Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Temperature and Grafting on the Growth and Development of Tomato Plants under Controlled Conditions

Tomato (Lycopersicon esculentum Mill.) is a warm season vegetable crop with an optimum temperature for production of 28/22 oC (day/night). Heat stress is a major environmental stress that limits tomato production during summer under arid conditions. A variety of control measures and techniques including cultural practices in the field have been tested previously under Sudan conditions. Grafting of heat sensitive tomato cultivars could be used as scion, heat tolerant tomato or eggplant cultivars as rootstock to enhance heat tolerance into tomato plants. The objective of this study is to examine if there is any positive effect of grafting on the vegetative and reproductive development in tomato plants under heat stress conditions. The heat tolerant tomato cultivar ‘Summer set’ and the eggplant cultivar ‘Black Beauty’ as rootstock as well as the less heat tolerant tomato cultivar ‘UC 82-B’as scion were selected. Plants were grown under two temperature regimes 30/22 oC and 38/27 oC (day/night) in plant growth chambers at the Department of Vegetable Crops, Institute for Horticultural Sciences, Faculty of Agriculture and Horticulture, Humboldt University of Berlin. The experiments were set up in a complete randomized design with five replicates for each treatment. The following characteristics were recorded: leaf area, fresh and dry weight of leaves and stem, fresh and dry weight of roots, number of clusters, number of flowers, and the number of pollen grains per microscopic field. In addition, chlorophyll fluorescence and electric conductivity were measured, suggested as a screening technique for heat tolerance. Significant differences were encountered between treatment ‘UC 82-B/Black Beauty’ and ‘UC 82-B’ under 38/27 °C for chlorophyll fluorescence, electrolyte leakage and some vegetative and reproductive parameters. On the whole, grafting may have slightly positive effects on tomato vegetative growth and development under high temperature conditions.
Aerial Computing Offloading by Distributed Deep Learning in Collaborative Satellite-terrestrial Networks

With the rapid development of the Internet of Things (IoT) and its derivative technologies such as Aerial IoT (AIoT), the number of smart devices and their computing requirements have exploded. It makes it difficult for the existing ground base stations to guarantee the user's computing requirements and low latency requirements. The edge computing architecture of orbiting satellites, which provides feasible solutions to the above challenges. In this paper, we design a computing offloading strategy for users to minimize the weighted sum of latency and energy consumption in the cloud-edge-ground three layers hybrid AIoT architecture. Tasks can be calculated locally, on the satellite side, or forwarded to the cloud computing center. Then, the collaborative satellite-terrestrial network distributed offloading algorithm(CNDO) is proposed based on parallel neural networks to give multiple optimal offloading decisions in AIoT. Experimental results show that this scheme has a better performance compared with other comparison schemes.
A robust optimization method for IES with adjustable degree of conservation

Integrated energy system (IES) will become the main bearing forms of energy. Owing to the massive coupling, the energy structure and energy utilization efficiency of the IES are improved. In the meantime, IES's optimal scheduling is more complex than the traditional energy structure. The uncertainty of renewable energy, which consists of wind and PV, is a significant consideration affecting the safe operation of IES. For the sake of solving this problem, firstly, a robust constraint model of renewable energy with adjustable degree of conservation is established. Secondly, establishing the price-based demand response model to have the power load curve optimized. On this basis, the robust optimization model of industrial system with economy as the objective function is established. Finally, verifying the effectiveness of this method by giving a simulation example.
Unsupervised Infrared and Visible Image Fusion with Pixel Self-attention

In this paper, we propose a convolutional neural network (CNN) based unsupervised infrared and visible image fusion method. The proposed method optimizes both network structure and loss functions to obtain better fused images. Specifically, an effective pixel self-attention module is applied to emphasize the importance of different pixel locations of the feature map, which enables the network to better integrate the salient information in infrared images and the detail information in visible images. As to the loss function, we adopt the perceptual loss and texture loss to preserve the detail information as well as improve the visual perception of the fused image. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.
Multi-objective Evolutionary Algorithm for String SMT Solver Testing

String Satisfiability Modulo Theories (SMT) solver is widely used in academia and industry. The runtime efficiency of the solvers may have great impact on various software engineering tasks such as automated reasoning and formal verification. Although many studies have been conducted to test SMT solvers, they mainly focus on detecting soundness bugs of SMT solvers. In contrast, only very few studies concentrate on detecting performance defects of SMT solvers. Moreover, in the existing literatures, we observe two major barriers in generating test cases that trigger performance defects for string SMT solvers, i.e., the guidance information barrier and the diversity barrier.In this paper, we propose a multi-objective evolutionary algorithm, MulStringFuzz, to detect performance defects of string SMT solvers. The unique feature of MulStringFuzz lies in the combination of the multi-objective model and the diversity maintenance mechanism. To tackle the guidance information barrier, MulStringFuzz employs multiple objective functions, i.e., the running time, the code coverage, and the test case complexity to guide the test case generation. To tackle the diversity barrier, a tracing based crowding distance mechanism is proposed to ensure the diversity of generated test cases. Extensive experiments are conducted to evaluate the effectiveness of MulStringFuzz, and we investigate how each proposed mechanism contribute to the overall framework. The test cases generated by MulStringFuzz can cover nearly 5,000 more lines of code and trigger 3.25 times performance defects than StringFuzz, which shows that MulStringFuzz can effectively detect performance defect of the String SMT solver.
What Do They Want?: A Study of Changing Employer Expectations of Information Professionals

