Proline-mediated redox regulation in wheat for mitigating nickel-induced stress and soil decontamination.

Nickel (Ni) is known as a plant micronutrient and serves as a component of many significant enzymes, however, it can be extremely toxic to plants when present in excess concentration. Scientists are looking for natural compounds that can influence the development processes of plants. Therefore, it was decided to use proline as a protective agent against Ni toxicity. Proline (Pro) is a popularly known osmoprotectant to regulate the biomass and developmental processes of plants under a variety of environmental stresses, but its role in the modulation of Ni-induced toxicity in wheat is very little explored. This investigation indicated the role of exogenously applied proline (10 mM) on two wheat varieties (V1 = Punjab-11, V2 = Ghazi-11) exposed to Ni (100 mg/kg) stress. Proline mediated a positive rejoinder on morphological, photosynthetic indices, antioxidant enzymes, oxidative stress markers, ion uptake were analyzed with and without Ni stress. Proline alone and in combination with Ni improved the growth, photosynthetic performance, and antioxidant capacity of wheat plants. However, Ni application alone exhibited strong oxidative damage through increased H2O2 (V1 = 28.96, V2 = 55.20) accumulation, lipid peroxidation (V1 = 26.09, V2 = 38.26%), and reduced translocation of macronutrients from root to shoot. Application of Pro to Ni-stressed wheat plants enhanced actions of catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and total soluble protein (TSP) contents by 45.70, 44.06, 43.40, and 25.11% in V1, and 39.32, 46.46, 42.22, 55.29% in V2, compared to control plants. The upregulation of antioxidant enzymes, proline accumulation, and uptake of essential mineral ions has maintained the equilibrium of Ni in both wheat cultivars, indicating Ni detoxification. This trial insight into an awareness that foliar application of proline can be utilized as a potent biochemical method in mitigating Ni-induced stress and might serve as a strong remedial technique for the decontamination of polluted soil particularly with metals.

A novel lens of stock market capitalization and environmental degradation.

This research article examines the impact of stock market capitalization on carbon emissions using forty high carbon-emitting countries from 1996 to 2018. This study adopts the Driscoll-Kraay method that simultaneously tackles heteroscedasticity, autocorrelation, and contemporaneous correlation issues. We find an inverted U relationship between stock market capitalization (SMC) and environmental degradation. We propose an extended environmental Kuznets curve based on SMC while energy intensity, industrialization, and urbanization increase emissions in sample countries. The quadratic method, SLM test, and derivative graphing detect the consensus of the inverted U relationship. The weak-negative SMC2 coefficient reveals that the dangerous impact of capitalization declines gradually and finally curbs the environmental degradation challenges. The relationship is strong in highly polluted countries with overvalued stock markets. The study catches no policy synergies between the growing stock market and increased carbon emissions. Stock market capitalization should be integrated into climate change adaptation strategies at national and regional levels, primarily to address the dark effect of environmental degradation.

Smart Distribution Boards (Smart DB), Non-Intrusive Load Monitoring (NILM) for Load Device Appliance Signature Identification and Smart Sockets for Grid Demand Management.

Traditionally, the choices to balance the grid and meet its peaking power needs are by installing more spinning reserves or perform load shedding when it becomes too much. This problem becomes worse as more intermittent renewable energy resources are installed, forming a substantial amount of total capacity. Advancements in Energy Storage System (ESS) provides the utility new ways to balance the grid and to meet its peak demand by storing un-used off peak energy for peak usage. Large sized ESS-mega watt (MW) level-are installed by different utilities at their substations to provide the high speed grid stabilization to balance the grid to avoid installing more capacity or triggering any current load shedding schemes. However, such large sized ESS systems and their required inverters are costly to install, require much space and their efficacy could also be limited due to network fault current limits and impedances. In this paper, we propose a novel approach and trial for 3000+ homes in Singapore of achieving a large capacity of demand management by developing a smart distribution board (DB) in each home with the high speed metering sensors (>6 kHz sampling rate) and non-intrusive load monitoring (NILM) algorithm, that can assist home users to perform the load/appliance profile identification with daily usage patterns and allow targeted load interruption using the smart sockets/plugs provided. By allowing load shedding at device or appliance level, while knowing their usage profile and preferences, this can allow such an approach to become part of a new voluntary interruptible load management system (ILMS) that requires little user intervention, while minimizing disruption to them, allowing ease of mass participation and thus achieving the intended MW demand management capacities for the grid. This allows for a more cost effective way to better balance the grid without the need for generation capacity growth, large ESS investment while improving the way to perform load shedding without disruptions to entire districts. Simply, home users can now know and participate with the grid in interruptible load (IL) schemes to target specific home appliance, such as water heaters or air conditioning, allowing interruptions during certain times of the day, instead of the entire house, albeit with the right incentives. This allows utilities to achieve MW capacity load shedding with millions of appliances with their preferences, and most importantly, with minimal disruptions to their consumers quality of life. In our paper, we will also consider coupling a small sized Home Energy Storage System (HESS) to amplify the demand management capacity. The proposed approach does not require any infrastructure or wiring changes and is highly scalable. Simulation results demonstrate the effectiveness of the NILM algorithm and achieving high capacity grid demand management. This approach of taking user preferences for appliance level load shedding was developed from the results of a survey of 500 households that indicates >95% participation if they were able to control their choices, possibly allowing this design to be the most successful demand management program than any large ESS solution for the utility. The proposed system has the ability to operate in centralized as part of a larger Energy Management System (EMS) Supervisory Control And Data Acquisition (SCADA) that decide what to dispatch as well as in autonomous modes making it simpler to manage than any MW level large ESS setup. With the availability of high-speed sampling at the DB level, it can rely on EMS SCADA dispatch or when disconnected, rely on the decaying of the grid frequency measured at the metering point in the Smart DB. Our simulation results demonstrate the effectiveness of our proposed approach for fast grid balancing.