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The presence of weeds at the beginning of cultivation can negatively affect cassava productivity because of the slow initial development of the crop. To minimize weed interference, pre-emergence herbicides can be used, provided they are selective to crops. In this study, we evaluated the selectivity and efficacy of herbicides applied in pre-emergence of cassava crop in weed control. Two experiments were conducted in the field during the years 2017, 2018 and 2019. Each year, ten different herbicides were evaluated, along and untreated controls. In the 2017 efficacy experiment, the mixture of atrazine + s-metolachlor provided the highest rate of weed control at 42 days after application (DAA) (76%). The herbicides sulfentrazone, flumioxazin, clomazone, s-metolachlor and metribuzin caused the lowest poisonings in 2017, with rates below 20% in all evaluation periods. Diuron and metribuzin reduced cassava yield. In the second experiment, only the combinations of ametryn + clomazone and ametryn + flumioxazin maintained control levels greater than 85% in all evaluations. Cassava plants experienced less than 25% poisoning for all herbicides tested. However, the use of linuron and the application of paraquat alone did not result in yield reductions. Regarding the relative productivity of the roots, in 2017, only ametryn + clomazone did not result in reductions, while in 2018, the combination s-metolachlor + clomazone was the only one that did not reduce productivity. The results indicate selective and effective herbicides can help reduce production costs and increase yield in cassava crops.

Quantum Key Distribution (QKD) systems have the potential to provide secure communication by offering unconditionally secure key exchange between distant parties. However, the integration of QKD into existing communication networks presents challenges, particularly when it comes to the deployment of trusted nodes and the establishment of secure connections over metropolitan distances. In this study, we present a novel approach to address these issues by introducing a trusted-node-free design on a metropolitan scale for the seamless integration of a QKD system into the Berlin OpenQKD optical fiber testbed. Our innovative design leverages conventional telecom modulators and a standard Wavelength-Division Multiplexing - Arrayed Waveguide Grating (WDM-AWG), offering a practical solution for flexible multi-partner communication and enabling full connectivity between non-adjacent nodes. In this study, we not only observed relatively low QBER ($< $ 5.5%) but also a rather stable key rate between nodes in the Qline system over approximately 130 hours ($\approx$ 5.4 days) for a lab-based back-to-back setup. Later on, a more complex experimental scheme is integrated into Berlin OpenQKD optical fiber testbed encompassing multiple C-band frequencies (191.75–195.75 THz) and extending over metropolitan distances (up to 39.8 km), providing a foundation for secure quantum communication networks with enhanced scalability and versatility.

Modern wind turbines with increasing power levels are continuously emerging. These power levels are reaching a point where excessively high currents are obtained if the traditional low-voltage wind-turbine structure is adopted. High currents lead to excessively high losses and the need for bulky and heavy conductors to carry them. A medium-voltage structure should be a more competitive alternative to be adopted in high-power wind turbines. In this article, a new converter solution with a modular multilevel structure suitable for driving modern/future high-power medium-voltage wind turbines is proposed. This converter topology has desirable features common to modular multilevel converters, such as the improved reliability at high voltage levels, and the possibility to synthesize high-power-quality staircase-shape voltages, which leads to low requirements for harmonic and ${dv}/{dt}$ filters. Furthermore, the proposed converter presents reduced capacitance and inductance requirements compared with other well-established modular multilevel converter topologies, which should result in a lighter and more compact solution. The proposed solution could potentially allow for the reduction of the overall costs of the supporting structure required to withstand the weight of the wind turbine. Simulation and experimental results are presented in this article to validate the proposed topology. [ABSTRACT FROM AUTHOR]

The seismic surveys conducted by the oil and gas sector result in very large datasets, often exceeding terabytes of data, leading to high costs and technical challenges regarding storage and transmission of such large quantities of data. Therefore, data compression is crucial to address the challenges related to communication and storage demands. This paper investigates a compression strategy based on the combined application of the Discrete Cosine Transform and Principal Component Analysis and its ability to achieve higher compression ratios than the application of each of those methods alone. A theoretical motivation for the increased compression performance is presented, emphasizing that the application of Principal Component Analysis to signals in the transformed domain make the resulting signal more suitable for the posterior Thresholding, Quantization and Entropy Encoding steps. The proposed method was evaluated using a dataset containing passive seismic data collected during an oil and gas reservoir monitoring survey, achieving compression ratios of up to 1000:1 with a normalized reconstruction residue energy of less than 20%, relative to the original signal energy, for the more relevant frequency range between 0 and 20 Hz, outperforming other seismic data compression strategies considered in the literature.

