We first introduce the general framework – applicable to any infant gaze experiment – and then demonstrate its utility by analyzing data from three studies investigating the role of attentional cues in infant learning. Finally, we discuss general implications for construction and testing of quantitative linking hypotheses.
Autonomous navigation for Mars final approach using X-ray pulsars and Mars network based on RKF

Final approach phase is one of the key phases in Mars landing progress. The performance of the navigation system in this phase influences the actualization of Mars high-precision pinpoint landing. Conventional navigation strategies in this phase mainly rely on the Earth-based measurement, whose time delay is serious because of the long distance between the spacecraft and the ground telemetering station. Therefore, in order to ensure the acquisition of real-time measurement information and enhance the reliability of the navigation system, an autonomous navigation algorithm adopting X-ray pulsars and Mars network is proposed in the current study. This study also considers the atomic clock bias error in the measurement model to further improve the navigation accuracy. Moreover, the rank Kalman filter (RKF) is introduced in the algorithm. Compared with the unscented Kalman filter (UKF), RKF improves the sampling method to obtain optimal state estimations in the filtering process. Numerical simulation results show that the proposed navigation algorithm can make full use of the navigation information provided by X-ray pulsars and Mars network, and can effectively reduce the influence of clock bias errors and guarantee a better performance than conventional methods.
Hard Real-Time Scheduling of Streaming Applications Modeled as Cyclic CSDF Graphs

Recently, it has been shown that the classical hard real-time scheduling theory can be applied to streaming applications modeled as acyclic Cyclo-Static Dataflow (CSDF) graphs. However, many streaming applications are modeled as cyclic CSDF graphs, thus they are not supported by such scheduling theory. Therefore, in this paper, we propose an approach which enables to apply the classical hard real-time scheduling theory on streaming applications modeled as cyclic CSDF graphs. The proposed approach converts each task in a cyclic CSDF graph to a constrained-deadline periodic task. This conversion enables the utilization of many hard real-time scheduling algorithms which offer properties such as temporal isolation and fast calculation of the required number of processors for scheduling the tasks. We evaluate the performance of our approach in comparison to existing scheduling approaches. The evaluation, on a set of real-life benchmarks, demonstrates that our approach can schedule the tasks in an application, modeled as a cyclic CSDF graph, with guaranteed throughput equal or comparable to the throughput obtained by existing scheduling approaches while providing hard real-time guarantees for every task in the application thereby enabling temporal isolation among concurrently running tasks/applications on a multi-processor platform.
Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver’s Heart Rate Monitoring

The driver’s physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human’s health status. When detecting the driver’s HR, using traditional contact-type devices might bring about the driver’s distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver’s HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver’s HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers’ HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver’s HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the success-rate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).
Patients' perception of pain after total hip arthroplasty.

A study was undertaken to determine the frequency with which patients had pain that they attributed to their hip after total hip arthroplasty. Pain drawings were used to allow patients to localize the area of their symptoms, and the degree of pain was quantified with visual analog scales. Complete clinical and radiographic data were collected on all patients so that the occurrence of pain could be correlated with a number of parameters previously reported to affect the incidence of pain, including age, sex, activity level, length of follow-up, stem size, bone type (Dorr index), and type of stem fixation (proximally coated, fully coated, or cemented). Results indicated that type of stem fixation was the only parameter statistically correlated with a higher incidence of thigh pain. Patients with proximally coated stems were more than twice as likely to complain of pain than patients with fully coated or cemented hips (P < .01). Although the incidence of thigh pain was significantly higher with proximally coated stems, the severity was not, averaging 3.0 to 3.5 out of 10 on visual analog scale in all 3 groups. The results indicate that patients perceived pain as originating in the hip in a high percentage of cases, particularly when proximally coated stems were used.
Path Planning Strategy of Mobile Nodes Based on Improved RRT Algorithm