ABSTRACTThis paper reports the findings of an exploratory study of position vacant announcements appropriate for library and information studies (LIS) graduates appearing in the Sydney Morning Herald over a four week period in each of the following years: 2004, 1994, 1984 and 1974. The period studied witnessed change-demanding developments in information technologies as well as changes in workplace conditions and client expectations. The study collected data on the demands of employers as expressed through job advertisements that included data on work status (full-time, part-time, contract, casual), qualifications and the experience required of the information professional during the periods selected. To investigate similarities and differences between periods a content analysis and co-word analysis of the job advertisements was undertaken. The advertisements indicated a movement from simple advertisements in 1974 inviting applications for reference or technical services librarians, to complex and special.
Detection of Vital Signs Based on Variational Mode Decomposition Using FMCW Radar

In this paper, a non-contact method of human vital signs detection is proposed. Frequency modulated continuous wave (FMCW) radar is used, and the variational mode decomposition (VMD) algorithm is applied to the separation of respiratory and heartbeat components in the vital signs. The FMCW radar measures the weak chest surface vibration caused by human respiration and heartbeat movement. After signal windowing and pre-processing, the vital signs of human body at different distances are obtained, and the respiratory and heartbeat components are separated by VMD algorithm. Compared with empirical mode decomposition (EMD), adaptive ensemble empirical mode decomposition (CEEMDAN), the method based on VMD improves the accuracy of the separated instantaneous frequencies of respiratory and heartbeat components, and reduces the fluctuation of real-time vital sign detection results.
Biogas Purification and Bottling into CNG Cylinders: Producing Bio-CNG from Biomass for Rural Automotive Applications

The paper presents development of biogas scrubbing and bottling system for production of Bio-CNG to substitute compressed natural gas used in automobile and transport applications. Biogas can be used in automobiles after its purification (removing CO2, H2S and vapour present in it). A CO2 scrubbing and bottling technology has been designed and developed at Indian Institute of Technology Delhi based on physical absorption of CO2 in water at elevated pressure. The developed scrubbing system is able to remove CO2 from raw biogas when pressurized raw biogas was fed into the packed bed scrubbing column and pressurized water is sprayed from top in counter-current action. The enriched biogas was compressed up to 20 MPa pressure using a three stage compressor after moisture removal and filled in special high pressure steel cylinders (as used in CNG filling). The enriched biogas filled CNG cylinders were tested in a vehicle. Biogas enrichment and compression system can be recommended for large size biogas plants as there is sufficient biogas available for bottling, thus make it an economical venture of methane farming. The technology ensures sustainable development and energy security with employment generation in rural areas using cattle dung and biomass.
Fault-Tolerant Trajectory Tracking Control of a Quadrotor Suffering a Complete Rotor Failure

The problem of fault-tolerant position tracking control is proposed for a quadrotor under a complete rotor failure in this paper, which sacrifices the yaw control due to the underactuation. The hierarchical control scheme consists of a model predictive control (MPC) method to track the reference trajectory by taking the actuator saturation into account, together with an incremental nonlinear dynamic inversion (INDI) based inner controller to stabilize the reduced attitude of the quadrotor and eliminate the effect of external disturbance. A detailed aerodynamic model may be obtained from sensor measurements, which also reduces the model dependency of the INDI approach. To demonstrate the efficiency of the proposed method, numerical simulations are carried out for a quadrotor under external disturbance, model uncertainty and a complete rotor failure.
Cross-Polarization suppression by Comb-Shaped slots in Microstrip Patch

Cross-polarization (XP) of the microstrip antenna was suppressed by etching comb-shaped slots in the patch. By cutting longitudinal comb-shaped slots on the rectangular patch, the transverse current at the patch edges that lead to cross-polarization was dammed up. Then, the co-to cross-polarization (CP-XP) ratio over entire angular range around the broadside direction was increased from 20 dB of the rectangular microstrip antenna (RMA) to 35 dB of the defected patch structure (DPS). Moreover, as the longitudinal current was little affected by the slots, the proposed structure has little effect on the other performances of the antenna (including gain, directivity, radiant principle and impedance bandwidth). As the presented structure is achieved by simple adding slots to the rectangular patch, it is very easy to be fabricated.
A Frequency-Reconfigurable Open-loop Antenna Based on Liquid Metal Alloy

A frequency-reconfigurable open-loop antenna based on liquid metal alloy (LMA) is proposed in this work. The Polymethylmethacrylate (PMMA) structure with two channels is loaded on the substrate of F4BM, providing a condition for frequency-reconfigurable. The lower frequency band can be obtained by injecting LMA into the outer channel and higher frequency band can be obtained by injecting LMA into inner channel. The Simulated impedance bandwidth ranges from 2.08-2.12 GHz and 3.5-3.59 GHz, respectively.
Speech Emotion Recognition Based on Mixed MFCC

