A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture

The presence of weeds poses a common and persistent problem in crop cultivation, affecting both yield and overall agricultural productivity. Common solutions to the problem typically include chemical pesticides, mulching, or mechanical weeding performed by agricultural implements or humans. Even if effective, those techniques have several drawbacks, including soil and water pollution, high cost-effectiveness ratio or stress for operators. In recent years, novel robotic solutions have been proposed to overcome current limitations and to move towards more sustainable approaches to weeding. This work presents a mixed-autonomous, robotic, weeding system based on a fully integrated three-axis platform and a vision system mounted on a mobile rover. The rover’s motion is remotely controlled by a human operator, while weeds identification and removal is performed autonomously by the robotic system. Once in position, an RGB-D camera captures the portion of field to be treated. The acquired spatial, color and depth information is used to classify soil, the main crop, and the weeds to be removed using a pre-trained Deep Neural Network. Each target is then analyzed by a second RGB-D camera (mounted on the gripper) to confirm the correct classification before its removal. With the proposed approach, weeds are all the plants not classified as the main crop known a priori. The performance of the integrated robotic system has been tested in laboratory as well as in open field and in greenhouse conditions. The system was also tested under different light and shadowing conditions to evaluate the performance of the Deep Neural Network. Results show that the identification of the plants (both crop and weeds) is above 95%, increasing to 98% when additional information, such as the intra-row spacing, is provided. Nevertheless, the correct identification of the weeds remains above 97% ensuring an effective removal of weeds (up to 85%) with negligible crop damage (less than 5%)

Spatially variable organic-matter-driven clogging in a stormwater infiltration pond: Isotopic, microbiological and hydrogeological evidence

Stormwater infiltration ponds (SIPs) are nature-based solutions which tend to decrease their infiltration capacity over time due to pore clogging. Organic matter (OM) is a well-known clogging driver, but how OM affects the physical and biochemical processes in a SIP remains largely unknown. An analysis encompassing soil organic carbon (SOC) stable isotopes, extracellular polymeric substances (EPS) of biofilms, DNA-based identification of microbiological communities and hydrogeological tests was carried out to elucidate the main clogging mechanisms in a large SIP in Italy. Open pits revealed a stratified soil composed of different textures and compositions, associated with artificial recharge sequences and on-site maintenance practices. A very different isotopic and microbiological signature of soil samples collected at different depths within the first meter of the soil surface was observed. Such diversity was linked to the spatially variable permeability of OM-enriched sediments limiting the infiltration. The isotopic signature beneath the more permeable (i.e., less clogged) OM-enriched layers was similar to that of the isotopic value of the biological surficial crust (δ13C → −27 ‰). Below the less permeable (i.e., more clogged) OM-enriched layers, isotopic values were more consistent with advanced degradation of organic matter (δ13C → −23 ‰). The selective hydraulic isolation of the analyzed trenches could lead to the formation of microbial microenvironments, with direct consequences on local composition of EPS and biofilm production. Based on this multidisciplinary approach, a new conceptual model could be proposed to the site managers and authorities dealing with the SIP's maintenance

Behavioral macromodeling of high-speed drivers via compressed tensor representations

This paper addresses the behavioral modeling of digital drivers for Signal and Power Integrity co-simulations. State-of-the-art two-piece model representations are combined with a compact description of the device static characteristics. The latter are considered as multivariate mappings that are functions of the device electrical variables, and of additional parameters defining process corners and device settings. Overall model complexity is reduced through a compressed tensor representation obtained via a high-order singular value decomposition. Several application examples demonstrate the feasibility and the advantages of the proposed approac

Enhanced macromodels of high-speed low-power differential drivers

High-speed differential interfaces implementing specific solutions for low-power consumption and low-EMI disturbances are vastly used in mobile platforms. In these devices, the slew rate is suitably controlled, the communication scheme alternates data-bursts followed by power-saving states, the voltage swing and the common-mode level are reduced. To achieve these targets, a key role in voltage-mode output drivers is played by an internal voltage-regulator. The latter exhibits a rich dynamic behavior, with non-negligible effects on the transmitter outputs, that need to be carefully characterized. In this paper, a modeling strategy based on a few key enhancements of state-of-the-art solutions is presented, leading to compact and accurate models. The feasibility and strengths of the proposed approach are verified on a low-power high-speed voltage-mode driver

