Assimilation Capability Research Articles

Influence of Knowledge Capabilities on Performance of Kenyan Fintech Companies

Purpose: The purpose of the study was to examine the influence of knowledge capability on performance in Kenyan fintech companies. The research questions that guided the study were to establish the influence of knowledge acquisition, knowledge assimilation, knowledge transformation and knowledge exploitation on performance of Kenyan fintech companies. The theoretical review of the study used the dynamic capabilities theory. Methodology: The study adopted a descriptive research design with a target population of 850 employees at management level working at 85 Kenyan fintech companies. Stratified random sampling technique was used to select a sample size of 272 management level employees. Primary data was collected using a questionnaire and analysed using descriptive and inferential statistics. The statistical package for the Social Science was used as a data analysis tool and thereafter the data was presented in figures and tables. Findings: Findings on the first research question, indicated that there was a negative correlation between fintech performance and knowledge acquisition capability. The multiple linear regression results revealed that knowledge acquisition had no statistically significant influence on the performance of fintech. The results from the regression coefficients indicated that knowledge acquisition capability did not significantly predict the performance of fintech firms in Kenya. For the second research question, the correlation analysis findings indicated that knowledge assimilation capability had a negative relationship with performance of the fintech companies. The regression coefficient results indicated that knowledge assimilation capability did not significantly predict the performance of fintech organizations in Kenya. Regarding the third research question, correlation results noted that knowledge transformation capability had a negative relationship with performance of fintech firms. Findings of the regression coefficients indicated that knowledge transformation capability significantly did not predict performance of fintech organizations in Kenya. On the last research question, correlation results revealed that knowledge exploitation capability had a negative relationship with performance of fintech firms. Regression coefficients result indicated that knowledge exploitation capabilities did not significantly predict the performance of fintech firms in Kenya. Unique Contribution to Theory, Policy and Practice: The study concluded that knowledge capabilities did not significantly influence the performance of Kenyan fintech companies. This is mainly attributed to the other elements and factors that influence performance more. The study recommends that fintech institutions should increase their focus on knowledge capabilities to compete sustainably in turbulent business environments. Fintech organizations can fully exploit the knowledge they receive to effectively achieve strategic objectives. Additionally, due to other factors being more influential in predicting performance, further research is required with these additional variables considered.

Study on Selenium Assimilation and Transformation in Radish Sprouts Cultivated Using Maillard Reaction Products.

The organic selenium (Se), particularly in the form of selenoamino acids, in non-edible sections or by-products of Se-enriched plants, has the potential to generate Maillard reaction products (MRPs) during thermal treatment or fermentation. To elucidate the recycling process of organic selenium in foods and improve the utilization rate of Se, the biotransformation of organic selenium was studied by the cultivation of edible radish sprouts with Se-MPRs. Maillard reactions were simulated using selenoamino acids (SeAAs; selenomethionine and methylselenocysteine) and reducing sugars (glucose and fructose) for preparing Se-MRPs. The structures of the possible dehydrated Se-MRPs were analyzed using a HPLC-ESI-MS/MS system based on their fragmentation patterns and Se isotopic characteristics. Se absorption by the radish sprouts cultivated using Se-MRPs was estimated by the corresponding Se in the SeAAs and the total Se contents. The capabilities of SeAA transformation and total Se assimilation by the sprouts were related to the substrate composition during the Se-Maillard reaction. A particular Se-MRP (selenomethionine + fructose) increased SeAAs transformation by 33.8% compared to selenomethionine. However, glucose and fructose seemed to inhibit the transformation of the Se-MRPs to SeAAs by 10.0 to 59.1% compared to purified Se-MRPs. These results provide key references for the efficient utilization of organic Se in the cultivation of Se-enriched sprouts.

Efficient production 2,3-butanediol from biomass-derived sugars by Raoultella ornithinolytica TH-21, a newly isolated lignocellulose-degrading bacterium

Efficient bioconversion of lignocellulose to valuable products can be performed by bacteria that are both capable of degrading biomass and producing platform chemicals. This study reported a newly isolated lignocellulolytic strain, Raoultella ornithinolytica TH-21 showing great potential as an ideal biocatalyst for producing 2,3-butanediol (2,3-BDO). Strain TH-21 exhibited the capability of assimilation of multiple carbon sources for 2,3-BDO production. It could directly convert 50 g/L of xylose, mannitol, cellobiose and xylan into 2,3-BDO with the maximum production of 20.04 g/L, 16.56 g/L, 20.06 g/L and 11.25 g/L, respectively. In addition, the lignin, cellulose, and hemicellulose present in sugarcane bagasse were degraded by strain TH-21 with a rate of 26.96%, 24.6%, and 33% after 7 days of treatment, respectively. Furthermore, FTIR, XRD and SEM analysis revealed that strain TH-21 can effectively depolymerize the lignin and holocellulose in sugarcane bagasse through its efficient secretion of lignocellulolytic enzymes. Thus, these findings indicated that strain TH-21 might be a promising biocatalyst for the effective bioconversion of lignocellulose to high-value chemicals.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