Due to MFCC characteristic parameter in speech recognition has low identification accuracy when signal is intermediate, high frequency signal, this paper put forward a improved algorithm of combining MFCC, Mid-MFCC and IMFCC, using increase or decrease component method to calculate the contribution that MFCC, Mid-MFCC and IMFCC each order cepstrum component was used in speech emotion recognition, extracting several order cepstrum component with highest contribution from three characteristic parameters and forming a new characteristic parameter. The experiment results show that under the same environment new characteristic parameter has higher recognition rate than classic MFCC characteristic parameter in speech emotion recognition.
6-DOF grasp planning of manipulator combined with self-supervised learning

To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained; it is then used to train the grasp-quality classification model for the manipulator. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator.
Investigation of the Influence of Speckle Noise on the Accuracy of Object Detection by Convolutional Neural Networks

Synthetic aperture radars are widely used to obtain high-resolution radar images without the influence of weather and lighting conditions, at large distances and in a wide swath. Object detection and classification is one of the main tasks in radar image processing. One of the ways to do this is to use convolutional neural networks. However, neural network training requires a large set of images (dataset). There are few radar image datasets in the public domain and they are not labeled. This article discusses the possibility of using convolutional neural networks trained on optical images for detecting and classifying objects in radar images. Optical and radar images have both similarities and differences. One important feature of SAR images is a high level of speckle noise that is not present in optical images and must be reduced. Therefore, the influence of speckle noise on the detection accuracy is investigated.
Indoor Dynamic Scene Visual SLAM Based On Human Detection And Geometric Constraints

The existence of dynamic objects dramatically depresses the performance of localization and mapping of SLAM systems. In this paper, we propose a novel indoor dynamic visual SLAM framework based on human detection and geometric constraints to solve the problem caused by the dynamic object. We first use semantic segmentation model, DeeplabV3, to detect human and then use spatial geometric constraints to further remove dynamic feature points. Several experiments on TUM datasets and Bonn datasets have shown that our performances, in terms of absolute trajectory error (ATE), are 91% to 99% higher than that of ORBSLAM2.
Bearing-based Formation Control of Multi-agents with Dual-leaders Structure

In this paper, bearing rigidity theory and Routh-Hurwitz stability theory are used to analyze the formation maneuvering of the multi-agent system with dual leaders structure. Different control laws are designed for the second leader and followers respectively and the global formation stability is analyzed. The control law of the followers is distributed because it only needs neighbor information. In the target formation, followers are subjected to bearing constraints while leaders are subjected to bearing and distance constraints. The formation scaling maneuvering can be achieved by adjusting the distance constraint, and the control of the formation's orientation and rotation can be achieved by adjusting the bearing constraint between two leaders. Finally, some simulation results are provided to verify our analysis.
A Comprehensive Evaluation for Burr-Type NHPP-based Software Reliability Models

In this paper, we summarize the so-called Burr-type software reliability models (SRMs) based on the non-homogeneous Poisson process (NHPP) and comprehensively evaluate the model performances by comparing with the existing NHPP-based SRMs. Two kinds of software fault count data are considered; fault-detection time-domain data and fault-detection time-interval data (group data). For 8 data sets in each fault count type, we estimate the model parameters by means of the maximum likelihood estimation and evaluate the performance metrics in terms of goodness-of-fit and prediction. It is shown that the Burr-type NHPP-based SRMs could show the better performances than the existing NHPP-based SRMs in many cases.
Detection of False Data Injection Attacks in smart grids based on cubature Kalman Filtering

The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.
Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.
A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics

In order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28 ± 42ms, and the mean absolute difference of TO is 27 ± 43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.
Is Social Diversity Related to Misinformation Resistance? An Empirical Study on Social Communities

Online misinformation has become a pervasive issue that threatens to disrupt humanitarian efforts during times of crisis by overloading the public discourse with conflicting information and sowing distrust in credible sources. While every individual is susceptible to misinformation, social media groups tend to have clusters of individuals with similar levels of misinformation resistance. The goal of this research is to examine the correlation between the diversity characteristics of social media groups and the groups' misinformation resistance. This project utilizes the CrowdTangle API, a tool to gather data from Facebook groups, along with machine learning models, to generate quantitative measures of different types of diversity in Facebook groups and their correlation with misinformation resistance. We were able to find correlations between some diversity metrics with misinformation resistance, which may be useful knowledge for future research on ways to counteract the impact of misinformation.
A Parametric Simulative Study for Si and SiC Semiconductor Devices Under Various Accelerated Testing Conditions Using Rate- and Temperature Dependent Inelastic Material Data

The usage of wide-bandgap semiconductor material such as silicon carbide and gallium nitride for power modules in electric vehicle applications is accompanied by higher power densities and higher operating temperatures compared to standard power modules employing silicon-based technology. This demands for new packaging materials like sintered silver which is able to cope with the higher thermo-mechanical stress. Within this study, comparative finite element simulations for silicon and silicon carbide as die material are presented. In addition, the material data used is gained from tensile tests and the viscoplastic behavior of sintered silver is modeled with the Anand model.