Magic™ Anti-β-Amyloid, 1-42 monoclonal antibody, clone 23G5

An integrated research coupling a field study with an agronomic-economic model (ANSWER-APP) was conducted to investigate the combined effects of irrigation levels and crop loads on fruit quality, water productivity and profitability of mature date palms. The study took place in Israel's hyper-arid Arava Valley, where date palm trees are widely cultivated and rely exclusively on brackish water irrigation. Three fruit load intensities (low, FL1; commercial, FL2; high, FL3) and two irrigation levels (W1 and W2, equal to and higher than the local irrigation regime, respectively) were applied in a 22-year-old date palm orchard in 2018-2019. Irrigation amount, fruit quality and yield were measured. Profitability for each treatment was analyzed with the ANSWER-APP. Higher fruit load resulted in higher yield and water productivity, but on the other hand led to reduced fruit physical properties (size and mass), regardless of the irrigation treatment. The improved fruit physical properties and net profits in W2 compared to W1 treatment in each fruit load group resulted from excess salt leaching in the root zone. Notably, trees under W2FL2 treatment were found to have highest net profits. It is concluded that adequate irrigation amounts contribute to net profits in date palm trees treated with high fruit load intensity under saline irrigation conditions.

Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing mode greatly solves the last mile issue of the commuters, and better meets their real bike usage demand. However, it also poses new challenges for operators to manage the system. The first primary challenge is how to accurately estimate the real bike usage demand in different areas of a city and in different time intervals, which is crucial for the system planning and operation. This paper for the first time proposes a data driven approach for bike usage demand inference in stationless bike-sharing systems. The idea is that we first estimate the demands in some regions and time intervals from a small number of observed bike check-out/in data directly, and then use them as seeds to infer the region-level bike usage demands of an entire city. Specifically, we formulate this problem as a matrix completion task by modeling the bike usage demand as a matrix whose two dimensions are time intervals of a day and regions of a city, respectively. With the observation that POI distribution of a region is an important indicator to bike demand, we propose to utilize inductive matrix factorization by considering POIs as side information. As the bike usage data are highly correlated in both spatial and temporal dimensions, we also incorporate the spatial-temporal correlations as well as the balanced bike usage constraint into a joint optimization framework. We evaluate the proposed model on a large Mobike trip dataset collected from Beijing, and the experimental results show its superior performance by comparison with various baseline methods.