Miracle-dz/UniFAR-FTUs huggingface dataset:
Below‐ground herbivory and root toughness: a potential model system using lignin‐modified tobacco

Plants exploit an array of defences against insect herbivores based on chemical and biomechanical properties. There is increasing evidence that plant toughness comprises a particularly effective defence against herbivory, yet studies to date have focussed exclusively on leaf toughness and folivore behaviour. The relationship between root mechanical properties and the chewing behaviour of a root‐feeding insect, the Agriotes spp. wireworm (Coleoptera: Elateridae L.), feeding on tobacco (Nicoiana tabacum) is investigated. Root toughness is manipulated using introduced transgenes for the down‐regulation of key enzymes in the lignin biosynthesis pathway: cinnamoyl‐CoA reductase (CCR line) and caffeate O‐methyltransferase and cinnamyl alcohol dehydrogenase (CO line). Two biomechanical analyses (i.e. conventional cutting and notched tensile) are conducted to quantify root toughness on both lines. Roots from the CO line are significantly tougher than those of the CCR line in terms of fracture toughness and fracture energy, although not for cutting energy or stiffness. Bioassays that compel wireworms to chew through roots demonstrate that only 30% can penetrate roots of the CO line compared with 90% on the CCR line. It takes wireworms over twice as long to penetrate roots from the CO line (8 h) compared with CCR roots (3.5 h). There is a statistically significant positive relationship between penetration time and fracture toughness evaluated with tensile tests, although not with cutting energy from cutting tests. Using this exploratory model system, it is concluded that root toughness derived from tensile tests is a practical indicator of the ability of root‐feeding insects to penetrate roots.
Phytosociology of sown pasture weeds under two levels of degradation in Brazilian savanna areas, Mato Grosso do Sul State, Brazil.

Cultivated pastures, in general, go through a decline process, associated with the occurrence of weeds, whose successful management begins with the survey of species and their biology. This study aimed to carry out phytosociological surveys, during the rainy and dry seasons, in two pastures cultivated with Brachiaria ( Urochloa ) spp. , with cattle (AI) and without cattle (AII), in Cerrado (Brazilian savanna) areas of the Mato Grosso do Sul State, Brazil. The occurrence of 104 weedy herbaceous and subshrubby Phanerogams species was recorded. The families with the highest number of species were Fabaceae (23 species), Poaceae (16), and Asteraceae (15). Hemicryptophytes predominate with 41% of the species. The species with the highest importance value and relative coverage are B. decumbens and Sida rhombifolia , in AI, and B. brizantha and Desmodium incanum , in AII, plus Paspalum notatum , in both pastures. The Shannon diversity index was 4.19 and 4.43 nats, for AI and AII, respectively, indicating considerable weed species richness. The floristic dissimilarity index between AI and AII, obtained from the Euclidian distance, is 0.7468, indicating just a few species in common (44%). In the dry season, the number of species in AI decreased 10% while in AII it increased 4%. KEY-WORDS: Agroecosystem; Brachiaria ; flora; plant ecology; Urochloa .
Self-Healing Properties of Bioinspired Amorphous CaCO3/Polyphosphate-Supplemented Cement

There is a strong interest in cement additives that are able to prevent or mitigate the adverse effects of cracks in concrete that cause corrosion of the reinforcement. Inorganic polyphosphate (polyP), a natural polymer that is synthesized by bacteria, even those on cement/concrete, can increase the resistance of concrete to progressive damage from micro-cracking. Here we use a novel bioinspired strategy based on polyP-stabilized amorphous calcium carbonate (ACC) to give this material self-healing properties. Portland cement was supplemented with ACC nanoparticles which were stabilized with 10% (w/w) Na–polyP. Embedding these particles in the hydrated cement resulted in the formation of calcite crystals after a hardening time of 10 days, which were not seen in controls, indicating that the particles dissolve and then transform into calcite. While there was no significant repair in the controls without ACC, almost complete closure of the cracks was observed after a 10 days healing period in the ACC-supplemented samples. Nanoindentation measurements on the self-healed crack surfaces showed a similar or slightly higher elasticity at a lower hardness compared to non-cracked surfaces. Our results demonstrate that bioinspired approaches, like the use of polyP-stabilized ACC shown here, can significantly improve the repair capacity of Portland cement.
Integrating data mining and transmission theory in the ecology of infectious diseases

Abstract Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.
A approximation algorithm to solve the maximum multicommodity flow problem with local dominant