The RRT algorithm is widely used in the high-dimensional path planning in a dynamic environment, and well adapted to the dynamics of motion of the mobile node needs. However, in large scale wireless sensor networks (WSN), the RRT algorithm lacks stability and is easy to deviate from the optimal path. In this paper we proposes a path planning algorithm called E-RRT to improve the problems that RRT has. The method proposed includes the coverage density of obstacle for initialize searching area for the exploring random tree, and the gradually extended region used to ensure the path to be found. The method also adopts the greedy algorithm to delete the intermediate point in the point sequence of path for an optimal path, and the quadratic Bezier curve to smooth the path for the mobile sensor node. The path found can be the shortest, collision-free and smoothing, and therefore to satisfy the requirement of path planning for mobile sensor nodes. The simulation results show that the E-RRT algorithm outperforms the RRT algorithm.
A Two-Step Deep Convolution Neural Network for Road Extraction from Aerial Images

Road extraction has been one of the important research topics in field of remote sensing imagery due to its significant role in various areas such as traffic management, urban planning, GPS navigation, disaster management etc. In this paper, we investigate and exploit a deep convolution network, U-net, for road extraction from aerial images. We propose a model which is a union of a high precision network and a high recall network. Both the networks are based on deep U-net. Massachusetts road dataset is used in the experiments. The results demonstrate that our proposed model outperforms state-of-the-art frameworks in terms of accuracy, precision, recall, and F-score.
Correlation Analysis between Quantified Uncertainty Factors and Optimal Operation Cost of Commercial Building-distributed Energy Resources

In this study, uncertainties arising from distributed energy resources (DER) of a commercial building is quantified, and the relationship is analyzed between optimal DER operating costs and the variation of uncertainties. Firstly, an optimization algorithm for DER management of a commercial building is introduced. A photovoltaic (PV) generation with energy storage system (ESS) is utilized to reduce peak load consumed by the building and to contribute surplus energy to the electricity grid system. Demand response (DR) issuance is assumed from the given historical data. An electric vehicle (EV) charging station is applied to the DER system which can exchange charging and discharging power with the grid system. Furthermore, uncertainties are quantified based on the distributed resource characteristics. It is inserted in the process of determining the optimal solution and is used as an indicator of the correlation with the minimum operating cost result. Finally, a criterion is proposed to ensure sufficient predictive accuracy of resources that contain randomness for meaningful optimal operation.
Performance Comparison of OFDM-QAM and FBMC-QAM Filtered in Multi-User Massive MIMO Channel Characterization

The current 4G standard or long-term evolution (LTE) system is based on the use of orthogonal frequency division multiplexing-quadrature amplitude modulation (OFDM-QAM) for the downlink and uplink communications. OFDM is an orthogonal block transmission scheme which, in the presence of multi-path fading channels, plain orthogonal multicarrier modulation formats are not able to maintain orthogonality due to inter-symbol interference (ISI) among consecutive multicarrier symbols. A new modulation format is a filter bank multicarrier (FBMC). In this paper, we compare a performance of OFDM-QAM and FBMC-QAM to filter the ISI problem at the receiver. We evaluate the performance under the assumption of multiuser massive MIMO channel as a simplicity. The experimental results are depicted with eye-diagrams and the performance of bit error rates (BERs). As the result, the FBMC-QAM is a potential successor of the dominant OFDM-QAM where BER is acceptable with no ISI and it is definitely a promising modulation to use in 5G signal processing.
Induction of cardiomyocytes by GATA4 in Xenopus ectodermal explants

The earliest step in heart formation in vertebrates occurs during gastrulation, when cardiac tissue is specified. Dorsoanterior endoderm is thought to provide a signal that induces adjacent mesodermal cells to adopt a cardiac fate. However, the nature of this signalling and the precise role of endoderm are unknown because of the close proximity and interdependence of mesoderm and endoderm during gastrulation. To better define the molecular events that underlie cardiac induction, we have sought to develop a simple means of inducing cardiac tissue. We show that the transcription factor GATA4, which has been implicated in regulating cardiac gene expression, is sufficient to induce cardiac differentiation in Xenopus embryonic ectoderm (animal pole) explants, frequently resulting in beating tissue. Lineage labelling experiments demonstrate that GATA4 can trigger cardiac differentiation not only in cells in which it is present, but also in neighbouring cells. Surprisingly, cardiac differentiation can occur without any stable differentiation of anterior endoderm and is in fact enhanced under conditions in which endoderm formation is inhibited. Remarkably, cardiac tissue is formed even when GATA4 activity is delayed until long after explants have commenced differentiation into epidermal tissue. These findings provide a simple assay system for cardiac induction that may allow elucidation of pathways leading to cardiac differentiation. Better knowledge of the pathways governing this process may help develop procedures for efficient generation of cardiomyocytes from pluripotent stem cells.
Can HIV infection be eradicated through use of potent antiviral agents?

