Soil Enzyme Activity in Soils Subjected to Flooding and the Effect on Nitrogen and Phosphorus Uptake by Oilseed Rape

Waterlogging presents one of the greatest constraints for agricultural crops. In order to elucidate the influences of waterlogging stress on the growth of oilseed rape, a pot experiment was performed investigating the impact of waterlogging on nitrogen (N) and phosphorus (P) accumulation in oilseed rape, and mineral N and available P profiles and enzyme activities of soils. The experiment included waterlogging treatments lasting 3 (I), 6 (II), and 9 (III) days, and a control treatment without waterlogging (CK). Results showed that waterlogging lasting 3 or more days significantly depressed the growth of oilseed rape, and prolonged the recovery time of plant growth with the period of flooding. Waterlogging notably influenced the N and P concentrations in plant tissues, and also affected mineral N, available P profiles, and activities of enzymes (including urease, phosphatase, invertase, and catalase) in the soils. With the exception of catalase, flooding suppressed the activity of urease, phosphatase, and invertase to varying degrees, and the longer the flooding time, the greater the suppression. The effect of waterlogging on mineral N and P profiles resulted from the altered proportions of NH4+-N and NO3--N, and the decreased available P concentrations in these soils, respectively. The effect on P was more significant than on N in both soil nutrient profile and plant utilization

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

A functional genomic approach to actionable gene fusions for precision oncology

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize similar to 100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology

Trade-off of security and performance of lightweight block ciphers in Industrial Wireless Sensor Networks

Abstract Lightweight block ciphers play an indispensable role for the security in the context of pervasive computing. However, the performance of resource-constrained devices can be affected dynamically by the selection of suitable cryptalgorithms, especially for the devices in the resource-constrained devices and/or wireless networks. Thus, in this paper, we study the trade-off between security and performance of several recent top performing lightweight block ciphers for the demand of resource-constrained Industrial Wireless Sensor Networks. Then, the software performance evaluation about these ciphers has been carried out in terms of memory occupation, cycles per byte, throughput, and a relative good comprehensive metric. Moreover, the results of avalanche effect, which shows the possibility to resist possible types of different attacks, are presented subsequently. Our results show that SPECK is the software-oriented lightweight cipher which achieves the best performance in various aspects, and it enjoys a healthy security margin at the same time. Furthermore, PRESENT, which is usually used as a benchmark for newer hardware-oriented lightweight ciphers, shows that the software performance combined with avalanche effect is inadequate when it is implemented. In the real application, there is a need to better understand the resources of dedicated platforms and security requirement, as well as the emphasis and focus. Therefore, this case study can serve as a good reference for the better selection of trade-off between performance and security in constrained environments

Investigation on Selective Laser Melting AlSi10Mg Cellular Lattice Strut: Molten Pool Morphology, Surface Roughness and Dimensional Accuracy

AlSi10Mg inclined struts with angle of 45° were fabricated by selective laser melting (SLM) using different scanning speed and hatch spacing to gain insight into the evolution of the molten pool morphology, surface roughness, and dimensional accuracy. The results show that the average width and depth of the molten pool, the lower surface roughness and dimensional deviation decrease with the increase of scanning speed and hatch spacing. The upper surface roughness is found to be almost constant under different processing parameters. The width and depth of the molten pool on powder-supported zone are larger than that of the molten pool on the solid-supported zone, while the width changes more significantly than that of depth. However, if the scanning speed is high enough, the width and depth of the molten pool and the lower surface roughness almost keep constant as the density is still high. Therefore, high dimensional accuracy and density as well as good surface quality can be achieved simultaneously by using high scanning speed during SLMed cellular lattice strut

Hierarchical Weighting Vicsek Model for Flocking Navigation of Drones

Flocking navigation, involving alignment-guaranteed path following and collision avoidance against obstacles, remains to be a challenging task for drones. In this paper, we investigate how to implement flocking navigation when only one drone in the swarm masters the predetermined path, instead of all drones mastering their routes. Specifically, this paper proposes a hierarchical weighting Vicsek model (WVEM), which consists of a hierarchical weighting mechanism and a layer regulation mechanism. Based on the hierarchical mechanism, all drones are divided into three layers and the drones at different layers are assigned with different weights to guarantee the convergence speed of alignment. The layer regulation mechanism is developed to realize a more flexible obstacle avoidance. We analyze the influence of the WVEM parameters such as weighting value and interaction radius, and demonstrate the flocking navigation performance through a series of simulation experiments