Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Biological Safety of Polyvinyl Alcohol/Collagen Hydrogel as Tissue Substitute (I)

The hydrogel based on polyvinyl alcohol (PVA) and collagen was prepared by using a freeze-dried method. To evaluate the biological safety of the PVA/collagen hydrogel, which has a potential application in tissue substitute, cell viability and growth were assessed by using methylthiazoletetrazolium (MTT) assay and Alamar Blue assay; Micronucleus test was reckoned to identify genotoxicity, and sensitivity test was aimed to detect allergic response to skin. The results demonstrated that no significant cytotoxicity of the PVA/collagen hydrogen against L929 mouse fibroblasts was observed. The micronucleus frequency of PVA/collagen was (2.5 ± 0.79)‰, which showed no genotoxic effects either. In addition, there was no allergic response in long-term application. This study suggests that PVA/collagen hydrogen does not demonstrate any significant toxic effects to fibroblastic cells in-vitro and has the potential to be used as scaffolds in tissue engineering applications.
A Two-Order Transfer Model for Gearbox Fault Diagnosis

To enhance gearbox fault diagnosis performance under varying working conditions, a two-order transfer model based on manifold regularization (MR) projection/maximum variance (MV) projection and domain selection machine (DSM) is proposed. In the first order transfer learning (TL), each source domain is mapped with MR and the target domain is mapped with MV from high dimensional spaces to the low dimensional space. Meanwhile, the minimum mean difference (MMD) is used to minimize the difference between the two domains in the low dimensional space. In the second order, the DSM model is utilized to select high-quality source domains by designing the domain selection vector in new space. Experimental results using the SpectraQuest’s Drivetrain Dynamics Simulator show that the proposed method has better gearbox diagnosis accuracy under varying working conditions than each single transfer learning model.
Distributed Consensus Algorithm For Multi-Missile Networks With Switching-Topologies And Dynamically-Changed Delay (Dcams)

The Department of Defense conducts missions that include multiple missiles that communicate in order to successfully achieve mission goals. To accomplish this goal, delay in the multi-missile systems must be minimized. Algorithms exist that address portions of this problem by providing a consensus protocol that considers time-delayed communications for synchronized networks with fixed topologies. This paper expands this approach to provide a common consensus protocol that can be used in both fixed topology and switched topology communications networks. The effect of this enhancement is to dramatically reduce delay for multi-missile communications systems; thereby enabling more time-efficient consensus and improving mission success.
Generating Systolic Array Accelerators With Reusable Blocks

Systolic array architecture is widely used in spatial hardware and well-suited for many tensor processing algorithms. Many systolic array architectures are implemented with high-level synthesis (HLS) design flow. However, existing HLS tools do not favor of modular and reusable design, which brings inefficiency for design iteration. In this article, we analyze the systolic array design space, and identify the common structures of different systolic dataflows. We build hardware module templates using Chisel infrastructure, which can be reused for different dataflows and computation algorithms. This remarkably improves the productivity for the development and optimization of systolic architecture. We further build a systolic array generator that transforms the tensor algorithm definition to a complete systolic hardware architecture. Experiments show that we can implement systolic array designs for different applications and dataflows with little engineering effort, and the performance throughput outperforms HLS designs.
Autonomous Vehicles in Underground Mines, Where We Are, Where We Are Going?

The mining industry has been acting as a leader in the development of fully autonomous vehicles. Mining equipment manufacturers have been developing and testing autonomous vehicle technologies for many years. There are many ongoing innovations in autonomous vehicle technology. One of these is the Dedicated Short Range Communications (DSRC). The DSRC is a one-way or two-way short-to-medium-range wireless communications capability. In this paper, we present a review of the DSRC large-scale fading channel at 5.9 GHz in the tunnels and underground mines. The requirements for DSRC receiver performance for VANET- Vehicular Ad-hoc Networks applications in an underground mine is calculated. This paper also reports the overall performance evaluation of three existing routing protocols, namely, Emergency Message Dissemination for Vehicular Environments (EMDV), Enhanced Multi-Hop Vehicular Broadcast (MHVB), and Efficient Directional Broadcast (EDB) for active safety applications. Finally, a comparative study of these three routing protocols for cooperative collision warning in underground mining galleries was evaluated.
Evidence of morphometric differentiation among Antarctic moss populations as a response to local microenvironment

