Liu, K., Zhang, T. Q. and Tan, C. S. 2011. Processing tomato phosphorus utilization and post-harvest soil profile phosphorus as affected by phosphorus and potassium additions and drip irrigation. Can. J. Soil Sci. Sci. 91: 417–425. Phosphorus (P) applied in agricultural land not only affects crop P utilization, but can also cause environmental concerns when excessive P applied moves off-site to surrounding water systems. A 2-yr study, 2007–2008, was conducted to determine the effects of fertilizer P (four rates: 0, 30, 60, and 90 kg P ha⁻¹) and potassium (K) (four rates: 0, 200, 400, and 600 kg K ha⁻¹) additions and drip irrigation on crop P utilization and post-harvest agronomic (i.e., Olsen P) and environmental [i.e., water extractable P (WEP)] soil test P under processing tomato in loamy sand soils. Plant P uptake increased, but apparent P recovery decreased with increases in fertilizer P rate. Cumulative soil WEP in the 0- to 100-cm soil profile and Olsen P in the 0- to 20-cm depth increased linearly with increases in fertilizer P rate, regardless of water management. No effects of K were found on plant P utilization, soil WEP, or soil Olsen P. Drip irrigation increased plant P uptake by 35% and apparent P recovery by 44%, relative to non-irrigation. Drip irrigation consequently decreased the post-harvest soil profile WEP by 14% and Olsen P by 6.5% averaged across the 2 yr, compared with non-irrigation. Drip irrigation reduced the potential for post-harvest soil P losses by improving P utilization. the addition of fertilizer P needs to be optimized by considering crop P needs in association with actual yield production to ensure processing tomatoes are produced in an environmentally sustainable manner.

This paper addresses the critical challenge of optimizing the energy efficiency of induction motors, which are pivotal components across diverse industrial sectors due to their substantial energy consumption. Given the non-measurable internal parameters of induction motors, parameter identification becomes a complex, multidimensional optimization problem characterized by highly nonlinear and multimodal error surfaces. Traditional optimization algorithms often weaken, yielding suboptimal results due to an inadequate balance between the exploration and exploitation phases. To overcome these limitations, this study introduces an Adaptive Weight Grey Wolf Optimizer (AWGWO) to enhance the accuracy and reliability of induction motor parameter estimation. The AWGWO incorporates an adaptive weight mechanism that dynamically adjusts the exploration and exploitation balance, effectively mitigating issues such as premature convergence to local optima. Extensive simulation validation was conducted across various induction motor models, including eight commercial motors, and demonstrated that AWGWO consistently outperforms state-of-the-art algorithms in terms of convergence speed, solution accuracy, and robustness in multimodal optimization landscapes. The AWGWO consistently exhibited faster convergence, significantly reducing premature convergence. Moreover, the adaptive weight mechanism enabled a more effective balance between exploration and exploitation, leading to higher accuracy in parameter estimation. Comparative analyses reveal that AWGWO outperforms existing algorithms not only in achieving lower error rates, but also in maintaining stability. This study significantly contributes to progress in the field by providing an effective tool for induction motor parameterization, thereby offering potential improvements in energy efficiency.

This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units’ operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations.

Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities Robert Margolis, Pieter Gagnon, Jennifer Melius et al. SOLFAST, a Ray-Tracing Monte-Carlo software for solar concentrating facilities J P Roccia, B Piaud, C Coustet et al. The Ag/g-C3N4 layered composites with enhanced visible light photocatalytic performance Lu Chen, Yuhong Man, Zhiqian Chen et al. The response of a silicon diode designed for use as a detector for direct solar radiation