X-ray properties and obscured fraction of AGN in the J1030 Chandra field

The 500ks Chandra ACIS-I observation of the field around the z = 6.31 quasar SDSS J1030+0524 is currently the fifth deepest extragalactic X-ray survey. The rich multi-band coverage of the field allowed an effective identification and redshift determination of the X-ray source counterparts; to date, a catalog of 243 extragalactic X-ray sources with either a spectroscopic or photometric redshift estimate in the range z ≈ 0−6 is available over an area of 355 arcmin2 . Given its depth and the multi-band information, this catalog is an excellent resource to investigate X-ray spectral properties of distant active galactic nuclei (AGN) and derive the redshift evolution of their obscuration. We performed a thorough X-ray spectral analysis for each object in the sample, and measured its nuclear column density NH and intrinsic (de-absorbed) 2–10 keV rest-frame luminosity, L2−10. Whenever possible, we also used the presence of the Fe Kα emission line to improve the photometric redshift estimates. We measured the fractions of AGN hidden by column densities in excess of 1022 and 1023 cm−2 (f22 and f23, respectively) as a function of L2−10 and redshift, and corrected for selection effects to recover the intrinsic obscured fractions. At z ∼ 1.2, we found f22 ∼ 0.7−0.8 and f23 ∼ 0.5−0.6, respectively, in broad agreement with the results from other X-ray surveys. No significant variations in X-ray luminosity were found within the limited luminosity range probed by our sample (logL2−10 ∼ 42.8−44.3). When focusing on luminous AGN with logL2−10 ∼ 44 to maximize the sample completeness up to large cosmological distances, we did not observe any significant change in f22 or f23 over the redshift range z ∼ 0.8−3. Nonetheless, the obscured fractions we measure are significantly higher than is seen in the local Universe for objects of comparable intrinsic luminosity, pointing toward an increase in the average AGN obscuration toward early cosmic epochs, as also observed in other X-ray surveys. We finally compared our results with recent analytic models that ascribe the greater obscuration observed in AGN at high redshifts to the dense interstellar medium (ISM) of their hosts. When combined with literature measurements, our results favor a scenario in which the total column density of the ISM and the characteristic surface density of its individual clouds both increase toward early cosmic epochs as NH,ISM∝ (1 + z) δ , with δ ∼ 3.3−4 and Σc,∗ ∝ (1 + z) 2 , respectively

Recurrent, founder and hypomorphic variants contribute to the genetic landscape of Joubert syndrome

Background Joubert syndrome (JS) is a neurodevelopmental ciliopathy characterised by a distinctive mid-hindbrain malformation, the 'molar tooth sign'. Over 40 JS-associated genes are known, accounting for two-thirds of cases.Methods While most variants are novel or extremely rare, we report on 11 recurring variants in seven genes, including three known 'founder variants' in the Ashkenazi Jewish, Hutterite and Finnish populations. We evaluated variant frequencies in similar to 550 European patients with JS and compared them with controls (>15 000 Italian plus gnomAD), and with an independent cohort of similar to 600 JS probands from the USA.Results All variants were markedly enriched in the European JS cohort compared with controls. When comparing allele frequencies in the two JS cohorts, the Ashkenazim founder variant (TMEM216 c.218G>T) was significantly enriched in American compared with European patients with JS, while MKS1 c.1476T>G was about 10 times more frequent among European JS. Frequencies of other variants were comparable in the two cohorts. Genotyping of several markers identified four novel European founder haplotypes. Two recurrent variants (MKS1 c.1476T>G and KIAA0586 c.428delG), have been detected in homozygosity in unaffected individuals, suggesting they could act as hypomorphic variants. However, while fibroblasts from a MKS1 c.1476T>G healthy homozygote showed impaired ability to form primary cilia and mildly reduced ciliary length, ciliary parameters were normal in cells from a KIAA0586 c.428delG healthy homozygote.Conclusion This study contributes to understand the complex genetic landscape of JS, explain its variable prevalence in distinct geographical areas and characterise two recurrent hypomorphic variants