Studies on phenotypic variation among populations growing in different microenvironments may provide information about plasticity related to environmental pressures, and thus help to elucidate the potential evolutionary forces contributing to the origin and maintenance of diversity in any region. In this study we investigate morphometric variation on a small geographic scale for three species of Antarctic mosses. All species revealed significant differentiation among populations for all evaluated traits. The comparison of morphometric measures of populations of Polytrichum juniperinumfrom Nelson Island and from southern Brazil suggests that the effects of a small geographic scale in Antarctica are the same as a large geographic scale in environments where the climate is more homogeneous and microhabitats have minor influence on vegetation. However, further investigations over a larger area, evaluating more species, and using controlled garden experiments are recommended in order to evaluate the capacity for plasticity of moss species in different climatic conditions and on different geographic scales.
Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning

Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions result in an inaccurate diagnosis due to their high similarity. Accurate classification of the skin lesions in their early stages enables dermatologists to treat the patients and save their lives. This paper proposes a model for a highly accurate classification of skin lesions. The proposed model utilized the transfer learning and pre-trained model with GoogleNet. The model parameters are used as initial values, and then these parameters will be modified through training. The latest well-known public challenge dataset, ISIC 2019, is used to test the ability of the proposed model to classify different kinds of skin lesions. The proposed model successfully classified the eight different classes of skin lesions, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and Squamous cell carcinoma. The achieved classification accuracy, sensitivity, specificity, and precision percentages are 94.92%, 79.8%, 97%, and 80.36%, respectively. The proposed model can detect images that do not belong to any one of the eight classes where these images are classified as unknown images.
A prairie plant community data set for addressing questions in community assembly and restoration

By assisting the recovery of disturbed or destroyed ecosystems, ecological restoration plays an important role in biodiversity conservation. Moreover, restoration has been heralded as an “acid test” of ecological understanding, by affording the ability to study community assembly, ecosystem function, and human influence over ecosystems across large spatial and long temporal scales. These data sets report the outcome of community assembly, in terms of plant community composition and structure and one important ecosystem function (aboveground biomass production), in 29 prairie restorations in southwestern Michigan. We also report putative forces shaping the outcome of assembly including the species pools (seed mixes applied during restoration), site conditions, landscape context, and land-use history. Detailed knowledge of each restoration effort, including seed mixes used, is unusual and makes these data sets uniquely suited to addressing questions in community assembly by comparing the sown seeds and resu.
UPFlight: An enabler for Avionic MEC in a drone-extended 5G mobile network

The interest in extending the range of mobile networks by means of drones, which carry a cellular radio base station or even a complete mobile core, is continuously increasing in research as well as in industry. Whereas the asset in such solutions is well motivated, various practical aspects are not fully considered, such as weight, power consumption and associated flight time of such drones, which usually carry limited and expensive mobile energy resources. In this paper we leverage various advantageous architectural and operational aspects of a 5G Mobile Core and propose the use of a low-cost system-on-chip solution on board of a drone to realize a flying 5G User Plane Function, which serves as data plane anchor to mobile devices attaching to the cellular base station co-located with the drone and enables offloading traffic to services, which can run on the system-on-chip board as well. We prototyped a standard-conform User Plane Function by means of the open source fd.io project for Vector Packet Processing and enabled mobile access to flying lightweight mobile edge services. The paper describes how the drone system operates with the architecture backend in the fixed infrastructure and presents experimentally determined performance figures, comparing the developed low-costs flying edge services with a setup, which deploys the 5G User Plane Function as well as services in the fixed infrastructure.
A Novel Decentralized and Flexible Policy for Flow Mobility Management

