Miracle-dz/UniFAR-FTUs huggingface dataset:
An Electronic Measuring System to Record Aphid Salivation and Ingestion1

A detailed description of the system used to record salivation and ingestion activities of the pea aphid, Acyrthosiphon pisum (Harris), is presented. Recent modifications to the system also have been included. Selected electronic principles are discussed and are related to applicable parts of the system. Results of some voltage and resistance measurements are given, and these are used to calculate the current through, the voltage across, and the resistance of pea aphids when probing broadbean, Vicia faba L. Twelve resistance measurements were made on 3 leaves at 4 distances from the point where voltage was applied to the leaves. The voltage measurements were made during maximum conductance of current through the salivary canal of the aphids. Twenty-five aphids were used for the voltage measurements. Three broadbean leaves were used as the probed substrates. With 0.5 volts a-c applied to each leaf, the current through the aphid was found to be 3.72×10−6 amperes. The voltage across the aphid at this time was calculated to be 0.495 volts. The resistance of the aphid was 99 megohms during the time of maximum conductance.
Identification and Pathogenicity of Subgroup of AIV

Using the known antigen of the subgroup toxic strains of AIV, identificated the A type of AIV from chickens with clinical symptom of AI in Shaanxi Province by serological mothod and Virulence deteminated test. The results showed that three strains of AIV is H_(9)N_(2) (A_(1), A_(2), A_(4)), One is H_(11) N_(6)(A_(5)). The death rate of the four strains were 37.5%, 25.0%, 37.5%, 12.5% by chicken experimenaly infected with allantoic fluid from embryonated egg, it indicated the four strains were medium Virulence.
Integrating an Observer in Interactive Reinforcement Learning to Learn Legible Trajectories

An important aspect of Human-Robot-cooperation is that the robot is capable of clearly communicating its intentions to its human collaborator. This communication of intentions often requires the generation of legible motion trajectories. The concept of legible motion is usually not studied together with machine learning. Studying these fields together is an important step towards better Human-Robot cooperation. In this paper, we investigate interactive robot learning approaches with the aim of developing models that are able to generate legible motions by taking observer feedback into account. We explore how to integrate the observer feedback into a Reinforcement Learning (RL) framework. We do this by proposing three different observer algorithms as observer strategies in an interactive RL scheme and compare with one non-interactive RL algorithm as baseline. For the observer strategies we vary the method how the observer estimates how likely the agent is going for the target goal. We evaluate our approach on five environments and calculate the legibility of the learned trajectories. The results show that the legibility of the learned trajectories is significantly higher while integrating the feedback from the observer compared with a standard Q-Learning algorithm not using the observer feedback.
Modeling IoT Enabled Automotive System for Accident Detection and Classification

Millions of people get injured, disabled or die in automotive accidents each year. Knowledge about the type of road accident is invaluable to the emergency medical services providers for optimal planning and execution of the rescue operation. An IoT based system has been developed in this work to report the occurrence, location as well as the type of road accident. The system uses in-built sensors of passenger smartphone to detect and classify the accident as head-on collision, rollover or fall-off. The accuracy of the proposed system, which uses Naïve Bayes classifier for classification, has been evaluated using precision, recall, F1 score and ROC curve.
Classification Of Eligibility Consumption Of Manalagi Apple Fruit Varieties Using Backpropagation

This research aims to clasify the quality of Manalagi apple fruit varieties prior to sorting them automatically.The sorting system used is still manually in fruit sorting with the help of human labor that takes a long time. The sorting process is slow therefore the sorted result is low in number and inconsistent in quality. The parameters used in the Manalagi apple classification are based on the skin color of the fruit. The color of the fruit skin will be taken color features with average RGB. In this study, the classification system is automatically developed for the Manalagi apple fruit with the extraction of the average RGB color feature with the Backpropagation algorithm. The purpose of the developed system is to distinguish the Manalagi apple fruit of worth consumption and not worth the consumption. The fruit classification is based on the average RGB color composition of the fruit skin. Systems developed using image processing such as image segmentation, color feature extraction with average RGB models and fruit quality classification decisions with the Backpropagation algorithm. The developed system can take the decision automatically fruit worth consumption and not worth the consumption with an accuracy rate of 90%.
Recognition and 3D Pose Estimation for Underwater Objects Using Deep Convolutional Neural Network and Point Cloud Registration

Acquiring high-precision spatial position and posture of the object is essential for operation of underwater vehicle. The traditional methods of pose estimation based on point cloud require the manual extraction of the object point cloud, which have poor autonomy and low efficiency. For this problem, a novel method based on Convolutional Neural Network(CNN) and point cloud registration is proposed. In this paper, we use the center and range recognized by CNN of object to guide clustering segmentation of the point cloud data, and then extract the point cloud of the object. Finally, we complete the precise positioning of the object through coarse point cloud registration and fine point cloud registration. We build underwater experimental environment, and the results show that our method can effectively estimate the position of irregular object with high accuracy.
Evaluation of Strategies for Emergency Message Dissemination in VANETs