Purpose of review This review focuses on recent advances in HIV research and therapy that seek to eradicate persistent HIV in patients on suppressive therapy. Recent findings The source of persistent HIV in patients on suppressive therapy is debated. Recent studies of treatment intensification have produced varied results: no reduction in low-level plasma viremia indicating the source of persistent viremia is long-lived HIV-infected cells that release HIV when activated and increase in episomal HIV DNA indicating active replication persists in some infected individuals on suppressive therapy. In addition, clonal HIV sequences found in plasma from patients on long-term suppressive therapy are rarely found in CD4+ memory T cells. These results indicate that persistent viremia may arise from several different sources. Recent studies emphasize the complexity of HIV latency. Current strategies for HIV eradication focus on compounds that activate viral transcription in memory CD4+ T cells by many routes, including inhibiting histone deacetylation and activating nuclear factor kappa B. Several compounds and combinations of these compounds appear to induce the expression of integrated HIV in different latency models. Summary The eradication of HIV requires the elimination of persistent HIV during suppressive therapy. Recent studies have focused on the source of persistent viremia, mechanisms of intracellular HIV latency, and reactivation of latent HIV. It remains to be seen whether alternative treatment strategies may be required to eradicate HIV.
Rapid Building of Software-based SDN Testbed using SDN Owl

This paper presents the development of a software tool called SDN Owl that is used to facilitate a quick and easy construction of SDN testbed using open source software. SDN OWL is based on using a few computers, Linux operating system, Ansible, and OpenvSwitch to build an SDN testbed. The prototype testbed has been built using SDN OWL. The experimental results show that the SDN testbed that is built can function properly with a reasonable performance. In addition, the performance of hardware installation and container installation has been investigated as well. The result of this work enables an easy creation of a cost-effective SDN testbed from commodity computing components. This is very crucial for many small companies and research teams that want to study and develop the next generation SDN services and tools
Cloud-Aided Cognitive Ambient Backscatter Wireless Sensor Networks

Cognitive ambient backscatter is a wireless communication paradigm that allows a secondary backscatter device to superimpose its information-bearing data on a primary signal, without requiring any type of power-consuming active components or other signal conditioning units. In such a network, the performance of the backscatter system can be severely degraded by channel estimation errors and co-channel direct-link interference (DLI) from the primary system. To overcome these shortcomings, we consider a cloud radio access network (C-RAN) architecture, where both the primary and secondary edge nodes are connected to a cloud processor via high-speed links. In this centralized architecture, secondary edge nodes provide network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the problem of acquiring channel state information and suppressing the DLI is managed by the cloud processor. In particular, we assess the performance of the secondary backscatter sensor transmission in a realistic system setup, which takes into account training-based channel estimation, practical modulation constraints, and imperfect DLI suppression. In addition, we formulate and solve an optimization problem aimed at maximizing the transmission rate of the secondary transmission, subject to limits on channel estimation error, average symbol error rate, power consumption, and energy storage capabilities of the backscatter sensor. The validity of our analysis and the performance of the secondary system based on the proposed designs are corroborated through the Monte Carlo simulations.
A Word Vector Based Review Vector Method for Sentiment Analysis of Movie Reviews Exploring the Applicability of the Movie Reviews

Based on word embedding method, this paper presents a word vector based review vector method for sentiment analysis of movie reviews. As a result, it is achieved that 86.18% classification accuracy using the method. Meanwhile, the method is applicable to multiple languages such as Chinese and English, and it is extensible for larger scale contents as well. What’s more, the influence of word vector dimensions on the sentiment analysis accuracy and the method’s applicability on sentences of varied lengths are also discussed in this paper. The experimental result proved that the word vector based review method for sentiment analysis is not only an efficient and simple way to analyze emotional expression, but also has extensibility and applicability for comments in varied lengths and multiple languages.
Embedded Device Free Passive (EDfP) System: Effect of WiFi Protocols

