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MESOSCALE SIMULATIONS OF FUGITIVE PM10 EMISSION FROM HARBOUR ACTIVITIES IN COMPLEX TERRAIN
No full textPublisher Copyright: © 2024 22nd International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, HARMO 2024. All rights reserved.A modelling system consisting of the Weather Research and Forecasting model to simulate the meteorology and CALPUFF for pollutants dispersion is used in this study to simulate the impact of fugitive PM10 emissions related to harbour activities in nearby populated areas. Results for wind speed and direction show that the model performs well in the area, although some differences exist. PM10 concentrations show high differences respect to observations. Binarizing the PM10 signal points to the high uncertainty in emission factors as the main source of error.Peer reviewe
Effects on the ZnO nanorods array of a seeding process made under a static electric field
No full textPublisher Copyright: © 2024 Elsevier LtdThe seeding process is a crucial step in achieving a homogeneous nanorods semiconductor layer by hydrothermal growth techniques. Previous studies have demonstrated the significant impact of the seeds on the morphology and orientation of these structures. In this study, we investigate the effect of a static electric field with values of 5 kV/m and 30 kV/m, applied during the seeding process to improve the vertical alignment of the resulting nanostructures that will grow over the seed layer. Due to the small size of the seeds and the c-axis dipole moment of ZnO, resulting nanoparticles are expected to have a dipole moment that interacts with a static electric field. In this work, we found evidence that this interaction promotes vertical alignment and also lateral growth of subsequent ZnO nanorods. This effect promotes, at the same time, a more compact growth of thicker nanorods at higher electric field values. The compactness increased from 21 % to 80 % and the transversal diameter from 91 nm to 150 nm. These interesting results are shown through X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-Vis spectrophotometry and impedance spectroscopy (IS) characterization.Peer reviewe
Clinical evaluation of antifungal de-escalation in Candida infections: A systematic review and meta-analysis
No full textPublisher Copyright: © 2024 The Author(s)Objectives: De-escalation (DES) from echinocandins to azoles is recommended by several medical societies in Candida infections. We summarise the evidence of DES on clinical and microbiological cure and 30-day survival and compare it with continuing the treatment with echinocandins (non-DES). Methods: We searched MEDLINE, Embase, Web of Science and Scopus. Studies describing DES in inpatients and reporting any of the outcomes evaluated were included. Pooled estimates of the tree outcomes were calculated with a fixed or random-effects model. Heterogeneity was explored stratifying by subgroups and via meta-regression. This systematic review is registered with PROSPERO (CRD42023475486). Results: Of 1853 records identified, 9 studies were included, totalling 1575 patients. Five studies stepped-down to fluconazole; one to voriconazole and three to any of azoles. The mean day of DES was 5.2 (4.6-6.5) days. The clinical cure OR was 1.29 (95% CI: 0.88-1.88); the microbiological cure 1.62 (95% CI: 0.71-3.71); and 30-day survival 2.17 (95% CI: 1.09-4.32). The 30-day survival data into subgroups showed higher effect on critically ill patients and serious-risk bias studies. Meta-regression did not identify significant effect modifiers. Conclusions: DES is a safe strategy; it showed no higher 30-day mortality and a trend towards greater clinical and microbiological cure.Peer reviewe
Receding horizon based collision avoidance for UAM aircraft at intersections
No full textPublisher Copyright: © 2024 The AuthorsUrban Air Mobility (UAM) is an emerging aviation sector which the goal is to transform air transportation with safe, on-demand air travel for both passengers and cargo. UAM flight planning strategically separates flows of aircraft on intersecting routes vertically by allocating distinct flight levels to them, and aircraft are required to maintain the flight level when crossing the intersection. However, there is a possibility that an aircraft may fail to maintain the assigned flight level, leading to a potential conflict at intersections. This paper aims to address conflicts at intersections in the context of UAM, focusing on decentralized conflict detection and resolution. A novel approach is developed to facilitate information exchange among UAM components, including the provider of services to UAM, UAM operators, and the pilot in command. A receding horizon trajectory planning approach is proposed for the execution of conflict resolution, optimizing trajectory planning by eliminating potential problems and challenges associated with geometric approaches. The proposed trajectory planner considers the model and constraints of UAM aircraft, offering optimal solutions for safe separation at UAM airspace intersections. The significance of the proposed planning framework is demonstrated through simulations considering conflict at intersections by communicating the UAM components through request and replay services and generating resolution maneuvers on-the-fly for each aircraft involved in the conflict.Peer reviewe