Maritime operations, such as ship hull inspections, infrastructure assessments, and rapid response to emergencies, often expose humans to hazardous environments. The deployment of unmanned vehicles offers a potential solution to mitigate risks and reduce operational costs in such scenarios. Collaborative efforts between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) can enhance mission endurance and portability. This paper introduces a cooperative UAV-USV system designed to expand the operational coverage area. It leverages off-the-shelf satellite-based technology to establish robust communication links between UAVs, USVs, and the ground station. This connection facilitates the development of an autonomous launch and recovery system, relying on GPS and visual servoing. Furthermore, the system incorporates a Battery Hot Swap-system (BHS) on the USV, allowing the UAV to resume its mission within 3 minutes. The effectiveness of this proposed solution was demonstrated through successful inspection trials conducted at the port of Hamburg, Germany.

Open-set segmentation has caught the community’s attention only in the last few years, and it is a growing and active research area with many challenges ahead. To better identify open-set pixels, we address two known issues by improving data representation and ensuring semantic consistency in open-set predictions. First, we present a method called Open Gaussian Mixture of Models (OpenGMM) that allows for multimodal statistical distributions in known class pixels using a Gaussian Mixture of Models instead of unimodal approaches, like Principal Component Analysis. The second approach improved semantic consistency by applying a post-processing technique that uses superpixels to enforce homogeneous regions to have similar predictions, rectifying erroneously classified pixels within these regions and providing better delineation of object borders. We also developed a novel superpixel method called Fusing Superpixels for Improved Semantic Consistency (FuSC) that produced more homogeneous superpixels and enhanced, even more, the open-set segmentation prediction. We applied the proposed approaches to well-known remote sensing datasets with labeled ground truth for semantic segmentation tasks. The proposed methods improved the highest AUROC quantitative results for the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets. Using FuSC, we achieved novel open-set state-of-the-art results for both datasets, improving AUROC results from 0.850 to 0.880 (3.53%) for Vaihingen and 0.764 to 0.797 (4.32%) for Potsdam datasets. The official implementation is available at: https://github.com/iannunes/FuSC.

This paper presents a control technique for a boost converter that is connected to a power source via an LC input filter in a continuous conduction mode. The proposed method employs the Lyapunov stability criterion to determine the switching command that guarantees system stability. An estimator is introduced to track the load resistance and losses of the system in real-time. Our control strategy regulates all state variables of the system and additional state variables in the estimator concurrently. Additionally, the start-up transient response is considered in the control algorithm. Finally, experimental results are provided to validate the effectiveness of the proposed control method.

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration; modified feedback, in which we applied a hidden augmentation of error to these probabilities; and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that relative to the baseline, the modified feedback condition led to significantly improved accuracy. Class separation also improved, though this trend was not significant. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS ‘spot’ over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

Control-inspired design, as the name suggests, involves drawing inspiration from control theory to design other engineering systems. Engineers may use the principles of feedback control to design systems that can adapt and self-correct in response to changing conditions. This technique is known as Control Co-design (CCD), and it focuses on the redesign of dynamics and subsystem interactions. CCD offers several benefits, such as improved performance, reduced design time and cost, and increased reliability, and has been applied to a variety of areas. In this paper, we present a review of 197 articles related to CCD and highlight the main topics of its applications, such as renewable energy, vehicular and aircraft control systems and communication systems in control. We delimit the applications of CCD in the field of engineering, providing an introductory understanding of this topic and presenting the main works developed in this field in recent years, as well as discussing the tendencies and benefits of CCD. The paper offers an in-depth conceptualisation of CCD. A theoretical example is provided to illustrate CCD’s application in a Hybrid Wind-Wave Platform (HWWP), detailing the interaction between aerodynamic and hydrodynamic design domains and their control challenges, along with discussions on simultaneous and nested CCD formulations.

