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Studying weed spatial distribution patterns and implementing precise herbicide applications requires accurate weed mapping. In this study, a simple unmanned aerial vehicle (UAV) was utilized to survey 11 dry onion (Allium cepa L.) commercial fields to examine late-season weed classification and investigate weeds spatial pattern. In addition, orthomosaics were resampled to a coarser spatial resolution to simulate and examine the accuracy of weed mapping at different altitudes. Overall, 176 weed maps were generated and evaluated. Pixel and object-based image analyses were assessed, employing two supervised classification algorithms: Maximum Likelihood (ML) and Support Vector Machine (SVM). Classification processes resulted in highly accurate weed maps across all spatial resolutions tested. Weed maps contributed to three insights regarding the late-season weed spatial pattern in onion fields: 1) weed coverage varied significantly between fields, ranging from 1 to 79%; 2) weed coverage was similar within and between crop rows; and 3) weed pattern was patchy in all fields. The last finding, combined with the ability to map weeds using a low cost, off-the-shelf UAV, constitutes an important step in developing precise weed control management in onion fields.

Grasses within the Sporobolus genus have been classified as problematic weeds of pastures in many countries. In Florida, giant smutgrass is the most common and troublesome weedy Sporobolus grass. The use of unmanned aerial vehicles (UAVs) for mapping, combined with site-specific weed control has the potential to optimize giant smutgrass management and decrease the use of herbicides. In this research, RGB ortho-mosaics captured from a simple UAV were examined to detect and map giant smutgrass in bahiagrass pastures in Florida. Two sampling dates (May and August) and four flight altitudes (50, 75, 100 and 120 m) were investigated for optimal classification accuracy. Spectral, texture and combined (spectral and texture) analyses served as the basis for supervised (random forest) and unsupervised (k means) classifications. Giant smutgrass cover was successfully mapped and best evaluated by integrating the combined analysis with supervised algorithm, reaching a correlation of 0.91 with the ground truth cover. Flight altitude had a negative relationship with giant smutgrass detection; however, satisfactory results were also obtained from 120 m with an average correlation of 0.76 when using combined supervised classification. Additionally, both sampling dates were found adequate for giant smutgrass mapping. These findings demonstrate that low-cost UAV platforms can successfully be used to generate accurate giant smutgrass infestations maps, allowing for site-specific management in bahiagrass pastures. Results from this work also broaden the general knowledge on the impacts that different settings and parameters (e.g. time of the year, altitude and image-analyses methods) can have on aerial image classification.

Invasive species serve as natural experiments to study adaptive evolution over contemporary time scales, as their native and invasive populations are exposed to different climatic settings and hence may experience strong selection pressures. In the life cycle of plants, germination stands as a cornerstone; thus, when invasive plant species encounter new surroundings, it is primarily expected that their germination niche will adapt to align with the thermal characteristics of the environment. However, adaptive responses in germination traits, specifically in cardinal temperatures, have not been explored. The objective of this study is to compare the Amaranthus albus (tumble pigweed) germination thermal niche and ecological niche between its native and invaded ranges. Considering geographic areas and climatic conditions, populations of A. albus were gathered from both cold and warm habitats in its native range (US) and its invaded range (Israel), with populations being at least 20 km apart from each other. First, populations were grown under similar conditions in a common garden experiment until seed production. The progeny seeds produced were then subjected to a set of germination trials under gradually changing temperature regimes. Cardinal temperatures and thermal niches for each population were estimated and analyzed in relation to the local climatic conditions of their respective habitats. Finally, a germination window was calculated to characterize the germination of a population throughout the year within its habitat. The invaded range studied here represents a subset of the species’ native range, with the A. albus establishing in similar climatic surroundings. Similarly, the germination niche for the native range was wider than for the invaded range. Thermal differentiation is evident in both native and invaded ranges. Populations from colder habitats exhibit lower base temperatures than those from warmer ones. This association was more evident for native populations. The remarkable variability observed within the germination pattern completely disappeared in the ‘germination window’ produced by those models, resulting in a similar pattern across the year for all populations. Our findings demonstrate the capability of species to adapt to new environmental conditions that may arise due to climatic changes. It emphasizes the role of cardinal temperatures, specifically the base temperature, as a potential adaptive characteristic.

PurposeThe incidence of stroke in patients undergoing hemodialysis (HD) is eight-to-ten times greater than that of the general population. However, data on the outcome of stroke in these patients are limited.MethodsIn this retrospective observational cohort study, electronic medical records of all patients undergoing HD from 1.1.2014 to 31.12.2017 at Meir Medical Center, Israel, were reviewed. Stroke was defined as a focal neurological deficit of cerebrovascular origin, and confirmed as ischemic or hemorrhagic by computed tomography. Age- and sex-matched HD patients who did not experience a stroke (HD-NS) and hospitalized stroke patients with normal kidney function (NRF-S) served as the two control groups. Baseline demographic, clinical, and laboratory data were collected. Thrombolytic therapy, duration of hospital stay, and mortality were recorded. Functional status at discharge was assessed by the Modified Rankin Scale.ResultsIn the cohort study group (HD-S), 52 strokes occurred during 248.3 patient years, an incidence rate of 8.13%, and a stroke rate of 0.19% patients/month. Most strokes in HD patients were ischemic, and only four patients were administered tissue plasminogen activator. HD-S had longer hospitalization than did NRF-S (10.6 ± 9.9 vs. 5.96 ± 5.3 days, p = 0.004) and lower functional status at discharge (Rankin score 3.75 ± 1.57 vs. 2.29 ± 1.89, p < 0.001). HD-S patients had a higher mortality than both HD-NS and NRF-S patients.ConclusionsStroke outcome in these patients is dismal with prolonged hospital stay, poor functional status at discharge, very limited response to rehabilitation, and increased mortality.

This paper is concerned with the defense of deep models against adversarial attacks. Inspired by the certificate defense approach, we propose a maximal adversarial distortion (MAD) optimization method for robustifying deep networks. MAD captures the idea of increasing separability of class clusters in the embedding space while decreasing the network sensitivity to small distortions. Given a deep neural network (DNN) for a classification problem, an application of MAD optimization results in MadNet, a version of the original network, now equipped with an adversarial defense mechanism. MAD optimization is intuitive, effective and scalable, and the resulting MadNet can improve the original accuracy. We present an extensive empirical study demonstrating that MadNet improves adversarial robustness performance compared to state-of-the-art methods.

The modulation of channel conductance in field-effect transistors (FETs) via metal-oxide-semiconductor (MOS) structures has revolutionized information processing and storage. However, the limitations of silicon-based FETs in electrical switching have driven the search for new materials capable of overcoming these constraints. Electrostatic gating of competing electronic phases in a Mott material near its metal to insulator transition (MIT) offers prospects of substantial modulation of the free carriers and electrical resistivity through small changes in band filling. While electrostatic control of the MIT has been previously reported, the advancement of Mott materials towards novel Mott transistors requires the realization of their charge gain prospects in a solid-state device. In this study, we present gate-control of electron correlation using a solid-state device utilizing the oxide Mott system \\(La_{1-x}Sr_xVO_3\\) as a correlated FET channel. We report on a gate resistance response that cannot be explained in a purely electrostatic framework, suggesting at least \\(\\times100\\) charge gain originating from the correlated behavior. These preliminary results pave the way towards the development of highly efficient, low-power electronic devices that could surpass the performance bottlenecks of conventional FETs by leveraging the electronic phase transitions of correlated electron systems.