Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels' speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported

Image-based sensing of salt stress in grapevine

Grapevine is among the most economically important crops suffering environmental constraints, including drought and salt stress. Although imaging is increasingly used to detect abiotic stress in agriculture, image-based phenotyping in grapevine still needs optimisation. This study presents the RGB-(red, green, blue)-based phenotyping of the early stage of salt stress response in potted grapevine (Aleatico/SO4) irrigated with saline water (100 mM NaCl) for 9 days in contrast with vines irrigated with fresh water. The response was measured using stomatal conductance (gs), net photosynthetic rate (A), transpiration (E), maximum potential photosynthetic efficiency (Fv/Fm), stem water potential (SWP) concurrently with RGB imaging via a robotised platform. The image-based phenotyping of salt-stressed vines employed two sets of measurements: (i) the pixel fraction of specific colour bands (Yellow, Green, Brown and Dark Green) and (ii) the mean pixel value of R, G and B and other RGB-based colorimetric indexes. Results show that the responses of gs, A, E, Fv/Fm were closely related to increasing soil electrical conductivity (EC) and that imaging could detect the EC threshold of approx. 4 dS m-1 causing a ~60 % decrease in these physiological traits compared to the pre-stress level. The SWP declined to about –0.7 MPa at the end of the experiment. The change of the relative pixel fraction of Dark Green to increasing EC has been analysed within a dose-response context, showing that a decrease of 1 % of the Dark Green colour band corresponded to the 4 dS m-1 EC threshold. This study also examined the use of the mean pixel value of the R, G and B channels as proxies of EC along with new RGB-based indexes resulting from the rearrangement of original R, G and B mean pixel values. Results show the suitability of the mean pixel value of R and Coloration Index [(R-B)/R] to serve as predictors of EC (R2 >= 0.80)

Progression of coronary artery calcification and cardiac events in patients with chronic renal disease not receiving dialysis

We tested for the presence of coronary calcifications in patients with chronic renal disease not on dialysis and studied its progression in 181 consecutive non-dialyzed patients who were followed for a median of 745 days. Coronary calcifications (calcium score) were tallied in Agatston units by computed tomography, and the patients were stratified into two groups by their baseline calcium score (100 U or less and over 100 U). Survival was measured by baseline calcium score and its progression. Cardiac death and myocardial infarction occurred in 29 patients and were significantly more frequent in those patients with calcium scores over 100 U (hazard ratio of 4.11). With a calcium score of 100 U or less, the hazard ratio for cardiac events was 0.41 and 3.26 in patients with absent and accelerated progression, respectively. Thus, in non-dialyzed patients, the extent of coronary calcifications was associated to cardiac events, and progression was an independent predictive factor of cardiac events mainly in less calcified patients. Hence, assessment of coronary calcifications and progression might be useful for earlier management of risk factors and guiding decisions for prevention of cardiac events in this patient population

Large-Scale Recombinant Production of the SARS-CoV-2 Proteome for High-Throughput and Structural Biology Applications

The highly infectious disease COVID-19 caused by the Betacoronavirus SARS-CoV-2 poses a severe threat to humanity and demands the redirection of scientific efforts and criteria to organized research projects. The international COVID19-NMR consortium seeks to provide such new approaches by gathering scientific expertise worldwide. In particular, making available viral proteins and RNAs will pave the way to understanding the SARS-CoV-2 molecular components in detail. The research in COVID19-NMR and the resources provided through the consortium are fully disclosed to accelerate access and exploitation. NMR investigations of the viral molecular components are designated to provide the essential basis for further work, including macromolecular interaction studies and high-throughput drug screening. Here, we present the extensive catalog of a holistic SARS-CoV-2 protein preparation approach based on the consortium’s collective efforts. We provide protocols for the large-scale production of more than 80% of all SARS-CoV-2 proteins or essential parts of them. Several of the proteins were produced in more than one laboratory, demonstrating the high interoperability between NMR groups worldwide. For the majority of proteins, we can produce isotope-labeled samples of HSQC-grade. Together with several NMR chemical shift assignments made publicly available on covid19-nmr.com, we here provide highly valuable resources for the production of SARS-CoV-2 proteins in isotope-labeled form

Proceedings of 2023 IEEE International Workshop on Metrology for Agriculture and Forestry

Imaging is an emerging contact-less high throughput technology employed to retrieve quantitative and qualitative plant traits. In addition, it is often combined with artificial neural networks (ANNs) to further improve the reliability of image-based digital proxies. The olive oil industry is expanding globally as olive oil is increasingly recognized as a functional food. Fast and reliable determination of fruit quality traits is challenging in the agricultural sector. This study summarizes recent advances in the use of RGB-based imaging combined with ANNs to (i) predict oil and phenol concentrations in olive fruit and (ii) classify fruit at harvest according to colour and defects. Opportunities and limitations are also discussed

Isolated pontocerebellar leukoencephalopathy in HIV-related PML: focus on the "shrimp sign"

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