Emergency services play an important role in the intelligent transportation systems based on mobile communication networks in smart cities. However, the characteristics of Vehicular Ad-hoc Networks (VANETs), such as high mobility, intermittent connectivity, scalability and constant changes in the network topology make this type of message dissemination a challenge. To improve emergency message dissemination between vehicles, we propose strategies that take advantage of the location, direction, speed, number of vehicles’ neighbors and characteristics of the region of the city, so that the message reaches all vehicles in the shortest time with the lowest network overhead. To show the effectiveness of our strategies, we deployed an emulation platform with real connectivity and mobility data. Our results show a delivery rate between 92% and 100% at various densities and vehicle speeds, for various zone sizes of relevance and range of the communication technology.
Individual Targeting Increases Control Over Inter-Individual Variability in Simulated Transcranial Electric Fields

Transcranial electric stimulation (tES) induces electric fields that propagate in the brain and depend on individual anatomies. The interaction between the electric fields and individual anatomies may contribute to the heterogenous results that are commonly observed across tES studies in humans. Targeted tES is able to account for some of these individual factors by adapting the electric field to the stimulation target. Here, the effect of individually targeted tES on simulated intracranial electric fields was evaluated in head models of twenty-one participants using the finite-element method (FEM). For all participants, two individually targeted tES montages were compared to a fixed stimulation montage that was not individually optimized. For a simulated parietal stimulation target with three different orientations, individual current densities showed varying intensities near the lower limit at which physiological efficacy of electric fields can be assumed. However, targeting algorithms were able to control different electric field properties, by either maximizing the target current densities or by increasing the specificity of electric fields with respect to target location and orientation. Electric fields were constrained by individual anatomical properties, but still showed considerable variation for the given parietal stimulation target across participants. Thus, we present findings of inter-individual variability within the same cortical region to complement recent studies that showed large variation across cortical regions in a single FEM head model. Our results support the usage of individual targeting for enhancing the efficacy of tES and for elucidating the underlying mechanisms of tES. At the same time, residual variability in electric fields is suggested to be utilized for the explanation of individual differences in the tES outcome.
The structure and diversity of rain forests at bajo calima choco region western colombia

The rain forests at Bajo Calima, Colombia are described for woody plant composition. Two upland plots, 1.0 and 0.5 ha in size, were selected and all trees ? 10 cm dbh were measured and identified. Trees > 2.5 cm and ' 9.9 cm dbh were sampled in 0. 1 ha subplots. Biomass was estimated using allometric equations. Biomass levels are low, at 210 tons/ha, and tree canopy heights rarely exceed 30-35 m. Few trees over 100 cm dbh were found. Forests at Bajo Calima are among the most species-rich in the world, with over 250 tree species ? 10 cm dbh per ha. Palms are numerically abundant in the overstory, with Jessenia bataua being most common. Free-climbing lianas are uncommon. Only 11 species had more than 8 individuals - 10 cm dbh per ha. Measures of soil nutrients indicate low fertility and possible aluminum toxicity. The pluvial rain forests at Bajo Calima lend support to previous findings that high diversity is correlated with both high rainfall and low nutrient levels.
An Improved Simultaneous Stage-wise Weak Orthogonal Matching Pursuit Algorithm for Microwave Brain Stroke Imaging

Stroke is a dangerous disease with a high recurrence rate. Therefore, postoperative patients need timely monitoring of stroke conditions in their rehabilitation stage for early treatment. Recent studies in biomedical imaging have shown that strokes produce variations in the electric permittivity of brain tissues, which can be detected by microwave imaging techniques. Assuming that we have obtained the image of electromagnetic parameters in previous treatment, we can use differential imaging to detect the bleeding points when stroke recurs. However, the computational cost of traditional methods could be prohibitively large, as the bleeding points are small in the early stages of recurrence and the data of scatterers are sparse. Compressive sensing is a potential candidate for efficient microwave imaging algorithms. In this paper, we propose a new method for microwave differential imaging. The difference of the scattering field is related to sparse unknown scatterers by the Green's function of the inhomogeneous medium. An improved simultaneous stagewise weak orthogonal matching pursuit algorithm is then proposed to solve the problem in the framework of compressive sensing. The numerical results validate the accuracy and efficiency of the proposed method.
Load Modelling Effects on Power System Inertia Response