The present work studies a maximum multicommodity flow problem with local constrains, which not only enrich the content of the multicommodity flow problem, but also can be used to operate optimization decisions of some practical problems, such as the logistics distribution problems stemming out of transportations. The major contributions are as follows: (A) A multicommodity flow problem is developed and the existence of its solutions is studied. (B) An approximation algorithm to solve the defined problem is designed. (C) The approximation measures as well as the time complexity of the designed algorithm is proposed and proved.
An Acceleration Model of Multiple Accelerating Stresses for Typical Electronic Products

Acceleration test is one of the most commonly used tools to assess the life of a product. In an acceleration test, the model to describe degradation feature is the key. In this paper, a method to build an acceleration model of multiple accelerating stresses for typical electronic products is studied. First, commonly used acceleration models of both single and multiple stresses are briefly reviewed. Then, with the help of general Eyring model and polynomial model for acceleration life test, a general comprehensive model is given. After that, a framework of application of the general model is presented with the example of one specific model of temperature, humidity, vibration and velocity as acceleration stresses. At last, an application example of this method in the testing data of a power supply of communication product is given.
Graph Based User Clustering for HAP Massive MIMO Systems With Two-stage Beamforming

We propose a user clustering algorithm based on graph theory with two-stage beamforming for high-altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. First, we construct a conflict graph, where each vertex is the user and each edge is measured by the similarity of correlation matrix distance (CMD) between users. Then, in the aim of alleviating the self-cluster interference (SCI), a novel low-complexiy user clustering method is introduced, where the algorithm is Bron-Kerbosch to enumerate all the maximal cliques, and maximal clusters are obtained by the cluster formation algorithm. As shown in the numerical results, the performance of the proposed algorithm has a significant increase.
Design of Distributed Storage Multi-core System Based on NoC Architecture

The distributed storage multi-core system is designed and implemented based on the NoC (network-on-chip Network) architecture. Compared with the traditional bus-based interconnection architecture, it aims to solve the problem which data processing becomes the bottleneck of the whole system. The system also adopts adaptive routing algorithm, which can flexibly set the priority of data communication according to different applications, and improves the overall data throughput of the system. Finally, by applying this system to the experiment of decoding h. 264 video, it can be verified that the distributed multi-core storage system with NoC architecture can achieve a great improvement in performance.
SIMPLE: A Remote Attestation Approach for Resource-constrained IoT devices

Remote Attestation (RA) is a security service that detects malware presence on remote IoT devices by verifying their software integrity by a trusted party (verifier). There are three main types of RA: software (SW)-, hardware (HW)-, and hybrid (SW/HW)-based. Hybrid techniques obtain secure RA with minimal hardware requirements imposed on the architectures of existing microcontrollers units (MCUs). In recent years, considerable attention has been devoted to hybrid techniques since prior software-based ones lack concrete security guarantees in a remote setting, while hardware-based approaches are too costly for low-end MCUs. However, one key problem is that many already deployed IoT devices neither satisfy minimal hardware requirements nor support hardware modifications, needed for hybrid RA.This paper bridges the gap between software-based and hybrid RA by proposing a novel RA scheme based on software virtualization. In particular, it proposes a new scheme, called SIMPLE, which meets the minimal hardware requirements needed for secure RA via reliable software. SIMPLE depends on a formally-verified software-based memory isolation technique, called Security MicroVisor (Sμ V). Its reliability is achieved by extending the formally-verified safety and correctness properties to cover the entire software architecture of SIMPLE. Furthermore, SIMPLE is used to construct SIMPLE+, an efficient swarm attestation scheme for static and dynamic heterogeneous IoT networks. We implement and evaluate SIMPLE and SIMPLE+ on Atmel AVR architecture, a common MCU platform.
Time Series Segmentation of Flood Flow Based on Bi-LG-LSTM Neural Network