Intelligent Transport Systems rely extensively on the proper management of vehicular resources, as well as the underlying vehicular communication. Services and applications are made accessible through the communication of vehicles, which is expected to happen without any interruption; however, the high mobility of vehicles is detrimental towards their connectivity and communication stability. In this highly dynamic and heterogeneous vehicular scenario, we assume the existence of multiple communication interfaces and high movement speed of nodes. Thus, we propose the development of a flow mobility management policy. The policy is devised following a decentralized design, being flexible and capable of providing transparent flow management to users while maintaining continuous service access. We conducted experimental simulations for evaluating the proposed architecture, as well as making performance comparisons with three related flow management works. The results showed that the proposed policy reduces the delay of information exchange to approximately 50 ms, decreases handover time (0.5s), and maximizes the input of information around 95%.
Incomplete alleviation of nickel toxicity in bean by nitric oxide supplementation.

The aim of the experiment was to test the capacity of NO to reverse harmfull effects of nickel on bean ( Phaseolus vulgaris L.) seedlings. Bean seedlings were grown on culture medium and treated with NO-donor – sodium nitroprusside (0.3 mmol/L) and Ni (0.2 mmol/L NiCl 2 ). After 4 days, the parameters of antioxidative response were determined in roots and leaves, as well as the concentrations of essential cations and Ni. In the presence of Ni alone, soluble protein, proline and superoxide-dismutase activity were increased, while peroxidase and especially catalase activities were supressed. Also, Ni induced a depletion of K, Ca, Mg, Mn and Zn, while the contents of Cu and Fe in the roots were increased. In the presence of NO, Ni-induced stimulation of superoxide-dismutase activity, soluble protein and proline accumulations was decreased, while the inhibition of peroxidase and catalase activities was eliminated. Calcium and Zn concentrations were increased by Ni in NO-treated seedlings, suggesting specific activation of the uptake of these elements as part of the protective processes regulated by NO. However, NO had no effect on the impact of Ni on K, Cu, Fe, and Mn concentrations. In conclusion, exogenous NO efficiently attenuates oxidative stress in bean, but does not prevent Ni-induced ion leakage.
Nonlinear Convex Control Design for Differential Algebraic Equation Systems via LMIs

This document presents an approach for nonlinear control design in singular systems by combining Lyapunov's direct method with exact convex structures. The conditions obtained are given in terms of linear matrix inequalities, the methodology proposed is illustrated by applying the nonlinear sector methodology to the mathematical model of a parallel robot for stabilizing it in a desired set point.
Intrusion Detection System Using Machine Learning Techniques for Increasing Accuracy and Distributed & Parallel Approach for Increasing Efficiency

In the current era of wide use of internet by the users across the globe, the data generated and its security both are the important issues. Hence, in any network to prevent the malicious activities from the intrusions, the early and correct detection of intruders is important. For that we are using Intrusion Detection systems (IDS), developed with many effective algorithms which are helping us to get rid of these attacks. But, sometimes these systems do respond with the detection of intruders who are in real, not the intruders, but the normal users. This makes these users difficult to continue with the normal communication and can be prevented from accessing the important services over the World Wide Web. Similarly, not tolerable at all, when it’s false negative i.e. intruder is allowed to enter into network traffic being a normal user, as it may cost to an organization to the extent of being demolished their future. Though the percentage of these false positives and false negatives is less, it is creating issues in many important services. For that we can have a machine learning techniques which can effectively detect intrusions in the system without creating obstacles of false positives and false negatives, also we are proposing it for the online streamed data. Also, this IDS implementation using machine learning techniques is proposed with Distributed & Parallel approach for simultaneous processing of network traffic data by these different machine learning techniques. This approach enables us to implement this IDS design for Online streamed data for faster detection i.e. increasing efficiency of IDS.
Diversity-Multiplexing Tradeoff for MIMO-FSO System Under Different Transmission Scenarios With Limited Quantized Feedback

