Miracle-dz/UniFAR-FTUs huggingface dataset:
DRIFT-POTENTIAL-INDEX : EINE NEUE KENNZAHL ZUR BEURTEILUNG VON PFLANZENSCHUTZDUSEN HINSICHTLICH IHRES ABTRIFTPOTENTIALS

Es wird eine Kennzahl vorgestellt, mit deren Hilfe im Pflanzenschutz eingesetzte Zerstauber hinsichtlich ihres Abtriftpotentials beurteilt werden konnen - der Drift-Potential-Index ( DIX ). Diese Kennzahl basiert auf der theoretischen Uberlegung zur Abtriftentstehung und -ausbreitung, wonach das Abtriftrisiko sowohl von der driftfahigen Menge als auch von der Hohenverteilung dieser Menge in der Driftwolke im Nahbereich der Duse abhangt. Diese Komponenten des DIX konnen durch relativ einfache Messungen an Einzeldusen in einem Windkanal bestimmt werden. Die Darstellung dieser Ergebnisse in einem doppeltlogarithmischen Diagramm erlaubt eine einfache Einordnung der Zerstauber in noch zu definierende Drift-Potential-Klassen. Es wird gezeigt, das sowohl die Modellvorstellungen als auch die im Windkanal ermittelten DIX -Werte gut mit Abtriftmesergebnissen aus Freilandversuchen korrelieren. Drift Potential Index - a new parameter for the evaluation of agricultural nozzles concerning their dritt potential A parameter is presented that enables an evaluation of agricultural atomisers conceming their drift potential - the Drift Potential Index ( DIX ). This parameter is based on a theory of drift rise and spreading. Following this theory, the drift risk depends upon the driftable amount as weil as on its vertical distribution near the nozzle. These DIX components can be determined using relative simple methods in a wind tunnel. Representing these results in a log-log diagram, it is easy to arrange atomisers into drift potential classes. lt is shown that the theory as weil as the DIX values from wind tunnel measurements correlate well with drift measurement results from field trials.
Reliable and Failure-Free Workspaces for Motion Planning Algorithms for Parallel Manipulators Under Geometrical Uncertainties

Manufacturing tolerances and other uncertainties may play an important role in the performance of parallel manipulators since they can affect the distance to a singular configuration. Motion planning strategies for parallel manipulators under uncertainty require decision making approaches for classifying reliable regions within the workspace. In this paper, we address fail free and reliable motion planning for parallel manipulators. Failure is related to parallel kinematic singularities in the motion equations or to ill-conditioning of the Jacobian matrices. Monte Carlo algorithm is employed to compute failure probabilities for a dense grid of manipulator workspace configurations. The inverse condition number of the Jacobian matrix is used to compute the distance between each configuration and a singularity. For supporting motion planning strategies, not only failure maps are constructed but also reliable and failure-free workspaces are obtained. On the one hand, the reliable workspace is obtained by minimizing the failure probabilities subject to a minimal workspace area. Differently, a failure-free workspace is found by maximizing the workspace area subject to a probability of failure equal to zero. A 3RRR manipulator is used as a case study. For this case study, the usage of the reliable strategy can be useful for robustifying motion planning algorithm without a significant reduction of the reliable regions within the workspace.
The impact of pandemic and infectious diseases in economic growth: a neoclassical point of view

: We develop a neoclassical growth model introducing a Richard production function to describe the shock of pandemic and infectious diseases aﬀecting the supply side. e model shows that in the proportion of exogenous shocks, countries with low-level technologies in the productive sectors reduce the labor productivity and, under some conditions, may fall into a poverty trap. Countries with high-level technology also reduce labor productivity but have a faster recovery. We show that incorporating modern technology and improvements in human capital are necessary conditions to overcome the poverty trap.
STUDENTS’ ANXIETY IN LEARNING ENGLISH

This research was about students’ anxiety in learning English by students of SMPN 03 Mempawah Hilir, West Kalimantan. Anxiety in learning English is often reported to negatively affect English learning. This research aims to determine the level of anxiety and the factors that dominate the anxiety of students at SMPN 03 in Mempawah Hilir, West Kalimantan in learning English, which will then be carried out
Towards Conversational Co-Creation of Learning Content in Digital Higher Education

Today formal education like higher education relies on digital learning content like learning videos or quizzes. Using such online learning material enables students to learn independently from time and place. While improvements have been made, there are still many issues as the two-year long crisis in 2020 has revealed. Many offerings do not consider the learners’ needs and can result in unsuccessful learning. One way to address these short comings is to actively include learners in the creation process of learning content. However, co-creation oftentimes relies on face to face and or group settings that may not be possible for all students at all times. Therefore, we undertake a long-term action design research project to investigate the novel concept of conversational co-creation of learning material using a conversational agent and persuasive design to engage and motivate learners. In this article we present an early-stage prototype and concept of conversational co-creation.
A Multi-Heuristic Search-based Motion Planning for Automated Parking