With fast development of big data technology, how to mine useful information from a large amount of time series data is an important task in hydrology. Such process of information mining should be built on the basis of pre-processing, including data cleaning, aligning, segmentation and so on. In this paper, we mainly focus on an efficient algorithm to accurately segment time sequences in hydrology domain. Although traditional methods of time series segmentation can segment time series easily, there are three drawbacks in traditional methods; First, traditional segmentation methods pay more attention to the evaluation of the compression ratio and the fitting rate, but ignore the accuracy of segmentation points; Secondly, traditional segmentation methods do not take the information of time series data itself into account, such as it is easy to divide a whole time series into two time series segments, which leads to losing semantic information in time series analysis. Finally, in these traditional evaluations of time series segmentation, the number of segments and the position of the segmentation points are not comprehensively taken into account. In this paper, we propose a time series segmentation method based on Bi-LG-LSTM neural network, which is modified on the base of the LSTM/Bi-LSTM neural network. Through the supervised learning method on flood flow data sets, we can extract a large number of effective segments of the time series. Meanwhile, we propose a novel evaluation method, which utilizes the dynamic DTW distance method to evaluate the results of the time series segmentation method. The evaluation method takes the position of segmentation points and the number of time series segments into consideration. Experiments on the flood flow time series show that our proposed method has an obvious effect.
A Survey on Green Internet of Things

Internet of Things(IoT) is a broad term which enables the object to collect data, exchange data and also reduces human intervention. It allows Machine to Machine communication with embedded devices, which enables sending-receiving of the data. Green Internet of things which can be either software or hardware adopted majorly deals with reducing greenhouse effects and the entire life cycle from production to utilization plays major on the environment. This paper gives the summary of Green IoT with different Tags and their uses to achieve energy efficiency and how they impact on the society for a greener environment.
Performance Improvement for Multi-User Millimeter-Wave Massive MIMO Systems

In this paper, we study the problem of optimizing the performance of multi-user millimeter-wave (mmWave) communications in three steps. The first one is given by the use of a new pilot mapping to reduce the inter-user interference effect and to perform more accurate channel estimation. In the second step, we designed a hybrid receiver that, based on the accuracy of the channel state information, chooses between the minimum mean square error (MMSE) and the multi-user regularized zero-forcing beamforming (RZFBF) receivers, to combine/precode the massive multiple-input multiple-output (MIMO) signal. In the third step, we propose to improve the beam direction with a slight change in the azimuth angle during the uplink communications to increase the multi-user efficiency and reduce inter-user interference. Numerical results show the performance increase using the proposed solutions in terms of the spectral efficiency by comparing the MMSE, RZFBF, and hybrid receivers.
Natural Data-driven Approaching Behaviors of Humanoid Mobile Robots for F-Formations

In this work we address the problem of approaching a group of persons based on a data-driven model, which is gathered in real-world human interaction. Our model estimates an adequate pose for approaching groups of different sizes, providing a mobile robot with the ability to decide how to approach a group.The model is capable of automatically detecting and distinguishing individuals from groups of humans, although only approaches to groups will be focused. From group detection, it computes the 2D poses of the group’s elements and, based on the poses, estimates an approaching pose to join it. The model uses an F-Formation oriented representation of the world that allows estimating an approaching pose to groups regardless of the environment and the arrangement of the elements of the group in space. The model was tested in simulations and real-world experiments. Results show that the model was fully capable of estimating, in both tested scenarios, human-like and socially acceptable approaches to groups.
The Role of Currents and Waves in the Dispersal of Submersed Angiosperm Seeds and Seedlings

We tested the hypothesis that currents, waves, and sediment grain size affect the dispersal of seeds and seedlings of the submersed angiosperms Ruppia maritima, Potamogeton perfoliatus and Stuckenia pectinata. Seed settling velocities and initiation of motion of seeds and seedlings and distance transported were quantified on four sediment types under a range of currents and waves in a flume. The rapid settling velocities of R. maritima and S. pectinata seeds and the increased settling velocity of P. perfoliatus in currents above 8 cm/second suggest that primary dispersal of these species is localized to the general area colonized by their parents. Once settled within a bed, seeds are exposed to weak currents and waves, and are likely to be subject to sediment deposition which may further limit dispersal. In contrast, in restoration projects, the absence of vegetation is likely to make seeds more vulnerable to grazing and transport, and may contribute to the lack of plant establishment. If seeds germinate without being buried, they are susceptible to secondary dispersal at relatively low current velocities and small wave heights due to the drag exerted on the cotyledon. Sand grains tend to stick to the seed coat and rootlet of P. perfoliatus seedlings, perhaps a mechanism to reduce the chances of being displaced following germination. These data reveal the close links between sediment, water flow, and submersed angiosperm seedling establishment; these parameters should be considered when using seeds for restoration of submersed angiosperms.
Constrained Shortest Path by Parameter Searching