Analyzing the implementation of predictive control systems and application of stored data in non-residential buildings
No full textPublisher Copyright: © The Author(s) 2024.In non-residential buildings, building energy management systems (BEMS) and the application of data hold significant promise in reducing energy consumption. Nevertheless, BEMS have different levels of complexity, benefit, and limitation. Despite the advanced technologies and improvements in building operation, there is a clear gap in the actual performance of buildings that has been attributed to the adoption of advanced technologies. Consequently, there is an increasing need for researchers and practitioners to study current practices in order to identify and address the challenges that compromise the core objectives of BEMS. For this reason, this paper aims to validate three research questions: (i) to examine the current state of BEMS and its functionalities; (ii) to analyze the type of control used; (iii) and to determine the availability of historical data compiled by BEMS and its application in non-residential buildings. A survey of 676 buildings and interviews with building professionals were conducted. The findings confirmed that most of the buildings applied BEMS with scheduled control. In addition, a lack of digitized data for analysis and predictions was detected. Indeed, only 0.60% of the investigated buildings implemented predictive control. Finally, using hierarchical clustering analysis, responses were grouped to analyze similarities between them. The study findings help to develop targeted actions for implementing predictive control in non-residential buildings.Peer reviewe
Leveraging Driver Attention for an End-To-End Explainable Decision-Making from Frontal Images
No full textPublisher Copyright: © 2000-2011 IEEE.Explaining the decision made by end-To-end autonomous driving is a difficult task. These approaches take raw sensor data and compute the decision as a black box with large deep learning models. Understanding the output of deep learning is a complex challenge due to the complicated nature of explainability; as data passes through the network, it becomes untraceable, making it difficult to understand. Explainability increases confidence in the decision by making the black box that drives the vehicle transparent to the user inside. Achieving a Level 5 autonomous vehicle necessitates the resolution of that challenging task. In this work, we propose a model that leverages the driver's attention to obtain explainable decisions based on an attention map and the scene context. Our novel architecture addresses the task of obtaining a decision and its explanation from a single RGB sequence of the driving scene ahead. We base this architecture on the Transformer architecture with some efficiency tricks in order to use it at a reasonable frame rate. Moreover, we integrate in this proposal our previous ARAGAN model, which obtains SOTA attention maps, to improve the performance of the model thanks to understand the sequence as a human does. We train and validate our proposal on the BDD-OIA dataset, achieving on-pair results or even better than other state-of-The-Art methods. Additionally, we present a simulation-based proof of concept demonstrating the model's performance as a copilot in a close-loop vehicle to driver interaction.Peer reviewe
Retraction notice to “Improved mixed-dimensional 3D/2D perovskite layer with formamidinium bromide salt for highly efficient and stable perovskite solar cells” [Chem. Eng. J. 428 (2022) 131185] (Chemical Engineering Journal (2022) 428, (S1385894721027662), (10.1016/j.cej.2021.131185))
No full textPublisher Copyright: © 2024 Elsevier B.V.This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Executive Editor due to concerns about the integrity of the research reported. Concerns were raised about the reliability of the data presented in Figure 4A and 4C (detailed on Pubpeer), whereby unexpected similarities were observed in the noise features of the XRD spectra of different experimental samples. The authors were contacted for comment about the above-mentioned concerns but were unable to provide a suitable explanation for how the similarities came about. The Editor has therefore lost confidence in the reliability of the findings presented in this article as a whole. In light of the above, the Editor believes that the most responsible course of action is to retract the paper. Apologies are offered to the readers that this was not detected sooner
A dataset of voltage and current waveforms in an electric arc under low pressure for aircraft power systems