Two‐Line Elements Based Thermospheric Mass Density Specification From Parameter Optimization of a Physics‐Based Model

AbstractAccurately estimating the mass density of the thermosphere can assist satellite operators in planning missions and optimizing satellite orbits, reducing the risk of collisions. The newly developed GOFT (Global Observation‐based Forecast for the Thermosphere) model specifies thermospheric mass density by optimizing uncertainty parameters in a physics‐based model based on model errors. This study extends the model to specify thermospheric mass density based on Two‐Line Elements (TLEs) from numerous space objects. The resulting mass density is validated against accelerometer‐derived mass density from different satellites. The comparison results revealed that assimilating TLEs into the GOFT model significantly improved the accuracy of the thermospheric mass density. In addition, the GOFT model also improved the accuracy of the electron density in the ionosphere, indicating that the assimilation capability of GOFT allows simultaneous specification of coupled ionosphere and thermosphere in future applications.

Construction Digital Technology Assimilation and Absorption Capability Using Measurement Invariance of Composite Modeling

Digital technology is evolving, and construction companies now have various choices of these technologies to implement into daily business. This has, however, resulted in a growing argument on how digital technologies can be accepted in various contexts. However, most arguments are based on technology acceptance models with the core concept of individual behaviors, which ignores the influence of other stakeholders and organizational characteristics. This paper aims to present a novel model representing the successful practice of the assimilation process for digital construction technologies that should be considered before any technology acceptance analysis. The model was tested via empirical investigation with 372 practitioners from Australia involved in building and infrastructure construction projects. Structural equation modeling, measurement invariance of composite models, and some semistructured interviews were used to test and verify the relationships between key variables of the model. The results suggest that the capability of assimilation can be enhanced by improving innovativeness and the relevant operational processes, with a t-value of >6.27. The findings show that a project’s likelihood of successful technology assimilation can also be increased if vendors are supportive and the company improves its operating performance assessment and absorption capacity. The findings clarify theoretical and practical contributions for a better understanding of critical factors of assimilation and provide a tool to potential company users for predicting the success of technology assimilation.

Implementation and Testing of Radar Data Assimilation Capabilities Within the Joint Effort for Data Assimilation Integration Framework With Ensemble Transformation Kalman Filter Coupled With FV3‐LAM Model

AbstractCapabilities to directly assimilate radar data are implemented within the local ensemble transform Kalman filter (LETKF) and the gain‐form LETKF (LGETKF) algorithms of the Joint Effort for Data assimilation Integration (JEDI) system. The capabilities are evaluated for the analysis and forecast of a severe convection case of 20 May 2019 in the Southern Great Plains using the limited area model version of the FV3 dynamical core (FV3‐LAM) from a recent release for Short‐Range Weather Application (SRW App). The LETKF and LGETKF implementations are shown to produce analyses and short‐range forecasts comparable to those using the ensemble square‐root Kalman Filter (EnSRF) within the Gridpoint Statistical Interpolation (GSI) framework used by current NCEP operational models. In addition, LGETKF retaining only 60% variances for model‐space vertical localization performs similarly to LGETKF retaining 99% of variance and LETKF using observation error‐based vertical localization. JEDI LETKF shows better parallel scalability than LGETKF and GSI EnSRF.

Composting as an Alternative for the Treatment of Solid Waste from the Kraft Pulp Industry

