Physiological and metabolic alterations in basil (Ocimum basilicum L.) varieties under distinct soil water levels

Basil (Ocimum basilicum L.) is a medicinal species used in several areas, such as food, medicines and cosmetics, and the understanding of its physiological behavior under environmental conditions is of paramount importance for the improvement of cultivation methods. The objective of this study was to evaluate the influence of different water availability under physiological, biochemical and metabolic characteristics, in three distinct genotypes: 'Alfavaca basilicão', 'Gennaro de menta' and 'Grecco à palla', during two different phenological stages (vegetative and reproductive). It was found that the water deficit promotes physiological changes to tolerate water stress, and the studied genotypes have different routes to achieve this physiological tolerance, which culminates in a distinct accumulation of metabolites in plants, and can be considered interesting if the final product is the production of essential oils.

White-matter free-water diffusion MRI in schizophrenia: a systematic review and meta-analysis

White-matter abnormalities, including increases in extracellular free-water, are implicated in the pathophysiology of schizophrenia. Recent advances in diffusion magnetic resonance imaging (MRI) enable free-water levels to be indexed. However, the brain levels in patients with schizophrenia have not yet been systematically investigated. We aimed to meta-analyse white-matter free-water levels in patients with schizophrenia compared to healthy volunteers. We performed a literature search in EMBASE, MEDLINE, and PsycINFO databases. Diffusion MRI studies reporting free-water in patients with schizophrenia compared to healthy controls were included. We investigated the effect of demographic variables, illness duration, chlorpromazine equivalents of antipsychotic medication, type of scanner, and clinical symptoms severity on free-water measures. Ten studies, including five of first episode of psychosis have investigated free-water levels in schizophrenia, with significantly higher levels reported in whole-brain and specific brain regions (including corona radiata, internal capsule, superior and inferior longitudinal fasciculus, cingulum bundle, and corpus callosum). Six studies, including a total of 614 participants met the inclusion criteria for quantitative analysis. Whole-brain free-water levels were significantly higher in patients relative to healthy volunteers (Hedge’s g = 0.38, 95% confidence interval (CI) 0.07–0.69, p = 0.02). Sex moderated this effect, such that smaller effects were seen in samples with more females (z = −2.54, p < 0.05), but antipsychotic dose, illness duration and symptom severity did not. Patients with schizophrenia have increased free-water compared to healthy volunteers. Future studies are necessary to determine the pathological sources of increased free-water, and its relationship with illness duration and severity.

Screening of Soybean Genotypes for Waterlogging Stress Tolerance and Understanding the Physiological Mechanisms

Waterlogging is a common form of abiotic stress that severely impedes global soybean production. Targeting this issue, an experiment was carried out at Sher-e-Bangla Agricultural University during August–November 2019 to screen out the waterlogging tolerance and yield performances of selected soybean genotypes. The experiment was laid out in a completely randomized design (CRD) with three replications consisting of 2 water levels (control and waterlogging) and 12 genotypes (Sohag, BARI Soybean-5, BINAsoybean-1, BINAsoybean-2, BINAsoybean-3, BINAsoybean-5, BINAsoybean-6, SGB-1, SGB-3, SGB-4, SGB-5, and GC-840). On the 15th day after sowing, plants were exposed to waterlogging for 12 days. Waterlogging remarkably declined the growth and yield of all the soybean genotypes compared to control. Reduced plant height, relative water content, above-ground fresh and dry weight, SPAD value, leaf area, number of leaves, branches, pods, seeds pod−1, 100-seed weight, and seed yield plant−1 were observed under waterlogging stress. Conversely, mortality rate and electrolyte leakage were increased under the same condition. The waterlogged plants showed delayed flowering and maturity compared with the control plants. However, among the 12 genotypes, Sohag, BARI Soybean-5, GC-840, BINAsoybean-1, and BINAsoybean-2 showed better waterlogging tolerance. These genotypes showed a greater number of adventitious roots in the base of their stem, which probably helped plants to thrive under waterlogging conditions.

Complex effects of acidification, habitat properties and fish stock on littoral macroinvertebrate assemblages in montane standing waters

Littoral macroinvertebrates in acidified waterbodies are affected by the interaction of acidification and local environmental conditions. Understanding the interplay of these factors in the structuring of communities is essential for interpreting responses to and/or recovery from acidification. Here, we analyse the species composition and richness of littoral macroinvertebrates in a range of acidified montane standing waters in relation to water chemistry, littoral characteristics and fish stock. The main species composition gradients were related to pH and conductivity; however, considerable variation along these gradients was associated with local habitat characteristics (changing water levels and littoral structure) and concentration of ionic aluminium and dissolved organic carbon. Although fish stock effects were confounded by correlated acidity, we observed a significant decline in abundance of macroinvertebrates vulnerable to fish predation at sites with fish stock. Overall, littoral macroinvertebrates of acidic waterbodies were diverse due to the heterogeneity of local habitat properties, despite they were dominated by acid-tolerant species. Acidic humic sites with dense, heterogeneous littoral vegetation were species-rich, hosting numerous habitat specialists and rare species, while chronically acidified lakes with high aluminium concentrations and sparse littoral vegetation had species-poor assemblages, characteristic of strong acid-stress. Water level manipulation resulted in serious assemblage impoverishment, overriding the effects of more favourable water chemistry. This study shows that the littoral fauna of acidic waterbodies is structured by complex effects induced by local factors in addition to acidity, resulting in acid-stressed assemblages with relatively high variability, emphasising a need to analyse local habitat factors when evaluating the impact of acidification on macroinvertebrates.

