Deterministic loading of a single strontium ion into a surface electrode trap using pulsed laser ablation

Trapped-ion quantum technologies have been developed for decades toward applications such as precision measurement, quantum communication and quantum computation. Coherent manipulation of ions' oscillatory motions in an ion trap is important for quantum information processing by ions, however, unwanted decoherence caused by fluctuating electric-field environment often hinders stable and high-fidelity operations.. One way to avoid this is to adopt pulsed laser ablation for ion loading, a loading method with significantly reduced pollution and heat production. Despite the usefulness of the ablation loading such as the compatibility with cryogenic environment, randomness of the number of loaded ions is still problematic in realistic applications where definite number of ions are preferably loaded with high probability. %The ablation loading is proven to be useful, being even compatible with cryogenic environment, except for the randomness of the number of loaded ions. In this paper, we demonstrate an efficient loading of a single strontium ion into a surface electrode trap generated by laser ablation and successive photoionization. The probability of single-ion loading into a surface electrode trap is measured to be 82\,\%, and such a deterministic single-ion loading allows for loading ions into the trap one-by-one. Our results open up a way to develop more functional ion-trap quantum devices by the clean, stable, and deterministic ion loading.

Study on observation system of seismic forward prospecting in tunnel: A case on tailrace tunnel of Wudongde hydropower station

Seismic method is a major approach for detecting the seismic geological features ahead of the tunnel, understanding the distribution of unfavorable geology, and ensuring the safety of tunnel construction. Observation system is the key for seismic detection, many studies have been conducted to optimize the observation system; however, most of them focused on the surface seismic investigation and numerical simulation rather than in tunnel field environment (limited aperture and full space environment). How to obtain better wavefield information with limited observation aperture is a great challenge. In this study numerical simulation and instrumental techniques (GPR, DC, etc.) were implemented to further check the result of seismic detection at the 1# tailrace tunnel at the Wudongde hydropower station. In the field case, observation detectors were arranged spatially in the tunnel and source points were placed in four ways: linearly along a single side, on the tunnel face, in front of the detectors, and behind the detectors. Then, after data acquisition, the data processing is conducted to carry out the migration results. The imaging results indicate that the observation system with sources and detectors in liner arrangement (with equal interval) helps to suppress artifacts, further supporting the advantages of spatial observation system with liner observation line (detectors). Moreover, the study provides suggestions for geological prospecting in similar tunnel projects.

Potato genotypes and environments under potato in the Brazilian Cerrado biome

ABSTRACT: The objective was to estimate the physiological and productive performance of potato genotypes in two organic cultivation systems. Two trials were conducted, being one in a protected and on in an open-field environment. A randomized complete block design with four replicates and five treatments (genotypes ‘Agata’, ‘Atlantic’, ‘BRS Ana’, ‘BRSIPR Bel’ and ‘BRS Clara’) was used. Physiological, productive characteristics and defects associated with tubers were obtained. The protected environment is the most appropriate to produce organic potatoes under the evaluated conditions. Genotypes ‘BRSIPR Bel’ and ‘BRS Ana’ exhibit a high productive potential in organic conditions.

A Lightweight Powdery Mildew Disease Evaluation Model for Its In-Field Detection with Portable Instrumentation

Powdery mildew is a common crop disease and is one of the main diseases of cucumber in the middle and late stages of growth. Powdery mildew causes the plant leaves to lose their photosynthetic function and reduces crop yield. The segmentation of powdery mildew spot areas on plant leaves is the key to disease detection and severity evaluation. Considering the convenience for identification of powdery mildew in the field environment or for quantitative analysis in the lab, establishing a lightweight model for portable equipment is essential. In this study, the plant-leaf disease-area segmentation model was deliberately designed to make it meet the need for portability, such as deployment in a smartphone or a tablet with a constrained computational performance and memory size. First, we proposed a super-pixel clustering segmentation operation to preprocess the images to reduce the pixel-level computation. Second, in order to enhance the segmentation efficiency by leveraging the a priori knowledge, a Gaussian Mixture Model (GMM) was established to model different kinds of super-pixels in the images, namely the healthy leaf super pixel, the infected leaf super pixel, and the cluttered background. Subsequently, an Expectation–Maximization (EM) algorithm was adopted to optimize the computational efficiency. Third, in order to eliminate the effect of under-segmentation caused by the aforementioned clustering method, pixel-level expansion was used to describe and embody the nature of leaf mildew distribution and therefore improve the segmentation accuracy. Finally, a lightweight powdery-mildew-spot-area-segmentation software was integrated to realize a pixel-level segmentation of powdery mildew spot, and we developed a mobile powdery-mildew-spot-segmentation software that can run in Android devices, providing practitioners with a convenient way to analyze leaf diseases. Experiments show that the model proposed in this paper can easily run on mobile devices, as it occupies only 200 M memory when running. The model takes less than 3 s to run on a smartphone with a Cortex-A9 1.2G processor. Compared to the traditional applications, the proposed method achieves a trade-off among the powdery-mildew-area accuracy estimation, limited instrument resource occupation, and the computational latency, which meets the demand of portable automated phenotyping.

Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks

The intelligent identification and classification of plant diseases is an important research objective in agriculture. In this study, in order to realize the rapid and accurate identification of apple leaf disease, a new lightweight convolutional neural network RegNet was proposed. A series of comparative experiments had been conducted based on 2141 images of 5 apple leaf diseases (rust, scab, ring rot, panonychus ulmi, and healthy leaves) in the field environment. To assess the effectiveness of the RegNet model, a series of comparison experiments were conducted with state-of-the-art convolutional neural networks (CNN) such as ShuffleNet, EfficientNet-B0, MobileNetV3, and Vision Transformer. The results show that RegNet-Adam with a learning rate of 0.0001 obtained an average accuracy of 99.8% on the validation set and an overall accuracy of 99.23% on the test set, outperforming all other pre-trained models. In other words, the proposed method based on transfer learning established in this research can realize the rapid and accurate identification of apple leaf disease.

Calibration of Automatic Sun Photometer with Temperature Correction in Field Environment

Aerosol optical depth (AOD) is an important atmospheric correction parameter in remote sensing. In order to obtain AOD accurately, the surface-based automatic sun photometer needs to carry out calibration regularly. The normally used Langley method can be effective only when the AOD and the calibration coefficients of the instrument remain unchanged throughout the day. However, when observing the AOD with CE318 sun photometer in field environment, it was found that the AOD of silicon (Si) detector at 1020 nm and indium gallium arsenide (InGaAs) detector at 1639 nm was strongly influenced by temperature due to the large temperature difference at the Dunhuang site. Based on the corresponding relationship between AOD and wavelength, the model of the calibration coefficients varying with temperature was established by nonlinear regression method in field environment. By comparing the AOD before and after temperature correction with the theoretical one, the ratio of data with relative error (RE) less than 5% increased from 0.195 and 0.14 to 0.894 and 0.355, respectively. By this method, calibration can be carried out without the limit of constant AOD. In addition, it is simpler, more convenient, and less costly to perform temperature correction in a field environment than in a laboratory.

COPING THE ARSENIC TOXICITY IN RICE PLANT WITH MAGNESIUM ADDENDUM FOR ALLUVIAL SOIL OF INDO-GANGETIC BENGAL, INDIA

Arsenic (As3+) is a toxic metalloid found in the earth’s crust, its elevated concentration is a concern for human health because rice is the staple grain in eastern part of India and the waterlogged rice field environment provides opportunity for more As3+ uptake. Magnesium (Mg2+) is an important plant nutrient. Present work is a search for reducing As3+ toxicity in plants through Mg2+ application. The findings are quite impressive, the root to shoot biomass ratio showed more than 1.5 times increase compared to the control. Total protein content increased 2 folds. Carbohydrate and chlorophyll content increased two to three times compared to control. On the other hand, Malondialdehyde content showed a decline with the application of increased Mg2+ dose. The in-silico study shows a better interaction with As3+ in presence of Mg2+ but interestingly without stress symptoms. These findings from the research indicate that Mg2+ application can be effective in reducing As3+ induced stress in plants.

Evaluating carbonate dissolution and precipitation in a short time-frame using SEM: techniques and preliminary results from Postojna Cave, Slovenia

Carbonate dissolution and precipitation are important geological processes whose rates often require quantification. In natural settings, these processes may be taking place at a slow rate, and thus, it may not be easily visible which of these processes is occurring. Alternatively, if the effects of precipitation/dissolution are visible, it may not be clear if they are still underway or an artefact of past conditions. Moreover, these two opposing processes may flip states depending on the environmental conditions, such as, on a seasonal basis. Here, we present the technical details and preliminary results of a method using carbonate tablets and Scanning Electron Microscopy (SEM) to evaluate which process (carbonate dissolution or precipitation) is occurring, using as an example, a cave environment. Our method involves making tablets by encasing blocks of carbonate rock into resin and polishing these to form a completely flat and smooth “zero surface”. These tablets are observed under SEM in exactly the same points both before and after exposure to the field environment, using a system of marking lines at specific locations on the resin. Our results show significant differences in the before and after images of the tablet surface after just six weeks in the cave. Furthermore, the use of the insoluble resin zero surface permits a comparison of the starting height with the new dissolved/precipitated surface that can be used to quantitatively estimate the rate of dissolution/precipitation happening at a field location in a relatively short time-frame (weeks/months). This method could be used in numerous natural and industrial settings to identify these processes that can be caused purely geochemically, but also through microbialmediation and physical weathering.

Cerebellar contributions to a brainwide network for flexible behavior

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for posterior cerebellum in guiding flexible behavior, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, high-throughput automated analysis of complex whole-body movement revealed deficiencies in adaptation across days to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. In a Y-maze task, mice could learn but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (thalamus and habenula) and crus I (hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. Taken together, these experiments reveal parts of a brainwide system for cerebellar influence to guide flexible learning.