Given an undirected graph, one start point and one end point belong to this graph, and nonnegative groups of weights for each edge, we consider the problem of finding a path that has lowest total cost with respect to cost weight and has constrained budget with respect to constrains weight [9]. Different from the traditional Lagrangean relaxation method, we proposed a new parameter space searching method for solving the multi-constrained shortest path problem (MCSP) in this paper. For this newly proposed method all the solutions found are in the original problem’s feasible region, there is no need to do the hard work to close the gap between the original problem and its Lagrangean relaxation. The proposed algorithm is very easy to parallelize, and can take full advantage of multi-core multi-thread processors to improve problem solving efficiency. For most optimization algorithms, properly selecting the super parameters is not an easy work. Through multi-rounds computer numerical simulation with different super parameters, we can see that our algorithm is robust to super parameters.
Face Detection and Recognition of the Seven Emotions via Facial Expression: Integration of Machine Learning Algorithm into the NAO Robot

This study revolves around NAO, a programmable and interactive robot, to do Emotion Recognition via Facial Expression, and to give an appropriate response for the identified emotion whilst accumulating images to further enhance the accuracy. To develop this cohesive system, the authors utilized Computer Vision and Haar-Cascade Classifier along with the feature descriptors, Histogram of Oriented Gradient and Local Binary Pattern to train the Support Vector Machine Learning Algorithm. Gathering and increasing data with Asian/Filipino participants whilst being added to the CK+ database in different stages of retraining, optimization, and undertaking cross-fold validation yielded improved results with the highest average accuracy across the seven emotions of 87.14%. Suggesting that adding images with a specific ethnicity yields a higher accuracy model for a more diverse testing dataset and in the wild image classifications. With the machine learning model capable of accurately recognizing the emotion and reinforcing it with the accumulated local images, NAO equipped with emotion recognition capabilities can better aid and support the individuals in need.
An efficient method for driver fatigue state detection based on deep learning

The driver fatigue detection method based on traditional image processing technology suffers from low accuracy due to object rotation, illumination change, and object occlusion. This paper proposed an efficient method for detecting driver fatigue state based on face infrared image and deep learning. The method performed face detection and feature point locating through a cascaded network with depthwise separable convolution as the core, then acquired the eye region according to the feature point coordinates and then performed eye state recognition. Finally, the PERCLOS criterion was used for fatigue judgment, and then the result was output. The experimental results showed that the accuracy of the proposed method is 96.2%, and the average time is 32.4 milliseconds, which is better than the existing methods and can effectively detect the fatigue state of the driver. This method is important for the protection of drivers and traffic safety.
A Spatio-Temporal Structured LSTM Model for Short-Term Prediction of Origin-Destination Matrix in Rail Transit With Multisource Data

Passenger assignment of rail transit has recently attracted increasing research interest due to its potential applications in large-scale intelligent transportation systems. In the rail transit system, the foundation of passenger assignment is passengers’ origin and destination demand (OD matrix). However, due to the nature of stochastic of the short-term dynamic OD matrix, how to accurately predict the distribution of passenger travel spatio-temporally is still an open challenge. In this paper, combined multisource data with deep learning method is proposed to improve prediction of dynamic OD matrix accuracy. Firstly, multisource data such as smart card data, weather data and mobile phone data are introduced. And after quantitative analysis of the influencing factors, choosing 31 features as model inputs. Secondly, considering the superiority of Long Short-term Memory Network in time series, we improve the structure of LSTM by redesigning the hidden layer and neuron, in view of the spatio-temporal characteristics of spatio-temporal Long Short-term Memory Network (STLSTM) of rail transit passenger flow. Finally, using the Beijing subway network which had 54,056OD for verification. Extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of STLSTM over commonly used prediction models and standard LSTM model for short-term prediction of dynamic OD matrix. In addition, the application method of multisource data in OD prediction in this paper can deal with more data from other sources to further improve the information exploit effect on passenger flow law.
Classification of Press Plate Image Based on Attention Mechanism

Power secondary pressure plate is an important means to ensure the safe operation of relay protection and automatic devices. Pressure plate detection method -based video has been applied more and more. And attention mechanism aims is selecting the key targets of images according to people’s attention. In order to improve the accuracy of extracting candidate regions and the descriptive ability of pressing plate features, this paper proposes an image classification algorithm combining channel attention and spatial attention. This method combines two branch networks of spatial attention and channel attention into a sub-network and adds this sub-module to each convolution block in the network to extract high-level features. In order to avoid the loss of the underlying semantic information, the output features of the sub-module and the input features of the branch are fused through the maximum pooling layer as the input of the next stage of the network. Finally, a neural network model is trained to classify the images. The experimental results show that good results are achieved in image classification of press plate.