WATER LEVEL FLUCTUATION USING SURVEILLANCE CAMERA

Floods are the most frequent type of natural disaster that cause loss of life and damages to personal property and eventually affect the economic state of the country. Researchers around the world have been made significant efforts in dealing with the flood issue. Computer vision is one of the common approaches being employed which include the use of image segmentation techniques for image understanding and image analysis. The technique has been used in various fields including in flood disaster applications. This paper explores the use of a hybrid segmentation technique in detecting water regions from surveillance images and introduces a flood index calculation to study water level fluctuations. The flood index was evaluated by comparing the result with water level measured by sensor on-site. The experimental results demonstrated that the flood index reflects the trend of water levels of the river. Thus, the proposed technique can be used in detecting water regions and monitoring the water level fluctuation of the river.

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Data Mining and Deep Learning for Predicting the Displacement of “Step-Like” Landslides

Landslide displacement prediction is one of the unsolved challenges in the field of geological hazards, especially in reservoir areas. Affected by rainfall and cyclic fluctuations in reservoir water levels, a large number of landslide disasters have developed in the Three Gorges Reservoir Area. In this article, the Baishuihe landslide was taken as the research object. Firstly, based on time series theory, the landslide displacement was decomposed into three parts (trend term, periodic term, and random term) by Variational Mode Decomposition (VMD). Next, the landslide was divided into three deformation states according to the deformation rate. A data mining algorithm was introduced for selecting the triggering factors of periodic displacement, and the Fruit Fly Optimization Algorithm–Back Propagation Neural Network (FOA-BPNN) was applied to the training and prediction of periodic and random displacements. The results show that the displacement monitoring curve of the Baishuihe landslide has a “step-like” trend. Using VMD to decompose the displacement of a landslide can indicate the triggering factors, which has clear physical significance. In the proposed model, the R2 values between the measured and predicted displacements of ZG118 and XD01 were 0.977 and 0.978 respectively. Compared with previous studies, the prediction model proposed in this article not only ensures the calculation efficiency but also further improves the accuracy of the prediction results, which could provide guidance for the prediction and prevention of geological disasters.

A Model of Evapotranspirative Irrigation to Manage the Various Water Levels in the System of Rice Intensification (SRI) and Its Effect on Crop and Water Productivities

Evapotranspirative irrigation is a simple idea in a watering field based on the actual evapotranspiration rate, by operating an automatic floating valve in the inlet without electric power to manage water levels. The current study introduces a model of evapotranspirative irrigation and its application under different water levels. The objectives were (1) to evaluate the performances of evapotranspirative irrigation under various irrigation regimes, and to (2) to observe crop and water productivities of the system of rice intensification (SRI) as affected by different types of irrigation. The experiment was performed during one rice planting season, starting from July to November 2020, with three irrigation regimes, i.e., continuous flooded (CFI), moderate flooded (MFI) and water-saving irrigation (WSI). Good performance of the system was achieved; low root mean square error (RMSE) was indicated between observed water level and the set point in all irrigation regimes. Developing a better drainage system can improve the system. Among the regimes, the WSI regime was most effective in water use. It was able to increase water productivity by up to 14.5% while maintaining the crop yield. In addition, it has the highest water-use efficiency index. The index was 34% and 52% higher than those of the MFI and CFI regimes, respectively. Accordingly, the evapotranspirative irrigation was effective in controlling various water levels, and we recommend the system implemented at the field levels.

Determining Extreme Still Water Levels for Design and Planning Purposes Incorporating Sea Level Rise: Sydney, Australia

This paper provides an Extreme Value Analysis (EVA) of the hourly water level record at Fort Denison dating back to 1915 to understand the statistical likelihood of the combination of high predicted tides and the more dynamic influences that can drive ocean water levels higher at the coast. The analysis is based on the Peaks-Over-Threshold (POT) method using a fitted Generalised Pareto Distribution (GPD) function to estimate extreme hourly heights above mean sea level. The analysis highlights the impact of the 1974 East Coast Low event and rarity of the associated measured water level above mean sea level at Sydney, with an estimated return period exceeding 1000 years. Extreme hourly predictions are integrated with future projections of sea level rise to provide estimates of relevant still water levels at 2050, 2070 and 2100 for a range of return periods (1 to 1000 years) for use in coastal zone management, design, and sea level rise adaptation planning along the NSW coastline. The analytical procedures described provide a step-by-step guide for practitioners on how to develop similar baseline information from any long tide gauge record and the associated limitations and key sensitivities that must be understood and appreciated in applying EVA.