The increasing industrial pulp production has led to a negative growth of the associated solid wastes, thus making necessary alternative ways of handling them in suitable sanitary landfills to minimize adverse effects on the environment and well-being of people. Solid waste treatment prior to its disposal is a target to minimize pollution of the natural resources (air, soil, water) due to accidental leaching. This paper aims to determine better experimental conditions in the container to develop an optimal composting design for pulp solid wastes. For this, an experimental methodology is introduced. This paper presents the results about the influence of independent control variables (grits addition and composting process time) on dependent variables (chemical and biological), for which a composting design was used, and a face-centered central composite factor was applied. The results showed mature compost over 60-day treatment, with the following experimental observations (i) the grits addition did not decrease the pH in the first stage of the composting process; and (ii) the microbial activities were high during the active stage of the composting progress and evolved to stable, lower values together with a proper trend of N–NH4+ and N–NO3− at the end. Grits addition of around 6% is the optimal experimental amount to use for the composting process of the secondary sludge from the Kraft mill industry. In conclusion, treating secondary sludges and grit residues from the Kraft mill industry to produce compost is feasible and sustainable. This action reduces the environmental pollution risk (evidenced by soil pH change and possible water pollution) and improves the soil assimilation capability of inorganic micronutrients and organic compounds after application. Thus, the controlled waste reuse will pass from a negative input to the environment to a positive, sustainable solution, which can be used as a soil-nutrient improver in agriculture.

Increasing the organizational performance of online sellers: the powerful back-end management systems

PurposeThis study examines how customer-linking capabilities, the flexibility of back-end management systems, strategic agility and the perceived absorptive capacity of e-commerce influence the organizational performance of online sellers.Design/methodology/approachA performance model based on the adaptive structuration theory was employed to test the hypotheses using structural equation modeling (SEM) on a dataset of 325 samples with acceptable responses.FindingsThe results reveal that the customer-linking capability and flexibility of back-end management systems, strategic agility and assimilation of e-commerce activity significantly influence organizational performance in a virtual environment.Originality/valueThe agility of online sellers is becoming increasingly important, and from the perspective of adaptive structuration theory, back-end management systems have been built to manage buyer orders for online sellers.

Evaluation of Aerosol Optical Depth Forecasts from NOAA’s Global Aerosol Forecast Model (GEFS-Aerosols)

Abstract The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model. Significance Statement The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

Acute stress of the typical disinfectant glutaraldehyde-didecyldimethylammonium bromide (GD) on sludge microecology in livestock wastewater treatment plants: Effect and its mechanisms

Glutaraldehyde and didecyldimethylammonium bromide (GD) is a disinfectant widely used to prevent African swine fever (ASF) in livestock farms. However, the effect of residual GD on the activated sludge microbial ecology of receiving wastewater treatment plants (WWTPs) remains largely unknown. In this study, seven simulated systems were established to research the effects of GD on WWTPs and reveal the underlying mechanisms of microecological responses to GD at different concentrations. Both the nitrogen and carbon removal rates decreased with increasing GD concentrations, and nitrogen metabolism was inhibited more obviously, but the inhibition weakened with increasing stress duration. Microorganisms activated their SoxRS systems to promote ATP synthesis and electron transfer to support the hydrolysis and efflux of GD by producing a small number of ROS when exposed to GD at less than 1 mg/L. The overproduction of ROS led to a decrease of antioxidant and nitrogen removal enzyme activities, and upregulation of the porin gene increased the risk of GD entering the intracellular space upon exposure to GD at concentrations higher than 1 mg/L. Some denitrifiers survived via resistance and their basic capabilities of sugar metabolism and nitrogen assimilation. Notably, low concentrations of disinfectants could promote vertical and horizontal transfer of multiple resistance genes, especially aminoglycosides, among microorganisms, which might increase not only the adaptation capability of denitrifiers but also the risk to ecological systems. Therefore, the risks of disinfectants targeting ASF on ecology and health as well as the effects of disinfectant residuals from the COVID-19 epidemic should receive more attention.

Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation

In Wang & Pan (J. Fluid Mech., vol. 918, 2021, A19), the authors developed the first ensemble-based data assimilation (DA) capability for the reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS) method. In this work, we continue to enrich the method by allowing it to simultaneously estimate the ocean current field, which is in general not known a priori and can (slowly) vary in both space and time. To achieve this goal, we incorporate the effect of ocean current (as unknown parameters) on waves to build the HOS-C method as the forward prediction model, and obtain a simultaneous estimation of (current) parameters and (wave) states via an iterative EnKF (IEnKF) method that is necessary to handle the complexity in this DA problem. The new algorithm, named the IEnKF-HOS-C method, is first tested in synthetic problems with various forms (steady/unsteady, uniform/non-uniform) of current. It is shown that the IEnKF-HOS-C method is able to not only estimate the current field accurately, but also boost the prediction accuracy of the wave field (even) relative to the state-of-the-art EnKF-HOS method. Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C method successfully recovers the current speed that matches the in situ measurement by a floating buoy.

Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains

Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation

Abstract. The Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM's capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented in both the finite-volume and spectral-element dynamical cores and an online ocean data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation” constraints by ADA and ODA. The weakly coupled data assimilation (CDA) in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air–sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air–sea interface. A 3-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. The success of the online CESM-ECDA system is the first step to implementing a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

Evaluation of Surface Conditions from Operational Forecasts Using in situ Saildrone Observations in the Pacific Arctic

Abstract Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June – September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multi-model means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multi-model means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System

AbstractThe NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Comparative differences in photosynthetic characteristics, ion balance, and nitrogen metabolism between young and old wild soybean leaves under nitrogen deficiency

It is a hot issue and an effective method in botany and agronomy to improve cultivation crops using existing wild plant resources without creating invasive plants. Wild soybean is not only a wild ancestor of cultivated soybean, but also an excellent germplasm resource to improve it. The photosynthetic characteristics, ion balance, and nitrogen (N) metabolism of young and old leaves in two wild soybean genotypes (common wild soybean: W1; barren tolerant wild soybean: W2) were analyzed under different intensities of N deficiency. The results showed that N deficiency inhibited the growth and total N content in both genotypes, but W2 was less inhibited. Under the high intensity of N deficiency, the root length in W2 was significantly longer than that in the control group (P < 0.05), which suggested that N deficiency tolerance in W2 was stronger than that in W1. W2 in young leaves maintained stable net photosynthetic rate (pN) and photosynthetic pigment content. Mg2+ and K+ were transported from old leaves to young leaves. Meanwhile, GOGAT and GDH activities were increased in W2 old leaves and GOT activity was increased in W2 young leaves. W2 young leaves maintained stable photosynthetic assimilation capability, provided energy and basic skeleton for old leaves, and stable N metabolism in old leaves could provide nitrogen metabolites for carbon metabolism in young leaves. The aim of this study was to reveal the physiological mechanisms by which wild soybean seedlings resist low nitrogen stress from the perspective of carbon and nitrogen metabolism by comparing the differences in carbon and nitrogen metabolism between common wild soybean and barren tolerant wild soybean in response to nitrogen deficiency. This study provides a theoretical foundation for the protection and utilization of wild soybean resources.12Nitrogen (N); common wild soybean (W1); barren tolerant wild soybean (W2); photosynthetic rate (pN); glutamate synthase (GOGAT); glutamate dehydrogenase (GDH); aspartate aminotransferase (GOT).

Multifaceted Role of Microalgae for Municipal Wastewater Treatment: A Futuristic Outlook toward Wastewater Management

AbstractMunicipal wastewater (MWW) or urban wastewater (UWW) is generated by the domestic consumption of freshwater, which contains a huge amount of nutrients. The release of unprocessed wastewater causes eutrophication and harms aquatic life. Moreover, ingestion of polluted MWW causes a severe negative impact on human health. Microalgae are unicellular, eukaryotic photosynthetic organisms and have the capability of nutrient assimilation in the presence of light. Moreover, the produced biomass can be used for the generation of value‐added bioproducts such as bioenergy. However, conventional microalgae‐based MWW treatment is not as sustainable on a commercial scale. Therefore, more advanced approaches using microalgae need to be integrated in wastewater cultivation systems to improve nutrient removal efficiency. Thus, the present review explores the use of microalgae for the removal of nutrients from MWW, provides an outlook of direct and indirect methods of nutrient uptake from wastewater and the effects of the influencing factors in biomass growth. Moreover, the review also gives insight into recent approaches used for MWW treatment and the applications of algal biomass resulting from treated wastewater. It is predicted that microalgae‐based MWW treatment systems will be a significant green approach to help eliminate nutrient loads and implement circular economy.

ДИАГНОСТИКА АССИМИЛЯЦИОННОГО ПОТЕНЦИАЛА ВОДНЫХ РЕСУРСОВ РЕГИОНА НА ПРИМЕРЕ ЮГА РОССИИ

General theoretical approaches to the research of the assimilation potential essence and capabilities to carry out a quantitative assessment are presented. Special attention is paid to the assessment of assimilation potential of the waters of the South of Russia. Current state of the water resources of the Republic of Crimea is presented. Territories of the South of Russia are differentiated according to the assimilation capacity of the water resources. There’s proposed an authors’ modal and analytical toolkit to assess the assimilation potential of the water bodies. The assimilation potential of the Black sea and the Sea of Azov to biological oxygen consumption is calculated. It is identified that drainage to the rivers and the sea surrounding the Crimean peninsula greatly exceeds the assimilation capabilities of the water bodies to self-cleaning. A great attention is brought to the recommendations to optimize water management in the area and increase its touristic attractiveness and opportunities of effective management of the territorial and sectoral infrastructure