As countries around the world begin to increase the contribution of renewable energy generation into their interconnected power systems, a large portion of this generation is interfaced to the power system through power electronic converters. As the increase in their contribution begins to displace traditional synchronous generation through end of life and/or early decommissioning, the power system inertia will decrease. This increases the rate of change of frequency (ROCOF) making the frequency of the system respond quicker to power imbalances. In this paper, load modelling and their effects on ROCOF are explored to highlight the key considerations when deriving conclusions based on the power system inertia outlook. The outcome showed that more realistic composite load structures based on type of loads that reflect the corresponding economic activity will present different outlooks on the inertia response of the power system. The outcome of this research also demonstrates how composite load modelling effects on the network and interlinked transmission line loading can result in lower frequency nadir and ROCOF. This outcome is very critical in understanding the dynamic loading conditions on transmission lines against possible contingencies and the network inertia status.
The impact of varying system flexibility on market prices for electricity and balancing reserves

The goal of this paper is twofold. At first, we provide a modeling framework to derive a consistent view on spot and balancing reserve markets. Secondly, we use this approach to determine the impact of increasing system flexibility via scenario analysis for day-ahead electricity and capacity reservation for balancing reserves, focusing particularly on the German electricity system for the year 2030. While fundamental modeling of spot market prices in the mid-term or long-term has been well established, it is quite ambitious to derive a consistent view on future balancing reserve markets. To achieve this goal, we use a two-step modeling approach to determine the equilibrium on both markets. Minimizing the opportunity costs for capacity reservation of the supply-side, we determine balancing reserve market bids that are consistent with spot market prices. The scenario analysis reveals a strong market position for battery storage regarding Frequency Containment Reserve (FCR) and positive automatic Frequency Restoration Reserve (aFRR). Due to the lower opportunity cost compared to fossil-fueled power plants, capacity prices for these market segments are diminished by battery storages.
The Service Node Placement Problem in Software-Defined Fog Networks

Nowadays, cloud computing has become a key paradigm in distributed applications thanks to the rise of low-power Internet-connected devices as commonplace. However, stringent Quality of Service (QoS) requirements are complicated to achieve when a pure cloud computing paradigm is applied, due to the physical distance between end devices and cloud servers. This motivated the appearance of fog computing, a paradigm that adds computation and storage resources, named fog nodes, closer to the end devices in order to reduce response time and latency. However, the placement of fog nodes, as well as the relative placement of the end devices each fog node serves, can affect the QoS obtained. This can be crucial to those services that have stringent QoS requirements. In this work, we analyze the effects that different placements of fog nodes have on QoS and present the problem of placing fog nodes to obtain an optimal QoS, with a focus on the Industrial Internet of Things domain because of its strict QoS requirements. We conclude that an optimized placement of the fog nodes can minimize latency to support the QoS requirements of IIoT applications.
Magnetic Microdevices for MRI-Based Detection of SARS-CoV-2 Viruses

Goal: To develop a micron-scale device that can operate as an MRI-based reporter for the presence of SARS-CoV-2 virus. Methods: Iron rod microdevices were constructed via template-guided synthesis and suspended in phosphate buffered saline (PBS). Heat-inactivated SARS-CoV-2 viruses were added to the samples and imaged with low-field MRI. Results: MRI of microdevices and viruses showed decreased signal intensity at low concentrations of viruses that recovered at higher concentrations. Electron micrographs suggest that reduced MRI intensity may be due to concentration-dependent shielding of water protons from local magnetic inhomogeneities caused by the iron microdevices. Conclusions: The preliminary results presented in this letter provide justification for further studies exploring the potential diagnostic role of magnetic microdevices in assessing the presence and concentration of SARS-CoV-2 viruses.
Modeling of a Cost-Effective Implementation and Utilization Scheme for Micro-Hybrid Plants in Rural Areas: A Case of Mayukwayukwa, Zambia

Energy plays a very important role in people's lives, at the same time it contributes to economic development and growth. A sustainable energy system, supplying clean, affordable and reliable energy, is required in line with almost 70% of Sustainable Development Goals (SDG) - SDG7 in rural areas where energy poverty is prominent due to no energy access. A model and simulation of various energy source combinations were conducted using HOMER Pro Software with data collected from a survey done in Mayukwayukwa, Kaoma district in Zambia. A review of current energy scenario and electrification plans for Mayukwayukwa is discussed and evaluated and the aim is to demonstrate the economic viability of hybrid micro power plants using local resources and provide alternative energy solutions that would meet the end-users energy demand. Biomass-solar PV combination is found to be an affordable, reliable, sustainable and continuous clean energy for a micro hybrid power plant.
Internet-of-Things-Based Smart Transportation Systems for Safer Roads