High-performance computing (HPC) systems need to handle ever-increasing data sizes for fast processing and quick response times. However, modern processors’ caches are unable to handle massive amounts of data, leading to significant cache miss penalties that affect performance. In this context, selecting an effective cache replacement policy is crucial to improving HPC performance. Existing cache replacement policies fall short of Bélády’s optimal algorithm, and we propose a new approach that leverages the coherence state and sharers’ bit-vector of a cache block to make better decisions. We suggest a reinforcement learning-based strategy that learns from past eviction decisions and applies this knowledge to make better decisions in the future. Our approach uses a next-attempt method that combines the results from classic cache replacement algorithms with reinforcement learning. We evaluated our approach using the Sniper simulator and seven kernels from CAP Benchmarks. Our results show that our approach can significantly reduce the cache miss rate by 41.20% and 27.30% in L1 and L2 caches, respectively. In addition, our approach can improve the IPC by 27.33% in the best case and reduce energy consumption by 20.36% compared to an unmodified policy.

This study investigates the commandability in a DC-DC converter for fuel cell applications. In this modular system, each module is established on a three-level converter. The operation areas of the studied converter in terms of commandability have already been investigated. In this paper, a manner to increase the commandable area is suggested. An anti-windup scheme is used to enhance stability under duty-cycle saturation. An indirect sliding-mode controller is utilized to balance DC voltages and control currents with dynamical properties regardless of the operating settings. Simulations and experiments consistent to the theoretical study are conducted to confirm the suggested scheme.

Considering the importance of education especially for the newer field of education, the current study proposes a novel teaching method based on storytelling that aims to enhance the creativity of students and improve the effectiveness of learning. The method was applied and analyzed through a one-year teaching course case study to introduce topics regarding renewable energy. Despite traditional education, in which learning occurs through the direct transfer of information, here a plot with the theme of mountaineering and nature using real photos and narration of a fictional story was set for the implementation of the proposed method. Therefore, the unintentional consequences of conventional teaching including quick content forgetting, failure to define the main purpose, improper reaction, and low attractiveness and even effectiveness were prevented. Considering the affective and cognitive domains of Bloom’s Taxonomy as well as analyzing the performances of students during the teaching course and their final feedback, the results showed promising improvements including more participation (100%), better retention of content (receiving (75%) and remembering (83.33%)). While the learners preferred the proposed method and found it more effective (75%) and attractive (79.17%) than conventional teaching. Moreover, it was shown that the proposed method can improve education criteria in other affective and cognitive domains and is recommended to be considered for implementation in other complex engineering topics.

Preventive tree pruning is an efficient way to reduce the risks of outages in the electrical network, such as cable rupture, short circuits, and fires, which may occur due to interferences between vegetation and urban electrical distribution lines. These interferences are listed as one of the main causes of power supply interruptions by distribution companies and, consequently, generate significant financial losses due to the decline in quality indicators and the need for maintenance. Vegetation management inspection is essential to maintain the reliability and continuity of the power distribution, but manual monitoring is expensive, time-consuming, and subjective. This paper proposes a new method based on Deep Learning (DL) and high-resolution sensors for automated vegetation inspection in urban power distribution lines. The method combines Light Detection and Ranging (LiDAR) and spherical images and overcomes the limitations of aerial images using vehicle-borne sensors. The dataset was obtained using the proposed capture system in four Brazilian cities. The results demonstrated the suitability of the proposed method in an innovative way through a proof of concept. Among the main contributions, the functional evaluation of the system for Artificial Intelligence-based real-time vegetation inspection in power line distribution is highlighted. The proposed method achieved high rates of interference identification using a web platform and mobile application (achieving a precision of over 94%). The results demonstrate the potential of the proposed approach for automated vegetation inspection in real-world scenarios and open up opportunities for the deployment and evaluation of large-scale applications.