Search

A solid phase extraction-electrospray ionization-ion mobility spectrometry (SPE-ESI-IMS) method was developed to determine six organic acids in cucumber leaves. The multiplexing IMS using correlation technique was adopted to optimize the signal-to-noise ratio (SNR) and resolution. 90% methanol/water was selected to be the solvent, the flow rate was 120 µL·h−1, the stop frequency of ion gate was 5000 Hz, the ion gate opening period was 0.1 ms, the air flow velocity was 2.0 L·min−1, the electrospray voltage was 3.5 kV, and the drift tube temperature was 90 °C. The cucumber leaves were treated with ultrasound in 50% methanol/water. The analytes were subsequently eluted from an SPE cartridge with 0.1 mol·L−1 HCl, and the extract was neutralized to pH 2–3 with ammonia before freeze-drying. SPE-ESI-IMS enabled the effective determination of citric acid, fumaric acid, lactic acid, malic acid, pyruvic acid, and succinic acid based upon the drift time. The limits of detection were from 0.008 µg·mL−1 to 0.2 µg·mL−1, and the recoveries of spiked samples were from 87.00% to 107.20%. The convenient, sensitive, and efficient developed SPE-ESI-IMS method may be applicable for the determination of organic acids in leaves. [ABSTRACT FROM AUTHOR]

In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the effectiveness of network coverage, a directional sensor network coverage optimization algorithm is developed based on the principles of virtual force and particle swarm optimization. Firstly, the article introduces the concept of a segmented virtual negative centroid model. This model revolutionizes the configuration of the virtual negative centroid, thereby enabling a more efficient adjustment of the gravitational forces exerted by the coverage blind areas on the sensor nodes. Therefore, the influence of these blind areas on the improvement of network coverage is significantly amplified. Secondly, taking into account the characteristics of global optimization and the inherent randomness of particle swarm optimization, the algorithm synergistically combines the principles of virtual force and particle swarm optimization. This integration effectively fine-tunes the sensing direction of the sensor nodes, thereby optimizing their overall performance. The algorithm in this study incorporates an adjusted inertia weight strategy and introduces Gaussian disturbance in the local optimization enhancement phase to prevent local optimization, accelerate particle convergence, and facilitate the sensor network's attainment of an optimal distribution for coverage optimization. Simulation experiments were conducted to verify the algorithm's effectiveness. The initial sensor network coverage was 31.04%. After applying the algorithm, the average coverage increased to 80.16%, with a maximum coverage of 84.2%. These results verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

China is facing serious water scarcity, and the effects of international trade on its water resources have been widely examined. Processing exports account for nearly half of China's gross exports. Adopting China's multi-regional input–output table that captures processing exports, we enrich the literature on virtual water exports by accounting for trade heterogeneity. The results show that China's virtual water exports show a significant trade heterogeneity. Normal and processing exports are attributed to 86.7% and 13.3% of the Agriculture sector's water use induced by exports respectively. Conversely, normal and processing exports are attributed to 31.8% and 68.3% of the Communications Equipment, Computers sector's water use induced by exports respectively. In addition, a cross-regional compensation is needed to deal with the unequal regional distribution of water uses and economic benefits related to exports. [ABSTRACT FROM AUTHOR]