The diversity-multiplexing tradeoff (DMT) in a multiple-input multiple-output (MIMO) free-space optical (FSO) communication with limited channel state information at the transmitter (CSIT) is investigated. Using the limited CSIT based power and rate control strategy, we optimally allocate the power among the good and bad channels in such a fashion that the DMT performance of the system enhances significantly unlike the no-CSIT based MIMO-FSO DMT. In this way, a new limited CSIT based technique/model with optimal power and rate control strategy is proposed to enhance the DMT performance. The optimal DMT is studied for two different transmission scenarios: single-rate and adaptive-rate transmission. It is shown that how the optimal DMT is influenced when the concept of minimum guaranteed multiplexing gain in the forward link is taken into account. It is illustrated that power control based on the feedback plays a vital role in attaining the optimal DMT, and rate adaptation is significant in obtaining a high diversity gain, especially at high rates. Moreover, the analysis of upper and lower bounds on the optimal DMT is done by giving useful insights. Furthermore, a novel study based on the optimal tradeoff between the degrees of freedom and the number of transmit apertures in a coherent MIMO-FSO channel is also done. To validate the results of the proposed model, we compare the derived results with the no-CSIT based MIMO-FSO DMT. It is observed that the proposed technique/model outperforms the no-CSIT based MIMO-FSO DMT.
EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis

This paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels.
Unifying Person and Vehicle Re-Identification

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system’s generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.
Assessment of photosynthetic radiation‐use efficiency with spectral reflectance

Reflectance changes at 531 nm, associated with the zeaxanthin-antheraxanthin-violaxanthin interconversion and the related thylakoid energization, are widespread among plant species. We evaluated an index based on 531 nm, reflectance ('PRI', Photochemical Reflectance Index, calculated as (R 531 -R 570) /(R 531 +R 570 )) as an indicator of efficiency of photosynthetic radiation use in seven species representing both C 3 and CAM photosynthetic pathways. Leaves exposed to a dark-light-dark transition in a steady-state laboratory gas exchange system exhibited nearly parallel changes in PRI and PS II quantum yield (ΔF/F m '). Similar PRI and ΔF/F m ' responses were seen in leaves exposed to diurnally changing sunlight levels outdoors. PRI was linearly related to ΔF/F m ', and both ΔF/F m ' and PRI were exponentially related to instantaneous efficiency of photosynthetic radiation-use in different species over a range of different field conditions. These results extend previous studies by indicating a functional relationship between PRI, ΔF/F m ', and photosynthetic radiation-use efficiency. The narrow-band PRI index offers a simple, portable means of assessing PS II radiation-use efficiency, analogous to ΔF/F m ', and with the potential for remote applications at scales larger than the leaf.
Random Forest-based predictive modelling on Hungarian Myocardial Infarction Registry

The objective of the current study is to compare how our two tree-based machine learning algorithms can predict 30-day and 1-year mortality of patients hospitalized with acute myocardial infarction. The two algorithms were decision tree and random forest, and the source of dataset is Hungarian Myocardial Infarction Registry (n=47,391). As a result, we found that the ROC AUC values of Random Forest models for predicting 30-day mortality were 0.843 and 0.847 (training and validation set), while for the 1-year models these were 0.835 and 0.836, respectively. These numbers mean that, the Random Forest models were at least 5-6% better than the decision tree models, but in some cases the improvement is above 9%.
Prediction-Based Portfolio Optimization Models Using Deep Neural Networks

Portfolio optimization is a hot research topic, which has attracted many researchers in recent decades. Better portfolio optimization model can help investors earn more stable profits. This paper uses three deep neural networks (DNNs), i.e., deep multilayer perceptron (DMLP), long short memory (LSTM) neural network and convolutional neural network (CNN) to build prediction-based portfolio optimization models which own the advantages of both deep learning technology and modern portfolio theory. These models first use DNNs to predict each stock’s future return. Then, predictive errors of DNNs are applied to measure the risk of each stock. Next, the portfolio optimization models are built by integrating the predictive returns and semi-absolute deviation of predictive errors. These models are compared with three equal weighted portfolios, where their stocks are selected by DMLP, LSTM neural network and CNN respectively. Also, two prediction-based portfolio models built with support vector regression are used as benchmarks. This paper applies component stocks of China securities 100 index in Chinese stock market as experimental data. Experimental results present that the prediction-based portfolio model based on DMLP performs the best among these models under different desired portfolio returns, and high desired portfolio return can further improve the performance of this model. This paper presents the promising performance of DNNs in building prediction-based portfolio models.