From the beginning of civilizations, transportation has been one of the most important requirements for humans. Over the years, it has been evolved to modern transportation systems such as road, train, and air transportation. With the development of technology, intelligent transportation systems have been enriched with Information and Communications Technology (ICT). Nowadays, smart city concept that integrates ICT and Internet-of-Things (IoT) have been appeared to optimize the efficiency of city operations and services. Recently, several IoT-based smart applications for smart cities have been developed. Among these applications, smart services for transportation are highly required to ease the issues especially regarding to road safety. In this context, this study presents a literature review that elaborates the existing IoT-based smart transportation systems especially in terms of road safety. In this way, the current state of IoT-based smart transportation systems for safer roads are provided. Then, the current research efforts undertaken by the authors to provide an IoT-based safe smart traffic system are briefly introduced. It is emphasized that road safety can be improved using Vehicle-to-Infrastructure (V2I) communication technologies via the cloud (Infrastructure-to-Cloud – I2C). Therefore, it is believed that this study offers useful information to researchers for developing safer roads in smart cities.
Model Predictive Control-Adaptive Neuro-Fuzzy Inference System Control Strategies for Photovoltaic-Wind Microgrid: Feasibility Review

Microgrids are increasingly gaining attention in the modern power industry. This is because it provides an attractive technology for utilizing distributed renewable energy resources such as Photovoltaic (PV) and Wind. The main problem currently facing PV-Wind microgrids is that of lower power quality due to their intermittent nature. This leads to variations in voltage and frequency which must be regulated and managed using a microgrid control system. This paper reviews the application of Model Predictive Control (MPC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control strategies to PV-Wind based microgrids. The outcome of the review was that these two control strategies can be hybridized to complement each other when applied in a Multilevel Microgrid Control System (MMCS). This improves the power quality of the microgrid in terms of better regulation of voltage and frequency. The research directions in this field lean towards the hybridization of artificial intelligence with analytical methods to optimize microgrid performance.
A Pattern Reconfigurable Water Leaky-wave Antenna with Conical Beam

A novel pattern reconfigurable pure-water leaky-wave antenna with conical beam for maritime wireless communications is presented in this paper. It consists of several concentric water loops mounted on a glass cylindrical base fed by a probe from the back. A conical beam can be achieved when the dimensions of the structure are properly designed. By controlling the water flow of the loops, angle of the conical beam can be changed. This paper presents two situations of the water flow. Simulated results show that the proposed pure-water antenna can change the angle of conical beam between 20° and 35°. It should be mentioned that more angles can be achieved by employing denser water loops. With the advantages of fluidity, the proposed antenna can be useful in maritime communication systems.
Driving Auxiliary Circuit Based on SiC MOSFET Series Disturbance Suppression

With the wide-band-gap devices SiC MOSFETs being more widely used in high-frequency switching environments, bridge arm series disturbances caused by parasitic capacitance and stray inductance have become more attractive. In order to solve this phenomenon, a new auxiliary circuit for suppressing series disturbance was proposed through the structure of passive components. This circuit combined the two solutions of driving impedance control and negative voltage shutdown to effectively suppress the phenomenon of series disturbances without reducing switching speed. First, the traditional methods of crosstalk suppression and the causes of crosstalk were introduced. Secondly, the working principle of new auxiliary circuit and the calculation of parameters were analyzed. Finally, an experimental platform was built to verify the feasibility of the auxiliary circuit. The experimental results show that the auxiliary circuit can effectively suppress the series disturbance phenomenon, and at the same time, the device turn-off speed can be increased by it.
Teaching Robots Novel Objects by Pointing at Them

Robots that must operate in novel environments and collaborate with humans must be capable of acquiring new knowledge from human experts during operation. We propose teaching a robot novel objects it has not encountered before by pointing a hand at the new object of interest. An end-to-end neural network is used to attend to the novel object of interest indicated by the pointing hand and then to localize the object in new scenes. In order to attend to the novel object indicated by the pointing hand, we propose a spatial attention modulation mechanism that learns to focus on the highlighted object while ignoring the other objects in the scene. We show that a robot arm can manipulate novel objects that are highlighted by pointing a hand at them. We also evaluate the performance of the proposed architecture on a synthetic dataset constructed using emojis and on a real-world dataset of common objects.
Accuracy Evaluation of SLAM Algorithms in RGB-D Sensors

Since visualization is a powerful technique to reinforce human cognition, real time robots can be used to study its environment to make a map or a model. Mapping is a fundamental task in navigation through unknown environments. Due to its increased application in robotics, surveillance, tracking system used in security and many other areas researchers have propelled to continuously experiment in more efficient and competitive techniques. In this paper we present an experimental evaluation for mapping system that robustly generates 2D maps using a RGB-D sensor. The evaluation was done for our mobile system which comprises a Kinect. This work mainly presents research on earlier related techniques for mapping evaluation and a comparison of SLAM algorithms. The paper concludes with the analysis of mapping accuracy with the ground truth. Experiment indicate that Gmapping gives an accurate solution for 2D mapping among many ROS compatible algorithms for computationally limited robots.