Internet of Things (IoT) concept initiated the evolution of devices capable of wireless communication to cater potential IoT enablers. These devices require specific communication infrastructures for local as well as cloud connection. Ubiquitously available WiFi infrastructure in Malaysia provides a promising platform for the deployment of WiFi based IoT edge devices. In this study, the effect of WiFi protocols on the sensitivity of an ESP32 device to the presence of human entity is evaluated in a pathway of 4.8 square meters. The key objective of this study is to verify the effect of different WiFi protocols on the sensitivity of a potential IoT enabler that performs Device Free Passive (DfP) human presence detection using a single link with an Access Point (AP) without any intervention from an application server. Protocols namely 802.11 B, 802.11 BG, 802.11 BGN with 20 MHz bandwidth and 802.11 BGN with 40 MHz bandwidth are evaluated in this study. Sensitivity measure shows 802.11 BGN protocol with 40 MHz bandwidth has the highest accuracy of 83% for human presence detection. In overall, all the evaluated protocols can be utilized to perform DfP tasks using IoT enablers especially ESP32.
Impact of Electric Bus Charging on the Power Distribution System a Case Study IEEE 33 Bus Test System

Electric buses (Ebus) have become increasingly used, especially in the metropolitan area, to reduce pollution from carbon monoxide and dust. However, the use of an Ebus requires charging from the power distribution system, which may affect the stability of the system. This paper is a study of the impact of electric bus charging on the power distribution system a case study IEEE 33 bus test system. The study also included Ebus charging integrated with distributed generation (DG) system to improve voltage level. The power flow was calculated using the Newton-Raphson method and the size and location of DG calculated by the voltage deviation index (VDI). The simulation results show the impact of Ebus charging on the system. The fast charging of 6 minutes at 300 kW power, system voltage dropped 0.8139 p.u. , mean voltage 0.9014 p.u. The voltage improvement by installing DG on buses 18 and 33 at 1.0 MW and 1.4 MW respectively. The results show that the system voltage increased to 0.9533 p.u. , the voltage means 0.9735 p.u. The Ebus charging causes the voltage drop on the power system, which affects the reliability of the system. It is a larger load than the charging of electric vehicles. The installation of DG into the system makes it possible to adjust the voltage level to the standard value.
The WiggleZ Dark Energy Survey: constraining galaxy bias and cosmic growth with three-point correlation functions

Higher order statistics are a useful and complementary tool for measuring the clustering of galaxies, containing information on the non-Gaussian evolution and morphology of large-scale structure in the Universe. In this work we present measurements of the three-point correlation function (3PCF) for 187 000 galaxies in the WiggleZ spectroscopic galaxy survey. We explore the WiggleZ 3PCF scale and shape dependence at three different epochs z = 0.35, 0.55 and 0.68, the highest redshifts where these measurements have been made to date. Using N-body simulations to predict the clustering of dark matter, we constrain the linear and non-linear bias parameters of WiggleZ galaxies with respect to dark matter, and marginalize over them to obtain constraints on σ8(z), the variance of perturbations on a scale of 8 h^−1 Mpc and its evolution with redshift. These measurements of σ_8(z), which have 10–20 per cent accuracies, are consistent with the predictions of the Λ cold dark matter concordance cosmology and test this model in a new way.
Capacity Analysis of Downlink NOMA-Based Coexistent HTC/MTC in UDN

The coexistence of human-type communications (HTC) users and machine-type communications (MTC) devices is inevitable in the next generation of cellular communications. In this paper, we study the impact of the association schemes on the downlink capacity of ultra-dense networks (UDNs). We adopt non-orthogonal multiple access (NOMA) where HTC users and MTC devices share the spectrum based on power-NOMA approach. The capacity in terms of average rate and area spectral efficiency (ASE) is analyzed for two extreme association schemes, namely, connect to active (C2A) and connect to closest (C2C). Furthermore, we investigate the network performance while moving from one extreme to the other. On one hand, C2A is an efficient scheme from HTC users’ perspective where it can keep most of the existing base stations (BSs) in the idle mode; saving energy and reducing interference. However, it provides unsatisfactory performance for the MTC devices. On the other hand, moving towards C2C is favorable to MTC devices, however, it deteriorates the ASE of the HTC users and increases the transmission power density. Accordingly, we seek a compromise between these two contradicting schemes. Accurate expressions for the network performance are derived using tools from stochastic geometry and validated by Monte Carlo simulations.