In unstructured environments like parking lots or construction sites, due to the large search-space and kinodynamic constraints of the vehicle, it is challenging to achieve real-time planning. Several state-of-the-art planners utilize heuristic search-based algorithms. However, they heavily rely on the quality of the single heuristic function, used to guide the search. Therefore, they are not capable to achieve reasonable computational performance, resulting in unnecessary delays in the response of the vehicle. In this work, we are adopting a Multi-Heuristic Search approach, that enables the use of multiple heuristic functions and their individual advantages to capture different complexities of a given search space. Based on our knowledge, this approach was not used previously for this problem. For this purpose, multiple admissible and non-admissible heuristic functions are defined, the original Multi-Heuristic A* Search was extended for bidirectional use and dealing with hybrid continuous-discrete search space, and a mechanism for adapting scale of motion primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A* algorithm is benchmarked against a very popular heuristic search-based algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in both terms, computation efficiency and motion plan (path) quality.
Applicability of Python-based geospatial analysis for operational assessment of weather station deployment

This paper examines the feasibility of utilizing Python packages such as pandas, elevation, geopy, suncalc, gdal, and others to conduct quick geographical analyses of terrain for various field tasks. The paper explores the capabilities of these packages to obtain SRTM model data, analyze terrain features, and present results in a clear visual format. The focus of the paper is on the analysis of ad hoc weather stations and terrain features such as elevation ratios, slope, exposure, hillshading, and visibility to cardinal directions. A step-by-step timeline of the entire processing is provided, along with code snippets required for each analysis. Although the paper acknowledges the importance of GIS, it offers a practical, license-free alternative for operational use. The provided code has the potential to be utilized in the development of independent tools that could be incorporated into various operational applications for diverse field tasks.
Edge-Based Resilient Consensus Control of Islanded Microgrids

We present a resilient output voltage recovery strategy for AC microgrids (MGs). In the presence of network attacks, distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults in their actuators. These fault parameters can be heterogeneous and time-varying, posing significant challenges to voltage regulation. To address this, our approach incorporates adaptive techniques to handle the fault parameters while avoiding their direct dependence. Furthermore, we utilize boundary layers to design smooth controllers that mitigate the adverse effects of non-smooth controllers, ensuring stable and precise voltage regulation. Through extensive simulations, we validate the effectiveness of our proposed strategy in achieving reliable and robust load voltage regulation in AC MGs.
Research on Transmission Characteristics of Electromagnetic Signal in Downhole

In order to evaluate the transmission characteristics of electromagnetic signal of EM-MWD system in downhole, a novel theoretical model for prediction the transmission capacity of electromagnetic MWD system has been developed. Firstly, the theoretical model of electromagnetic signal transmission channel in downhole is established with drill string, casing, drill-bit, drilling fluid and formation being taken into account. Secondly, the model is analyzed in detail with COMSOL multiphysics platform. The simulation result shows that(i) the amplitude of received electromagnetic signal attenuates obviously with the increase of transmission depth;(ii) formation resistivity also has a great influence on EM-MWD transmission depth. Extremely high or low formation resistivity is not conducive to the transmission of electromagnetic signals. In addition, conductivity, inner radius and thickness of drill string also has a certain impact on the transmission of electromagnetic signal. The simulation model can be applied to predict the transmission capacity and transmission depth of electromagnetic MWD system.
A Method for Detecting Shallow Cavities in Roadbeds Based on Deep Learning and Ground Penetrating Radar Measurement Data

In the course of road use, cavity disease in the roadbed area is often a direct threat to the structural stability of the road. This is because unfavourable geological conditions in various roadbed areas often end up forming cavities, making it an important issue to investigate how to intelligently detect cavities in roadbeds. To this end, an experimental road has been built specifically for radar detection and a large number of cavity models of varying sizes and depths have been embedded in the roadbed area of the experimental road. Using a known real model and a software simulation model built partly by the time-domain finite difference method as the object of study, a deep learning framework was used to construct a mapping relationship between ground-penetrating radar images and subsurface dielectric constant maps, identifying the limitations of the current study, which has served to advance the application of deep learning in the analysis of road ground-penetrating radar images.
Incremental Call Graph Construction in Industrial Practice