No full textPublisher Copyright: © The Author(s) 2024.This paper presents an experimental dataset developed for the detection of parallel arc faults in aircraft electrical systems. This dataset is based on a total of 960 experiments performed in a low-pressure chamber under different conditions using two electrodes placed on the surface of an insulating material. These experiments correspond to 2 insulating materials, 12 electrode distances, and 10 pressure conditions representative of aircraft environments. Each experimental condition was repeated four times, resulting in 960 experimental recordings, each containing one million samples of time, current, and voltage signals of the electric arc induced on the surface of the insulating material. The dataset can be used to model arc behavior under different pressure conditions, to identify patterns that indicate the presence of an arc, and to accelerate the improvement of arc identification. This dataset has the potential to be used to develop arc fault detection and identification methods for more electric and all-electric aircraft and other electric vehicles.Peer reviewe
Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial
No full textPublisher Copyright: © 2024, The Author(s).Background: Restorative Brain–Computer Interfaces (BCI) that combine motor imagery with visual feedback and functional electrical stimulation (FES) may offer much-needed treatment alternatives for patients with severely impaired upper limb (UL) function after a stroke. Objectives: This study aimed to examine if BCI-based training, combining motor imagery with FES targeting finger/wrist extensors, is more effective in improving severely impaired UL motor function than conventional therapy in the subacute phase after stroke, and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training. Methods: Forty patients with severe UL paresis (< 13 on Action Research Arm Test (ARAT) were randomized to either a 12-session BCI training as part of their rehabilitation or conventional UL rehabilitation. BCI sessions were conducted 3–4 times weekly for 3–4 weeks. At baseline, Transcranial Magnetic Stimulation (TMS) was performed to examine CST integrity. The main endpoint was the ARAT at 3 months post-stroke. A binominal logistic regression was conducted to examine the effect of treatment group and CST integrity on achieving meaningful improvement. In the BCI group, electroencephalographic (EEG) data were analyzed to investigate changes in event-related desynchronization (ERD) during the course of therapy. Results: Data from 35 patients (15 in the BCI group and 20 in the control group) were analyzed at 3-month follow-up. Few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, 5/15 in the BCI group, 5/20 in control. An independent-samples Mann–Whitney U test revealed no differences between the two groups, p = 0.382. In the logistic regression only CST integrity was a significant predictor for improving UL motor function, p = 0.007. The EEG analysis showed significant changes in ERD of the affected hemisphere and its lateralization only during unaffected UL motor imagery at the end of the therapy. Conclusion: This is the first RCT examining BCI training in the subacute phase where only patients with severe UL paresis were included. Though more patients in the BCI group improved relative to the group size, the difference between the groups was not significant. In the present study, preserved CTS integrity was much more vital for UL improvement than which type of intervention the patients received. Larger studies including only patients with some preserved CST integrity should be attempted.Peer reviewe
Learning from Human Driver Demonstration for Speed Control of Ground Autonomous Vehicles
No full textPublisher Copyright: © 2024 IEEE.Recent developments in autonomous driving aim to emulate human decision-making, offering potential benefits for safety and user acceptance. Nevertheless, a significant challenge remains in accurately modeling the complex and adaptable nature of how humans navigate the roads. This work introduces a control system that learns from human driving behavior. It utilizes FIFO buffering to process a series of inputs and LSTM networks for making predictions. The inputs include historical buffered data, current sensor readings, and predicted averages of chunked pitches aimed at estimating axle torque and deceleration values. Validation tests using data from sensors installed on a Chevy Bolt EUV have confirmed the predictive accuracy of the system, favorably compared with ground truth measurements. Moreover, its ability to filter out additive noise with zero mean from both current and predicted averages of chunked pitch values makes it less susceptible to additive noises. Statistical analyses, including measures of central tendency, coefficient of correlation, mean absolute error and probability density plots have further validated the approach. These analyses demonstrate the ability of the proposed method to replicate human-like decision-making in driving, underscoring its potential to enhance autonomous driving. By ensuring safety, comfort, and efficiency, the proposed method closely mirrors the responsiveness of a human driver.Peer reviewe