Interprocedural program analysis is critical in finding hidden program defects and vulnerabilities in CI/CD pipelines. A pre-constructed call graph is a prerequisite for interprocedural analysis. However, the exhaustive call graph construction, i.e., analyzing the target program as a whole and constructing from scratch, often takes too much time. We made a scalable empirical study on both industrial and open-source projects and observed that most program updates only involve a very limited part of the code. The observation inspires an efficient approach that not wholely re-constructs a call graph but incrementally patches the old one with the partial graph affected by the update. We propose a sound incremental call graph construction algorithm that works in a reset-recompute way: first, prune invalid nodes and edges from the old call graph, then analyze the new code to patch it to construct the new one. We implemented the algorithm and built a benchmark suite consisting of 20 industrial and 10 open-source projects. The experimental evaluation shows that the efficiency improvement is encouraging. Compared with the exhaustive construction algorithm, the incremental way can speed up the construction by 20.0 times and reduce the memory and storage consumption to 58.1% and 10.4%, respectively.
Towards Automated Embedded Systems Programming

Writing code for embedded systems poses unique challenges due to hardware involvement. Developers often need to learn domain-specific knowledge to write embedded codes. Learning such knowledge is time-consuming and hinders de-velopers' productivity. This paper presents a proposal for an automated code generation approach, specifically designed for embedded systems. The work is composed of three milestones, i.e., understanding the needs of embedded developers by analyzing posts from discussion forums, developing a tool to recommend driver libraries of 110 hardware and generate its interface configurations and usage patterns, and improving the generation accuracy of the prior tool using program analysis techniques. The tool will be evaluated using various metrics from machine translation. classification. and information retrieval fields.
Online Fault-Tolerance for Memristive Neuromorphic Fabric Based on Local Approximation

Neural networks (NNs) are a widely-used problem-solving tool, but their high computational and power consumption makes them expensive. Computation-in-Memory (CiM) architecture, which uses resistive non-volatile memories, is a promising solution due to its high energy efficiency. However, manufacturing defects and in-field faults can reduce the reliability and inference accuracy of CiM-implemented neural networks. Existing sophisticated fault detection and tolerance techniques require long downtime for testing and repair. In certain applications, e.g., "always on" NN applications, such downtime may not be acceptable. Thus, in this paper, a low-cost online fault tolerance technique based on local approximations is proposed to ensure continuous neural network operation with acceptable accuracy. Our approach reduces hardware overhead by up to 99.37% compared to conventional redundancy-based approaches while still achieving accuracy within 2% of the trained NNs.
Synchronous Rectification for LLC Converter Based on Detecting Resonance Voltage

The early or later turned-on and turned-off has existed in the previous synchronous rectification (SR) method that cause the extra loss, emergence of circulating currents and so on. Therefore, an Analog-Digital Mixed Control (ADMC) that drives the SR MOSFETs by detecting resonance voltage has been proposed in this paper. First, the SR method is applied based on the model considering the parasitic capacitance which is closer to real-time dynamic response. Secondly, the driving signal for SR MOSFETs is generated by comparing with the pre-set voltage and the resonance voltage to avoid the disturbance from magnetic inductance and get high-precision performance. Furthermore, this method has combined digital and analog to prevent the mis-conduction which happens in the traditional artificial SR methods. This SR method has been proved by a 400V-36V-lkW prototype.
Model-Free Predictive Current Control for Three-Phase AC/DC Converters Based on Hybrid SVM to Minimize Current Harmonics

Model-based predictive current control based on space vector modulation (SVM-MBPCC) is widely used in three-phase pulse-width-modulated (PWM) AC/DC converters because of its good steady-state performance. However, the conventional SVM-MBPCC method depends on the accuracy of system model and parameters. Recently, model-free predictive current control based on SVM (SVM-MFPCC) has been proposed to solve the problem of parameter robustness. However, its steady-state performance is inferior to that of SVM-MBPCC with accurate parameters. This paper combines the conventional SVM-MFPCC with a hybrid SVM method (HSVM-MFPCC), which inherits the strong parameter robustness and reduces current harmonics significantly by selecting the optimal vector sequence of SVM online. The simulated results indicate that the proposed HSVMMFPCC can reduce the current total harmonic distortion (THD) by 27.4% than conventional SVM-MFPCC in high range of modulation indexes.
The Concept for High-Resolution ISAR-based Traffic Surveillance System

The paper presents a concept of classifying objects moving on a predefined or estimated trajectory based on their ISAR images. The main idea is to use model-based techniques with 3D CAD models of ground targets and modeled movement along an approximately known trajectory. An example of such a setup is an analysis of road traffic, where the aim is to classify different vehicles moving along the road, and the road layout constrains the trajectory partially. Still, the exact movement path is not known a priori. Thus, either the trajectory knowledge has to be refined from the acquired data, or the model needs to be robust enough to the trajectory variation. The concept presentation is backed by the results of simulations making use of a realistic SAR/ISAR signal simulator.
Model Predictive Torque Control of SRMs with Current Reconstruction for Split-DC Power Converters

In this paper, a model predictive torque control method combining current reconstruction techniques for split-DC power converters is proposed for the torque ripple suppression of the switched reluctance machine (SRM). The proposed current reconstruction method does not require topology modification, and the phase currents are obtained through a combination of the collected neutral current and total currents according to the SRM operating region. This method does not require pulsation injection and therefore avoids the appearance of current distortion and loss of efficiency. Based on the reconstruction phase current, a new model predictive control (MPC) applied to split-DC converters is implemented to minimize the torque ripple of the SRM. Simulation results show that the proposed method can effectively reduce torque ripple with fewer power components and current sensors.
Virtual Vectors Based Model Predictive Control for Single-Phase Cascaded H-Bridge Converters

This paper presents a model predictive control (MPC) scheme for grid-connected cascaded H-bridge (CHB) converters with extra designed virtual voltage vectors. To reduce the computational burden of MPC strategies for multilevel converters, a pre-selection method is proposed to identify redundant voltage vectors. Considering the computation time, switching frequency and performance quality, virtual voltage vectors constructed from real voltage vectors are proposed in this modified control strategy. The proposed control method has good performance, including the low total harmonic distortion of the grid current, fast response speed under the dc-link voltage reference variation. Simulation results on a developed CHB multilevel converter prototype with two power cells are conducted to validate the theoretical effectiveness of the proposed method.
Distributionally Robust Proximal Policy Optimization for Unit Commitment with Wind Power Uncertainty

Unit commitment problem plays an essential part in the electricity market. The uncertainty of wind power and power demand increases the difficulty of solving the unit commitment problem. Although the traditional method can effectively solve this problem, the computation time grows exponentially with the increasing scale. To overcome this difficulty, we use deep reinforcement learning (DRL) methods by the means of the formulation of the UC problem as a Markov decision process and propose a DRL-based algorithm to solve the problem. Meanwhile, we use distributionally robust optimization to improve the DRL method robustness. The IEEE 39-bus test case is used to conduct numerical results and demonstrate the method’s effectiveness. Finally, we compare it with some existing solutions to verify its effectiveness and superiority.
FlakyCat: Predicting Flaky Tests Categories using Few-Shot Learning

Flaky tests are tests that yield different outcomes when run on the same version of a program. This non-deterministic behaviour plagues continuous integration with false signals, wasting developers’ time and reducing their trust in test suites. Studies highlighted the importance of keeping tests flakiness-free. Recently, the research community has been pushing towards the detection of flaky tests by suggesting many static and dynamic approaches. While promising, those approaches mainly focus on classifying tests as flaky or not and, even when high performances are reported, it remains challenging to understand the cause of flakiness. This part is crucial for researchers and developers that aim to fix it. To help with the comprehension of a given flaky test, we propose FlakyCat, the first approach to classify flaky tests based on their root cause category. FlakyCat relies on CodeBERT for code representation and leverages Siamese networks to train a multi-class classifier. We train and evaluate FlakyCat on a set of 451 flaky tests collected from open-source Java projects. Our evaluation shows that FlakyCat categorises flaky tests accurately, with an F1 score of 73%. Furthermore, we investigate the performance of our approach for each category, revealing that Async waits, Unordered collections and Time-related flaky tests are accurately classified, while Concurrency-related flaky tests are more challenging to predict. Finally, to facilitate the comprehension of FlakyCat’s predictions, we present a new technique for CodeBERT-based model interpretability that highlights code statements influencing the categorization.
Tank Barrel Surface Damage Detection Algorithm

The article proposes a new algorithm for detecting damaged areas of the tank barrel based on image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrel. The algorithm also calculates volume of the detected damage from the depth map generated for example from the distance measurement unit. The proposed method was tested on data obtained by tank barrel bore explorer, which generate both surface image data and depth map. The Barrel bore explorer device is also discussed in the article.
Hybrid Ring Generators for In-System Test Applications

Ring generators are high speed devices formed by transformations that alter the structure of conventional linear feedback shift registers (LFSRs) while preserving a transition function of the original circuits [8]. They feature a reduced number of levels of XOR logic, minimized internal fan-outs, and simplified layout and routing. This paper discusses hybrid ring generators – a new class of lightweight linear finite state machines. While they use the principal design rules of conventional ring generators, the new devices can reduce the number of XOR gates up to seven times compared to conventional rings implementing the same characteristic polynomial. It makes a substantial contribution toward the performance of linear circuits used in a variety of test applications. Several issues related to hybrid ring generators such as designing MISRs, programable PRPGs, or phase shifters are also discussed in the paper along with data providing architectural details of hybrid ring generators for sizes up to 256 bits.